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SPORTS AND ATHLETICS PREPARATION, PERFORMANCE, AND PSYCHOLOGY
ATHLETE PERFORMANCE
AND INJURIES
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SPORTS AND ATHLETICS PREPARATION,
PERFORMANCE, AND PSYCHOLOGY
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SPORTS AND ATHLETICS PREPARATION, PERFORMANCE, AND PSYCHOLOGY
ATHLETE PERFORMANCE
AND INJURIES
JOÃO H. BASTOS
AND
ANDREIA C. SILVA
EDITORS
Nova Science Publishers, Inc.
New York
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Copyright © 2012 by Nova Science Publishers, Inc.
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Library of Congress Cataloging-in-Publication Data
Athlete performance and injuries / editors, Joco H. Bastos and Andreia C. Silva.
p. cm.
Includes index.
ISBN: (eBook)
1. Sports injuries. 2. Sports--Competitions. I. Bastos, Joco H. II. Silva, Andreia C.
RD97.A83 2011
617.1'027--dc23
2011051070
Published by Nova Science Publishers, Inc. † New York
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CONTENTS
Preface
vii
Chapter 1
Exercise, Injuries and Athlete Performance
Ana Luisa Miranda-Vilela
Chapter 2
Training Over the Edge: Understanding the Overtraining Syndrome
Fernando Rocha, Mário C. Marques and Aldo M. Costa
Chapter 3
Evaluating the Dynamic Model of Psychological Response
to Sport Injury and Rehabilitation
Diane M. Wiese-Bjornstal, Courtney B. Albinson,
Shaine E. Henert, Elizabeth A. Arendt, Susan J. Schwenz,
Shelly S. Myers and Diane M. Gardetto-Heller
Chapter 4
Chapter 5
Cardiometabolic Injury due to Recombinant Human Erythropoietin
Doping for Improvement of Sports Performance: Chronic (Training)
versus Acute (Extenuating) Aerobic Exercise
Edite Teixeira-Lemos, Helena M. Teixeira, Nuno Piloto,
Margarida Teixeira, Belmiro Parada, Paulo Rodrigues-Santos,
Lina Carvalho, Rui Alves, Elísio Costa, Luís Belo,
Alice Santos-Silva, Frederico Teixeira and Flávio Reis
Athletic Heart: The Possible Role of Impaired Repolarization
Reserve in Development of Sudden Cardiac Death
István Baczkó, Andrea Orosz, Csaba Lengyel and András Varró
Chapter 6
Sports Injuries and Risk-Taking Behaviors in Amateur Athletes
Vanessa Lentillon-Kaestner
Chapter 7
Oral Glycosaminoglycans for 8-Week Recovery of Functional
Abilities in Professional Male Athletes after Knee Injury
Sergej M. Ostojic, Senka Rendulic-Slivar, Marko Stojanovic,
Igor Jukic, Kemal Idrizovic and Boris Vukomanovic
Chapter 8
Evolution of the Achilles Tendon in Bipedal Locomotion:
Advantages and Flaws
B. Tucker and W. S. Khan
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1
51
79
99
123
145
159
169
vi
Chapter 9
Contents
Patellofemoral Syndrome
A. Yetkil, W. S. Khan and P. Pastides
Index
177
183
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PREFACE
In this book, the authors present current research in the study of athletic performance and
injuries. Topics discussed include the role of antioxidants in combating exercise-induced
oxidative stress; over-training syndrome and recovery; psychological response to sport
injuries and rehabilitation processes; the cardiometabolic effects of rhEPO treatment on
chronic vs. acute extenuating exercise; impaired repolarization reserve in the development of
sudden cardiac death in the young athlete; sports injuries and risk-taking behaviors in amateur
athletes; injuries in young martial arts athletes; the achilles tendon in bipedal locomotion and
patellofemoral syndrome.
Chapter 1 - Today it is unanimously accepted that physical exercise, when practiced on a
regular basis, is vital for a healthy lifestyle, acting as a therapeutic agent and/or preventing
numerous illnesses. However, despite its potential beneficial effects, exercise, especially if
unusual or exhausting, or training above habitual intensity, may exceed the endogenous
antioxidant system’s capacity and often results in oxidative stress and injuries. This oxidative
overload, although felt most intensely in skeletal muscles, has also been reported in many
other organs and body systems responsible for regulating and maintaining homeostasis,
including the heart, liver, kidneys, lungs, erythrocytes and immune and osteoarticular
systems. Sports-related injuries are one of the main reasons why athletes prematurely
abandon a sports career, spend long periods excluded from training and competitions, or
experience a decline in sports performance, even causing functional limitations at more
advanced ages. Thus, many athletes and even individuals participating in regular exercise
programs consume antioxidant supplements to prevent exercise-induced oxidative stress and
injuries. However, antioxidant supplementation can inhibit the beneficial adaptive responses
associated with improved athletic performance, which in turn is a multifactorial phenotype,
where genetic and environmental factors interact to produce the final phenotype. Research
into genetic variants that, when inherited, can lead to improved athletic performance, has
therefore increased greatly. This chapter presents a comprehensive summary of free radicals
and the antioxidant defense system and examines the role of reactive oxygen species in
exercise-induced oxidative stress and injuries to skeletal muscle, myocardium, liver,
erythrocytes, immune system, plasma lipoproteins and DNA. It also discusses the role of
antioxidants in combating exercise-induced oxidative stress and provides a subset of genetic
variants potentially related to exercise-induced oxidative stress and injuries, as well as some
others widely studied in the context of performance.
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viii
João H. Bastos and Andreia C. Silva
Chapter 2 - The improvement of elite athletes performance depends on two fundamental
training variables: volume and intensity. The physiological adaptations and supercompensation
require training and sufficient recovery periods. In fact, there is a strict relationship between
training volume and recovery time that can be considered critic for the athlete’s physiological
status. On this, overtraining syndrome appears to be caused by too much high intensity training
and/or too little recovery time often combined with other training and nontraining stressors
factors. The imbalance between effort and adequate recovery can bring serious physiological
consequences, like decrease of training tolerance and performance and even susceptibility to
respiratory tract infections. At present there is no one single diagnostic test that can define
overtraining. The recognition of overtraining requires the identification of stress indicators, which
do not return to baseline following a period of regeneration. Possible indicators include an
imbalance of the neuroendocrine system, suppression of the immune system, indicators of muscle
damage, depressed muscle glycogen reserves, deteriorating aerobic, ventilatory and cardiac
efficiency, a depressed psychological profile, and poor performance in sport specific tests, e.g.
time trials. Therefore, screening for overtraining and performance improvements must occur at the
culmination of regeneration periods.
Chapter 3 - Authors of the integrated model of psychological response to the sport injury
and rehabilitation process (Wiese-Bjornstal, Smith, Shaffer, & Morrey, 1998) conceptualized
sport injury as influenced by preinjury psychosocial factors (Williams & Andersen, 1998),
acting as a negative life event stressor, and comprising a dynamic process of ongoing
cognitive appraisals influencing emotional and behavioral responses affecting recovery
outcomes (Wiese-Bjornstal, 2009; 2010; Wiese-Bjornstal, Smith, & LaMott, 1995). The
purpose of this project was to simultaneously examine these three primary model components
and associated predictions while controlling for within team and school-related factors
through repeated measures sampling of injured and noninjured teammates. Within a
prospective mixed factorial study design, NCAA Division I male and female athletes (N = 74)
from four sports (women’s softball, track and field, and tennis, and men’s baseball)
completed multiple psychosocial measures at repeated time points from baseline to
postseason. Results supported (a) the ability of psychosocial variables to predict sport injury,
(b) conceptualizing sport injury as a stressor, and, (c) the role of affect as a precursor and
response to sport injury. A unique aspect to this study was reflected in the matching of
psychological data from injured and noninjured teammates during the specific weeks in which
injuries occurred, thus controlling for non-injury related factors such as team and school
related variables that may have influenced the mood state and life event stress of all athletes
on the teams aside from injury. Furthermore, this study lends support to the idea that negative
mood states are not only responses to but also risk factors for sport injury, and thus provides
grounding for identifying psychological interventions to ameliorate negative moods.
Chapter 4 - Athletes who abuse recombinant human erythropoietin (rhEPO) consider
only the benefit to performance and usually ignore the potential short and long-term
liabilities. Elevated haematocrit and dehydratation associated with intense exercise may
reveal undetected cardiovascular risk, but the mechanisms underlying it remain to be fully
explained. This chapter intended to compare the cardiometabolic effects of rhEPO treatment
on rats under chronic vs acute extenuating exercise.
The following male Wistar rat groups were assessed: control – sedentary (Sed); rhEPO –
50 IU/Kg/wk; Exercise (Ex) – swimming: 1 hr, 3 times/wk; Ex+rhEPO. For the chronic
exercise a period of 10 wks was assessed, while for the acute exercise, a single bout of
Preface
ix
extenuating swimming was performed, with a rhEPO treatment for 3 wks prior to the acute
section. Blood pressure and heart trophism were analysed. Blood and tissue samples were
assessed for: biochemical data, haematology, catecholamine and serotoninergic measures,
redox status and heart gene expression profile.
The chronic Ex+rhEPO rats showed higher values of RBC, Htc and Hb vs chronic Ex, as
well as vs acute Ex+rhEPO. Both chronic and acute swimming showed a remarkable
sympathetic and serotonergic activation. rhEPO treatment in chronic training has promoted
oxidative stress, in contrast with the antioxidant effect on the acute exercise. rhEPO in trained
rats promoted erythrocyte count increase, hypertension, heart hypertrophy, sympathetic and
serotonergic overactivation. One rat of the chronic Ex+rhEPO group suffered a sudden death
episode during the week 8 and the tissues analyzed showed: brain with vascular congestion;
left ventricular hypertrophy, together with a “cardiac liver”, suggesting the hypothesis of
heart failure as cause of sudden death. In the chronically trained rats, rhEPO per se promoted
apoptosis, proliferation and angiogenesis, which was partially or totally prevented in the
Ex+rhEPO rats.
In conclusion, the effects of rhEPO doping in rats under exercise is notoriously more
deleterious in circumstances that mimic high-performance athletes (chronic training) than in
occasional consumers (acute sessions), particular due to increased cardiovascular risk.
Chronic rhEPO doping in rats under chronic exercise promotes not only the expected RBC
count increment, suggesting hyperviscosity, but also other serious deleterious cardiovascular
and thromboembolic modifications, including mortality risk, which might be known and
assumed by all sports authorities, including athletes and their physicians.
Chapter 5 - A number of sudden deaths involving young competitive athletes were
reported in recent years. Sudden death among athletes is rare, but in a significant number of
these cases the cause is not established and is mostly attributed to ventricular fibrillation.
Physical conditioning in competitive athletes induces cardiovascular adaptation including
lower resting heart rate (increased vagal tone) and increased cardiac mass (hypertrophy) and
volume as a consequence of increased demand on the cardiovascular system, called "athlete’s
heart”. Myocardial hypertrophy has been shown to cause electrophysiological remodeling
where the expression of different ion channels is altered. Since the duration of repolarization
depends on cycle length, the low heart rate in athletes also leads to prolonged repolarization.
It is conceivable that prolonged repolarization and a possibly impaired repolarization reserve
due to myocardial hypertrophy-induced downregulation of potassium currents might
represent increased risk for the development of ventricular arrhythmias, including Torsades
de Pointes ventricular tachycardia (TdP) that can degenerate into ventricular fibrillation and
lead to sudden cardiac death in athletes.
The reliable prediction of TdP remains unsatisfactory. Short-term variability (STV) of the
QT interval is a novel parameter used in the assessment of arrhythmic risk. STV of
repolarization can increase in case of decreased repolarization reserve even when there are no
noticable changes in the duration of cardiac repolarization. STV may be significantly larger in
competitive athletes and may be an early indicator of increased instability of cardiac
repolarization and a higher arrhythmia propensity in this population.
Chapter 6 - Research conducted outside of the sports context has shown higher risk for all
injuries (e.g., intentional injuries, injured drivers, fatal and non-fatal injuries) among persons
with risk-taking behaviors (e.g., cannabis use, alcohol consumption). The purpose of this
study was to investigate whether: (1) risk-taking behaviors, such as alcohol, cannabis or
x
João H. Bastos and Andreia C. Silva
tobacco consumption increased the risk and the severity of sports injuries; (2) whether
differences emerged between male and female athletes; and (3) whether differences emerged
between recreational and competitive athletes. The sample consisted of 1,810 amateur
athletes (993 men, 817 women), aged 16 to 22 years old (M=18.72; SD=2.08). Respondents
completed a questionnaire, which queried frequency of risk-taking behaviors and sports
injuries recorded in their lifetime. Sixty-seven percent (67%) of amateur athletes indicated at
least one sports-related injury in their lifetime. For sixty-two percent (62%) of athletes, the
most frequent sports injuries required ten days to three months of sport interruption. Results
also indicated that risk and severity of sports injuries increased with increased alcohol
consumption. Cannabis use also increased the risk but not the severity of sports injuries,
while smoking was not associated with the risk but with the severity of sports injuries. Some
differences were observed between males and females as well as between recreational and
competitive athletes in associations between risk-taking behaviors, risk and/or severity of
sports injuries. Prevention measures for risk-taking behaviors in athletic pursuits should be
increased and improved to reduce the number and severity of injuries in sports played on an
amateur level.
Chapter 7 - The use of different glycosaminoglycans (GAGs; e.g. glucosamine salts,
chondroitin sulfates, hyaluronan) is a common practice among athletes at all ages and levels
of participation, with GAGs promoted as chondroprotective and therapeutic agents for
musculoskeletal healing. Yet, the effectiveness of different common GAGs intake after acute
joint injury in high-performance athletes is yet to be determined. The main aim of the present
chapter was to present the effects of eight-week of oral glucosamine chloride, chondroitin
sulfate and hyaluronic acid administration on the functional ability and the degree of pain
intensity in competitive male athletes after acute knee injury. This research was a
randomized, double-blind parallel trial of glucosamine chloride (1500 mg per day),
chondroitin sulfate (1500 mg per day), hyaluronic acid (90 mg per day) or a placebo
administration for 2 months, utilising 218 patients with an acute knee injury. Pain at rest and
while walking and functional ability (e.g. passive knee flexibility, degree of knee swelling)
were evaluated at the beginning of the study and every second week thereafter for the study
duration. No significant differences were found between the experimental protocols in mean
pain intensity scores for resting and walking, and degree of knee swelling during the study (p
> 0.05). There were no significant differences for passive knee flexibility between the groups
at the 14-day and 28-day assessment (p > 0.05). After 6 weeks of treatment the patients
supplemented with glucosamine chloride demonstrated significant improvement in both knee
flexion and extension as compared to other experimental protocols (p < 0.05). The findings of
the present study indicate that administration with GAGs does not significantly alter pain
score or degree of swelling after acute sports injury of knee. Yet, glucosamine chloride
supplementation appears to be suitable as a flexibility improvement strategy in athletes after 6
weeks of treatment. In prescribed doses GAGs do not induce any acute side-effects.
Chapter 8 - The Achilles tendon is a key structure separating humans from other
primates, allowing the upright bipedal stance. There are many advantages to being a biped
from hunting ability to energy expenditure. The Achilles tendon itself has the benefit of
greatly enhancing endurance running. However, there are disadvantages to having an Achilles
tendon such as its vulnerability to injury. This article outlines the advantages and
disadvantages of the tendon and highlights some theories as to why humans may have
evolved to have it.
Preface
xi
Chapter 9 - Patellofemoral pain syndrome (PFPS), an injury frequently observed in
runners and is a very common presentation to sports medicine clinics. Although the exact
cause is still unclear, the development of PFPS is almost certainly multifactorial. Disruption
to the physiological tracking of the patella due to overuse, muscular imbalance or injury may
changes the biomechanics of the joint and result in the development of PFPS. The focus of
this work will be to examine the suspected aetiology of PFPS and then suggest how the recent
evidence based exercise therapies in particular can be included to alleviate the pain and allow
return to a normal level of activities.
In: Athlete Performance and Injuries
Editors: João H. Bastos and Andreia C. Silva
ISBN 978-1-61942-658-0
© 2012 Nova Science Publishers, Inc.
Chapter 1
EXERCISE, INJURIES AND ATHLETE PERFORMANCE
Ana Luisa Miranda-Vilela
Department of Genetics and Morphology, Laboratory of Genetics,
Institute of Biological Sciences, University of Brasilia, Brasilia/DF, Brazil
ABSTRACT
Today it is unanimously accepted that physical exercise, when practiced on a regular
basis, is vital for a healthy lifestyle, acting as a therapeutic agent and/or preventing
numerous illnesses. However, despite its potential beneficial effects, exercise, especially
if unusual or exhausting, or training above habitual intensity, may exceed the endogenous
antioxidant system’s capacity and often results in oxidative stress and injuries. This
oxidative overload, although felt most intensely in skeletal muscles, has also been
reported in many other organs and body systems responsible for regulating and
maintaining homeostasis, including the heart, liver, kidneys, lungs, erythrocytes and
immune and osteoarticular systems. Sports-related injuries are one of the main reasons
why athletes prematurely abandon a sports career, spend long periods excluded from
training and competitions, or experience a decline in sports performance, even causing
functional limitations at more advanced ages. Thus, many athletes and even individuals
participating in regular exercise programs consume antioxidant supplements to prevent
exercise-induced oxidative stress and injuries. However, antioxidant supplementation can
inhibit the beneficial adaptive responses associated with improved athletic performance,
which in turn is a multifactorial phenotype, where genetic and environmental factors
interact to produce the final phenotype. Research into genetic variants that, when
inherited, can lead to improved athletic performance, has therefore increased greatly. This
chapter presents a comprehensive summary of free radicals and the antioxidant defense
system and examines the role of reactive oxygen species in exercise-induced oxidative
stress and injuries to skeletal muscle, myocardium, liver, erythrocytes, immune system,
plasma lipoproteins and DNA. It also discusses the role of antioxidants in combating
exercise-induced oxidative stress and provides a subset of genetic variants potentially
related to exercise-induced oxidative stress and injuries, as well as some others widely
studied in the context of performance.
2
Ana Luisa Miranda-Vilela
1. FREE RADICALS IN PHYSIOLOGICAL FUNCTIONS
AND OXIDATIVE STRESS
A free radical (FR) is any species capable of independent existence, containing one or
more unpaired electrons in its outer orbit. These configurations make them highly unstable,
very chemically reactive and with a very short half-life. They provoke or result from redox
(shorthand for reduction-oxidation) reactions and multiply quickly in cascade by stealing
electrons from other molecules, which also become FRs (Ferreira and Matsubara, 1997;
Hermes-Lima, 2004; Bahorun et al., 2006; Fisher-Wellman and Bloomer, 2009). They can
also be non-radical species (not presenting unpaired electrons in their outer orbit), but be
capable of generating highly reactive and harmful species (Ferreira and Matsubara, 1997;
Bahorun et al., 2006; Barreiros et al., 2006). Radicals are generally less stable than nonradical species, although their reactivity varies (Bahorun et al., 2006).
By the simple fact of consuming oxygen, the cellular metabolism, even in basal
situations, promotes a continuous generation of reactive oxygen species (ROS), FRs that
present the unpaired electron centered on the oxygen atom and that play an important role in a
variety of physiological and pathological processes (Niess et al., 1999; Hermes-Lima, 2004;
Schneider and Oliveira, 2004). Because of its electron configuration, oxygen has a marked
tendency to receive an electron from each time; the univalent reduction of oxygen leads to
formation of the superoxide anion (O2). With that as a starting point, the other ROS are
chemically derived from O2, formed as by-products of enzymatic reactions (Dröge, 2002;
Hermes-Lima, 2004; Schneider and Oliveira, 2004; Ferreira et al., 2007). Thus, ROS are
naturally produced in our bodies as a result of the normal oxidative metabolism that occurs in
mitochondria, endoplasmic reticulum, lysosomes, cell membranes, peroxisomes and cytosol
(Ferreira et al., 2007). ROS are also produced by phagocytic cells (neutrophils, monocytes,
macrophages, eosinophils) and help them to inactivate viruses and bacteria (Bahorun et al.,
2006).
Mitochondria use 85-90% of the oxygen we breathe; the remaining 10-15% are used by
various oxidases and oxygenases enzymes and also by direct oxidation of chemical reactions
that occur in the other cellular compartments mentioned above (Schneider and Oliveira, 2004,
Ferreira et al., 2007). Hence, mitochondria are the major intracellular sites of O2 generation
under physiological conditions, followed by the NADPH oxidase enzymatic system, which is
found in neutrophils, monocytes and macrophages. O2 is also generated by a variety of
cytosolic and membrane-bound enzymes, including molybdenum hydroxylase reactions
(xanthine, sufite, and aldehyde oxidases), the cytochrome 450 complex, the monoamine
oxidase enzymatic system and those of arachidonic acid metabolism (phospholipase A2,
lipoxygenases and cyclooxygenases) (Comhair and Erzurum, 2002; Bahorun et al., 2006).
In cells, one-electron abstraction of molecules can also yield sulfur-, carbon- and
nitrogen-centered radicals (Hermes-Lima, 2004), but those derived from oxygen and nitrogen
represent the most important class of FR generated in living systems (Fisher-Wellman and
Bloomer, 2009). Many regulatory effects are mediated by hydrogen peroxide (H2O2) and
other ROS chemically derived from O2 (Dröge, 2002), while nitric oxide (NO), a reactive
nitrogen species (RNS), plays a important role in cellular signalling, vasodilation, relaxing
smooth muscle tissue, inhibition of platelet adhesion and innate immune response (Dröge,
2002; Drew and Leeuwenburgh, 2002; Iovine et al., 2008). Thus, the most relevant radicals in
Exercise, Injuries and Athlete Performance
3
biological regulation are O2 and NO, the latter being typically generated by tightly
regulated enzymes such as constitutive or inducible NO synthases (NOS) isoforms (Comhair
and Erzurum, 2002; Dröge, 2002; Hermes-Lima, 2004; Iovine et al., 2008). Both radicals, as
well as the non-radical species created via interaction with FRs, are collectively referred to as
ROS/RNS or RONS (Fisher-Wellman and Bloomer, 2009).
Because FRs are continuously formed in small quantities by the normal processes of
aerobic metabolism, all body cells have an antioxidant defense system to soften their
aggressive effects. This system is divided into enzymatic and non-enzymatic antioxidants.
The former includes the enzymes superoxide dismutase (SOD), catalase (CAT) and
glutathione peroxidase (GPX), while the non-enzymatic system includes compounds
synthesized by the body such as haptoglobin, bilirubin, ceruloplasmin, estrogens, melatonin,
coenzyme Q, uric acid, and others obtained through the diet such as ascorbic acid (vitamin C),
D-tocopherol (vitamin E), carotenoids and phenolic compounds of plants (Sies, 1993;
Schneider and Oliveira, 2004; Tseng et al., 2004). So, radical and reactive non-radical species
derived from radicals exist in biological cells and tissues at low concentrations and their
concentrations are determined by the balance between their production rates and their
clearance rates by various antioxidant compounds and enzymes (Figure 1) (Dröge, 2002).
Despite their deleterious potential for cells, FRs are critical for maintaining normal
physiological functions. In the body, they are involved in energy production, phagocytosis,
cell growth regulation, intercellular signaling and synthesis of biologically important
substances (Harman, 1956; Saeed et al., 2005; Barreiros et al., 2006; Valko et al., 2007;
Lecarpentier, 2007). At moderate concentrations, RONS play an important role as regulatory
mediators in signaling processes, such as regulation of vascular tone, monitoring of oxygen
ETC, electron transport chain; SOD, superoxide dismutase; CAT, catalase; GPX, glutathione
peroxidase; GR, glutathione reductase; GSH, glutathione in the reduced state (reduced
glutathione); GSSG, glutathione in the oxidized state (oxidized glutathione)
Figure 1. Integrated action of enzymatic and non-enzymatic antioxidant system in neutralizing ROS.
4
Ana Luisa Miranda-Vilela
tension in the control of ventilation and erythropoietin production, and signal
transduction from membrane receptors in various physiological processes (Dröge, 2002).
ROS are also necessary for normal contractile activity of skeletal muscles (Lecarpentier,
2007). Many of the ROS-mediated responses actually protect the cells against oxidative stress
and reestablish redox homeostasis (Dröge, 2002) by modulating several major regulatory
systems of skeletal muscle performance such as mitochondria, sarcoplasmic reticulum,
glucose transport and numerous enzymatic systems involved in the cellular metabolism
(Lecarpentier, 2007). They may also have considerable positive effects on the immune system
(Cruzat et al., 2007; Lecarpentier, 2007).
However, under certain conditions, an imbalance between RONS generation and
antioxidant capacity leads to oxidative stress, a state in which an imbalance between the prooxidant and antioxidant system occurs in such way that the pro-oxidant system prevails. As a
result, RONS cause extensive cell damages, with harmful effects, such as the oxidation of the
cellular membrane lipidic layer (leading to cell lysis) and aggression to proteins,
carbohydrates and DNA (causing single and double strand breaks and chromosomal
aberrations) (Dizdaroglu, 1992; Imlay and Linn, 1988; Cooke et al., 2003; Schneider and
Oliveira, 2004; Barreiros et al., 2006; Traber, 2006; Valko et al., 2007; Radak et al., 2008).
Therefore, RONS can contribute to the pathogenesis of a number of human diseases, such as
cancer, neurological disorders, chronic inflammatory diseases, cardiovascular diseases and
even muscle fatigue during strenuous exercise, besides being implicated in the mechanism of
senescence, where they can be the cause or general aggravating factor (Comhair and
Erzurum, 2002; Dröge, 2002; Cooke et al., 2003, Hermes-Lima, 2004; Schneider and
Oliveira, 2004; Barreiros et al., 2006; Ferreira et al., 2007). Since oxidative stress can occur
by excessive FR production, by deficient antioxidant capacity, or by a combination of both
(Collins, 2009), the balance between RONS and the body's natural antioxidant functions plays
a crucial role in the prevention of oxidative stress and development of related diseases (Cooke
et al., 2003).
2. PHYSICAL EXERCISE  CLASSIFICATION
Physical exercise can be classified according to the effort intensity as mild, moderate and
intense, based on the performance of some maximal effort tests for evaluation of the
percentage of maximal oxygen uptake (VO2max), the percentage of oxygen uptake reserve
(VO2R), the percentage of maximal heart rate (HRmax) and the percentage of heart rate reserve
(HRR), where VO2R and HRR are calculated from the difference between resting and
maximal VO2 and resting and maximal heart rate respectively (ACSM, 1998; Baldwin et al.,
2000; Leandro et al., 2007).
Some studies have demonstrated that physiological and metabolic responses to exercise
at the same relative intensity are the same regardless of training status, while several other
studies have observed that the metabolic, cardiovascular, and hormonal changes differ
between endurance trained and untrained individuals during exercise at the same relative
oxygen consumption (45–75% VO2 peak) (Baldwin et al., 2000). Despite this, the amount of
improvement in VO2max increases with training frequency, but the magnitude of change is
smaller and tends to plateau when frequency exceeds 3 days/week. The value of the added
5
Exercise, Injuries and Athlete Performance
improvement in VO2max that occurs with training more than 5 days/week is minimal to none,
and the incidence of injury increases disproportionately (ACSM, 1998).
The lactate threshold (LT), which is based on blood lactate concentration behavior at
different exercise intensities, is another important indicator of cardiorespiratory endurance
and provides a rating of perceived exertion (RPE). The LT may be thought of as the highest
VO2 that can be maintained without a sustained rise in blood lactate, since it has been defined
as the exercise intensity above which blood lactate concentration increases gradually and
ventilation also intensifies in a non-linear way to the consumed oxygen, although other
previous definitions exist (ACSM, 1998; Baldwin et al., 2000; Barros et al., 2004).
Although the optimal training frequency for improving LT and metabolic fitness is not
known and may or may not be similar to that for improving VO2max, exercise below the LT
may be considered light-to-moderate RPE, while exercise above the LT may be considered
hard-to-very hard RPE, depending upon the degree to which the VO2 exceeds the LT (ACSM,
1998). In exercises of light or moderate intensity, the blood lactate is produced at lower rates
and its concentration remains steady (varying between 2 and 4 mmol/L) (Leandro et al.,
2007), while for exercise intensities well above the LT (≥ 85% VO2max), blood lactate
concentration rises continuously and exercise tolerance is compromised (ACSM, 1998).
During high-intensity exercise, the intramuscular accumulation of lactic acid has long been
considered one of the most important factors in fatigue (Cairns, 2006).
Even though relative exercise intensities vary with the exercise type (endurance or
resistance-type exercise), frequency, duration of training and age of subject, intensity
classification, based on physical activity lasting up to 60 min, can be given according to Table
1 (ACSM, 1998; Leandro, 2007).
Response to exercise is divided into acute response and chronic adaptation. Acute
response is understood as the temporary physiological changes caused by an exercise session,
while chronic adaptations are changes caused by multiple sessions of exercise, featuring
training (Santos et al., 2007). Isolated exercise sessions elicit acute, transient cardiovascular
and metabolic responses. Frequent repetition of these isolated sessions produces more
permanent adaptations, referred to as the exercise training response. Exercise training
increases exercise capacity, which permits longer individual exercise sessions and a greater
acute effect (Thompson et al., 2001).
Table 1. Classification of relative exercise intensities, based
on physical activity lasting up to 60 min
Relative intensities
Endurance-type exercise
VO2max (%) and HRmax VO2R (%) and
Intensity
RPE
(%)
HRR (%)
Light
20–49
20–39
10–11
Moderate
50–69
40–59
12–13
Hard
70–89
60–84
14–16
Very hard
≥ 90
≥ 85
17–19
*Based on 8–12 repetitions for persons under age 50–60 years and 10–15
50–60 yr and older.
Resistance-type exercise*
Maximal voluntary
contraction (%)
30–49
50–69
70–84
≥ 85
repetitions for persons aged
6
Ana Luisa Miranda-Vilela
3. EXERCISE ADAPTATION
Today it is unanimously accepted that exercise, when practiced regularly, is crucial for a
healthy lifestyle, acting as a therapeutic agent and/or preventing numerous illnesses (Ferreira
et al., 2007). Physical activity involves the entire body in terms of energy metabolism,
hormonal mobilization and action, signal transduction process, immunological responses and
adaptations (Ji et al., 2008). Exercise is a stress and the corresponding adaptations to regular
training include improved cardiovascular function and antioxidant capacity, changes in body
composition and body weight (mass), improved mood, lower blood pressure, insulin
sensitivity (glucose tolerance) and biochemical cell changes (Wannamethee and Shaper,
2001; Laufs et al., 2005; Vancini et al., 2005), and enhanced immune function producing an
anti-inflammatory environment and reducing the risk of infection (Gleeson, 2000; Nieman,
2000). Endurance athletes also have serum high-density lipoprotein (HDL) cholesterol (HDLC) concentrations 40-50% higher than their sedentary counter-parts, triglyceride (TG) levels
20% lower and low-density lipoprotein (LDL) cholesterol (LDL-C) concentrations often
approximately 5-10% lower (Thompson et al., 2001).
Epidemiological evidence indicates that physical activity is associated with reduced risk
of coronary heart disease and mortality due to cardiovascular problems in middle age
(Wannamethee and Shaper, 2001; Thompson et al., 2007). Moderate physical activity
improves endothelial function and collateralization of vascularization and prevents the
progression of carotid atherosclerosis. Regular training also favors increased expression
(upregulation) of antioxidant enzymes with a consequent reduction in oxidative stress (Laufs
et al., 2005), besides reducing the capacity of leukocytes for oxidant release; it also leads to
an adaptation of antioxidant mechanisms, which may contribute to a limitation of exerciseinduced oxidative stress (Niess et al., 1999). It has also been observed that the extent of
damage to the DNA of trained individuals is small compared to that of untrained individuals,
suggesting that adaptation to endurance training can reduce the effects of oxidative stress on
DNA damage (Nies et al., 1996; Mastaloudis et al., 2004; Schneider and Oliveira, 2004), thus
perhaps preventing related diseases such as cancer. In summary, humans involved in regular
exercise have shown reduced oxidative damage to DNA during physical exertion
(Mastaloudis et al., 2004; Ji et al., 2008).
Chronic exercise of moderate intensity positively alters the redox status of cells and
tissues by reducing the basal levels of oxidative damage and increases resistance to oxidative
stress, being beneficial to health (Vancini et al., 2005). Changes in the oxidant-antioxidant
balance act as a trigger for redox homeostasis (Lecarpentier, 2007) and regular moderate
exercise results in adaptations in antioxidant capacity, which protects cells against the
deleterious effects oxidative stress, preventing subsequent cell damage (Vancini et al., 2005).
After a temporary increase in cellular RONS concentrations, the initial redox state can be
reestablished by numerous compensatory mechanisms. Some antioxidant genes, for example,
can be rapidly activated to cope with acute oxidative stress caused by hypoxia and ischemia,
whereas other genes are upregulated more slowly in response to chronic oxidative stress
provoked by energy demand during exercise training (Ji, 2007; Lecarpentier, 2007).
Additionally, nitric oxide production induces a direct feedback inhibition of NO synthase by
NO.
Exercise, Injuries and Athlete Performance
7
If frequency, intensity, and duration of training are similar (total kcal expenditure), the
training adaptations appear to be independent of the mode of aerobic activity (ACSM, 1998).
Thus, although single bouts of aerobic and anaerobic exercise can induce an acute state of
oxidative stress, which is indicated by an increased presence of oxidized molecules in a
variety of tissues (Fisher-Wellman and Bloomer, 2009), the effects of exercise express
characteristics of the hormesis phenomenon, which is a dose-dependent relationship in which
a low dose of a substance may increase the body’s tolerance for greater toxicity (Ji, 2007; Ji et
al., 2008). As a consequence, in accordance with the principle of hormesis, this ROS increase
appears necessary to allow for an upregulation in endogenous antioxidant defenses (FisherWellman and Bloomer, 2009). Redox homeostasis can be maintained in a near-equilibrium
stationary thermodynamic state or quasi-stable state (Forsberg et al., 2001a; Lecarpentier,
2007), and in a physiological range, the antioxidant response to a moderate increase in ROS
may be sufficient to reset the balance between ROS production and ROS-scavenging
capacity. Thus, cross-training that emphasizes the use of a variety of large muscle groups
(activities) may be beneficial to achieving a more well-rounded training effect (ACSM,
1998).
4. EXERCISE, OXIDATIVE STRESS AND INJURIES
FR production during exercise depends on several factors, such as frequency, intensity,
duration and the type of exercise performed (aerobic or anaerobic), as well as the subject
population tested (Vancini et al., 2005; Fisher-Wellman and Bloomer, 2009). When the redox
system is subjected to dramatic and/or long-lasting perturbations, it may behave in a manner
that is far from equilibrium, where instability and thermodynamic bifurcations toward chronic
pathological states may appear (Lecarpentier, 2007). Consequently, in opposition to its
potential beneficial effects, exercise, especially under circumstances such as unaccustomed
intensity or duration, increases the production of RONS and can lead to oxidative stress, even
in trained individuals (Ji, 1995; Niess et al., 1999; Lamprecht et al., 2004; Sureda et al.,
2005).
In this way, acute exercise, mainly if exhausting, or training above habitual intensity
induces oxidative stress, causing muscular injuries with consequent inflammatory process (Ji
and Leichtweis 1997; Sureda et al. 2005; Cruzat et al. 2007; Ferreira et al. 2007). Other
harmful organic changes may also take place, especially when the tissues, organs or systems
are not sufficiently adapted to withstand, without major homeostatic changes, the different
types of burden imposed on them (Ferreira et al., 2007). These types of exercise generally
overload the endogenous antioxidant system’s capacity, leading to an increase in plasma lipid
peroxidation (Ferreira et al. 2007) plus oxidative damage to muscles and other tissues (Ji and
Leichtweis 1997; Sureda et al. 2005; Traber 2006; Ferreira et al. 2007). Strong hormonal
changes, as well as temperature changes associated with intense and exhaustive exercise, will
be felt more or less intensely in most body cells (Ferreira et al., 2007).
8
Ana Luisa Miranda-Vilela
Figure 2. Oxidative damage and injuries induced by strenuous exercise.
Oxidative overload, although felt more intensely in the skeletal muscles, has also been
reported in many systems (Cruzat et al., 2007; Ferreira et al., 2007; Kim et al., 2007;
Lecarpentier, 2007), including heart (Ferreira et al., 2007), liver (Nagel et al., 1990; De Paz et
al., 1995; Ferreira et al., 2007; Kim et al. 2007), kidneys, lungs (Ferreira et al., 2007),
erythrocytes (Schmidt et al. 1988; Sureda et al. 2005; Yusof et al., 2007) and immune (Ji,
1999; Mooren et al. , 2002; Sureda et al. 2005; Belviranli and Gokbel, 2006; Cruzat et al.,
2007; Kim et al., 2007) and osteoarticular (Ferreira et al., 2007; Kim et al., 2007) systems.
The heart is affected because of high oxidative metabolic demand and large numbers of
mitochondria (Ferreira et al., 2007; Judge and Leeuwenburgh, 2007). The liver and other
tissues and organs are mainly affected by ischemia and reperfusion phenomena (Cruzat et al.,
2007). Since circulation is deviated to the active muscles during exercise, other tissues and
organs can suffer temporary hypoxia. As a consequence, after exercise these tissues receive
large amounts of oxygen, favoring ROS generation (Cruzat et al., 2007; Radak et al., 2007).
Additionally, there is an increase in the mechanical stress imposed on required skeletal
muscle fibers and cells of the osteoarticular and cardiovascular systems (Figure 2) (Ferreira et
al., 2007; Kim et al., 2007). These damaging effects, with their consequent inflammatory
processes, can jeopardize performance and may lead to overtraining syndrome, besides
potentially contributing to an increased future risk of cardiovascular disease (CVD) (Urso and
Clarkson 2003; Cruzat et al. 2007; Ferreira et al. 2007; Radak et al. 2007; Thompson et al.
2007).
Exercise, Injuries and Athlete Performance
9
4.1. Skeletal Muscle
In skeletal muscle the mitochondria constitute the main source and, simultaneously, the
main target of ROS. However, it is the activities of the enzymes xanthine oxidase and
phospholipase A2, the deamination of catecholamines and the infiltration of leukocytes after
exercise that constitute additional sources of ROS and consequently contribute decisively to
oxidative damage (Ferreira et al., 2007). Myoglobin may also be oxidized by autooxidation or
by FRs during ischemia/reperfusion, with production of H2O2. Then myoglobin can interact
with H2O2 and produce other radicals such as the peroxyl radical (Finaud et al., 2006).
Periods of intense exercise can increase oxidative stress due to temporary hypoxia and
reoxygenation occurring in the exercised muscle, according to the cyclical contractions and
relaxations established. During contraction, vascular compression provides a framework of
ischemia and, therefore, hypoxia; in relaxation reperfusion occurs and, consequently,
reoxygenation (Schneider and Oliveira, 2004). Since during exercise blood flow is diverted to
the exercising muscles, other muscles and tissues may also suffer temporary hypoxia (Cruzat
et al., 2007). As a result, these deprived muscles and tissues receive a large amount of oxygen
after exercise, favoring ROS generation (Schneider and Oliveira, 2004; Finaud et al., 2006;
Cruzat et al., 2007).
In the ischemia/reperfusion process, increased oxidative stress during and after exercise is
favored by the catabolism of purines. It has been proposed that the conversion of xanthine
dehydrogenase to its oxidized form (xanthine oxidase) by proteases activated by intracellular
Ca2+ favors ROS increase. During hypoxia, ATP is degraded to hypoxanthine, which
accumulates in the tissues. As a result, there is failure of cellular homeostasis, allowing a Ca2+
influx into the cells, which activates intracellular proteases to convert the xanthine
dehydrogenase to xanthine oxidase. During reperfusion, xanthine oxidase uses O2 to promote
the conversion of hypoxanthine to xanthine and then uric acid, causing a univalent reduction
of molecular oxygen to O2, with consequent generation of H2O2 e HO (hydroxyl anion) (Ji,
1999; Campos and Yoshida, 2004; Finaud et al., 2006; Cruzat et al., 2007; Ferreira et al.,
2007).
Acute and chronic exercise is associated with ultrastructural muscle damage, mainly
centered on the Z disk that anchors thin filaments and several intermediate filaments within
the sarcomere. Disturbances of the mitochondria, sarcoplasmic reticulum, A band, and
extracellular matrix have also been reported (Magaudda et al., 2004). Muscle damage may
range from an ultrastructural lesion of muscle fibers to trauma involving complete muscle
rupture, and it can be directly accessed by histological techniques or electron microscopy, or
indirectly by determining the specific efflux of cytosolic enzymes into the bloodstream
(Cruzat et al., 2007; Foschini et al., 2007). The morphological and ultrastructural injuries
induced by exercise are well documented in animal and human models (Armstrong et al.,
1983; Gibala et al., 1995; Morgan and Allen, 1999; Proske and Morgan, 2001; Stupka et al.,
2001; Magaudda et al., 2004). Muscle damage induced by one session of eccentric exercise
may result from disruption of connective tissue attached to adjacent myofibrils, the muscle
cell itself, the basal lamina adjacent to the plasma membrane, plasma membrane of the
muscle cell, the sarcomere, the reticulum sarcoplasmic, or a combination of these components
(Cruzat et al., 2007).
In humans, for ethical and logistic reasons, the evidence for exercise-induced damage has
been essentially studied in the blood, in both plasma and blood cells (Ferreira et al., 2007). In
10
Ana Luisa Miranda-Vilela
this context, an increase in the concentration of cytosolic proteins in the circulation after
exercise reflects muscular injuries. The proteins that are often evaluated are creatine kinase
(CK), lactate dehydrogenase (LDH), aspartate aminotransferase (AST) and myoglobin, which
are usually unable to cross the plasma membrane. The presence of these proteins in the blood
reflects significant changes in the structure and permeability of the myofibrillar membrane
(Cruzat et al., 2007; Apple et al., 1988; Agarwal and Ankra-Badu, 1999; Barbosa et al., 2003;
Lac and Maso, 2004; Gasper and Gilchrist 2005; Foschini et al., 2007). Among them, CK is
often best described as an indirect marker of muscle damage, especially after strength training
or other exercises that require predominantly eccentric actions (Barbosa et al., 2003, Foschini
et al., 2007).
4.2. Myocardium
As described for skeletal muscle, today it is now widely accepted that acute exercise,
especially if extensive, also promotes increased oxidative stress in the heart, leading to an
increase in markers of oxidative damage in this organ (Ferreira et al., 2007). The myocardium
appears to be particularly susceptible to all situations that promote an increase in metabolism,
which include acute physical exercise, since the oxidative metabolic capacity is very high due
to the abundance of mitochondria (Ferreira et al., 2007; Judge and Leeuwenburgh, 2007).
Increased oxidative stress in this organ during acute exercise is the result of dramatic
increases in oxygen consumption (Judge and Leeuwenburgh, 2007). Under these conditions,
the CK reaction is important for rapid resynthesis of ATP from creatine phosphate and ADP,
since the heart increases its work (Putney et al., 1984; Nascimben et al., 1996; Foschini et al.,
2007). Considering that CK is a cytosolic enzyme, increased serum levels of this enzyme after
acute exercise can also be indicative of oxidative damage in the myocardium.
Evidence suggests that oxidative stress plays an important role in the pathogenesis of
cardiovascular disease (Wattanapitayakul and Bauer, 2001; Molavi and Mehta, 2004;
Abramson et al., 2005; Belardinelli, 2007; Kasap et al., 2007). The presence of elevated
plasma levels of some markers of the inflammatory state, such as C-reactive protein (CRP) is
a predictive risk factor for acute coronary syndrome, myocardial infarction, peripheral arterial
occlusive disease and sudden cardiac death, both in healthy subjects and in patients with
established atherosclerotic disease (Mitka, 2004; Dummer et al., 2007). Increased serum
levels of AST enzyme have been used in the diagnosis of acute myocardial infarction since
1954. This transaminase is found in the cytoplasm and mitochondria of many cells, especially
liver, heart, skeletal muscle, kidney, pancreas and blood cells (Dewar et al., 1958). Although
the mentioned markers are not specific to assess myocardial injuries, increases in plasma
levels of CRP and AST after acute exercise can also be indicative of oxidative damage in the
myocardium.
4.3. Liver
Exercise also induces ROS generation and oxidative damage in the liver, such as lipid
peroxidation (Witt et al., 1992; Nagel et al., 1990; De Paz et al., 1995; Ferreira et al., 2007;
Kim et al., 2007). The amount of damage will depend on the intensity of exercise and training
Exercise, Injuries and Athlete Performance
11
status (Witt et al., 1992). The liver has a high metabolic rate, which is naturally associated
with a high flow of oxygen. However, this flow decreases significantly during exercise,
appearing to be similar to the ischemia/reperfusion phenomenon (Radak et al., 2008). Unlike
skeletal muscle, liver contains high levels of xanthine dehydrogenase which, during exercise,
is converted to xanthine oxidase, which in turn contributes as an additional source of ROS
and, consequently, oxidative damage (Ferreira et al., 2007; Radak et al., 2008). While a single
bout of intense exercise stimulates adaptations in the skeletal muscle’s antioxidant system
(Cruzat et al. 2007; Ferreira et al., 2007; Radak et al. 2007), the same is not the case with the
liver, which is oxidatively stressed (Radak et al. 2007). In animals, however, it has been
shown that exercise training promotes improvements in the antioxidant capacity of the liver,
inducing antioxidant enzymes and reducing ROS production (Ferreira et al., 2007; Radak et
al., 2008).
Liver damage may be evidenced by the efflux of some cytosolic enzymes into the
bloodstream, particularly alanine aminotransferase (ALT) and AST; the first being found in
high concentrations only in the liver (Dewar et al., 1958; Miranda-Vilela et al., 2009b).
Therefore, increased serum levels of these enzymes after acute exercise serve as an indicator
of oxidative damage in this organ.
4.4. Blood
4.4.1. Erythrocytes
It has been suggested that intense exercise of long duration and exhausting training may
also compromise our ability to detoxify ROS within the blood cells, and red blood cells
(RBCs) appear much more vulnerable to oxidative damage (Petibois and Déléris, 2005;
Sureda et al., 2005). This reflects their very limited biosynthetic capacity (nucleus absence), a
poor repair mechanism (Santos-Silva et al., 2001; Cazzola et al., 2003; Sureda et al., 2005),
the presence of large amounts of thiols (RSH) and polyunsaturated fatty acids (PUFA) in
their membrane and the high internal concentration of oxygen and hemoglobin, sources that
potentially promote oxidative processes (Ferreira and Matsubara, 1997; Cazzola et al., 2003).
Despite having an elaborate antioxidant defense system, which includes enzymes such as
catalase, superoxide dismutase and glutathione peroxidase, the efficiency of this system is
overcome by the magnitude of oxidative processes. Thus, oxidative stress occurs, leading to
hemolysis (Ferreira and Matsubara, 1997; Cazzola et al., 2003). In this case, the extracellular
hemoglobin becomes toxic due to the nature of oxidative iron ions contained in the heme
group, which participate in the Fenton reaction to produce ROS that cause cellular injury
(Akimoto et al., 2010).
During oxidative stress, the most common changes are lipid and membrane protein
oxidation, which may destabilize the cytoskeleton and impair cell survival (Petibois and
Déléris, 2005; Sureda et al., 2005; Yusof et al., 2007). Additionally, RBCs are highly exposed
to mechanical stress, as well as changes in cytosolic and extracellular pH (Petibois and
Déléris, 2005).
ROS can alter the chemical and physical properties of the membrane by modifying the
composition, packaging and distribution of their lipids, which leads to a structural change to
reduce membrane fluidity. This in turn can modify the activity of several membrane proteins
and accelerate erythrocyte senescence or even cause their premature removal from circulation
12
Ana Luisa Miranda-Vilela
(Cazzola et al., 2003; Petibois and Déléris, 2005). The oxidizing agents can convert the thiol
groups into disulfide components (RSSG), leading to membrane protein denaturation. In this
process, intracellular damage may occur, with hemoglobin (Hb) being oxidized to
methemoglobin (metHb), which precipitates, forming erythrocytic inclusion bodies called
Heinz bodies (Ferreira and Matsubara, 1997). In the human body, 3% of total hemoglobin
(about 750 g) is transformed by self-oxidation. This reaction, which produces metHb and
O2, can increase with exercise (Belviranli and Gökbel, 2006; Finaud et al., 2006). The
products of the peroxidation of lipid components of erythrocyte membrane may also induce
intracellular oxidative stress. The association of the phenomena of SH group oxidation,
Heinz body formation and lipid peroxidation can promote damage in the RBC membrane
(Ferreira and Matsubara, 1997).
During exhaustive exercise or training, destruction of RBC can occur not only for the
reasons mentioned above, but also due to plasma volume reduction. The displacement of
water out of the vascular space may act as chemical stress, since the loss of water through the
blood leads to dehydration of the erythrocyte, usually as a result of potassium loss.
Dehydration of RBCs is also influenced by the ischemia/reperfusion phenomenon. However,
in response to chemical stress, the body releases renin, aldosterone and vasopressin
(antidiuretic hormone or ADH) and the end result is an increase in plasma volume (Eichner,
1998; Petibois and Déléris, 2005). The immediate consequence, known as "sports anemia", is
characterized by reduced values of RBCs, hemoglobin and hematocrit and a change in mean
cell volume (MCV) (Carlson and Mawdsley, 1986; Eichner, 1998; Fallon et al., 1999).
Therefore it can be detected by hemogram, by the reduction of RBCs and a decreased
hemoglobin concentration and hematocrit in the blood (Schmidt et al., 1988). However, sports
anemia is a pseudoanemia, since the reduction in the values of RBCs, hemoglobin and
hematocrit occurs as a physiological adaptation to strenuous exercise and is due to
hemodilution caused by an increase in plasma volume (Carlson and Mawdsley, 1986;
Eichner, 1998). As this is a false anemia, the hemolysis that occurs with physical activity is
unlikely to cause anemia, and inadequate iron intake is the main cause of true anemia in
athletes, particularly in females (Vilardi et al., 2001; Eichner, 1998).
4.4.2. Immune System
Besides increasing oxygen consumption and inducing oxidative stress as a result of
increased production of FR (Sureda et al., 2005), strenuous exercise can cause disturbances in
the immune system and immune depression, increasing the risk of opportunistic infections,
particularly those affecting the upper respiratory tract (Gleeson and Bishop, 2000; Woods et
al., 2000). Immunosuppression in athletes involved in heavy training is undoubtedly
multifactorial in origin (Gleeson and Bishop, 2000; Gleeson, 2007). The circulating numbers
and functional capacities of leukocytes may be decreased by repeated bouts of intense,
prolonged exercise, probably due to the increased levels of stress hormones during exercise
and entry into the circulation of less mature leukocytes from the bone marrow. Falls in the
blood concentration of glutamine have also been suggested as a possible cause of the
immunodepression associated with heavy training, although the evidence for this is less
compelling. Also, there is increased production of ROS, and some immune cell functions can
be impaired by an excess of FR. During exercise, exposure to airborne pathogens is increased
due to the higher rate and depth of breathing, while an increase in gut permeability may also
Exercise, Injuries and Athlete Performance
13
allow increased entry of gut bacterial endotoxins into the circulation, particularly during
prolonged exercise in the heat. Hence a variety of stressors can suppress immune function,
and these effects, together with increased exposure to pathogens, can make the athlete more
susceptible to infection (Gleeson, 2007).
During exercise, leukocytes are recruited to the blood, and if muscle damage occurs the
cytokine level is enhanced (Pedersen and Bruunsgaard, 1995). Initially neutrophils and later
monocytes and lymphocytes are recruited to the site of inflammation where they produce
ROS and proteolytic enzymes (Cruzat et al., 2007). Neutrophil infiltration is stimulated by
chemotactic factors, including prostaglandins, tumor necrosis factor (TNF)-α, and
interleukins (IL)-1β and IL-6 (Cruzat et al., 2007), while increased levels of adrenalin, and to
a lesser degree noradrenalin, are believed to be the main factors responsible for recruitment of
all lymphocyte subsets (NK, T and B cells) (Bruunsgaard and Pedersen, 2000). IL-6 acts as a
primary mediator of the acute phase reaction, stimulating the hepatic production of acute
phase proteins such as C-reactive protein (CRP) and protease inhibitors. It also restricts the
extent of the inflammatory response by activating synthesis of inflammatory cytokines and
stimulates the pituitary gland to release adrenocorticotropic hormone (ACTH), which
promotes increased release of the cortisol hormone by the adrenal cortex (Cruzat et al., 2007).
The catecholamines adrenalin and noradrenalin, together with growth hormone, may mediate
the acute effects on neutrophils, whereas cortisol exerts its effect within a time lag of at least
2 hours (Bruunsgaard and Pedersen, 2000). After prolonged, intense exercise, the hormonal
changes and the generation of ROS can inhibit the proliferation of lymphocytes, while
oxidative damage can activate apoptotic processes in these cells (Sureda et al., 2005). Thus,
the number of lymphocytes in the blood is reduced and the function of natural killer cells is
suppressed, impairing cellular-mediated immunity; furthermore, secretory immunity is also
impaired. During this temporary time of immunodepression, often referred to as “the open
window”, the host may be more susceptible to micro-organisms bypassing the first line of
defense (Pedersen and Bruunsgaard, 1995). Thus, training and competitive surroundings may
increase the athlete’s exposure to pathogens and provide optimal conditions for pathogen
transmission (Gleeson and Bishop, 2000).
The data on whether competitive athletes have an increased incidence of infections
compared with the general community has been inconclusive. However, it is generally agreed
that the period of vulnerability for elite athletes coincides with the intense training undertaken
immediately prior to or during a competition and may not follow the normal seasonal patterns
observed in the general community (Gleeson, 2000). The relationship between exercise and
susceptibility to infection has been modeled in the form of a “J”-shaped curve, where
moderate activity may enhance immune function above sedentary levels, while excessive
amounts of prolonged and high-intensity exercise may impair immune function (Nieman,
1994; Gleeson, 2007).
4.4.3. Plasma
The increased metabolism imposed by exercise promotes increased ROS production,
which can also induce oxidative damage in plasma, together with RBCs the fraction most
susceptible to lipid peroxidation (Sureda et al., 2005).
In humans, most studies investigating lipid peroxidation have examined the presence of
lipid peroxides or lipid peroxidation byproducts, such as conjugated diene, lipid hydrocarbons
(aliphatic hydrocarbons and polycyclic aromatic hydrocarbons - PAH) and thiobarbituric acid
14
Ana Luisa Miranda-Vilela
reactive substances (TBARS), such as malonaldehyde (MDA). Changes in plasma
concentrations of vitamins C and E and oxidized glutathione (GSSG) have also been used to
indicate increased oxidative reactions. It is thought that these antioxidant vitamins may be
mobilized from the tissues to combat oxidative stress in another part of the body. The GSSG
efflux into the plasma is considered an indicator of oxidative stress, since the reduced
glutathione (GSH) is oxidized to GSSG in cells in response to the increase in FR (Clarkson
and Thompson, 2000).
After exhaustive exercise, there are also changes in serum iron and ferritin, total binding
capacity of iron and transferrin saturation. Although such changes are similar to those found
in chronic anemia, they just reflect the acute phase response (Fallon et al., 1999). Moreover,
during intense exercise, large amounts of lactic acid can be produced by skeletal muscle and
released into the bloodstream for subsequent removal by peripheral tissues. Lactacidemia
exercises, i.e., with exercise intensity above that at which lactate begins to accumulate in the
bloodstream (anaerobic threshold - AT), also have a direct influence on the level of plasma
lipid peroxidation by inhibiting the antioxidant defense system of erythrocytes (Ribeiro et al.,
2004; Petibois and Déléris, 2005; Manchado et al., 2006).
4.4.3.1. Lipoproteins
There is significant evidence of the involvement of ROS in the pathogenesis of
atherosclerosis. The oxidative modification of low density lipoproteins (LDL) plays a key
role in amplifying the inflammatory response, mediating a variety of pro-inflammatory
immune processes that determine the progression of atherosclerosis (Magnusson et al., 1994;
Steinberg, 1997; Santos-Silva et al., 2001; Liu et al., 2004; Laufs et al., 2005; Kiechl et al.,
2007). ROS contribute to the onset and progression of atherosclerotic lesions, which favor the
infiltration and accumulation of lipids in the subendothelial space. ROS produced by smooth
muscle cells, endothelial cells or macrophages can modify LDL that, when oxidized,
promotes the recruitment of circulating monocytes, the accumulation of resident
macrophages, phagocytosis and finally the accelerated formation of "foam cells" (composed
of lipid-rich macrophages) and fatty streaks, characteristic of early lesions (Steinberg, 1997;
Santos-Silva et al., 2001). Additionally, epidemiological studies suggest that increased blood
viscosity is also related to atherogenesis and cardiovascular risk (Santos-Silva et al., 2001).
