11 Kelechi Opara: a case study of a competitor
coming in “too dialed” for the judges.
By Alan Aragon
Copyright © April 1st, 2010 by Alan Aragon
Home: www.alanaragon.com/researchreview
Correspondence: aarrsupport@gmail.com
2
Scientific American butters up our fear of carbs &
love for lard.
By Alan Aragon
5
Training with low muscle glycogen enhances fat
metabolism in well-trained cyclists.
Med Sci Sports Exerc. 2010 Mar 25. [Epub ahead of print]
[Medline]
6
Single vs. multiple sets of resistance exercise for
muscle hypertrophy: a meta-analysis.
Krieger JW. J Strength Cond Res. 2010 Apr;24(4):1150-9.
[Medline]
7
Citrulline malate enhances athletic anaerobic
performance and relieves muscle soreness.
Pérez-Guisado J, Jakeman PM. J Strength Cond Res. 2010
Apr 7. [Epub ahead of print] [Medline]
8
Comparison
of
pre-workout
nitric
oxide
stimulating dietary supplements on skeletal
muscle oxygen saturation, blood nitrate/nitrite,
lipid peroxidation, and upper body exercise
performance in resistance trained men.
Bloomer RJ, et al. J Int Soc Sports Nutr. 2010 7:16 [Epub
ahead of print] [JISSN]
9
Fat-free mass index in users and nonusers of
anabolic-androgenic steroids.
Kouri EM, et al. Clin J Sport Med. 1995 Oct;5(4):223-8.
[Medline]
Alan Aragon’s Research Review – April 2010
[Back to Contents]
Page 1
Scientific American butters up our fear of carbs & love
for lard.
By Alan Aragon
____________________________________________________
You see what I did there?
That was my version of a catchy headline. Like it? Scientific
American magazine offered their version of a catchy title of a
recent article entitled, “Carbs against Cardio: More Evidence
that Refined Carbohydrates, not Fats, Threaten the Heart” by
freelance science journalist Melinda Wenner Moyer.1 In the
coming discussion, I’ll take a look at how closely the reported
evidence supports the author’s conclusions, and provide some
slick sciency stuff of my own along the way.
Let the scapegoating & oversimplification begin
In the article’s introductory paragraph, Moyer comes out
swinging with the classical correlative statement, “…while
Americans have dutifully reduced the percentage of daily
calories from saturated fat since 1970, the obesity rate during
that time has more than doubled, diabetes has tripled, and heart
disease is still the country’s biggest killer.” She then goes on to
assert that research is mounting in favor of the idea that
processed carbohydrate is the culprit of the aforementioned
diseases – not fat. Hmmm… Is it really that simple, or does a
chronic surplus of unused calories in general have anything to
do with it?
The short answer is, chronic caloric excess – regardless of
macronutrient – has everything to do with it. Moyer committed
the first sin; she left out half of the picture by failing to mention
the “energy-out” side of the equation. According to the USDA’s
Economic Research Service, as of 2007, Americans were
consuming about 600 more daily kcal than they did in 1970.2
Importantly, this data showed a higher increase in added fat
intake than any other single type of food. Compounding this
problem, NHANES data has shown a 10% decrease in physical
activity in recent years compared to 2 decades ago.3 Now, I’ll
concede that survey data is often incomplete and relatively weak
in terms of its ability to establish relationships between
variables. However, just look around at the increased butt-sitting
at the job and at home versus what you observed in your
childhood, and this data doesn’t seem far-fetched at all.
Speaking of research limitations, let’s move on to the article’s
opening blow, which was an attempt to debunk the current
recommendation to moderate saturated fat intake.
Meta-analysis in AJCN
Siri-Tarino et al recently did a meta-analysis examining the
association of saturated fat with cardiovascular disease.
According to Moyer, “The analysis, overseen by Ronald M.
Krauss, director of atherosclerosis research at the Children’s
Hospital Oakland Research Institute, found no association
between the amount of saturated fat consumed and the risk of
heart disease.” Though the study’s findings would appear
conclusive to less-versed, it’s actually not this cut & dry or free
Alan Aragon’s Research Review – April 2010
of limitations. Meta-analyses are only capable of establishing
associations between variables; they cannot demonstrate causeand-effect. Furthermore, this wasn’t a pooling of data from
randomized controlled trials, but rather from prospective cohort
studies, which are observational and thus unable to demonstrate
causation due to a multitude of uncontrolled variables. There are
yet additional limitations to prospective cohort research, which
the authors themselves acknowledge as follows:4
However, such studies have caveats, including a reliance on
nutritional assessment methods whose validity and reliability may
vary (25), the assumption that diets remain similar over the long
term (26) and variable adjustment for covariates by different
investigators.
It’s also notable that Moyer failed to mention one of the largest
yet antithetical studies in this area. Jakobsen et al recently
examined the pooled data of 11 prospective cohort studies and
concluded that that replacing saturated fat with polyunsaturated
fat rather than monounsaturated fat or carbohydrates lowers the
risk for coronary heart disease.5 Unsurprisingly, the
epidemiological data is equivocal. But what about the controlled
intervention data? Here’s a study that would have greatly
lessened the impact of Moyer’s article had it been included.
Mozaffarian et al conducted a systematic review of the same
topic,5 except they examined the data from randomized
controlled trials (RCTs), the only type of research able to show
cause-and-effect. To quote them directly:
“In this meta‐analysis of RCTs, increasing PUFA consumption as a
replacement for SFA reduced the occurrence of CHD events by
19%; each 5%E greater PUFA consumption reduced CHD risk by
10%. Whereas nearly all these trials were insufficiently powered
to detect a significant effect individually, the pooled results
demonstrate a significant benefit of replacing PUFA for SFA on
clinical CHD events.”
Of course, the RCTs used in Mozzafarian et al’s meta-analysis
weren’t free of limitations, either. Nevertheless, when pitted
directly against each other, a body of data derived from
controlled interventions is inherently stronger than a body of
data derived from uncontrolled observations.
Now, is this to say that saturated fats indeed are the bad guys
and unsaturated fats are the good guys? Nope, that would be
oversimplifying things as well. The research in this area on the
whole does not examine ideal dietary conditions (i.e., inadequate
or sub-optimal protein intake is common), nor does it typically
examine physically active subjects. On the important note of
protein, a recent trial by Furtado et al found that a diet with 25%
protein improved the risk markers better than the two other diets
with 15% protein, despite one of the lower-protein diets having a
higher amount of unsaturated fat.6 To quote the authors:
“...the results that the Prot diet elicits the least atherogenic apo B
lipoprotein profile combined with the recent report of the
superiority of the Prot diet over the Carb diet in reducing blood
pressure (21) makes a strong case for choosing protein rather than
carbohydrate as a replacement for saturated fat to improve
cardiovascular health.”
