MULTISCALE RELATIONSHIPS OF LIGAMENT MECHANICS
by
Trevor Justin Lujan
A dissertation submitted to the faculty of
The University of Utah
in partial fulfillment of the requirements for the degree of
Doctor of Philosophy
Department of Bioengineering
The University of Utah
December 2007
Copyright © Trevor Justin Lujan 2007
All Rights Reserved
ABSTRACT
Healthy knee joints require structural stability through a full range of motion.
Knee stability is primarily provided by a network of ligaments that resist abnormal
forces and direct smooth articulations. Ligament malfunction has deleterious effects on
balance, agility, and knee laxity. Altered knee laxity redistributes the stress transmitted
across articulations, which may create regional stress concentrations that are potentially
damaging to articular cartilage.
The high incidence of knee ligament injuries and
shortfalls of related treatments are thus adverse to knee joint health. As the function of
ligament is essentially mechanical, understanding the tissue-scale and molecular-scale
relationships that mechanically influence ligament is critical to functional restoration.
The aim of this dissertation was to strengthen the scientific knowledge of mechanical
relationships that impact ligament function in the knee. Toward this objective, a tissuescale relationship was investigated between the anterior cruciate ligament (ACL) and the
medial collateral ligament (MCL). These knee ligaments are frequently injured and
have inter-dependent functionality.
At the molecular-scale, mechanical interactions
between glycosaminoglycans (GAGs) and collagen fibrils may impact gross ligament
function. Therefore, the influence of these molecular relationships on ligament material
behavior was inspected.
Experimental and numerical modeling techniques (finite element analysis) were
employed to fulfill these objectives. In an ACL-deficient knee, the MCL was more
susceptible to damage during anterior tibial translation, as strains reached values
corresponding to the threshold of microstructural failure. The MCL midsubstance and
insertions were functionally insensitive to ACL transection during valgus loading of the
knee.
Interfibrillar GAGs were found to have negligible influence on tissue-scale
mechanics during tensile loading. Therefore, interfibrillar GAGs do not directly support
ligaments’ primary function of resisting tensile loads, which contradicts the popular
theory that GAGs transmit forces between adjacent collagen fibrils. By determining the
effect of multiscale relationships on ligament function, improvements are possible in the
innovation and evaluation of ligament treatment modalities.
v
To my loving wife and family.
They are my joy and inspiration.
TABLE OF CONTENTS
ABSTRACT..................................................................................................................... iv
LIST OF TABLES........................................................................................................... ix
LIST OF FIGURES........................................................................................................... x
CHAPTER
1.
INTRODUCTION................................................................................................ 1
Motivation............................................................................................................. 1
Research Goals...................................................................................................... 3
Summary of Chapters............................................................................................ 3
References……………………..............................................................................6
2.
BACKGROUND ................................................................................................ 10
Knee Joint Overview............................................................................................10
Ligament Composition and Molecular Organization.......................................... 17
Ligament Contribution to Knee Joint Pathology................................................. 22
Experimental Biomechanics of Ligament........................................................... 26
Numerical Modeling of Ligament....................................................................... 28
References............................................................................................................ 34
3.
SIMULTANEOUS MEASUREMENT OF THREE-DIMENSIONAL
JOINT KINEMATICS AND LIGAMENT STRAINS WITH OPTICAL
METHODS. ........................................................................................................ 45
Abstract................................................................................................................ 45
Introduction.......................................................................................................... 46
Materials and Methods......................................................................................... 48
Results ................................................................................................................. 53
Discussion ........................................................................................................... 59
References............................................................................................................ 62
4.
EFFECT OF ACL DEFICIENCY ON MCL STRAINS AND JOINT
KINEMATICS..................................................................................................... 64
Abstract................................................................................................................ 64
Introduction.......................................................................................................... 65
Materials and Methods......................................................................................... 67
Results ................................................................................................................. 73
Discussion ........................................................................................................... 79
References............................................................................................................ 85
5.
MCL INSERTION SITE AND CONTACT FORCES
IN THE ACL-DEFICIENT KNEE...................................................................... 90
Abstract................................................................................................................ 90
Introduction......................................................................................................... 91
Materials and Methods........................................................................................ 93
Results ............................................................................................................... 100
Discussion ......................................................................................................... 106
References.......................................................................................................... 112
6.