In the presented context, exhaustive and high intensity exercise may promote
atherosclerosis and cardiovascular risk (Schneider and Oliveira, 2004), since this type of
exercise has been associated with increased oxidative stress in the vascular endothelium
(Laufs et al., 2005; Petibois and Déléris, 2005) and increase in blood viscosity (Santos-Silva
et al., 2001). Although prolonged exercise is associated with a small plasma TG reduction,
which may reflect lipoprotein (LDL rich in triglycerides) use as a fuel or be attributed to
reduced hepatic secretion of these lipoproteins (Hardman, 1998), inhibition of the antioxidant
defense system of erythrocytes by chemical stress induced by high-intensity exercise
(lactacidemia) may favor LDL oxidation and hence atherosclerosis (Santos-Silva et al., 2001;
Petibois and Déléris, 2005).
Exercise, Injuries and Athlete Performance
15
4.5. DNA
Normal cellular metabolism is well established as an indigenous source of ROS and
because these species are related to basal levels of DNA damage detected in normal tissues
(Cooke et al., 2003), exhaustive exercise can also lead to damaged bases in DNA (Moller et
al., 2001; Mastaloudis et al., 2004a). So, there has been growing interest in exercise-induced
DNA damage due to its potential involvement in various disease states, such as
carcinogenesis, the ageing process, lifestyle-related diseases and age-related degenerative
diseases (Osterod et al., 2001; Kasai and Kawai, 2006; Loft and Møller, 2006).
Endurance exercise increases whole body oxygen consumption 10-20 fold, which at the
level of the skeletal muscle increases 100-200 fold. This increase in oxygen utilization may
result in the production of ROS at rates that exceed the body’s detoxification capacity
(Mastaloudis et al., 2004a). Because the byproducts of oxidative phosphorylation reactions
can diffuse from mitochondria to reach nuclear DNA and induce damage (Zhao et al., 2007),
this type of exercise can result in DNA strand breaks and oxidatively damaged bases in DNA
(Mastaloudis et al., 2004a). Indeed, it has been shown that exhaustive exercise induces DNA
damage in circulating leukocytes (Nies et al., 1996; Mastaloudis et al., 2004a; Demirbag et
al., 2006) and that it can induce apoptosis through different mechanisms such as reduced
levels of intracellular glutathione, alteration of mitochondrial proteins or by directly
damaging the DNA (Mooren et al., 2002).
Past studies have primarily used the 8-hydroxydeoxyguanosine assay to assess DNA
damage, but this method has been criticized for its susceptibility to artifact formation and the
large amount of DNA required for analysis (Mastaloudis et al., 2004a). More recently, the
comet assay (also known as the single-cell gel electrophoresis assay) has come into favor to
assess oxidatively damaged DNA, thanks to its greater simplicity, sensitivity, stability and
accuracy (Mastaloudis et al., 2004a; Collins, 2009). This technique detects single and doublestrand breaks, alkali labile sites, incomplete repair sites, cross-linking DNA-DNA and DNAprotein in individual cells (Mastaloudis et al., 2004a; Brendler-Schwaab et al., 2005).
5. ANTIOXIDANTS AGAINST SPORTS-RELATED INJURIES
Sports-related injuries are one of the main reasons why athletes prematurely abandon a
sports career, spend long periods excluded from training and competitions, or experience a
decline in sports performance, even causing functional limitations at more advanced ages
(Artioli et al., 2007). Thus, many athletes and even individuals participating in regular
exercise programs consume antioxidant supplements to avoid exercise-induced oxidative
stress and injuries (Sureda et al. 2005; Cruzat et al. 2007; Ferreira et al. 2007; Radak et al.
2007; Yfanti et al. 2010). However, considering that ROS can act as signals that regulate
molecular events of cellular adaptation to exercise, the practical consequence is that
antioxidant supplements can inhibit beneficial adaptive responses associated with improved
athletic performance. Therefore, the prudent recommendation for physically active
individuals is a diet rich in antioxidants from natural foods (Clarkson and Thompson, 2000),
and the recommendation of the use of antioxidant supplements should be made only for those
16
Ana Luisa Miranda-Vilela
cases in which exhaustive exercise causes oxidative stress and cell damage (Ji and
Leichtweis, 1997; Gomez-Cabrera et al., 2008).
Biological antioxidants play a fundamental role in protecting against oxidative stress
induced by exercise. Deficiency or depletion of these antioxidants has been associated with
exacerbated tissue damage, while antioxidant supplementation has generated variable results
(Ji, 1995). Many studies have investigated the impact of antioxidant status in the oxidative
damage induced by exercise (Ji, 1995) and for this purpose, most interventions have focused
on nutritional factors such as antioxidant vitamins or drugs that serve as mediators of
oxidative stress (Alessio et al., 2002). In order to reduce the deleterious effects promoted by
strenuous exercise, the most studied alternatives include nutritional supplementation with
vitamin E, vitamin C, creatine and glutamine (Clarkson and Thompson, 2000; Cruzat et al.,
2007).
Studies that have examined the effects of antioxidant supplementation used athletic
performance only or together with changes in oxidative stress as outcome measures (Urso and
Clarkson, 2003) or else serum inflammation markers and/or cell damage and DNA damage
markers (Mastaloudis et al., 2004a,b; Miranda-Vilela et al. 2009a,b; Miranda-Vilela et al.,
2010a; Miranda-Vilela et al., 2011a). Different studies have shown that supplementation with
vitamin E, creatine and glutamine can attenuate oxidative stress or reduce the amount of cell
damage caused by exhaustive exercise (Cruzat et al., 2007). Vitamin E administered together
with vitamin C has also been shown to prevent increases in lipid peroxidation but had no
apparent effect on DNA and muscle damage (Traber, 2006); nor did this combination have
any effect on inflammatory markers (Mastaloudis et al., 2004). Vitamin C alone or in a
mixture with vitamin E and beta-carotene had little or no effect, although the reduction in
vitamin C body stores may contribute to increased oxidative stress (Kanter et al., 1993;
Cruzat et al., 2007).
On the other hand, there are some aspects that need to be better investigated before
antioxidant intervention, because its potential effects can depend on the nature of the
antioxidant, its dose and the prevailing oxygen partial pressure (PO2) in the tissues.
Carotenoids, for example, are an effective antioxidant under low PO2 conditions (Borek,
2004; Hermes-Lima, 2004; Ferreira and Matsubara, 1997); while under high PO2 they are less
efficient and may even act as pro-oxidants due to auto-oxidation (Borek, 2004). Conversely,
vitamin E (α-tocopherol) is an efficient antioxidant for cells submitted to high PO2, such as
those of the lungs (Borek, 2004). Compounds with antioxidant properties may also have
antioxidant or pro-oxidant effects, depending on dose. While in nutritional levels these
compounds seem to have a protective effect, at high doses they can have deleterious effects
(Panayiotidis and Collins, 1997; Hercberg et al., 1998; Antunes and Takahashi, 1999; Collins,
2001; Paolini et al., 2003; Hercberg et al., 2006).
Considering the entire context presented above, the importance of research on natural
antioxidants has increased greatly in recent years. Since the 1980s there has been a
considerable broadening of the search for natural antioxidants that can be included in the diet
as substitutes for synthetic antioxidants (Degáspari and Waszczynskyj, 2004). Following this
line, our group has demonstrated that a carotenoid-rich oil extracted from pequi (Caryocar
brasiliense), a typical fruit of the Brazilian Cerrado, has anti-inflammatory properties, besides
reducing arterial pressure, exercise-induced DNA and cell damages, lipid peroxidation and
anisocytosis in runners (Miranda-Vilela et al., 2009a; Miranda-Vilela et al., 2009b; MirandaVilela et al., 2010a; Miranda-Vilela et al., 2011a). However, pequi oil was particularly
Exercise, Injuries and Athlete Performance
17
efficient in reducing DNA damage for the age group of 20-40 years and a distance of 8-10
Km, indicating that long-distance races can be harmful, mainly for older athletes, due to
oxidative stress above organism adaptability (Miranda-Vilela et al., 2011b). Although the
protective effects of pequi oil are unquestionable, some of these responses were influenced by
genetic polymorphisms related to oxidative stress and inflammatory markers. Given this,
knowledge on how individual genetic differences can affect response to antioxidant
supplementation and how diet interacts with the human genome to influence performance,
health and disease is of unquestionable importance to the athlete’s performance and health.
6. ATHLETE’S PERFORMANCE
Although performance is a term difficult to define, athletic performance is usually seen as
a set of characteristics such as agility, muscle power, speed, equilibrium and coordination,
flexibility, force and muscular resistance, cardio-respiratory resistance and corporal
composition, among others, which lead to better physical aptitude. Endurance sports
performance, for example, is associated with the interplay of several physiological factors,
especially those related to lung and heart capacities, measured through VO2max, VO2R, HRmax,
HRR and LT, among others related to energy metabolism or cardiorespiratory fitness. Muscle
efficiency has been less studied in the scientific literature than other endurance phenotype
traits, although it may be a critical factor determining endurance performance (GómezGallego et al., 2009).
The phenomenon of human physical performance has long been of interest to specialists
in sports medicine and exercise physiologists (Dias et al., 2007). In this context,
anthropometric assessment, cardiopulmonary parameters and analysis of hormonal,
immunological and enzymatic processes have been carried out (Bouchard et al., 1995; Apple
et al., 1988; Lac and Maso, 2004; Ghorayeb et al., 2005; Cruzat et al., 2007; Dias et al., 2007;
Dourado, 2007; Foschini et al., 2007; Foschini et al., 2008). However, some of those
characteristics such as height, body mass, fat, muscle strength, flexibility, speed, aerobic
capacity, muscle fiber composition and ability to adapt to the training are, to some extent,
genetically determined (Bouchard et al., 1995; Beunen and Thomis, 1999; Smith, 2003; Lippi
et al., 2009). Thus, the status of human physical performance is a multifactorial phenotype,
influenced by several factors, including physique and biomechanical, physiological,
metabolic, behavioral, psychological, and social characteristics (Bouchard et al. 1997;
Rankinen et al. 2000; Ostrander et al 2009). Consequently, it requires the integrated
combination of environmental (such as specific training and nutritional counseling) and
genetic (which are beyond the control of athletes and technicians) factors; genetic
predisposition has major implications for the genetic characterization of the individual as an
outstanding athlete (Skinner, 2002; Smith, 2003, Dias et al., 2007, Lippi et al., 2009).
7. GENETICS-BASED PERFORMANCE
The variability of individual biological and mechanical responses, particularly in the elite
athletes of each specific modality, allows the screening of candidate genes. A large number of
18
Ana Luisa Miranda-Vilela
genetic markers have been well documented, showing association with physical performance
phenotypes. However, it is important to note that multiple biological and environmental
factors are determinants of performance, and the analysis of a single gene alone does not
determine the phenotype of an athlete, since the athletic characteristics responsible for
contributing to a good sports performance are determined by a number of genes which are
subjected to environmental action (Bouchard et al., 1997; Skinner, 2002; Dias et al., 2007).
Thus, human genetic research is based on the application of biomarkers to assess the genetic
characteristics and their relation to the environment, aiming to understand the desired
phenotype that, in this case, would be sports performance (Bouchard et al., 1997; Dias et al.,
2007). Currently, the genes themselves and their products have been used as biomarkers in
human genetic research (Bouchard et al., 1997; Dias et al., 2007), and genetic mapping with
newer approaches such as genome-wide association may yield novel insights into the
physiological responses to exercise.
Over the last two decades, a number of groups have begun to investigate the influence of
candidate gene polymorphisms on endurance performance and, arising from their studies, a
large number of genetic variants have been well documented, showing association with
physical performance-related phenotypes (Ostrander et al. 2009; Schoenfelder, 2010). By
2005, 170 genetic variations (165 autosomal and 5 X-linked) had been identified to improve
athletic performance when inherited (Dias et al., 2007; Bray et al., 2008; Ostrander et al.
2009); most of them involved some aspect of energy metabolism or cardiovascular function,
and presented heritability estimates varying from 20% to 75% (MacArthur and North 2005;
Schoenfelder 2010). Muscle efficiency has been less studied in the scientific literature than
these traits (Gómez-Gallego et al. 2009), and the genetic contribution to variation in the
relative proportions of skeletal muscle fiber types is estimated as lying between 40% and 50%
(MacArthur and North 2005). The 2007 update included 239 genes and quantitative trait loci
(QTL) related to human performance and health-related fitness genes, besides mitochondrial
genes that that have been shown to be associated with exercise intolerance, fitness, or
performance-related phenotypes (Bray et al., 2008). Moreover, superimposed on this genetic
variability, epigenetic1 modifications thought to be modulated by environmental and lifestyle
factors, such as nutrition and hormonal status, could amplify biological diversity
(Schoenfelder, 2010).
Given the wide range of genetic factors connected to performance, this chapter intends to
approach a small number of potential genetic variants related to oxidative stress and injuries
previously addressed, as well as some others widely studied in the context of performance
and health-related fitness phenotypes.
7.1. Polymorphisms Related to Oxidative Stress
Many potentially significant genetic variants related to oxidative stress have already been
identified (Forsberg et al., 2001; Morgenstern, 2004). Among them, several single nucleotide
polymorphisms (SNPs) in the antioxidant enzyme genes have been reported to produce
1
Epigenetics is any regulatory activity of genes that does not involve changes in DNA sequence (genetic code) and
can persist for a generation or more. Epigenetic processes include covalent modifications of histones,
methylation of cytosines in DNA, and gene regulation by noncoding RNA.
Exercise, Injuries and Athlete Performance
19
altered levels or activities of those enzymes, leading to abnormal free radical defense
mechanisms (Bastaki et al., 2006). In such circumstances, ROS may interact with cellular
biomolecules, such as DNA, with potentially serious consequences for the cell (Cooke et al.,
2003). Similarly, activation of the renin angiotensin system has been associated with
increased vascular superoxide anion production (Münzel and Keaney, 2001), so the
insertion/deletion polymorphism of the angiotensin I-converting enzyme (ACE) gene can
influence vascular oxidative stress, besides being associated with performance in endurance
sports and with an increased response to endurance training. Moreover, the ability of the
serum glycoprotein haptoglobin (Hp) to block hemoglobin-induced oxidative stress and
damage is reportedly phenotype-dependent (Carter and Worwood, 2007).
7.1.1. Polymorphisms of Antioxidant Enzymes’ Genes
7.1.1.1. Superoxide Dismutase (SOD)
Superoxide dismutase (SOD) is one of the most important enzymes to act against
superoxide anions in tissues, by catalyzing the dismutation reaction of O2• to molecular
oxygen and H2O2 (Akyol et al., 2005; Nakamura et al., 2005). It is divided into three
isoforms: copper zinc superoxide dismutase (Cu/ZnSOD or SOD1) in the intracellular
cytoplasmic compartments, manganese superoxide dismutase (MnSOD or SOD 2) in the
mitochondria, and extracellular SOD (ECSOD or SOD3) on the endothelial membrane
surface (Nakamura et al., 2005).
Cu/ZnSOD (EC 1.15.1.1) is coded by the SOD1 gene located at chromosome 21q22.1
and it is specifically expressed in cytosol and associated with organelles, including
mitochondrial intermembrane space (Niwa et al., 2007; Wang et al., 2008; Magrané et al.,
2009). More than 100 mutations in the SOD1 gene have been reported, with numerous
variants causing familial amyotrophic lateral sclerosis through the gain of a toxic function
(Niwa et al., 2007). There are several hypotheses for mutant SOD1 toxicity, and among them
are the contribution to mitochondrial dysfunction (Magrané et al., 2009) and enhanced redox
stress caused by dominant mutations (Harraz et al., 2008). Although none of these mutations
have been studied in the context of physical fitness or performance-related phenotypes, both
contributions could affect them, by increasing oxidative stress and decreasing the energy
supply during exercise. Moreover, Cu/ZnSOD has been shown to inhibit the superoxidestimulated osteoclastic bone resorption in vitro (Wang et al., 2008), besides exerting an
impact on femoral mechanical characteristics, impairing femoral bending strength and
stiffness in growing knockout female mice. Also, an important role has been suggested for
Cu/ZnSOD in inflammation, suggesting that alterations in the activity of Cu/ZnSOD may
affect the immune response and pathologies in which inflammation is involved (Marikovsky
et al., 2003).
Mitochondria are the major source of reactive oxygen species in a resting cell. If
antioxidant protection is inadequate this will result in oxidative stress and cause
mitochondrial dysfunction. MnSOD (EC1.15.1.1) is the only known enzyme that scavenges
the superoxide radical within mitochondria, providing the main defense against oxidative
stress in this organelle (Elsakka et al., 2007). It is coded by a nuclear gene located on
chromosome 6q25.3 and synthesized with a mitochondrial targeting sequence (MTS), which
is cleaved in the mitochondrial matrix to produce the active enzyme (Bastaki et al., 2006;
Akyol et al., 2005; Elsakka et al., 2007). The common polymorphism consisting of a single
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Ana Luisa Miranda-Vilela
nucleotide change in the region of the DNA encodes the signal sequence, such that the
presence of either an alanine or a valine has been suggested; these would induce a
conformational change from an α-helix to a β-sheet. This change has been reported to impact
mitochondrial processing efficiency, affect the transport of MnSOD to mitochondria, and
decrease MnSOD efficiency against oxidative stress, being associated with diseases related to
oxidative stress and abnormal free radical defense mechanisms (Rosenblum et al., 1996;
Mitrunen et al., 2001; Kinnula et al., 2004; Olson et al., 2004; Akyol et al., 2005; Choi et al.,
2008). Despite these associations, it has been suggested that an overexpression of MnSOD
such as that in the wild type genotype could increase production of H2O2, which, if not
subsequently neutralized and converted to H2O and O2, could contribute to further
generations of ROS (Slanger et al., 2006).
In this context, MnSOD heterozygosis has been suggested to favor defense against
oxidative stress in runners, since the Val/Ala genotype presented the lowest damages to DNA
and tissues, as well as the lowest lipid peroxidation levels, besides having a better response to
the antioxidant supplementation with pequi oil against exercise-induced damages (MirandaVilela et al., 2009a). Moreover, MnSOD has been reported to significantly influence results
of CK in runners, with a possible association between the variant genotype with lower muscle
damage and higher DNA damage, suggesting that the mechanisms of muscle and DNA
damages are independent and could operate in different ways (Akimoto et al., 2010). Because
MnSOD expression is induced by oxidative stress, hypoxia, tumor necrosis factor  (TNF)
and interleukin-1 (IL-1) (Elsakka et al., 2007), and all of these factors are involved in the
response to endurance exercise, this polymorphism in the MTS of MnSOD deserves to be
further investigated in the context of improving performance, mainly among elite athletes of
different modalities.
Extracellular SOD (EC 1.15.1.1) is encoded on 4p15.3-p15.1 (OMIM*185490) and is the
major SOD isoenzyme in extracellular fluids such as plasma, lymph, and synovial fluid,
although it also occurs in tissues. Ninety percent of ECSOD is located on the bond to the
endothelial cell surfaces (Juul eta l., 2004; Nakamura et al., 2005) and is distinguished from
the other SOD isoenzymes by its heparin-binding capacity: ECSOD binds to the surface of
endothelial cells through the heparan sulfate proteoglycan in the glycocalyx of endothelial
cell surfaces and in connective tissue matrix, especially in the arterial wall (Juul et al., 2004).
In the vascular system, ECSOD reduces free radical toxicity by scavenging superoxide anions
in and around the endothelial cells (Nakamura et al., 2005). It has also been reported that
ECSOD effectively prevents LDL oxidation in vitro and plays an important role in
attenuating free radical damage of LDL in the tissues (Nakamura et al., 2005).
Although ECSOD provides important antioxidant defense against damage from free
radicals, increased levels of this enzyme, such as those that occur in the polymorphism
Arg213Gly, may in some cases lead to cell damage by an overproduction of H2O2, especially
in individuals with a decreased capacity to remove this highly reactive compound by
glutathione peroxidase or catalase. Indeed, the concentration of ECSOD in the plasma of
individuals who are homozygous for this variant is increased 10- to 30-fold compared with
non-carriers, and it has been suggested that this phenotype is caused by a reduction in heparin
affinity (Juul et al., 2004; Petersen et al., 2005). ECSOD Arg213Gly cannot dismutate
superoxide anions in and around the endothelial cells and may facilitate atherosclerosis
because the mutated ECSOD has a lower affinity for endothelial cells via the heparin-binding
domain than the native form (Nakamura et al., 2005; Petersen et al., 2005). Because
Exercise, Injuries and Athlete Performance
21
inflammation is important in the start, progression, and clinical outcome of atherosclerosis
(Brull et al., 2003), strenuous exercise, mainly if above habitual intensity of effort, or training
with very elevated frequency, can exceed the benefits of physical activity, compromising
performance and potentially contributing to an increased future risk of cardiovascular disease
(CVD) in athletes (Urso and Clarkson 2003; Cruzat et al. 2007; Ferreira et al. 2007;
Thompson et al. 2007). Sudden deaths in athletes are usually caused by previously
unsuspected cardiovascular disease, with the vast majority of deaths in middle-aged athletes
caused by atherosclerotic coronary artery disease (CAD) (Maron et al., 1996; Maron and
Zipes, 2005; Thompson et al., 2007). Thus, the Arg213Gly polymorphism seems to be
important in contexts, performance and CAD risk for middle-aged athletes, needing to be
better investigated in these respects.
7.1.1.2. Catalase (CAT)
CAT (EC 1.11.1.6) is a heme enzyme whose main role involves controlling H2O2
concentrations in human cells, converting H2O2 into H2O and O2. With SOD and glutathione
peroxidase, CAT constitutes a primary defense against oxidative stress (Ambrosone et al.
2005). Although some polymorphisms in the CAT gene (locus 11p13) have been reported to
cause acatalasemia (Wen et al., 1990) or to be associated with sporadic aniridia (congenital
absence of the iris) (Boyd et al., 1986) or essential hypertension (Jiang et al., 2001), the CAT
gene presents a benign polymorphism, CAT 21A/T (ref SNP ID: rs7943316), located inside
the promoter region close to the start site (Ukkola et al. 2001; Góth et al. 2004). For this
polymorphism, no effects on catalase expression, catalase activity or association with
disease/pathological changes have been reported (Góth et al., 2004), besides the heterozygous
AT being associated with higher values of mean corpuscular hemoglobin (MCH) in runners.
Because MCH indicates the average amount of oxygen-carrying hemoglobin inside and in
view of the fact that CAT’s high concentrations in erythrocytes provides defense against high
concentrations of hydrogen peroxide, results suggest that the CAT 21A/T variant allele can
influence endurance performance to improve the blood oxygen-carrying capacity (MirandaVilela et al., 2010a). Moreover, the variant genotype (TT) has been positively influenced by
pequi-oil supplementation against DNA damage, which did not occur with the wild genotype
(Miranda-Vilela et al., 2011b).
7.1.1.3. Glutathione Peroxidase (GPX)
The glutathione peroxidase family is the largest of the selenoprotein gene families.
Glutathione peroxidases (GPX, EC 1.11.1.9) are named for their ability to use glutathione as a
reducing substrate. GPX1 and GPX2 appear to have similar substrate specificity, catalyzing
the reduction of hydrogen peroxide to water, but differ in their tissue distribution, with GPX1
expression being particularly abundant in erythrocytes and GPX2 expression being restricted
primarily to the gastrointestinal tract and the liver in humans (Chu et al., 1993; Foster et al.,
2006). GPX3 (extracellular or plasma) is a circulating plasma selenoprotein and is able to
utilize thioredoxin reductase, thioredoxin or glutaredoxin as reductants. GPX4 reduces
phospholipid hydroperoxides, localizes to the mitochondria or to the nucleus and the cytosol,
and appears to be essential for survival (Foster et al., 2006).
In view of what has been said in this chapter and elsewhere about exercise-induced
oxidative stress and injuries, the altered expression of GPX isoenzymes could compromise
performance by promoting oxidative stress and injuries mediated by H2O2, mainly for those
22
Ana Luisa Miranda-Vilela
athletes overexpressing SOD enzymes, but deficient in neutralizing H2O2, while decreased
GPX2 activity could also favor infections in athletes who exercise strenuously. From this
point of view, polymorphisms in the GPX genes associated with decreased enzyme activities
could be a target for further investigations in the context of performance. For example, it has
been reported that CAT 21A/T and GPX1 Pro198Leu polymorphisms influenced results of
mean cellular volume (MCV) and MCH of erythrocytes in runners, consequently influencing
their capacity to carry oxygen (Miranda-Vilela et al., 2010a). Moreover, GPX1 knockout
mice have a normal phenotype, but are highly sensitive to oxidative stressors, raising the issue
of the role of GPX1, especially under conditions of oxidative stress (de Haan et al., 1998).
Considering these findings, polymorphisms in the GPX1 gene which decrease the GPX1
enzyme activity could favor hemolytic events, mainly in those endurance athletes
overexpressing SOD enzymes, although this deleterious effect could be attenuated by CAT
activity. Also, plasma GPX3 deficiency related to genetic polymorphisms in the promoter
region of the GPX3 gene has been reported to increase extracellular oxidant stress, decreases
bioavailable nitric oxide, and promotes platelet activation (Voetsh et al., 2007). Such
decreased GPX3 activity can compromise performance and potentially contribute to an
increased future risk of cardiovascular disease (CVD) in athletes, mainly those who exercise
extraneously and had overexpression of ECSOD due to the Arg213Gly polymorphism.
7.1.2. Haptoglobin (Hp)
Haptoglobin (Hp) is an integral part of the immune acute phase response, which binds
free hemoglobin (Hb), preventing oxidative damage and modulating immune function. The
complex Hp-Hb also functions as a scavenger of nitric oxide (NO), a free radical vital in basal
blood flow regulation and vascular homeostasis, regulating NO bioavailability and vascular
homeostasis (Carter and Worwood, 2007). Several functional differences reported between
Hp phenotypes could have important biological and clinical consequences. These differences
are explained by a phenotype-dependent modulation of oxidative stress and prostaglandin
synthesis (Carter and Worwood, 2007; Alayash, 2011). Hp polymorphism is associated with
the prevalence and clinical evolution of many inflammatory diseases (Alayash, 2011) and
recently, it has also been suggested as a possible determining factor in runners’ performance
(Akimoto et al., 2010), influencing aerobic capacity (Miranda-Vilela et al., 2010a) and,
mainly when in interaction with angiotensin-converting enzyme (ACE) polymorphism, also
influencing lipid peroxidation and CK values (Akimoto et al., 2010).
7.2. I/D Polymorphism of the Angiotensin-Converting Enzyme (ACE)
The angiotensin-converting enzyme I (ACE, EC 3.4.15.1) plays an important role in
circulatory homeostasis and blood pressure control, by cleaving angiotensin I to produce the
potent vasoconstrictor angiotensin II and by inactivating the vasodilator bradykinin (Can et
al., 2005; Dias et al., 2007).
Plasma levels of ACE in humans are related to I/D polymorphism in the ACE gene (locus
17q23). This polymorphism consists of the absence (deletion or "D" allele) or presence
(insertion or "I" allele) of an Alu sequence of 287 base pairs (bp) in intron 16 of the ACE
gene (Fonseca and Izar, 2004; Dias et al., 2007). The deletion is associated with higher levels
of transcription of messenger RNA and, consequently, with higher expression of ACE. Thus,
carriers of the DD genotype have higher ACE levels than those with ID or II genotypes
(Fonseca and Izar, 2004).
Exercise, Injuries and Athlete Performance
23
The ACE I/D polymorphism has attracted considerable attention regarding its association
with human physical performance (Dias et al., 2007), since it has been associated with better
performance in endurance sports and with an increased response to endurance training (Can et
al., 2005). Studies have shown that the I allele is more frequent in endurance athletes,
whereas the D allele occurs more in athletes of strength and muscular explosion (Dias et al.,
2007). Additionally, activation of the renin angiotensin system has been associated with
increased vascular superoxide anion production (Münzel and Keaney. 2001), so the ACE I/D
polymorphism can influence vascular oxidative stress.
7.3. Polymorphisms Related to Aerobic Capacity
7.3.1. Erythropoietin (EPO) and Polymorphisms in EPO Gene and Its Receptor (EpoR)
Erythropoietin (EPO) is the main endogenous hormone regulator of erythropoiesis. It is a
glycoprotein and its expression is induced in the kidneys and liver by anemia or hypoxia
(Semenza et al., 1991; Bento et al., 2003). Due to its inherent ability to stimulate production
of red blood cells and consequently increase the oxygen supply to tissues, its use in sport has
been banned by the International Olympic Committee (IOC) since 1987, and its use is
considered as doping (Bento et al., 2003; Artioli et al., 2007). However, its recombinant
(synthetic) forms have been used indiscriminately by athletes, mainly in endurance sports, to
increase the concentration of red blood cells, generating higher oxygen supply to muscle
tissue (Pardos et al., 1999; Bento et al., 2003; De Rose et al., 2004).
Because changes in EPO production generate an imbalance in its plasma concentration
and can cause several pathologies related to the hematopoietic system, its use in sport is
certainly questionable (Bento et al., 2003). On the other hand, benign mutations in the gene of
its receptor (EPOR, locus 19p13.3-p13.2) may favor aerobic physical performance (de la
Chapelle et al., 1993a). In fact, so far only one allelic variant in the promoter region of the
EPO gene (locus 7q21) has been described, being associated with complications in diabetes
(Tong et al., 2008), while several polymorphisms have been described for EpoR (Prchal et al.,
1985; Juvonen et al.,1991; de la Chapelle et al., 1993a; Sokol et al., 1995; Le Couedic et al.,
1996; Arcasoy et al.; 1997; Kralovics et al., 1997; Kralovics et al., 1998; Watowich et al.,
1999), one of them associated with favoring performance in skiing competitions (OMIM
*133171).
7.3.2. Vascular Endothelial Growth Factor (VEGF) and Its Receptor (VEGFR)
Many polypeptide mitogens, such as fibroblast growth factor (FGFB) and plateletderived growth factors, are active in a wide range of different cell types. In contrast, vascular
endothelial growth factor (VEGF) is a mitogen primarily for vascular endothelial cells, being
structurally related to platelet-derived growth factor (OMIM +192240). VEGF constitutes a
family of hypoxia-inducible regulatory peptides capable of controlling blood vessel formation
and permeability, being a potent stimulator of endothelial cell proliferation, besides having
vasodilatory function, and neurotrophic and neuroprotective effects. It interacts with receptor
tyrosine kinases on endothelial cells to promote angiogenesis (Jin et al., 2002; Medford and
Millar, 2006). VEGF increases microvascular permeability 20,000 times more potently than
histamine (Medford and Millar, 2006) and its expression is stimulated by a great number of
proangiogenic factors, including the hypoxia-induced factor (HIF) and epidermal (EGF) and
24
Ana Luisa Miranda-Vilela
fibroblast (FGF) growth factors. Also, its level is influenced by the pH value of the blood and
the partial pressure and concentration of oxygen in inhaled air (Ahmetov et al., 2008).
VEGF expression significantly increases under aerobic physical exercise, and the
improvement of the VO2max as a result of training is mainly determined by an increased
maximal blood flow and higher density of muscle capillaries in active tissues (Ahmetov et al.,
2008). Moreover, VEGF expression in response to exercise is stronger in deep regions of the
muscle possessing a high proportion of oxidative fibers (Ahmetov et al., 2009). Because
individual differences in the degree of adaptive changes, such as growth of blood vessels of
skeletal muscles and the myocardium, are to a greater extent accounted by genetic factors that
determine the genetic predisposition to performing physical exercises of different intensities
and durations (Ahmetov et al., 2008), polymorphisms that increase VEGF expression could
result in a hyper supply of oxygen and other nutrients to the tissues. With better
vascularization in muscles, the heart and other parts of the body, exhaustion would be
delayed.
The VEGF gene is located in chromosome 6 (6p12) and alternate splicing of the the gene
transcript leads to the generation of several splice variants (isoforms) of differing sizes
(Medford and Millar, 2006; Ahmetov et al., 2008). Among the identified polymorphisms, of
special interest are the variants located in the promoter (regulatory) region (Ahmetov et al.,
2008). In this way, the substitution of cytosine for guanine at position –634 (the G-634C
polymorphism; rs2010963) increases the gene activity and, accordingly, determines
individual differences in the level of expression (Ahmetov et al., 2008), the VEGF C allele
being associated with a greater increase in the VO2max level as a result of aerobic physical
exercise (Prior et al., 2006).
All VEGF isoforms bind to the tyrosine kinase receptors, VEGF receptor 1 (VEGF-R1)
and VEGF receptor 2 (VEGF-R2), but VEGF-R2 is the main signaling receptor for VEGF
bioactivity (angiogenesis, proliferation and permeability) and can cause proliferation in cells
lacking VEGF-R1 (Medford and Millar, 2006). VEGFR2 is essential to induce the full
spectrum of VEGF angiogenic responses to aerobic training and one polymorphism in its
gene, His472Gln, has been pinpointed as important in the context of performance, where the
allele VEGFR2 472Gln has been associated with elite athlete status, endurance performance
and muscle fiber type composition in females (Ahmetov et al., 2009).
7.4. Polymorphisms Related yo Muscle Energy, Structure and Strength
7.4.1. Creatine Kinase (CK) and the Nco I and Taq I Polymorphisms in the 3'
Untranslated Region of the CKM Gene
Creatine kinase (CK) is an enzyme that catalyzes the rapid reaction of ATP resynthesis
from phosphocreatine (CP) and ADP, playing an important role in energy metabolism of
muscle cells and brain (Zhou et al., 2006; Foschini et al., 2007). In its active form it consists
of two subunits, M and B, expressed by distinct genes. The gene for the subunit M (CKM;
M= muscle), with 17.5 kilo base pairs (Kbp), eight exons and seven introns, is located on
chromosome 19q13.2-q13.3, while the gene for subunit B (CKB; B = brain) is located on
chromosome 14q32.3. By the combination of CKM and CKB subunits, three dimeric
isoforms are formed, structuring in CK-MM, predominantly in skeletal muscle; CK-BB,
predominantly in the brain; and CK-MB, predominantly in the myocardium. These three
Exercise, Injuries and Athlete Performance
25
isoforms are found in the cytosol or associated with the myofibrillar structures (Dias et al.,
2007; Foschini et al., 2007).
Skeletal muscle contains almost entirely CK-MM, with small amounts of CK-MB. The
major activity of this enzyme in heart muscle is also attributed to CK-MM, with
approximately 20% of CK-MB (Foschini et al., 2007). The muscle-specific creatine kinase
(CK-MM) is present both in type I fibers (slow twitch or slow oxidative fibers) and in type II
fibers (white fibers, fast twitch or fast oxidative fibers) (Zhou et al., 2006). However, these
fibers differ in their CK-MM activities, which are two times more active in fast-twitch fibers
(Zhou et al., 2006; Dias et al., 2007). Additionally, type I muscle fibers, predominantly
recruited in endurance sports and recognized by the predominance of aerobic oxidative
metabolism, present an inverse relationship with CK-MM activity. Consequently, lower CKMM activity may be essential for endurance athletes (Zhou et al., 2006; Dias et al., 2007).
Using the polymerase chain reaction technique (PCR) and subsequent DNA digestion
with restriction enzymes NcoI and TaqI (PCR-based RFLP or restriction fragment length
polymorphism), two polymorphisms were detected in the 3' untranslated region of the CKM
gene (Rivera et al., 1997a; Dias et al, 2007). For the NcoI enzyme, the allele with the
restriction site was first designated as 985 +185 bp and subsequently, after sequencing, as A
allele; while the allele without the restriction site was originally designated as 1170 bp and
later as G allele (Rivera et al., 1997a; Zhou et al., 2005; Zhou et al., 2006). For the TaqI
enzyme, the allele with the restriction site was designated as 1020 bp + 150 bp and the allele
without the restriction site as 1170 bp, because it is related to the size of the fragment
amplified by PCR (Rivera et al.a, 1997a).
These polymorphisms, analyzed together or separately, have been indicated as potential
contributors to athletic performance in some studies (Zhou et al., 2006; Rivera et al. 1997b;
Heled et al., 2007), while in others, no association was made (Rivera et al., 1997a). Because
both polymorphisms are located in the 3' untranslated region of the gene, outside the coding
region and regulatory region, it has been proposed that there is little likelihood that this
mutation is the direct cause of any observed association, suggesting thus that this
polymorphism could only serve as a marker of genetic difference (Dias et al., 2007).
However, because the nature of athletic performance is multigenic and multifactorial, further
investigation becomes necessary, mainly due to the fact that investigations have examined
these polymorphisms together with VO2max assessments and/or running economy, without,
however, assessing serum concentrations of total CK or CK-MM. Therefore, our group
recently demonstrated that CK-MM TaqI polymorphism significantly influenced results of
serum AST, total CK and high-sensitivity C-reactive protein (hs-CRP) (Miranda-Vilela et al.,
2011c), indicating that the heterozygous to the CK TaqI polymorphism can favor minor
exercise-induced damage and also the reduction of its subsequent inflammatory process as
shown by the hs-CRP results, thus corroborating previous reports of its contribution to
athletic performance.
7.4.2. R577X Polymorphism Alpha-Actinin 3 (ACTN3) Gene
The alpha-actinin 3 (ACTN3) gene (locus 11q13-q14) encodes alpha-actinin 3, a
structural protein of the sarcomeric Z line of type II muscle fibers, related to power and
muscular strength, and with predominance of anaerobic energy metabolism type (Dias et al.,
2007; Yang et al., 2007; Massidda et al., 2009).
26
Ana Luisa Miranda-Vilela
Genetic variation of ACTN3 has been associated with elite athletic status and resistance
training (MacArthur and North, 2004). In a common polymorphism in the ACTN3 gene, the
exchange of a cytosine (C) for a thymine (T) at position 1747 of exon 16 causes the amino
acid arginine (R) to be replaced by a premature stop codon (X) at position 577 of the protein
(arg577-to-ter) (North et al., 1999). When in homozygozis (577XX genotype), this
polymorphism, named R577X, results in complete deficiency of the protein alpha-actinin 3
and is present in about 16% of humans globally (North et al., 1999; Dias et al., 2007;
Massidda et al., 2009). In the presence of the R allele, the ACTN3 gene has a protective
function in the sarcomere of skeletal muscle against destructive mechanisms caused during
repetitive efforts, like short running distances (100-200 metros) (Moran et al., 2007).
Interestingly, the genotype for deficiency of alpha-actinin 3 (577XX) does not result in a
pathological phenotype such as muscular dystrophy or myopathy and has been found more
often in endurance athletes than in the general population, suggesting that it contributes to a
better performance in endurance tests (North et al., 1999; Dias et al., 2007; Yang et al., 2007;
Eynon et al., 2009). On the other hand, the 577RR genotype has been associated with better
performance in tests that require muscle strength and explosiveness (Druzhevskaya et al.,
2008; Eynon et al., 2009; Massidda et al., 2009).
7.4.3. Myostatin (MSTN or GDF8)
The transforming growth factor-beta (TGF-beta) superfamily encompasses a large
number of growth and differentiation factors that play important roles in regulating
embryonic development and in maintaining tissue homeostasis in adult animals (McPherron
et al., 1997). Myostatin (MSTN) or growth/differentiation factor-8 (GDF8) is a member of
this superfamily with a role in the control and maintenance of skeletal muscle mass
(McPherron et al., 1997; Gonzalez-Cadavid et al., 1998). During early stages of
embryogenesis, GDF8 expression is restricted to the myotome compartment of developing
somites. At later stages and in adult animals, it is expressed in many different muscles
throughout the body (McPherron et al., 1997; Gonzalez-Cadavid et al., 1998). Myostatin is a
genetic determinant of skeletal muscle growth (Gonzalez-Cadavid et al., 1998), exerting
negative regulation on muscle mass (inhibitor of muscle growth) (Schuelke et al., 2004;
Huygens et al., 2005; Ye et al., 2007), by inhibiting the activation of satellite cells, which are
stem cells resident in skeletal muscle (Schuelke et al., 2004).
MSTN gene (locus 2q32.2) comprises three exons and two introns and is highly
conserved in gene structure among vertebrate species (Thomis et al., 2004; Ye et al., 2007).
Several polymorphisms and mutations have been identified in this gene, with diverse
functional consequences (González-Freire et al., 2010; Santiago et al., 2011). It is transcribed
as a 3.1-kb mRNA species that encodes a 335-amino acid precursor protein, being expressed
uniquely in human skeletal muscle, in both type I and type II fibers, as a 26-kD mature
glycoprotein and secreted into the plasma (Gonzalez-Cadavid et al., 1998). Mice with null
mutations of the myostatin gene have increased muscle mass. Similarly, mutations of the
myostatin gene inactivating the protein cause bovine muscular hypertrophy (GonzalezCadavid et al., 1998; Miranda et al., 2002; Ye et al., 2007). In humans, a loss-of-function
mutation in the myostatin gene in a child increased muscle bulk and strength. This child was
the son of a woman who was a former professional athlete, and several members of her
family were reported to be unusually strong (Schuelke et al., 2004).
Exercise, Injuries and Athlete Performance
27
Of the identified polymorphisms, the Lys(K)153Arg(R) variation located in exon 2
(rs1805086, 2379 A>G replacement) is a candidate to influence skeletal muscle phenotypes
(González-Freire et al., 2010; Santiago et al., 2011), with the variant R allele appearing to
contribute to a worse performance (Thomis et al., 2004; Santiago et al., 2011). However,
reports on association studies of MSTN polymorphisms with baseline muscle strength or
responses to strength training in humans are scarce (Thomis et al., 2004). Moreover, the
frequency of the mutant R allele is low (about 3-4% among Caucasians), with the frequency
of the homozygotes RR (below 1%) being even lower, which certainly limits the possibility
of studying large groups of people carrying the R variant (Thomis et al., 2004; GonzálezFreire et al., 2010). Also other genes within the myostatin pathway as well as regulatory
elements in myostatin expression should be studied as candidate genes. In fact, it has been
reported that several genes involved in the myostatin pathway, but not the myostatin gene
itself, are important quantitative trait loci (QTLs)2 for human muscle strength. An additional
set of valuable candidate genes that were not part of the myostatin pathway was also found in
the chromosome 12 and 13 genomic regions, including the insulin-like growth factor-1 (IGF1), among others (Huygens et al., 2005).
7.4.4. Insulin-Like Growth Factor-1 (IGF-1)
The somatomedins or insulin-like growth factors (IGFs) comprise a family of peptide
hormones structurally related to insulin that play important roles in mammalian growth and
development, having a pleiotropic effect on cell growth and metabolism. Insulin-like growth
factor-1 (IGF-1 or somatomedin C) mediates many of the growth-promoting effects of growth
hormone (OMIM*147440; Lisa et al., 2011). The primary source of circulating IGF-I is the
liver, although the skeleton also contributes to total serum levels and has anabolic effects, its
concentration being related to that of growth hormone (GH) (Rosen et al., 1998; Haisma and
Hon, 2006). These biological actions include the ability to release cytokines, promotion of
angiogenesis and stimulation of extracellular matrix production (Lisa et al., 2011), besides
exerting mitogenic, myogenic and anabolic tissue actions (Nindl et al., 2002), stimulating
skeletal muscle hypertrophy by increasing protein synthesis, inhibiting proteolysis and
increasing the uptake of glucose and amino acids (Cordeiro et al., 2005). IGF-1 is also
important for bone cell proliferation, differentiation, and collagen synthesis (Rosen et al.,
1998). Accordingly, it has been proposed that a combination of resistance training and
overexpression of IGF-1 could be an effective measure for attenuating the loss of traininginduced adaptations (Lee et al., 2004). Because quantitative genetic analyses in humans,
mice, pigs and cattle have shown that the levels of circulating IGF-I are under genetic control,
with heritability estimates around 30% (Estany et al., 2007), polymorphisms in the IGF-1
gene which increase the hormone’s expression could favor performance of resistance training
athletes.
The IGF-1 gene (locus 12q23.2) contains 6 exons, 4 of which are alternatively spliced
depending on tissue type and hormonal environment (Smith et al., 2002). Sequencing of the
rat and human IGF-1 genes revealed the presence of a cytosine-adenosine short tandem repeat
2
Quantitative trait loci (QTLs) are stretches of DNA containing or linked to the genes that underlie a quantitative
trait, which in turn are phenotypes (characteristics) that vary in degree and can be attributed to polygenic
effects, i.e., product of two or more genes, and their environment.
28
Ana Luisa Miranda-Vilela
(STRs)3 in the promoter region (Estany et al., 2007). In humans evidence is accumulating that
the length of these highly polymorphic STR sequences may be associated with circulating
IGF-1 concentration (Rosen et al., 1998). There are also lines of evidence indicating that this
polymorphism is associated with body weight and height characteristics, although results
were not always coincident and their molecular basis remains to be elucidated (Estany et al.,
2007). Since it has been demonstrated that the injection of a recombinant adeno-associated
virus directing overexpression of IGF-1 in differentiated muscle fibers gave rise to an
increase in muscle bulk in young adult mice in the absence of any special exercise program
(Barton-Davis et al., 1998) and the combination of resistance training and overexpression of
IGF-1 induced greater hypertrophy than either treatment alone (Lee et al., 2004), this
polymorphism could favor performance by contributing, for example, to the strengthening a
tennis player’s shoulder muscles, a sprinter’s calves or a boxer’s biceps.
7.5. Polymorphisms in the Cytokine Genes and Inflammation
Intense physical activities, such as resistance exercise, with a strong eccentric component,
cause micro-injury to skeletal muscle, and inflammation appears to play an important role in
the repair and regeneration of skeletal muscle after damage (Dennis et al., 2004; Mann et al.,
2011). There is evidence that susceptibility to inflammation is influenced by genetic variation
in cytokine genes, and accumulated data have indicated that both pro- and anti-inflammatory
responses can play a role in athletic performance. They may influence muscle repair,
hypertrophy and strength (Cauci et al., 2010), particularly those SNPs located in the promoter
regions of the cytokine genes that may alter their expression. SNPs in the cytokine genes and
alterations in associated gene expression may also influence risk for upper respiratory
symptoms (URS) in some athletes (Cox et al., 2010).
7.5.1. Tumor Necrosis Factor Alpha (TNF-)
With micro-injury to the muscle, the first genes activated by the quiescent resident
macrophages are the pro-inflammatory cytokines TNF-α and IL-1β (Dennis et al., 2004). It is
of note that accumulation and activation of muscle resident macrophages is a rich source of
growth factors postulated to stimulate myogenesis. Thus, inflammation may serve as a
mechanism promoting hypertrophy. However, pre- and post-exercise levels of inflammatory
factors display considerable variation among people, and this is likely influenced, at least
partially, by genetic variation (Cauci et al., 2010).
TNF- is a multifunctional proinflammatory cytokine that has effects on lipid
metabolism, coagulation, insulin resistance and endothelial function. It is secreted
predominantly by monocytes/macrophages, although significant amounts are also secreted by
several other cell types (Skoog et al., 1999). TNF-α is also a potent catabolic factor to skeletal
muscle (Liu et al., 2008), besides stimulating the production of interleukin (IL-6) and thereby
inducing the hepatic production of C-reactive protein (CRP), a sensitive biomarker of the
3
STRs or microsatellites are hypervariable short sequences of DNA, normally of length 2-5 base pairs, that are
interspersed throughout the human genome and repeated numerous times in tandem (in a head-to-tail fashion
at a specific chromosomal locus). Ex.: the 16 bp sequence of "gatagatagatagata" would represent 4 head-tail
copies of the tetramer "gata". The polymorphisms in STRs are due to the dfferent number of copies of the
repeat element that can occur in a population of individuals.
Exercise, Injuries and Athlete Performance
29
inflammatory status of the individual and exercise-induced oxidative stress (Djarova et al.,
2011).
TNF- is synthesized as a 26-kD membrane-bound protein (pro-TNF) that is cleaved by
TNF-processing enzymes to release a soluble 17-kD TNF-α molecule. The mature TNF-α
protein can then bind to its main receptors TNFR1 and TNFR2, which are expressed in most
nucleated cells. After interacting with its receptors, a variety of responses are elicited which
affect the regulation of a large number of genes (Skoog et al., 1999).
TNF-α gene is located on the short arm of chromosome 6 (6p21.3), which is within the
highly polygenic and polymorphic major histocompatibility complex (MHC) region of the
human genome (Liu et al., 2008). TNF- expression is indicated to be partly genetically
determined, and polymorphic sites closely linked to the TNF-α locus (inside the MHC region)
are associated with differences in cellular TNF-α secretion. While there is evidence for
transcriptional regulation of TNF-α gene expression, polymorphisms in the promoter region
of the TNF-α gene may be important for TNF-α gene expression and protein production
(Skoog et al., 1999).
Many SNPs and microsatellites have been identified in the TNF locus, and the ones in the
promoter region are thought to influence TNF transcription rate and to affect the circulating
CRP levels (Lakka et al., 2006; Liu et al., 2008). It is also believed that the interaction
between nuclear proteins and these TNF SNPs is an important pathway for the allele-specific
modulation of TNF expression (Liu et al., 2008). Supporting this hypothesis, five SNPs in the
promoter region have been shown to influence gene expression (Liu et al., 2008), being
linked to various infectious and autoimmune diseases, obesity and obesity-associated insulin
resistance, age-related diseases, including sarcopenia (age-related loss of muscle mass and
strength), as well as longevity (Lakka et al., 2006; Liu et al., 2008). Among them, a guanine
(G) to adenine (A) substitution located at position −308 of the transcription start site in the
promoter region (308G/A; rs1800629) has been reported to affect the transcription rate of
the TNF-α gene, with association between the AA genotype with higher plasma CRP levels
and less favorable CRP response to regular exercise (Lakka et al., 2006). Because CRP can
amplify the proinflammatory response through complement activation, tissue damage, and
activation of endothelial cells (Libby et al. 2002), this polymorphism may contribute to a
worse performance and also, over time, to loss of muscle mass and strength and CVD risk in
athletes, particularly middle-aged athletes who exercise extensively.
7.5.2. Interleukins (IL)
The interleukin-1 (IL-1) family of cytokines and IL-6 are other cytokines involved in the
inflammatory and repair reactions of skeletal muscle during and after exercise (Cauci et al.,
2010; Eynon et al., 2011). IL-6 also plays a role in the regulation of metabolism during
physical exercise, improving skeletal muscle energy supply and assisting in the maintenance
of stable blood glucose levels by stimulating lipolysis in the adipose tissue and augmenting
glycogenolysis in the liver (Pedersen et al., 2004; Huuskonen et al., 2009).
In general, IL-1 acts synergistically with TNF-, activating proinflammatory responses
in a wide range of cells and promoting the acute phase response. IL-1 is able to induce the
secretion of several inflammatory factors, including IL-6 and TNF- (Cauci et al., 2010). IL6 plays an important role in the homeostasis of the neuroendocrine and immune systems, in
the balance of pro- and anti-inflammatory pathways and in response to oxidative stress,
30
Ana Luisa Miranda-Vilela
besides regulating hematopoiesis and bone resorption (Chung et al., 2003; Pereira et al.,
2011). Additionally, it may modify the regulation of energy balance, by actings as an energy
sensor, being dependent on the glycogen content in the muscle. IL-6 is released from
contracting muscles in high amounts and exerts its effect on adipose tissue, inducing lipolysis
and gene transcription in abdominal subcutaneous fat (Pedersen et al., 2004).
During exercise, IL-6 is produced by muscle fibers in contraction, even without any
muscle damage, via a TNF-independent pathway increasing plasma IL-6 levels dramatically
(100-fold) (Pedersen et al., 2004). IL-6 stimulates the appearance in the circulation of the
anti-inflammatory cytokine IL-10 and the cytokine inhibitors such as IL-1 receptor antagonist
(IL-1ra or IL-1RN) and TNF- receptor, inhibiting the production of the proinflammatory
cytokine TNF- (Pedersen et al., 2004; Petersen and Pedersen, 2005). Consequently, IL-6
induces an anti-inflammatory environment, may inhibit TNF--induced insulin resistance and
have an important role in mediating the beneficial health effects of exercise in inactivity and
obesity-related disorders such as diabetes and CVD (Pedersen et al., 2004; Petersen and
Pedersen, 2005; Petersen and Pedersen, 2006). Polymorphisms in the promoter region of the
IL-6 gene (locus 7p21) that affect the IL-6 expression level may consequently influence
performance, immunodepression and risk of upper respiratory symptoms (URS), besides
contributing to insulin resistance and CVD risk.
The IL-1 and IL-1 genes are located on the long arm of chromosome 2 (2q14) and are
tightly linked (D'Eustachio et al., 1987; Nicklin et al., 1994). They are synthesized as a large
precursor of 30.6 and 30.7 kD, respectively, which is processed to a smaller form (March et
al., 1985). Another gene map close to IL-1  and  genes is the IL-1ra gene (locus 2q14.2)
(Nicklin et al., 1994). IL-1ra is a protein that binds to IL-1 receptors (IL-1RI) and inhibits the
binding of IL-1 and IL-1, neutralizing the biologic activity of these 2 cytokines in
physiologic and pathophysiologic immune and inflammatory responses (Arend, 1991).
Because IL-1ra acts as an antagonist of IL-1RI and prevents IL-1-dependent signaling,
deficiency of IL-1ra in humans, which may be caused by certain polymorphisms, can lead to
IL-1-mediated systemic and local inflammation (Cauci et al., 2010).
Several studies showed that polymorphisms in the IL-1 and IL-1ra genes correlate with
altered protein expression (Cauci et al., 2010). Two SNPs in IL-1 representing C-to-T base
transitions have been studied for disease predisposition, one at position 511 in the promoter
region and another at position +3954 in exon 5 (TaqI restriction site polymorphism). In
addition, a polymorphism in the intron 2 region of the IL-1ra gene consisting of a variable
number of tandem repeats (VNTR)4 of 86 base pairs (bp) has been extensively investigated in
relation to a variety of pathological conditions, including inflammatory myopathies (Cauci et
al., 2010).
Whether polymorphisms in the interleukin genes can affect the severity of the
inflammatory response or the athletic status has been also investigated. A study performed on
sedentary subjects selected on the basis of their haplotype pattern of specific combinations of
five SNPs in the IL-1 gene cluster showed that the wild type genotype for IL-1 +3954 or the
variant genotype for IL-1 −3737 in combination with the variant allele at IL-1ra +2018 were
associated with inflammation of skeletal muscle, following acute resistance exercise. This
4
VNTRs or minisatellites are hypervariable regions of human DNA of length 10-100 base pairs that are repeated
numerous times in tandem (in a head-tail manner, like STRs). VNTRs are similar to STRs, the difference
being that in a VNTR, the repeated sequence is longer.
Exercise, Injuries and Athlete Performance
31
study indicated that IL-1 haplotype can influence the inflammatory response of skeletal
muscle after exercise and that it is necessary to test whether the specific IL-1 haplotype is
beneficial or detrimental for muscle repair and the adaptability to resistance training (Dennis
et al., 2004). Another study carried out with professional and non-professional Italian athletes
and non-athlete controls assessing the IL-1 511 and +3954 polymorphisms and the VNTR
IL-1ra polymorphism showed that variants in the IL-1ra gene was associated with athletic
status. The authors suggested that as VNTR IL-1ra polymorphism is implicated in several
disease conditions, athlete status may constitute a confounding variable that will need to be
accounted for when examining associations of this polymorphism with disease risk (Cauci et
al., 2010).
For IL-6 gene, of the three main polymorphisms reported so far in the promoter region of
the IL-6 gene, such as 174G/C, 572G/C and 597G/A (Wang et al., 2011), the −174G/C
polymorphism has been reported as a candidate to explain individual variations in health and
exercise related phenotypes, with GG genotype favoring sprint/power sports performance in
European (Spanish) Caucasian males (Ruiz et al., 2010) but not in Israeli Caucasians (Eynon
et al., 2011), and CG genotype favoring increased plasma IL-6 levels, greatest gains in
VO2max and decreased BMI (Huuskonen et al., 2009). These apparently contradictory findings
support the need to replicate association results between genetic polymorphisms and athletic
status in populations of different ethnic backgrounds with the largest possible population,
since they vary among different ethnicities.
The change of guanine bases to cytosine (G → C) at position 174 bp from the
transcriptional start site seems to affect the transcription of the IL-6 gene and therefore the
plasma levels of this cytokine in young, elderly and centenarian individuals (Pereira et al.,
2011), with an increased inflammatory response associated with the G allele (Bennermo et al.,
2004). It has been shown that, in German Caucasian surgical patients, the −174GG was not
associated with the incidence of sepsis, although it increased their survival in sepsis (Schlüter
et al., 2002), while in highly-trained athletes, this genotype has been associated with an
increased likelihood of ≥ 3 URS episodes in a 12 month period (Cox et al., 2010). These
contradictory results indicate the need to study cytokine haplotypes in association with studies
involving elite and trained athletes, at least those involved in the systemic IL-6 response, such
as IL-10, IL-1ra and TNF- receptor.