[Back to Contents]
Page 2
Comparison of 3 diets in NEJM
Moyer moves on to cite a trial by Shai et al, where 3 diets were
compared:7 American Heart Association (low-fat/calorierestricted); Mediterranean (moderate-fat/calorie-restricted), and
Atkins (low-carb/non-restricted). Quoting Moyer, “Although the
subjects on the low-carb diet ate the most saturated fat, they
ended up with the healthiest ratio of HDL to LDL cholesterol
and lost twice as much weight as their low-fat-eating
counterparts.” The latter statement is not entirely true, since out
of the 322 recruited, 277 completed the full 2-year trial. Among
those who completed the study, weight loss was 3.3 kg for the
low-fat diet, 4.6 for the moderate-fat diet, and 5.5 kg for the lowcarb diet, which ends up being a lot less dramatic than Moyer’s
article indicates.
She then deduces that, “Stampfer’s findings do not merely
suggest that saturated fats are not so bad; they indicate that
carbohydrates could be worse.” The latter statement does
nothing more than mislead the reader into writing off
carbohydrate in favor of saturated fat and/or cholesterol intake.
This simply was not the crux of the study findings. Although the
Atkins diet showed the greatest lipid profile improvements of the
bunch, the Mediterranean diet was superior to the rest of the
diets for improving glucose and insulin metabolism in diabetic
subjects. In contrast to the negative connotations placed on
carbohydrates by Moyer, the authors of the trial in question offer
a more moderate & flexible conclusion:
Consumption of monounsaturated fats is thought to improve
insulin sensitivity, an effect that may explain the favorable effect
of the Mediterranean diet on glucose and insulin levels. The
results imply that dietary composition modifies metabolic
biomarkers in addition to leading to weight loss. Our results
suggest that health care professionals might consider more than
one dietary approach, according to individual preferences and
metabolic needs, as long as the effort is sustained.
Let’s throw in glycemic response to make things exciting
In an interesting but not entirely unexpected diversion, Moyer
takes the carbohydrate scapegoating to a particularly
questionable area. She cites another prospective cohort study
(this time from back in 1997) by Stampfer et al, which found
that diets with a high glycemic load and low cereal fiber content
increase diabetes risk.8 No fundamental disagreement here, but
again, we’re talking about observational/survey-based research
that hinges upon the accuracy of the participants’ self-reported
data.
sack of potatoes, and adopting indifference toward vegetables
and fruit. A little absurd? Of course it is.
Moyer then cites yet another observational/epidemiological
study by Beulens et al, who found that high dietary glycemic
load and glycemic index increase the risk of cardiovascular
disease, particularly in overweight women.9 Nevertheless,
looking at the data more closely, we see the typically odd mishmash of outcomes attributable to numerous uncontrolled
variables. For example, subjects who had high glycemic loads
consumed less protein but more carbohydrates. However, those
with lower glycemic loads consumed less fiber, more alcohol,
and had a higher prevalence of smoking. Welcome to the wild
world of epidemiology, where correlation does not necessarily
mean causation. Bear in mind that correlational research is not
useless by any stretch; it’s great for gathering questions, hints,
and hypotheses to test under controlled conditions.
The section ends with a quote by David Ludwig, director of the
obesity program at Children’s Hospital Boston: “These trends
may be explained in part by the yo-yo effects that high glycemicindex carbohydrates have on blood glucose, which can stimulate
fat production and inflammation, increase overall caloric intake
and lower insulin sensitivity,” What we have here is an
Olympic-level leap to conclusions.
Now is a good time to highlight the fundamental fallacy of any
inherent detriment of high-GI carbohydrate. GI doesn’t truly
matter unless all of the following conditions are met, in no
particular order of importance: a) a high-carb/low-protein/lowfat diet is consumed; b) an emphasis on low-fiber refined
starches and sugars is actively pursued; c) hypocaloric
conditions are avoided; d) sedentary conditions are maintained;
e) carbohydrate is consumed in large amounts in a fasted state,
in isolation from the other macronutrients. If you know
individuals who meet all of the previous conditions, let them
know that the GI of their carbohydrate choices might be of some
concern, particularly if they have clinically diagnosed glucose
control issues.
Funny enough, most of the aforementioned criteria indeed
comprise the set-ups of most research examining GI. However,
if most of the above conditions are met, GI manipulation has for
the most part failed as a solution to weight control in long-term
controlled intervention trials, where causation can be
demonstrated.10-12 Thus, it’s not surprising that a recent literature
review by Niwano et al found that GI was not a reliable
predictor of appetite, hunger, and satiety.13
Public health implications
Needless to say, this type of research is fraught with validity
threats, and it’s often evident in contradictory outcomes that
often lack rhyme or reason. For example, this study found a
significant inverse relationship between cereal fiber and diabetes
risk, but no significant relationship between vegetable and fruitderived fiber and diabetes risk. Another nonsensical outcome
(expected of uncontrolled research) was the correlation of
various foods to diabetes risk. To illustrate, cold cereal was
inversely correlated with diabetes, while cooked potatoes was
positively correlated. If we were to take this data on faith,
imagine stocking up on Special K & Wheaties, throwing out the
Moyer manages to grab a refreshing quote from Robert C. Post,
deputy director of the U.S. Department of Agriculture’s Center
for Nutrition Policy and Promotion: “Findings that “have less
support are put on the list of things to do with regard to more
research.” Right now, Post explains, the agency’s main message
to Americans is to limit overall calorie intake, irrespective of the
source. “We’re finding that messages to consumers need to be
short and simple and to the point,” I intuitively endorse
simplicity when it comes to educating the lay public. This is
because of the tendency to screw up the fine details and come
away with more error than accuracy (which is most of the
Alan Aragon’s Research Review – April 2010
[Back to Contents]
Page 3
information dished out to patrons of the fitness industry).
However, Moyer doesn’t seem to buy this approach, as she
quickly moves on to quote an accusatory remark by Stampfer
toward the sugared beverage industry (as if the underlying
problem therein isn’t excess calories).
Parting shots
Before closing out the article, Moyer swings briefly back to
neutrality by warning against gorging on saturated fats. She then
gives positive plugs to fish oil, olive oil, and high-fiber
carbohydrates – so far, so good. But alas, she ends off by
quoting Ludwig and sending what ends up being the take-home
message: “If you reduce saturated fat and replace it with high
glycemic-index carbohydrates, you may not only not get
benefits—you might actually produce harm,” Ludwig argues.
The next time you eat a piece of buttered toast, he says, consider
that “butter is actually the more healthful component.” Too bad
this falsely dichotomous statement is simply not supportable by
the vast majority of controlled interventions. In the spirit of Gary
Taubes’ Good Calories Bad Calories, it seems that even a
magazine like Scientific American isn’t above letting the facts
get in the way of a good story.
11. Sichieri R, et al. An 18-mo randomized trial of a lowglycemic-index diet and weight change in Brazilian women.
Am J Clin Nutr. 2007 Sep;86(3):707-13. [Medline]
12. Raatz SK, et al. Reduced glycemic index and glycemic load
diets do not increase the effects of energy restriction on
weight loss and insulin sensitivity in obese men and women.
J Nutr. 2005 Oct;135(10):2387-91. [Medline]
13. Niwano Y, et al. Is glycemic index of food a feasible
predictor of appetite, hunger, and satiety? J Nutr Sci
Vitaminol (Tokyo). 2009 Jun;55(3):201-7. [Medline]
References
1.