EFFECT OF DERMATAN SULFATE GLYCOSAMINOGLYCANS
ON THE QUASI-STATIC MATERIAL PROPERTIES OF THE
HUMAN MEDIAL COLLATERAL LIGAMENT........................................... 116
Abstract.............................................................................................................. 116
Introduction........................................................................................................ 117
Materials and Methods....................................................................................... 121
Results ............................................................................................................... 127
Discussion ......................................................................................................... 132
References.......................................................................................................... 137
7.
VISCOELASTIC TENSILE BEHAVIOR OF HUMAN LIGAMENT
AFTER INTERFIBRILLAR GLYCOSAMINOGLYCAN DIGESTION........ 143
Abstract.............................................................................................................. 143
Introduction........................................................................................................ 144
Materials and Methods...................................................................................... 146
Results ............................................................................................................... 154
Discussion ......................................................................................................... 163
References.......................................................................................................... 169
8.
DISCUSSION.................................................................................................... 175
Summary............................................................................................................ 175
Limitations and Future Work............................................................................. 180
References.......................................................................................................... 187
viii
LIST OF TABLES
Table
Page
3.1. Results for measurement of 3D precision.........................................................54
3.2. Accuracy of 3D simulated strain measurement along
the z- and x-axes for all four strain levels......................................................... 56
3.3. Accuracy of 3D kinematic measurements........................................................ 58
6.1. Physical dimensions of the MCL test specimens............................................ 122
7.1. Mechanical properties for all eight test groups before and after treatment.....154
LIST OF FIGURES
Figure
Page
2.1. An x-ray picture of the medial knee................................................................ 11
2.2. Illustration of the anterior knee joint. .............................................................. 13
2.3. Illustration of the posterior knee joint.............................................................. 14
2.4. Illustration of the quadricep and hamstring muscle groups............................. 16
2.5. Schematic of the structural hierarchy of tendon and ligament......................... 19
3.1. Plane view of the camera setup........................................................................ 50
3.2. Photograph of test setup for simultaneous measurement
of MCL strain and knee joint kinematics......................................................... 50
3.3. Simulated 3D strain error along the z-axis and x-axis..................................... 57
3.4. Results for the measurement of 3D kinematics along the z-axis direction...... 58
4.1. Location of fiducial markers ........................................................................... 69
4.2. Schematic of the loading apparatus ................................................................. 70
4.3. Experimental results from anterior-posterior translation.................................. 74
4.4. Experimental results from varus-valgus rotation.............................................. 76
4.5. MCL strain changes due to ACL transection at peak anterior translation
and valgus rotation........................................................................................... 77
4.6. Interdependence of the ACL and tibial axial rotation....................................... 80
5.1. FE predicted vs. experimental fiber stretch for all knees............................... 102
5.2. Representative fringe plots of FE predicted fiber strain................................. 103
5.3. FE predictions of insertion site forces for femoral and tibial insertion sites.. 105
5.4. FE predictions of contact forces between the MCL and femur
and between the MCL and tibia...................................................................... 107
6.1. Multiscale schematic of the proposed glycosaminoglycan interaction
between collagen fibrils. ............................................................................... 119
6.2. Stress-Strain curves for tensile tests of control and
chondroitinase B treated ligament samples………........................................ 128
6.3. Stress-Strain curves for shear tests of control and
chondroitinase B treated ligament samples……………................................. 128
6.4. Specificity of Chondroitinase B...................................................................... 130
6.5. Decorin western blot of proteins extracted from 3 control
and 3 ChB treated ligament samples…………………................................... 131
6.6. Representative TEM images of tissue stained with
Cupromeroneic Blue after mechanical testing……........................................ 132
7.1. Illustration of specimen acquisition................................................................ 148
7.2. Diagram of the viscoelastic protocol............................................................... 149
7.3. Reduced relaxation as a function of oscillatory frequency............................. 155
7.4. Dynamic modulus as a function of oscillatory frequency............................... 157
7.5. Phase shift as a function of oscillatory frequency........................................... 159
7.6. Time history of the unloaded samples wet weight
through the course of the experiment. ............................................................ 161
7.7 Water content of the time-zero reference samples (native),
the incubated samples that were not mechanically tested (unloaded),
and the incubated samples that were mechanically tested (loaded)......…...... 161
.
xi
CHAPTER 1
INTRODUCTION
Motivation
The knee is the largest synovial joint in the human body and is also the most
vulnerable musculoskeletal structure [1]. In addition to transferring loads between the
femur and tibia of up to 7x body weight [2], the knee must functionally permit the broad
range of motion required for bipedal gait. To enable this mobility, a complex network
of tendons and ligaments primarily stabilize the knee and guide smooth articulations.