7.5.3. Methylenetetrahydrofolate Reductase (MTHFR) Gene
Polymorphisms in the MTHFR gene have been reported in association with an altered
plasma homocysteine (Hcy) level (Morita et al. 1997; van Bockxmeer et al. 1997; Fujimura et
al. 2000; Miller et al. 2005), which is in turn an independent factor risk for CVD (Graham et
al. 1997; Morita et al. 1997; Eikelboom et al. 1999; Anderson et al. 2000). Because elevated
Hcy levels are associated with coronary and peripheral vascular obstructive events (Misawa et
al., 2008), the variant alleles of the MTHFR gene could contribute, in the course of time, to a
lower oxygen supply to the heart in those endurance athletes who exercise strenuously,
reducing cardiovascular fitness and thus performance, since they have been associated with
increased Hcy. In fact, MTHFR C677T polymorphism has been associated with a lower
hemoglobin level in a healthy exercise-trained population (Fortunato et al. 2007). Moreover,
hemolysis can occur as a result of mechanical trauma in the capillaries of runners’ feet
32
Ana Luisa Miranda-Vilela
(Carlson and Mawdsley, 1986), which would compromise performance still more in carriers
of these variants.
Previous reports from our research group have demonstrated an association between both
MTHFR polymorphisms (C677T and A1298C) with plasma CRP levels, and thus with a
slightly higher inflammatory process (Miranda-Vilela et al., 2011c). Hence, these results
indicate that MTHFR polymorphisms should be better investigated in the context of
performance and future CVD risk in athletes, particularly middle-aged athletes who exercise
extensively, because besides the facts discussed above, CRP is present in atherosclerotic
plaques, where it might exert several potential proinflammatory and atherogenic actions that
include the binding of oxidized LDL cholesterol, induction of adhesion molecule expression,
activation of complement, and stimulation of tissue factor production by monocytes (Brull et
al. 2003). Furthermore, the existence of a sport-related hyperhomocysteinemia has been
reported, independent of the variables found in the general population such as decreased
folate or vitamin B12 (Borrione et al. 2008), particularly if a race took place close to the
anaerobic threshold speed (Benedini et al. 2010). This suggests that it would represent an
adaptation to training but the possibility of secondary vascular damage cannot be excluded
(Borrione et al. 2008).
7.6. Some Considerations
Although more than 200 performance enhancing polymorphisms have been reported until
now, several of them vary among ethnic groups. This possibly explains, at least in part, the
fact that athletes from a specific ethnic origin seem to have an advantage over others in
certain Olympic sports and affirms the need to replicate association results between genetic
polymorphisms and athletic status in populations of different ethnic backgrounds with the
largest possible population. Moreover, in contrast to monogenic inheritance, in which
mutations of a single gene result in a specific phenotype, sports performance is multigenic
and multifactorial. Thus, several genes are involved and, although genetic predisposition has
a strong influence on the characterization of the individual as an outstanding athlete, it alone
does not produce an elite athlete. Other aspects, such as psychological and environmental
factors, will ultimately determine whether these individuals will be top athletes.
CONCLUSION
Exercise has a classic Janus effect, which means that the difference between "medicine"
and "poison" is in the dose. When practiced moderately and regularly, it is crucial for a
healthy lifestyle, acting as a therapeutic agent and/or preventing numerous illnesses. When
exhaustive, exercise can leads to oxidative stress and cell damage even in trained individuals.
Thus, the prudent recommendation to maintain a healthy lifestyle is the regular practice of
moderate exercise and a diet rich in antioxidants from natural foods, whatever the genetic
inheritance. For those people who undergo strenuous exercise or training that exceeds the
antioxidant defenses, the use of antioxidant supplements may be of use, providing that they
are closely monitored by sports nutritionists. For these athletes, genetic screening may also be
Exercise, Injuries and Athlete Performance
33
used to select specific training methods to enhance or improve their genetic predisposition. It
is worth remembering, however, that sudden deaths in athletes are usually caused by
previously unsuspected cardiovascular disease, and the vast majority of deaths in middle-aged
athletes are caused by atherosclerotic cardiovascular disease. Moreover, even children of
Olympic athletes, who certainly have a genetic advantage over many other babies born, are
not guaranteed the same performance as their parents, because other aspects such as
psychological and environmental factors will ultimately determine whether these individuals
will be top athletes.
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In: Athlete Performance and Injuries
Editors: João H. Bastos and Andreia C. Silva
ISBN 978-1-61942-658-0
© 2012 Nova Science Publishers, Inc.
Chapter 2
TRAINING OVER THE EDGE: UNDERSTANDING
THE OVERTRAINING SYNDROME
Fernando Rocha1,2, Mário C. Marques1,2 and Aldo M. Costa1,2
1
University of Trás-os-Montes and Alto Douro,
Department of Sport Sciences, Vila Real, Portugal
2
Research Centre in Sports, Health and Human Development, Vila Real, Portugal
ABSTRACT
The improvement of elite athletes performance depends on two fundamental training
variables: volume and intensity. The physiological adaptations and supercompensation require
training and sufficient recovery periods. In fact, there is a strict relationship between training
volume and recovery time that can be considered critic for the athlete’s physiological status.
On this, overtraining syndrome appears to be caused by too much high intensity training
and/or too little recovery time often combined with other training and nontraining stressors
factors. The imbalance between effort and adequate recovery can bring serious physiological
consequences, like decrease of training tolerance and performance and even susceptibility to
respiratory tract infections. At present there is no one single diagnostic test that can define
overtraining. The recognition of overtraining requires the identification of stress indicators,
which do not return to baseline following a period of regeneration. Possible indicators include
an imbalance of the neuroendocrine system, suppression of the immune system, indicators of
muscle damage, depressed muscle glycogen reserves, deteriorating aerobic, ventilatory and
cardiac efficiency, a depressed psychological profile, and poor performance in sport specific
tests, e.g. time trials. Therefore, screening for overtraining and performance improvements
must occur at the culmination of regeneration periods.
Keywords: Overtraining, overreaching, recovery, training load, sports performance
52
Fernando Rocha, Mário C. Marques and Aldo M. Costa
INTRODUCTION
Top-level sport inevitably requires a rigorous training and control regimen and therefore
lies with the coach the decision of selecting the best training load. However, the complexity
of this task results from the dynamic nature of the athlete’s individual trainability. Thus, the
capacity to adapt to successive training loads during a defined period of time - translated into
improved performance – depends on a number of endogenous (age, gender, morphology,
training experience, etc.) and exogenous factors (nutrition, social support, etc.). As such, the
adaptation capacity varies over time, inclusively facing the same applied load.
Providing training loads that are effective in improving performance is not new for sports
coaches. Unfortunately, the common acceptance of the classical theory of training, do not
converge to the requirements of modern high-level sport. Here, the amount of volume and
training intensity imposed on athletes today has grown to the point of no return. Moreover,
this wild scheme of training can became dubious in its true effectiveness, beyond to the
possible deleterious effects on the athlete’s physical and mental integrity. So, nowadays,
consider a proper recovery for the current demands of training load and competition, became
a primary concern.
Successful training must involve overload; however the combination of excessive
overload plus inadequate recovery must be avoided. As consequence of a disrupted balance
between training stress and recovery, the athletes may experience acute feelings of fatigue,
changes in mood state, staleness and even decreases in overall performance. If no other
explanation for this observed changes can be found, the state of overtraining may be diagnose
- overtraining syndrome.
While many athletes and coaches unaware this phenomenon, its high prevalence in toplevel sport has gradually been highlighted. Therefore, the purpose of this chapter is to deepen
the knowledge about overtraining, bringing actual scientific data to help coaches and athletes
to recognize but particularly to avoid and overcame the overtraining syndrome. The following
issues will be discussed: (i) misconception of overtraining terminology (overtraining,
overtraining syndrome, overreaching); (ii) understanding the multifactorial etiology;; (iii) the
assessment of overtraining (monitoring performance, immunological, hematological,
hormonal and psychological parameters); (iv) prevention and treatment of the overtraining
syndrome.
MISCONCEPTION OF OVERTRAINING TERMINOLOGY
The definition of the term overtraining has considerably conflicting viewpoints.
Researchers have used too many terms in different ways to describe both processes and
outcomes associated with overtraining. Indeed, there has been confusion “about whether
overtraining may have positive or negative aftereffects; about whether it should be considered
a process, an outcome, or both; about whether various aspects of overtraining are causes or
consequences; and about the varied usage of terms in the fields associated with overtraining”
(Richardson, Anderson & Morris, 2008, p. 6). The difficulty of having a standardized
diagnosis helps this misconception, demonstrating that this issue needs further investigation.
Training Over the Edge
53
The competitive sports requires an athlete, beyond their natural talent, have their physical
and mental capacity in an optimal level and this can only happen through the training process.
Through training , coaches and athletes must adapt to and cope with all demands, in manner
to avoid exhaustion (Grantham, 2006). With this concern, which must be based not only in
training loads, but also in recovery process, it may be possible to reach the limits of human
performance. Sometimes (more often that we would like), by carelessness or lack of
knowledge, that ceiling are exceeded resulting in a state of chronic fatigue and in a decrease
of physical performance (Gleeson, 2002). The same author identifies this state as
overtraining, adding that “it is also a situation defined by excessive training, characterized by
long-lasting fatigue and worsening of competitive performance with further attempts to
improve physical condition” (p. 32). Indeed, overtraining is an imbalance between
training/competition and recovery with atypical cellular adaptations and responses
(Steinacker & Lehmann, 2002). Therefore, the state of overtraining is characterized by the
inability to recover properly after successive training sessions (Kuipers, 1998). That’s why
the feeling of fatigue persists even after a regular rest period and leads to an emotional,
physical and behavioral changes. This accumulation of training and/or non-training stress
results in long-term decrement in performance capacity (Kreider et al., 1998). However,
besides performance incompetence, many other clinical problems may arise as a result of
overtraining; including sports injuries, infections or mood disturbances (Steinacker &
Lehmann, 2002.) Moreover, stress factors not caused by training such as monotony, intra and
interpersonal conflicts, can exacerbate the risk of resulting in overtraining (Lehmann et al.,
1997). That’s why the term overtraining seems insufficient to describe what was going on
with athletes in their everyday battles to balance stressors with recoveries (Richardson,
Anderson & Morris, 2008).
With effect, quite a few authors (Hooper & Mackinnon, 1995; O’Toole, 1998, Steinacker
and Lehmann, 2002) have provided a definition that describes overtraining as a process and
also an outcome (i.e., overtraining syndrome). The term overtraining seems appropriate to
label the process, whereas overtraining syndrome is an outcome, representing the end state of
nonadaptation that results from overtraining (Hooper & Mackinnon, 1995). By using the
expression ’syndrome’, the emphasis is placed on a multifactorial etiology, recognizing that
exercise (training) is not necessarily the only cause of this phenomenon (Meeusen, Duclos,
Gleeson & Rietjens, 2005).
Israel (1976), says that the overtraining can be classified in two categories: the
parasympathetic and sympathetic. The sympathetic form, or the classic overtraining is
characterized by increase sympathetic nervous system activity at rest. The sympathetic
nervous system causes changes of the basic functions of the body, making easily the motor
response to acute stress or physical activity. It occurs more frequently in athletes that rely
primarily on anaerobic metabolism (lactic and alactic) to supply their muscle energetic
demands,. The parasympathetic overtraining form is characterized by the predominance of
parasympathetic tone at rest and during exercise, and is observed with greater frequency in
endurance athletes.
Lehmann, Foster, Gastmann, Keizer & Steinacker (1999) distinguished overtraining by
time frame (i.e., short- or long-term overtraining). The short-term overtraining is presented as
a common part of athletic training, which leads to a so-called state of overreaching. This
positive state “is characterized by transient underperformance, which is reversible within
short-term recovery period” (p. 2). Therefore, in search of peak performance, the state of
54
Fernando Rocha, Mário C. Marques and Aldo M. Costa
overreaching seems to be a regular part of athletic training in which restoration of
performance capacity usually take one or two weeks and can be rewarded by an increase in
performance ability. On the other hand, when overreaching is too profound or is extended for
too long (i.e., long-term overtraining) the athlete runs the risk of a resulting overtraining
syndrome.
Nederhof, Lemmink, Visscher, Meeusen & Mulder (2006) described the overtraining
process occurring in three progressive stages: (i) Functional overreaching; (ii) Non-functional
overreaching and; (iii) Overtraining syndrome. According to this author, functional
overreaching occurs as a result of heavy training process, where there is a momentary
decrease in performance, however, this reduction is reversible in a short time if we consider
an appropriate recovery plan. The functional overreaching occurs after several days of intense
training and is associated with muscle fatigue or peripheral and, according to Lehman, Foster
& Keul (1993), can be defined as pre-overtraining. Many coaches use training camps to
increase the training load (intensity and volume) so that athletes are subjected to a stimulus
that creates the functional overreaching. Promoting the so-called super -compensation period,
usually enable the athlete to reach higher performance levels. ,
Non functional overreaching or extreme overreaching, can occurs if the athlete neglecting
the balance between training and recovery, typically, situations where the training load is
markedly heavy during recovery periods; when the athlete drops down to a low level of
performance and energy are not restored after a planned short-term recovery period; and
when the impact of the non training stressors in life are underestimate (Saunders, 2009;
Meeusen et al., 2005). Non functional overreaching is, therefore a quite severe level of fatigue
where athletes can experience the first signs and symptoms of prolonged training distress
such as performance decrements, psychological disturbance (decreased vigor, increased
fatigue) and hormonal disorder. Recovery happens if athletes refrain from training for a few
weeks (or even mouths). At this stage, the action of the coach is very important because
realizing that the athlete is in a non-functional overreaching state, may delay the next training
session.. Facing such performance decrease, an anxious coach may even increase training
Table 1. Overload training progression
Process
Outcome
Training
(overload)
Acute fatigue
Intensified
Training
Functional
overreaching
(short-term
overreaching)
Moderate
Non-functional
overreaching
(extreme
overreaching)
Fatigue level
Ordinary
Moderatesevere
Recovery time
Day(s)
Days to Weeks Weeks to
Months
Performance
Increase
Temporary
Stagnation
performance
Decrease
decrement (e.g
training camp)
Based on Saunders (2009) and Meeusen et al. (2005).
Overtraining
syndrome
Severe
Months …
Decrease
Training Over the Edge
55
load, contributing to the deterioration of the non functional overreaching state, that is, a
deeper level of fatigue, impairing the capacity for regeneration and recovery of the body. If
this tune persists, may lead to overtraining syndrome.
Despite the importance of correct terminology, many coaches and athletes unaware this
phenomenon whereas their main object of interest is sport performance. Thus, sport scientist
should focus on distinguish and monitor positive from negative training adaptation in order to
get always positive results and avoid damaging the athlete health (Richardson et al., 2008). In
order to summarize and demonstrate how thin is the line between overtraining and
overreaching, we present the table 1, which represents the overload training progression,
referring to the fatigue level, recovery time and level of performance.
By this time, we are able to say that overtraining syndrome often can be accompanied by
several biochemical, physiological, psychological and hormonal changes, and some common
manifestation are chronic muscle pain, joint pain, mood and personality changes, elevated
resting heart rate, and of course, decreased performance (Gleeson, 2002; Brenner, 2011). The
difficulty of knowing whether an a athlete is in a state of peak fitness or if he is at the
beginning of a decline in performance due to overtraining is very complex, especially regard
to the physiological and biochemical factors (Meeusen et al., 2005, p.5). Moreover,
overtraining signs and symptoms vary from individual to individual, are non-specific,
anecdotal and numerous. These symptoms can also be confused with other clinical
disturbances, and many times, the chronic fatigue syndrome and clinical depression are the
most confoundable factors.
UNDERSTANDING THE MULTIFACTORIAL ETIOLOGY
The progress of knowledge in this area has been delayed because there are few scientific
prospective researches and lack of well-controlled studies about individual responses to
overload training (Halson &Jeukendrup, 2004). This lack of studies happens because is not
ethical to “overtrain” an athlete. Thus, identifying possible events that trigger or initiate
overtraining (imbalance between load and recovery, training monotony, exaggerated number
of competitions, glycogen deficiency, infections, emotional demands – affective and
professional) is, perhaps, a rational study design, although cannot fully explain the entire
mechanism of overload training. Since the phenomena involved in overtraining and recovery
are clearly multifactorial, qualitative descriptive case studies can also assist in understanding
the complex relationships involved (Botterill & Wilson, 2002). It could be useful to conduct
research looking into many variables as possible; nevertheless it is not an easy task in
understanding problems in a holistic way.
The physiopathology of overtraining syndrome, ranges from muscle soreness and
weakness, cytokine actions, moods swings, hormonal and hematological changes,
psychological depression and nutritional problems, but the number of symptoms reported by
overtrained athletes is very large, more than 200 (Fry et al., 1991). Table 2 shows, the
physiological and psychological symptoms that are most commonly associated with a clinical
diagnosis of overtraining (base on Gleeson, 2002).
56
Fernando Rocha, Mário C. Marques and Aldo M. Costa
Table 2. Common reported physiological and psychological
changes associated with overtraining
Symptoms
Underperformance
Muscle weakness
Chronic fatigue
Sore muscles
Increased perceived exertion during exercise
Reduced motivation
Sleep disturbance
Increased early morning or sleeping heart rate
Altered mood states (e.g. low scores for vigor; increase scores for fatigue and
depression)
Loss of appetite
Gastrointestinal disturbance
Recurrent infections
Reference: based on Gleeson (2002)
To understand the etiology of overtraining syndrome seek first to exclude some organic
diseases or infections and other nutritional factors (negative energy balance, insufficient
carbohydrates and proteins intake, iron and magnesium deficiency). Despite the existence of
several hypotheses about the causes of overtraining syndrome, there seem to be also some
consensus. Situations that can trigger overtraining syndrome are the imbalance between
training / load and recovery, excess competition, the monotony of training, emotional issues.
Other less mentioned causes, relies on exercise heat stress and training at altitude (Meeusen,
2005), but the scientific evidence to support or refute these hypotheses are scarce, and the
diagnosis is reached when you cannot identify and justify the cause of such symptoms.
In the following subsections we point some main reasons that seem to trigger
overtraining. What we need to retain, is that the etiology of overtraining syndrome varies
from individual to individual, depending a lot on your state (physical and psychological) and
stressors factors that are put upon it. Nevertheless, high intensity training and/or too little
regeneration (recovery) is always the starting point.
Variations of the Hypothalamic-Pituitary-Adrenal Axis
Lehmann et al., (1993) introduced the concept that hypothalamic function reflects the
state of overreaching or overtraining syndrome because the hypothalamus integrates many of
the stressors. The same author in 1998 suggested that a regulation disorder at the
hypothalamus-pituitary might be the central disorder in overtraining syndrome.
Increased training loads as well as other stresses can influence the neuroendocrine system
in a chronic way. The endocrine system acts to promote the adaptation to the stimulus (load
or other life stressors) through the activation of the autonomic nervous system. These actions
result in changes in blood catecholamine, glucocorticoid, testosterone levels (Cunha et al.,
2006), adrenocorticotrophin (ACTH), cortisol and prolactina (Gleeson, 2002).
Training Over the Edge
57
In response to stress, greater quantities of hormones are released by changing the
sensitivity of specific receptors for these hormones, and tissues became less responsive to its
action. Some authors (Fry et al., 1991; Lehmann et al., 1998) refers that the negative
feedbacks responses reduce sympathetic drive and down –regulation of anterior pituitary
gland receptors for hypothalamic releasing factors (corticotrophin) and/or inhibition of
pituitary hormone pulse generators could result in a decreased pituitary hormone - ACTH;
growth hormone; follicle stimulating hormone, (FSH); luteinising hormone, (LH)- response
to stress. This and/or a down regulation of receptors for ACTH on the cells of the adrenal
cortex could result in a decreased release of cortisol in response to stress.
In a normal training state, with high loads and other life stressors, there is a decrease in
the adrenal responsiveness; this decrease is compensated by an increase in the pituitary
release of ACTH. In an early stage of overtraining, we still record a decrease in the adrenal
responsiveness to ACTH, but at this time it is not compensated, and a decrease in cortisol
response will be verified. A more advanced state of overtraining continues to show a
reduction of the ACTH release by the pituitary, a decrease in sympathetic activity and a
decrease sensitivity to catecholamine’s (adrenaline and noradrenaline). Those
catecholamine´s and cortisol, are responsible to redistribute metabolic fuels, maintain blood
glucose and enhance responsiveness of the cardiovascular system. A repeated exposure to
stress can change the responsiveness, through alterations in neurotransmitter and receptors
functions, impairing the behavioral adaptations.
Imbalance of Circulating Amino Acids
During exercise there may be a decrease in circulating amino acids (including the
branched chain - BCCA´s, isoleucine, leucine and valine) due to oxidation in skeletal muscle
to ATP production, while there is the formation of an aromatic amino acid, tryptophan, it
binds to albumin in the blood.
Free fatty acids may also be oxidized to form ATP (when the muscle and liver glycogen
is depleted) and because they are not soluble, they also circulate in the blood bound to
albumin. Consequently, there will be a competition for that link - albumin-tripotophan and
albumin- free fatty acids (Petibois, Cazola, Poortmans & Deleris, 2002).
Tryptophan is the serotonin precursor. As 90% of tryptophan is bound to albumin, and
the 10% remaining is free in blood. The more free fatty acids bound to albumin, greater
amount of free tryptophan will exist. Tryptophan also competes with BCAA's to pass the
blood brain barrier.
During physical activity there is a decrease in BCCA's circulating and a greater
concentration of tryptophan than BCCA's will take place, thus, tryptophan will have the
preference to pass to the brain, and that can result in fatigue of cerebral origin (Budgett,
1998). In the brain, tryptophan acts as a neurotransmitter (5HT), and level changes in that
neurotransmitter can provoke overtraining symptoms (see figure 1), causing central fatigue,
loss of appetite, affecting sleeping, and even inhibiting the release of factors from the
hypothalamus that control pituitary hormones (Blomstrand, 1989; Rang 1987 cited by
Budgett, 1998).
58
Fernando
cited by Budgett,
1998). Rocha, Mário
C. Marques and Aldo M. Costa
Exercise
Free fatty acids
Oxidation
Formation
(bind to albumin)
Triptophan
(bind to albumin)
Oxidation
BCAA´S
Competition for the link with
ALBUMIN
Brain Barrier
Triptophan
Competitive
barrier to cross
Triptophan
Triptophan
Brain Barrier
BCCA´S
BCCA´S
Triptophan
Acts like a neurotransmissor
OVERTRAINING
SYMPTONS
that leads to central fatigue.
Figure 1. Process
Figure 1. Process
that leads
to central fatigue.
The glutamine, a nonessential amino acid synthesized by isoleucine and valine, very
abundant in skeletal muscle, also seems to play a role in the overtraining syndrome.
Glutamine can be used for hepatic gluconeogenesis and its main target is the kidneys, where it
is used in maintaining the pH balance (Rowbottom, Goodman & Morton, 1995). A negative
arterio venous difference in plasma glutamine concentration occurs during prolonged exercise
(Graham, 1995) and some evidences shows that, this concentration of amino acid is higher in
slow-twitch fibers compared with fast-twitch fibers. Long-duration exercise, with aerobic
characteristics and in periods of intense training, the concentration of glutamine rises,
decreasing during the recovery period.
Since the white blood cells (lymphocytes in particular) cannot synthesize glutamine for
energy, being dependent on syntheses and release by skeletal muscles the decrease in
glutamine cause a muscle acidosis (cannot do the buffering of hydrogen ions) and provoke a
decline in the immune response, especially in overtraining (Gleeson, 2008). Indeed, plasma
glutamine has been suggested to be a potential cause of the exercise-induced immune
impairment and increased susceptibility to infection in athletes and therefore, as a possible
indicator of excessive training stress. However, not all studies have found a fall during
periods of increased training and overtraining (Walsh, Blannin, Robson & Gleeson, 1998).
Training Over the Edge
59
Cytokine and Inflammation
Cytoquines have also been linked to overtraining as these appear to be mediators of this
syndrome, a situation justified by the activation of monocytes to produce and release
inflammatory cytokines such as IL-1b, IL-6 and TNF-α. Repetitive exercise, high volume and
inadequate rest generate a high inflammatory response, which can cause micro-trauma in
joints, muscles and connective tissue (Mackinnon, 2000). These cytokines would then initiate
a ‘whole-body’ response, involving chronic systemic inflammation, ‘sickness behavior’,
suppressed immune function and mood state changes. It has also been suggested that
cytokines may activate the hypothalamic-pituitary adrenal axis, and therefore, may underlie
the neuroendocrine changes observed in overtrained athletes.
With overtraining there are also increases in the plasma concentrations of others
substances that are known to influence leukocyte functions (besides the ones that already
were pronounced) like the inflammatory cytokines (Mackinnon, 1998b quoted in Gleesson,
2002). It appears that the high release of pro-inflammatory cytokines (interleukins 1, 2 and 6,
interferon α, tumor necrosis alfa and protein c-reactive) triggered by the systemic
inflammation process – due to excessive training – acts on the central nervous system,
changing the hormonal balance. Cytokines also activate the sympathetic nervous system,
while suppressing the activity of hypothalamic-pituitary-gonad, and thus responsible for the
observed changes in blood concentrations of gonadal hormones and catecholamines, which
are present in a state of overtraining athletes (Rogero, Mendes & Tirapegui, 2005).
THE ASSESSMENT OF OVERTRAINING
At present, it still is a very hard task to differentiate acute fatigue and decreased
performance resulting from isolated training sessions from any overtraining progression states
(Halson & Jeukendrup 2004). Additionally, it is also complicated to identify a specific marker
that can register difference between the states of overtraining and overreaching.
acoording to Meeusen, Nederhof, Buyse, Roelands, Shutter & Piacentini (2010), a
keyword in the detection and recognition of the overtraining syndrome may be the prolonged
inability to adapt, not only to the level of aspects of athletic performance, but also in relation
to other regulatory mechanisms, such as biological mechanisms, hormonal and
neurochemicals. The marker of choice for detecting overtraining syndrome should address the
following two criteria: (i) the marker should be sensitive to training load and, preferably,
should not be affected by other factors such as diet; (ii) changes in the marker value should
occur before reaching the state of overtraining syndrome, and responses due to the acute
exercise should be possible to distinguish in relation to chronic responses. As this marker
would be extremely useful for coaches and athletes, a criterion of easy applicability and low
cost also is a point to be fulfilled (Meuseen, 2005), however, so far, the literature does not
identify any marker that has all these requirements.
The mechanisms that are consistently documented to occur with overtraining and
together may provide a significant support to expose the overtraining syndrome, include the
list below. (Mackinnon, 2000, p. 503):
60
Fernando Rocha, Mário C. Marques and Aldo M. Costa







Performance decrements;
Reduce ability to performance high intensity exercise;
Persistent high fatigue ratings;
Decreased maximal heart rate;
Changes in blood lactate variables, such as the blood lactate threshold or blood
lactate concentration at maximal exercise;
Neuroendocrine changes, such as reduced nocturnal excretion of noreepinephrine
(Nep);
Changes in athletes self-reported indicators of “wellbeing” such as fatigue and
quality of sleep
Prevention is a important point in this thematic, therefore, a very well structured planning
train is necessary, where coaches and athletes can register and track all adaptations to short
and long term training.
Monitoring Performance
Identify the prevalence of overtraining is difficult because it requires a long-term
monitoring of several athletes from different sports, and on the other hand, the coaches have a
great reluctance to identify athletes who are overtrained, but some studies indicate that about
7 % to 20% of athletes in specific individuals active phase of his sports life may have
symptoms of overtraining (Hooper, 1993;, 1987; Raglin, 1994).
The type of sport most likely to cause overtraining appears to be the endurance modes,
where the very intense training volume is more present than those where the strength is the
predominant capacity. But in sports like judo and weight-lifting can also occur overtraining
symptoms (Callister, Fleck & Dudley, 1990).
Meeusen (2005, citing Budgett, 2000; Lehmann, 1999 and Urhausen, 1995) refer that
athletes suffering from overtraining syndrome, normally are able to start a regular training
sequence at their usual capacities, but they are not capable to complete the training load, so,
as mentioned before, one very good indicator is the unexplainable decrease in performance.
Of course it is clear that the type of tests should be sport-specific. How to apply it is still
involved in academic discussions: maximal or incremental test? Halson (2004) refers that in
general, time to fatigue test are more likely to show greater changes in exercise capacity as a
result of overreaching and overtraining syndrome than incremental exercise tests, beyond
that, allows the evaluation of substrate kinetics, hormonal response and the possibility of
setting specific intensities and durations for the collection of sub-maximal results.
Meeusen et al., (2010), used a two-bout maximal exercise protocol to objectively and
immediately make a distinction between non-functional overreaching and overtraining
syndrome in underperforming athletes who were diagnosed with suspicion of non-functional
overreaching or overtraining syndrome. With this protocol, they measured physical
performance and stress induced hormonal reactions. The protocol was applied with 4 hours of
interval, obtaining the following main results: the maximal blood lactate was lower in
overtraining syndrome subjects, compared with the non-functional overreaching subjects
Training Over the Edge
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while resting concentrations of cortisol, adrenocorticotrophic hormone (ACTH) and prolactin
(PRL) concentrations were higher. However, sensitivity of these measures was low. Both,
ACTH and PRL had a higher reaction in the second bout in non-functional overreaching
athletes compared with overtraining syndrome and showed the highest sensitivity for making
that distinction. This study suggests that using a two-bout maximal exercise protocol can be
useful to early detect non-functional overreaching and overtraining syndrome. The authors
used the cycle ergometer and the treadmill, obviously, depending on the specific type of
athlete/sport to be tested, proving that the specificity of the test may be sensitive not only to
variation in performance but also in the variation of other parameters that may be associated
with overtraining syndrome.
Monitoring sports performance involves having a set of standardized and validated
instruments, for the changes over time can be explained. However there are no ways to
measure the individual capacity of response or the athelet’s adaptation to exercise/training.
For such task we can always use questionnaires, diaries, monitoring physiological parameters
or even use direct observation (Borresen & Lambert2009) to track the physiological
adaptations of training. The baseline individual data and the need of high standardized
conditions are the most frequent problems and represent a limitation for the use of performing
test as a detector of overtraining syndrome.
Monitoring Heart Rate
Heart rate (HR) appears as one the most preferable indicator for the evaluation of training
load response and physical fitness. In addition to HR responses to exercise, research has
recently focused on heart rate variability (HRV). HRV is an index of interbeat intervals; the
higher the HRV, the higher the cardiovascular autonomic responsiveness (Bosquet et al.,
2008), which also means a increase in vagal (parasympathetic) tone relative to sympathetic
activity (Uusitalo et al., 2000). It seems that trained individual have higher HRV than
untrained individuals. As enunciated in the following texts, both HR and HRV, could
potentially play a role in the prevention and detection of overtraining (Achten & Jeukendrup,
2003).
Training stress interferes with the autonomic nervous system and therefore with HR.
According to Fry et al., (1991), this influence may be one of the reasons why HR is
considered an indicator for overreaching and overtraining syndrome. However, the effects of
overreaching on submaximal HR are controversial, with some studies showing decreased
rates and others no difference. Maximal HR appears to be decreased in almost all
'overreaching' studies, but concerning the HRV, it appears that in overreaching or
overtraining there is no differences (Achten and Jeukendrup, 2003) or the one´s are very
inconsistent (Uusitalo et al., 2000).
Meeusen et al. (2005) underlined the study of Halson et al., 2005, in which they sought to
understand the influence of increased training intensity for 7 days (overreaching) on HRV.
The results showed a significant effect on HRV values when the intensity of training was
intensified. This suggests an increase in the relative contribution of parasympathetic to
sympathetic nervous system activity.
In a meta-analysis developed by Bosquet et al. (2008), overeaching resulted in a small
decrease in the HR measured during submaximal and maximal exercise, together with a small
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Fernando Rocha, Mário C. Marques and Aldo M. Costa
increase in the cardiovascular autonomic balance at rest. The decrease in HR in a submaximal
effort was more evident during long term increase in training load, suggesting that this marker
cannot be used as valid short-term fatigue indicator; it probably suits better for long-term
fatigue. The results also show that maximum HR, in addition to identifying the training
intensity, can also be a possible indicator of overtraining syndrome, functional overreaching
and non-functional overreaching, because it was the only variable that changes with increased
training load during short and long term periods. The overall effect size showed only a small
increase in resting HR, suggesting that it cannot be a valid indicator for overtraining
syndrome or for both states of overreaching. Although, the results also show a moderate
increase in resting HR after short term interventions (2 weeks) of increasing training load, but
no alterations when the intervention was longer than 2 weeks. According to the authors the
increasing in resting HR suggests that this indicator can be used as a valid marker of shortterm fatigue, probably for functional overreaching, but not for a long-term intervention of
increasing load (possibly non functional overreaching or overtraining syndrome). Another
variable widely used in training is the heart-rate recovery. Bosquet et al. (2008) found no
data/studies supported by experimental data that would enable them to make considerations
about this parameter, noting that any conclusion about the validity of post-exercise HR
recovery as a marker of functional overreaching, non-functional overreaching and
overtraining syndrome will be hazardous.
In a meta-analysis is important to note that the results are primarily statistical, and in this
context Bosquet et al reported that the moderate amplitude of the alterations founded in their
research limits the clinical usefulness, as this difference may be justified with the day-to-day
variability. Consequently, the correct interpretation of HR or HRV fluctuations during the
training process requires the comparison of these markers with other objective signs and
symptoms of functional overreaching, non-functional overreaching and overtraining
syndrome. Indeed, HR or HRV alone does not provide consistent results due to the difficulty
of standardized procedures. Moreover, it seems that it is also difficult to to distinguish
between changes in physiological measures resulting from functional overreaching, nonfunctional overreaching and overtraining syndrome (Meeusen et al., 2005).
Immunological Parameters
The immune system composed by several white cells seems to be affected with exercise.
For instance, leukocytes, whose change in number and its functions are closely correlated
with being active. According to Mackinnen (1998b, quoted by Gleeson, 2002) in intense and
repeated exercises bouts there is a decreased in the leukocytes ability, suspecting that the
change in plasma concentration of hormones such as adrenaline, cortisol, growth hormone
and b-endorphin is considered the neuroendocrine cause that leads to immunosuppression
induced by exercise (Niemann, 1997).The falls in blood concentration of glutamine as seen
before, also seems to be implicated in causing immunossupression associated with heavy
training.
One situations that is often reported with increased training intensity and the
overreaching and overtraining syndrome is the risk of upper respiratory tract infections
(URTI) (Niemann, 1997; Mackinnon, 2000; Meeusen et al., 2005), but it has been suggested
that the predisposition increase of high performance athletes to URTI is not necessary
Training Over the Edge
63
accompanied by a state of overreaching or overtraining syndrome; URTI can be the
consequence of an intense workout.
In a study using swimmers submitted to training intensified training for 4 weeks, it was
found that the high rate of URTI among athletes who have not reached a state of overreaching
was a protective factor since, in a way, forced them to reduce the training loads for some
time, allowed sufficient rest to prevent overreaching (Jonsdottir et al. in IV International
Symposium Exercise and Immunology, 1999 quoted by Mackinnen, 2000). When trying to
support the theory that overtraining alone is associated with increased risk of URTI, the
studies are inconclusive, but there are good evidence that when the training is truly intense
(beyond the limits of an individual athlete capacities and before the presence of some
monotony in training), the risk of URTI is increased, suspecting that the overtraining
syndrome and URTI may result from a common denominator - excessive training load and
insufficient recovery time.
How intense training causes an immunosuppression has several explanations. The
question of increasing the duration and degree of “open window” is a trigger to induce
overtraining syndrome and to infections. On the other hand, when a athlete is subjected to
prolonged exercise, there is an increase in neutrophils (bone marrow) and if that training
continues to occur over extended time for weeks and/or months, the bone marrow can deplete
the ability to neutrophils release, particularly those who have reached a mature state. This
decreased number of neutrophils in overtrained athletes may also predispose athletes to
infections.
During the recovery period after a workout, the number of neutrophils increases,
however, the number of lymphocytes decreases and the ratio neutrophil/lymphocyte seems to
be a good indicator of stress induced by exercise, and also for the recovery capacity (Nieman,
1998). The normal value of this ratio usually takes 6-9 hours after exercise to be replaced, but
if it is a prolonged and intense training, the same ratio can be elevated for 24 hours after
exercise (Gleeson, 2002).
The same author also claims that using the indicator given by expression of CD45RO+
over the CD4+ cells can indentify overtrained athletes with high sensitivity and specificity.
CD45RO+ and CD4+ cells are subsets of T lymphocytes, changing both with exercise or
training. CD45RO cells are markers of T-memory cells and T-cells activators; therefore, an
expression of CD45RO on T-cells may be only an indicator of the presence of an acute
infection, which may be a possible cause of underperformance (Meeusen et al., 2005).
Another changing in the leukocytes function with intense training is the ratio
CD4+/CD8+ (helper/suppressor). This ratio under conditions of intense training falls, but it
seems not be different enough in overtrained athletes when compared with healthy or well
trained athletes (Gleeson et al, 2005). Regarding the number and functions of natural killer
cells (NK, CD16*/CD56*) the percentage and number are normal in athletes, although their
cytotoxic activity at rest may be higher in athletes than in non-athletes (Nieman, 1995, cited
by Mackinnon, 2000). Nieman and coworkers in Suzui et al., (2004) reported that NK cell
cytolytic activity was greater in marathon runners, rowers, and active elderly than in
untrained individuals, although there were no intergroup differences in NK cell count. Such
results suggest that chronic exercise increases cytotoxicity per NK cell. On the other hand,
many studies have failed to establish positive relationships between NK cell cytolytic activity
and chronic exercise (Boas et al, 1996; Shepard, 1997; Shepard and Shek, 1999, cited by
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Fernando Rocha, Mário C. Marques and Aldo M. Costa
Suzui et al., 2004). Acute exercise temporarily increases NK citolytic activity (Gleeson, 2002)
but decreases after exercise, usually for no more than a few hours (Suzui et al., 2004).
Natural killer cells citotoxic activity have been shown to sensitive increases in the
training load in already well trained athletes, however, it is not possible to identify differences
between overtraining and healthy athletes (Verde, Thomas & Shepard,., 1992). Mackinnon
says that in terms of functionality and number of NK is not possible, yet, to distinguish a state
of overtraining and overreaching, but during periods of intense training the number of NK
cells may decrease: a military study found that for 10 days of intense workout (running) the
number of CD56* cells decreased more than 40%, and these values remained low for 5 days
of light training for recovery (Fry et al., 1992).In another study whose reference is direct to
the number of NK and citotoxic activity, it is reported that both conditions (number and
activity) after an intense workout plan for 4 weeks in swimmers, are lower. A special feature
of this intervention is that athletes who reported these results are not achieved during the four
weeks a state of overreaching (Gedge, Mackinnon & Hooper, 1997).
For competitive athletes who often train twice a day is possible that the number of NK
cells and their function need more time to recover, since it is reported that intense and
prolonged training sessions has a transient response of suppression of citotoxic activity of NK
cells, which may take at least 6 hours, and perhaps can reach 12 hours to normal activity
(Nieman et al., 1995, Mackinnon et al., 1997 as cited in Mackinnon, 2000). That
downregulates of Nk function, seem to support the “open window” theory, whereby some
athletes become susceptible to upper respiratory infections (URTI) for a brief period after
heavy exercise (Pedersen and Ullum, 1994, as cited in Suzui et al., 2004).
Another important parameter in relation to the immune system is the concentration of
immunoglobulin A (IgA), which also constitutes a barrier to infectious agents in the body,
particularly against pathogens that cause URTI. IgA is found in external secretions (eg.
mucous, saliva, tears), and with intensified training, it seems that the concentration of IgA
falls, continuing low after several hours in the recovery period (Nieman, 1997). Some studies
documented a negative relationship between salivary IgA and the concentration and
occurrence of URTI: for example, lower IgA levels early in the training season have been
correlated with the number of URTI episodes throughout the season (Mackinnon, 2000).
Low levels of IgA have been reported in overtrained athletes (Mackinnon, 1996, as cited
in Gleeson, 2002) demonstrating that monitoring salivary IgA may be useful in indicating
overtraining, although the inter individual variation in salivary IgA is quite large.
It seems that the immunity system is fairly sensitive to intense training, and although it is
not possible to distinguish those alterations (mainly in functions and not in numbers) from the
well trained stage (overreaching) and a maladaptation state (overtraining). Like for all stress
parameters that we have referred before is important to establish a reference/normal value for
the each individual athlete. Given the biological variability, comparing values between
individuals seem not a sufficiently reliable method. Moreover, testing immunological markers
for overtraining, despite being wrapped up in lengthy and costly process, the data
presented in the scientific literature are inconsistent, which leads us to use and
analyze these markers with relative care.
Training Over the Edge
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Hematological Parameters
The values of hematological parameters are affected by a number of factors even in
apparently healthy populations. These factors include age, sex, ethnic background, social,
nutritional and environmental factors, and it has been shown in several studies that some of
the hematological parameters exhibit considerable variations at different periods of life (ElHazmi & Warsy, 2001). For the overtraining syndrome and overreaching a variety of
responses to the increased training load have been studied in an attempt to achieve a reliable
indicator for those two states.
Mackinnon et al. (1997, quoted by Silva, 2006) refers that the hematocrit tends to
decrease with overtraining in swimmers after two and four weeks of gradually increasing the
training volume. In the same study, the amount of red blood cells also decreased with
overtraining after four weeks of increased training volume. The reduction was about 8-12%,
very similar to the decline in magnitude to the concentration of hemoglobin in amounts of 59%. Likewise, according to Silva, (2006) others authors (Newhouse and Clement, 1988;
Smith, 1995) also reported a reduction concentration of red blood cells and hemoglobin after
high intensity training.
The concentration of plasma glutamine has been suggested as a possible indicator of
excessive training stress (Rowbottom et al., 1995, as cited is Meeusen et al, 2005; Rowbottom
et al, 1996, as cited in Gleeson, 2002). During periods of high demand of immune system a
increased production of glutamine is observed, but during prolonged period of training,
glutamine levels fall - the same don´t happen in short and intense training bouts – the same
decrease of glutamine can also been seen during the existence of physical trauma, burns,
inflammations and infections (Walsh et a.l, 1998;Gleeson, 2002).
Because of these changes in glutamine levels and also because of its relationship with the
immune system, it has been associated this amino acid with the overtraining. Halson, (2004)
refer Parry-Byllings et al. (1992) where they reported a lower plasma glutamine
concentrations in 40 athletes diagnosed as overtrained when compared with controls; the
same study reported lower increased glutamate levels in overtrained athletes. We refer the
glutamate because the mechanisms behind the performance decrements associated with
overtraining are unclear, and a combination of a number of markers is needed for early
diagnosis. Smith and Norris (2000), in an attempt to track the training tolerance through the
glutamine and glutamate concentrations founded changes in the plasma glutamine/glutamate
ratio (Gln/Glu) suggesting this ratio as a predictor of overreaching or overtraining in athletes.
They also observed a elevated plasma glutamate and hence a reduced Gln/Glu ratio in athletes
who were classified as overtrained. However, no studies have investigated changes in
glutamine, glutamate, and reported concurrent performance measures during a period of
intensified training that has resulted in overreaching (Halson et al., 2003)
Although not all studies have found a fall during periods of increased training and
overtraining, plasma glutamine may provide a useful biochemical marker of overtraining, but
since plasma glutamine level is influenced by short term exercise, nutritional status, diet,
infection and physical trauma, it is important that standardized evaluations of these
parameters are taken into account (Walsh et al., 1998; Gleeson, 2002).
Other marker that seems to be also a help to detect overtraining is urea. Urea is an end
product of the degradation of nitrogenous or protein materials. Measurements recorded in the
field as a component of the training program represents the concentration of serum urea (can
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Fernando Rocha, Mário C. Marques and Aldo M. Costa
be regarded as equal to plasma), i.e., balance of urea synthesized in the liver and urea
excreted renally (Hartmann & Mester, 2000), briefly, the extent of protein degradation can be
very helpful to the coach. An increase in exercise can promote an increase in serum urea
value, but the conclusion of catabolic/metabolic activity does not automatically result from
such elevated values. If those increased values are associated with a reduced exercise
tolerance after a long phase of intensive physical effort, the possibility of a
catabolic/metabolic activity or insufficient exercise tolerance becomes much more likely
(Hartmann & Mester, 2000). According to the same investigators, it is only appropriate to
speak of a catabolic/metabolic activity only when serum urea levels are elevated for 2-3 days.
If the levels remain high for more than 3 to 5 days, it may be suspect a massive loss of
protein.
In Halson et al., (2003) study, plasma urea concentration tended to be slightly elevated
during a intense training period, and also declined to pre-intensive training levels after
recovery, which means that elevation is temporary, and the concentration of urea is markedly
influenced by the recent diary protein intake, so serum urea hardly fulfill a reliable indicator
for the onset of overtraining (Gleeson, 2002).
Other parameter very associated with muscle function is the creatine kinase, an enzyme
present in the blood when the muscle cell membranes are damaged as a result of an intense
muscle contraction, and that is often used as an indicator of muscle damage (Diaz, Ruiz,
Hoyos, Zubero, Gravina, Gil & Irazusta 2010). One consequence of the high level of creatine
kinase in plasma is the temporary decline in athletic performance, probably caused by muscle
aches, muscle stiffness, decreased range of motion, changes in lactate concentrations, loss of
strength and decrease in the maximum dynamic power (Jones, Newham, Round & Tolfree,
1986). Although Gleeson (2002) noted that in highly trained athletes the eccentric work does
not cause large increases in creatine kinase activity, yet, athletes experience muscle aches.
The same author quoting O´Reilly et al. (1997), also notes that once installed elevated
creatine kinase levels in the body impairs the re-synthesis of muscle glycogen, resulting in
decrease performance.
Halson et al (2003), in the same study that evaluated the serum urea, found that plasma
creatine kinase activity was significantly elevated during the intensive training period, and
also returned to baseline levels during recovery. Regarding the activity of creatina kinase, it
seems that the characteristics of the effort, intensity and volume, are both important, since
they have an influence on the reduction of high-energy phosphate in muscle cell. Creatine
kinase seems to have the highest concentrations in men than in women, probably due to the
influence of sex hormones, the greatest resistance to muscle cell damage, or simple due to a
smaller amount of this enzyme in women (Hartmann & Mester, 2000). There is another
peculiarity of creatine kinase, which is the inter and intra-individual variability. As mentioned
by Hartman and Mester, there are athletes who have low levels of creatine kinase at rest,
others a mean value and there are still those who have high values when compared with
normal values. In this study, athletes who had chronic low values of creatine kinase have
demonstrated a lower variability, whereas those with chronically higher values exhibited
considerable variability of this parameter. This information becomes important for the coach,
since he can better tailor training schemes to a more individualized intervention.
As noted for serum urea, increases in creatine kinase concentrations at rest when
measured on standardized conditions, can provide a set of information concerning an elevated
Training Over the Edge
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muscle and /or metabolic strain, but they are not suitable to indicate an overreaching or
overtraining state (Urhausen et al., 1998a, as cited in Meeusen et al., 2005).
Another proposed marker of overtraining is a paradoxical decrease in plasma lactate
levels in submaximal and maximal exercise. While lower lactate levels during submaximal
exercise generally indicate improved endurance capacity (Foster et al., 1988; Jacobs, 1986 as
cited in Jeukendrup and Hessellink, 1994), in overtraining, paradoxically lower maximal and
submaximal lactate values have been reported (Jeukendrup et al., 1992; Lehmann, 1988 as
cited in Jeukendrup and Hesselink, 1994). This has been explained on the basis of low muscle
glycogen levels, a decreased catecholamine response to exercise or decreased muscle tissue
responsiveness to the effects of catecholamines (Jeukendrup et al., 1992, as cited in Gleeson,
2002).
Associate ratings of perceived exertion with lactate values, seems to be a possible way to
distinguish the state of training with overtraining. This is supported by the explanation that
for a given exercise intensity, a decrease in blood lactate concentration is accompanied by a
increase of rating of perceived exertion during overtraining, while ratings of perceived
exertion remains unchanged or decreases when the athlete is tested during intensive training
(Snyder et al., 1993, as cited in Bosquet et al., 2000). So, the blood lactate /rating of perceived
exertion quotient would be expected to decrease with overtraining, but stay relatively the
same with intensive training. This theory seems to be true in overreaching athletes, but never
has been tested with overtraining athletes.
Bosquet et al., (2000) in a study with the objective of determine if it is possible to
disassociate the changes in the lactate curve brought about by training and overtraining, with
the hypotheses that overtraining would result in a decrease in the blood lactate /ratings of
perceived exertion quotient after an overtraining period and a 2 weeks recovery period. The
study showed that rating of perceived exertion does not provide useful information for
detecting overtraining during an incremental test. Therefore, the proposed ratio is not a better
marker for overtraining than blood lactate alone.
Another interesting fact was that the authors noted that the right shift of the curve of
lactate was accompanied by a decrease in the peak blood lactate when there was a decrease in
performance capacity, which remained after the 2 recovery weeks when athletes were in
overtraining, but not when they were in overreaching. Following this observation, Bosquet et
al. propose to retain a decrease in the peak blood lactate as a marker of overtraining in events
of long duration, and repeating its measurement after a sufficient period of rest to make the
distinction with overreaching.
Beyond what already mentioned, the literature presents some limitations as to lactate be a
marker for overtraining - lactate differences are sometimes subtle (lying within the measuring
error of the apparatus) and depend on the modus of the exercise test used; and no lactate
changes reported in strength athletes (Meeusen et al., 2005).
Hormonal Parameters
Halson et al, (2000, cited by Urhausen et al., 1998) reported that in overtraining
endurance athletes there was no significant changes in cortisol, normal vs overtrained
athletes, when subjects were examined prior to and during a state of short-term overtraining,
however, maximal cortisol response appear to be reduced during overtraining. Compared to
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Fernando Rocha, Mário C. Marques and Aldo M. Costa
testosterone, some studies are contradictory, Urhausen referring to Flynn et al., (1994),
indicates that after intensive training, a decrease in testosterone levels may be found, which
can also coincide with a decrease in performance capacity. Vervoorn et al., (1991, as cited in
Halson and Jeukendrup, 2004) obtained the same decrease in testosterone concentrations,
however, no significant loss of performance was obtained.
The ratio testosterone/cortisol is referred in some studies as a marker of the anbaboliccatabolic balance, which can be a tool for the diagnosis of overtraining syndrome
(Adlercreutz et al., 1986, as cited in Urhausen and Kindermann, 2002). Cortisol and
testosterone are both released in response to high intensity (>60% maximal oxygen uptake,
VO2max) aerobic and anaerobic exercise and is believed that this ratio decreases in relation to
the intensity and duration of training , and it is the indicator of the positive and negative
effects of training due to the opposing effects that hormones have on growth, protein
synthesis and muscle metabolism (Kreider et al., 1998 as cited in Halson and Jeukendrup,
2004).
This ratio only indicates the actual physiological strain of training and cannot be used for
diagnosis of overreaching or overtraining syndrome (Lehmann et al., 1995; Meeusen, 2005
quote Urhausen et al., 1995; Meeusen et al., 2004) because the ratio has been shown to
remain unchanged in overreached athletes, although, a decrease ratio has been reported in
athletes who show no performance decrements after intensive training (Vervoorn et al., 1991,
as cited in Halson and Jeukendrup, 2004).
The decrease in nocturnal urinary excretion of catecholamines has been suggested as sign
of an advance state of overtraining syndrome, in overtraining athletes, and has also been
interpreted as low intrinsic sympathetic activity (Lehmann et al., 1992, Mackinnon et al.,
1997, as cited in Urhausen and Kindermann, 2002). This excretion appears to be lower than
normal in overtrained athletes (Foster and Lehmann, 1999, as cited in Gleeson, 2002)
indicating a negative correlation with fatigue ratings. Catecholamine levels in urine and
plasma can reflect the activity of the sympathetic nervous system and can, therefore, examine
the possibility of parasympathetic-sympathetic imbalance or autonomic imbalance (Halson et
al., 2003).
Lehmann et al., (1997) reported that athletes after been submit a intensive ergometer
training, revealed 60% higher pituitary adrenocorticotropic hormone (ACTH) release to
corticotrophin releasing hormone, which was also followed by a decrease of about 25% of
adrenal cortisol release.
Barron et al, (1985, as cited in Gleeson, 2002) have also presented evidence of an
adrenocortical deficiency in athletes suffering from overtraining syndrome. They found that
growth hormone, prolactin and ACTH responses to insulin-induced hypoglycaemia (a potent
stimulus to sympathetic nervous activity) were lower in a small group of overtrained athletes
compared with healthy well-trained controls.
Urhausen et al, (1998, as cited in Halson and Jeukendrup, 2004), reported lower resting
ACTH levels and lower exercise-induced ACTH release in overreached athletes. A reduced
maximal plasma growth hormone (GH) concentration was also reported.
In order to protect the target organs of inadequate or pathological loads during the
process of overtraining, the body has several adaptations, and one that appears to prevent a
catabolic state is a slight increase in pituitary sensitivity to GHRH (increased release of GH),
which is more anabolic (Lehmann et al., 1993). This also happened in response to the
unchanged testosterone/cortisol ratio.
Training Over the Edge
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About the limitations for the use of hormonal markers to identify overtraining, the
baseline data also appears a problem to avoid; it is needed to have a baseline measure to allow
the comparisons among different stages/periods for the same athlete. Moreover, nutrients
through the food intake can change the concentration of some hormones at rest and/or in
response to exercise (Meeusen et al., 2005). The same author also highlighted that the
concentrations of blood hormone are linked with the sample conditions, i.e., conservation and
the assay variability.
Psychological Parameters
Is clear the multifactorial etiology of the overtraining process, and several methods using
physiological markers, hematological or immunological parameters have been used, however,
Shepard &Shek (1994) show that the psychological evaluation is an easier and less expensive
to detect the overtraining syndrome. In fact, there is a general agreement that the overtraining
syndrome is characterized by psychological disturbances and negative affective states
(Hooper et al., 1997, as cited in Halson and Jeukendrup, 2004).
O´Connor (1998, as cited is Kentta and Hassmén, 1998) identify four advantages for
using psychological markers: 1) Psychological changes are more reliable, i.e. mood shifts
coincide with increases and decreases in training and are also highly replicable; 2) Some
mood states are highly sensitive to increases in the training load (changes in these states occur
early on and have large effects) while others are more sensitive to the staleness (overtraining)
syndrome; 3) Variations in measures of mood often correlate with those of physiological
markers; and 4) The titration of training loads based on mood responses to overtraining
appears to have good potential for preventing overtraining. On the other end, psychological
testing may reveal early-warning signs more readily than the various physiological or
immunological markers (Kenta & Hassmén, 1998).
The great advantage of psychometric instruments is the quick availability of information
(Kellmann, 2002, as cited in Meeusen et al., 2005), therefore, some questionnaires such has
the “Profile of Mood State” (POMS) (Morgan et al., 1988; Raglin et al., 1994; O´Connor,
1997; O´Connor et al., 1989; Rietjens et al., 2005, as cited in Meeusen et al., 2005) the
“Recovery – Stress Questionnaire” (RestQ-Sport) (Kellmann, 2002, as cited in Meeusen et
al., 2005), the “Daily Analysis of Life Demands of Athletes” (DALDA) (Halson et al., 2002,
as cited in Meeusen et al., 2005), and the “Self-condition Scale” (Urhausen et al., 1998b, as
cited in Meeusen et al., 2005) have been used to monitor psychological parameters in athletes.
Although these questionnaires give a set of information that can predict a state of
overreaching or overtraining syndrome, the results should not be interpreted without an
association of decreased performance measurements.
It has been said that sometimes there is confusion between burnout and overtraining,
where the first is a sequel to the second. In psychological terms, it is necessary to treat the two
situations differently, once an athlete burnout, have their motivation levels far below, and this
is a central issue of burnout. Overtrained athletes may be highly motivated at the point of
increasing their levels of training/load in order to try to reverse the decline in performance.