Moyer MW. Carbs against cardio: more evidence that
refined carbohydrates, not fats, threaten the heart. Scientific
American, May 2010 [SciAm]
2. Economic Research Service, USDA. Loss-Adjusted Food
Availability Data. Updated Feb 27, 2009. [ERS/USDA]
3. King DE, et al. Adherence to healthy lifestyle habits in US
adults, 1988-2006. Am J Med. 2009 Ju; 122(6):528-34.
[Medline]
4. Siri-Tarino PW, et al. Meta-analysis of prospective cohort
studies evaluating the association of saturated fat with
cardiovascular disease. Am J Clin Nutr. 2010
Mar;91(3):535-46. [Medline]
5. Jakobsen MU, et al. Major types of dietary fat and risk of
coronary heart disease: a pooled analysis of 11 cohort
studies. Am J Clin Nutr. 2009 May;89(5):1425-32.
[Medline]
6. Mozaffarian D, et al. Effects on coronary heart disease of
increasing polyunsaturated fat in place of saturated fat: a
systematic review and meta-analysis of randomized
controlled trials. [Medline]
7. Shai I, et al. Weight loss with a low-carbohydrate,
Mediterranean, or low-fat diet. N Engl J Med. 2008 Jul
17;359(3):229-41. [Medline]
8. Salmerón J, et al. Dietary fiber, glycemic load, and risk of
non-insulin-dependent diabetes mellitus in women. JAMA.
1997 Feb 12;277(6):472-7. [Medline]
9. Beulens JW, et al. High dietary glycemic load and glycemic
index increase risk of cardiovascular disease among middleaged women: a population-based follow-up study. J Am
Coll Cardiol. 2007 Jul 3;50(1):14-21. [Medline]
10. Aston LM,, et al. No effect of a diet with a reduced
glycaemic index on satiety, energy intake and body weight
in overweight and obese women. Int J Obes (Lond). 2008
Jan;32(1):160-5. [Medline]
Alan Aragon’s Research Review – April 2010
[Back to Contents]
Page 4
Training with low muscle glycogen enhances fat
metabolism in well-trained cyclists.
Med Sci Sports Exerc. 2010 Mar 25. [Epub ahead of print]
[Medline]
PURPOSE To determine the effects of training with low muscle
glycogen on exercise performance, substrate metabolism, and
skeletal muscle adaptation. METHODS: Fourteen well-trained
cyclists were pair-matched and randomly assigned to HIGH or
LOW-glycogen training groups. Subjects performed 9 aerobic
training (AT; 90 min at 70% VO2max) and 9 high-intensity
interval-training sessions (HIT; 8 x 5 min efforts, 1 min
recovery) during a 3-wk period. HIGH trained once daily,
alternating between AT on day 1 and HIT the following day,
whereas LOW trained twice every second day, firstly performing
AT and then 1 h later performing HIT. Pre and post-training
measures were a resting muscle biopsy, metabolic measures
during steady state cycling (SS), and a time trial (TT).
RESULTS: Power output during HIT was 297 +/- 8 W in LOW
compared with 323 +/- 9 W in HIGH (P<0.05), however, TT
performance improved by ~10% in both groups (P<0.05). Fat
oxidation during SS increased after training in LOW (from 26+/2 to 34+/-2 mumol/kg/min, P<0.01). Plasma FFA oxidation was
similar before and after training in both groups but musclederived triacylglycerol oxidation increased after training in
LOW (from 16+/-1 to 23+/-1 mumol/kg/min, P<0.05). Training
with low muscle glycogen also increased beta-hydroxyacylCoA-dehydrogenase protein content (P<0.01). CONCLUSION:
Training with low muscle glycogen reduced training intensity
and, in terms of performance, was no more effective than
training with high muscle glycogen. However, fat oxidation was
increased after training with low muscle glycogen, which may
have been due to enhanced metabolic adaptations in skeletal
muscle. SPONSORSHIP: Supported by a research grant from
GlaxoSmithKline, Nutritional Healthcare, R&D.
position and allow isotopic equilibrium to be reached. Like
many trial of this nature, the applicability of fasted endurancetype work is limited to those whose performance goal is
secondary to specifically targeting fat oxidation during training.
In actual endurance competition, there is never an instance
where competitors show up to a race on an empty tank after an
overnight fast. It simply does not happen. Although the diets
were controlled 24 hours prior to each trial, they were still selfselected otherwise, and no diet composition analysis via
software was made.
Comment/application
One thing that’s easy to automatically assume based on the study
title was that the low-glycogen group oxidized more fat, and thus
reduced their percent body fat by the end of the trial.
Unfortunately, body fat was not measured. This was more of a
during-training substrate use investigation. If the trial were
drawn out for a longer period than 3 weeks, then it would have
been interesting to see if any fat loss advantage could have been
detected in the low-glycogen group, whose training involved a
double-session every second day instead of a single session
daily. Nevertheless, a couple of notable things occurred in the
low-glycogen group: greater activity of fat-oxidative enzymes,
and a greater use of intramuscular triacylglycerol:
Study strengths
This study investigates the relatively unstudied idea that training
under conditions of low glycogen might enhance training
adaptations. In previous work by Hansen et al on trained
cyclists,1 whole body fat oxidation was greater in the lowglycogen treatment, but time trial cycling performance was
improved to a similar degree in both the high- and low-glycogen
groups. The present study aimed to confirm Hansen’s findings
by using a more direct method (stable isotope tracers) to
measure plasma and muscle-derived carbohydrate and fat
oxidation as a result of high- vs. low-glycogen status.
Endurance-trained cyclists were used, eliminating the newbie
effect, wherein subtle differences in outcomes can be masked by
greater responsiveness to a broader range of stimuli. Although
no specific details were given, the authors stated that the
nutritional status of the subjects was controlled for 24 hours
before each trial.
Another notable (and also somewhat unsurprising) finding was
that self-selected intensity was reduced when the high-intensity
interval training was done under low-glycogen conditions.
Perhaps the most notable (and unexpected) finding here was a
lack of significant advantage of the high-glycogen group in the
performance test. At the end of the 60-minute steady-state
period, subjects cranked out a fixed amount of work (1017 kJ;
243 kcal) as fast as possible. There was no significant difference
in the total amount of work performed. It’s my speculation that
the high-glycogen group would have had the edge if a greater
amount of work was assigned for the time trial.
Subjects were tested on the morning after an over-night fast –
with additional 60 minutes needed to rest in a semi-supine
This was the first trial to ever examine the training effect of low
glycogen on GLUT 4, which is the rate-limiting enzyme for
glucose utilization. Given that GLUT 4 activity tended to be
greater in the high-glycogen group, the authors offered the
practical note that, “training with low muscle glycogen may be
counterproductive for athletes who compete in high-intensity
events where CHO oxidation plays a significant role in
performance, and that this type of training may be more suited
to preparation for ultra-endurance activities.