Through overuse, repetitive physical activity and abnormal loading, knee ligaments are
prone to tear and are responsible for over 6 million hospital visits a year [1, 3]. In the
short-term, knee ligament damage will restrict participation in certain sports and
activities [4-6], while in the long-term, ligament damage can predispose the knee to
debilitating osteoarthritis by altering gait patterns [7-9].
In order to return patients with knee ligament damage to normal activity levels
and to obstruct osteoarthritic progression, clinicians and scientists have developed both
surgical and nonsurgical treatment strategies. There are over 100,000 reconstructions of
knee ligaments performed annually in the United States [10]. Ligament reconstruction
vastly improves the gait and function of joints [11][12]. However, reconstructed knees
are still beset with abnormal gait [13-15] and athletic performance is significantly
hampered postsurgery [16]. Even ligaments that are adept at healing with conservative
2
treatment experience reductions in mechanical properties [17, 18] that can alter knee
laxity [19, 20]. Moreover, ligament injury can cause physical [21, 22] and chemical
alterations [23] to intact ligaments that may hamper the effectiveness of clinical
treatments.
The inadequacies that exist in the present management of ligament injury can be
attributed to technological limitations and deficiencies in our underlying knowledge of
ligament function. It is self-evident that a ligament’s function derives from molecular
interactions within its structure and is dependent on intrinsic relationships with other
joint stabilizers. Therefore, a fundamental understanding of these inter- and intraligament relationships is absolutely necessary to develop strategies (drug therapy,
functional tissue engineering, novel reconstruction procedures) that restore or enhance
the native function of ligament. Since the role of ligament is mechanical by nature,
mechanically based experimental and theoretical studies are appropriate to examine
these tissue and molecular scale (multiscale) relationships.
Advances
in
experimental
technologies,
computational
methods
and
biochemistry have recently enabled multiscale relationships to be investigated in detail.
High resolution digital cameras have progressed to give an accurate and efficient means
to track tissue deformations for mechanical analysis [24]. The astounding growth of
computer power combined with improvements in medical imaging has propelled the
science of numerical modeling [25-27]. Moreover, burgeoning progress in molecular
biology has impelled the quantification and manipulation of tissue microstructure [2830]. By blending these tools, the multiscale relationships that define ligament function
3
can be comprehensively examined and applied to the treatment and prevention of
ligament injuries.
Research Goals
The overall objective of this research was to clarify multiscale biomechanical
relationships in ligament. Advancement of the treatment and prevention of joint injury
and disease will be facilitated by a concise understanding of the mechanical
relationships within and between joint stabilizers.
Experimental and theoretical
biomechanics, along with biochemical techniques, were utilized to accomplish the
research objective. At the tissue level, ex vivo structural experiments were employed to
determine the functional interrelationship of primary knee stabilizers. Computational
finite element models were then generated to examine aspects of this interaction that
would otherwise be difficult or impossible to measure experimentally. At the molecular
level, the mechanical relationship of collagen molecules with ground substance
constituents was examined by performing a series of elastic and viscoelastic material
tests before and after enzymatic treatment. Finally, the solid-fluid phase interaction of
ligament was briefly examined both experimentally and computationally to investigate
the mechanical relevance of fluid flow during tensile deformation.
Summary of Chapters
The mechanical relationships investigated in this dissertation start at the tissue
scale and move to the molecular scale. The primary purpose of Chapter 2 was to
provide sufficient background on the topics that will be addressed in the later chapters.
This background information includes an overview of the structure and function of the
4
knee joint along with the pathologies of the knee joint associated with ligament injury.
The microstructural composition and organization of ligament is detailed along with
mathematical models and numerical methods that describe ligament material behavior.
To comprehensively study the structural interactions of ligaments, an
experimental technique was developed in Chapter 3 that simultaneously measures threedimensional joint kinematics and localized ligament strain.
These coupled
measurements link specific movements to functional ligament regions.
A high-
resolution optical tracking system was appropriate for this type of measurement, and
was rigorously validated. The excellent precision and accuracy of this system make it
an attractive tool for biomechanical experiments.
Chapter 4 describes the use of this validated technique to experimentally
examine the relationship between the anterior cruciate ligament (ACL) and the medial
collateral ligament (MCL). These two ligaments are primary knee stabilizers, have
overlapping roles and account for 70% of multiligament injuries in the knee [31]. The
objective of this study was to determine how ACL deficiency affects the local strain in
the MCL during loading configurations applicable to these ligaments. To meet this
objective, kinematic motions were applied to a prescribed force before and after ACL
transection under a variety of loading conditions. The results of this study provide
physiological and clinical insight into the interaction of these two primary knee
stabilizers.