Once again we present the limitations of use of questionnaires to assess psychological
parameters/mood changes described in the Overtraining Position Statement, Task Force
(Meesen et al., 2005): the application of the questionnaires must be in a well standardized
70
Fernando Rocha, Mário C. Marques and Aldo M. Costa
conditions to avoid different in mood; the timing is also important for example, normally
during the day the mood in the morning, afternoon and evening changes. Also in this
parameter the measures of an individual must be compared with baseline data and the
questionnaire as an instrument must detect the influenced in the results of other psychological
parameters independently of moods state
PREVENTION AND TREATMENT OF OVERTRAINING SYNDROME
Scientific research in the field of sports science has been keen on getting a set of effective
strategies that promote physical recovery, indicating that talent is not enough to ensure
success in sports. The concept that body needs a recovery time to adapt to the load of training
and/or competition is not new, however, athletes and coaches often rely on the empiricism to
act at this stage of training.
Before one can develop a recovery strategy, it must be define what kind of fatigue we
want to reverse. Because fatigue is a multifactorial condition, the type of stimulus induced
will define the forms of fatigue that may manifest itself (Gleeson, 2002). The issue focuses on
the occasion (the shortest amount of time) that a new load of training or competition can be
applied, causing the athlete benefits from previous load. This requires that the same athlete is
subjected to a recovering method at the point of fatigue that allows having a recovery time of
the stimulus that was submitted, avoiding overtraining and all its negative repercussions on
athletic and sports performance.
Avoiding / preventing is the primary measure in the fight against the overtraining
syndrome. Uusitalo (2001) refers that because there is still a huge difficulty in diagnosing the
overtraining syndrome and distinguish it from a state of overreaching, it is best to prevent that
overtraining happen, and the first step is to understand the fundamental principles of
progressive load before of understanding the significance of recovery (Grantham, 2006, p. 12):






Training is designed progressively to overload body systems and stores;
If the training stress is insufficient to overload the body´s capabilities, no adaptations
will occur;
If the workload is to great (progressed too quickly, performed too often without
adequate rest), then fatigue follows and subsequent performance will be reduced;
Work alone is not enough to produce the best results; it takes time to adapt to training
stress;
To encourage adaptation to training, it is important to plan recovery activities that
reduce residual fatigue;
The sooner the recovery from fatigue, and fresher when undertake a training session,
the better the chance of improving.
Figure 2 (based on Grantham, 2006) shows the principle of progressive overload, where
it enters a recovery strategy in the fatigue point where it will decrease the length of time it
will take to recover from training.
Training Over the Edge
71
Figure 2. Progressive overload.
As in Grantham manuscript (2006), the broken line represents the gain in the recovery
time, and the light gray shaded area, the opportunity window to applied another
training/competitive load, which will be sooner than if it hadn’t been a training unit for
recovery.
To achieve the situation described above is necessary to have a training plan, based on
periodicity. Periodicity means that loads are given in an appropriate stimulus, followed by
periods of recovery. This will also decrease the monotony of training (Uusitalo, 2001) and
allow taper. Taper is a type of reduced training, which is implemented to maintain fitness and
skill levels. Taper is NOT de-training and is needed to reduce the residual effects of fatigue
resulting from training and peak performance will occur at a point where fitness and fatigue
differences are maximized (Banister & Calvert, 1980).
Training and recovery must be in balance to prevent non-functional overreaching and
overtraining syndrome, and for that, it is very important that coaches and athletes make the
registration of the load applied in practice/training session and total week training using a
training log.
The four methods, most frequently used to monitor training and prevent overtraining are:
retrospective questionnaires, training diaries, physiological screening and the direct
observational method (Hopkins, 1991, as cited in Meeusen et al., 2005). Also the
psychological screening of athletes (Berglund & Safstrom, 1994, as cited in Meeusen et al.,
2005; Hooper et al., 1995, as cited in Meeusen et al., 2005; Hooper & McKinnon, 1995, as
cited in Meeusen et a.l, 2005; McKenzie 1999, as cited in Meeusen et al., 2005; Raglin et al.,
1991, as cited in Meeusen et al., 2005; Urhausen et al., 1998b, as cited in Meeusen et al.,
2005; Morgan et al., 1988, as cited in Meeusen et al., 2005; Kellmann, 2002, as cited in
Meeusen et al., 2005; Steinacker et al., 2002, as cited in Meeusen et al., 2005 ) and the
Ratings of Perceived Exertion (RPE) (Acevedo et al., 1994, as cited in Meeusen et al., 2005;
Callister et al., 1990; Foster et al., 1996, as cited in Meeusen et al., 2005; Foster, 1998, as
cited in Meeusen et al., 2005; Hooper et al., 1995; Hooper & McKinnon 1995, as cited in
Meeusen et al., 2005; Kentta & Hassmen 1998; Snyder et al., 1993, as cited in Meeusen et al.,
2005) have received more and more attention nowadays.
So that the records of training load were objectives, Foster et al, (1996, 1998, as cited in
Meeusen, 2005), have determined training load as the product of the subjective intensity of a
72
Fernando Rocha, Mário C. Marques and Aldo M. Costa
training session using ‘session RPE’ and the total duration of the training session expressed in
minutes. If these parameters are summated on a weekly base it is called the total training load
of an individual. The ‘session RPE’ has been shown to be related to the average percent heart
rate reserve during an exercise session and to the percentage of a training session during
which the heart rate is in blood lactate derived heart rate training zones. With this method of
monitoring training they have demonstrated the utility of evaluating experimental alterations
in training and have successfully related training load to its performance. But, as training load
are clearly not the only parameters that influences overtraining syndrome, the same
investigators additionally to the weekly training load, daily mean training load as well as the
standard deviation of training load were calculated during each week. The daily mean divided
by the standard deviation was defined as the monotony. The product of the weekly training
load and monotony was calculated as strain. The incidence of simple illness and injury was
noted and plotted together with the indices of training load, monotony and strain. They noted
the correspondence between spikes in the indices of training and subsequent illness or injury
and thresholds that allowed for optimal explanation of illnesses were computed
Kentta and Hassmé (1998), attest that there are many methods used to measure the
training process but few with which to match the recovery process against it. One such
framework for this is referred to as the total quality recovery (TQR) process. By using a TQR
scale, structured around the scale developed for ratings of perceived exertion (RPE), the
recovery process can be monitored and matched against the breakdown (training) process
(TQR versus RPE). The TQR scale emphasizes both the athlete’s perception of recovery and
the importance of active measures to improve the recovery process. Furthermore, directing
attention to psychophysiological cues serves the same purpose as in RPE, i.e. increasing selfawareness, as opposed to relying on physiological cues alone.
The TQR has a correspondence with the RPE, and is divided into two subscales, one
more subjective (perception) and other more objectives (action). The idea is to integrate
quantitative and qualitative aspects of overtraining syndrome, in order to speed up the
recovery process with interventions and strategies that are optimized for one particular
stimulus.
In order to best overcome and prevent the overtraining is necessary to take into
consideration that the more intense the training, the greater the breakdown. High intensity
training therefore demands higher quality recovery than low intensity training. Consequently,
high intensity training also demands a longer recovery period than low intensity training. The
athlete undertaking high intensity training would therefore benefit from high quality recovery
more than an athlete undertaking low intensity training (Kentta and Hassmén, 1998) and the
whole process of recovery depends, among other factors (gender, age, level of experience,
weather…) of the energy system used. Bompa and Cornacchia (1998), recommend the
following recovery time (see table 3).
According to Terjing and Hood (1988, cited by Bompa and Cornacchia, 1998), reported
that to overcome the effects of short-term overtraining, the sessions should be discontinued
for 3-5 days. After this rest period the training should be lowered, alternating one day of
training and one day of rest. If overtraining is more severe and the initial rest period is longer,
then for each week of rest will require two weeks training for the athlete reach his prior
fitness state.
Training Over the Edge
73
Table 3. Suggested recovery time after intense training
Recovery Process
ATP/CP restoration
Restoration of muscle glycogen
After prolonged exercise
After intermittent exercise (weight training)
Renewal of blood and muscle lactic acid
Restoration of enzymes and vitamins
Recovery of strength training of high intensity
(metabolic supercompensation and CNS)
Payment of alactic O2 debt
Payment of the lactic debt.
Recovery Time
3-5 minutes
10-48 hours
24 hours
1-2 hours
24 hours
2-3 days
5 minutes
30-60 minutes
Table 4. Type of fatigue and how they occur
Occurs as a result of …
 High volume training
 Repeated workloads
 Aerobic/anaerobic conditions
 Mmultiple training sessions throughout day
Tissue damage
 Plyometrics
 Eccentric loading
 Contact sports
Neurological
 High intensity work
(peripheral nervous system)
 Resistance
training
(strength
and
power
development)
 Speed work
 Skill sessions and introduction of new training
techniques
Psychological
 Training monotony
(central nervous system and
 Lifestyle issues
emotional fatigue)
 Heavy game/competition/training periods
 Pressure plays (training simulating match conditions
 New training techniques
Environmental
 Hot and cold environmental
 Travel (local, national, international)
 Time differences
 Competitions
Reference: based on Grantham, 2006.
Type of Fatigue
Metabolic
(energy stores)
It is important to take notice about the type of training effort, because this is what will
determine which forms of fatigue an athlete will experience (Calder, 1996, as cited in
Grantham, 2006, p.2). Table 4 illustrates the various types of fatigue (according to Grantham,
2006). The author, define an order in which recovery strategies should be applied. He called it
“the recovery pyramid”. The pyramid is composed of four levels, namely:
74
Fernando Rocha, Mário C. Marques and Aldo M. Costa




Level 1 (base) – covers the rest (passive and active), sleep and nutrition (refueling
and rehydratation);
Level 2 – covers periodisation (training changes), reactive programming, cooldown,
stretching);
Level 3 – encompasses recovery pool work, compression skins, ice baths, massage,
contrast bathing;
Level 4 – is responsible for strategies that involve psychological/environmental
(flotation tanks, etc), omega wave, integrated approach with individual focus.
The list is not exhaustive, and strategies at levels 3 and 4 should not form part of the
equation until and unless the basic (levels 1 and 2) has already an established regime; first we
should look for the simple intervention, sleep, nutrition and training.
The repair of damage muscle tissue is in the category of short-term overtraining,
requiring 5-7days to complete the process, while the total regeneration of muscle tissue takes
approximately 20 days (Ebbing and Clarkson, 1989, as cited in Bompa and Cornacchia,
1998). The recovery of muscle damage in the acute phase is best treated with ice, elevation,
compression and passive and active rest, depending on the degree of injury. After three days
you can begin to introduce other methods of therapy such as massage. Alternate hot and cold
can also be an effective way to decrease the stiffness associated with muscle damage caused
by exercise (Cornheim, 1988, as cited is Bompa and Cornacchia, 1998; Prentice, 1990,
Bompa and Cornacchia, 1998).
The diet has a direct connection with the overtraining because it can be an important
factor in the recovery of muscle tissue. Carbohydrates are essential to maintain muscle
glycogen levels during intense training and become crucial in intense high-volume workouts,
because the glycogen is the primary source of stored energy in the muscles being used. After
exercise, in the first 30 min there is a window of opportunity to replenish muscle glycogen,
this windows is caused by insulin-like effect of exercise which lasts for some time after
exercise. If this time is spent with the consumption of carbohydrates, the replacement will
happen much faster than if the intake of carbohydrates happen later. This action is sufficient
to prevent the non-functional overreaching and give to athlete the opportunity to get the most
out of the training, as showing in the figure 3 (based on Saunders, 2009).
The effects of protein intake seems to add some benefits in the recovery process,
especially when it is mixed with carbohydrates, however, there is no consensus regarding the
role that protein play in the overtraining. Some studies have shown faster rates of muscle
glycogen replenishment when carbohydrate-protein is consumed immediately following
endurance exercise, compared to carbohydrate alone; a better protein balance, increasing
protein synthesis and decreased protein breakdown; improvements in some muscle damage
markers, resulting in lower blood creatina kinase, less muscle soreness and improve muscle
function. But not all studies have shown significant improvements in subsequent performance
following carbohydrate-protein intake. However, the positive effects of protein seem to
appear more regularly in the studies that provide the more demanding training/recovery
periods. So, it also seem that the longer and harder is the training, the more important the
details of the recovery nutrition, including the inclusion of protein, become.
Training Over the Edge
75
Figure 3. Effects of carbohydrate intake during intensified training.
CONCLUSION
The overtraining is a challenge for coaches, athletes and researchers, since there isn’t,
yet, a valid and reliable instrument for diagnosing it. This syndrome is wrapped in a set of
situations that can lead to a difficult, time consuming and tricky diagnosis. So far the best
possible diagnosis is by exclusion of diseases that can mask the overtraining.
The targets for diagnostic markers are lacking, although some such as heart rate
variability, the perception of mood changes and feelings/self awareness of the athletes is a
promising diagnostic tools. Until further studies reveal specific indicators, confirming the
effectiveness of physiotherapists and psychotherapists interventions as a treatment, prevention
is still the best cure.
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In: Athlete Performance and Injuries
Editors: João H. Bastos and Andreia C. Silva
ISBN 978-1-61942-658-0
© 2012 Nova Science Publishers, Inc.
Chapter 3
EVALUATING THE DYNAMIC MODEL OF
PSYCHOLOGICAL RESPONSE TO SPORT
INJURY AND REHABILITATION
Diane M. Wiese-Bjornstal*, Courtney B. Albinson,
Shaine E. Henert, Elizabeth A. Arendt, Susan J. Schwenz,
Shelly S. Myers and Diane M. Gardetto-Heller
University of Minnesota, Twin Cities, Minneapolis, MN, US
ABSTRACT
Authors of the integrated model of psychological response to the sport injury and
rehabilitation process (Wiese-Bjornstal, Smith, Shaffer, & Morrey, 1998) conceptualized
sport injury as influenced by preinjury psychosocial factors (Williams & Andersen,
1998), acting as a negative life event stressor, and comprising a dynamic process of
ongoing cognitive appraisals influencing emotional and behavioral responses affecting
recovery outcomes (Wiese-Bjornstal, 2009; 2010; Wiese-Bjornstal, Smith, & LaMott,
1995). The purpose of this project was to simultaneously examine these three primary
model components and associated predictions while controlling for within team and
school-related factors through repeated measures sampling of injured and noninjured
teammates. Within a prospective mixed factorial study design, NCAA Division I male
and female athletes (N = 74) from four sports (women’s softball, track and field, and
tennis, and men’s baseball) completed multiple psychosocial measures at repeated time
points from baseline to postseason. Results supported (a) the ability of psychosocial
variables to predict sport injury, (b) conceptualizing sport injury as a stressor, and, (c) the
role of affect as a precursor and response to sport injury. A unique aspect to this study
was reflected in the matching of psychological data from injured and noninjured
teammates during the specific weeks in which injuries occurred, thus controlling for noninjury related factors such as team and school related variables that may have influenced
the mood state and life event stress of all athletes on the teams aside from injury.
* Correspondence concerning this article should be addressed to Diane M. Wiese-Bjornstal, School of Kinesiology,
Cooke Hall, University of Minnesota, 1900 University Ave. SE, Minneapolis, MN 55455. E-mail:
dwiese@umn.edu.
80
Diane M. Wiese-Bjornstal, Courtney B. Albinson, Shaine E. Henert et al.
Furthermore, this study lends support to the idea that negative mood states are not only
responses to but also risk factors for sport injury, and thus provides grounding for
identifying psychological interventions to ameliorate negative moods.
Keywords: intercollegiate athletes, sports medicine, sport psychology, mood state, life event
stress
INTRODUCTION
Once thought of in purely physical terms, interest in sport injury has long since moved
into the psychosocial domain. Conceptual models of sport injury have focused on preinjury
and postinjury dimensions. From a preinjury standpoint, the conceptual model of stress and
athletic injury developed by Andersen and Williams (1988) has generated a significant
amount of interest in psychosocial factors influencing injury vulnerability. This model
provides a framework for the prediction and prevention of sport injuries and includes
personality, stress history, and coping resources variables that may influence the occurrence
of injury through the mechanism of the stress response. Andersen and Williams proposed that
an athlete who has a lot of stress in his or her life, who possesses personality characteristics
that tend to exacerbate the stress response, and who has few coping resources will, when
placed in a potentially stressful situation, be more likely to appraise the situation as stressful
and exhibit greater muscle tension and attentional disruptions. Considerable research support
has been obtained for this preinjury stress model, particularly with respect to the history of
stressors and coping resources portions (see Williams & Andersen, 1998 for a review).
Other researchers have looked into to the postinjury psychosocial processes occurring
among athletes after they have sustained sport injuries. A stress process based model of
response to sport injury was first developed by Wiese and Weiss in 1987, who conceptualized
sport injury as a stressor and the recovery from sport injury as a dynamic process of cognitive
appraisals and emotional and behavioral responses influencing recovery outcomes. WieseBjornstal and Smith (1993) and Wiese-Bjornstal et al. (1995) expanded the development of
this conceptual model of response to injury based on an inductive approach, analyzing the
specific research findings of existing empirical research pieces and ordering them into the
broader generalizations and patterns of a predictive model. Wiese-Bjornstal et al. posited that
the stress-based precursors to injury described by Andersen and Williams continue to affect
athletes’ postinjury responses by filtering through other moderating and mediating factors
(classified into personal and social categories; see Wiese-Bjornstal et al. for a list of
hypothesized moderators and mediators) to influence postinjury psychological responses.
Athletes’ responses to injuries are considered cyclic longitudinal dynamic processes, in which
athletes’ cognitions (defined as interpretations, beliefs and appraisals) influence their
emotions (defined as affects, feelings, and moods) and behaviors (defined as efforts, actions,
and activities). These psychological response cycles affect athlete recovery outcomes (defined
as results, effects, and consequences) such as health status, recovery progress, or return-toplay (see Wiese-Bjornstal, 2009, 2010 for further specific examples of cognitions, emotions,
behaviors, and outcomes).
Since the initial development of this model several investigations have provided support
for various postinjury components, particularly with respect to cognitive and emotional
Evaluating the Dynamic Model of Psychological Response …
81
responses (e.g., Albinson & Petrie, 2003; LaMott, 1994; Morrey, Stuart, Smith, & WieseBjornstal, 1999; Shaffer, 1991; Smith, Stuart, Wiese-Bjornstal, Milliner, O’Fallon, &
Crowson, 1993). The prospective repeated measures designs employed by several of the
studies showed the greatest strengths for documenting that postinjury changes are a
consequence of the injury and not of other stressors in the sport or school environment.
Research has also supported the idea that injury is a source of stress for athletes (Gould, Udry,
Bridges, & Beck, 1997; Selby, Weinstein, & Bird, 1990), although not conducted in direct
tests of the Wiese-Bjornstal et al. (1998) model predictions.
No one study has examined the model as a whole, yet testing the central predictions of
the model simultaneously within the same sample would have great theoretical and practical
value. The information could confirm or disconfirm stress-based models of response to injury
and would allow sport psychologists and coaches to better assist injured athletes in coping
with their injuries by understanding their influences and consequences. Therefore, the
purpose of this project was to simultaneously examine the primary model components and
associated predictions while controlling for within team- and school-related factors through
prospective repeated measures sampling of injured and noninjured teammates. The following
three research questions were examined. First, do the preinjury factors specified by the
Williams and Andersen (1998) model, and incorporated into the Wiese-Bjornstal et al. (1998)
model, predict future injury status among initially noninjured male and female intercollegiate
athletes? Second, is sport injury a stressor, as predicted by the Wiese-Bjornstal et al. (1998)
model? Third, do cognitive appraisal and emotional response factors differ between injured
and noninjured teammates in the specific times surrounding injury, as predicted by the WieseBjornstal et al. (1998) model?
METHOD
Participants
Intercollegiate athletes were chosen for model evaluation because all athletes shared
similar school and sport-related influences and thus it would be possible to control for more
competing explanations if differences were found between injured and noninjured athletes.
Therefore, the overall sample of athletes used in this investigation consisted of 74 National
Collegiate Athletic Association (NCAA) Division I level university athletes competing on the
following spring intercollegiate sport teams during the 1996 – 1997 season: women’s tennis,
women’s softball, women’s track and field, and men’s baseball. The athletes ages ranged
between 18 and 23 years (M = 20.32 years, SD = 1.40) and they self-reported having
participated in their respective sports an average of 9.43 years (SD = 4.57). Participants were
included in this study if they were a member of the university women’s varsity track and field
(n = 32), softball (n = 20) or tennis (n = 7) team or men’s baseball team (n = 15), if they
signed an informed consent form, and if they were not injured at the outset of the study. The
NCAA definition of sport injury from the Injury Surveillance System (ISS; National
Collegiate Athletic Association, 1995 - 96) was used in this study for establishing non-injury
status at baseline and for reporting injury occurrence during the course of the study.
Specifically, a sport injury was defined as: (a) having occurred as a result of participation in
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Diane M. Wiese-Bjornstal, Courtney B. Albinson, Shaine E. Henert et al.
an organized intercollegiate practice or game, (b) as requiring medical attention by a team
athletic trainer or physician, and (c) as having resulted in a restriction of the athlete’s sport
participation or performance for one or more days beyond the day of injury.
PREINJURY
STRESS
FACTORS
 AIMS
 ISP (base)
 ALES (base)
 WES (base)
 Scholarship
Personality
History of
SPORT
Coping
Stressors
INJURY
Resources
RESPONSE
 PSOM (base)
 ACSI
Interventions
RESPONSE TO SPORT INJURY AND REHABILITATION PROCESS
SITUATIONAL MODERATORS
PERSONAL MODERATORS
 Gender
 Race/ethnicity
 Time played this sport
COGNITIVE APPRAISAL







BEHAVIORAL RESPONSE
 SIRAS (weekly for injured)
WES hassles (post)
ALES negative (post)
WES hassles (weekly)
WES uplifts (weekly)
PSOM (weekly)
WT (weekly)
HUS for validity
 Sport type
 University competitive
seasons
EMOTIONAL RESPONSE


ISP (weekly)
POMS-SF for validity
Figure 1. Measures included in the present study to evaluate multiple components of the integrated
model of psychological response to the sport injury and rehabilitation process (Wiese-Bjornstal et al.,
1998).
Evaluating the Dynamic Model of Psychological Response …
83
Measures
Decisions were made about which measures to use in this study based on evaluating key
components and predictions of the Wiese-Bjornstal et al. (1998) model. In line with the
model, multiple measures were selected to assess constructs related to personality, history of
stressors, coping resources, cognitions, affects, and behaviors (see Figure 1). Measures were
identified based on previous use in publications related to the psychosocial aspects of sport
injury prediction and response. Because of the large number of measures employed, efforts
were made to use the most brief but reliable forms of assessment for these constructs. All
measures were completed in a paper-and-pencil format.
Athletic identity. The Athletic Identity Measurement Scale (AIMS) measures athletic
identity, defined as the extent to which one identifies with the athlete role (Brewer, Van
Raalte, & Linder, 1993). It is considered to be a part of the broader construct of self-concept.
The scale contains 10 items each rated on a 1 to 7 continuum (1 = “strongly agree” to 7 =
“strongly disagree”). Example items include, “I consider myself an athlete”, “Sport is the
most important thing in my life”, and “I would be very depressed if I were injured and could
not compete in sport”. A single total score is calculated that can range from 10 to 70, with
higher scores indicating a greater identification with the athlete role. Developers of the scale
report data supporting it as a reliable and valid measure of athletic identity (Brewer et al.).
This measure was completed one time as part of the baseline packet, and scores were used as
a personality measure in the preinjury analysis.
Coping resources. The Athletic Coping Skills Inventory (ACSI) was used as a measure
of coping skills and resources. The ACSI was developed by Smith, Smoll, Schutz, and Ptacek
(1995) and consists of 28 items loading on seven sport-specific coping subscales (coping with
adversity, peaking under pressure, goal setting/mental preparation, concentration, freedom
from worry, confidence and achievement motivation, coachability) that are summed to create
a personal coping resources score. Example items include “When I fail to reach my goals it
makes me try even harder”, “I maintain emotional control no matter how bad things are going
for me”, and “I handle unexpected situations in my sport very well”. Each of the 28 items is
rated on a 4-point Likert scale (0 = “almost never” to 3 = “almost always”). Each subscale
contains four items with scores with scores ranging from 0 to 12; the composite personal
coping resources score ranges from 0 to 84. Ratings of internal consistency have been
reported to range from .56 to .86 for individual subscales and from .85 to .88 for the scale as a
whole (Smith et al.).
Demographics. The demographic data sheet consisted of questions concerning the
athlete’s age, gender, and athletic scholarship status (i.e., full, partial, none). In addition,
participants were asked to indicate on which sport team they participated, the number of
competitive seasons they completed at their university, and the total number of years they had
participated in their respective sports. Athletic scholarship status was used as one of the life
event stressors.
Injury. The injury data sheet consisted of questions regarding the athlete’s name, the
original injury diagnosis, the dates of the original and/or recurrent injuries, where the injury
occurred (e.g., practice, competition, other), illnesses or any other complications the athlete
was having, and additional comments. This form was to be completed by university certified
athletic trainers (ATCs) each time an athlete became injured and sought medical attention.
The only injury data that were ultimately allowed to use was whether or not the athlete was
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Diane M. Wiese-Bjornstal, Courtney B. Albinson, Shaine E. Henert et al.
injured during the course of the season. Thus athletes were categorized as to “yes” or “no”
based on whether or not they sustained an injury that met the ISS definition.
Major life event stress. Major life event stress was measured with a modified version of
the Athletic Life Experiences Survey (ALES; Passer & Seese, 1983). Major life events are
those significant events and changes that can result in feelings of stress. The scale used in this
study consisted of the 19 sport-related items from the ALES, and the Life Experiences Survey
(LES; Sarason, Johnson, & Siegel, 1978) in its entirety, including the 10 academic-related
items. The modified scale consisted of 75 items examining positive and negative life events
among athletes. In addition, 5 blank items were included to allow the athletes to fill in and
rate other major life events that had impacted their lives, but were not listed on the scale.
Individuals were asked to report each major life event they experienced over the past
twelve months (baseline) or during the course of the sport season (posttest), whether the event
was positive or negative, and the degree of impact or strain (i.e., 0 = no, 1 = some, 3 =
moderate, or 4 = extreme) the event had on their lives at the time of its occurrence. Example
items include: “conflict with head coach,” “failing an important exam,” and “death of a
family member.” Three scores were calculated from the life event stress measure: total life
event stress, the sum of the absolute value of positive and negative impact scores; negative
life event stress, the sum of the absolute value of the negative impact scores; and positive life
event stress, the sum of the positive impact scores. Passer and Seese (1983) did not report
reliability and validity information for the ALES; Sarason et al. (1978) reported test-retest
reliability coefficients between .56 and .88 for the LES negative life event stress score,
between .19 and .53 for the LES positive life event stress score, and between .63 and .64 for
the LES total life event stress score.
Minor life event stress. The Weekly Events Scale (WES), a 16-item hassles and uplifts
survey, was created for the purposes of this study to measure minor life event stress (hassles)
and uplifts. Minor life event stress is “the stress from many minor daily problems, irritations,
or changes” (Williams & Andersen, 1998, p. 10). Sixteen major themes of the Hassles and
Uplifts Scale (HUS) (DeLongis, Folkman & Lazarus, 1988; see also DeLongis, 1985 for
reliability and validity descriptions) relevant to university students were extracted to create a
shorter version (WES) of the scale that could be used with a college athlete population on a
weekly basis. Example themes included: “family,” “finances,” “school”, and “sport.”
At baseline, posttest, and each week during the season participants provided WES
responses to the following questions for each item: “How much of a hassle was this item for
you this past week?” and “How much of an uplift was this item for you this past week?” using
two 4-point Likert scales (0 = “none/not applicable” to 3 = “a great deal”). Thus, two scores
were calculated each week: a) a WES-hassles score was calculated by summing the values of
the responses to the hassles question (scores range 0 to 48) and b) a WES-uplifts score was
calculated by summing the values of the responses to the uplifts question (scores range 0 to
48).
Comparisons were made between the HUS and WES at one week within this
investigation to determine the acceptability of using the shorter WES on a weekly basis in
lieu of the longer but established HUS. A total of 52 athletes completed both the WES and the
HUS. With this sample, adequate concurrent validity was found for both the WES hassles
subscale (r = .44, p < .00) and the WES uplifts subscale (r = .79, p < .00) with the
corresponding subscale on the HUS. Both the WES hassles subscale and the WES uplifts
Evaluating the Dynamic Model of Psychological Response …
85
subscale showed reasonably high internal consistency with Cronbach’s alpha equaling .69 and
.77 respectively.
Mood state. The Incredibly Short Profile of Mood States (ISP; Dean, Whelan, & Meyers,
1990) is a 6-item state mood assessment using one item to assess each mood dimension. This
instrument was created as a very brief alternative to the original validated 65-item Profile of
Mood States (POMS; McNair, Lorr, & Droppleman, 1971) and the 30-item POMS-Short
Form (POMS-SF) that could be administered in less than one minute (Dean et al.). Each week
participants rated their current mood on the same 5-point Likert Scale as the POMS-SF (0 =
“not at all” to 4 = “extremely”) expressing to what degree each adjective reflected how they
had been feeling the past week. In line with the procedure established for the POMS, total
mood disturbance scores were calculated by adding scores for the five negative mood states
(anxious, sad/depressed, confused, angry, fatigued) and subtracting the score for the positive
mood state (energetic). Thus ISP total mood disturbance scores could range between –4 to 20.
As opposed to one-word adjectives, items on the ISP are phrased as questions and ask
participants how they are feeling right now (e.g., “How anxious do you feel right now?”). For
the purposes of this study, however, the questions were reworded to assess how participants
were feeling during the past week (e.g., “How anxious have you felt this past week?”).
The ISP has been shown previously to have good reliability with correlations between .91
to .97 for the individual subscales and .97 for total mood disturbance when correlated with the
original 65-item POMS with a sport population (Fleming, Bourgeois, LeUnes, & Meyers,
1992). During one week of the present study, both the ISP and the POMS-SF were
administered in order to verify the acceptability of using the ISP as a measure of mood in this
sample. The POMS-SF is an established 30-item inventory consisting of adjectives
corresponding to six subscales (i.e., depression, tension, fatigue, anger, confusion, and vigor).
Concurrent validity was established for the ISP, in that each item on the ISP significantly
correlated with the corresponding subscale on the POMS-SF (r = .32 to .66), and the total
mood disturbance score on the ISP significantly correlated with the total mood disturbance
score on the POMS-SF (r = .74, p < .001). The ISP also showed reasonably high internal
consistency within this study; Cronbach’s alpha was .78. It was used as a predictor of injury
and as a measure of emotional response to injury.
Positive states of mind. The Positive States of Mind (PSOM) measure is an indicator of
satisfying states of mind (Adler, Horowitz, Garcia, & Moyer, 1998). Scale developers suggest
individuals’ capacities to enter positive states of mind help them to better tolerate negative
stressful circumstances. The questionnaire asks about the respondent’s kinds of satisfying
states of mind experienced in the last week. The scale consists of six items (focused attention,
productivity, responsible caretaking, able to relax, enjoyment, sharing) each ranked in a four
choice response format (“unable to”, “trouble in”, “limited in”, “able to”). Example items
include, “Feeling able to attend and focus to whatever you want or need to do, without many
distractions” and “Feeling that you are doing what you should do to take care of yourself or
someone else”. Each item on the scale is scored separately for six subscale scores ranging
from 0 (“unable to”) to 3 (“able to”), and a total positive indicator is based on a summation,
thus having a range of 0 to 18. Adler et al. report data supporting satisfactory reliability and
validity for the scale. This was used as a measure of coping resources.
Rehabilitation behavior. The Sport Injury Rehabilitation Adherence Scale (SIRAS;
Brewer , Van Raalte, Petitpas, Sklar, Pohlman, Krushell et al., 2000) typically completed by
ATCs or other clinicians as a measure of the athlete’s quality of adherence to rehabilitation
86
Diane M. Wiese-Bjornstal, Courtney B. Albinson, Shaine E. Henert et al.
sessions. It contains three items (athlete intensity of effort, adherence to ATC advice, and
receptivity to changes in rehabilitation protocol) each rated in this study by the ATC on 1 to 5
Likert Scales, with higher scores being indicative of better quality of adherence. Athletes who
do not attend a rehabilitation session receive scores of zero for each item. A total score is
calculated by summing the three ATC rankings for the rehabilitation session; thus the scores
can range from 0 to 15 for a specific rehabilitation session. Scale developers suggest that the
SIRAS shows good construct validity, test-retest reliability, internal consistency, and
unidimensionality (Brewer et al.). This measure was used as an indicator of postinjury
behavioral response in the present investigation.
Self-efficacy and coping. The Weekly Thoughts (WT) inventory was developed for use
in this investigation as a brief weekly measure of the cognitive appraisals of athletes
regarding their confidence in physical performance and success in coping with stressful
demands. Essential the inventory assessed their self-efficacy regarding these specific aspects,
and as such, this scale was designed using based on Bandura’s (1997) recommendations for
self-efficacy measures. This measure consisted of seven total items. The first three items were
responded to with 7-point Likert scale ratings of confidence general physical abilities,
performing the physical skills specific to the sport, and in completing physical conditioning
and/or rehabilitative activities. The next two questions asked for two-part responses, first to
fill-in-the-blank descriptive questions (major demands of sport participation, major sport
and/or rehabilitation setbacks during the past week), and then for each a 7-point Likert scale
rating of how well those demands and setbacks were handled or coped with during the past
week.
Procedure
Once Institutional Review Board approval was received the study was launched. At the
beginning of the sport season and prior to the start of the competitive season, the head coach
of each team was contacted to request permission to use practice and/or team meeting time to
collect data from his or her athletes. After the coaches’ permission was obtained, the
investigators met separately with each team to take the baseline assessments from consenting
participants. The baseline packet consisted of the informed consent form, demographics, and
the AIMS, ACSI, ALES, ISP, PSOM, WES, and WT. Athletes choosing not to participate in
the study were instructed not to sign the consent form and to return the packet to the
investigators. All athletes present at these initial meetings agreed to participate in the study.
This first meeting lasted approximately 20 minutes.
Following the baseline assessment period, meeting times were set each week after a
practice or team meeting to take the weekly assessments. Weekly packets were given only to
those athletes who had completed the first baseline assessment. If participating athletes were
not present at a weekly meeting, psychological data were not collected from them that week.
The weekly packets consisted of the ISP (6 items), PSOM (6 items), WES (16 items) and WT
(7 items). Each meeting lasted approximately 5 minutes. The weekly assessments covered an
interim period of 11 weeks; however, data was not able to be collected from all teams each
week due to scheduling conflicts. Teams averaged 7 data collection weeks each.
During the week following the week 11 assessment, the athletes were given the posttest
packets. The posttest packets consisted of the ALES, HUS, ISP, POMS-SF, PSOM, WES and
Evaluating the Dynamic Model of Psychological Response …
87
WT. The life event stress measure was reworded to assess to occurrence and impact of life
events occurring since the study began (i.e., over the course of the sport season). The POMSSF and the HUS were included this week with the ISP and the WES to establish concurrent
validity for the newly established scales. This final meeting lasted approximately 25 minutes.
Throughout the course of the study, university ATCs were expected to recorded injury
data on the injury data sheet. Each time an athlete sought medical attention for a sportsrelated injury, the ATCs recorded the type of injury, the date the injury originally occurred,
and the date of the recurrence of the injury (for recurrent injuries). The ATCs also recorded
where the injury was sustained (e.g., practice or competition) and whether the athlete was
experiencing any other illness or complications. Each week, the investigators collected the
previous weeks’ injury data from the ATCs and matched the injury dates with the
corresponding weekly psychological data. For purposes of statistical analyses, a participant
was included in the injured group if they sustained an injury at any point during the study.
At the end of these data collection efforts, it was found that data from two of the
psychosocial measures was inconsistent and therefore unusable. First, while the PSOM data
for baseline and posttest was sufficient, there was not sufficient weekly data to allow for
further analysis. Second, despite researcher efforts, the SIRAS was not completed by the
ATCs on a consistent enough basis to be usable. Further, in terms of injury reports, the only
injury data that investigators were ultimately allowed to include in the published report is the
injury status of each athlete, i.e., whether they were injured or not during the course of the
season based on the NCAA definition of injury. Thus no descriptive information about the
nature of the specific injuries sustained is included in this report.
Table 1. Demographic Information for All Participant Groups
Variable
Age (M) yrs
Time Played this Sport
(M) yrs
University Competitive
Seasons (M) yrs
Gender (%)
Female
Male
Ethnicity (%)
European American
African American
Asian American
Other Ethnicities
Sport Team (%)
Track & Field (Women)
Softball (Women)
Tennis (Women)
Baseball (Men)
Scholarship Status (%)
Full
Partial
None
Overall GroupInjured
(n = 23)
20.6
10.9
Overall GroupNoninjured
(n = 51)
20.2
8.8
Matched SubsetInjured
(n = 6)
21.0
11.3
Matched SubsetNoninjured
(n = 6)
20.0
7.5
2.1
1.8
87.0
13.0
76.5
23.5
100.0
100.0
73.9
8.7
66.7
33.3
83.3
16.7
17.4
84.4
3.9
3.9
7.8
43.5
39.1
4.3
13.1
43.1
21.6
11.8
23.5
66.7
33.3
66.7
33.3
47.8
39.1
13.1
19.6
35.3
45.1
83.3
33.3
50.0
16.7
16.7
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Diane M. Wiese-Bjornstal, Courtney B. Albinson, Shaine E. Henert et al.
RESULTS
Participant Characteristics
Table 1 displays participant characteristics for all of the groupings used in the analyses of
results. The Overall Groups (Injured and Noninjured) represent all of the athletes in the
investigation from whom data was sufficient. The Matched Subsets (Injured and Noninjured)
represent the matched samples. Out of the Overall Group-Injured sample, six injured female
athletes had complete injury data and complete psychological data corresponding to 1 to 2
weeks prior to the occurrence of their injuries, the actual week of their injury, 1 to 2 weeks
postinjury, and the posttest. These six athletes were closely matched by sport, scholarship
status, race/ethnicity and age with six noninjured teammates who also had complete data for
these corresponding weeks, becoming the Matched Subsets.
Research Question Findings
Psychosocial factors as sport injury predictors. The first research question was: Do the
preinjury factors specified by the Williams and Andersen (1998) model, and incorporated into
the Wiese-Bjornstal et al. (1998) model, predict future injury status among initially
noninjured male and female intercollegiate athletes? The Overall Group members with
complete data on involved measures (n=70) were used to answer this research question.
Baseline variables were selected for inclusion in the study and this analysis based on the
theoretical predictors identified by the Williams and Andersen model of stress and athletic
injury and incorporated into the dynamic model of psychological response to sport injury and
rehabilitation of Wiese-Bjornstal et al. (1998): personality (athletic identity [AIMS], mood
state [ISP total mood disturbance]), history of stressors (major life event stress [ALES total],
minor life event stress [WES hassles], athletic scholarship status), and coping resources
(uplifts [WES uplifts], positive states of mind [PSOM total]).
These variables were entered into a logistic regression to determine the impact of
psychosocial predictors on subsequent categorical injury status (injured [n = 21] vs.
noninjured [n = 49]) during the course of the season). A test of the full model against a
constant only model was statistically significant (chi square = 17.20, p = .02, with df = 7),
indicating that the psychosocial predictors as a set reliably distinguished between athletes
who were injured versus noninjured. Nagelkerke’s R2 of .31 indicated a modest relationship
between prediction and grouping. Prediction accuracy overall was 71.4% (38.1% for injured
and 85.7% for noninjured). The Wald criterion showed that athletic scholarship status (p =
.00), baseline total mood disturbance (p = .05), and baseline minor life event stress (p = .05)
made significant contributions to prediction. Athletes with a higher level of athletic
scholarship (injured M = 1.65, SD = .71 versus noninjured M = 2.25, SD = .77), greater total
mood disturbance (injured M = 6.33, SD = 4.36 versus noninjured M = 5.43, SD = 4.34), and
less minor life event stress (injured M = 7.00, SD = 3.69 versus noninjured M = 8.54, SD =
5.32) at baseline were more likely to be subsequently injured. Athletic identity, major life
event stress, uplifts, and positive states of mind at baseline did not contribute significantly to
prediction of injury status during the season.
Evaluating the Dynamic Model of Psychological Response …
89
Sport injury as a stressor. The second research question was: Is sport injury a stressor, as
predicted by the Wiese-Bjornstal et al. (1998) model? In the Wiese-Bjornstal et al. (1998)
model, sport injury is conceptualized as a stressor that would add perceived negative stress to
an athlete’s life above and beyond that attributable to those stressors already inherent in the
university, sport, and personal structures. Therefore it was hypothesized that injured athletes
would report greater changes in negative stress variables from baseline to posttest than would
noninjured teammates; baseline scores serve as controls for the initial stress variable status of
athletes. Data from those in the Overall Group with complete baseline and posttest
psychosocial stress-related data (n = 73) were used to answer this research question. Two
measures of stress were included to evaluate individual score changes from baseline to
posttest contrasting injured (n = 23) and noninjured (n = 50) athletes. Change scores were
created by subtracting individual posttest scores from baseline scores for each individual for
the following two stress assessment variables: major negative life event stress (ALES
negative change), minor negative life event stress (WES hassles change). Higher scores on
either of these measures indicate greater perceived negative stress, and therefore negative
change scores would indicate more deterioration in negative stress perceptions from baseline
to posttest.
The two change scores for major and minor negative life event stress were entered as
dependent variables into a one-way multivariate analysis of variance (MANOVA) comparing
injured and noninjured groups to ascertain if injury occurrence affected negative stress levels.
Results indicated a significant difference between injured and noninjured athletes, Wilks’
Lambda = .92, F (2, 70 df) = 3.17, p = .05; partial eta squared = .08. Follow-up univariate
analyses of variance (ANOVAs) showed that both major (F = 4.20, p = .04, partial eta
squared = .06) and minor (F = 4.34, p = .04, partial eta squared = .06) negative life event
stress change scores distinguished between injury status groups. Injured athletes reported
minimal change in their minor negative life event stress (hassles) at posttest compared to their
own baselines (M change score = -.09, SD = 4.72) whereas noninjured athletes reported
substantially less negative life event stress (hassles) at posttest compared to baseline (M
change score = 2.62, SD = 5.34). Injured athletes reported far more major negative life event
stress at posttest compared to their own baselines (M change score = -5.28, SD = 7.26)
whereas noninjured athletes reported only a slight change from baseline to posttest (M change
score = -.75, SD = 9.37). These findings should be interpreted with some caution because
significant variability in major and minor life event stress change scores was noted in the
data. In theory, being all on the same teams at the same university provides some control for
other stressful life event factors related to sport or university, and comparing athletes changes
from their own baseline scores provide stronger evidence that the changes in life event stress
are related to injury rather than other personal or situational factors.
Using data from the Matched Subsets (n = 12) allowed us to further examine the answer
to this question in a tightly controlled way. A 2 x 2 (participant group by time) ANOVA with
repeated measures on the last factor was conducted for total life event stress (ALES total) and
revealed a significant main effect for time (F (1, 10 df) = 5.46, p = .04), with both the
matched injured and the matched noninjured athletes scoring higher at baseline than at
posttest. This was not entirely surprising since the athletes were responding to different time
period at baseline (12 months) than at posttest (3 months). The more important finding is that
the main effect for participant group was also significant for total life event stress (F (1, 10 df)
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Diane M. Wiese-Bjornstal, Courtney B. Albinson, Shaine E. Henert et al.
45
40
35
ALES total
30
25
Matched Injured
20
Matched Noninjured
15
10
5
0
Baseline
Posttest
Figure 2. Mean baseline and posttest total life event stress (ALES total) scores for the Matched SubsetInjured (n = 6) and the Matched Subset-Noninjured (n = 6). Higher scores indicate greater total major
life event stress. The ANOVA main effects for time and participant group were statistically significant.
= 13.16, p = .00), with the matched injured athletes scoring higher than the matched
noninjured athletes at both baseline and posttest. Figure 2 displays these results, which also
support the model contention that sport injury was a stressful experience.
Differences in cognitive and emotional factors over time. The third research question
was: Do cognitive appraisal and emotional response factors differ between injured and
noninjured teammates in the specific times surrounding injury, as predicted by the WieseBjornstal et al. (1998) model? One of the powerful and unique aspects of this study was its
collection of weekly data throughout the course of a season for both injured and noninjured
teammates. This simultaneous data collection allowed for some measure of control of noninjury related factors that likely affect cognitions and emotions for all athletes, such as time in
the academic year or sport season success. In this way the influence that injury has on
cognitions and emotions could be better isolated. Using the Matched Subsets for these
analyses allowed direct comparison of injured athletes with their noninjured team
counterparts at the exact time points surrounding the injury occurrence.
Thus, in order to evaluate changes in two cognitive appraisal elements at key time points
surrounding injury, minor negative life event stress (hassles) and uplifts over these weeks
were examined for the Matched Subsets. A separate 2 x 4 (injury status by time) ANOVA
with repeated measures on the last factor was conducted each for minor negative life event
stress (WES hassles) and WES uplifts. No results were statistically significant for either
dependent variable. A visual inspection of the data graph for WES hassles (see Figure 3)
proves interesting for future consideration, however. Although not found to be statistically
significant perhaps due to large variability within the data, the finding illustrates the
possibility that injuries themselves are a hassle.
Evaluating the Dynamic Model of Psychological Response …
91
12
10
WES hassles
8
Matched Injured
6
Matched Noninjured
4
2
0
Preinjury
Injury
Postinjury Posttest
Figure 3. Mean minor negative life event (WES-hassles) scores during the preinjury, injury, postinjury,
and posttest weeks for the Matched Subset-Injured (n = 6) and the Matched Subset-Noninjured (n = 6).
Higher scores indicate greater minor life event stress. The ANOVA results were not significant.
10
ISP total mood disturbance
9
8
7
6
5
Matched Injured
4
Matched Noninjured
3
2
1
0
Preinjury
Injury
Postinjury
Posttest
Figure 4. Mean weekly mood disturbance (ISP-total mood disturbance) scores during the preinjury,
injury, postinjury, and posttest weeks for the Matched Subset-Injured (n = 6) and the Matched SubsetNoninjured (n = 6). Higher scores indicate more negative mood state.The ANOVA main effect for
participant group was statistically significant.
To evaluate changes in emotional response at key time points surrounding injury, a 2 x 4
(injury status by time) ANOVA with repeated measures on the last factor was conducted with
mood disturbance (ISP total mood disturbance) data for the Matched Subsets. The only
significant finding was a main effect for injury status, F (1, 10 df) = 6.95, p = .03. Post-hoc
comparisons illustrated that matched injured athletes showed significantly (p < .05) higher
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Diane M. Wiese-Bjornstal, Courtney B. Albinson, Shaine E. Henert et al.
total mood disturbance scores than the matched noninjured athletes at all four time points
surrounding the specific injury weeks for each injured athlete (see Figure 4). It was
interesting to note that in addition to the significant magnitude differences at each of the four
time points, in a qualitative sense the patterning of the mood state data was quite similar for
injured and noninjured athletes, likely illustrating the role of school and sport factors affecting
all athletes.
DISCUSSION
Evaluating various aspects of the Wiese-Bjornstal et al. (1998) integrated model of
psychological response to the sport injury and rehabilitation process led generally to support
for the model, with some exceptions. Findings from this study regarding the first research
question examining preinjury and baseline predictors derived from the model of stress and
athletic injury (Andersen & Williams, 1998) showed higher athletic scholarship status to be
the most powerful predictor of sport injury. This finding demands further attention in future
investigation in order to examine the stressors and actions that appear to differentially affect
scholarship athletes. More negative mood state at preseason was also a significant predictor of
injury, which is in line with other investigations. Individual mood states such as fatigue have
been shown to predict sport injury (e.g., Smith, Stuart, Wiese-Bjornstal, & Gunnon, 1997),
and higher levels of tension can interfere with normal skill execution and quality sleep in
ways that could increase vulnerability. Minor life event stress in the form of weekly hassles
proved predictive of injury, but in an inverse way contrary to what was expected. Unlike
previous research findings other constructs such as major life event stress and athletic identity
did not contribute to the prediction of injury status in the present investigation. Psychological
variables had a stronger ability to predict status among noninjured athletes than they did
among injured athletes; perhaps noninjured athletes are more stable or similar in their
psychological profiles in a way that works to a protective advantage.
With respect to the second research question, the results of this study support WieseBjornstal et al.’s (1995) model conceptualization of sport injury as a stressor. The injured
athletes in this study reported a similar amount of major negative life event stress during the
13 week sport season as they did during the entire year preceding the sport season, while the
noninjured athletes reported significantly less total major life event stress during the sport
season than they did for the year prior to it. The results from minor negative life event stress
(hassles) were less clear; although the hassles of noninjured athletes dropped significantly
from pre- to posttest, those for the injured athletes did not. When a matched subset of injured
athletes was compared with a subset of noninjured athletes from the same team, support for
the conceptualization of sport injury as a stressor was again evident. This subset of injured
athletes reported greater amounts of total major life event stress than the subset of noninjured
athletes during both the year preceding the sport season and during the course of the sport
season. Thus, it appears that injured athletes experience a significant amount of major life
event stress as a result of their injuries, particularly in terms of total life event stress.
As for the third research question, the findings surrounding the model predictions about
cognitive and emotional responses of athletes to injury were somewhat mixed. Statistical
results did not support the model hypothesis that injured athletes would experience a greater
Evaluating the Dynamic Model of Psychological Response …
93
intensity of weekly hassles than their noninjured teammates, although a visual inspection of
the mean scores showed the matched injured athletes to experience an increase in weekly
hassles during the injury and postinjury weeks. The matched noninjured athletes, on the other
hand, showed a steady decrease in weekly hassles over the same time periods. Thus, injuries
may be a temporary hassle that athletes soon adjust to, or, since we did not have the actual
injury data, it could be that injured athletes were fully recovered and returned to play within a
couple of weeks following injury and therefore the hassle (injury) was removed. Based on the
model it would also be expected that the injured athletes would experience significantly less
uplifts than their noninjured teammates. But no significant differences were found between
the matched injured and matched noninjured athletes in terms of weekly uplifts. Thus, it does
not appear that the experience of uplifts is affected by the occurrence of sport injury. A larger
sample size and a more sport injury-sensitive weekly hassles and uplifts measure could
possibly have increased the significance of the results.
Support was found for the model prediction that the injured athletes would experience
greater mood disturbance than their noninjured teammates. The matched injured athletes
reported greater weekly mood disturbance than the matched noninjured athletes during the
preinjury, injury, postinjury, and posttest weeks. Interestingly, an examination of both the
matched injured and the matched noninjured athletes’ weekly mood disturbance scores over
all four time points revealed that the temporal pattern of the matched noninjured athletes’
weekly mood disturbance scores approximated the pattern found with the matched injured
athletes, but to a lesser degree. It was suggested that these patterns may reflect to a certain
extent, team and academic-related factors that could influence both the injured and noninjured
athletes. But the degree or magnitude aspect of the mood disturbance scores at baseline and
prior to injury seems to speak to the observation that negative mood state is a risk factor for
sport injury as much as a response. This was supported by the results for research question
one, in which total mood disturbance at baseline was in fact found to be one of the significant
predictors of subsequent injury.
As with all forms of scientific inquiry, this investigation had a number of strengths and
limitations. The main strength of this study was its prospective, repeated measures design.
The prospective research design permitted both pre- and postinjury assessments of the
psychological variables of interest to be made. Such a design is beneficial because it allows
researchers to have a better understanding of cause and effect relationships. Since stress and
mood disturbance were assessed both prior to, and following the athletes’ injuries, greater
confidence was obtained that the postinjury changes in these variables were actually due to
the injuries. Without the use of a prospective research design, less certainty could be obtained
as to whether the differences between the injured and noninjured athletes were due to sport
injury or simply pre-existing differences. Thus, this approach allowed for greater confidence
in the results.
Another major strength of this investigation was the use of repeated measurements.
Repeated measurements are important for a couple of reasons. First, the levels of many
psychological variables are not static, but rather change over time. Therefore, measurements
taken at the beginning of the sport season may not yield the same results as measurements
taken during the middle of the season or at the end of the season. The use of only one or two
assessment periods (e.g., baseline and/or posttest) would likely not provide an accurate
indication of the psychological variable levels around the time of injury. The state-like
variables assessed in this study (e.g., hassles, uplifts, mood state, self-efficacy and coping,
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Diane M. Wiese-Bjornstal, Courtney B. Albinson, Shaine E. Henert et al.
positive states of mind) are by nature, in constant flux, and thus require multiple assessments.
Infrequent assessments of such variables have plagued previous sport injury research;
therefore, this investigation represents a significant improvement over past research. Second,
with repeated measurements, each athlete serves as his or her own control. In other words,
repeated measurements allow for intraindividual comparisons to be made—an individual
athlete’s earlier scores can be compared to his or her later scores. Since the same participant’s
scores are compared over time, greater confidence can be obtained that the resulting effects
are due to a certain treatment or event (e.g., sport injury), as opposed to between-subjects
variability.
The short recall period used to assess life event stress, hassles, uplifts, and mood
disturbance was another advantage to this study. For the posttest life event stress
measurement, the athletes were asked to recall life events occurring during the sport season
(i.e., over a period of 3 months). Such a short recall period is beneficial because it maximizes
the accuracy of the athletes’ reports (Petrie & Falkstein, 1998). For the weekly measures (i.e.,
hassles, uplifts, mood states, positive states of mind, self-efficacy and coping) athletes
reflected upon the past week and provided the appropriate responses. The weekly
measurements essentially allowed for the assessment of the psychological variables as they
were occurring. Therefore, the athletes were able to provide more immediate and accurate
reflections of their stress levels and mood states, and thus, more immediate reactions to their
injuries. This constitutes a significant improvement over past investigations which usually
assess the psychological variables of interest only at baseline and/or posttest. If a longer recall
period were used, as often done with previous research, considerable time lapse could have
resulted between the time sport injury occurred and the time the individual responded to it,
introducing the possibility of memory loss and contamination from the occurrence of
subsequent events and outcomes.
Finally, the selection of noninjured athletes to match the injured athletes primarily in
terms of team membership, and secondarily, in terms of scholarship status, race, and age, was
another benefit to this study. The use of a subset of matched noninjured athletes enabled the
effects of team and academic-related factors on stress, uplifts, and mood disturbance to be
controlled. Past sport injury research has not considered the importance of controlling for
such factors. The benefit of this approach was particularly evident with the analyses of the
matched subsets’ weekly mood disturbance scores. It was found that the pattern of the
matched noninjured athletes’ weekly mood disturbance scores approximated that of the
matched injured athletes’ scores. The difference between the two subsets was that the
matched injured athletes’ scores were significantly higher than the matched noninjured
athletes’ scores at each time point. The similarity in weekly mood disturbance patterns was
likely due to team-related and academic-related occurrences that affected all of the athletes
from that particular team. For instance, a team loss in a big competition or intense travel
demands during a certain week would have affected the entire team, not just the injured or the
noninjured athletes. Furthermore, academic-related occurrences such as midterms or finals
week would have affected both the matched injured and the matched noninjured athletes,
since psychological data were examined from the same weeks for both groups. If a subset of
matched noninjured athletes was not used, such team and academic-related effects on mood
disturbance could have been mistakenly attributed to injuries, resulting in inaccurate results
and conclusions. Future injury-related research should incorporate groups of noninjured
athletes that match injured athletes on various factors such as team membership, scholarship
Evaluating the Dynamic Model of Psychological Response …
95
status, gender, race, and age, and should collect data from both groups during the same points
in time whenever possible to minimize such inaccuracies.
While there were several advantages to this investigation, certain weaknesses and
limitations were also present. First, a major disadvantage of this study was its small sample
size. It is likely that several of the analyses could have reached statistical significance had a
larger sample size resulted from the initial pool of athletes. A larger sample size would have
been particularly beneficial for the matched subset analyses, especially with the weekly
hassles and weekly uplifts measures. While there are more advantages to using prospective,
repeated measures designs with matched noninjured groups than there are disadvantages,
significant disadvantages of such research designs are that they require considerably large
sample sizes and data collection is quite labor intensive. These research designs are often
affected by participant attrition, incomplete data sets, and a limited sample of injured athletes,
therefore, the beginning sample size needs to be quite large to counteract these effects. The
present study was affected by all of these factors. But with larger samples more elegant and
parsimonious statistical analyses such as structural equation modeling would be possible for
simultaneously testing model predictions and paths of influence.
Second, this investigation was unable to assess injury severity, time loss from sport, or
the athletes’ perceived rates of recovery. Consequently, the sample of injured athletes in this
study was comprised of athletes with varying levels of injury severity and varying lengths of
rehabilitation periods. The investigators originally intended to collect information regarding
injury severity and time loss from sport; however, despite initial approval from institutional
review boards, athletes and ATCs, they were ultimately denied access to the athletes’
personal medical records kept by the university ATCs. Therefore, these variables could not be
incorporated into this investigation. Furthermore, due to the extensive number of inventories
already employed in this investigation, an inventory asking the injured athletes to rate their
perceived recovery statuses was not included. Nonetheless, researchers should continue to
make attempts to include all of these factors in their investigations, if at all possible, as they
have been related to subsequent mood disturbance in several studies (e.g., Smith et al., 1993).