Alan Aragon’s Research Review – April 2010
[Back to Contents]
Study limitations
Page 5
Single vs. multiple sets of resistance exercise for
muscle hypertrophy: a meta-analysis.
Krieger JW. J Strength Cond Res. 2010 Apr;24(4):1150-9.
[Medline]
PURPOSE: Previous meta-analyses have compared the effects
of single to multiple sets on strength, but analyses on muscle
hypertrophy are lacking. The purpose of this study was to use
multilevel meta-regression to compare the effects of single and
multiple sets per exercise on muscle hypertrophy. METHODS:
The analysis comprised 55 effect sizes (ESs), nested within 19
treatment groups and 8 studies. Multiple sets were associated
with a larger ES than a single set (difference = 0.10 +/- 0.04;
confidence interval [CI]: 0.02, 0.19; p = 0.016). RESULTS:
Multiple sets were associated with a larger ES than a single set
(difference = 0.10 +/- 0.04; confidence interval [CI]: 0.02, 0.19;
p = 0.016). In a dose-response model, there was a trend for 2-3
sets per exercise to be associated with a greater ES than 1 set
(difference = 0.09 +/- 0.05; CI: -0.02, 0.20; p = 0.09), and a
trend for 4-6 sets per exercise to be associated with a greater ES
than 1 set (difference = 0.20 +/- 0.11; CI: -0.04, 0.43; p = 0.096).
Both of these trends were significant when considering
permutation test p values (p < 0.01). There was no significant
difference between 2-3 sets per exercise and 4-6 sets per exercise
(difference = 0.10 +/- 0.10; CI: -0.09, 0.30; p = 0.29). There was
a tendency for increasing ESs for an increasing number of sets
(0.24 for 1 set, 0.34 for 2-3 sets, and 0.44 for 4-6 sets).
Sensitivity analysis revealed no highly influential studies that
affected the magnitude of the observed differences, but one
study did slightly influence the level of significance and CI
width. No evidence of publication bias was observed.
CONCLUSIONS: In conclusion, multiple sets are associated
with 40% greater hypertrophy-related ESs than 1 set, in both
trained and untrained subjects. SPONSORSHIP: None listed.
between set volume and training experience. Another notable
limitation was that most of the studies compared 1 set with 3 sets
per exercise, which limits the statistical power to compare 3 sets
with greater set volumes. I’ll quote Krieger directly on this
point: “Given that the ES [effect sizes] for 4–6 sets (0.44) is
considered a moderate effect, whereas the ES for 2–3 sets (0.34)
is considered a small effect according to Cohen’s classifications
(9), more research involving >4 sets is needed to clarify whether
this is a chance difference or a true difference.” Related to this
problem of a small number of studies is the exclusion of studies
that compared single versus multiple sets, but did not measure
hypertrophy. As such, there’s a considerable grey area regarding
how the well-designed studies might have influenced the
outcomes if hypertrophy was one of their endpoints. However,
it’s noted that most of the studies excluded showed greater
strength gains in the multiple-set treatments. Therefore, since
strength and size are related (size increases are associated with
strength increases), it’s not likely that the addition of more
studies would change the present outcomes other than
strengthening them statistically. Finally, meta-analyses cannot
establish cause-and-effect.
Comment/application
Study strengths
Previous meta-analyses have focused primarily on strength
gains.2-5 This is perhaps the first meta-analysis to carefully
examine the effect of set volume on hypertrophy. The present
study had a number of strengths lacking in previous work. Only
studies comparing single with multiple sets that held all other
variables constant were included. Multilevel statistical models
were used in order to account for variations between studies,
treatment groups, and effect sizes within each treatment group.
To protect against spurious (false) associations, both standard
and permutation test p values were used.6 In order to find the
relative consistency of difference between single and multiple
sets, a sensitivity analysis was performed. Finally, no evidence
of publication bias was detected.
On a personal note, it amazes me to this day that it’s not a strict
requirement for papers published in the major journals to discuss
their limitations with any depth. In the present paper, the
limitations are laid out very clearly and thoroughly. The main
limitation of this meta-analysis is the small number of studies
examined, which limits the statistical power. As noted by the
author, the small number of studies prevented the analysis of
other important variables/predictors, such as relationships
As seen in the chart above, a dose-response effect of set volume
on hypertrophy was detected. An important aspect to bear in
mind is that 3 studies in the meta-analysis had subjects training
the muscle groups 2 times per week,7-9 while in 4 studies,
subjects trained the muscle groups 3 times per week,10-13 while
one study had subjects train a total of 4 days per week, and
accounted for total sets in the week, rather than sets per
exercise.14 Overall, the studies in this meta-analysis vary
markedly in total set volume per week. So, while it’s useful to
know that multiple sets are superior to single sets per exercise
for muscle hypertrophy, optimal frequency and total set volume
per week are important considerations for this purpose. A
relatively recent review by Wernbom et al suggested that
hypertrophy can by maximized with up to 3-6 sets per muscle
group, trained 2-3 times per week (a range of 6-18 sets total per
week).15 Max strength gain seems to concur with this. A metaanalysis by Peterson et al found that in non-novice subjects, a
mean volume of 8 sets per muscle group, trained 2 times a week
(16 sets total per week) maximizes the rate of strength gain.16
Alan Aragon’s Research Review – April 2010
[Back to Contents]
Study limitations
Page 6
Citrulline malate enhances athletic anaerobic
performance and relieves muscle soreness.
Pérez-Guisado J, Jakeman PM. J Strength Cond Res. 2010 Apr
7. [Epub ahead of print] [Medline]
PURPOSE: The purpose of the present study was to determine
the effects of a single dose of citrulline malate (CM) on the
performance of flat barbell bench presses as an anaerobic
exercise and in terms of decreasing muscle soreness after
exercise. DESIGN: Forty-one men performed 2 consecutive
pectoral training session protocols (16 sets). The study was
performed as a randomized, double-blind, 2-period crossover
design. Eight grams of CM was used in 1 of the 2 training
sessions, and a placebo was used in the other. The subjects'
resistance was tested using the repetitions to fatigue test, at 80%
of their predetermined 1 repetition maximum (RM), in the 8 sets
of flat barbell bench presses during the pectoral training session
(S1-4 and S1'-4'). The p-value was 0.05. RESULTS: The
number of repetitions showed a significant increase from
placebo treatment to CM treatment from the third set evaluated
(p <0.0001). This increase was positively correlated with the
number of sets, achieving 52.92% more repetitions and the
100% of response in the last set (S4'). A significant decrease of
40% in muscle soreness at 24 hours and 48 hours after the
pectoral training session and a higher percentage response than
90% was achieved with CM supplementation. The only side
effect reported was a feeling of stomach discomfort in 14.63% of
the subjects. CONCLUSIONS: We conclude that the use of CM
might be useful to increase athletic performance in highintensity anaerobic exercises with short rest times and to relieve
postexercise muscle soreness. Thus, athletes undergoing
intensive preparation involving a high level of training or in
competitive events might profit from CM. SPONSORSHIP:
None listed.