Finally, certain limitations were present due to the weekly hassles and uplifts inventory
employed in this investigation. The Weekly Events Scale (WES), which was developed for
the purposes of this study, may not be the most appropriate scale for assessing the hassles and
uplifts specific to sport and sport injury. While this scale was found to be reliable, valid, and
useful for repeated measures investigations, it is not particularly sport- or injury-sensitive. It
is possible that the analyses of weekly hassles and weekly uplifts in this study did not reach
statistical significance because the WES was not sensitive to hassles and uplifts that would be
most affected by sport injury. For instance, items on the WES such as “sexual/intimate
relationships,” “legal matters,” and “world issues,” would most likely not be affected by sport
injury. Therefore, future research investigating pre- and postinjury hassles and uplifts would
benefit from the employment of a more age and sport-specific weekly measure.
CONCLUSION
The evaluation of the integrated model of psychological response to the sport injury and
rehabilitation process (Wiese-Bjornstal et al., 1998) with respect to conceptualizing sport
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Diane M. Wiese-Bjornstal, Courtney B. Albinson, Shaine E. Henert et al.
injury as influenced by preinjury psychosocial factors (Williams & Andersen, 1998), acting as
a negative life event stressor, and comprising a dynamic process of emotional responses
(Wiese-Bjornstal, 2009; 2010; Wiese-Bjornstal et al., 1995) leads to the conclusion that the
model is valid in these regards. Psychosocial factors did predict sport injury, sport injury was
a stressful experience for injured athletes above and beyond the shared stressors of school and
sport, and emotional responses did differ between injured and uninjured athletes. The
repeated measures research approach used in this investigation was effective in gathering data
over time for matched injured athletes with noninjured teammate controls when examining
psychosocial changes linked to injury. Evaluating the core model predictions demonstrates
the significant ways in which stress and mood disturbance affect injury vulnerability, and the
impact sport injury can have on athletes in terms of stress and emotional disturbance.
Researchers must continue to explore the effects injuries have on an athlete’s well-being, so
that the appropriate interventions can be employed. Such information would greatly aid sport
psychologists, coaches, and other members of the sports medicine team in facilitating the
athlete’s return to physical and psychological health.
ABOUT THE AUTHORS
Diane M. Wiese-Bjornstal, Courtney B. (Heniff) Albinson, Shaine E. Henert, Susan J.
Schwenz, Shelly M. (Shaffer) Myers, and Diane M. (Gardetto)-Heller, School of Kinesiology,
University of Minnesota, Twin Cities; Elizabeth A. Arendt, Department of Orthopaedic
Surgery, University of Minnesota, Twin Cities.
Courtney B. Albinson is now at Counseling and Psychological Services, Northwestern
University, Evanston, IL; Shaine E. Henert is now at Division of Fitness, Wellness, and
Sport, Rock Valley College, Rockford, IL; Susan J. Schwenz is now at School of Physical
Therapy, Regis University, Denver, CO; Shelly S. Myers is now at Westlake Academy
Foundation, Westlake, TX; and Diane M. Gardetto-Heller is now in Friday Harbor, WA.
This research was supported in part by a Grant-in-Aid from the Graduate School at the
University of Minnesota. Portions of this paper are derived from the analyses of project data
presented in the University of Minnesota M.A. in Kinesiology thesis of Courtney B. Heniff
(Albinson), A Comparison of Life Event Stress, Weekly Hassles, and Mood Disturbance
between Injured and Noninjured Female University Athletes.
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In: Athlete Performance and Injuries
Editors: João H. Bastos and Andreia C. Silva
ISBN 978-1-61942-658-0
© 2012 Nova Science Publishers, Inc.
Chapter 4
CARDIOMETABOLIC INJURY DUE TO RECOMBINANT
HUMAN ERYTHROPOIETIN DOPING FOR
IMPROVEMENT OF SPORTS PERFORMANCE:
CHRONIC (TRAINING) VERSUS ACUTE
(EXTENUATING) AEROBIC EXERCISE
Edite Teixeira-Lemos1,2, Helena M. Teixeira3,4,5,
Nuno Piloto1, Margarida Teixeira1,6, Belmiro Parada1,
Paulo Rodrigues-Santos6, Lina Carvalho7, Rui Alves8,
Elísio Costa9,10, Luís Belo10,11, Alice Santos-Silva10,11,
Frederico Teixeira1 and Flávio Reis*1
1
Laboratory of Pharmacology & Experimental Therapeutics,
IBILI, Medicine Faculty, Coimbra University;
2
ESAV, Polytechnic Institute of Viseu;
3
National Institute of Legal Medicine – North Branch and CENCIFOR – Forensic
Sciences Centre, Portugal;
4
Medicine Faculty, Porto University;
5
Medicine Faculty, Coimbra University;
6
Laboratory of Immunology and Oncology,
Center for Neuroscience and Cell Biology, Coimbra;
7
Institute of Anatomic Patology and
8
Department of Nephrology,Medicine Faculty, Coimbra University;
9
Institute of Health Sciences, Catholic University, Porto;
10
Institute for Molecular and Cellular Biology, Porto University;
11
Biochemistry Department, Pharmacy Faculty, Porto University. Portugal
* Corresponding author: Flávio Reis, PhD, Laboratory of Pharmacology and Experimental Therapeutics, IBILI,
Medicine Faculty, Sub-Unit 1 (Polo 3), Coimbra University, 3000-548 Coimbra, Portugal, Tel: +351 239 480
053; Fax: +351 239 480 065, E-mail: freis@fmed.uc.pt.
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Edite Teixeira-Lemos, Helena M. Teixeira, Nuno Piloto et al.
ABSTRACT
Athletes who abuse recombinant human erythropoietin (rhEPO) consider only the
benefit to performance and usually ignore the potential short and long-term liabilities.
Elevated haematocrit and dehydratation associated with intense exercise may reveal
undetected cardiovascular risk, but the mechanisms underlying it remain to be fully
explained. This chapter intended to compare the cardiometabolic effects of rhEPO
treatment on rats under chronic vs acute extenuating exercise.
The following male Wistar rat groups were assessed: control – sedentary (Sed);
rhEPO – 50 IU/Kg/wk; Exercise (Ex) – swimming: 1 hr, 3 times/wk; Ex+rhEPO. For the
chronic exercise a period of 10 wks was assessed, while for the acute exercise, a single
bout of extenuating swimming was performed, with a rhEPO treatment for 3 wks prior to
the acute section. Blood pressure and heart trophism were analysed. Blood and tissue
samples were assessed for: biochemical data, haematology, catecholamine and
serotoninergic measures, redox status and heart gene expression profile.
The chronic Ex+rhEPO rats showed higher values of RBC, Htc and Hb vs chronic
Ex, as well as vs acute Ex+rhEPO. Both chronic and acute swimming showed a
remarkable sympathetic and serotonergic activation. rhEPO treatment in chronic training
has promoted oxidative stress, in contrast with the antioxidant effect on the acute
exercise. rhEPO in trained rats promoted erythrocyte count increase, hypertension, heart
hypertrophy, sympathetic and serotonergic overactivation. One rat of the chronic
Ex+rhEPO group suffered a sudden death episode during the week 8 and the tissues
analyzed showed: brain with vascular congestion; left ventricular hypertrophy, together
with a “cardiac liver”, suggesting the hypothesis of heart failure as cause of sudden death.
In the chronically trained rats, rhEPO per se promoted apoptosis, proliferation and
angiogenesis, which was partially or totally prevented in the Ex+rhEPO rats.
In conclusion, the effects of rhEPO doping in rats under exercise is notoriously more
deleterious in circumstances that mimic high-performance athletes (chronic training) than
in occasional consumers (acute sessions), particular due to increased cardiovascular risk.
Chronic rhEPO doping in rats under chronic exercise promotes not only the expected
RBC count increment, suggesting hyperviscosity, but also other serious deleterious
cardiovascular and thromboembolic modifications, including mortality risk, which might
be known and assumed by all sports authorities, including athletes and their physicians.
Keywords: rhEPO doping – cardiometabolic injury – chronic vs acute aerobic exercise –
hypertension – hyperviscosity – sympathetic and serotoninergic overactivation –
oxidative stress – apoptosis, proliferation and angiogenesis profile
INTRODUCTION
Erythropoietin (EPO) is a circulating glycosylated protein hormone, synthesized mainly
in the kidneys, that is the primary regulator of RBC formation (Lacombe and Mayeux, 2006).
The production of recombinant human erythropoietin (rhEPO), which has been widely used
for correction of anaemia, allowed many patients to resume their normal daily activities due
to increased energy (Fliser et al., 2006). The rationale for the use of rhEPO in sport, as
doping, is based on the increased O2 capacity it provides, due to augmented erythropoietic
stimulation (Adamson and Vapnek, 1991; Elliott et al., 2008). The availability of rhEPO
allowed its use in doping (Gareau et al., 1996; Scott and Phillips, 2005). As soon as the anti-
Cardiometabolic Injury due to Recombinant …
101
doping authorities were able to distinguish between the endogenous and the rhEPO (Lasne
and de Ceaurriz, 2000), the scandal of its use in sport was revealed, with particular emphasis
to cycling and cross-country skiing, compared to other sports modalities (Robinson et al.,
2003; Bento et al., 2003). Sports authorities prohibited the use of rhEPO in 1988. The idea
was, first, to limit the degree of health risk and, second, the degree of performance
enhancement.
Athletes who abuse rhEPO consider only the benefit to performance and usually ignore
the potential short and long-term liabilities (Gauthier, 2001; Lipsic et al., 2006; Mastromarino
et al., 2011). Illegal and abusive utilization of this hormone has been found in both endurance
and short-duration sports, which require distinct energetic sources, but the potential
deleterious effects and mechanism underlying, remain to be fully elucidated. In the early
1990s, there was a considerable speculation about the involvement of rhEPO doping in the
death of professional cyclists (Gareau et al., 1996; Thein et al., 1995; Scheen, 1998). The
artificial increase in RBC count and haematocrit, further enhanced by dehydratation during
prolonged exercise, predisposes to thromboembolic complications, which might be connected
to sudden death in sport practice (Thein et al., 1995). However, the cellular/molecular
mechanisms underlying those sudden death episodes are poorly clarified, as well as whether
rhEPO use was linked to this outrageous phenomenon.
EPO has been recognized as a key player in a broad variety of processes in
cardiovascular pathophysiology, including apoptosis, cell proliferation, ischaemia and the
nitric oxide (NO) pathway, which reinforces their putative use in non-haematological
conditions, as a pleiotropic protective factor (Ghezzi and Brines, 2004; Manolis et al., 2005),
namely due to its cardio and neuroprotective actions (Parsa et al., 2003; Bogoyevitch, 2004;
Riksen et al., 2008; Latini et al., 2008). Nevertheless, for a significant percentage of patients,
rhEPO treatment losses efficacy, becoming resistant, recommending dose increment with
further deterioration of heart function, most probably due to the expected hyperviscosity and
thromboembolisms (van der Putten et al., 2008; Lipsic et al., 2006; Mastromarino et al.,
2011). Therefore, although enhanced EPO synthesis is viewed as an appropriate
compensatory mechanism in the cardio-renal syndrome, excessive EPO synthesis in the
advanced stages of both the chronic renal failure and congestive heart failure appears to be
predictive of higher mortality (Mastromarino et al., 2011). For rhEPO abuse as sports doping
the same issues should be hypothesizes, but remains to be fully elucidated. Therefore,
whether the above mentioned putative protective actions are present and could protect athletes
from rhEPO use risks or prolonged and intense rhEPO abuse promotes the known deleterious
cardiac and thromboembolic effects, remains to be fully characterized.
This chapter intended to compare the cardiometabolic effects of rhEPO treatment on rats
under chronic vs extenuating acute exercise, focusing on haemogram, lipid profile, blood
pressure, circulating and tissue catecholamines and serotonin contents, redox status and heart
gene expression profile.
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MATERIAL AND METHODS
Animals and Experimental Protocol
Male Wistar rats (Charles River Laboratories Inc., Barcelona, Spain), weighting 220250g, were maintained in an air conditioned room (22-24ºC) with humidity of 60%, subjected
to 12-h dark-light cycles and given standard rat chow (AO4, Panlab, Letica, Barcelona,
Spain) and water ad libitum. All experiments with animals were performed in accordance
with the European Convention for the Protection of Vertebrate Animals used for
Experimental and other Scientific Purposes (Council of Europe no123, Strasbourg, 1985), as
well as with ethical laws of the National Institutions for Science and Technology.
For the chronic exercise (swimming), after a period of adaptation of 2 week, 4 groups
(n=7) were evaluated for 10 wks-treatment: control – sedentary (Sed); rhEPO – 50 IU/Kg/wk
beta-EPO Recormon®, Roche Pharm. (EPO); Exercised (Ex) – swimming (1 hr, 3 times/wk);
Ex+rhEPO. The swimming rats were submitted to a 1 wk period of adaptation for minimizing
the water stress (bath set at 351ºC). Sessions started with 15 min, increased 5 min each day
until a 60 min continuous period was achieved. Excepting one animal of the EX+rhEPO
group, which suffered a sudden death episode during an exercise session (week 8), all the
animals have completed the 10-week protocol.
For the acute exercise, the same 4 groups were tested and those treated with rhEPO
received 50 IU/Kg/wk for 3 wks prior to the extenuating swimming session. Exercise was
made without previous adaptation and until extenuation and duration was monitored.
Body weight (BW) was monitored during the study, and blood pressure (BP) and heart
rate (HR) measured using a tail-cuff sphygmomanometer LE 5001 (Letica, Barcelona, Spain).
Sample Collection and Preparation
Blood: At the end of treatments the rats were subjected to intraperitoneal anesthesia with
a 2 mg/kg BW of a 2:1 (v:v) 50 mg/mL ketamine (Ketalar®, Parke-Davis, Lab. Pfeizer Lda,
Seixal, Portugal) solution in 2.5% chlorpromazine (Largactil®, Rhône-Poulenc Rorer, Lab.
Vitória, Amadora, Portugal) and blood samples were immediately collected by venipuncture
from the jugular vein into syringes without anticoagulant (for serum samples collection) or
with the appropriate anticoagulant: EDTA, heparin or a solution of ACD (acid citratedextrose). Blood was centrifuged (160 g for 10 min. at 20ºC) to obtain platelet rich plasma
(PRP), which was then centrifuged (730 g for 10 min. at 20ºC) to obtain the platelet pellet and
poor platelet plasma (PPP).
Tissues: The rats were sacrificed by cervical dislocation and the heart, the adrenals, the
kidneys, the liver and the gastrocnenius muscle were immediately removed, placed in ice-cold
Krebs’ buffer and carefully cleaned of adherent fat and connective tissue. The BW and the
weights of heart (HW) and left ventricle (LVW) were measured in all the rats under study in
order to be used as trophy indexes. The following tissues were removed from the rat that
suffered a sudden death episode during an exercise session after 8 weeks of treatment: lungs,
kidneys, brain, heart/left ventricle and liver. Tissues were analyzed for histomorphology with
haematoxilin-eosin (H&E) staining.
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103
An aliquot of the following tissues collected were stored for further analysis: adrenals,
left ventricle (LV) and brain in HClO4 and gastrocnenius muscle in liquid nitrogen.
Haematological Data
Red blood cell (RBC) count, haematocrit (Hct), haemoglobin (Hb), platelets count, mean
platelet volume (MPV), platelet distribution width (PDW) and plaquetocrit (PCT) were
assessed by using an automatic Coulter Counter® (Beckman Coulter Inc., USA).
Biochemical Data
Serum creatinine, ureia and uric acid concentrations were used as renal function indexes,
aspartate (AST) and alanine aminotransferase (ALT) levels were assessed for liver evaluation
and creatine cinase (CK) activity was assesses as a measure of muscle lesion, through
automatic validated methods and equipments (Hitachi 717 analyser). Plasma glucose levels
were measured using a Glucose oxidase commercial kit (Sigma, St. Louis, Mo, USA). Serum
total cholesterol (Total-c) and triglycerides (TGs) were analysed on a Hitachi 717 analyser
(Roche Diagnostics Inc., MA, USA) using standard methods.
Catecholamine and Serotonin Assay
Noradrenaline (NA) and adrenaline (A) concentrations in plasma, platelet, adrenals, left
ventricle and brain, as well as plasma, platelet and brain 5-hydroxy-tryptamine (5-HT) and 5hydroxyindoleacetic acid (5-HIAA) contents, were evaluated by high performance liquid
chromatography with electrochemical detection (HPLC-ECD), according to previously
described (Reis et al., 2005).
Catecholamine measurement: The platelet pellet and the plasma samples from all the rat
groups were prepared as previously described. In brief, to 2 ml of these fraction, in reduced
glutathione (0.250 M) to prevent amine degradation, 100 ng/ml of DHBA, 50 mg of alumina
and 1 ml of Tris-HCl buffer (1.5 M, pH 8.6) containing 0.1 M Na2-EDTA were added. The
mixture was then shaken for 10 min, allowed to stand for a few min to sediment alumina, and
the supernatant was aspirated. The alumina was then washed three times with ultra-pure water
and transferred to an appropriate microfilter system, where adsorbed CAs were finally
obtained by centrifugation (1,000 g, 1 min) after having added HClO4 (0.1 M). Concerning
adrenals, ventricles and brain preparation, at the end of treatments, the rats were sacrificed
and the adrenals, the heart and the brain were immediately removed, placed in ice-cold Krebs’
buffer and carefully cleaned of adherent fat and connective tissue. The two adrenals and
ventricles and the brain were then homogenized in HClO4 (0.1 M, Sigma) at 4 °C and then
centrifuged at 2,500 g for 15 min at 4 °C. The supernatant was filtered by microcentrifuge
filter (Spin-X HPLC, Costar®, Corning Inc. NY, USA) and the filtrate was used for the CA
assay.
Serotonergic measures: The platelet pellet, obtained as above described, was suspended
in 1 ml of a buffer solution (pH 7.4) containing (in mM): NaCl (145), KCl (5), MgSO4 (1),
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CaCl2 (1), Dglucose (10) and ascorbic acid (20 μM), in order to prevent metabolic
degradation. After protein disruption, by adding perchloric acid followed by 15 min at ice
temperature, the suspension was centrifuged (730 g for 10 min at 20 °C) and the supernatant
containing the released serotonin (5-HT) and 5-hydroxyindol-acetic-acid (5-HIAA) was
collected. One ml of plasma was prepared by adding 40 μM of ascorbic acid and 1 ml of
perchloric acid (3 M) with EDTA (1%). After vigorous vortex, the samples were maintained
all the night at 4 °C. The suspension was finally centrifuged at 1,200 g for 15 min at 4 °C and
the supernatant collected. Platelet, plasma and brain (collected and prepared as above
mentioned) 5-HT and 5-HIAA contents were determined by HPLC-ECD, according to the
following chromatographic conditions.
Chromatographic conditions: A Gilson Applied Chromatographic System with a 305
model pump and a 231 injection valve model, with a 50 μl loop, was used. A Biophase ODS
RP18 analytical column (250×4.6, ∅=5 μ; Bioanalytical Systems Inc., U.S.A.) was used and
separation was made possible by using an isocratic solvent system consisting of an acetatecitrate buffer (sodium acetate 0.1 M, citric acid 0.1 M), containing sodium 1-octanesulfonate
(0.5 mM), EDTA (0.15 mM), dibutylamine (1 mM) and 10 % methanol. A flow rate of 1
ml/m was maintained and detection achieved by using a 141 Gilson electrochemical detector
model (650 mV).
Catecholamine and serotonin levels quantification: NA, AD, 5-HT and 5-HIAA contents
were measured by using appropriate standards (Sigma Chemical Co., St. Louis, MO, U.S.A.)
and software (Gilson 710). NA and AD concentrations were expressed in ng/ml for plasma
and platelets and in g/g for adrenals, left ventricle and brain. 5-HT and 5-HIAA values were
expressed in ng/ml for platelet and plasma levels and in ng/g for the brain contents.
Redox Status in Serum and Muscle
The thiobarbituric acid reactive-species (TBARs) assay was used to assess serum and
muscle products of lipid peroxidation (via malondialdehyde: MDA), according to previously
described (Estepa et al., 2001; Baptista et al., 2008). In brief, a portion of the gastrocnemius
muscle was homogeneized with a Tissumizer (Tekmar Industries, CI, USA) in 9 vol. of cold
PBS, then quickly sonicated and thereafter centrifuged at 1,200 g, 10 min at 4 °C. The
supernatant was recovered for determination of tissue MDA according to the same method. In
brief, in a propylene tube 100 μl of serum or a sample of tissue extraction supernatant was
mixed with 100 μl of FeCl3 .6H2O (2.7 g/l), 100 μl of butylated hydroxytoluene (BHT)
dissolved in absolute ethanol (2.2 g/l), 1.5 ml HCl-glycin buffer solution (pH 3.5) and 1.5 ml
of thiobarbituric acid (TBA) in sodium dodecyl sulphate (SDS) 0.3% (v/v). The tubes were
kept in the dark at 5 °C for 1 h and then heated at 95 °C for further 1 h. Samples were
thereafter cooled and extracted with a mixture nbuthanol-piridine-H2O (15:1:0.5 v/v). They
were then submitted to a centrifugation at 1,200 g for 10 min. The organic phase was
analysed spectrophotometrically at 532 nm using 1,1,3,3-tetramethoxypropane as external
standard. The concentration of lipid peroxides (in MDA) was expressed as μmol/l in the
serum and as μmol/g tissue in the skeletal muscle. Ferric reducing antioxidant potential
(FRAP) assay was used to estimate serum and muscle total antioxidant status (TAS) (Benzie
and Strain, 1996).
Cardiometabolic Injury due to Recombinant …
105
Heart gene Expression Analysis by RT-qPCR
Total RNA isolation: Hearts were isolated in autopsy and stored in RNA laterTM solution
(Ambion, Austin, USA). Samples were removed from preservation solution and 1200 μl of
RLT Lysis Buffer were added to proceed with disruption and homogenization for 2 minutes at
30Hz using TissueLyser (Qiagen, Hilden, Germany). Tissue lysate were processed according
to the protocol from RNeasy® Mini Kit (Qiagen, Hilden, Germany). Total RNA was eluted in
50 µl of RNase-free water (without optional treatment with DNAse). In order to quantify the
amount of total RNA extracted and verify RNA integrity (RIN, RNA Integrity Number),
samples were analyzed using 6000 Nano Chip® kit, in Agilent 2100 bioanalyzer (Agilent
Technologies, Walbronn, Germany) and 2100 expert software, following manufacturer
instructions. The yield from isolation was from 0.5 to 3 µg; RIN values were 6.0-9.0 and
purity (A260/A280) was 1.8-2.0.
Reverse Transcription: RNA was reverse transcribed with SuperScriptTM III First-Strand
Synthesis System for RT-PCR (Invitrogen Corp., Carlsbad, CA, USA). One microgram of
total RNA was mixed with a 2X First-Strand Reaction Mix and a SuperScript™ III Enzyme
Mix (Oligo(dT) plus Random hexamers). Reactions were carried out in a thermocycler Gene
Amp PCR System 9600 (Perkin Elmer, Norwalk, USA), 10 min at 25 ºC, 50 min at 50 ºC and
5 min at 85 ºC . Reaction products were then digested with 1 µl RNase H for 20 min at 37 ºC
and, finally, cDNA eluted to a final volume of 100 μl and stored at -20ºC.
Relative quantification of gene expression: Performed using 7900 HT Sequence
Detection System (Applied Biosystems, Foster City, USA). A normalization step preceded
the gene expression quantification, using geNorm Housekeeping Gene Selection kit for Rattus
norvegicus (Primer Design, Southampton, UK) and geNorm software (Ghent University
Hospital, Center for Medical Genetics, Ghent, Belgium) to select optimal housekeeping genes
to this study (Vandesompele et al., 2002). RT-PCR reactions used optimized specific primers
(Proligo, Boulder, USA) for genes of interest, Bax, Bcl2, Fas, Faslg, Caspases 3 and 9,
interleukin-2 (IL-2), tumour necrosis factor α (TNF-α), nitric oxide synthase 2 and 3 (NOS2
and NOS3), transforming growth factor1 (TGF-1), vascular endothelial growth factor
(VEGF) and proliferating cell nuclear antigen (PCNA) and endogenous controls Actb, Gapdh
and Top1 together with QuantiTect SYBR Green PCR Kit Gene expression. RT-PCR
reactions were carried out with: 100ng cDNA sample, primers (50-200 nM) and 1X
QuantiTect SYBR Green PCR Master Mix. Non template control reactions were performed
for each gene, in order to assure no unspecific amplification. Reactions were performed with
the following thermal profile: 10 min. at 95ºC plus 40 cycles of 15 seconds at 95ºC and 1
min. at 60ºC. Real-time PCR results were analyzed with SDS 2.1 software (Applied
Biosystems, Foster City, USA) and quantification used the 2-ΔΔCt method (Livak and
Schmittgen, 2001).
Data Analysis
For statistical analysis, we used the GraphPad Prism version 5.00 software from
GraphPad Software (San Diego, California, USA). Results are expressed as mean values ±
standard errors of the mean (SEM). Differences between groups were tested by performing
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Factorial (two-way) analysis of variance (ANOVA), followed by the Bonferroni test Post’hoc
test. Differences were considered to be significant at P<.05.
RESULTS
Acute Exercise Performance
The animals from the acute Ex group have swim for 50.67 ± 2.19 min, while the
Ex+rhEPO rats have performed a longer swimming period (56.33±5.24 min). All the animals
from the chronically trained groups have swim for identical periods, in a protocol of 1 hr, 3
times/wk, for 10 weeks. In this case, doping effects of rhEPO treatment could only be
accompanied by reporting the ability to complete the 1 hr exercise, which was notoriously
easier in the rhEPO-treated animals.
Haematological Data (Table 1)
The most important findings were that rhEPO in the chronic exercise was able to increase
RBC count (p<0.05), with a trend to increased Hct and Hb, while in the acute exercise there
Table 1. Effects of rhEPO on haematological data in chronic (training)
and acute (extenuating session) exercise protocols
CHRONIC EXERCISE (training)
Control
rhEPO
RBCs
RBC count (x1012/L)
Hb (g/dL)
Hct (%)
Platelet
Platelet count (x109/L)
PCT (%)
MPV (fL)
PDW (%)
7.31 ± 0.16
14.45 ± 0.65
41.45 ± 1.65
7.67 ± 0.08*
14.11 ± 0.15
39.59 ± 0.45
Exercise
Ex+rhEPO
7.59 ± 0.15
14.86 ± 0.25
41.40 ± 0.82
8.23 ± 0.14*
15.45 ± 0.45
44.05 ± 1.45
904.0 ± 9.0
986.3 ± 41.5
1008.4 ± 35.9 1021.0 ± 57.0
0.55 ± 0.02
0.56 ± 0.02
0.57 ± 0.02
0.60 ± 0.07
6.15 ± 0.25
5.69 ± 0.06*
5.63 ± 0.14
5.85 ± 0.35
16.85 ± 0.15 16.73 ± 0.18
16.37 ± 0.26 17.00 ± 0.10
ACUTE EXERCISE (extenuating session)
Control
rhEPO
Exercise
Ex+rhEPO
RBCs
RBC count (x1012/L)
8.11 ± 0.23
6.65 ± 0.21**
7.97 ± 0.20
7.96 ± 0.21
Hb (g/dL)
14.70 ± 0.12 13.05 ± 0.55**
15.17 ± 0.20 14.83 ± 0.03
Hct (%)
40.73 ± 0.42 35.80 ± 1.70**
42.67 ± 0.64 41.87 ± 0.62
Platelet
Platelet count (x109/L)
993.3 ± 40.7 1223.5 ± 144.5*
929.0 ± 69.0 774.0 ± 18.2
PCT (%)
0.55 ± 0.01
0.64 ± 0.08
0.44 ± 0.06
0.46 ± 0.01
MPV (fL)
5.50 ± 0.17
5.25 ± 0.05
5.87 ± 0.47
5.93 ± 0.23
PDW (%)
16.17 ± 0.43 15.70 ± 0.00
16.47 ± 0.58 16.63 ± 0.46
Results are means ± s.e.m. of 7 rats per group. *p<0.05, **p<0.01 and ***p<0.001 vs the column on the
left (without rhEPO).
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107
was a significant reduction (p<0.01) of those parameters. Platelet count showed a trend to
increased values in the chronic Ex+rhEPO rats vs Ex (without rhEPO). Similar pattern, but
significant (p<0.05), was found in the Ex+rhEPO of the acute extenuating swimming.
Biochemical Data (Table 2)
In the chronically treated rats, urea content was lower (P<0.05) in the rhEPO group vs the
control one, without significant changes on creatinine and uric acid levels. Exercised rats also
presented significantly lower values of urea (P<0.05) and uric acid (P<0.01). This reduction
was prevented in the rats under exercise and rhEPO treatment (Ex+rhEPO). In the acute
exercised animals, urea, creatinine and uric acid contents increased vs the sedentary control
animals, while no further changes occurred in the EX+rhEPO vs Ex.
Table 2. Effects of rhEPO on biochemical data: renal and liver function, glucose,
CK activity and lipid profile in chronic (training) and acute (extenuating session)
exercise protocols
CHRONIC EXERCISE (training)
Control
rhEPO
Renal Function
Urea (mg/dl)
Creatinine (mg/dl)
Uric Acid (mg/dl)
Liver Function
AST (UI/l)
ALT (UI/l)
Lipid Profile and Glucose
Total-c (mg/dl)
HDL-c (mg/dl)
LDL-c (mg/dl)
TGs (mg/dl)
Glucose (mg/dl)
CK Activity (UI/l)
Renal Function
Urea (mg/dl)
Creatinine (mg/dl)
Uric Acid (mg/dl)
Liver Function
AST (UI/l)
ALT (UI/l)
Lipid Profile and Glucose
Total-c (mg/dl)
HDL-c (mg/dl)
LDL-c (mg/dl)
TGs (mg/dl)
Glucose (mg/dl)
CK Activity (UI/l)
Exercise
Ex+rhEPO
18.84 ± 0.55
0.57 ± 0.01
0.67 ± 0.05
17.37 ± 0.46*
0.54 ± 0.02
0.77 ± 0.05
17.35 ± 0.26
0.57 ± 0.01
0.40 ± 0.06
18.60 ± 0.63
0.56 ± 0.01
0.50 ± 0.01*
27.20 ± 0.37
50.20 ± 0.86
27.50 ± 0.50
70.00 ± 2.28***
30.60 ± 2.84
65.20 ± 1.59
32.40 ± 2.27
63.50 ± 2.63
53.17 ± 1.66
55.00 ± 1.94
43.67 ± 1.20
38.25 ± 1.18*
36.29 ± 2.51
40.00 ± 2.60
28.86 ± 1.87
26.17 ± 2.71
9.00 ± 1.00
16.22 ± 0.86***
12.00 ± 0.89
12.00 ± 0.63
151.80 ± 7.17 185.6 ± 16.21*
131.00 ± 5.58
136.33 ± 8.03
186.17± 5.11 195.00 ± 3.35
178.75 ± 5.76
178.40 ± 6.72*
165.8 ± 14.60 541.40 ± 67.27*** 540.25±11.76
265.75±35.00***
ACUTE EXERCISE (extenuating session)
Control
rhEPO
Exercise
Ex+rhEPO
18.54 ± 0.45
0.53 ± 0.02
0.63 ± 0.04
18.45 ± 0.51
0.54 ± 0.06
0.86 ± 0.05
22.57 ± 1.17
0.63 ± 0.03
1.03 ± 0.09
21.57 ± 0.92
0.64 ± 0.03
0.83 ± 0.30
26.60 ± 0.33
48.90 ± 0.69
27.50 ± 0.45
62.40 ± 0.96*
37.67 ± 5.17
101.67 ± 8.95
33.67 ± 2.03
82.67 ± 13.54
51.25 ± 1.43
35.66 ± 1.95
8.16± 0.65
147.45 ± 6.65
182.55± 4.72
155.6 ± 11.33
58.33 ± 2.04
38.88 ± 2.91
10.55 ± 2.02
168.41 ± 9.12
196.15 ± 6.09
365.8 ± 34.60*
61.67 ± 4.84
42.67 ± 4.10
18.00 ± 2.89
136.33 ± 11.29
187.67 ± 21.07
590.7 ± 36.79
58.33 ± 13.64
42.67 ± 10.73
11.67 ± 1.67*
100.33 ± 13.64
243.00 ± 14.73
375.00 ± 130.71
Results are means ± s.e.m. of 7 rats per group. *p<0.05, **p<0.01 and ***p<0.001 vs the column on the
left (without rhEPO).
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Serum AST was unchanged between groups in the chronic exercise protocol, but there
was a higher value of ALT in the rhEPO and in the Ex rats vs the control. The change in the
Ex rats was not prevented by concomitant rhEPO treatment. In the acute extenuating exercise,
AST also increased in the rhEPO and in the Ex animals; Ex+rhEPO showed identical values.
Concerning the lipid profile of the chronically treated rats, while the rhEPO-treated
animals presented a trend to higher Total-c contents and significantly increased TGs levels
(P<0.05), the Ex animals showed the opposite profile. The values encountered for the
Ex+rhEPO rats were similar to those of the Ex (without rhEPO) animals. In the acute
extenuating exercise, no significant changes were encountered for rhEPO and Ex vs the
control, but there was a trend to lower values of Total-c, LDL e TGs in the Ex+rhEPO vs Ex
without rhEPO.
Figure 1. Effects of rhEPO on blood pressures (systolic, diastolic and mean BP), heart rate and heart
and left ventricle weights and trophy indexes in the rats of the 4 groups under study for 10 weeks: control (sedentary); - rhEPO (50 IU/kg s.c., 3 times/wk); - exercised, which swam for 1hr, 3 times/wk,
and - Ex+rhEPO. Results are means ± s.e.m. of 7 rats per group. *P<0.05, **P<0.01 and ***P<0.001.
Adapted from Piloto et al., 2009.
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109
Glucose levels were identical in all the groups of the chronic study, but there was a trend
to higher values in the acute exercised animal treated with rhEPO. Finally, CK activity
increased in the rhEPO group of the chronic study, as well in the trained animals, which was
partially prevented by concomitant rhEPO treatment. Similar profile was encountered for the
acute exercise protocol groups.
Blood Pressure, Heart Rate and Cardiac Hypertrophy Indexes (Figure 1)
While no significant changes were encountered between the acute exercised animals,
with or without rhEPO treatment, when compared with the control sedentary animals, there
was profound changes on the chronically-exercised rats. Therefore, blood pressure (SBP,
DBP and MBP) and HR values were higher in the rhEPO group when compared with control.
The same pattern was found for Ex group. In the exercised animals, rhEPO treatment further
increased blood pressures (P<0.001) and HR (P<0.05). Body weight showed a lower value
(P<0.05) in Ex rats (0.46 ± 0.10 kg) vs control (0.51 ± 0.01 kg), without further changes
between groups. HW and HW/BW were significantly higher in rhEPO group vs control,
together with significant lower LVW and LVW/BW. In the rats under chronic exercise
practise, rhEPO treatment promoted a further increment in HW and HW/BW, with a trend to
increased values of LVW and LVH/BW.
Catecholamine and Serotonin Measures (Table 3)
In the chronic Ex+rhEPO animals, there was an increment in plasma NA (p<0.05) and
AD (p<0.01) contents, accompanied by a trend to NA reduction in adrenals and platelets and
a significant decrease in the LV (p<0.001), together with a trend to AD increment in adrenals
and a significant reduction (p<0.001) in platelets. An identical plasma NA and AD (p<0.001)
pattern of changes was found for the acute exercised animals. The NA and AD increment in
the acute Ex+rhEPO rats was accompanied by a reduction in platelet NA and by a rise in
brain AD. While in the chronic training the changes were non-significant for 5-HT and 5HIAA in the plasma, platelets and brain, in the Ex+rhEPO of the acute extenuating sessions
there was an increment in plasma measures and a reduction in platelets.
Serum and Muscle Redox Status (Table 4)
In serum samples, the redox status, evaluated by the MDA/TAS levels, increased
(p<0.05) in the Ex+rhEPO group of chronic training, while there was a trend to a reduction in
the Ex+rhEPO rats of the extenuating exercise. Similar pattern was found for the muscle
assays, showing that rhEPO was pro-oxidant when given in chronic training conditions but
antioxidant in acute.
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Table 3. Effects of rhEPO on peripheral and central catecholamine and serotoninergic
measures in chronic (training) and acute (extenuating session) exercise protocols
CHRONIC EXERCISE (training)
Control
rhEPO
Exercise
Ex+rhEPO
Catecholamines measures
Plasma NA (ng/ml)
3.71 ± 0.60
4.81 ± 0.37
5.10 ± 0.96
9.32 ± 1.43
AD (ng/ml)
1.48 ± 0.21
1.52 ± 0.06
1.04 ± 0.09
1.96 ± 0.18
Platelet NA (ng/ml)
4.54 ± 0.61
0.60 ± 0.08***
8.02 ± 0.68
7.01 ± 0.47
AD (ng/ml)
0.69 ± 0.04
0.36 ± 0.08**
9.15 ± 2.26
0.50 ± 0.09
164.1 ± 8.0
130.4 ± 9.6*
149.8 ± 15.8 133.8 ± 7.9
Adrenals NA (g/g)
626.0 ± 47.6 602.0 ± 66.7
433.1 ± 24.6 579.4 ± 40.6
AD (g/g)
***
0.14
±
0.02
0.71
±
0.05
0.92 ± 0.04*** 0.12 ± 0.02***
L.Ventricl. NA (g/g)
0.02 ± 0.01
0.05 ± 0.01
0.15 ± 0.02*** 0.13 ± 0.02***
AD (g/g)
0.20 ± 0.004 0.18 ± 0.007
0.21 ± 0.008 0.19 ± 0.008
Brain NA (g/g)
2.03 ± 0.09
2.38 ± 0.18
1.79 ± 0.25
2.57 ± 0.15*
AD (g/g)
Serotoninergic measures
Plasma 5-HT (ng/ml)
18.56 ± 1.46 5.82 ± 0.60***
11.08 ± 0.65
30.07 ± 4.45***
**
5-HIAA (ng/ml)
11.53 ± 0.93
17.56 ± 1.20
18.00 ± 2.94
25.07 ± 2.38*
***
Platelet 5-HT (ng/ml)
556.7 ± 40.9
830.0 ± 27.2
1610.8 ± 55.1
1640.4 ± 39.6
5-HIAA (ng/ml)
3.92 ± 0.24
2.74 ± 0.18**
2.99 ± 0.22
3.68 ± 0.30
Brain 5-HT (ng/g)
0.25 ± 0.01
0.30 ± 0.01*
0.24 ± 0.01
0.22 ± 0.01
5-HIAA (ng/g)
0.13 ± 0.004
0.12 ± 0.007
0.13 ± 0.005
0.13 ± 0.006
ACUTE EXERCISE (extenuating session)
Control
rhEPO
Exercise
Ex+rhEPO
Catecholamines measures
Plasma NA (ng/ml)
3.91 ± 0.85
5.29 ± 1.95
1.52 ± 0.33
2.29 ± 0.55
AD (ng/ml)
1.95 ± 0.33
4.60 ± 0.41**
1.76 ± 0.02
1.15 ± 0.29
Platelet NA (ng/ml)
4.41 ± 0.79
2.13 ± 0.37
2.68 ± 1.46
1.50 ± 0.36
AD (ng/ml)
1.09 ± 0.01
1.59 ± 0.30
2.01 ± 0.86
2.72 ± 0.46
188.3 ± 11.0 188.1 ± 96.7
96.9 ± 24.1
158.4 ± 16.1
Adrenals NA (g/g)
865.0 ± 107.6 778.5 ± 423.6
411.5 ± 117.4 645.0 ± 40.4
AD (g/g)
0.48 ± 0.11
0.43 ± 0.07
0.48 ± 0.01
0.45 ± 0.05
L.Ventricl. NA (g/g)
0.10 ± 0.03
0.11 ± 0.02
0.24 ± 0.03gg
0.16 ± 0.01i
AD (g/g)
0.16 ± 0.01
0.18 ± 0.03
0.15 ± 0.01
0.22 ± 0.02
Brain NA (g/g)
3.95 ± 0.06
10.35 ± 2.25*
7.57 ± 1.40
5.90 ± 1.57
AD (g/g)
Serotoninergic measures
Plasma 5-HT (ng/ml)
85.97 ± 20.70 533.4 ± 60.7*** 75.83 ± 39.12
27.31 ± 6.43
5-HIAA (ng/ml)
17.10 ± 0.71
21.72 ± 0.62
18.60 ± 2.75
21.62 ± 1.51
Platelet 5-HT (ng/ml)
1122.2 ± 135.9 727.6 ± 45.2
622.8 ± 266.6
528.8 ± 104.9
5-HIAA (ng/ml)
4.34 ± 0.74
0.65 ± 0.14*
4.75 ± 0.91
2.82 ± 0.54
Brain 5-HT (ng/g)
0.14 ± 0.04
0.21 ± 0.03
0.16 ± 0.01
0.18 ± 0.02
5-HIAA (ng/g)
0.25 ± 0.02
0.24 ± 0.03
0.27 ± 0.03
0.30 ± 0.06
Results are means ± s.e.m. of 7 rats per group. *p<0.05, **p<0.01 and ***p<0.001 vs the column on the
left (without rhEPO).
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111
Table 4. Effects of rhEPO on serum and muscle redox status markers in chronic
(training) and acute (extenuating session) exercise protocols
CHRONIC EXERCISE (training)
Control
rhEPO
Exercise
Serum redox status
MDA (mol/L)
TAS (mol/L)
MDA/TAS
Muscle redox status
MDA (mol/L)
TAS (mol/L)
MDA/TAS
0.40 ± 0.02
0.24 ± 0.01
1.76 ± 0.16
0.38 ± 0.04
0.36 ± 0.03***
1.13 ± 0.23*
0.30 ± 0.02*
0.25 ± 0.01
1.27 ± 0.09*
0.29 ± 0.03
0.36 ± 0.02
0.38 ± 0.04
0.14 ± 0.003 0.12 ± 0.001
0.12 ± 0.003
0.41 ± 0.04
0.63 ± 0.04**
0.58 ± 0.03*
ACUTE EXERCISE (extenuating session)
Control
rhEPO
Exercise
Ex+rhEPO
0.34 ± 0.01
0.22 ± 0.01
1.53 ± 0.05
0.47 ± 0.04*
0.12 ± 0.009
0.73 ± 0.06*
Ex+rhEPO
Serum redox status
0.29 ± 0.08
0.22 ± 0.13
0.29 ± 0.10
0.36 ± 0.06
MDA (mol/L)
0.45
±
0.14
0.65
±
0.20
0.56
±
0.13
0.71 ± 0.04
TAS (mol/L)
MDA/TAS
0.37 ± 0.02
0.28 ± 0.14
0.67 ± 0.33
0.53 ± 0.11
Muscle redox status
1.04 ± 0.31
0.73 ± 0.09
1.82 ± 0.61
1.47 ± 0.34
MDA (mol/L)
1.36
±
0.22
1.57
±
0.34
1.04
±
0.32
1.31 ± 0.20
TAS (mol/L)
MDA/TAS
0.84 ± 0.35
0.48 ± 0.10
1.89 ± 0.58
1.19 ± 0.33
Results are means ± s.e.m. of 7 rats per group. *p<0.05, **p<0.01 and ***p<0.001 vs the column on the
left (without rhEPO).
Histomorphological Analysis of Tissues from the Suddenly Death Rat
under Chronic Exercise and Rhepo Treatment (Figure 2)
In the week 8 of exercise practice, one rat of the Ex+rhEPO group suffered a sudden
death episode in the initial 15 minutes of the swimming session. All efforts with reanimation
procedures were, unfortunately, useless. As the blood collection was compromised due the
time taken in the animal assistance, we removed and weighted the kidneys, the lungs, the
brain, the heart and the liver to assess the possible cause of death. The rat heart weight was
1.82 g and the heart//body weight ratio was 4.04 g/kg, significantly hypertrophic when
compared with the values of the Ex group (1.23 ± 0.06 g and 2.66 ± 0.13 g/kg, respectively),
demonstrating the tremendous effort of the heart to maintain its functions. The
histomorphological studies provided the following results, when compared with the normal
lungs, brain, liver and left ventricle (Fig. 2A1 to 2A4, respectively): the kidneys from the
suddenly death rat showed eosinophilia and congestion (data not shown); the lungs showed
signs of blood congestion, alveolar hemorrhage and anoxia, without markers of drowning
(Fig. 2B1); the brain tissue from the suddenly death rat demonstrated vascular congestion
(Fig. 2B2); the liver showed centre-lobular congestion and signals of “cardiac-liver”,
probably due to the heart failure (Fig. 2B3); there was LVH (2.0 mm3) when compared with
the values of the control group (1.5 mm3) and deregulation of cardiac fibers, suggesting the
hypothesis of heart failure (eventually ventricular fibrillation) as cause of death (Fig. 2B4).
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Figure 2. Histomorphological H&E staining pictures from the lung (1), the brain (2), the liver (3) and the
left ventricle (4) from the control rats (A) when compared with those of the suddenly death rat of the
EX+rhEPO group (B). During the week 8 of study, one rat of the EX+rhEPO group (exercise and
rhEPO treatment) suffered a sudden death episode. As the blood collection was compromised due the
time taken in the animal assistance, we removed and weighted several tissues to assess the possible
cause of death. The histomorphological studies provided the following results, when compared with the
normal lungs, brain, liver and left ventricle (A1 to A4, respectively): lungs with signs of blood
congestion, alveolar hemorrhage and anoxia, without markers of drowning (B1); brain with vascular
congestion (B2); liver showing centre-lobular congestion and signals of “cardiac-liver” (B3) and left
ventricular hypertrophy (2.0 mm3) when compared with the values of the control group (1.5 mm3), and
desregulation of cardiac fibers, suggesting the hypothesis of heart failure (eventually ventricular
fibrillation) as cause of death (B4). From Piloto et al., 2009.
Heart Gene Expression Analysis by RT-Qpcr (Figures 3 and 4)
In the chronically trained animals, the following genes were assessed in the heart tissue,
as a measure of the rhEPO effects on: apoptotic machinery (Bax, Bcl2, Fas, Faslg, caspases 3
and caspase 9); inflammatory mechanisms [interleukin-2 (IL-2) and tumour necrosis factor α
(TNF-α)]; proliferation/angiogenesis profile [transforming growth factor1 (TGF-1),
proliferating cell nuclear antigen (PCNA) and vascular endothelial growth factor (VEGF)]
and nitric oxide synthase isoforms 2 and 3 (NOS2 and NOS3).
Cardiometabolic Injury due to Recombinant …
113
Figure 3. Effects of rhEPO on heart gene expression of apoptotic pathway markers in chronically
exercised rats: Bax (A), Bcl2 (B) and Bax/Bcl2 (C) in the rats of the 4 groups under study for 10 weeks:
- control (sedentary); - rhEPO (50 IU/kg s.c., 3 times/wk); - exercised, which swam for 1hr, 3 times/wk,
and - Ex+rhEPO. Results are means ± SEM of 7 rats per group. P<0.05, P<0.01 and P<0.001 for one,
two or three letters, respectively: a vs control group; b vs rhEPO group and c vs Exercise group.
The main findings encountered were: rhEPO treatment, per se, showed a pro-apoptotic
profile, viewed by the significantly (p<0.001) increased Bax/Bcl2 ratio (Fig. 3C).
Furthermore, rhEPO also demonstrated the well-recognized pro-proliferative and angiogenic
character, viewed by the significant overexpression of TGF-1 (p<0.05), PCNA (p<0.001)
and VEGF (p<0.01), when compared with the control sedentary animals (Fig. 4A, 4B and 4C,
respectively). The pro-apoptotic, pro-proliferative and pro-angiogenic effects were totally
prevented or at least highly attenuated in the exercised animals under rhEPO treatment (Fig. 3
and 4). For all the other markers analyzed, no relevant changes were encountered between the
4 groups of animals under evaluation (data not shown).
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Figure 4. Effects of rhEPO on heart gene expression of proliferative and angiogenic markers, TGF-1
(A), PCNA (B) and VEGF (C), in the rats of the 4 groups under study for 10 weeks: - control
(sedentary); - rhEPO (50 IU/kg s.c., 3 times/wk); - exercised, which swam for 1hr, 3 times/wk, and Ex+rhEPO. Results are means ± SEM of 7 rats per group. P<0.05, P<0.01 and P<0.001 for one, two or
three letters, respectively: a vs control group; b vs rhEPO group and c vs Exercise group.
Cardiometabolic Injury due to Recombinant …
115
DISCUSSION
The therapeutic use of rhEPO, particularly for the treatment of anaemia, allowed a
significant reduction in the associated adverse effects and improved patient’s quality of life
(Fliser et al., 2006). Since rhEPO became available as an erthropoiesis-stimulating drug, its
abuse by athletes of endurance aerobic sports has been speculated and studied (Elliott, 2008;
Robinson et al., 2003; Bento et al., 2003). rhEPO doping remains one of the negative
highlights of world sport, with recurrent news about the distortion of sport values and ethics,
by athletes, who, desperate to enhance their performance, try, illegally, to improve oxygen
delivery to the muscles by using rhEPO (Cazzola, 2002; Scott and Phillips, 2005; Cruz,
2006). In endurance sports, such as long-distance running, cycling and skiing, performance
relies on an adequate O2-supply to the heart and skeletal muscle. Hence, the rate of maximal
O2-uptake is an important determinant of aerobic physical power (Adamson and Vapnek,
1991; Elliott et al., 2008). Unfortunately, some athletes and their coaches were eager to abuse
rhEPO because it increases the O2 supply to muscles and boots performance in endurance
sports. However, athletes who abuse rhEPO seem to consider only the benefit to performance
and ignore the short and long-term side-effects. This led to a view among some athletes that
to compete successfully doping with rhEPO was required, forgetting the increased health risk.
There is a suspicion that rhEPO-induced erythrocytosis caused the death of about 20
world-class Dutch and Belgian Cyclists, although this was never proven (Gareau et al., 1996;
Thein et al., 1995; Scheen, 1998), probably due to the lack of methodological capacity to
distinguish between the endogenous and the recombinant EPO as well as due the lack of
knowledge concerning the mechanisms underlying the side-effects of rhEPO. When Lasne
and de Ceaurriz (2000) were able to separate and distinguish by electrophoresis the
endogenous and the rhEPO in human urine, the scandal of rhEPO use in sports was revealed.
However, we should recognize that nowadays the recombinant erythropoietins and analogues
remains a huge challenge for doping control authorities, due to new molecules and forms,
including gene doping, as well as due to possible false-positive detection, namely after
strenuous physical exercise, as reported by several authors (Pascual et al., 2004; DiamantiKandarakis et al., 2005; Beullens et al., 2006; Abellan et al., 2007; Delanghe et al., 2008;
Lamon et al., 2009; Voss et al., 2010). In any case, the possibility raised in 1990s of
distinguish endogenous EPO from exogenous recombinant human EPO has alerted the
authorities and stimulated the scientific and medical community for the high health risks for
the athletes under rhEPO doping.
The main risks of erythrocytosis (Hct>0.55 l/l) include heart failure, myocardial
infarction, seizures, peripheral thromboembolic events and pulmonary embolism. Endurance
athletes are at increased risk during the competition, if their blood viscosity increases further
due to the great loss of fluid associated with sweating (Bento et al., 2003; Gareau et al., 1996;
Thein et al., 1995; Cazzola, 2002). Interestingly, some deaths allegedly caused by rhEPO
have not occurred during exercise but during periods of physical inactivity, suggesting that
the deleterious effects are prolonged. Abusive use of rhEPO might be viewed in both
endurance and short-duration sports, which require distinct energetic sources, but the
potential deleterious effects and mechanisms underlying remain to be fully elucidated. Our
data confirmed that rhEPO promoted an augmented sports performance.
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The consequences of physical exercise on the EPO concentrations have been poorly
investigated and the data available is not consensual. According to Abellan et al. (2007),
physical fitness, sport and different training workload during the sport season had no
substantial effect on serum EPO and sTfR concentrations, except in recreational athletes after
a 21-km race. Serum erythropoietin concentration and hemoglobin concentration were
determined by Berglund et al. (1998) during the winter season in 41 male and 31 female welltrained, cross-country skiers. The results showed that normal serum EPO concentration is to
be expected during the winter season at sea level in cross-country skiers living and training at
low altitude (below 300 m above sea level). In a study with marathon athletes under rhEPO
treatment, serum EPO levels increased after both 3 and 31 hrs after exercise, but were
unchanged immediately after the end of running (Schwandt et al., 1991). In our study, rhEPO
treatment was able to increase the RBC, the Hct and Hb in chronic exercise, without
significant changes in acute. Thus, prolonged rhEPO treatment (10 wks in chronic) seems to
be able to promote important changes on erythropoiesis, while in short-term (3 wks prior
acute) do not produce identical stimulation. This should have distinct implications in CV risk,
with an expected hyperviscosity in chronic exercise with rhEPO use.
The increased RBC count vs the EX group without rhEPO treatment was confirmed, as
expected. This was accompanied by development of hypertension and tachycardia. Increased
blood pressure is a common feature in patients and athletes under rhEPO treatment (Bento et
al., 2003; Gareau et al., 1996; Thein et al., 1995; Gauthier, 2001), and might result both from
increased blood viscosity and loss of hypoxia-induced vasodilatation. rhEPO treatment was
also able to promote heart hypertrophy, which might be due to the blood hyperviscosity,
suggested by the RBC count increment, and could be viewed as a need to ensure proper blood
circulation to peripheral tissues. Increased tachycardia might be explained by the increment in
sympathetic activity, revealed by the higher values of plasma noradrenaline and adrenaline
concentrations. This effect of rhEPO was previously documented, namely on hemodialyzed
patients under rhEPO therapy (Torralbo et al., 1995). In our study, both the chronic and the
acute swimming exercise showed a remarkable sympathetic and serotonergic activation,
which might be due to the cardio-respiratory involvement, favouring the CV risk.
Furthermore, there was an increment in plasma serotonergic measures, which might result
from platelet overactivation, thus releasing the granule contents. The increased platelet
reactivity was reported by others (Stohlawetz et al., 2000), and is in favour of an increased
vascular reactivity, blood pressure and thromboembolic complications.
Distinct types and intensities of exercise have been associated with different effects on
oxidative stress. Regular training is able to promote antioxidant actions (Meilhac et al., 2001),
while high intensity exercise or in non-adapted individuals might produce harmful effects, in
a dual effect known as “exercise paradox”. The final effect seems to depend, thus, on the
intensity as well as on the type of protocol. Recombinant human EPO treatment have been
associated with beneficial therapeutic effects on non-anaemic conditions, due to its cardio and
neuroprotective actions (Bogoyevitch, 2004; Riksen et al., 2008; Latini et al., 2008; Parsa et
al., 2003; Lipsic et al., 2006), attributed to a pleiotropic action (Ghezzi and Brines, 2004;
Manolis et al., 2005), such as its antioxidant ability (Katavenin et al., 2007; Maiese, 2008). In
our study, rhEPO was notoriously more deleterious when used in chronic conditions,
demonstrating a pro-oxidative action, contrasting to its putative antioxidant effect when used
in acute extenuating exercise. This effect might be due to the lower duration of rhEPO
treatment prior to extenuating exercise session (3 wks), when contrasting with the 10 wks for
Cardiometabolic Injury due to Recombinant …
117
the chronic exercise, as well as with particular characteristics of acute exercise. Therefore,
extenuating exercise leads to important autonomic and haemodynamic adaptations that
influence the CV system in order to maintain homeostasis in response to the increase of
metabolic needs. This includes augment of cardiorespiratory responses to promote increase of
O2 supply to peripheral tissues; SNS activation, which increases HR and cardiac output and,
then, BP fluxes to peripheral tissues, particularly to the muscles that needs more energy to
produce work (Smith et al., 2006). Under those conditions, rhEPO seem to be needed, playing
thus an antioxidant effect, contrasting with its deleterious pro-oxidant profile when used in
prolonged and regular training condition, mimicking chronic rhEPO doping.