Study strengths
Prior to this study, the single positive ergogenic effect citrulline
malate (CM) in human caused was an improvement in finger
flexion performance.17 So, one of the main merits of this study is
that it’s the first one to put CM through a meaningful/relevant
test for athletic purposes. This was a multi-center study (6
gyms), enabling a sample size that’s uncommonly large in
supplementation research (41 subjects). Adding a crossover
strengthened the outcomes by reducing variability between
subjects. In order to qualify for participation, subjects had to
have been on a current regimen of more than 3 hours of training
per week. Subjects had been training for the previous 6 months
for an average of 6 hours per week, with an average of 4
sessions per week. Clearly, these were not rank novices. This
makes any potential effect of CM more meaningful, since
raining experience accompanies a reduced sensitivity to
supplemental agents.
rather odd testing/training protocol. Chest work (which was the
only work tested) was trained on Monday, where the testing
consisted of the maximal number of reps at 80% 1RM during 4
sets of flat barbell bench press, 4 sets of incline barbell bench
press, 4 sets of incline dumbbell flyes (load reduced to 60%
1RM), and finally 4 sets of barbell bench press again (load
increased back to 80% 1RM). Back was trained on Tuesday, legs
on Wednesday, shoulders on Thursday, arms on Friday, and no
training on Saturday and Sunday. Although I can see how using
the 16-set chest session as a testing modality for the effect of
CM on anaerobic capacity, athletes in team sports or mixed
sports almost never undergo this type of bodypart split.
Comment/application
The chart above indicates that during the first 4 sets of flat bench
press, the CM group did an average of 0.53 reps more per set,
with a total of 2.15 more reps than the placebo group by the end
of the 4 sets. In the final 4 sets of flat bench press (after the 4
sets each of 2 other chest exercises), the CM group did an
average of 1.55 more reps per set, with a total of 6.21 more reps
than the placebo group by the end of the final 4 sets. In addition
to the increased performance, CM also caused less muscle
soreness at 24 and 48 hours after testing.
The authors propose 3 hypothetical mechanisms through which
CM exerts its effects: 1) The excess availability of citrulline
facilitates the clearance of ammonium (thus reducing fatigue). 2)
Malate is capable of acting as a metabolic shuttle between the
cytoplasm and mitochondria, limiting the accumulation of lactic
acid by redirecting it toward the formation of pyruvate and
gluconeogenesis. 3) Citrulline can act as a precursor to arginine,
which in turn can incrise nitric oxide (NO) production, which in
turn can various functions in skeletal muscle including blood
flow, glucose uptake, mitochondriogenesis, fatty acid oxidation,
and satellite cell activation. As we’ll soon see in the following
study critique, this 3rd proposed mechanism for CM is least
likely to be a strong contributor to its ergogenic effects.
The obvious limitation of this trial is its short-term nature.
Habituation or reduced sensitivity to the effects of CM over
longer periods remains speculative. Another limitation was the
What does this data mean to the end-user? To the skeptics, it’s
unknown whether this trial will or will not be a one-hit-wonder.
This is the first study showing CM’s effectiveness in a context
relevant to athletic goals, complete with an omission of funding
source. Independent replication with relevant designs would
strengthen this compound’s promise. To the optimistic, CM
might be the next supplement to add to the shopping list.
Alan Aragon’s Research Review – April 2010
[Back to Contents]
Study limitations
Page 7
Comparison of pre-workout nitric oxide stimulating
dietary supplements on skeletal muscle oxygen
saturation, blood nitrate/nitrite, lipid peroxidation, and
upper body exercise performance in resistance trained
men.
the pump; that is, amplify the engorgement of muscles with
blood. In addition to the subjective rating, upper torso
circumferences were also measured. Subjects’ 24-hour
food/beverage intake was analyzed via nutritional software, and
they were instructed to duplicate this intake before testing.
Bloomer RJ, et al. J Int Soc Sports Nutr. 2010 7:16 [Epub ahead
of print] [JISSN]
Study limitations
BACKGROUND: We compared Glycine Propionyl-LCarnitine (GlycoCarn(R)) and three different pre-workout
nutritional supplements on measures of skeletal muscle oxygen
saturation (StO2), blood nitrate/nitrite (NOx), lactate (HLa),
malondialdehyde (MDA), and exercise performance in men.
DESIGN: Using a randomized, double-blind, cross-over design,
19 resistance trained men performed tests of muscular power
(bench press throws) and endurance (10 sets of bench press to
muscular failure). A placebo, GlycoCarn(R), or one of three
dietary supplements (SUPP1, SUPP2, SUPP3) was consumed
prior to exercise, with one week separating conditions. Blood
was collected before receiving the condition and immediately
after exercise. StO2 was measured during the endurance test
using Near Infrared Spectroscopy. Heart rate (HR) and rating of
perceived exertion (RPE) were determined at the end of each set.
RESULTS: A condition effect was noted for StO2 at the start of
exercise (p=0.02), with GlycoCarn(R) higher than SUPP2. A
condition effect was also noted for StO2 at the end of exercise
(p=0.003), with SUPP1 lower than all other conditions. No
statistically significant interaction, condition, or time effects
were noted for NOx or MDA (p>0.05); however, MDA
decreased 13.7% with GlycoCarn(R) and increased in all other
conditions. Only a time effect was noted for HLa (p<0.0001),
with values increasing from pre- to post-exercise. No effects
were noted for HR, RPE, or for any exercise performance
variables (p>0.05); however, GlycoCarn(R) resulted in a
statistically insignificant greater total volume load compared to
the placebo (3.3%), SUPP1 (4.2%), SUPP2 (2.5%), and SUPP3
(4.6%). CONCLUSION: None of the products tested resulted in
favorable changes in our chosen outcome measures, with the
exception of GlycoCarn(R) in terms of higher StO2 at the start
of exercise. GlycoCarn(R) resulted in a 13.7% decrease in MDA
from pre- to post-exercise and yielded a non-significant but
greater total volume load compared to all other conditions.
These data indicate that 1) a single ingredient (GlycoCarn(R))
can provide similar practical benefit than finished products
containing multiple ingredients, and 2) while we do not have
data in relation to post-exercise recovery parameters, the tested
products are ineffective in terms of increasing blood flow and
improving acute upper body exercise performance.
SPONSORSHIP: Sigma-tau HealthSciences, Inc.
As noted by the authors themselves, although water intake was
matched, actual hydration status wasn’t measured, and variations
in hydration status could potentially alter the outcomes. Another
limitation was the use of near infrared spectroscopy (NIRS) to
measure muscle tissue oxygen saturation, which is not as
sophisticated as other methods such as magnetic resonance
imaging (MRI). As is common with preworkout supplement
studies, questions remain of how diminished their effects might
be as a result of taking them in a fed state. It would have been
nice if they listed the actual brands of the supplements
compared, but they were probably kept anonymous due to
potential legal conflicts. Finally, this was an acute study whose
effects over the long term are unknown.