Strengthening the idea of a higher cardiovascular risk in the chronic exercised rats under
chronic rhEPO treatment, our study showed that all the changes reported for the Ex+rhEPO
rats seem to be in agreement with the sudden death episode occurred in one rat of the group,
after 8 weeks of protocol, during the initial minutes of exercise. The anatomo-pathological
tissue evaluation of the suddenly death rat, demonstrated that there were no drowning signs in
the lungs, but marked vascular congestion in the lungs, brain and liver. Furthermore, and even
more relevant, there was some LVH and deregulation of cardiac fibers, together with a
“cardiac liver”, suggesting the hypothesis of heart failure as the cause of death, which is in
agreement with the increased risk of cardio/cerebrovascular and thromboembolic events that
the functional studies in the Ex+rhEPO also indicate. Further studies will be important to
assess the nature of the changes in myocardial structure encountered in the rhEPO rats and in
particular in those suffering sudden dead episodes, in order to evaluate the influence on
myocyte size, capillary density and reactive myocardial fibrosis, and its relationship with the
deleterious effects and mortality risk.
Our findings are in agreement with other studies, both in humans and animals under
rhEPO treatment. In end stage chronic kidney disease patients, for example, rhEPO is able to
correct the associated anemia but there is hematocrit increment, often associated with
hypertension, thromboembolism and higher morbidity and mortality (Regidor et al., 2006). In
mice transgenic for EPO, the increased hematocrit was linked with left and right ventricular
hypertrophy and cardiac oedema, as well as with a reduced life expectancy (Wagner et al.,
2001). Thus, erythrocytosis seems to increase the risk for myocardial infarction and stroke,
which was observed in our experimental model of rhEPO sports doping in rats under aerobic
chronic exercise and rhEPO treatment.
Finally, since rhEPO have been demonstrating cardioprotective actions (Parsa et al.,
2003; Bogoyevitch, 2004; Riksen et al., 2008; Latini et al., 2008), due to its pleiotropic
activity as anti-apoptotic, antioxidant, pro-proliferative and cytoprotective (Ghezzi and
Brines, 2004; Katavenin et al., 2007; Manolis et al., 2005), we were interested in assess the
effects of rhEPO supply on chronic training, through evaluation of gene expression profile
related to apoptosis machinery, inflammation, angiogenesis, proliferation and inducible and
constitutive NOS isoforms. Thus, in the chronically trained animals, the following genes were
assessed in the heart tissue: Bax, Bcl2, Fas, Faslg, caspases 3 and 9, IL-2, TNF-α, TGF-1,
PCNA, VEGF and NOS2 and NOS3. We found that rhEPO was able, per se, to induce
overexpression of markers of apoptotic machinery, proliferation and angiogenesis, viewed by
increased Bax/Bcl2 ratio, TGF-1, PCNA and VEGF. Those effects were totally prevented or
at least highly attenuated in the rhEPO-treated animals under chronic exercise practice. This
cytoprotective effect of chronic exercise training was, however, unable to prevent or attenuate
the effects above mentioned: increased blood pressure, tachycardia, blood hyperviscosity,
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Edite Teixeira-Lemos, Helena M. Teixeira, Nuno Piloto et al.
sympathetic and serotonergic systems overactivity and oxidative stress, which might be
linked not only with the expected thromboembolic complications, but also with the cardiac
and metabolic injure that most probably predispose to high cardiovascular and even live risk
for the athletes under rhEPO doping.
CONCLUSION
The effects of rhEPO doping in rats under exercise is notoriously more deleterious in
circumstances that mimic high-performance athletes (chronic training) than in occasional
consumers (acute sessions), particular due to increased cardiovascular risk. Therefore, rhEPO
use, as doping, in situations of chronic/regular physical exercise, promotes not only the
expected increased erythrocytosis, suggesting hyperviscosity, but also other serious
deleterious cardiovascular and thromboembolic modifications, such as hypertension, heart
hypertrophy, sympathetic and serotonergic overactivity, despite some compensatory
beneficial effects of exercise training against the rhEPO deleterious actions on cardiac gene
expression profile of markers of apoptosis and proliferation/angiogenesis. Thus, our
experimental model of rhEPO sports doping demonstrates that athletes under similar
conditions are submitted to a serious cardiovascular/metabolic and even mortality risk, which
might be known and believed by all sports authorities and in particular by them and their
physicians and themselves.
ACKNOWLEDGMENTS
We are very grateful to Roche Pharmaceuticals for providing the rhEPO used. We also
thank Dra. Cristina Ramos, from IBILI Library, for her continuous support on bibliography.
CONFLICTS OF INTEREST
The authors declare no conflicts of interest.
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In: Athlete Performance and Injuries
Editors: João H. Bastos and Andreia C. Silva
ISBN 978-1-61942-658-0
© 2012 Nova Science Publishers, Inc.
Chapter 5
ATHLETIC HEART: THE POSSIBLE ROLE
OF IMPAIRED REPOLARIZATION RESERVE IN
DEVELOPMENT OF SUDDEN CARDIAC DEATH
István Baczkó1, Andrea Orosz1,
Csaba Lengyel2 and András Varró1,3
1
Department of Pharmacology and Pharmacotherapy, University of Szeged,
Szeged, Hungary
2 st
1 Department of Internal Medicine, Faculty of Medicine, University of Szeged,
Szeged, Hungary
3
Division of Cardiovascular Pharmacology, Hungarian Academy of Sciences,
Szeged, Hungary
ABSTRACT
A number of sudden deaths involving young competitive athletes were reported in
recent years. Sudden death among athletes is rare, but in a significant number of these
cases the cause is not established and is mostly attributed to ventricular fibrillation.
Physical conditioning in competitive athletes induces cardiovascular adaptation including
lower resting heart rate (increased vagal tone) and increased cardiac mass (hypertrophy)
and volume as a consequence of increased demand on the cardiovascular system, called
"athlete’s heart”. Myocardial hypertrophy has been shown to cause electrophysiological
remodeling where the expression of different ion channels is altered. Since the duration
of repolarization depends on cycle length, the low heart rate in athletes also leads to
prolonged repolarization. It is conceivable that prolonged repolarization and a possibly
impaired repolarization reserve due to myocardial hypertrophy-induced downregulation
of potassium currents might represent increased risk for the development of ventricular
arrhythmias, including Torsades de Pointes ventricular tachycardia (TdP) that can
degenerate into ventricular fibrillation and lead to sudden cardiac death in athletes.
The reliable prediction of TdP remains unsatisfactory. Short-term variability (STV)
of the QT interval is a novel parameter used in the assessment of arrhythmic risk. STV of
repolarization can increase in case of decreased repolarization reserve even when there
are no noticable changes in the duration of cardiac repolarization. STV may be
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István Baczkó, Andrea Orosz, Csaba Lengyel et al.
significantly larger in competitive athletes and may be an early indicator of increased
instability of cardiac repolarization and a higher arrhythmia propensity in this population.
SUDDEN CARDIAC DEATH IN ATHLETES
While sports activities definitely improve life expectancy and quality of life, there have
been a number of high profile tragic sudden deaths of athletes, many of them elite soccer
players, reported in the media in recent years (Foe, Feher, O’Donnell, Puerta, Kolonics).
Sudden death in athletes is relatively rare (1:50 000 to 1:100 000), however, its frequency is
reportedly 2 to 4 times higher in this population compared to age-matched controls (Corrado
et al. 2007). Numerous congenital and acquired heart diseases have been identified as causes
of SCD (see review by Pigozzi and Rizzo 2008), however, in 5-10% of SCD cases no
structural abnormalities are detected in the heart during autopsy (Maron et al. 1980; Maron et
al. 1996), and the death is attributed to ventricular fibrillation most of the time. Inconclusive
autopsy findings often lead to a suspicion of ischemic origin of SCD without hard evidence,
however, in young athletes SCD mostly does not occur during peak performance and
ischemia specific ECG signs or proof of myocardial infarction are rarely found. Moreover,
regular physical training is considered to lead to the powerful cardioprotective phenomenon,
cardiac preconditioning, that would significantly increase survival chances during these SCD
episodes (Parratt and Végh 1997; Kavazis 2009). Myocardial ischemia and infarction are
important contributors to SCD in athletes older than 35, however, for athletes younger than 35
these causes for SCD are unlikely (Pigozzi and Rizzo 2008). Importantly, state-of-the-art
sports medical examinations mostly fail to detect abnormalities in those individuals who later
fall victim to SCD and exhibit normal hearts upon autopsy, for example, the soccer player
Antonio Puerta died only 3 days after completing his routine medical examination which he
passed. Therefore, it is clear that the prediction and screening procedures to prevent these
events leading to SCD in athletes needs significant improvement. There are two major
prerequisites leading to the development of TdP and consequent lethal ventricular fibrillation:
a sufficient arrhythmia substrate (prolongation of repolarization, enhanced spatial and
temporal repolarization inhomogeneity favoring the formation of re-entry paths) and a trigger
(an extrasystole in the vulnerable period). In this chapter, after introducing the concept of
repolarization reserve, we outline the mechanisms how impairment of ventricular
repolarization and repolarization reserve likely create an arrhythmia substrate and lead to
SCD in young competitive athletes. The factors contributing to repolarization reserve
narrowing in competitive athletes and their links to SCD, as well as possible sources of
triggers are also discussed.
REPOLARIZATION RESERVE
The repolarization process of myocardial cells is governed by the balanced and
simultaneous activities of inward and outward currents via different ion channels and
electrogenic ion pumps (Figure 1.). The concept of repolarization reserve was first described
by Dr. Roden in 1998 (Roden, 1998) and refers to the redundant repolarizing capacity of the
myocardium. According to this concept, the heart can compensate for the loss or impaired
Athletic Heart
0 mV
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inward current
outward current
200 ms
transmembrane current
INa
ICa
Ito
Channel proteins
Nav1.5, Nav1.7, Nav2.1,
Nav1, Nav2
Cav1.2, Cav3.17, Cav1.3,
Cav21, Cav2
Kv1.4, Kv1.7, Kv3.4,
Kv4.2, Kv4.3, KCHIP2
IKr
(H)ERG, MIRP2 + MIRP3
IKs
Ik1
KvLQT1 + MinK + MIRP2 + MIRP3
Kir 2.1, Kir2.2, Kir2.3,
Kir2.4, TWIK1, TASK2
INCX
NCX1
INa/K
Na/K ATP-ase 
Figure 1. A representative ventricular action potential recording and schematic illustration of the most
important underlying transmembrane ionic currents and electrogenic pumps. The channel proteins
responsible for mediating the given current are indicated on the right. Upward and downward
deflections and arrows refer to outward and inward currents, respectively.
function of one or more potassium currents that are important for normal repolarization
(Roden 1998). In case repolarization reserve is narrowed, this does not necessarily lead to
clinically manifest repolarization disturbances on the recorded ECG, however, the heart will
be more susceptible to the development of arrhythmias (Roden 1998; Roden and Yang 2005;
Varró and Papp 2006). In other words, repolarization reserve can be described as the ability
of the heart to withstand additional repolarization challenges. There are several repolarizing
currents that can significantly contribute to repolarization reserve.
Based on experimental evidence from dog, rabbit and human, the slow delayed rectifier
outward potassium current (IKs) has been shown to play a key role in repolarization reserve
(Varró et al. 2000; Lengyel et al. 2001; Jost et al. 2005). This current activates slowly (5001000 ms) during the plateau phase of the action potential followed by rapid (100-200 ms)
deactivation at more negative membrane potentials (for a recent review, consult Jost et al.
2007). In normal circumstances, due to its small amplitude and slow activation kinetics, there
is relatively little current that is activated (Varró et al. 2000; Jost et al. 2005).
On the other hand, when the action potential is prolonged and plateau membrane voltage
shifts to more positive values (Han et al. 2001), IKs represents "an available reserve of
channels that are ready to open on demand” (Carmeliet 2006). Thus IKs functions as a
repolarizing potassium current that opposes excessive action potential duration prolongation
(Varró et al. 2000; Jost et al. 2005). Sympathetic stimulation activates IKs via PKA (Yazawa
and Kameyama 1990; Walsh and Kass 1991) and the increased IKs amplitude and a shift in its
activation voltage towards negative potentials enhances IKs current density when sympathetic
tone is elevated, which is the case in athletes during and after performance. The L-type
calcium current (ICa,L) is also enhanced by sympathetic stimulation leading to positive
membrane potential shifts during the plateau phase and to prolonged repolarization. IKs is
further activated by these changes and acts as a negative feedback mechanism limiting
prolongation of repolarization (Han et al. 2001; Volders et al. 2003). In summary, IKs
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István Baczkó, Andrea Orosz, Csaba Lengyel et al.
contributes to repolarization significantly during enhanced sympathetic activation and when
repolarization is prolonged, providing a countermeasure mechanism that shortens action
potential duration only when necessary (Johnson et al. 2010). It is, therefore, evident how
impairment of IKs either due to loss of function mutations (LQT1), IKs blocking drugs or IKs
downregulation (as in the case in cardiac hypertrophy, see below) can lead to narrowing of
repolarization reserve.
In addition to the critical role of IKs, other repolarizing potassium currents may also
contribute to repolarization reserve. In this regard, the transient outward potassium current
(Ito) has been shown to be a major factor in repolarization reserve in dog ventricle (Virág et
al. 2011). Also, observations from our laboratory confirmed that inhibition of IK1 in rabbits
and dogs also diminished repolarization reserve and rendered these animals susceptible to the
development of TdP (Baczkó et al. 2011).
Importantly, according to recent data repolarization reserve is not static but changes
dynamically. In an elegant series of in vitro experiments, Xiao and co-workers (Xiao et al.
2008) have shown that in continuously paced and cultured canine ventricular myocytes 24 h
incubation with the selective IKr blocker dofetilide the action potential duration shortened,
therefore the repolarization prolonging effect of this compound was blunted.
The authors found that IKs was enhanced in dofetilide incubated cells compared to control
cardiomyocytes using patch-clamp measurements. These results were confirmed by elevated
KvLQT1 and MinK protein levels, while the mRNA for both of these proteins did not change,
suggesting a role for post-transcriptional regulatory mechanisms in this process. Indeed, the
expression of microRNA 133a and 133b (miR-133a and miR-133b) were reduced in
dofetilide incubated cells. In an earlier study, it was found that muscle-specific microRNAs
repressed IKs-encoding genes without changung KvLQT1 mRNA (Luo et al. 2007).
Further studies, including in vivo experiments, are needed to confirm these data, however,
these results strongly suggest that chronic administration of drugs with different degrees of
cardiac potassium channel blocking effects (including certain antibiotics, antihistamines,
NSAIDs, etc.) may cause the compensatory upregulation of IKs in an attempt to restore
repolarization capacity of the myocardium. Theoretically, if IKs function is already impaired
due to either genetic mutations (e.g. as in the case of LQT1 syndrome) or downregulation of
the current (as in the case of cardiac hypertrophy, discussed later), such seemingly harmless
drug therapy in athletes may cause more pronounced repolarization disturbances than
expected.
MECHANISM OF ARRHYTHMIA DEVELOPMENT AND SUDDEN
CARDIAC DEATH IN ATHLETES WITH IMPAIRED REPOLARIZATION
RESERVE
It is well established that there are marked transmural and regional differences in the
expression of cardiac transmembrane ion channels, including potassium channels, that create
some heterogeneity of repolarization already in normal circumstances (Gaborit et al. 2007;
Antzelevitch and Fish 2001). These differences in repolarization can be significantly
enhanced by impaired repolarization reserve, thus creating an arrhythmia substrate.
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Figure 2. Schematic illustration of the mechanism for arrhythmia development when dispersion of
repolarization is increased (represented by exaggerated APD differences). In the normal setting, sinus
impulses (black arrows) travel via physiological pathways. Early extrasystoles ("triggers”) propagate in
directions (red arrows) where propagation is not blocked by refractoriness and where action potentials
are in their vulnerable periods (1, 3, 4, 6), while conduction is blocked in refractory cells (2 and 5). The
extra stimulus can travel back to its site of origin, creating a re-entry pattern (7). From Varró and
Baczkó 2010, with permission.
The proposed mechanism of arrhythmia development due to increased repolarization
heterogeneity is illustrated on Figure 2.
In the heart, conduction is fast in normal conditions (1-2 m/s) and action potential
duration of ventricular myocardial cells is long (200-300 ms). These cells cannot be
stimulated early since they are in a refractory state, the duration of which is characterized by
the effective refractory period (ERP). In the normal setting, action potential duration and ERP
differences between well coupled adjacent cells are very small (small heterogeneity). The
relatively homogenous repolarization and fast conduction prevents the formation of circular
re-entry excitation and arrhythmia will not develop. On the other hand, when repolarization
and therefore the ERP are prolonged in a heterogenous fashion due to enhancement of
intrinsic regional/transmural repolarization heterogeneities by different degrees of
repolarization reserve impairment in separate regions, an arrhythmia susbstrate is created.
Consequently, an extrasystole generated following a normal sinus beat can travel in the
direction of cells with shorter APD, however, conduction is blocked in the direction of cells
with longer APD (Figure 2). The extra stimulus then can travel back in a complicated
pathway back towards the site of origin as well as in other directions where excitability is
restored, resulting in the development of TdP or ventricular fibrillation and SCD. It should be
emphasized that the creation of an arrhythmia substrate, i.e. the increased repolarization
heterogeneity following repolarization prolongation, is not enough in itself to precipitate
arrhythmias. A trigger extrasystole critically timed to the vulnerable period that can travel reentry paths is also needed for arrhythmia induction. Enhanced repolarization heterogeneity
results in longer vulnerable periods and with more frequent extrasystoles the chance for
serious arrhythmia generation is greater.
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István Baczkó, Andrea Orosz, Csaba Lengyel et al.
Figure 3. Experimental demonstration of transmural heterogeneity of repolarization during bradycardia
and following IKr blocker d-sotalol administration leading to impaired repolarization reserve in a canine
wedge long QT syndrome 2 model. Action potential duration is color coded on upper panel, from where
optical action potentials are shown on bottom panel from selected transmural sites (A-D). EPI,
epicardium; ENDO, endocardium. Adapted from Akar et al. (2002), with permission.
Development of TdP via this mechanism was demonstrated by Akar and co-workers
(2002) in dog ventricular wedge preparations, where increased transmural heterogeneity of
repolarization was found and TdP incidence depended on both bradycardia and administration
of an IKr blocker (Figure 3).
POSSIBLE MECHANISMS FOR IMPAIRMENT OF REPOLARIZATION
RESERVE IN ATHLETES
Athlete’s Heart, Cardiac Hypertrophy and Electrical Remodeling
In competitive athletes, strong physical exercise induces physiological adaptation of the
cardiovascular system that includes lower resting heart rate due to increased vagal tone,
increase in cardiac mass (hypertrophy) and cardiac volume as a result of increased demand,
collectively described as the "athlete’s heart” (Atchley and Douglas 2007). Since cardiac
repolarization is cycle length dependent, the lower heart rate in athletes favors prolongation of
repolarization and increased inhomogeneity of repolarization duration. Numerous
echocardiography studies have proven that cardiac hypertrophy develops as a result of longterm training and sports activities (Scharhag et al. 2002; Atchley and Douglas 2007). This
hypertrophy is larger in males compared to female athletes and the most significant left
ventricular wall thickness increase (larger than 75%) was reported in cyclists, rowers, water
polo players, cross-country skiers and football players (Maron and Pelliccia 2006). There are
only few conclusive animal experimental studies available on the effect of exercise training
Athletic Heart
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on the development of cardiac hypertrophy in species electrophysiologically relevant to
human (i.e. other than mouse, rat). Interestingly, some of these studies found decreased heart
rate, prolongation of the QT interval and ECG signs of cardiac hypertrophy in sled dogs
(Constable et al. 1994; Constable et al. 2000). In a reasonably good analogue model for
athlete’s heart, in dogs with chronic atrioventricular (AV) block, marked bradycardia and
reversible myocardial hypertrophy develops 3 weeks following AV block induction (Volders
et al. 1998; de Groot et al. 2000). In these animals, IKs downregulation was found to develop
as part of ventricular electrical remodeling, and these dogs were more susceptible to lethal
ventricular arrhythmias when subjected to different challenges on repolarization (Volders et
al. 1999; Vos et al. 1998). Whether IKs downregulation occurs in a similar way in athletes
needs to be confirmed, however, it was suggested that cardiac hypertrophy can lead to the
downregulation of potassium channels (Hart 2003).
Cardiac Diseases and Genetic Defects
Based on autopsy findings, hypertrophic cardiomyopathy (HCM) is the most common
cause of SCD in young athletes (Basavarajaiah et al. 2008). This familial malformation is
relatively common (1 in 500 of the general population), leads to cardiac hypertrophy,
cardiomegaly and interstitial fibrosis and it is due to mutations identified in a number of
sarcomeric genes (Maron 2002). In athletes, it is quite difficult to distinguish normal
compensatory cardiac hypertrophy from HCM, and only following a 2-3 month sports
activity-free period can echocardiographic studies reliably identify HCM, since this
hypertrophy is irreversible (Calderon Montero et al. 2007; Williams et al. 2009). There are a
number of other cardiac diseases and pathologies that have been associated with SCD in
athletes, including arrhythmogenic right ventricular cardiomyopathy, congenital coronary
artery anomalies, myocarditis, commotio cordis, aortic stenosis, Wolff-Parkinson-White and
Brugada syndromes (Basso et al. 2007; Pigozzi and Rizzo 2008), however, these are mostly
identified upon autopsy.
Some of the more common forms of congenital long QT (LQT) syndromes lead to loss of
function mutations in genes encoding repolarizing current mediating channel proteins,
thereby prolonging the action potential and QT interval and leading to impairment of
repolarization reserve and predisposes to lethal ventricular arrhythmias (Saenen and Vrints
2008). The prevalence of these LQT syndromes is similar in competitive athletes and in the
general population and is estimated at 1:5 000 (Basavarajaiah et al. 2007). It is estimated that
10% of patients with LQT die due to SCD. In competitive athletes, the significance of LQT
syndrome is increased since they have impaired repolarization reserve due to cardiac
hypertrophy and mutations that would otherwise result in mild repolarization abnormalities
can precipitate serious arrhythmias. In this scenario, when mutations in the IKs alpha subunit
(KvLQT1) and/or beta subunit (minK) protein encoding genes occur, the significantly
reduced IKs current (also reduced by cardiac hypertrophy) could not compensate for
prolonged repolarization caused by increased ICa,L as a consequence of elevated sympathetic
tone.
Catecholaminergic polymorphic ventricular tachycardia (CPVT) is another pathological
condition that can seriously affect athletes (Benton and Maginot 2007). CPVT is related to
mutations in genes encoding sarcoplasmic reticulum calcium releasing channels (RYR gene)
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István Baczkó, Andrea Orosz, Csaba Lengyel et al.
or calcium-binding proteins (CASQ2). Increased sympathetic tone during sports activities and
training leads to calcium release from the sarcoplasmic reticulum in CPVT even during
diastole, initiating trigger extrasystoles that can provoke serious ventricular arrhythmias via
mechanisms detailed earlier. In athletes with cardiac hypertrophy and impaired repolarization
reserve, these extrasystoles can develop more easily since the compensation of processes
favoring depolarization in CPVT is reduced.
Ion channel polymorphisms represent another possibility for impaired repolarization
reserve, where mild ion channel dysfunction can be the result. These might have relatively
high incidence in the general population and were found to be associated with drug-induced
cardiac arrhythmias (Sesti et al. 2000; Roden 2006; McBride et al. 2009). These
polymorphisms lead to individual differences in repolarization reserve and consequently in
susceptibility to drug-induced arrhythmias.
Doping Agents
High level competitive sports have been primarily associated with doping, however, it is
becoming more common in sports (de Rose 2008). It is extremely hard to obtain reliable data
due to the fact that application of doping agents is illegal, and newer agents continue to
emerge, therefore the true extent of this problem is not known. However, steroid use for
doping purposes can lead to skeletal and myocardial hypertrophy and can also impair
exercise-induced cardioprotection (Payne et al. 2004; Pereira et al. 2006; Chaves et al. 2006).
A very recent study indicates that chronic administration of anabolic steroids results in
repolarization disturbances in small experimental animals (Medei et al. 2010). The
application of growth hormone may similarly lead to cardiac hypertrophy and increased
cardiovascular mortality (Lombardi et al. 2006). Based on these data it seems that doping
agents may contribute to repolarization disturbances and can create an arrhythmia substrate
for ventricular arrhythmias. On the other hand, amphetamine-type doping agents can increase
intracellular cAMP levels and promote trigger extrasystole development, especially in
individuals with inhomogenous repolarization (e.g. athlete’s heart).
Medications
A large number of compounds can cause mild to significant inhibition of the IKr current,
leading to repolarization prolongation, occasionally to proarrhythmic side effects and very
rarely to SCD. These drugs can be frequently used non-cardiac medications, for example H1
antihistamines and antibiotics have been shown to exhibit such adverse effects (Anderson et
al. 2001; Berul and Morad 1995). Athletes can take these medications since they are not
illegal substances, however, those individuals with marked repolarization reserve impairment
due to myocardial hypertrophy are at greater risk for proarrhythmic side effects of these
compounds. Moreover, many of the compounds used by athletes were introduced to the
market when drug safety studies, including QT prolongation studies, were less rigorous.
Therefore, some of these drugs, in addition to other repolarization challenges, may contribute
to the development of arrhythmias and SCD in athletes. As an example, our preliminary data
indicate that the nonsteroid antiinflammatory drug (NSAID) diclofenac moderately inhibits
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131
IKr and IKs in isolated canine ventricular myocytes (Virág et al. 2010). These data justify the
detailed cardiac electrophysiological investigation of NSAID drugs, even if their effects on
repolarization are mild or marginal. Athletes use NSAID compounds in large doses and
frequently to treat sports injuries. In this regard, celecoxib has been shown to inhibit Kv2.1
channels (Frolov et al. 2008), however, IKr inhibition is still one of the most important cardiac
electrophysiological side effects of cardiac and non-cardiac drugs.
Diet and Dietary Supplements
Competitive athletes often apply special diets and consume dietary supplements to
improve their performance. Many of these compounds are present in natural form in food or
drinks constituting the human diet, however, it is important to remember that a significant
number of over-the-counter nutritional products can contain these substances in large
amounts as concentrated extracts. It should be noted that contrary to their frequent use, little
is known about the cardiac electrophysiological effects of these substances.
Animal experimental evidence suggests that soy products can enhance myocardial
hypertrophy and worsen heart failure, and these effects were more pronounced in male
animals (Csáky and Fekete 2004; Stauffer et al. 2006). In recent years, following the
recognition of the multiple beneficial (antioxidant, anti-carcinogenic, anti-atherosclerotic etc.)
effects of different polyphenolic and flavonoid compounds, the advantages of consumption of
green tea, red wine and citrus juices, including grapefruit juice has been emphasized (Chen et
al. 2011; Yang and Wang 2010; Melgarejo et al. 2010; Pignatelli et al. 2006; Folts 2002;
Vinson et al. 2002). The cardiac electrophysiological effects of most of these compounds
have not been fully characterized, and the available results suggest that these compounds be
consumed with some caution.
The main flavonoid compound found in green tea, epigallocathecin-3-gallate (EGCG) has
been shown to inhibit hERG channels expressed in HEK293 cells and Xenopus oocytes
(Kelemen et al. 2007), and to alter the shape of the ST-T wave segment in isolated guinea-pig
hearts (Kang et al. 2010). These results suggest that the cardiac electrophysiological effects of
EGCG may be significant in athletes and in individuals with impaired repolarization reserve,
especially when consumed in multigram doses using over-the-counter nutritional
supplements. On the other hand, resveratrol, a polyphenolic compound found in red wine,
with well documented beneficial effects in a range of pathologies from cancer to
cardiovascular disease (Baur and Sinclair 2006), has been shown to exert cardiac effects that
can, at least in part, counterbalance repolarization reserve impairment. In this regard,
resveratrol blocked late INa in isolated rat ventricular cardiomyocytes (Wallace et al. 2006)
and exerted myocardial antihypertrophic activity (Chan et al. 2008; Dolinsky et al. 2009).
Moreover, resveratrol protected against arrhythmias in several small rodent models and
enhanced IKs in isolated guinea-pig ventricular myocytes (Zhang et al. 2006), while having no
effect on IKr in guinea-pigs up to 100 M concentration (Zhang et al. 2006). These results
show that resveratrol does not impair repolarization reserve, however, these results should be
confirmed in other species that have ventricular electrophysiological characteristics, including
IKs, that resemble those found in human ventricle. There are two main reasons for grapefruit
juice to be considered in this context. Firstly, grapefruit contains many different polyphenolic
compounds and some of these (naringenin, hesperetin, morin) have been demonstrated to
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István Baczkó, Andrea Orosz, Csaba Lengyel et al.
possess hERG blocking effects and the ingestion of one liter of fresh grapefruit juice
significantly prolonged the QT interval in healthy volunteers (Scholz et al. 2005; Zitron et al.
2005). The question arises whether the even more often consumed orange juice has any
influence on repolarization in humans, since it contains large amounts of hesperetin.
Interestingly, in our laboratory we did not find any QTc prolonging effect of the consumption
of fresh orange juice in healthy volunteers (Figure 4). In contrast, grapefruit juice prolonged
QTc and incerased short-term QT variability (Figure 4), the parameter used for the
characterization of temporal beat-to-beat instability of repolarization (Thomsen et al. 2004;
Lengyel et al. 2007; Hinterseer et al. 2010). Pink grapefruit ingestion has also been shown to
increase QT variability index in patients with dilated and hypertensive cardiomyopathies
(Piccirillo et al. 2008). Orange juice and grapefruit juice share a number of polyphenolic
compounds albeit in different concentrations, probably leading to distinct net effects on
cardiac repolarization. Another important aspect of compounds found in grapefruit that some
of them are strong inhibitors of drug metabolizing cytochrome P-450 3A4 enzyme system
that transform a number of compounds including those with potassium channel blocking
properties. While it takes approximately 5 hours for hesperetin and naringenin to reach their
respective maximum plasma concentrations following single ingestion of grapefruit juice in
healthy volunteers (Erlund et al. 2001) with a concomitant 47% decrease in intestinal CYP
3A4 concentration (Dahan and Altman 2004), this effect can persist up to 72 hours (Takanaga
et al. 2000). Based on these data, ingestion of grapefruit can contribute to impairment of
repolarization reserve and may enhance proarrhythmic risk in athletes via two distinct
mechanisms. This impairment may be even more significant in individuals who have
potassium channel mutations and/or receive medications that have potassium channel
blocking effects. Indeed, an interesting case study of a young woman with asymptomatic
LQT syndrome who developed frequent episodes of TdP following grapefruit juice ingestion
was published (Hermans et al. 2003). Creatine is one of the most widely used ergogenic
supplements by athletes to build muscle and to improve repeated high-intensity exercise
performance (Bemben and Lamont 2005; Williams 2006). To date, the identified adverse
effects of creatine supplementation did not include cardiac side effects (Terjung et al. 2000).
Figure 4. Left panel: QTc intervals at 4 and 6 h following ingestion of 1L fresh orange and grapfruit
juice in healthy adult individuals. Right panel: Short-term beat-to-beat varibility of the QT interval
(STVQT) in the same individuals right before and 10 h following ingestion of 1L fresh orange and
grapfruit juice. *p<0.05 vs. control, n = 12.
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133
However, creatine kinase has been shown to be physically associated with the cardiac
sarcolemmal ATP-sensitive potassium (KATP) channel and creatine kinase activity has been
found to alter KATP channel function (Crawford et al. 2002). KATP channels were identified
first in cardiac tissue (Noma 1986) and has been shown to couple cell metabolism to
membrane excitability (Deutsch et al. 1991). A number of studies confirmed that activation of
KATP channels under hypoxic or ischemic conditions is cardioprotective (Cole et al. 1991;
Hearse 1995; Baczkó et al. 2005). KATP channels also play a vital role in adaptation of
calcium homeostasis and contractile function to physiological and pathological stress
(Zingmann et al. 2002), and in the powerful cardioprotective mechanism of preconditioning
(Parratt 1994; Cohen and Downey 1996). Based on the above, indirect cardiac
electrophysiological effects due to altered cardiac KATP channel function following long-term
creatine administration cannot be ruled out. We emphasize that the examples of dietary
constituents mentioned in this section may represent only a fraction of compounds concerned,
since only scarce information is available on the cardiac electrophysiological effects of food
colorants, preservatives and additives. Importantly, performance enhancing supplements can
be consumed in a large number of combinations, and additional studies are needed to
characterize the cardiac electrophysiological effects of these combinations in athletes.
Hypokalemia
Temporary hypokalemia can occur during sports activities in case fluid and electrolyte
replenishment is not adequate. It can contribute to decreased repolarization reserve and
repolarization abnormalities in athletes with myocardial hypertrophy, since a substantial
action potential prolongation develops that is partly due to reduced IKr and IK1 during the late
phase of the action potential. Hypokalemia is also recognized therefore as a factor that
increases susceptibility to TdP arrhythmias (for a recent review see Cubeddu 2009).
Gender of the Athlete
Women have been shown to be at greater risk for the development of TdP in response to
compounds that prolong cardiac repolarization due to having longer QTc interval and reduced
repolarization reserve compared to men (James et al. 2007). Gender related differences are
likely to be related to the effects of sex hormones on repolarization and underlying
transmembrane ionic currents (Jonsson et al. 2010). At birth, QTc intervals are similar in both
sexes (Stramba-Badiale et al. 1995) and during puberty, QTc interval shortens in men
(Rautaharju et al. 1992). Oestrogen has been found to reduce IKr and prolong replarization
(Kurokawa et al. 2008), while testosterone increased IKr and activated hERG channels via
androgen receptors (Ridley et al. 2008). On the other hand, progesterone has been shown to
oppose IKr inhibition in a study that investigated IKr block induced QTc prolongation in
women at different stages of the menstrual cycle (Rodriguez et al. 2001).
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István Baczkó, Andrea Orosz, Csaba Lengyel et al.
SHORT-TERM VARIABILITY OF THE QT INTERVAL: A NOVEL
PARAMETER FOR THE ESTIMATION OF PROARRHYTHMIC RISK
The current screening methods need to be improved to identify athletes without apparent
structural cardiac abnormalities but with increased susceptibility for arrhythmias to decrease
the incidence of SCD in young athletes. State-of-the art techniques based on measurements of
repolarization prolongation for the prediction of TdP and other serious, potentially lethal
arrhythmias remain unsatisfactory. Recent evidence indicates that in addition to QTc interval
prolongation, the beat-to-beat short-term variability of repolarization can be a more reliable
predictor of TdP development both in humans (Haigney et al. 2004) and in dogs with chronic
AV-block induced electrical remodeling (Thomsen et al. 2004). Also, STVQT has shown good
correlation with the incidence of TdP in dogs and rabbits with pharmacologically narrowed
repolarization reserve (Figure 5; Lengyel et al. 2007). These results were confirmed in in
Figure 5. The increase of STVQT showed a better correlation with the incidence of Torsades de Pointes
(TdP) than prolongation of the QTc interval in conscious dogs. Representative Poincaré plots illustrate
changes in QTc and STVQT following combined IKr (dofetilide) and IKs (HMR-1556) block in a dog
later developing TdP (TdP+) compared to another animal that did not exhibit TdP (TdP-). Modified
from Lengyel et al. 2007, with permission.
Figure 6. Representative Poincaré-plot (where the QT interval is plotted against its former value) from a
soccer player on left panel illustrating increased instability of repolarization. Right panel shows
grouped data on higher short-term beat-to-beat variability of the QT interval (STVQT) in professional
soccer players before and after a competitive game compared to age-matched vounteers with no sports
activities. n=76 individuals/group; ***p<0.001 vs. control; ##p<0.01 vs. before game value. Modified
from Lengyel et al. 2011, with permission
Athletic Heart
135
vitro experiments, where the increase in short-term variability of the action potential duration
predicted drug induced pro-arrhythmia in dog left ventricular myocytes (Abi-Gerges et al.
2010). Moreover, the increase in STVQT has been associated with disorders of repolarization
and increased arrhythmia susceptibility in clinical settings, including patients with LQT
syndrome and with dilated cardiomyopathy (Hinterseer et al. 2009; Hinterseer et al. 2010).
Recently, we have shown that STVQT is significantly higher in professional soccer
players compared to age-matched controls who did not participate in sports activities (Figure
6; Lengyel et al. 2011). These results suggest that beat-to-beat short-term variability of the
QT interval should be added to existing ECG evaluation as part of preparticipation athlete
screening worked out by Corrado et al. (2008).
CONCLUSION
Sudden cardiac death is more frequent in top competitive athletes compared to the agematched control population. In many SCD cases in athletes the underlying cause can be
identified, however, in a significant number of SCD events the cause remains unknown and
the autopsy is negative. Lethal ventricular arrhythmias developing on the basis of
repolarization disturbances are suspected in the background of these deaths. Athlete’s heart
develops as part of a physiological adaptive response to physical conditioning in competitive
athletes. Myocardial hypertrophy is a characteristic of athlete’s heart, and cardiac hypertrophy
is associated with the down-regulation of certain repolarizing potassium currents, including
IKs, which has been shown to play a key role in cardiac ventricular repolarization reserve.
Impairment of repolarization reserve due to myocardial hypertrophy may render the athlete’s
heart more susceptible to ventricular arrhythmia development and SCD when the
myocardium of the athlete is subjected to further hits on repolarization, for example cardiac
and/or non-cardiac drugs, dietary constituents, certain doping agents and performance
enhancing food supplements. This notion is also supported by the observation that de-training
significantly descreased extrasystole development in athletes (Biffi et al. 2004). We suggest
that current ECG evaluation is key in pre-participation screening of athletes, and the
measurement of the novel parameter of repolarization instability, short-term beat-to-beat
variability of the QT interval, should be added to the evaluation of the athlete ECG. However,
a reliable, easy to perform and cheap screening procedure should be established and
implemented for identification of athletes at higher risk for cardiac ventricular arrhythmias
and sudden cardiac death.
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In: Athlete Performance and Injuries
Editors: João H. Bastos and Andreia C. Silva
ISBN 978-1-61942-658-0
© 2012 Nova Science Publishers, Inc.
Chapter 6
SPORTS INJURIES AND RISK-TAKING BEHAVIORS
IN AMATEUR ATHLETES
Vanessa Lentillon-Kaestner
University of Teacher Education (HEP-VD), Department of Research and Teaching in
Sport and Physical Education (UER-EPS) / Institute of Sport Sciences of the
University of Lausanne (ISSUL), Lausanne, Switzerland
ABSTRACT
Research conducted outside of the sports context has shown higher risk for all
injuries (e.g., intentional injuries, injured drivers, fatal and non-fatal injuries) among
persons with risk-taking behaviors (e.g., cannabis use, alcohol consumption). The
purpose of this study was to investigate whether: (1) risk-taking behaviors, such as
alcohol, cannabis or tobacco consumption increased the risk and the severity of sports
injuries; (2) whether differences emerged between male and female athletes; and (3)
whether differences emerged between recreational and competitive athletes. The sample
consisted of 1,810 amateur athletes (993 men, 817 women), aged 16 to 22 years old
(M=18.72; SD=2.08). Respondents completed a questionnaire, which queried frequency
of risk-taking behaviors and sports injuries recorded in their lifetime. Sixty-seven percent
(67%) of amateur athletes indicated at least one sports-related injury in their lifetime. For
sixty-two percent (62%) of athletes, the most frequent sports injuries required ten days to
three months of sport interruption. Results also indicated that risk and severity of sports
injuries increased with increased alcohol consumption. Cannabis use also increased the
risk but not the severity of sports injuries, while smoking was not associated with the risk
but with the severity of sports injuries. Some differences were observed between males
and females as well as between recreational and competitive athletes in associations
between risk-taking behaviors, risk and/or severity of sports injuries. Prevention
measures for risk-taking behaviors in athletic pursuits should be increased and improved
to reduce the number and severity of injuries in sports played on an amateur level.
Keywords: sports injuries, risk-taking behaviors, amateur level, gender, recreational and
competitive athletes
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Vanessa Lentillon-Kaestner
1. INTRODUCTION
In recent years, the number of children and teenagers participating in sports has grown
due to the increased number of children in sports, the younger ages of participation, and an
increase in individuals engaged in multiple sports (Collard, Verhagen, Chin A Paw, & Van
Mechelen, 2008; NSKC, 2004). Participation in sports has many health benefits. However, a
drawback of an increase in athletic activity is the risk of sports-related injuries. A large
proportion of reported injuries among young people occur at sports facilities during
participation in a sport (Bijur, et al., 1995; Molcho, et al., 2006; Williams, Wright, Currie, &
Beattie, 1998). For example, the survey of Bijur et al. (1995) conducted on 11,840 children
and adolescents aged from 5 to 17 years showed that sports account for 36% of injuries from
all causes.
The epidemiology of sports-related injuries is well explored in the literature. Previous
research has been done on incidence of injury by sport, location of injury, type of injury (e.g.,
concussion, fracture, dislocation, contusion, sprain), and duration of absence (e.g., no
absence, 1-7 days, 8-21 days, >21 days) (Bijur, et al., 1995; Caine, Caine, & Maffulli, 2006;
Collard, et al., 2008; Cumps, Verhagen, & Meeusen, 2007; Gottschalk & Andrish, 2011;
Junge, Cheung, Edwards, & Dvorak, 2004; Junge, Chomiak, & Dvorak, 2000; Langevoort,
Myklebust, Dvorak, & Junge, 2007; Olsen, Myklebust, Engebretsen, & Bahr, 2006; Pikora,
Braham, Hill, & Mills, 2011).
Previous research has also identified potential risk factors for sports-related injuries,
including individual characteristics (e.g., age, height, weight, body mass index, previous
injury, physical condition, skill level, success of treatment and rehabilitation), play
characteristics (e.g., field position, sports experience, level of competition, pre-season
conditioning, amount and quality of training, frequency of participation, field conditions,
match intensity), protective or risky behaviors (e.g., use of protective equipment or behaviors,
use of stretching exercises, technical training, rule violations), and psychological factors (e.g.,
stress, anxiety, social support, coping skills, neuroticism, global self-esteem, locus of control,
perfectionism, mood states) (Chomiak, Junge, Peterson, & Dvorak, 2000; Collard, et al.,
2008; Cumps, et al., 2007; Deroche, Stephan, Brewer, & Le Scanff, 2007; Gabbett, 2004;
Iverson, Gaetz, Lovell, & Collins, 2004; Johnson, 2007; Noh, Morris, & Andersen, 2005;
Tsigilis & Hatzimanouil, 2005). For example, level of play is related to incidence of injury
(Cumps, et al., 2007). Incidence decreases with the play level; beginners, being less skillful
and experienced, generally sustain more injuries. Jacobson (2006) showed that the overall
injury incidence was 9.6 injuries / 1000 person-hours of football in the second division and
4.6 injuries / 1000 person-hours of football in the premiere league. Deroche et al. (2007)
studied factors associated with perceived susceptibility to sports-related injuries were
examined. The study showed that neuroticism predicted perceived susceptibility to a greater
degree than global self-esteem or previous experiences with injury. Noh et al. (2005) showed
that coping skills were moderately correlated with frequency of injury; in particular, peaking
under pressure, goal setting and mental preparation, freedom from worry, confidence levels
and achievement motivation subscales were related to injury frequency. In predicting duration
of injury, negative life stress and negative sports-related stress had significant correlations,
whereas higher stress levels correlated to longer recovery times. The review of Johnson
(2007) showed that levels of psychosocial variables such as high competitive anxiety, low and
Sports Injuries and Risk-Taking Behaviors in Amateur Athletes
147
high emotional state, high levels of life changes, low coping resources, and low levels of
social support were directly or indirectly related to injury outcome. Johnson (2007)
distinguished two types of interrelated risk factors: (a) extrinsic, where risk was related to the
type of sport, the way it is practiced, contextual factors, and equipment; and (b) intrinsic,
where risk was related primarily to an individual’s physical and psychological features. Risktaking behaviors were rarely mentioned as risk factors in sports injuries. Nevertheless,
research conducted outside of the sports context has shown associations between types of
injuries (intentional injuries, injured drivers, fatal and non-fatal injuries, etc.) and risk-taking
behaviors (Gmel, Kuendig, Rehm, Schreyer, & Daeppen, 2009; Macdonald, et al., 2003;
McDonald, Duncan, & Mitchell, 1999; Wadsworth, Moss, Simpson, & Smith, 2006). There is
sufficient evidence indicating alcohol use as a contributing causal factor for injury (Gmel,
Kuendig, Rehm, et al., 2009; Macdonald, et al., 2003; Vinson, et al., 1995), while results are
inconsistent for cannabis (Gmel, Kuendig, Rehm, et al., 2009; Macdonald, et al., 2003;
McDonald, et al., 1999; Wadsworth, et al., 2006). Wadsworth et al. (2006) showed that
cannabis use was associated with both minor injuries and accidents, particularly among those
with high levels of other associated risk factors. Gmel, Kuendig, Rehm, et al. (2009) showed
that cannabis use decreased risk of injury; however, the study’s sample size of cannabis users
was small. Macdonald et al. (2003) reviewed the results and limitations of injury studies of
risk associated with cannabis and cocaine use; the majority of laboratory studies showed that
being under the influence of drugs reduces psychomotor performance. However,
epidemiological studies using drug testing technology have failed to find evidence that
cannabis use is related to increased injury risk.
Few studies have considered risk-taking behaviors in relation to sports injuries; only a
single study was found in which the focus was on alcohol consumption. According to Gmel,
Kuendig, Rehm, et al. (2009), alcohol consumption increased the risk of sports injuries.
However, it should be noted that the severity of sports injuries was not taken into account in
the study. Because of the lack of studies surrounding risk-taking behaviors and sports injuries,
the aim of this study was to focus on the three most widely used drugs in Europe: alcohol,
tobacco and cannabis (Calafat, et al., 1999) and to investigate whether: (1) risk-taking
behaviors, such as alcohol, cannabis or tobacco consumption, increased the risk and the
severity of sports injuries; (2) whether differences emerged between male and female athletes;
and (3) whether differences emerged between recreational and competitive athletes.
2. METHODS
2.1. Sample
The sample consisted of 1,920 French Swiss athletes from 24 different schools. Of 1,920
questionnaires, 90 questionnaires were incorrectly filled out and were not included in the
analyses. Our final sample consisted of 1,810 athletes (993 men, 817 women, 55% vs. 45%)
aged between 16 and 22 years old (M=18.7 years, SD=2.1). Sixty per cent (60%) of athletes
were high school students (M=17.5 years, SD=1.5) and 40% were from institutions of higher
education (university, professional schools (HES))1 (Mage=20.6 years, SD=1.3).
1
3 athletes did not specify their education level.
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Vanessa Lentillon-Kaestner
Sixty six per cent (66%) of the 1,810 athletes were involved in a second sport in addition
to their primary sport, and 30% played a third sport. In all, 50% of the athletes practiced at
least one sport competitively.
2.2. Materials
Our data were collected using a self-administered questionnaire, designed by the authors
of this study. Pilot surveys were conducted to ensure that the questionnaire could be
understood and completed by adolescents. Our questionnaire included 21 questions
concerning social and demographic information, sports practice, risk-taking behaviors and
sports injuries. In this chapter, only demographic information (male/female), information
concerning athletic practice (recreational/competitive sports, number of hours of sports
practice per week), sports injuries and risk-taking behaviors (alcohol, cannabis and tobacco
consumption) were used.
All sports injuries up until the date of the questionnaire were recorded. Only injuries
requiring sport interruption were considered: “Have you already had an injury requiring a
sport interruption?” If the athlete answered “Yes”, he or she was asked to specify the
duration of the rest and rehabilitation period into one of several categories: “Less than ten
days”, “From ten days to three months”, “From three to six months”, or “More than six
months”. The duration of the rest and rehabilitation period was used as a proxy for the
severity of injury.
A list of 30 substances, both legal and banned, was proposed to athletes in the following
question: “Have you already used the following substances in your sport practice?” For each
substance, athletes indicated frequency of use on a 4-point scale: “never (1), sometimes (2),
often (3), every day or almost (4)”. In this chapter, the focus is restricted to alcohol and
cannabis data.
Smoking was evaluated using the following consumption categorization: “At present, do
you smoke cigarettes?” Possible responses to the question on smoking were: “never (1)”, “I
have stopped since … months (2)”, “Irregularly: ... cigarettes per week (3)”, or “Regularly:
… cigarettes per day (4)”.
2.3. Procedure
Permission for the study was obtained from the University of Lausanne’s Research Ethics
Board. Permission was also obtained from the heads of the various schools in the sample to
distribute the questionnaire, and teachers were asked to set aside time during class for
students to fill out the questionnaire. Before administering the survey, athletes and school
administrators were informed that participation was voluntary. The 21-item self-report
instrument was administered in different schools in 2008 by a survey researcher, whose role
was to answer questions and ensure that there was no communication between subjects. Only
students who participated in sports outside of their school were asked to fill out the survey.
Athletes were reminded to answer all questions independently and honestly and that
anonymity and confidentiality were guaranteed. Participants did not write their names on the
surveys. Surveys were completed at the beginning of a physical education class in high
Sports Injuries and Risk-Taking Behaviors in Amateur Athletes
149
schools, and at the beginning of a lecture in institutions of higher education. After completing
the surveys, which took 15 minutes on average, athletes placed them in a closed box.
2.4. Data Analysis
Data was managed and analyzed using Statistica Software (Version 8.0, StatSoft, Inc.,
Tulsa, OK, USA). Descriptive analyses were conducted to explore relationships between risktaking behavior, sports-related injury risk and severity in each group (global sample,
male/female athletes, and competitive/recreational athletes). Bivariate comparisons of athletes
with and without injury, female and male athletes, recreational and competitive athletes were
made using the two-tailed Student’s t-tests for continuous variables and chi-square tests for
categorical variables. Correlation tests were used between two continuous variables.
3. RESULTS
In the following passage, only significant differences (p<0.05) and results that tend to be
statistically significant (0.05<p<0.1) are presented.
3.1. Injury Statistics
Sixty-seven percent (67%) of amateur athletes recorded at least one sport injury in their
lifetime. The most frequent sports injuries were those requiring ten days to three months of
sport interruption (62% of athletes). Injuries categorized as requiring fewer than ten days or
more than three months were less frequent. Forty-five percent (45%) of amateur athletes
suffered at least one injury that forced them to stop playing the sport for a period of less than
ten days. Twenty-two percent (22%) of athletes sustained at least one injury requiring a break
of three to six months. Eleven percent (11%) of amateur athletes were concerned by an injury
that required a break of more than six months.
Competitive athletes had more sports injuries than recreational athletes (82% vs. 55%,
Chi2(1809)=140.30, p<0.0001). Moreover, competitive athletes had more minor injuries
(requiring a break of less than ten days) than recreational ones (49% vs. 41%,
Chi2(1218)=8.60, p=0.003). A tendency for the injuries to last ten days to three months was
observed (64% vs. 59%, Chi2(1218)=3.33, p=0.07). These results may be explained by their
greater personal investment in the sport: the competitive athletes practice more hours per
week than recreational athletes (11h24 vs. 7h12, t(1808)=12.06, p<0.0001). Injuries also
increased with time investment: the more time investment (hours of practice per week), the
higher the risk of sports-related injuries (r(1810)=0.19, p<0.0001). However, practice time
was only weakly related with minor injuries (i.e. injuries lasting less than ten days, r(1219)=0.06, p=0.03) and with more serious injuries lasting three to six months (r(1219)=-0.07,
p=0.01).
Sports injuries were more frequent among males than females (72% vs. 62%,
Chi2(1809)=21.69, p<0.0001). Differences in the severity of injuries were also observed.
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Vanessa Lentillon-Kaestner
Injuries lasting three to six months were more frequent among males than females (25% vs.
18%, Chi2(1219)=7.22, p=0.007) and a similar trend was observed in injuries lasting ten days
to three months (64% males vs. 59% females, Chi2(1219) =3.21, p=0.07). The more frequent
and serious sports injuries among males could be related to the difference in time devoted to
practice: males practiced an average 6h49 of sports per week and females 5h14 per week
(t(1716)=-6.43, p<0.000001). Moreover, males practiced more competitive sports than
females (52% vs. 37%, Chi2(1809)=41.39, p<0.00001).
3.2. Associations between Sports Injuries and Risk-Taking Behaviors
Three risk-taking behaviors were evaluated among amateur athletes: alcohol, cannabis
and tobacco consumption.
3.2.1. Alcohol Consumption and Sports Injuries
With increasing alcohol consumption, the risk and severity of sports injuries increased.
Athletes who had had at least once an injury that required a period of rehabilitation
consumed alcohol more frequently during practice than athletes who had never had sports
injuries (t(1808)=-2.59, p=0.01). The more severe injuries were associated with alcohol
consumption. Athletes who had had at least once a severe injury (requiring a rehabilitation
period of more than six months) drank alcohol more frequently than athletes who had never
had such serious injuries (t(1217)=2.10, p=0.04).
Separating competitive and recreational athletes, the relationship between alcohol
consumption and sports injuries' risk was significant among competitive athletes (t(659)=2.82, p=0.005) but not among recreational ones (t(1146)=-1,21, p=0.22). In competitive
sports, athletes who had had at least one injury lasting more than six months answered that
they consume alcohol more frequently than those who had never had such serious injuries
(t(366)=2.86, p=0.004). A similar trend was observed among recreational athletes
(t(849)=1.73, p=0.08).
Separating males and females in analyses, the association between alcohol consumption
and sports injuries' risk was significant in females (t(811)=-2.21, p=0.03) but not in males
(t(1808)=-0.68, p=0.49). Similarly, the severity of injuries was associated to alcohol
consumption only among females. Females who had been seriously injured (injuries lasting
more than six months) consumed alcohol more frequently than females who had never had
such serious injuries (t(498)=1.94, p=0.05).
3.2.2. Cannabis Use and Sports Injuries
Cannabis use increased the risk but not the severity of sports injuries. Athletes who had
had at least once an injury requiring a rehabilitation period used cannabis more frequently
during practice than those who had never had sports injuries (t(1808) =-2.30, p=0.02). No
difference was observed concerning the severity of injuries.
Comparing competitive and recreational athletes, a significant association between
cannabis use and sports-related injury risk appeared among competitive athletes (t(659)=2.57, p=0.01) but not among recreational ones (t(1146)=-1.21, p=0.22). Nevertheless, a
significant difference was shown in the severity of injury among recreational athletes only;
Sports Injuries and Risk-Taking Behaviors in Amateur Athletes
151
athletes who had had at least one sports injury lasting three to six months used cannabis more
frequently than those who never had such injuries (t(849)=2.24, p=0.02).
Separating males and females in analyses, the significant association between cannabis
and sports injury risk disappeared among females (t(811)=-0.93, p=0.35) and males (t(990)=1.59, p=0.11). Similar to the global sample, no associations between the severity of injuries
and cannabis use were observed in male and female athletes.
3.2.3. Smoking and Sports Injuries
Smoking was not associated with risk (t(1808)=-0.15, p=0.88) but was associated with
sports-related injury severity. Athletes who underwent serious injuries, requiring a
rehabilitation period of three to six months (t(1217)=2.51, p=0.01) or more than six months
(t(1217)=3.04, p=0.002) smoked more than those who had never had such serious injuries.
Similar results were obtained when comparing competitive and recreational athletes or
separating males and females: risk of injury was not associated with smoking but significant
associations were observed between smoking and the severity of injury.
Recreational athletes who had had at least one injury requiring a rehabilitation period of
more than six months smoked more than those who had never had such a serious injury
(t(849)=2.68, p=0.007). This trend was observed in competitive athletes (t(366)=1.62,
p=0.10).
Females who had had at least one injury lasting longer than six months smoked more
than females who had never had such serious injury (t(498)=2.83, p=0.005). A similar trend
was observed in males (t(713)=1.74, p=0.08) and a significant result was obtained for injuries
requiring three to six months of rehabilitation for males only (t(713)=2.21, p=0.03).
CONCLUSION
Results showed that the percentage of athletes who had had at least one injury requiring
rehabilitation was high (67%). This percentage was higher than results obtained in previous
research. That may be explained by the fact that, in this study, all previous sports injuries
leading up to the questionnaire were evaluated. In previous studies, athletes were often asked
to report their injuries during a limited period (e.g., the last 24 months, during the course of
the study - 10 months, in the past 12 months) (Noh, et al., 2005; Pikora, et al., 2011; Tsigilis
& Hatzimanouil, 2005). Moreover, comparisons with other studies are difficult because the
definition of sports injuries and of their severity differ from study to study (Junge & Dvorak,
2000). In their study on basketball injuries, Cumps et al. (2007) defined an acute injury as
“being a basketball accident with a sudden, direct cause/onset, which required at least
minimum medical care including, e.g. ice, tape, etc. and which caused the injured player to
miss out on at least one training or game session” (Cumps, et al., 2007, p.205). Junge et al.