Comment/application
An important point made by the authors of the present study is
that of the serving size of a given product is 20 grams, and 10
grams of it is carbohydrate with the other 10 grams consisting of
a “proprietary blend” of 30-60 ingredients, each one of them is
defaulted to a miniscule amount. Below certain dose thresholds,
many of the ingredients commonly found in multi-ingredient
preworkout supplements are simply inert:
“Our data clearly show that ingredient number has no influence
on product effectiveness. In fact, the use of a very inexpensive
maltodextrin powder yields similar effects as all products used for
comparison in this design. Considering a preserving cost of
approximately $2 (when using the amount of powder included
within the present design), the reasonable choice for an athlete
may simply be to use a carbohydrate powder.”
The authors acknowledge, however, that the products containing
creatine would not have a chance to exert ergogenic effects due
to the short-term nature of the study. Another important point is
that many preworkout supplements contain caffeine and other
stimulants, which might be contraindicated for certain intolerant
individuals or those on medications that might interact adversely
to stimulants. The stimulant-free, single-compound nature of
GlycoCarn® is part of its appeal, aside from its small degree of
superiority to the other treatments for total volume load
completed in the bench press.
This trial is particularly relevant since nitric oxide (NO) boosters
are among the most popular class of supplements on the market
today. Trained subjects were used, reducing the chance for
newbie gains. A crossover was done to reduce the possibility of
intersubject variability. This was the first trial I’ve come across
where subjects were asked to rate their subjective perception of
having a “pump” by using a visual analog scale. One of the main
marketing points of NO booster supps is the ability to enhance
In the April 2009 AARR, I reviewed a study examining the
effect of GlycoCarn®, which showed its ability to increase peak
power and reduce lactate accumulation.18 Refer back to that
issue for more details on its possible mechanisms of action aside
from its ability to increase nitric oxide levels. I’d speculate that
the lackluster results of the preworkout supplements can be
attributed to their insufficient caffeine dosing within their
“proprietary blends.” I didn’t expect the arginine content of the
multi-ingredient supplements to affect performance since
arginine’s track record for enhancing athletic pursuits has been a
repeated disappointment.19-24
Alan Aragon’s Research Review – April 2010
[Back to Contents]
Study strengths
Page 8
.
Fat-free mass index in users and nonusers of
anabolic-androgenic steroids.
Kouri EM, et al. Clin J Sport Med. 1995 Oct;5(4):223-8.
[Medline]
PURPOSE: To develop a simple method by which a clinician or
sports official can assess whether an athlete’s muscularity is
within the naturally attainable range or is beyond that which
could reasonably be expected without pharmacological
assistance. METHODS: We calculated fat-free mass index
(FFMI) in a sample of 157 male athletes, comprising 83 users of
anabolic-androgenic steroids and 74 nonusers. FFMI is defined
by the formula (fat-free body mass in kg) x (height in meters)-2.
We then added a slight correction of 6.3 x (1.80 m - height) to
normalize these values to the height of a 1.8-m man.
RESULTS: The normalized FFMI values of athletes who had
not used steroids extended up to a well-defined limit of 25.0.
Similarly, a sample of 20 Mr. America winners from the
presteroid era (1939-1959), for whom we estimated the
normalized FFMI, had a mean FFMI of 25.4. By contrast, the
FFMI of many of the steroid users in our sample easily exceeded
25.0, and that of some even exceeded 30. CONCLUSION:
Thus, although these findings must be regarded as preliminary, it
appears that FFMI may represent a useful initial measure to
screen for possible steroid abuse, especially in athletic, medical,
or forensic situations in which individuals may attempt to deny
such behavior. SPONSORSHIP: In part by NIDA training grant
T32 DAO7252 and NIDA grant RO1 DA-06543.
Study strengths
This is one of those classic studies that innovatively examined
an important, yet very politically volatile topic. The latter aspect
is probably why this type of research has not been replicated for
over a decade now. The sample consisted of a relatively even
split of drug-free athletes, and those on anabolic/androgenic
steroids (AAS). Each subject’s history was assessed via personal
interview, and confirmed with urine testing. Among the nonusers, some effort was made to include subjects who were likely
to be at the upper limits of fat-free mass. To quote the authors,
“The nonusers included many dedicated bodybuilders. Several
had competed in successfully in “natural bodybuilding contests,
two held world records in strength events, and many others were
recognized by their associates as highly successful
weightlifters.”
Study limitations
Aside from the limitations of estimating body composition, the
sampling methodology was perhaps the most profound
limitation. While the authors specified that they drew their
sample from gyms in the Boston and Los Angeles areas, they
were not specific about the actual number of subjects that were
formally/actively competitive in a given sport (versus merely
recreationally active). A systematic but more difficult way of
doing this would be to recruit subjects through the natural
bodybuilding federations, and choose the top-placing
competitive athletes through each weight class. The same would
be done with non-tested and professional bodybuilding
Alan Aragon’s Research Review – April 2010
federations. To beef up the sample a bit, a good pool to draw
from would be the top-level college and professional athletes
involved in strength/power sports. As it stands, although the
sample is an appreciable size given the difficulty of getting
reliable (& honest) study participants, it still could have been
more selectively constructed. That is, I feel the authors could
have done some better hunting for the big game.
Comment/application
Calculating FFMI goes as follows: divide fat-free mass in kg by
height in meters squared. An online FFMI calculator can be
found here. The main finding of this study was that the maximal
fat-free mass index (FFMI) of a drug-free athlete is
approximately 25. According the authors, Individuals in marked
excess of this are potentially dabbling in AAS.
Perhaps due to realizing the potential limits of their sample, the
authors compiled a list of the FFMIs of Mr. America winners
from the pre-steroid era (1939-1959). Collectively, their average
FFMI was 25.4, which on face value seems to lend validity to
the findings of the present study. However, it’s also important to
note that 13 of the 20 competitors (65%) exceeded FFMI of 25,
the proposed max in nonusers. Furthermore, the Mr. America
contest, like any bodybuilding contest, is judged on shape,
definition, and aesthetic balance rather than just mass. So, it
wouldn’t be necessarily be fair to apply the FFMI-25 cut-off
point of to athletes who aren’t competitive bodybuilders in nearcontest shape or leaner.
To further cross-check their findings, the authors compiled the
(estimated) FFMIs of 33 modern competitive bodybuilders in
contest-shape who appeared collectively in 60 issues of popular
bodybuilding magazines from 1989-1994. Although they
excluded bodybuilders in the lightweight class, they included
competitors who weren’t even top amateurs. To quote the
authors: “…it must be remembered that many of the modern
bodybuilders had not even won a national contest like the Mr.
America competition, and some had not even ranked among the
top 10 contenders in the contests described in the magazines.”
Interestingly, despite the loose inclusion criteria, the sample of
modern bodybuilders had markedly greater FFMIs than the
sample of Mr. America winners of the pre-steroid era. The bulk
of the modern sample’s FFMIs concentrated roughly in the range
of 30-34, with the lowest at somewhere between 26-26.9 and the
highest between 39-39.9. the reason for the markedly greater
FFMI of modern bodybuilders remains open to speculation, but
there are only 3 possibilities: a) greater use of AAS, b) better
training, nutrition, and supplementation, or c) both a & b. The
authors of the present study suggest that AAS is likely the single
factor that has jacked up the FFMIs of the modern-day
bodybuilder. My speculation is drugs may have been the
dominant factor, but improved nutrition and training played
significant roles as well.
The authors duly mention that their data is merely preliminary,
and potentially inapplicable to, as they bluntly put it, “fat
individuals”, since body fat gain can be accompanied by gains in
lean mass that could tip FFMI past 25 in nonusers. Ultimately,
I’d like to see this study redone, with a sample consisting
exclusively of competitive upper-tier drug-tested bodybuilders
and lean drug-tested elite strength/power athletes.
[Back to Contents]
Page 9
1.
2.
3.
4.
5.
6.
7.
8.
9.
10.
11.
12.
13.
14.
15.
16.
17.
18.
Hansen AK, et al. Skeletal muscle adaptation: training twice
every second day vs. training once daily. J Appl Physiol.
2005 Jan;98(1):93-9. [Medline]
Krieger JW. Single versus multiple sets of resistance
exercise: a meta-regression. J Strength Cond Res. 2009
Sep;23(6):1890-901. [Medline]
Rhea MR, et al. Single versus multiple sets for strength: A
meta-analysis to address the controversy. Res Q Exerc
Sport. 2002 Dec;73(4):485-8. [Medline]
Rhea MR, et al. A meta-analysis to determine the dose
response for strength development. Med Sci Sports Exerc.
2003 Mar;35(3):456-64. [Medline]
Wolfe BL, et al. Quantitative analysis of single- vs. multiple
set programs in resistance training. J Strength Cond Res.
2004 Feb;18(1):35-47. [Medline]
Higgins JP, Thompson SG. Controlling the risk of spurious
findings from meta-regression. Stat Med. 2004 Jun
15;23(11):1663-82. [Medline]
Galvão DA, Taaffe DR. Resistance exercise dosage in older
adults: single- versus multiset effects on physical
performance and body composition. J Am Geriatr Soc. 2005
Dec;53(12):2090-7. [Medline]
Marzolini S, et al. Aerobic and resistance training in
coronary disease: single versus multiple sets. Med Sci
Sports Exerc. 2008 Sep;40(9):1557-64. [Medline]
McBride JM, et al. Effect of resistance exercise volume and
complexity on EMG, strength, and regional body
composition. Eur J Appl Physiol. 2003 Nov;90(5-6):626-32.
[Medline]
Munn J, et al. Resistance training for strength: effect of
number of sets and contraction speed. Med Sci Sports
Exerc. 2005 Sep;37(9):1622-6. [Medline]
Rhea MR et al. Three sets of weight training superior to 1
set with equal intensity for eliciting strength. J Strength
Cond Res. 2002 Nov;16(4):525-9. [Medline]
Rønnestad BR, et al. Dissimilar effects of one- and three-set
strength training on strength and muscle mass gains in upper
and lower body in untrained subjects. J Strength Cond Res.
2007 Feb;21(1):157-63. [Medline]
Starky DB, et al. Effect of resistance training volume on
strength and muscle thickness. Med Sci Sports Exerc. 1996
Oct;28(10):1311-20. [Medline]
Ostrowski KJ, et al. The effect of weight training volume on
hormonal output and muscular size and function. J Strength
Cond Res. 1997;11(3):148-54. [JSCR]
Wernbom M, et al. The influence of frequency, intensity,
volume and mode of strength training on whole muscle
cross-sectional
area
in
humans.
Sports
Med.
2007;37(3):225-64. [Medline]
Peterson MD, et al. Maximizing strength development in
athletes: a meta-analysis to determine the dose-response
relationship. J Strength Cond Res. 2004 May;18(2):377-82.
[Medline]
Bendahan D, et al. Citrulline/malate promotes aerobic
energy production in human exercising muscle. Br J Sports
Med. 2002 Aug;36(4):282-9. [Medline]
Jacobs PL, et al. Glycine propionyl-L-carnitine produces
enhanced anaerobic work capacity with reduced lactate
Alan Aragon’s Research Review – April 2010
19.
20.
21.
22.
23.
24.
accumulation in resistance trained males. J Int Soc Sports
Nutr. 2009 Apr 2;6(1):9. [Medline]
Bescós R, et al. Effects of dietary L-arginine intake on
cardiorespiratory and metabolic adaptation in athletes. Int J
Sport Nutr Exerc Metab. 2009 Aug;19(4):355-65. [Medline]
Fahs CA, et al. Hemodynamic and vascular response to
resistance exercise with L-arginine. Med Sci Sports Exerc.
2009 Apr;41(4):773-9. [Medline]
Liu TH, et al. No effect of short-term arginine
supplementation on nitric oxide production, metabolism and
performance in intermittent exercise in athletes. J Nutr
Biochem. 2009 Jun;20(6):462-8. Epub 2008 Aug 15.
[Medline]
Kanaley JA. Growth hormone, arginine and exercise. Curr
Opin Clin Nutr Metab Care. 2008 Jan;11(1):50-4. [Medline]
Marcell TJ, et al. Oral arginine does not stimulate basal or
augment exercise-induced GH secretion in either young or
old adults. J Gerontol A Biol Sci Med Sci. 1999
Aug;54(8):M395-9. [Medline]
Collier SR, et al. Oral arginine attenuates the growth
hormone response to resistance exercise. J Appl Physiol.
2006 Sep;101(3):848-52. [Medline]
[Back to Contents]
Page 10
37 days & the clock is ticking: initial impressions
Kelechi Opara: a case study of a competitor coming in
“too dialed” for the judges.
By Alan Aragon
At the beginning of March, I got an email from a lifetime natural
athlete named Kelechi Opara. He broke it to me that he had 37
days to get everything rocking for a show. After seeing his pic, I
decided we could make a go of it. Now, here’s the catch – it was
a male fitness model contest rather than a bodybuilding contest
(you’ll see that there’s a punchline to this detail by the end of
this write-up). In any case, I didn’t want to take any chances, and
this was by far the shortest prep time that’s ever been thrown at
me by a client about to compete. My thought process was this:
get him dialed as possible and let the chips fall where they may,
because judging is so preposterously subjective in bodybuilding,
but even more so in fitness and figure competition.
So, I assessed his habitual intake. It was exceptionally nonneurotic, but then again, Kelechi had been following my writing
for quite some time before contacting me. He has my book and
is subscribed to AARR, so this tells you a lot about his
exceptional interest level in the scientific aspects of the game.
Anyway, I saw he was a coffee drinker, so I knew we had at
least 1 thing in common (this is a good thing). So he has cream
in his coffee… Seriously, who gives a crap. Keep it in there. My
objective was to keep as much things static as possible so that
Kelechi could focus on the important stuff with minimal
distractions. I saw that he could tolerate dairy – great, I kept it in
his diet. I specifically wanted him to know that life does not
have to be miserable while dieting down. Many folks think that
it boils down to an epic battle of will that involves boiled
chicken breasts and raw spinach every day except for your
monthly cheat meal. But this is just not the case, nor is it optimal
for contest prep.