(2004) defined sports injury as “any physical complaint caused by soccer or rugby during
school training and matches. The duration of absence due to an injury was categorized
according to the severity grading of the National Injury Registration System (NAIRS): up to
one week, 8-21 days, or more than 21 days” (Junge, et al., 2004, p.169). Carmeli et al. (2003)
defined sports injury as a single event that resulted in hospital or medical referral. The various
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Vanessa Lentillon-Kaestner
definitions influence the incidence rates and relationships between the injury risk, severity of
injuries and risk factors taken into account in studies.
Results of our study showed differences according to athletes’ type of involvement in
sports (recreational vs. competitive sports) and gender. Competitive athletes sustained more
injuries than recreational ones. High levels of participation are significantly associated with
increased risk of injury in general and in sports-related injury. In particular, injured players
had more hours of practice per week than the non-injured (Carmeli, Azencot, Wertheim, &
Coleman, 2003; Tsigilis & Hatzimanouil, 2005; Williams, et al., 1998).
Concerning athletes’ gender, males were injured more frequently and seriously than
females in this study. According to the National Safe Kids Campaign (NSKC, 2004), among
children aged 5 to 9 years old in the United States, sports injuries occurred more frequently
among girls than boys. However, during puberty (ages 10 to 14), boys are injured more
frequently and severely than girls. Our results were similar among amateur athletes aged 16 to
22 years old. The risk of physical injury is inherent in sports participation (NSKC, 2004). We
have shown in this study that males were more invested in sports than females: they practiced
more hours of sports per week and participated in more competitive sports than females.
Moreover, males are more aggressive, have larger body mass, and experience greater contact
compared with females (Collard, et al., 2008; Emery, 2003). They are also more likely to
participate in vigorous exercise and sport (Collard, et al., 2008; Taimela, Kujala, & Osterman,
1990). Consequently, males have a greater risk of sports injuries.
Compared to tobacco and cannabis use, alcohol consumption was the risk-taking
behavior with the highest relation to sports injuries. With increased alcohol consumption, the
risk and severity of sports injuries also increased. Alcohol consumption has negative
cognitive and psychomotor effects, such as deteriorations of vigilance, reaction time, eyehand coordination, accuracy, balance and cognitive processing (Gutgesell & Canterbury,
1999). Some gender differences were observed in our study; consumption of alcohol was
associated with higher risk and severity of sports injuries for females but not for males. Our
results are similar to those of Gmel, Kuendig and Daeppen (2009) and Gmel, Kuendig, Rehm
et al. (2009). On examining whether sports injuries were associated with alcohol consumption
before the injury (acute intake) and with usual consumption patterns (chronic high intake and
heavy intake on single occasions) in hospital patients, Gmel, Kuendig and Daeppen (2009)
showed that with increasingly acute intake (consumption 6 h before injury), the risk of sportsrelated and other injuries increased. Alcohol consumption was associated with an increasingly
higher risk of sports-related injuries compared with other injuries among females. Regarding
typical consumption patterns, both males and females injured while exercising were more
often at-risk drinkers (males: 44%; females: 25%) compared with those injured during other
activities (males: 37%; females: 13%). Results indicated that both males and females, but
particularly females, should not practice sports after alcohol ingestion. Biologically, females
are less resistant to alcohol effects compared to males: females reach higher blood alcohol
concentrations than men for the same amount of alcohol consumed because of weight
differences, lower body water percentage compared with men, and differences in metabolism
of alcohol (Gmel, Kuendig, & Daeppen, 2009; Graham, Wilsnack, Dawson, & VogeltranzHolm, 1998).
Comparing recreational and competitive athletes, associations between alcohol
consumption, risk and severity of sports injuries were observed almost in competitive
athletes. This result may be explained by two factors. First, in recreational sports, alcohol
Sports Injuries and Risk-Taking Behaviors in Amateur Athletes
153
consumption during play may lead to a decrease in personal investment in the sport when
athletes feel the effects of alcohol. In competitive sports, the decrease in personal investment
is more difficult. Because training, regular practice and performance improvement are more
important in competitive sports, competitive athletes may be more likely to continue to
playing a sport than recreational athletes even if they have consumed substances. Second,
according to Gmel, Kuendig and Daeppen (2009), competitive investment is often linked to a
higher degree of personal investment in the sport that is often accompanied by an increase in
alcohol consumption due to the social occasions to drink alcohol after trainings or
competitions. Nevertheless, in our sample, no differences in alcohol consumption were
observed between competitive and recreational athletes.
Concerning cannabis use, results of this study showed an increase of sports-related injury
risk with cannabis use but not in the severity of sports-related injuries in the global sample.
The significance of the association between injury risk and cannabis use was low and
disappeared when males and females were separated in analyses. Moreover, this significant
relationship was only seen in competitive athletes. Concerning the severity of sports injuries,
a single significant association was found in recreational athletes for serious injuries lasting
three to six months. In previous studies, mostly focused on traffic injuries, the relationship
between cannabis use and injury was unclear and results were inconsistent: some suggested a
causal relationship between cannabis consumption and injury, while others did not (Gmel,
Kuendig, Rehm, et al., 2009; Macdonald, et al., 2003; Mura, et al., 2003). Macdonald et al.
(2003) reviewed the direct effects of cannabis on psychomotor performance in order to better
understand how cannabis use might be related to injuries. Laboratory research has found, in
general, that the cannabis use is related to performance deficits and indicated that the most
deleterious effects of cannabis use were found for attention and tracking and psychomotor
skills, among others. The sedative effects of cannabis use are well established, with users
typically reporting mental slowness, tiredness, anxiety and paranoia as well as relaxation and
euphoria (Macdonald, et al., 2003; Wadsworth, et al., 2006). These cognitive factors could
influence injury risk (Macdonald, et al., 2003). These acute effects on cognition and
performance have been well-documented but they are limited to periods of intoxication
(Heishman, Arasteh, & Stitzer, 1997). Fewer studies have focused on the long-term effects of
chronic cannabis use on cognitive performance. However, the evidence suggests that longterm cannabis use leads to subtle and selective impairments of specific higher cognitive
functions (e.g., schizophrenia or depression, cognitive impairments of various types,
permanent effects on memory, information processing and executive functions) (Kalant,
2004; Wadsworth, et al., 2006). Nevertheless, these associations were often weak and often
observed only in regular users (Kalant, 2004). The low association between cannabis use and
sports injuries observed in our study may be explained by the fact that athletes were often not
regular cannabis users but only occasional users. Moreover, the timing of cannabis use in
relation to sports injuries was not available in our questionnaire, which only queried
frequency of cannabis use. The consumption of cannabis was perhaps too low or too
temporally removed from sports practice to be highly associated with risk or severity of sports
injuries. Alcohol consumption may be higher related to sports injuries because amateur
athletes consume alcohol more frequently than cannabis.
Results concerning smoking showed generally that smoking was not associated with the
risk but instead with the severity of sports injuries. Smoking could deteriorate the natural
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Vanessa Lentillon-Kaestner
physical defenses of athletes, increasing the severity of sports injuries. Investigation is needed
to describe and explain the relationship between smoking and sports injuries further.
Some caution is needed in interpreting our data due to several methodological limitations.
First, self-reported data may have introduced bias into the results of this study. Despite of the
announced confidentiality, it is possible that athletes may have under-reported risk-taking
behaviors. This problem has been researched for the use of illegal substances during sports
(Lentillon-Kaestner & Ohl, 2010). According to the World Anti-Doping Code (Code, 2010),
alcohol is prohibited in competition only in particular sports (e.g., archery, karate,
motorcycling, aeronautics) and cannabis is prohibited in all competitive sports. Some athletes
of these particular sports and competitive athletes may choose not to reveal their consumption
of cannabis and alcohol, even if the anonymity and the confidentiality are guaranteed, leading
to an underestimation of their consumption (Laure, 1997). In fact, the associations between
cannabis use, alcohol consumption and sports injuries may be higher. This methodological
problem is permanent when we focus on uses of illegal substances (Lentillon-Kaestner & Ohl,
2010).
Second, this study was limited by its retrospective data collection concerning sports
injuries. In the questionnaires completed by the athletes, the incidence of injuries was
significantly lower than the incidence found in weekly follow-up examinations by a
physician. Comparing these two different methodologies, Junge and Dvorak (2000) found
that approximately every third moderate injury and less than 10% of mild injuries were
recalled retrospectively. The shorter the period of symptoms and the longer the duration since
the injury occurred, the more difficult to recall. Even severe injuries, such as fractures, were
not reported in the retrospective investigation.
Third, this study focused only on typical consumption patterns and not on acute alcohol
and cannabis intake. Due to these limitations, associations and not causality were determined
in this study. Regarding the negative effects of alcohol and cannabis use on psychomotor
skills and the results of Gmel, Kuendig, Rehm et al. (2009), we can hypothesize that alcohol
and cannabis increase the risk and severity of sports injuries. In future studies, it would be
interesting to distinguish typical consumption patterns and acute intake for cannabis and
alcohol consumption, where acute intake is defined as consumption six hours or fewer before
sports-related injuries (Gmel, Kuendig, Rehm, et al., 2009).
Some previous research concluded that the number and severity of sports injuries could
decrease by focusing on elements other than risk-taking behaviors: using protective
equipment, safe playing conditions (e.g., field surfacing, maintenance), development and
enforcement of safety rules, etc. (NSKC, 2004). The effectiveness of stretching before
participation in athletic activities was also discussed as an injury prevention method;
according to the review of literature of Thacker, Gilchrisit, Stroup and Kimsey (2004), there
is not sufficient evidence to endorse or discontinue routine stretching before and after
exercise to prevent injury among competitive or recreational athletes. Our study showed that
risk-taking behaviors, mostly alcohol consumption, should be taken into account as factors
that can prevent sports injuries. The increase and improvement of prevention on risk-taking
behaviors in sports could help to reduce the number and severity of injuries in sports played
on an amateur level.
Sports Injuries and Risk-Taking Behaviors in Amateur Athletes
155
ACKNOWLEDGMENTS
We would like to thank the Federal Office of Public Health (OFSP) in Switzerland for its
support of this study and all participating athletes, principals and teachers.
Chapter edited by the American Journal Experts (AJE) (cf. certificate)
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In: Athlete Performance and Injuries
Editors: João H. Bastos and Andreia C. Silva
ISBN 978-1-61942-658-0
© 2012 Nova Science Publishers, Inc.
Chapter 7
ORAL GLYCOSAMINOGLYCANS
FOR 8-WEEK RECOVERY OF FUNCTIONAL
ABILITIES IN PROFESSIONAL MALE ATHLETES
AFTER KNEE INJURY
Sergej M. Ostojic, Senka Rendulic-Slivar, Marko Stojanovic,
Igor Jukic, Kemal Idrizovic and Boris Vukomanovic
Center for Health, Exercise and Sport Sciences, Belgrade, Serbia
ABSTRACT
The use of different glycosaminoglycans (GAGs; e.g. glucosamine salts, chondroitin
sulfates, hyaluronan) is a common practice among athletes at all ages and levels of
participation, with GAGs promoted as chondroprotective and therapeutic agents for
musculoskeletal healing. Yet, the effectiveness of different common GAGs intake after
acute joint injury in high-performance athletes is yet to be determined. The main aim of
the present chapter was to present the effects of eight-week of oral glucosamine chloride,
chondroitin sulfate and hyaluronic acid administration on the functional ability and the
degree of pain intensity in competitive male athletes after acute knee injury. This research
was a randomized, double-blind parallel trial of glucosamine chloride (1500 mg per day),
chondroitin sulfate (1500 mg per day), hyaluronic acid (90 mg per day) or a placebo
administration for 2 months, utilising 218 patients with an acute knee injury. Pain at rest
and while walking and functional ability (e.g. passive knee flexibility, degree of knee
swelling) were evaluated at the beginning of the study and every second week thereafter
for the study duration. No significant differences were found between the experimental
protocols in mean pain intensity scores for resting and walking, and degree of knee
swelling during the study (p > 0.05). There were no significant differences for passive
knee flexibility between the groups at the 14-day and 28-day assessment (p > 0.05). After
6 weeks of treatment the patients supplemented with glucosamine chloride demonstrated
significant improvement in both knee flexion and extension as compared to other
experimental protocols (p < 0.05). The findings of the present study indicate that
administration with GAGs does not significantly alter pain score or degree of swelling
after acute sports injury of knee. Yet, glucosamine chloride supplementation appears to
160
Sergej M. Ostojic, Senka Rendulic-Slivar, Marko Stojanovic et al.
be suitable as a flexibility improvement strategy in athletes after 6 weeks of treatment. In
prescribed doses GAGs do not induce any acute side-effects.
Keywords: Knee injury; Flexibility; Placebo; Glucosamine; Chondroitin; Knee swelling
INTRODUCTION
The management of joint repair in people with different articular conditions is often
facilitated by several glycosaminoglycans (GAGs; e.g. glucosamine salts, chondroitin
sulfates, hyaluronan) that are found in human cartilage, as a way to relieve pain and increase
range of motion [Braham et al. 2003]. GAGs are thought to prevent the breakdown of
cartilage and stimulate the production of cartilage, although the exact biochemical mechanism
is not known [Bassleret al. 1993; Axe & Shields 2005; Cibere et al. 2005; Waddell 2007].
Several clinical studies have shown that GAGs are better than a placebo and equal to but not
better than non-steroidal anti-inflammatory drugs to reduce pain and increase range of motion
in osteoarthritis patients [Crolle & D'Este, 1980; Drovanti et al. 1980; Muller-Fassbender et
al. 1994; Noack et al. 1994; Houpt et al. 1999; Reginster et al. 2001; Braham et al. 2003;
Kelly et al. 2004]. Others have also reported that the majority of improvements are present
after twelve weeks of treatment or more [Houpt et al. 1999; Delafuente 2000; Poolsup et al.
2005], with the usual doses of different GAGs are thought to be safe in the short-term (up to
12 weeks) while long-term safety is not known. However, the methods used in these studies
have been highly criticized and the results may be overstated due to insufficient subject
numbers, low dosage and duration of administration and the lack of inclusion of functional
tests [McAlindon et al. 2000]. Several studies [Buckwalter 2003; Haspel et al. 2006; Maroon
et al. 2006] have shown that use of GAGs are common practice among athletes at all ages and
levels of participation. However, there is little if any evidence currently available to support
claims about anti-inflammatory, analgesic or protective effects of different GAGs in the
athletic environment [Gorsline & Kaeding 2005]. Therefore, the main aim of the present
study was to examine the effects of eight-week of oral glucosamine chloride, chondroitin
sulfate and hyaluronic acid administration on the functional ability and the degree of pain
intensity in competitive male athletes after acute knee injury.
MATERIALS AND METHODS
Subjects
Patients were eligible to participate in the study if they had a recent history of acute
sports injury of the knee and had clinical findings consistent with trauma. Acute sports injury
was defined as direct or indirect trauma an athlete incurred in any sport-related activity that
caused absence from training or from match. During the 2009 season (from February to
December) subjects (professional athletes) were recruited and examined by certified sports
medicine specialist in the out-patient clinics of the Center for Health, Exercise and Sport
Sciences in the first 24 hours after injury was sustained. Clinical findings were graded
Oral Glycosaminoglycans for 8-Week Recovery …
161
according to modified Outerbridge classification [Fu et al. 1994]. Female athletes, patients
who had been treated earlier with GAGs, who were not ambulatory, or who had clinical
findings classed as more severe than grade II were excluded from the study. All subjects gave
their informed consent and volunteered to participate in the study that had the approval of the
local IRB. At the first assessment session, participants were fully informed verbally and in
writing about the nature and demands of the study as well as the known health risks. They
completed a health history questionnaire, and were informed that they could withdraw from
the study at any time, even after giving their written consent. All subjects were in good health
(free from diabetes, heart disease, musculoskeletal dysfunction, cancer, and smoking),
participating in consistent training (average of 12 hours per week) for the past five or more
years, and not currently taking a drug or dietary supplement that contained GAGs (or any
similar preparation).
Experimental Procedures
The subjects were allocated to a double-blind design to four randomly assigned trials.
During the period of 8 weeks subjects in the glucosamine group (GLU) ingested tablets that
contained glucosamine chloride at a dose of 1500 milligrams per day (in three divided doses
of 500 mg each); subjects in the chondroitin group (CHO) ingested tablets that contained
chondroitin sulfate at a dose of 1500 milligrams per day (in three divided doses of 500 mg
each); subjects in the hyaluronic group (HYA) ingested tablets that contained hyaluronic acid
at a dose of 90 milligrams per day (in three divided doses of 30 mg each), while subjects in
the placebo group (PLA) ingested an equal number of identical looking tablets that contained
cellulose. During the administration period all subjects refrained from training. No other
interventions were made. Participants were evaluated at the beginning of the study and at
every second week thereafter. Baseline testing was performed prior to administration, and
subjects were instructed not to change their current dietary habits. Pain intensity was assessed
using a visual analog scale [Flandry et al. 1991; Flaherty 1996]. Participants completed two
visual analog assessments at each visit, one representing pain intensity while at rest, and the
other representing pain while walking. Passive knee flexibility (flexion and extension) of the
injured limb was measured using a modified goniometer with spirit level (Creative Health
Inc., Plymouth, CA, USA). The range of motion was assessed according to Bull & Amis
[1998], with full knee extension defined at 180 degrees in this case. The degree of knee
swelling was measured and compared with the non-injured limb according to Mendelsohn &
Paiement [1996]. In order to assess potential side effects to the supplementation regimen, all
subjects were instructed to report any adverse effects of supplementation (e.g. nausea,
vomiting, gastrointestinal upset, cramps, headache, bloating, dry mouth, tenderness in knee)
at every visit to the Center.
Statistical Analyses
The data are expressed as means ± standard deviation. A one-way ANOVA with repeated
measures was used to analyze the data. Where appropriate, post hoc tests (paired t test with
Bonferroni corrections) were used to determine the location of any significant differences. A
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Sergej M. Ostojic, Senka Rendulic-Slivar, Marko Stojanovic et al.
significance level of p < 0.05 was considered to be statistically significant. The data were
analyzed using the statistical package SPSS for Windows version 14.0 (SPSS Inc., Somers,
NY, USA).
RESULTS
Although 218 participants were enrolled in the study, 26 patients (9 patients in the GLU
group, 5 patients in the CHO group, 5 patients from HYA group and 7 patients in the PLA
group) were lost due to follow up and were not included in the analysis. Data were analyzed
on 192 patients, 50 in GLU group, 48 in CHO group, 42 in HYA group and 52 in PLA group.
Table 1. Demographic and pre-administration characteristics of patients
Treatment
GLU
50
24.6 ± 3.7
7.0 ± 2.3
180.2 ± 6.3
74.8 ± 5.6
Number of men
Age (years)
Professional experience (years)
Height (cm)
Weight (kg)
Pain intensity scores
Resting
3.2 ± 1.8
Walking
5.2 ± 2.0
Knee flexibility (degrees)
Flexion
119.1 ± 22.5
Extension
143.8 ± 28.1
Degree of swelling (%)
10.2 ± 2.5
Note Values are shown as mean ± SD.
CHO
48
23.9 ± 4.3
6.1 ± 3.0
181.5 ± 7.2
75.1 ± 4.9
HYA
42
23.7 ± 2.9
6.4 ± 2.8
182.4 ± 5.6
76.4 ± 7.0
PLA
52
24.8 ± 4.1
7.1 ± 2.2
179.5 ± 7.1
75.3 ± 6.2
3.0 ± 1.7
5.3 ± 1.9
3.0 ± 2.0
4.9 ± 2.1
3.3 ± 1.9
5.1 ± 2.2
118.7 ± 19.6
145.3 ± 26.7
10.7 ± 1.9
120.2 ± 20.3
140.4 ± 19.6
10.0 ± 3.4
115.8 ± 21.8
141.2 ± 25.6
9.8 ± 2.9
Table 2. Scores of pain intensity as measured with a visual analog scale
Treatment
GLU
(n = 56)
CHO
(n = 48)
HYA
(n = 42)
PLA
(n = 52)
Week 2
Resting
3.0 ± 2.0
3.1 ± 1.7
3.0 ± 1.9
3.0 ± 2.2
Walking
4.8 ± 2.1
4.8 ± 1.9
4.6 ± 1.4
4.8 ± 2.3
Week 4
Resting
2.8 ± 1.9
3.0 ± 1.5
2.7 ± 1.7
2.9 ± 1.8
Walking
4.5 ± 2.1
4.6 ± 1.9
4.5 ± 1.3
4.6 ± 2.0
Week 6
Resting
2.5 ± 1.8
2.6 ± 1.3
2.5 ± 1.3
2.6 ± 2.0
Walking
4.0 ± 1.9
4.2 ± 2.0
4.1 ± 2.0
4.3 ± 2.1
Week 8
Resting
2.1 ± 1.7
2.3 ± 1.1
2.2 ± 1.8
2.1 ± 1.9
Walking
3.7 ± 2.1
3.8 ± 2.0
3.7 ± 1.9
4.1 ± 1.8
Note Values are shown as mean ± SD.
Score of 0 indicated "no discomfort" while score of 10 indicated "severe discomfort".
Oral Glycosaminoglycans for 8-Week Recovery …
163
Table 3. Range of motion (degrees) of the injured knee during the study
Treatment
GLU
(n = 56)
CHO
(n = 48)
HYA
(n = 42)
Week 2
Knee flexion
124.1 ± 20.3
125.7 ± 19.7
122.4 ± 17.3
Knee extension
151.3 ± 22.5
147.8 ± 24.0
148.6 ± 19.2
Week 4
Knee flexion
127.4 ± 19.6
126.9 ± 22.4
125.5 ± 20.2
Knee extension
157.3 ± 22.5
152.1 ± 17.8
150.9 ± 23.4
Week 6
Knee flexion
137.3 ± 20.2 †
128.9 ± 18.6
129.7 ± 19.9
Knee extension
163.8 ± 21.1 †
155.0 ± 20.7
152.8 ± 27.3
Week 8
Knee flexion
143.4 ± 18.7 †
133.7 ± 25.1
138.0 ± 14.6
Knee extension
174.2 ± 24.1 †
159.4 ± 21.3
161.1 ± 22.2
Note Values are shown as mean ± SD.
† Indicates significant difference as compared to other groups at p < 0.05.
PLA
(n = 52)
120.1 ± 24.3
144.9 ± 26.1
123.1 ± 21.0
151.4 ± 22.3
127.3 ± 17.1
154.1 ± 22.8
130.1 ± 21.4
160.3 ± 27.2
Figure 1. Degree of knee swelling between the groups during the study.
Demographic and pre-administration characteristics were shown in Table 1. There were no
differences in demographic data, baseline functional and clinical tests between groups (p >
0.05). No statistically significant differences were found between the groups in mean pain
intensity scores for resting and walking after 2, 4, 6 and 8 weeks of intervention (Table 2, p >
0.05). There was also no significant difference between passive knee flexibility (both flexion
and extension) at the 14 and 28-day assessment (Table 3, p > 0.05). However, we found a
significant difference between GLU and other experimental groups after 6 and 8 weeks of
treatment, with a significantly improved knee flexion and extension in the glucosamine group
(p < 0.05). When we analyzed the degree of knee swelling we found no differences between
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Sergej M. Ostojic, Senka Rendulic-Slivar, Marko Stojanovic et al.
the two groups during the study (p > 0.05, Fig. 1). No subject reported any untoward side
effects from the GAGs or placebo administration.
DISCUSSION
In comparison with other investigations, we believe the present study provides the well
controlled, direct analysis of effects of different oral GAGs on functional ability and pain
scores after sports injury in competitive male athletes. The results of the present study
indicate that GAGs administration for eight weeks in male professional athletes had little
effects on recovery of acute knee injury.
In the context of knee pathology management, several GAGs (sole substances or coadministered with other agents) have been promoted as analgesic, anti-inflammatory and
regenerative agents [Crolle & D'Este 1980; Barclay et al. 1998; Braham et al. 2003;
McAlindon & Biggee 2005]. The mechanism of action of GAGs is not clear, but it is
hypothesized that may inhibit lysosomal enzymes and may stimulate proteoglycan synthesis
[Bassler et al. 1993; Setnikar et al. 1993; Deal & Moskowitz 1999; Waddell 2007]. Recent
studies have indicated that GAGs can provide relief from arthritic pain related symptoms
[Houpt et al. 1999; Pavelka et al. 2002; McAlindon et al. 2004]. Since the condition of
arthropathy is manifested by degeneration of the joints in the body, glucosamine, chondroitin
and hyaluronan as constituents of GAGs, could regenerate and reconstruct damaged cartilage
[Noack et al. 1994]. Pavelka et al. [2002] claimed that long-term treatment with GAGs
retarded the progression of knee osteoarthritis, possibly determining disease modification.
Braham and co-workers [2003] suggested that GAGs supplementation may result in
decreased pain ratings and self reported improvements in functional ability of subjects
suffering from chronic knee pain. However, McAlindon and co-workers [2000] concluded
that different GAGs are only moderately effective for improving outcomes in knee
osteoarthritis but the magnitude of effect is unclear because of inconsistencies in study
methods and dependence on industry support for study execution. Small sample size, short
trial duration, lack of randomization of subjects, absence of double-blinding, and use of
hospitalized patients rather than free-living subjects have raised questions about the reliability
and validity of the results [Poolsup et al. 2005]. The results of our study are not in accordance
with findings of other investigators who have suggested that GAGs provides some degree of
pain relief and decrease inflammation to subjects who experienced cartilage damage [Houpt
et al. 1999; Reginster et al. 2001]. We found no improvement in degree of pain intensity
between placebo and intervention groups both at rest and while walking or degree of swelling
of injured limb. Disagreement between the results of our study and results of previous
investigators could be due to several factors. In this study, subjects tended to be younger with
acute minor sports injury of knee suggesting that our patients had less pronounced
artrhopathy, inflammation and/or cartilage damage. Older patients with osteoarthritis may
have more damage to their cartilage and their cartilage could be more responsive to the
effects of GAGs [Drovanti et al. 1980; Noack et al. 1994; McAlindon 1999; Christgau et al.
2004]. The majority of acute knee injuries presenting to the sports physician result from a
direct blows to the joint or from indirect trauma leading to damage of osseous, muscular,
tendinous, ligamentous or cartilaginous tissue [Ostojic 2004; Stojanovic & Ostojic 2011].
Oral Glycosaminoglycans for 8-Week Recovery …
165
Since the positive effects of GAGs are mainly related to joint cartilage damage it could be
postulated that GAGs could be effective only if major sport injuries are present with severe
harm of cartilage [Ostojic et al. 2007]. Minor knee injuries (less that grade II according to
Outerbridge classification) analyzed in the present study seem to be less responsive to GAGs
administration. Furthermore, our results could be due to the time period of treatment was
insufficient to effect pain ratings in our patients. Other investigators have found improvement
in pain ratings after more than 6 weeks of treatment with significant improvement at eight
weeks but not in the preceding weeks [Muller-Fassbender et al. 1994; Reginster et al. 2001;
Poolsup et al. 2005]. Additional studies are necessary to examine the analgesic and antiinflammatory effects of GAGs treatment in elite and recreational athletes of any age with
longer duration of treatment and different types and severity of sports injuries.
GAGs are often cited as stiffness-reducing agents which could improve articular
flexibility in patients with degenerative joint conditions [Barclay et al. 1998; da Camara &
Dowless, 1998; Pavelka et al. 2002]. No published reports compare the effectiveness of
different GAGs at improving the range of motion with patients suffering from acute articular
injury. According to results of our study, there were no marked changes in passive knee
flexibility (flexion and extension) of the injured limb after 14 and 28 days of treatment with
administration of different GAGs as compared to placebo. However, after 6 weeks, the knee
flexion and extension are significantly improved in glucosamine group. Improved flexibility
after six weeks of glucosamine treatment following acute knee injury could be of particular
interest in athletic environment. Regaining adequate range of movement (or absence of
stiffness) after traumatic injury is an important factor for physical performance along with
prevention of reinjury. Since glucosamine is incorporated into proteoglycans, which could
attract water into the joint space, enhanced articular flexibility after administration of
glucosamine could be due to increase of lubrication of the cartilage during movement
[McCarty 1998; Ostojic et al. 2007].
The most obvious limitation of this study was the subjective and non-discriminatory
injury scoring system which is not overly sensitive. Low reliability reported for the measures
of range of motion and knee swelling [Wood et al. 2006] along with large standard deviation
of presented results could have account for some of the non-significant findings. Therefore,
future research should use advanced evaluation procedures (e.g. CT, MRI) to investigate and
quantify the effects of glucosamine consumption on acute or overuse sports injuries. In
addition, the present study did not analyze the nature, etiology and severity of knee injury,
factors that could affect the efficacy of this preparation [Gorsline & Kaeding 2005].
Treatment with GAGs or placebo only, without other interventions made (e.g. cryomassage,
physical therapy, proprioception exercise), could account for another possible limitation of
the present study. It seems that GAGs administration coupled with a standard treatment
protocol and rehabilitative training program may be necessary to determine if
glycosaminoglycans have a considerable regenerative effect in active persons. Moreover,
further investigation will be greatly improved if some biochemical markers of
glycosaminoglycans metabolism, collagen degradation or proteoglycan synthesis have to be
measured in this particular population.
Subjects in our study reported no acute side effects, yet caution should be used before
recommending widely GAGs to athletes. Both animal and human studies reported different
occasional mild side effects (e.g. gastrointestinal discomfort, hyperglycaemia, headache, knee
sensitivity) of GAGs administration with fewer adverse effects than common pain-relieving
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Sergej M. Ostojic, Senka Rendulic-Slivar, Marko Stojanovic et al.
medications (e.g. non-steroidal anti-inflammatory drugs) [Muller-Fassbender et al. 1994; Deal
& Moskowitz 1999; Delafuente 2000; Waddell 2007]. Several authors [Crolle & D'Este,
1980; McAlindon 1999; Reginster et al. 2001] concluded that GAGs were well tolerated
throughout the administration and they could be treated as safe and nontoxic agents which is
in accordance with results of our study. Moreover, it is unclear whether the addition of other
agents (e.g. sulfur, methylsulfonilmethane, plant extracts) to GAGs would have influenced
the outcome of this study. In addition to glucosamine, the synthesis of glycosaminoglycans
requires a substantial amount of sulfate and it is known that sulfate depletion leads to a
decrease in glycosaminoglycan synthesis so the sulfate found in glucosamine sulfate may be
an important element in the efficacy of this preparation [van der Kraan et al. 1988]. It is not
clear is there any positive effect of combination treatment in athletes after acute sports injury.
CONCLUSION
The main advantages of the present study includes the use of experienced competitive
athletes, controlled and comparable conditions for all subjects during the study and a doubleblind, placebo-controlled design. Nonetheless, it is apparent that GAGs ingestion had
minimal benefit for the relatively small sample of individuals in our study. The findings of
the present study indicate that administration with GAGs does not significantly alter pain
score or degree of swelling after acute sports injury of knee. Yet, glucosamine chloride
supplementation appears to be suitable as a flexibility improvement strategy in athletes after
six weeks of treatment. In prescribed doses GAGs do not induce any acute adverse effects.
ACKNOWLEDGMENTS
The authors wish to thank dedicated group of subjects who made this project possible,
and Limax Belgrade for providing intervention at no cost. The study was supported in part by
a grant from Serbian Ministry of Science (Grant No. 175037).
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In: Athlete Performance and Injuries
Editors: João H. Bastos and Andreia C. Silva
ISBN 978-1-61942-658-0
© 2012 Nova Science Publishers, Inc.
Chapter 8
EVOLUTION OF THE ACHILLES TENDON IN BIPEDAL
LOCOMOTION: ADVANTAGES AND FLAWS
B. Tucker1 and W. S. Khan*2
1
2
University College London Medical School, Gower Street, London, UK
University College London Institute of Orthopaedics and Musculoskeletal Sciences,
Royal National Orthopaedic Hospital, Stanmore, Middlesex, UK
ABSTRACT
The Achilles tendon is a key structure separating humans from other primates,
allowing the upright bipedal stance. There are many advantages to being a biped from
hunting ability to energy expenditure. The Achilles tendon itself has the benefit of greatly
enhancing endurance running. However, there are disadvantages to having an Achilles
tendon such as its vulnerability to injury. This article outlines the advantages and
disadvantages of the tendon and highlights some theories as to why humans may have
evolved to have it.
Keywords: Achilles tendon; evolution; bipedal stance; injury
The Achilles tendon plays a major part in modern human bipedalism. The evolution of
the Achilles tendon is not well known but there are many theories of the origin of bipedalsim.
This article outlines the advantages and flaws of this form of locomotion in relation to the
Achilles tendon. The Achilles tendon is the common tendon of the soleus and gastrocnemius.
It attaches these muscles to the calcaneus bone of the heel. Its key role is therefore in
elevation of the heel. The effect of the Achilles tendon is to lift the whole body weight and
it’s therefore the toughest and strongest of human tendons (Palastanga et al. 2006).
*
Corresponding Author: Mr Wasim S Khan, Clinical Lecturer, University College London Institute of
Orthopaedics and Musculoskeletal Science, Royal National Orthopaedic Hospital, Stanmore, Middlesex,
London, HA7 4LP, UK. Telephone number: +44 (0) 7791 025554; Fax number: +44 (0) 20 8570 3864; E-mail
address: wasimkhan@doctors.org.uk
170
B. Tucker and W. S. Khan
The Achilles tendon is absent in Australopithecus and modern apes (Bramble et al. 2004).
It is likely to have originated in Homo over 3 million years ago. The Achilles tendon in
humans is very long, making about 65% of the total muscle length. In quadrapedal animals,
including gorillas orang-utans and chimpanzees (Channon et al. 2009), this tendon is a lot
shorter, too short to store any elastic potential. In both humans and apes the attachment is at
mid-level to the calcaneus. Therefore at some point humans have evolved to have a much
longer tendon that allows them to move on their hind legs. Apes can stand on two legs but
choose to walk on four. Their tendon must therefore be strong enough to stabilise them whilst
doing this and to hold their weight.
COMPOSITION
In a healthy state a tendon is composed of 70% water and 21% collagen, of which 95% is
type I collagen providing tensile strength, and a small amount of elastin. Collagen type III and
collagen type V are also present: types III and V help to regulate the diameter of the collagen
type I fibril. The flexibility of the tendon is obtained by allowing the collagen fibrils partial
independence. The collagen fibres have a wavy course which also contributes to their
flexibility. This composition of the Achilles tendon varies to that of an ape, for example. The
tendon in apes does not contain as much elastin, reducing its elasticity recoil property which
is essential for efficient running. The ape tendon is stiffer allowing one-off propulsive
movements, needed for moving their body weight up trees (Woo et al. 2007).
EXERCISE
It is shown that exercise strengthens a tendon by causing the synthesis of type I collagen
and increases the cross-links of the collagen fibrils. Exercise increases the diameter of
collagen fibres allowing them to withstand greater tensile forces as they contain a higher
number of intrafibrillar covalent cross-links (Palastanga et al. 2006). The development of the
Achilles tendon may be due to the load placed on the Achilles tendon when an ape stands up.
This will increase collagen production making the tendon bigger and stronger. Whilst wading
through a lake, for example, the force of the water would promote the upright position and
this will have to be maintained, again making the tendon stronger. As this happens over time
the tendon will be getting bigger and become more able to hold these forces. The advantages
of standing and moving on two feet will increase the frequency at which the tendon is being
used until the tendon is strong enough to hold the body upright constantly. Early humans had
a much shorter Achilles tendon than we do now, this would not have had much effect on their
walking, but running would be greatly affected (Crompton et al. 2008). Efficient running is
needed to move from a herbivorous diet to a carnivorous diet where hunting skills are needed.
Evolution of the Achilles Tendon in Bipedal Locomotion
171
THE ORIGIN OF BIPEDALISM
It has been suggested that humans evolved from an arboreal primate, suggesting the
upright posture originated in trees rather than on the ground (Meldrum et al. 2004). There are
many theories suggesting the origin of bipedalism, including the postural feeding hypothesis
in which apes stood on two feet to pick fruit from trees (Hunt 1996). The behavioural model
suggests the male will hunt and bring food back to the female and offspring. If they are better
at bipedal locomotion they will bring back more food and the offspring will survive (Lovejoy
1981). Another theory is that the pressure of a hot climate forced the upright stance as this
causes greater heat loss and decreased heat gain. Standing upright will put the body higher up
where the air will be cooler and the amount of direct sunlight onto the skin will be reduced
(Wheeler 1991). There is an adaptationist hypothesis that the reduced energy cost of upright
walking would have provided advantages by decreasing the energy cost of foraging.
Bipedalism uses less energy than quadrupedal knuckle-walking (Leonard et al. 1995).
Animals often stood on two feet to appear a threat to other animals; another theory suggests
bipedalism is an extension of this (Fleagle 1999).
ADVANTAGES OF BIPEDALISM
The change from quadrapedal to bipedal locomotion is largely attributed to the change of
the Achilles tendon, as well as other physical factors such as a narrower hip, straighter back
and others (Leonard et al. 1995). Bipedal locomotion has many benefits over quadrapedal
locomotion such as having the upper limb free to carry things. Although bipedalism isn’t
what freed the hands for activities, sitting was, it did allow things to be carried whilst moving
(Meldrum et al. 2004). This has allowed humans to carry food away from the site it was
found in, to a safer location. This will stop the risk of predators whilst eating and will inhibit
other animals from eating their food. They can also carry water, allowing them to hunt in dry
land, which was previously inaccessible (Aiello et al. 1990). As well as carry water they
could carry their young allowing early humans to travel further for food. As the human brain
developed humans began to use their hands to make and carry things such as tools to help
with hunting. The upright stance of bipedalism means they can walk through deeper rivers
and can see over long grass and bushes. All of these will increase their chances of catching
prey and they should not go without food. Being highly nutritional will increase strength
allowing them to further their hunting and other skills.
Being bipedal minimises the amount of direct sunlight onto the skin at midday reducing
the chance of heat stress especially on the brain. Respiration in quadrupeds is linked to upper
limb activity, whereas for humans it is not. This means our respiration is independent of our
speed, allowing us to run long distances by reducing the problems associated with thermal
stress and heat exhaustion (Meldrum et al. 2004).
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ENERGY EXPENDITURE
In order to stand upright the centre of gravity must fall into the rectangle formed by the
feet. The posture muscles must maintain balance to keep the centre of gravity in this
rectangle. In humans this can be done by the iliofemoral ligament and the cruciate ligaments
of the knees. Due to the small parameters of the body, only small adjustments need be made.
This reduces the oxygen consumption needed to maintain an upright posture. A quadruped
has a much larger rectangle in which its centre of gravity must fall into, which makes it easier
to remain upright. However, the legs of a quadruped must be kept flexed to stand, this
requires continuous muscle activity. Bipedal standing therefore has smaller oxygen
consumption than quadrupedal standing. There is a lot more energy expenditure by a
chimpanzee on two feet than a bipedal human. The bent-legged chimpanzee is very inefficient
because it requires constant use of the hamstrings, the quadriceps and the gluteal muscles to
keep the hip and knee joint from collapsing under the weight of the body (Aiello et al. 1990).
This shows our bipedal posture is very energy efficient.
ENDURANCE RUNNING
The Achilles tendon has reinvented the way we move, allowing us to maintain an upright
posture. Compared to other animals, which do not have Achilles tendons and are quadrapedal,
we have a much lower top running speed. This may be due to the decreased muscle mass.
However, humans can sustain running over much longer distances compared to other animals.
For example, in a distance race a trained human can out run a dog (Lieberman et al. 2009).
This is likely to be due to the Achilles tendon allowing us to run efficiently. It acts like a
spring to store and release energy. It has elastic potential which, when combined with the leg
muscles, can give very efficient force production (Woo et al. 2007). Stretching a tendon, as
when running, produces a store of elastic energy, since tendons have low mechanical
hysteresis, the majority of this energy is returned during the recoil. Thus saving energy that
would otherwise be needed to propel the body forward. Other primates do not have an
Achilles tendon like ours, and are poor runners. It would therefore appear that we have
sacrificed speed for endurance.
ELASTICITY OF THE TENDON
Bill Sellers designed a computer model of human running (model A) (Sellers et al. 2010).
He then made all the tendons in the body 100 times stiffer (model B). This tripled the energy
usage and almost halved their top speed. He then restored the Achilles tendon back to normal
stiffness (model C) this allowed them to run a lot faster but still not as fast as a normal human
with full elasticity in all tendons. The other design was of a human with all elastic tendons
except the Achilles tendon which remained stiff (model D). Model C ran faster than models B
or D. This shows the importance of elasticity in the Achilles tendon for efficient, highperformance running. Elasticity minimises the total energy spent by the body and increases
the power of the push-off. Cyclic tensile loading, as in running, has been shown to
Evolution of the Achilles Tendon in Bipedal Locomotion
173
significantly increase the elastic modulus compared to underused tendons (Palastanga et al.
2006). Model A ran faster than model C showing elasticity in all tendons is important and not
just in the Achilles tendon.
OTHER BIPEDAL ANIMALS
The bipedal ostrich is able to run up to 40mph for a short distance and can maintain a
speed of 30mph over long distances (Arnold 1990). A condor, on the other hand, which is
also a large bird but is not adapted to land running, can only run very slowly and only for
short distances. Ostriches have long (about 80cm), thin tendons of the gastrocnemius and
digital flexors (Rubenson et al. 2004). This allows the tendon to be more compliant than the
human equivalent, the Achilles tendon. This means to stretch the tendon by a given amount,
the ostrich will not need as much muscle force as a human would require to stretch the tendon
by the same amount. This tendon is well suited to elastic energy storage and it is this tendon
which generates most of the force needed during locomotion (Smith et al. 2006). In the
condor however, this tendon no longer exists (Raikow et al. 1979).
DISADVANTAGES OF THE ACHILLES TENDON
The structure of tendons gives them high tensile strength in the direction of the collagen
fibre. For the Achilles tendon, this will be vertically to withstand forces produced during
locomotion. This means, however, in the other direction, horizontally, it will be a lot weaker
as only a few collagen fibres will run this way, leaving the tendon vulnerable to lateral forces
(Scott 1980). Whilst running off-road may be better for the tendon in terms of stress, it also
gives uneven terrain which places greater shear loads onto the tendon which will act sideways
(Benjamin et al. 1991;Burbridge 2008).
ANATOMICAL POSITION
A number of properties of bipedal locomotion put the Achilles tendon under strain,
making it vulnerable to both sudden and chronic injuries. This includes its anatomical
position as it is the first structure to take up the impact forces involved with many activities.
The upright stance of a biped puts the foot at a right angle to the leg thus generating heavy
torque (Woo et al. 2007).
MUSCLE FUNCTIONS
The different functions of the muscles soleus and gastrocnemius, which make up the
Achilles tendon, add to its vulnerability. The soleus plantar flexes the foot and mainly acts in
posture maintenance to ensure we do not fall forward when standing; it thus contains mainly
slow twitch fibres. The gastrocnemius on the other hand, flexes the knee as well as plantar
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flexing the foot and contains mainly fast twitch fibres necessary for propulsive movements
needed during sprinting and jumping. The fibres of gastrocnemius attach to the lateral part of
the calcaneus and the fibres of soleus to the medial part. When the knee is flexed it can rotate;
as the gastrocnemius crosses the knee joint, the tendon derived from these fibres can be
twisted relative to the fibres of soleus. This causes the gastrocnemius fibres to cut into the
soleus fibres causing a ‘sawing’ action on them (Benjamin et al. 2007).
HEAT STRESS
Stretching of the tendon during running will cause the energy to be released during recoil.
Some of this energy is lost as heat. The amount of heat lost during a single stretch cycle is
small and does not affect the tendon. However, if this is done repeatedly as in running or
walking, the heat may accumulate and cause thermal damage and injury to the tendon,
predisposing the tendon to rupture. Hyperthermia may be involved in exercise related trauma
(Woo et al. 2007).
OVERUSE INJURY
During running the Achilles tendon can transmit up to seven times body weight
compared to its normal half body weight that it transmits whilst standing. Running and
standing on two legs compared to four legs increases the strain on the tendon as each one will
be carrying more weight. This will make the Achilles tendon more prone to overuse injuries.
The most common overuse injury being tendinosis (Woo et al. 2007). When walking on two
legs, injury to the Achilles tendon will have a larger negative effect than when walking on
four legs. If one leg is injured, for a biped this is 50% of their legs which would have a greater
effect than 25% as would be seen in a quadruped. The hypovascularity of the Achilles tendon
means it has a low healing capacity and therefore takes a long time to repair itself (Benazzo et
al. 2000;Benjamin et al. 1991). This lack of blood flow and oxygen predisposes the tendon to
injury (Woo et al. 2007).
There are a number of intrinsic properties of the Achilles tendon which makes it more
susceptible to chronic overuse injuries. For example, the Achilles tendon attaches medially to
the subtalar joint axis, producing a supinatory force. Excessive pronation will cause internal
tibial rotation, moving the tendon medially. There may be supination of the subtalar joint, to
counteract this. This force can cause microtears in the Achilles tendon. This further decreases
the blood flow to the tendon, over time this can lead to degeneration (Burbridge 2008).
IMMOBILISATION
Immobilsation of the tendon during a sedentary lifestyle causes it to atrophy (Woo et al.
2007). Both the density and size of the collagen fibres decreases causing impaired tensile
strength. When the tendon is then used for unaccustomed exercise, it is very prone to rupture
as it has lost its strength. It therefore appears the Achilles tendon has to be used to the correct
Evolution of the Achilles Tendon in Bipedal Locomotion
175
degree. If it is overused it is prone to overuse injuries such as tendinosis and if it is not used it
becomes prone to rupture.
AGE
Degeneration of the tendon occurs with ageing. The amount of collagen type III in the
tendon increases with age which causes a decrease in collagen fibril diameter. The amount of
collagen crosslinks increases (Benjamin et al. 1991), increasing the stiffness and decreasing
the elasticity of the tendon; making it more likely to rupture, as it is less resistant to tensile
forces (Woo et al. 2007). Low-load training can increase the elasticity of the tendon. Ageing
of the tendon will occur faster in a biped than a quadruped as it is being used more frequently.
CONCLUSION
The modern anatomy of a human Achilles tendon puts many intrinsic stresses onto the
structure. These can predispose the tendon to injury. It is also liable to degeneration with age.
There are precautions that can be taken to minimize the risk of injury, such as wearing
padded, correctly fitting shoes or gradually increasing exercise intensity, rather than suddenly.
A balance is required between a sedentary lifestyle in which tendon ruptures are common and
an athletic lifestyle which may lead to tendinosis. The benefits of bipedalism must be
weighed up against the probability of injury. In our opinion, the benefits we have gained
through bipedalism, such as finger dexterity and all the skills it has allowed us to develop, far
outweigh the risk of injury.
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Aiello, L. & Dean, C. 1990, "Bipedal Locomotion and the Postcranial Skeleton," in An
Introduction to Human Evolutionary Anatomy, L. Aiello & C. Dean, eds., Elsevier.
Arnold, C. 1990, Ostriches and other flightless birds Lerner Publications.
Benazzo, F., Zanon, G., & Maffulli, N. 2000, "An Operative Approach to Achilles
Tendinopathy", Sports Medicine and Arthroscopy Review, vol. 8, no. 1.
Benjamin, M., Theobald, P., Suzuki, D., & Toumi, H. 2007, "The Anatomy of the Achilles
Tendon," in The Achilles Tendon, N. Maffulli & L. Almekinders, eds., Springer.
Benjamin, M., Tyers, R., & Ralphs, J. 1991, "Age-related changes in tendon fibrocartilage", J
Anat, vol. 179, pp. 127-136.
Bramble, D. & Lieberman, D. 2004, "Endurance running and the evolution of Homo",
Nature, vol. 432, no. 7015, pp. 345-352.
Burbridge, P. 2008, "Clinical presentation, diagnosis, treatment and rehabilitation of Achilles
Tendon injury", Podiatry Now, vol. 11, no. 11.
Channon, A., Guenther, M., Crompton, R., & Vereecke, E. 2009, "Mechanical constraints on
the functional morphology of the gibbon hind limb", Journal of Anatomy, vol. 215, no. 4,
pp. 383-400.
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Crompton, R., Vereecke, E., & Thorpe, S. 2008, "Locomotion and posture from the common
hominoid ancestor to fully modern hominins, with special reference to the last common
panin/hominin ancestor", Journal of Anatomy, vol. 212, no. 4, pp. 501-543.
Fleagle, J. 1999, "Hominids, The Bipedal Primates," in Primate Adaptation and Evolution, 2
edn, Elsevier.
Hunt, K. 1996, "The postural feeding hypothesis: An ecological model for the evolution of
bipedalism", South African Journal of Science, vol. 92, no. 2, pp. 77-90.
Leonard, WR. & Robertson, ML. 1995, "Energetic efficiency of human bipedality", American
Journal of Physical Anthropology, vol. 97, no. 3, pp. 335-338.
Lieberman, D., Bramble, D., Raichlen, D., & Shea, J. 2009, "Brains, Brawn and the Evolution
of Human Endurance Running Capabilities," in The First Humans – Origin and Early
Evolution of the Genus Homo, F. Grine, J. Fleagle, & R. Leakey, eds..
Lovejoy, C. 1981, "The Origin of Man", Science, vol. 211, no. 4480, pp. 341-350.
Meldrum, D. & Hilton, C. 2004, From Biped to Strider: The emergence of Modern Human
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Palastanga, N., Soames, R., & Field, D. 2006, Anatomy and Human Movement: Structure and
Function, 5 edn, Butterworth-Heinemann.
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Ancestral Muscle in the Bowerbird Assemblage", The Condor, vol. 81, no. 2, pp. 203206.
Rubenson, J., Heliams, D., Lloyd, D., & Fournier, P. 2004, "Gait selection in the ostrich:
mechanical and metabolic characteristics of walking and running with and without an
aerial phase.", Proc Biol Sci, vol. 271, no. 1543, pp. 1091-1099.
Scott, J. 1980, "Collagen--proteoglycan interactions. Localization of proteoglycans in tendon
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Sellers, W., Pataky, T., Caravaggi, P., & Crompton, R. 2010, "Evolutionary Robotic
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Anatomy, vol. 209, no. 6, pp. 765-779.
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Reviewed by: Dr Atif Malik, Department of Trauma and Orthopaedics, Queens Hospital,
Barking Havering and Redbridge NHS Trust, Romford, Essex, UK
In: Athlete Performance and Injuries
Editors: João H. Bastos and Andreia C. Silva
ISBN 978-1-61942-658-0
© 2012 Nova Science Publishers, Inc.
Chapter 9
PATELLOFEMORAL SYNDROME
A. Yetkil1, W. S. Khan21 and P. Pastides2
1
2
University College London Medical School, London, UK
University College London Institute of Orthopaedics and Musculoskeletal Sciences
Royal National Orthopaedic Hospital, Stanmore, Middlesex, UK
ABSTRACT
Patellofemoral pain syndrome (PFPS), an injury frequently observed in runners and
is a very common presentation to sports medicine clinics. Although the exact cause is still
unclear, the development of PFPS is almost certainly multifactorial. Disruption to the
physiological tracking of the patella due to overuse, muscular imbalance or injury may
changes the biomechanics of the joint and result in the development of PFPS. The focus
of this work will be to examine the suspected aetiology of PFPS and then suggest how the
recent evidence based exercise therapies in particular can be included to alleviate the pain
and allow return to a normal level of activities.
Keywords: Patellofemoral syndrome, treatment, physiotherapy
INTRODUCTION
Patellofemoral pain syndrome (PFPS), an injury frequently observed in runners and a
very common presentation to sports medicine clinics where it can constitute up to 25% of all
new running injuries [1,2]. The patient will often complain of generalised pain of gradual
onset around or behind the patella which is exacerbated by excessive loading of the knee
joint.
Running has recognised benefits on cardiovascular health and is enjoyed by all ages on a
recreational level aswell as competitively. The positive benefits to the heart however may
come at a cost to the knees. The repetitive loading of the knee joint can lead to anterior knee
pain Due to the non specific location of pain the importance of excluding other causes is
1
E-mal address: wasimkhan@doctors.org.uk
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A. Yetkil, W. S. Khan and P. Pastides
paramount before one can confidently diagnose PFPS. PFPS has been described as an enigma
[3] and is probably reflected by the varied approaches to treatment with the evidence base
lacking rigor and a limited number of randomised controlled trials [4]. There is difficulty in
defining the condition as there are many differential diagnosis to consider; anterior knee pain,
chondromalacia patella, runner’s knee and patellofemoral joint arthralgia.
The focus of this work will be to examine the suspected aetiology of PFPS and suggest
how recent evidence based exercise therapies can be used to treat this condition and allow
return to normal level of activities.
AETIOLOGY
The patella and femoral trochlea articulate to form the patellofemoral joint. This key joint
is further stabilised by the attachement of the quadriceps muscles, tendons and ligaments. The
movement brought about by the structures within the patellofemoral grove is referred to as
patella tracking.
Although the exact cause is still unclear, the development of PFPS is almost certainly
multifactorial. Disruption to the physiological tracking of the patella due to overuse, muscular
imbalance or injury may change the biomechanics of the joint and results in the development
of PFPS [2].
PFPS is thought to be an overuse injury. In lay terms is sometimes referred to as
‘runner’s knee’. Descending or climbing stairs, walking, kneeling and squatting will also
aggravate the condition and once symptoms have been established then prolonged sitting can
elicit pain due to the increased amount of knee flexion (movie-goer’s knee) [5].
Lower limb malalignment due to factors such as pes planus, pes cavus, Q Angle and
subtalar pronation have been suggested to play a role in the development of PFPS. These tend
to be measured and analysed in static positions and are therefore less predictive of injury
following functional activity [6]. It is only until recently that dynamic studies have been
conducted in runners and have shown how hip, knee and foot kinematics influence the
process of pain development [7,8]. In particular, a comparison between uninjured runners and
those with PFPS revealed that the hip abductors were weaker in PFPS runners. With
increased exertion and progression of the run, the hip adduction angle increased in those with
weaker abductors. These factors suggested that strengthening of the musculature proximally
to the knee may correct the biomechanical process occurring in PFPS and address problems
with malalignment or patella maltracking7. A more recent study by the same team examined
the specific kinematics occurring in subgroup of PFPS patients and each displayed a unique
kinematic mechanism [8]. This lends further consideration that if individual variances among
patients exist then a very personalised exercise treatment programme will be required to
target their deficiencies.
If there is an imbalance of forces on the patellar, either due to weak vastus medialis
obliquus (VMO) or excessive lateral force, then lateral patella tracking will be observed at
full extension of the knee [9]. Hence VMO retraining is considered the ‘gold standard’ of
treatment as it encourages tracking of the patella to remain within the femoral trochlea.
Patellofemoral Syndrome
179
EXERCISE TREATMENT APPROACHES
PFPS is associated with overuse so it is prudent that rest and avoidance of high impact
sports is advisable. Given that this is a common injury in athletes, who need to maintain their
physical condition, non impact aerobic sports such as swimming can still be performed. Use
of anti inflammatories to alleviate pain or associated swelling can also be recommended.
The longer term treatment goal is to address the contributing factors particularly with
regards to intrinsic factors such as biomechanical abnormalities, or extrinsic factors such as
training regimes and suitable footwear. Muscle strengthening and correction of any
abnormalities associated with pes planus, for example, will go a long way to addressing
biomechanical abnormalities However as previously mentioned, regimens need to be tailored
to individual patients but overall, the benefits of physical therapy is well established.
[4,10,11,12].
A systematic review by Crossley et al [4] showed a significant reductions in PFPS
symptoms were found with a corrective foot orthosis and a progressive resistance brace, but
there was no evidence to support the use of patellofemoral orthoses, acupuncture, low-level
laser, chiropractic patellar mobilization or patellar taping. Overall they found that
physiotherapy interventions had significant beneficial effects however non of the trials in the
literature met the criteria of a level I evidence study. .Another systematic review by Heintjes
et al in 2003 showed that there is limited evidence of the effectiveness of exercise therapy vs
no exercise in reducing pain and inconsistent data with regards to improving knee function
[13].
Conservative therapy is still the preferred approach for treatment of this syndrome.
Crossley et al [10] conducted a multicenter, randomized, double-blinded, placebo-controlled
trial involving seventy-one patients PFPS of 1 month or longer. They were randomly
allocated to a physical therapy or placebo group. A standardized treatment program consisted
of six treatment sessions, once weekly. Physical therapy included quadriceps muscle
retraining, patellofemoral joint mobilization, and patellar taping, and daily home exercises.
The placebo treatment consisted of sham ultrasound, light application of a nontherapeutic gel,
and placebo taping At 6 weeks, the physical therapy group demonstrated significantly greater
reduction in the scores for average pain, worst pain, and disability than did the placebo group.