So, I proceeded to assess Kelechi’s habitual intake and figured
that it was underestimated by about 10%. I was perfectly open
to him having an unusually high degree of reporting accuracy,
but I also kept it in mind that obese individuals under-report
their intake by as much as 47%, and over-report their exercise by
roughly 51%.1 Those are big errors, but that’s the reality.
Experienced athletes (especially bodybuilders) tend to have a
much greater degree of meticulousness and accuracy in
estimating their intakes, but I wanted to be as conservative as
possible, given the very narrow timeframe I had to work with.
Projecting progress
One of the things that struck me about Kelechi’s goals was that
he wanted to lose a minimal amount of weight en route to the
stage. Since this was his first competition, neither of us had any
dieting-down history as a point of reference. So, it was important
for me to communicate the inevitability of weight loss – perhaps
more weight loss than he would anticipate or want. The thing to
remember with contest prep is that the competitors don’t get on
stage holding a neon sign indicating their bodyweight. All that
matters is how they look. At a height of 5’9”, Kelechi started off
at 181 lbs with visible abs; I’d estimate about 6% with calipers.
My goal was to cut that in half without sacrificing too much size
and fullness. If figured that he could sharpen up as much as he
needed to without crossing under the 170’s. Thankfully, we
accomplished that mission, and busted a lot of myths in the
process. On the day of the contest, Kelechi was 172 lbs. This
was a net loss of 9 lbs in 37 days, and it happened with fruit,
Alan Aragon’s Research Review – April 2010
[Back to Contents]
Page 11
milk, bread, and cereal in his diet. Oh, the taboos. There was no
“peak week” carb, sodium, or water manipulations, either. Those
last-minute adjustments are primarily based on unsubstantiated
folklore. They rarely make any visible differences, and tend to
pose more risk than benefit, so I purposely avoid that practice.
Training
Kelechi had about 13 years of training experience under his belt
when he approached me for help, so I took the position of letting
him simply continue what he was accustomed to doing, as long
as I didn’t see anything overly brotastic about it. Being well-read
in this area, Kelechi’s training program was solid. Total cardio
per week was 1-1.5 hours (3-4 sessions per week, mostly highintensity interval sprints, which he had a strong preference for),
and 7.5 hours of resistance training per week (5 sessions per
week, approximately 90 minutes each). Bodyparts were trained
twice weekly, and ancillary work was done on the fifth day.
Reps rarely exceeded 10 per set, most work sets were taken to
concentric failure. Approximately 16-18 sets per muscle group
were performed per week, and the training sessions involved 1-2
partners, hence the relatively long duration (90 minutes). In a
way, this was good, since it allowed greater inter-set recovery
for moving more iron.
Diet & supplementation
To put it plainly, I think that traditional bodybuilding diets are
effective, but they have a lot of stupid lore and voodoo built into
them. I concentrated on having Kelechi hit his macronutrient
targets over a good balance of healthy foods of his personal
preference. Unlike traditional bodybuilding diets that eliminate
dairy and fruit, Kelechi consumed two fruits per day and 2 dairy
servings per day, and this included chocolate milk or fruit juice
(with whey protein) during his somewhat long training bouts,
right up until the day of the contest. Numbers-wise, 3 of his
training days tended to be more exhaustive than the others, so
those days he had approximately 140 g carbs, while the rest of
the days they were kept at 90 g. Remember, we had 37 days to
dial it in, so I normally would have a guy his size take in about
double that much carbs if we had a normal 12-16 weeks of prep.
Protein was at 220 g on harder training days, and 190 g the rest
of the time. Fat was pretty stable each day at roughly 70 g.
Supplementation was minimal; a simple multi, calcium/vitamin
D, creatine, and fish oil. In retrospect, I would have had him take
some magnesium to round out the bases, but this obviously
wasn’t a make-or-break. He took no stimulants or fat burners.
Heck, he almost lost 10 lbs in a month without too much hunger,
so there was no need for stims + appetite suppressants (although
he did have coffee, like mentioned earlier).
of them to take to prejudging, and munch on them right in front
of his competitors. The purpose of this was 3-fold: to put his
mood through the roof, keep his energy from dipping, and most
importantly, to confuse & subtly intimidate the other
competitors. I talked to Kelechi right after prejudging, and it was
clear that he outclassed his competitors in both muscular size
and definition by a significant margin (and they knew it; many
of them congratulated Kelechi pre-emptively).
The kicker & some pleasant twists
Ready for the kicker? Kelechi didn’t even place in the
competition, yet he was easily the best-conditioned guy in the
line-up. But keep in mind, this was a “male fitness model”
competition, which I had no prior experience in prepping
someone for, so my mindset was to treat it like a bodybuilding
competition. Amazingly, the judges told him he was too big
and too ripped. Hah! Imagine my delight when I heard that.
Kelechi wasn’t too disappointed either. Although winning or
placing highly is always nice, being told by the judges
themselves that you’re “too big and too shredded for fitness
modeling competition” in a show that’s not drug-tested
is…priceless. Funny enough, I warned him of the possibility of
that very outcome the day of the show after he let me know that
he was in better shape than the other guys. The other pleasant
twist in the saga was that Kelechi was approached by at least 5
different magazines within a few days after the contest. Judging
from his picture, it’s easy to see why.
Postscript
To tie this case study in with an earlier section of this issue,
Kelechi is an exceptional specimen among natural trainees, and
people are invariably incredulous that he’s been completely
drug-free since birth. Most people guess his weight at about 200,
even though his normal off-season weight is in the low-to-mid
180’s. His fat-free mass index at 37 days precontest exceeded
the ‘natural’ cut-off of 25 generated by Kouri et al2 (see page 9)
despite him having single-digit bodyfat percent. On the contest
day it was just under that threshold.
References
1. Lichtman SW, et al. Discrepancy between self-reported and
actual caloric intake and exercise in obese subjects. N Engl J
Med. 1992 Dec 31;327(27):1893-8. [Medline]
2. Kouri EM, et al. Fat-free mass index in users and nonusers of
anabolic-androgenic steroids.Clin J Sport Med. 1995
Oct;5(4):223-8. [Medline]
Peanut M&Ms
One of the fun things about prepping clients for shows, other
than watching them get lean while eating more than just tuna and
broccoli, is getting into some of the psychological aspects of
competition. A fun tradition I have with clients is the M&M
trick. In a nutshell, during and surrounding prejudging, there can
be time lapses that leave the competitors even more hungry,
depleted, and nervous (most competitors on show day are
already dehydrated and miserable to begin with). This is where
peanut M&Ms come into the picture. I had Kelechi buy 3 bags
Alan Aragon’s Research Review – April 2010
Credit to Jamie Hale for tipping me off to this, I’d like to share
an impressive piece of work that delves into the fundamentals of
bro-proofing. Click here to download Using Research & Reason
in Education, by Paula & Keith Stanovich.
If you have any questions, comments, suggestions, bones of
contention, cheers, jeers, guest articles you’d like to submit, or
any feedback at all, send it over to aarrsupport@gmail.com.
[Back to Contents]
Page 12