The obvious limitation of this study is due to the large differences in treatment options, bias
may skew results. However, the magnitude of effect was large enough to outweigh any these.
Another randomised controlled trial, by Linschoten et al [14], compared an intervention
group that received a standardised exercise programme for 6 weeks tailored to individual
performance and supervised by a physical therapist to a control group, which comprised a
"wait and see" approach of rest during periods of pain and refraining from pain provoking
activities [14]. This trial was comparable to Crossley et al with regards to the duration of the
exercise programme which was for 6 weeks in both cases. After 3 months, the intervention
group showed better outcomes than the control group with regard to pain at rest, pain on
activity and function.
The target of treatment has primarily been directed at the quadriceps, while other
approaches looking at techniques such as bracing, taping, stretching and the effect of using
foot orthoses have shown inferior results [4, 14, 15].
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However, more recent research has linked this condition to impairment of the hip
musculature. Dolak et al [17] undertook a randomised trial to determine if hip strengthening
prior to functional exercises demonstrated greater improvements than quadriceps
strengthening prior to the same functional exercises in females with patellofemoral pain
syndrome (PFPS). Thirty-three females with PFPS performed either initial hip strengthening
(hip group) or initial quadriceps strengthening (quad group) for 4 weeks, prior to 4 weeks of a
similar program of functional weight-bearing exercises. Self-reported pain, function, and
functional strength were measured. Isometric strength was assessed for hip abductors,
external rotators, and knee extensors. After 4 weeks, there was less pain in the hip group than
in the quad group. From baseline to 8 weeks, the hip group demonstrated a 21% increase in
hip abductor strength, while that remained unchanged in the quad group. All participants
demonstrated improved subjective, objective function and hip external rotator strength. It is
clear that both rehabilitation approaches improved function and reduced pain however initial
hip strengthening may allow an earlier dissipation of pain than exercises focused on the
quadriceps for patients suffering with PTFS.
FUTURE DIRECTION
The gold standard of exercise treatment for PTFS is quadriceps strengthening but recent
work has highlighted the importance of surrounding structures and devices that can be
incorporated into the treatment protocols. The long term goal of building strong quadriceps
cannot be replaced but the additional techniques would be effective supplements to achieving
this. It appears that there are real benefits to addressing hip weakness prior to quadriceps
weakness before functional exercise is commenced and the use of orthoses has shown
effectiveness.
As useful as evidence based therapies are and the emergence of new evidence will be
critical in shaping future approaches it must be stressed that these interventions are useless if
not implemented correctly or patients are not compliant. The physician – patient relationship
in this setting plays a vital role beyond prescribing a set of home exercises. The competitive
athlete is a motivated individual who desires to be pushing their limits so they may disregard
advice in the interests of achieving their goal and increase their training volume by too much
too soon. At the more detrimental end, a more recreational sports person may abandon
activity altogether for fear of experiencing further pain. Future work should look closer at the
patient characteristics and their level of compliance. The study by Linschoten et al should
also encourage clinicians to use more supervised approaches where possible as this could be a
vital motivating factor and offer that much needed tailored therapy. Sophisticated technology
in the form of biofeedback offers the quantitative aspect of improving strength and muscle
activation but it is the dynamic relationship between a multidisciplinary team and the patient
in a safe environment that will offer more in terms of a patient’s long term recovery. This will
ultimately allow them to return to their full potential within a reasonable time period.
Patellofemoral Syndrome
181
CONCLUSION
The gold standard of exercise treatment for PTFS is quadriceps strengthening but recent
work has highlighted the importance of surrounding structures and devices that can be
incorporated into the treatment protocols. Non surgical treatment appears to produce good
results but regimens may need to be tailored to individual patient needs. The long term goal
of building strong quadriceps cannot be replaced but the additional techniques would be
effective supplements to achieving this. It appears that there are real benefits to addressing
hip weakness prior to quadriceps weakness before functional exercise is commenced and the
use of orthoses has shown effectiveness.
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INDEX
A
abstraction, 2
abuse, viii, 100, 101, 115, 119, 120
access, 95
achilles tendon, vii
acid, x, 2, 3, 5, 9, 13, 14, 26, 37, 57, 58, 65, 73, 77,
102, 103, 104, 107, 159, 160, 161, 168
acidosis, 58
ACTH, 13, 56, 57, 61, 68
action potential, 125, 126, 127, 128, 129, 133, 135,
139
acupuncture, 179
acute extenuating exercise, vii, viii, 100, 108, 116
acute fatigue, 59
acute infection, 63
acute lung injury, 43
acute respiratory distress syndrome, 43
acute stress, 53
AD, 35, 104, 109, 110
adaptability, 17, 31
adaptation, ix, 5, 6, 12, 15, 32, 46, 47, 48, 49, 52, 55,
56, 61, 70, 102, 123, 128, 133, 136
adaptations, viii, 5, 6, 7, 11, 27, 39, 51, 53, 57, 60,
61, 68, 70, 117, 118, 137
additives, 133
adduction, 178
adenine, 29
adenosine, 27
ADH, 12
adhesion, 2, 32
adipose, 29, 30
adipose tissue, 29, 30
adjustment, 96
administrators, 148
adolescents, 47, 146, 148, 155, 157
ADP, 10, 24
adrenaline, 57, 62, 103, 116
adrenocorticotropic hormone, 13, 68
adults, 33, 49
adverse effects, 115, 130, 132, 161, 165, 166
aerobic capacity, 17, 22
aerobic exercise, 100
aetiology, xi, 177, 178
age, 5, 6, 15, 17, 29, 43, 52, 65, 72, 83, 88, 94, 95,
124, 134, 135, 141, 146, 165, 175
age-related diseases, 29
aggression, 4
agility, 17
alanine, 11, 20, 103
alanine aminotransferase, 11, 103
albumin, 57
alcohol consumption, ix, 145, 147, 150, 152, 154,
155
alcohol dependence, 156
alcohol use, 147
aldosterone, 12
ALI, 43
allele, 21, 22, 23, 24, 25, 26, 27, 29, 30, 31, 35
ALS, 40
ALT, 11, 103, 107, 108
alters, 6
American Heart Association, 40
amine, 103
amino, 26, 27, 57, 58, 65
amino acid, 26, 27, 57, 58, 65
amplitude, 62, 125
amyotrophic lateral sclerosis, 19
anabolic steroids, 130, 140, 141
analgesic, 160, 164
anatomy, 175, 176
androgen, 133
anemia, 12, 14, 23, 35, 43, 117, 119
anger, 85
angiogenesis, ix, 23, 24, 27, 100, 112, 117, 118
angiotensin II, 22
aniridia, 21, 35
184
Index
anisocytosis, 16, 43
ANOVA, 89, 90, 91, 106, 161
anoxia, 111, 112
anterior cruciate, 97
antibody, 33
anticoagulant, 102
antidiuretic hormone, 12
antigen, 105, 112
antihistamines, 126, 130, 136
anti-inflammatory agents, 167
anti-inflammatory drugs, 160, 166
antioxidant, vii, ix, 1, 3, 4, 6, 7, 11, 14, 15, 16, 17,
18, 19, 20, 32, 34, 36, 39, 40, 41, 43, 44, 45, 47,
48, 49, 76, 100, 104, 109, 116, 117, 120, 131, 137
anxiety, 146, 153
aorta, 120
aortic stenosis, 129
apoptosis, ix, 15, 44, 100, 101, 117, 118
appetite, 56, 57
appraisals, viii, 79, 80, 86, 96
aptitude, 17
ARDS, 43
arginine, 26, 47
aromatic hydrocarbons, 13
arrhythmia, ix, 124, 126, 127, 130, 135, 136, 138,
139, 140, 141
ARRHYTHMIAS, 125, 130, 131, 133, 134, 135, 143
artery, 21, 44, 129
arthralgia, 178
arthritis, 38
ascorbic acid, 3, 104
aspartate, 10, 103
assessment, ix, x, 17, 45, 52, 83, 85, 86, 89, 93, 94,
123, 135, 159, 161, 163, 168
asymptomatic, 132
atherogenesis, 14
atherosclerosis, 6, 14, 20, 42, 120
atherosclerotic plaque, 32
athletic performance, vii, 1, 15, 16, 17, 18, 25, 28,
45, 59, 66, 118
athletic pursuits, x, 145
ATP, 9, 10, 24, 57, 73, 133, 137, 138, 140
atrioventricular block, 143
atrophy, 174
attachment, 170
authorities, ix, 100, 101, 115, 118
autoimmune disease, 29
autoimmune diseases, 29
autonomic nervous system, 56, 61
autooxidation, 9
autopsy, 105, 124, 129, 135
autosomal dominant, 41
B
bacteria, 2
basal lamina, 9
base, 22, 24, 28, 30, 45, 55, 72, 74, 81, 178
base pair, 22, 24, 28, 30
baths, 74
BD, 34, 181
beef, 43
behavioral change, 53
behaviors, vii, ix, 80, 83, 97, 145, 146, 147, 148,
150, 154
Belgium, 105
bending, 19
beneficial effect, vii, 1, 7, 118, 120, 131, 179
benefits, 21, 70, 74, 138, 146, 167, 171, 175, 177,
179, 180, 181
benign, 21, 23, 36
beta-carotene, 16
bias, 154, 179
bilirubin, 3, 36
bioavailability, 22, 137
biochemistry, 40, 156
biofeedback, 180
biomarkers, 18, 118, 167
biomechanics, xi, 177, 178
biomolecules, 19
bipedal, vii, x, 169, 171, 172, 173
birds, 175
blood, 5, 6, 9, 10, 11, 12, 13, 14, 21, 22, 23, 24, 29,
39, 40, 44, 47, 56, 57, 58, 59, 60, 62, 65, 66, 67,
69, 72, 73, 74, 78, 101, 102, 103, 108, 109, 111,
112, 115, 116, 117, 119, 120, 152, 174
blood circulation, 116
blood flow, 9, 22, 24, 174
blood pressure, 6, 22, 40, 44, 78, 101, 102, 108, 109,
116, 117
blood vessels, 24
bloodstream, 9, 11, 14
BMI, 31
body composition, 6, 44, 45
body mass index, 146
body weight, 6, 28, 111, 169, 170, 174
bone, 12, 19, 27, 30, 36, 46, 63, 169
bone marrow, 12, 63
bone resorption, 19, 30
boxer, 28
bradycardia, 128, 129
bradykinin, 22
brain, ix, 24, 57, 100, 102, 103, 104, 109, 111, 112,
117, 171
Brazil, 1
breakdown, 72, 74, 160
Index
breast cancer, 44, 47
breathing, 12
burnout, 69
by-products, 2
C
Ca2+, 9
CAD, 21
calcium, 125, 129, 133, 136
Cameroon, 136
cancer, 4, 6, 36, 44, 45, 47, 49, 131, 137, 143, 161
cannabis, ix, 145, 147, 148, 150, 151, 152, 153, 154,
155, 156, 157
capillary, 117
carbohydrate, 39, 74, 75, 77
carbohydrates, 4, 56, 74
carbon, 2
carcinogen, 45
carcinogenesis, 15
cardiac arrhythmia, 130, 141
cardiac muscle, 140
cardiac output, 117, 137
cardiomyopathy, 129, 135, 136, 137, 140, 142, 143
cardioplegia, 136
cardiovascular disease, 4, 8, 10, 21, 22, 33, 39, 44,
49, 120, 131, 137, 140
cardiovascular disease XE "cardiovascular disease"
s, 4, 120, 137
cardiovascular function, 6, 18
cardiovascular risk, viii, ix, 14, 35, 100, 117, 118
cardiovascular system, ix, 8, 57, 123, 128, 140
carnivorous diet, 170
carotene, 16
carotenoids, 3
cartilage, 41, 160, 164, 165, 167, 168
cartilaginous, 164
case studies, 55
case study, 132
caspases, 112, 117
catabolism, 9
catecholamines, 9, 13, 59, 67, 68, 101
categorization, 148
cattle, 27, 43
Caucasians, 27, 31
causal relationship, 153
causality, 154
CD8+, 63
cDNA, 105
cell membranes, 2, 66
cell metabolism, 133
cell surface, 20
cellular homeostasis, 9
185
cellulose, 161
central nervous system, 59, 73
certificate, 155
ceruloplasmin, 3
challenges, 125, 129, 130
chemical, 2, 11, 12, 14
chemical reactions, 2
childhood, 76
children, 33, 49, 146, 152, 155
chimpanzee, 172
China, 50
cholesterol, 6, 32, 103
chondroitin sulfate, x, 159, 160, 161
chondromalacia, 178
chondromalacia patella, 178
chromatography, 103
chromosome, 19, 24, 27, 29, 30, 37
chronic fatigue, 53, 55
chronic fatigue syndrome, 55
chronic kidney XE "kidney" disease, 117
chronic renal failure, 101
circulation, 8, 10, 11, 12, 30, 116
City, 40, 97, 105
classes, 155
classification, 5, 161, 165
climate, 171
clinical application, 46
clusters, 166
CNS, 73
coaches, 52, 53, 54, 55, 59, 60, 70, 71, 75, 81, 86,
96, 115
cocaine, 147, 156
coding, 25
codon, 26
coenzyme, 3
cognition, 153
cognitive process, 152
cognitive processing, 152
collagen, 27, 45, 165, 167, 170, 173, 174, 175
collateralization, 6
color, 128
colorectal cancer, 36
commercial, 103
communication, 148
community, 13, 115, 157
compensation, 54, 130
competition, 13, 47, 52, 53, 56, 57, 70, 73, 83, 87,
94, 115, 146, 154
competitive sport, 53, 130, 148, 150, 152, 153, 154
complement, 29, 32
complementary DNA, 42
complexity, 52
compliance, 180
186
complications, 23, 48, 83, 87, 101, 116, 118
composition, 6, 11, 17, 24, 44, 45, 170
compounds, 3, 16, 130, 131, 133, 141
compression, 9, 74
computer, 172
conceptual model, 77, 80
conceptualization, 92
concussion, 146, 156
conditioning, ix, 86, 123, 135, 146
condor, 173
conduction, 127
confidentiality, 148, 154
configuration, 2
conflict, 84
congestive heart failure, 101
connective tissue, 9, 20, 59, 102, 103
consensus, 56, 74, 98
Consensus, 137
consent, 81, 86, 161
conservation, 69
constituents, 133, 135, 164
construct validity, 86
consumers, ix, 100, 118
consumption, x, 4, 10, 12, 15, 74, 131, 132, 145,
147, 148, 150, 152, 153, 154, 155, 165, 172
consumption patterns, 152, 154
contamination, 94
control group, 111, 112, 113, 114, 179
controlled studies, 55
controlled trials, 168, 178
controversial, 61
contusion, 146
cooling, 176
coordination, 17, 152
copper, 19, 48
coronary artery disease, 21, 44
coronary heart disease, 6, 33
correlation, 68, 134, 138
correlations, 85, 146
cortex, 13, 57
cortisol, 13, 56, 57, 61, 62, 67, 68, 76
cost, 59, 166, 171, 177
Council of Europe, 102
counseling, 17
counterbalance, 131
creatine, 10, 16, 25, 33, 34, 46, 50, 66, 103, 132,
133, 142
creatinine, 103, 107
CRP, 10, 13, 25, 28, 29, 32, 35, 43
CT, 35, 165
cues, 72
cure, 75
CV, 116, 117
Index
CVD, 8, 21, 22, 29, 30, 31, 32
cycle length, ix, 123, 128
cycles, 80, 102, 105
cycling, 101, 115
cytochrome, 2, 132
CYTOKINES, 13, 27, 28, 29, 30, 59
cytoplasm, 10
cytosine, 24, 26, 27, 31
cytoskeleton, 11
cytotoxicity, 63
D
damages, 4, 16, 20
data collection, 86, 87, 90, 95, 154, 156
data set, 95
DBP, 109
deaths, ix, 21, 33, 115, 123, 124, 135
defense mechanisms, 19, 20
defibrillator, 138
deficiencies, 178
deficiency, 22, 26, 30, 45, 55, 56, 68
degenerate, ix, 123
degenerative joint disease, 166
degradation, 65, 103, 104, 165, 167
dehydrate, 77
dehydration, 12
demographic data, 83, 163
denaturation, 12
dependent variable, 89, 90
depolarization, 130
depression, 12, 55, 56, 85, 153
depth, 12
deregulation, 111, 117
destruction, 12
detection, 34, 44, 59, 61, 103, 104, 115, 119
detoxification, 15
deviation, 72, 161, 165
diabetes, 23, 30, 48, 161
diagnostic markers, 75
diastole, 130
diet, 3, 15, 16, 32, 59, 65, 74, 131, 142, 170
dietary habits, 161
differential diagnosis, 178
digestion, 25
dilated cardiomyopathy, 135
dimorphism, 48
direct observation, 61, 71
disability, 179
discomfort, 162, 165
diseases, 4, 6, 15, 20, 22, 29, 34, 35, 36, 39, 46, 56,
75, 120, 124, 129, 137
disintegrin, 35
Index
dislocation, 102, 146, 181
disorder, 54, 56
dispersion, 127, 138, 139, 142
displacement, 12
distress, 43, 54, 78
distribution, 11, 21, 36, 46, 103, 155
diversity, 18
DNA, vii, 1, 4, 6, 15, 16, 18, 19, 20, 21, 25, 27, 28,
30, 36, 37, 40, 41, 42, 43, 44, 45, 46, 49
DNA damage, 6, 15, 16, 17, 20, 21, 36, 37, 40, 43,
44, 45
DNA strand breaks, 15, 44
DNAs, 42
doctors, 169, 177
dogs, 126, 129, 134, 137, 139, 142, 143
DOI, 156
doping, ix, 23, 34, 100, 101, 106, 115, 117, 118, 119,
120, 130, 135, 156
dosage, 160
down-regulation, 135
drug interaction, 137
drug safety, 130
drug testing, 147
drug therapy, 126
drugs, 16, 119, 126, 130, 135, 143, 147, 156, 160,
166
dysplasia, 142
E
education, 147, 148, 155
elastin, 170
elderly population, 35
electrolyte, 133
electron, 2, 3, 9, 176
electron microscopy, 9, 176
electrons, 2
electrophoresis, 15, 115
ELISA, 33
embolism, 115
embryogenesis, 26
emergency, 155, 157
emotional responses, 81, 92, 96
emotional state, 147
employment, 95
encoding, 126, 129, 140
endocardium, 128
endocrine, 56
endocrine system, 56
endogenous antioxidant, vii, 1, 7
endothelial cells, 14, 20, 23, 29
endothelium, 14
endotoxins, 13
187
endurance, x, 4, 5, 6, 17, 18, 19, 20, 21, 22, 23, 24,
25, 26, 31, 33, 46, 47, 49, 53, 60, 67, 74, 77, 78,
101, 115, 118, 136, 137, 169, 172
energy, x, 3, 6, 17, 18, 19, 24, 25, 29, 30, 54, 56, 58,
66, 72, 73, 74, 100, 117, 169, 171, 172, 173, 174
energy expenditure, x, 169, 172
energy supply, 19, 29
enforcement, 154
environment, 6, 18, 27, 30, 81, 160, 165, 180
environmental factors, vii, 1, 18, 32, 33, 65
environments, 176
enzyme, 10, 13, 18, 19, 20, 21, 22, 24, 25, 39, 46, 48,
66, 132
enzymes, 2, 3, 6, 9, 11, 13, 19, 22, 25, 29, 39, 73,
137, 164
eosinophilia, 111
eosinophils, 2
epicardium, 128
epidemiology, 146, 156
EPS, 145
equilibrium, 7, 17
equipment, 146, 154
erythrocytes, vii, 1, 8, 14, 21, 22
erythrocytosis, 34, 36, 41, 49, 115, 117, 118, 121
erythropoietin, viii, 4, 34, 36, 41, 47, 48, 49, 100,
116, 118, 119, 120, 121
ethanol, 104
ethics, 115
ethnic background, 31, 32, 65
ethnic groups, 32
ethnicity, 88
etiology, 52, 53, 56, 69, 165
euphoria, 153
Europe, 102, 147, 155
European Commission, 155
evidence, xi, 6, 9, 12, 14, 28, 29, 43, 56, 63, 68, 89,
124, 125, 131, 134, 136, 147, 153, 154, 160, 177,
178, 179, 180
evolution, 22, 42, 141, 169, 175, 176
examinations, 124, 154
excitability, 127, 133
excitation, 127
exclusion, 75
excretion, 60, 68
execution, 92, 164
executive function, 153
executive functions, 153
exercise performance, 40, 132, 136
exercise programs, vii, 1, 15
exercise-induced oxidative stress, vii, 1, 6, 15, 21,
29, 33
exertion, 5, 6, 56, 67, 72, 77, 178
exons, 24, 26, 27
188
Index
exploitation, 141
exposure, 12, 13, 57
extracellular matrix, 9, 27
extraction, 104
extracts, 131, 166
F
families, 21
fat, 17, 30, 102, 103
fatty acids, 11, 57
fear, 180
feedback inhibition, 6
feelings, 52, 75, 80, 84
ferritin, 14
fiber, 17, 18, 24
fibers, 8, 9, 24, 25, 26, 28, 30, 58, 111, 112, 117
fibrillation, ix, 111, 112, 123, 124, 127, 138
fibroblast growth factor, 23
fibrosis, 42, 117, 129
fitness, 5, 17, 18, 19, 31, 33, 35, 55, 61, 71, 72, 116,
118
flavonoids, 138, 143
flaws, 169
flexibility, x, 17, 33, 159, 161, 162, 163, 165, 166,
170
flexor, 173
flotation, 74
fluctuations, 62
fluid, 20, 115, 133, 168
folate, 32
follicle, 57
follicle stimulating hormone, 57
food, 69, 131, 133, 135, 171
food intake, 69
football, 128, 136, 140, 142, 146, 155, 156, 167, 168
football injuries, 156
footwear, 179
force, 17, 39, 140, 170, 172, 173, 174, 178
formation, 2, 12, 14, 15, 23, 57, 100, 124, 127
fractures, 154
frameshift mutation, 47
France, 136
free radicals, vii, 1, 20, 40, 41, 47
freedom, 83, 146
G
gastrocnemius, 104, 169, 173
gastrointestinal tract, 21
gel, 15, 179
gender differences, 142, 152, 155
gene combinations, 39
gene expression, ix, 28, 29, 100, 101, 105, 113, 114,
117, 118
gene regulation, 18
genes, 6, 17, 18, 22, 24, 27, 28, 29, 30, 32, 36, 37,
38, 39, 44, 48, 105, 112, 117, 121, 126, 129, 138,
140, 141
genetic code, 18
genetic factors, 18, 24
genetic marker, 18
genetic mutations, 126
genetic predisposition, 17, 24, 32, 33
genetic screening, 32
genome, 17, 18, 28, 29
genomic regions, 27
genomic stability, 36
genomics, 141
genotype, 20, 21, 22, 26, 29, 30, 31, 48
genotyping, 43
Germany, 105
gland, 13, 57
glucocorticoid, 56
gluconeogenesis, 58
glucosamine chloride supplementation, x, 159, 166
glucose, 4, 6, 27, 29, 57, 103, 107
glucose tolerance, 6
glutamate, 65
glutamine, 12, 16, 39, 58, 62, 65, 76
glutathione, 3, 11, 14, 15, 20, 21, 34, 36, 38, 49, 103
glycogen, viii, 30, 51, 55, 57, 66, 67, 73, 74
glycosaminoglycans, x, 159, 160, 165, 166
goal setting, 83, 146
grading, 151
graph, 90
grass, 171
gravity, 172
Greeks, 44
grounding, viii, 80
grouping, 88
growth, 3, 13, 23, 24, 26, 27, 28, 34, 37, 40, 42, 43,
46, 57, 62, 68, 105, 112, 130
growth factor, 23, 26, 27, 28, 34, 37, 40, 42, 43, 46,
105, 112
growth hormone, 13, 27, 57, 62, 68, 130
guanine, 24, 29, 31
guidelines, 97
H
habitual intensity, vii, 1, 7, 21
half-life, 2
haplotypes, 31
haptoglobin, 3, 19, 39, 43, 48
harmful effects, 4, 116
189
Index
HE, 35, 141
headache, 161, 165
healing, x, 159, 174
health, 6, 17, 18, 30, 31, 35, 36, 46, 55, 80, 96, 97,
98, 101, 115, 120, 138, 142, 146, 155, 156, 161,
177
health effects, 30, 142
health risks, 115, 161
health status, 80
heart disease, 6, 33, 124, 137, 142, 161
heart failure, ix, 100, 101, 111, 112, 115, 117, 119,
120, 131, 138, 142
heart rate, ix, 4, 55, 56, 60, 61, 72, 75, 102, 108, 123,
128, 141, 142
heart rate (HR), 102
heat exhaustion, 171
heat loss, 171, 176
height, 17, 28, 146
hematocrit, 12, 65, 117
hematopoietic system, 23
heme, 11, 21
hemodialysis, 120
hemoglobin, 11, 12, 19, 21, 22, 31, 65, 116, 120
hemorrhage, 111, 112
heritability, 18, 27
heterogeneity, 126, 127, 128, 136, 140
high fat, 60
high school, 147, 149
higher education, 147, 149
histamine, 23, 140
histones, 18
history, 38, 80, 83, 88, 160
HIV, 39, 47
HM, 33, 39, 41
homeostasis, vii, 1, 4, 6, 7, 9, 22, 26, 29, 117, 133
homocysteine, 31, 34, 39
hormone, 12, 13, 23, 27, 57, 61, 62, 68, 69, 101, 130
hormones, 12, 27, 57, 59, 62, 66, 68, 69, 133
host, 13
House, 105
human, viii, 4, 9, 12, 17, 18, 21, 23, 26, 27, 28, 29,
30, 34, 35, 36, 37, 39, 41, 42, 44, 46, 47, 48, 49,
53, 76, 77, 100, 115, 116, 118, 119, 125, 129,
131, 136, 137, 138, 139, 160, 165, 166, 169, 171,
172, 173, 175, 176
human body, 12
human brain, 171
human genome, 17, 28, 29
human skin, 41
human subjects, 48
humidity, 102
Hungary, 123
hunting, x, 169, 170, 171
hydrocarbons, 13
hydrogen, 2, 21, 36, 41, 43, 58
hydrogen peroxide, 2, 21, 36, 41, 43
hydroperoxides, 21
hydroxyl, 9
hyperglycaemia, 165
hypersensitivity, 49
hypertension, ix, 21, 100, 116, 117, 118
hypertrophic cardiomyopathy, 129, 136, 137, 143
hypertrophy, ix, 26, 27, 28, 42, 47, 100, 112, 116,
117, 118, 123, 126, 128, 129, 130, 131, 133, 135,
136, 138, 142, 143
hypokalemia, 133
hypothalamus, 56, 57
hypothesis, ix, 29, 77, 92, 100, 111, 112, 117, 171,
176
hypoxia, 6, 8, 9, 20, 23, 44, 116, 138
hysteresis, 172
I
ibuprofen, 168
ID, 21, 22, 46
identification, viii, 49, 51, 83, 135
identity, 83, 88, 92, 97
idiosyncratic, 141
IMF, 39
immune function, 6, 13, 22, 39, 59, 78
immune response, 2, 19, 39, 58, 77
immune system, vii, viii, 1, 4, 12, 29, 35, 41, 45, 51,
62, 64, 65, 78
immunity, 13, 44, 45, 64, 77
immunoglobulin, 64
immunosuppression, 62, 63
impairments, 153
improvements, viii, 11, 51, 74, 160, 164, 179
impulses, 127
in vitro, 19, 20, 40, 76, 126, 135, 142
in vivo, 35, 40, 126, 136, 137, 142
incidence, 5, 13, 31, 47, 72, 128, 130, 134, 146, 152,
154, 156
independence, 170
individual character, 146
individual characteristics, 146
individual differences, 24, 130, 155
individuals, vii, 1, 4, 6, 7, 15, 20, 28, 31, 32, 60, 61,
63, 64, 85, 116, 124, 130, 131, 132, 134, 146, 166
induction, 32, 34, 127, 129, 138
industry, 164
infarction, 10, 37, 42, 115, 117, 124
infection, 6, 13, 44, 58, 63, 65
infectious agents, 64
190
Index
inflammation, 13, 16, 19, 21, 28, 30, 34, 41, 43, 59,
117, 140, 164
inflammatory disease, 4, 22
inflammatory responses, 28, 30
information processing, 153
informed consent, 81, 86, 161
ingestion, 44, 132, 152, 166
inheritance, 32
inhibition, 2, 6, 14, 57, 126, 130, 133, 136
inhibitor, 26
inhomogeneity, 124, 128
injure, 118
injuries, vii, viii, ix, 1, 7, 8, 9, 10, 15, 18, 21, 53, 79,
80, 81, 83, 87, 88, 90, 92, 93, 94, 96, 97, 98, 131,
145, 146, 147, 148, 149, 150, 151, 152, 153, 154,
155, 156, 157, 164, 165, 166, 168, 173, 174, 175,
181
injury, viii, x, xi, 5, 11, 28, 34, 43, 72, 74, 79, 80, 81,
82, 83, 85, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96,
97, 98, 100, 145, 146, 148, 149, 150, 151, 152,
153, 154, 155, 156, 157, 159, 160, 164, 165, 166,
168, 169, 174, 175, 177, 178, 179, 181
injury prevention, 154, 168
insertion, 19, 22, 48
institutions, 147, 149
insulin, 6, 27, 28, 29, 30, 34, 37, 42, 46, 68, 74
insulin resistance, 28, 29, 30
insulin sensitivity, 6
integrity, 52, 105
interferon, 59
internal consistency, 83, 85, 86
International Olympic Committee, 23
interpersonal conflict, 53
interpersonal conflicts, 53
intervention, 16, 62, 64, 66, 74, 156, 163, 164, 166,
179
intoxication, 153
intron, 22, 30, 37
introns, 24, 26
investment, 149, 153
ion channels, ix, 123, 124, 126
ions, 11, 58
iris, 21
iron, 11, 12, 14, 38, 42, 56
ischemia, 6, 8, 9, 11, 12, 119, 124, 137
isolation, 105
isoleucine, 57, 58
Israel, 53, 76, 142, 155
issue focus, 70
issues, 52, 56, 73, 95, 97, 101
Italy, 43
J
joint pain, 55
joints, 59, 164
K
K+, 137, 138, 140, 143
kidney, 10, 33, 48, 117, 121
kidney failure, 121
kidneys, vii, 1, 8, 23, 58, 100, 102, 111
killer cells, 13, 63, 64
kinase activity, 66, 133
kinetics, 60, 125
knees, 172, 177
L
laboratory studies, 147
lactate dehydrogenase, 10
lactate level, 67
lactic acid, 5, 14, 73
lateral sclerosis, 19
laws, 102
LDL, 6, 14, 20, 32, 107, 108
lead, vii, ix, 1, 7, 8, 15, 17, 20, 30, 55, 75, 123, 124,
125, 126, 129, 130, 153, 174, 175, 177
left ventricle, 102, 103, 104, 108, 111, 112, 120
legs, 170, 172, 174
leptin, 45
lesions, 14
leucine, 57
life changes, 97, 147
life expectancy, 117, 124
lifetime, x, 145, 149
ligament, 97, 172
light, 5, 64, 71, 102, 179
light cycle, 102
Likert scale, 83, 84, 86
lipid metabolism, 28
lipid peroxidation, 7, 10, 12, 13, 14, 16, 20, 22, 41,
43, 104
lipid peroxides, 13, 104
lipids, 11, 14
lipolysis, 29, 30
lipoproteins, vii, 1, 14
liquid chromatography, 103
liver, vii, ix, 1, 8, 10, 11, 21, 23, 27, 29, 33, 44, 48,
57, 66, 100, 102, 103, 107, 111, 112, 117
loci, 18, 27
locus, 21, 22, 23, 25, 26, 27, 28, 29, 30, 38, 146
longevity, 29
Index
loss of appetite, 57
low-density lipoprotein, 6
lung disease, 36
Luo, 39, 48, 126, 140, 143
lying, 18, 67
lymph, 20
lymphocytes, 13, 34, 58, 63
lysis, 4
M
machinery, 112, 117
macrophages, 2, 14, 28
magnesium, 56
magnitude, 4, 11, 65, 92, 93, 164, 179
major histocompatibility complex, 29
majority, 21, 33, 147, 160, 164, 172
male animals, 131
man, 139, 168
management, 136, 143, 160, 164, 167, 182
manganese, 19, 37, 44, 48
manipulation, 49
mapping, 18
marijuana, 156
marrow, 12, 63
martial art, vii
mass, ix, 6, 17, 26, 29, 43, 123, 128, 146, 152, 172
materials, 65
matrix, 9, 19, 20, 27
matter, 83
MB, 24, 25, 138, 142, 181
MBP, 109
measurement, 35, 67, 94, 97, 103, 135, 167
measurements, 69, 93, 94, 126, 134
mechanical stress, 8, 11
media, 124
medical, 82, 83, 87, 95, 115, 124, 151
medical care, 151
medicine, xi, 17, 32, 80, 96, 155, 156, 157, 160, 177
melatonin, 3
membership, 94
membranes, 2, 66
memory, 63, 94, 153, 156
memory loss, 94
mental capacity, 53
MES, 44
messenger RNA, 22
meta-analysis, 61, 62, 168
Metabolic, 73
metabolic responses, 4, 5
metabolism, 2, 3, 4, 6, 10, 13, 15, 17, 18, 24, 25, 27,
28, 29, 38, 39, 48, 53, 68, 133, 152, 165
metabolizing, 132
191
metalloproteinase, 35
methanol, 104
methylation, 18
MHC, 29
mice, 19, 22, 27, 28, 43, 45, 49, 117, 120, 121, 142
microRNA, 126
microsatellites, 28, 29
microscopy, 9, 176
military, 64
milligrams, 161
minisatellites, 30
Minneapolis, 79, 97
misuse, 118, 120
mitochondria, 2, 4, 8, 9, 10, 15, 19, 21, 42, 48
mitochondrial DNA, 46
mitogen, 23
mitogens, 23
models, 9, 80, 81, 131, 172
moderate activity, 13
moderators, 80
modifications, ix, 18, 100, 118
modulus, 173
molecular oxygen, 9, 19
molecules, 2, 7, 115
molybdenum, 2
monoclonal antibody, 33
mood change, 69, 75
mood states, viii, 56, 69, 80, 85, 92, 94, 97, 146
morbidity, 117
morphology, 52, 175
mortality, ix, 6, 49, 100, 101, 117, 118, 120, 130
mortality risk, ix, 100, 117, 118, 120
motivation, 56, 69, 83, 146
MR, 37, 42, 46
MRI, 165
mRNA, 26, 47, 126
MTS, 19, 20
multidimensional, 98
multivariate analysis, 89
muscle contraction, 66
muscle mass, 26, 29, 43, 172
muscle performance, 4
muscle strength, 17, 26, 27
muscles, vii, 1, 4, 7, 8, 9, 24, 26, 28, 30, 41, 56, 58,
59, 74, 97, 115, 117, 169, 172, 173, 178
muscular dystrophy, 26
musculoskeletal, x, 78, 159, 161, 167
mutant, 19, 27
mutation, 25, 26, 36, 41, 45, 46, 47, 49
mutations, 19, 23, 26, 32, 39, 41, 49, 126, 129, 132,
141
myocardial infarction, 10, 37, 42, 115, 117, 124
myocarditis, 129
192
Index
myocardium, vii, 1, 10, 24, 124, 126, 135, 137, 139,
143
myocyte, 117
myogenesis, 28
myoglobin, 9, 10
myopathy, 26
N
Na+, 141
NaCl, 103
natural food, 15, 32
natural killer (NK) cells, 76
natural killer cell, 13, 63
nausea, 161
necrosis, 13, 20, 35, 47, 59, 105, 112
negative effects, 68, 154
negative mood, viii, 80, 85, 91, 92, 93
negative relation, 64
nervous system, 53, 56, 59, 61, 68, 73, 118, 121
neuroendocrine system, viii, 51, 56
neurogenesis, 40
neuroprotection, 119
neurotransmitter, 57
neutrophils, 2, 13, 63
NHS, 176
nitric oxide, 2, 6, 22, 48, 101, 105, 112
nitric oxide synthase, 48, 105, 112
nitrogen, 2, 103
NK cells, 64
NO synthases, 3
non-enzymatic antioxidants, 3
nonsense mutation, 45
non-steroidal anti-inflammatory drugs, 160, 166
North America, 78, 167
NSAIDs, 126, 167
nucleus, 11, 21
null, 26, 36
nutrients, 24, 40, 69
nutrition, 18, 39, 52, 74, 76, 78, 155
nutritional status, 65
O
obesity, 29, 30
ODS, 104
oedema, 117
OH, 49
oil, 16, 20, 21, 43, 44
Olympic sports, 32
olympics, 45
orbit, 2
ores, 73
organ, 10, 11
organelles, 19
organic disease, 56
organism, 17
organs, vii, 1, 7, 8, 68
osteoarthritis, 160, 164, 166, 167, 168
osteoarticular systems, vii, 1
ovarian cancer, 45
overproduction, 20
overtraining, viii, 8, 51, 52, 53, 54, 55, 56, 57, 58,
59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71,
72, 74, 75, 76, 77, 78
ox, 28
oxidation, 2, 4, 11, 12, 14, 16, 20, 48, 57
oxidative damage, 6, 7, 9, 10, 11, 13, 16, 22, 49
oxidative reaction, 14
oxidative stress, vii, ix, 1, 4, 6, 7, 9, 10, 11, 12, 14,
15, 16, 17, 18, 19, 20, 21, 22, 23, 29, 32, 33, 34,
36, 38, 39, 40, 43, 45, 47, 48, 100, 116, 118, 120,
138, 141
oxygen, vii, 1, 2, 3, 4, 5, 8, 9, 10, 11, 12, 15, 16, 19,
21, 22, 23, 24, 31, 40, 47, 68, 115, 119, 172, 174
oxygen consumption, 4, 10, 12, 15, 172
P
pacing, 141
pain, x, xi, 55, 78, 159, 160, 161, 162, 163, 164, 165,
166, 167, 177, 178, 179, 180, 181, 182
pancreas, 10
parallel, x, 159
paranoia, 153
parents, 33
participants, 83, 84, 85, 86, 161, 162, 180
patella, xi, 177, 178
Patellofemoral pain syndrome (PFPS), xi, 177
pathogenesis, 4, 10, 14
pathogens, 12, 13, 64
pathology, 164
pathophysiology, 101
pathways, 29, 49, 127
PCR, 25, 105, 119, 121
PCT, 103, 106
penetrance, 142
peptide, 27
peptides, 23
perfectionism, 146
performance measurement, 69
periodicity, 71
peripheral nervous system, 73
permeability, 10, 12, 23, 24
permission, 86, 127, 128, 134
Index
peroxidation, 7, 10, 12, 13, 14, 16, 20, 22, 41, 43,
104
peroxide, 2, 21, 36, 41, 43
personality, 55, 80, 83, 88
personality characteristics, 80
pH, 11, 24, 58, 103, 104
phagocytic cells, 2
phagocytosis, 3, 14
pharmacokinetics, 142
pharmacology, 139
phenolic compounds, 3
phenotype, vii, 1, 17, 18, 19, 20, 22, 26, 32, 139
phenotypes, 18, 19, 22, 27, 31, 33, 35, 42, 44
Philadelphia, 181
phosphate, 10, 66
phosphocreatine, 24
phosphorylation, 15
physical activity, 5, 6, 12, 21, 35, 36, 53, 57, 78, 155
physical characteristics, 37
physical education, 148, 155
physical exercise, vii, 1, 10, 24, 29, 45, 47, 115, 116,
118, 119, 120, 128
physical fitness, 19, 61, 116, 118
physical inactivity, 115
physical properties, 11
physical therapist, 179
physical therapy, 165, 179
physicians, ix, 100, 118
Physiological, 2, 37, 40, 41, 76
physiological factors, 17
physiology, 140, 141, 155
physiopathology, 55
pigs, 27, 131
pilot study, 139, 157
pituitary gland, 13, 57
placebo, x, 159, 160, 161, 164, 165, 166, 167, 168,
179, 181
plants, 3
plasma levels, 10, 31, 104
plasma lipoproteins, vii, 1
plasma membrane, 9, 10
platelets, 103, 104, 109, 120
playing, 24, 117, 149, 153, 154
PM, 34, 36, 39, 42, 43, 48, 141
Poincaré, 134
poison, 32
polycyclic aromatic hydrocarbon, 13
polycythemia, 41, 45, 47
polymerase, 25
polymerase chain reaction, 25
polymorphism, 19, 20, 21, 22, 23, 24, 25, 26, 28, 29,
30, 31, 33, 37, 38, 39, 40, 41, 43, 44, 46, 47, 48,
49, 50, 141
193
polymorphisms, 17, 18, 21, 22, 23, 24, 25, 26, 27,
28, 29, 30, 31, 32, 33, 35, 36, 37, 38, 42, 43, 46,
47, 48, 49, 130
polypeptide, 23
polyphenols, 137
polyunsaturated fat, 11
polyunsaturated fatty acids, 11
poor performance, viii, 51
population, ix, 7, 26, 28, 31, 32, 35, 45, 47, 50, 84,
85, 124, 129, 130, 135, 165
Portugal, 51, 99, 102
positive mood, 85
positive relationship, 63
potassium, ix, 12, 123, 125, 126, 129, 132, 133, 135,
136, 138, 139, 141, 143
predators, 171
premature death, 121
preparation, 83, 103, 146, 161, 165, 166
preservation, 105
prevention, 4, 40, 52, 61, 75, 80, 96, 119, 137, 143,
154, 156, 165, 168
primate, 171
principles, 70
probability, 175
prodrugs, 137
progesterone, 133
prognosis, 143
programming, 74
pro-inflammatory, 14, 28, 59
project, viii, 79, 81, 96, 166
prolactin, 61, 68
proliferation, ix, 13, 23, 24, 27, 100, 101, 112, 117,
118
promoter, 21, 22, 23, 24, 28, 29, 30, 31, 36, 40, 42,
47, 48
propagation, 127
propylene, 104
prostaglandins, 13
prostate cancer, 36
protease inhibitors, 13
protection, 19, 138, 141
protein oxidation, 11
protein synthesis, 27, 68, 74
proteins, 4, 10, 11, 13, 15, 29, 56, 125, 126, 129, 130
proteoglycans, 165, 176
proteolysis, 27
proteolytic enzyme, 13
psychological health, 96
psychological variables, 93, 94
psychology, 80, 156
psychopharmacology, 157
psychosocial factors, viii, 79, 80, 96
psychosocial measures, viii, 79, 87
194
Index
psychosocial stress, 89
puberty, 133, 152
pulmonary embolism, 115
pulp, 44
pumps, 124, 125
pure water, 103
purines, 9
purity, 105
Q
QT interval, ix, 123, 129, 132, 134, 135, 136, 138,
140, 141, 142
quadriceps, 172, 178, 179, 180, 182
quality of life, 115, 124
quantification, 75, 104, 105
questionnaire, x, 70, 85, 145, 148, 151, 153, 161
quinolone antibiotic, 135
R
race, 32, 49, 76, 88, 94, 116, 137, 141, 172, 179
radiation, 39, 41
radicals, vii, 1, 2, 3, 9, 20, 40, 41, 45, 47, 48
RE, 138
reaction time, 152
reactions, 2, 14, 15, 29, 60, 94, 105
reactive oxygen, vii, 1, 2, 19
reactivity, 2, 116, 120
recall, 94, 154
receptors, 4, 24, 29, 30, 41, 57, 133
recognition, viii, 47, 51, 59, 131
recombinant human erythropoietin (rhEPO), viii, 100
recommendations, 86, 136
recovery, vii, viii, 51, 52, 53, 54, 55, 56, 58, 62, 63,
64, 66, 67, 70, 71, 72, 73, 74, 76, 77, 78, 79, 80,
95, 98, 146, 164, 166, 168, 180
recovery plan, 54
recovery process, 53, 72, 74
recreation, 155
recreational, x, 116, 145, 147, 148, 149, 150, 151,
152, 153, 154, 157, 165, 177, 180, 181
recurrence, 87
red blood cells, 11, 23, 39, 65
red wine, 131
regenerate, 164
regeneration, viii, 28, 51, 55, 56, 74
regression, 88
regulatory systems, 4
rehabilitation, vii, viii, 79, 82, 85, 86, 88, 92, 95, 97,
98, 146, 148, 150, 151, 175, 180
relaxation, 9, 153
relevance, 137
reliability, 84, 85, 86, 164, 165
relief, 164, 167
remodelling, 142, 143
renal failure, 101
renin, 12, 19, 23
repair, 11, 15, 28, 29, 31, 36, 42, 46, 74, 160, 174
repetitions, 5
replication, 38
repression, 140
requirements, 52, 59
researchers, 75, 80, 93, 95
reserves, viii, 51
resistance, 5, 6, 17, 26, 27, 28, 29, 30, 37, 39, 42, 66,
121, 179
resources, 80, 83, 85, 88, 97, 147
respiration, 171
respiratory distress syndrome, 43
respiratory tract infections, viii, 51, 62
response, vii, viii, 2, 5, 6, 7, 12, 13, 14, 17, 19, 20,
22, 23, 24, 29, 30, 31, 34, 37, 41, 48, 53, 57, 58,
59, 60, 61, 64, 67, 68, 69, 75, 76, 77, 79, 80, 81,
82, 83, 85, 86, 88, 90, 91, 92, 93, 95, 97, 98, 117,
133, 135, 139, 168
response format, 85
responsiveness, 57, 61, 67, 167
restoration, 54, 73
restriction enzyme, 25
restriction fragment length polymorphis, 25
resveratrol, 131, 136
reticulum, 2, 4, 9, 129
RH, 35, 43, 44, 49, 141
risk, vii, viii, ix, 6, 8, 10, 12, 14, 21, 22, 28, 29, 30,
31, 32, 35, 36, 39, 42, 44, 45, 47, 49, 53, 54, 62,
63, 80, 93, 98, 100, 101, 115, 116, 117, 118, 120,
123, 130, 132, 133, 135, 139, 145, 146, 147, 148,
149, 150, 151, 152, 153, 154, 155, 156, 157, 171,
175
risk factors, viii, 80, 146, 152, 155, 157
risks, 101, 115, 140, 157, 161
risk XE "risk" -taking, vii, ix, 145, 147, 148, 149,
150, 152, 154
risk-taking behaviors, vii, ix, 145, 147, 148, 150, 154
RNA, 18, 22, 105
rugby, 151, 155, 156
rules, 154
S
safety, 130, 137, 139, 154, 156, 157, 160
saliva, 64
salts, x, 159, 160
sarcopenia, 29, 39
saturation, 14
Index
schizophrenia, 33, 153
scholarship, 83, 88, 92, 94
school, viii, 79, 81, 84, 92, 96, 147, 148, 151
science, 70, 155, 157
sclerosis, 19
sea level, 116
secretion, 14, 29
sedative, 153
sedentary lifestyle, 174, 175
sediment, 103
selenium, 36
self-awareness, 72
self-concept, 83
self-efficacy, 86, 93, 94, 97
self-esteem, 98, 146
senescence, 4, 11
sensitivity, 6, 15, 25, 41, 47, 57, 61, 63, 68, 165
sepsis, 31, 47, 48
sequencing, 25
Serbia, 159
serine, 47
serotonergic overactivation, ix, 100
serotonin, 57, 101, 104
serum, 6, 10, 11, 14, 16, 19, 25, 27, 33, 34, 36, 46,
65, 66, 102, 104, 109, 111, 116, 168
serum EPO, 116
sex, 43, 65, 66, 133, 139
sex differences, 139
sex hormones, 66, 133
sham, 179
shape, 131
shear, 173
Short-term variability (STV), ix, 123
showing, 18, 61, 74, 109, 112, 173
sickle cell, 33
side effects, 130, 132, 161, 164, 165
signal transduction, 4, 6
signalling, 2
signals, 15, 111, 112
significance level, 162
signs, 46, 54, 55, 62, 69, 111, 112, 117, 124, 129
skeletal muscle, vii, 1, 4, 8, 9, 10, 11, 14, 15, 18, 24,
26, 27, 28, 29, 30, 34, 39, 40, 41, 42, 43, 46, 57,
58, 104, 115
skeleton, 27
skin, 41, 143, 171
smoking, x, 145, 148, 151, 153, 161
smooth muscle, 2, 14
SNP, 21
SNS, 117
soccer, 36, 124, 134, 135, 140, 151, 156, 168
social resources, 97
social support, 52, 146
195
sodium, 41, 104
software, 104, 105
soleus, 169, 173
solution, 102, 103, 104, 105
South Africa, 176
SP, 35, 37, 181
Spain, 102
specialists, 17
species, vii, 1, 2, 3, 15, 19, 26, 104, 129, 131
speculation, 101
sphygmomanometer, 102
sport psychologists, 81, 96
sprain, 146
sprains, 155
SS, 33, 36, 49
stability, 15, 36
standard deviation, 72, 161, 165
standard error, 105
state, viii, 3, 4, 6, 7, 10, 49, 52, 53, 54, 55, 56, 57,
59, 63, 64, 67, 68, 69, 70, 72, 79, 80, 85, 88, 91,
92, 93, 98, 124, 127, 147, 170
states, viii, 7, 15, 56, 59, 62, 65, 69, 80, 85, 88, 92,
94, 96, 97, 146
stem cells, 26
stenosis, 129
steroids, 130, 140, 141
stimulation, 27, 32, 100, 116, 125, 139, 143
stimulus, 54, 56, 68, 70, 71, 72, 127
storage, 173
strength training, 10, 27, 48, 73
stress, vii, viii, ix, 1, 4, 6, 7, 8, 9, 10, 11, 12, 14, 16,
17, 18, 19, 20, 21, 22, 23, 29, 32, 33, 34, 36, 38,
39, 40, 41, 43, 44, 45, 46, 47, 48, 51, 52, 53, 56,
57, 58, 60, 61, 63, 64, 65, 70, 78, 79, 80, 81, 84,
87, 88, 89, 90, 91, 92, 93, 94, 96, 97, 98, 100,
102, 116, 118, 120, 133, 138, 141, 146, 171, 173
stress factors, 53
stress response, 80
stressors, viii, 13, 22, 51, 53, 54, 56, 57, 80, 81, 83,
88, 89, 92, 96, 97
stretching, 74, 146, 154, 179
stroke, 49, 117
STRs, 28, 30
structural equation modeling, 95
structural protein, 25
structure, x, 10, 26, 117, 169, 173, 175
structuring, 24
substitutes, 16
substitution, 24, 29, 47
substrate, 21, 60, 124, 126, 127, 130, 138
successful aging, 46
sulfate, x, 20, 159, 160, 161, 166, 167, 168
sulfur, 2, 166
196
Index
Sun, 40, 48
supplementation, vii, x, 1, 16, 17, 20, 21, 43, 44, 48,
49, 76, 132, 136, 142, 159, 161, 164, 166
suppression, viii, 51, 64
survival, 11, 21, 31, 42, 120, 124
susceptibility, viii, 13, 15, 28, 36, 48, 51, 58, 130,
133, 134, 135, 143, 146, 155
swelling, x, 159, 160, 161, 162, 163, 164, 165, 166,
179
Switzerland, 145, 155
sympathetic nervous system, 53, 59, 61, 68, 118, 121
symptoms, 28, 30, 36, 54, 55, 56, 57, 60, 62, 154,
164, 168, 178, 179
syndrome, vii, viii, xi, 8, 10, 43, 51, 52, 53, 54, 55,
56, 58, 59, 60, 61, 62, 63, 65, 68, 69, 70, 71, 72,
75, 76, 77, 78, 101, 126, 128, 129, 132, 135, 137,
138, 139, 141, 177, 179, 180, 181, 182
synovial fluid, 20, 168
synthesis, 3, 13, 22, 27, 47, 66, 68, 74, 101, 164,
165, 166, 168, 170
T
T lymphocytes, 63
tachycardia, ix, 116, 117, 123, 129, 138
talent, 47, 53, 70
tandem repeats, 30
tanks, 74
tar, 136
target, 9, 22, 58, 68, 78, 142, 178, 179
target organs, 68
Task Force, 69, 77
TCC, 49
teachers, 148, 155
team members, 94
teams, viii, 79, 81, 86, 89, 140
techniques, 9, 73, 134, 179, 180, 181
technology, 147, 180
temperature, 7, 104
tendon, vii, x, 169, 170, 171, 172, 173, 174, 175, 176
tendons, 169, 172, 173, 178
tensile strength, 170, 173, 174
tension, 4, 80, 85, 92
testing, 35, 64, 69, 81, 95, 139, 147, 161
testosterone, 56, 68, 133, 141
test-retest reliability, 84, 86
TGF, 26, 43, 105, 112, 113, 114, 117
therapeutic agents, x, 159, 167
therapeutic effects, 116
therapeutic use, 115
therapist, 179
therapy, 74, 116, 126, 141, 142, 165, 167, 179, 180,
181
thymine, 26
time frame, 53
time periods, 93
tissue, ix, 2, 9, 16, 20, 21, 23, 26, 27, 29, 30, 32, 36,
45, 59, 67, 74, 100, 101, 102, 103, 104, 111, 112,
117, 119, 133, 138, 164
tissue homeostasis, 26
TNF, 13, 20, 28, 29, 30, 31, 37, 41, 42, 47, 59, 105,
112, 117
TNF-alpha, 41, 47
TNF-α, 28, 29, 59, 105, 112, 117
tobacco, x, 145, 147, 148, 150, 152
tonic, 138
total cholesterol, 103
total energy, 172
toxicity, 7, 19, 20, 40, 42
trafficking, 140
traits, 17, 18, 49
transcription, 22, 29, 30, 31, 38
transduction, 4, 6
transferrin, 14
transforming growth factor, 26, 105, 112
transmission, 13
transport, 3, 4, 20, 119
trauma, 9, 31, 59, 65, 156, 160, 164, 166, 174
treatment, vii, viii, ix, x, 28, 52, 75, 77, 78, 94, 100,
101, 102, 105, 106, 107, 108, 109, 112, 113, 115,
116, 117, 120, 137, 140, 146, 159, 160, 163, 164,
165, 166, 167, 168, 175, 177, 178, 179, 180, 181
trial, x, 138, 159, 164, 167, 168, 179, 181, 182
triggers, 124, 127
triglycerides, 14, 103
tryptophan, 57
tumor, 13, 20, 59
tumor necrosis factor, 13, 20
turnover, 47, 167
type 2 diabetes, 48
tyrosine, 23, 24
U
UK, 105, 169, 176, 177
ultrasound, 179
ultrastructure, 39
United Kingdom, 169, 177
United States, 152
univariate analyses of variance, 89
upper respiratory infection, 64
upper respiratory tract, 12, 62
urea, 65, 66, 107
uric acid, 3, 9, 37, 103, 107
urine, 33, 68, 115, 119
197
Index
USA, 34, 36, 39, 40, 41, 46, 47, 48, 79, 97, 103, 104,
105, 141, 149, 161, 162
usual dose, 160
V
Valencia, 155
validation, 96, 98
valine, 20, 57, 58
valuation, 117
valve, 104
variables, viii, 32, 51, 55, 60, 79, 80, 88, 89, 92, 93,
94, 95, 97, 146, 149
variations, 18, 31, 65
vascular endothelial growth factor (VEGF), 23, 105,
112
vascular system, 20
vascularization, 6, 24
vasodilation, 2
vasodilator, 22
vasopressin, 12
vastus medialis, 178
VEGF expression, 24
VEGFR, 23
vein, 102
venipuncture, 102
ventilation, 4, 5
ventricle, 102, 103, 104, 108, 111, 112, 120, 126,
131
ventricular arrhythmias, ix, 123, 129, 130, 135, 138,
141
ventricular fibrillation, ix, 111, 112, 123, 124, 127
ventricular tachycardia, ix, 123, 129, 138
vessels, 24
viruses, 2
viscosity, 14, 115, 116
vitamin B1, 32
vitamin B12, 32
vitamin C, 3, 16, 34
Vitamin C, 16
vitamin E, 3, 16, 48
vitamins, 14, 16, 40, 73
VLA, 49
vomiting, 161
vulnerability, x, 13, 80, 92, 96, 169, 173
W
walking, x, 159, 161, 163, 164, 170, 171, 174, 176,
178
Washington, 157
water, 12, 21, 102, 103, 105, 128, 152, 157, 165,
170, 171
weakness, 55, 56, 180, 181
weight ratio, 111
well-being, 96, 97
West Africa, 49
Western Australia, 157
white blood cells, 58
WHO, 156
wild type, 20, 30
windows, 74
Wisconsin, 136
workers, 126, 128, 164
workload, 70, 116
worldwide, 35
worry, 83, 146
Y
yield, 2, 18, 93, 105
young adults, 49
young people, 146
Z
zinc, 19, 48