Certain physical phenomena associated with ligament mechanics, such as
insertion and contact forces, are difficult or impossible to measure experimentally. One
method to approximate these inhomogeneous forces is by means of a validated finite
5
element model [27]. Therefore, to further examine the relationship between the ACL
and MCL, Chapter 5 focuses on using the experimental kinematics and strain
measurements from Chapter 4 to generate subject-specific finite element (FE) models
[26].
As a means of validation, the experimental strain values at peak load were
compared to the FE predictions. This study not only improved upon past advances in
subject-specific modeling, but helped clarify the specific tensile and compressive
stresses projected upon the MCL after ACL transection.
In Chapters 6 and 7, the focus of this dissertation shifts to the influence of
microstructure on tissue-scale mechanics.
The chapter objectives are to determine
whether the mechanical interactions of noncollagenous constituents play a role in the
continuum material behavior of ligament.
Understanding the origins of ligament
material behavior is applicable to functional tissue engineering and basic science. The
possible mechanical role of sulfated glycosaminoglycans (sGAGs) was investigated.
SGAGs include dermatan and chondroitin sulfate, which have been widely implicated as
contributors to the mechanical response of ligament. By performing experiments before
and after sGAG degradation, the mechanical influence of these noncollagenous
constituents was elucidated. Chapter 6 inspects the unique role of dermatan sulfate in
the elastic mechanics of ligament, while Chapter 7 researches the contribution of
dermatan and chondroitin sulfate on the viscoelastic material properties. Results are
related to relevant prior investigations, along with basic and applied science.
The final chapter highlights the major contributions that this dissertation has
made to the field of ligament biomechanics and comments on future work that is
necessary to expand upon this research.
The discussion on future work includes
6
preliminary work looking at the roles of other noncollagenous constituents and
experimental factors that could be improved to better assess functionality of the ground
substance. In this section, a strong emphasis was placed on a biphasic mathematical
representation of ligament that describes the physical solid-fluid interaction that occurs
in soft-tissue. The current state of this work was included along with the long-term
perspective.
References
[1]
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Summary," Ret. July 5, 2007, from http://www.cdc.gov/nchs/data/ad/ad374.pdf.
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Taylor, W. R., Heller, M. O., Bergmann, G., and Duda, G. N., 2004, "Tibiofemoral Loading During Human Gait and Stair Climbing," J Orthop Res, 22(3),
pp. 625-632.
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Miyasaka, K., et al., 1991, "The Incidence of Knee Ligament Injuries in the
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[4]
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J., 1995, "Injury Surveillance at the USTA Boys' Tennis Championships: A 6-yr
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with an ACL Deficient Knee," Br J Sports Med, 40(5), pp. 451-453.
[6]
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Changes at the Knee after ACL Injury Cause Cartilage Thinning," Clin Orthop
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[9]
Fink, C., Hoser, C., Hackl, W., Navarro, R. A., and Benedetto, K. P., 2001,
"Long-term Outcome of Operative or Nonoperative Treatment of Anterior
7
Cruciate Ligament Rupture--Is Sports Activity a Determining Variable?," Int J
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[11]
Nakayama, Y., Shirai, Y., Narita, T., Mori, A., and Kobayashi, K., 2000, "Knee
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[12]
Bulgheroni, P., Bulgheroni, M. V., Andrini, L., Guffanti, P., and Giughello, A.,
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Jomha, N. M., Pinczewski, L. A., Clingeleffer, A., and Otto, D. D., 1999,
"Arthroscopic Reconstruction of the Anterior Cruciate Ligament with Patellartendon Autograft and Interference Screw Fixation. The Results at Seven Years,"
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Laxdal, G., Kartus, J., Ejerhed, L., Sernert, N., Magnusson, L., Faxen, E., and
Karlsson, J., 2005, "Outcome and Risk Factors After Anterior Cruciate Ligament
Reconstruction: A Follow-up Study of 948 Patients," Arthroscopy, 21(8), pp.
958-964.
[15]
Georgoulis, A. D., Ristanis, S., Chouliaras, V., Moraiti, C., and Stergiou, N.,
2007, "Tibial Rotation is Not Restored After ACL Reconstruction with a
Hamstring Graft," Clin Orthop Relat Res, 454, pp. 89-94.
[16]
Carey, J. L., Huffman, G. R., Parekh, S. G., and Sennett, B. J., 2006, "Outcomes
of Anterior Cruciate Ligament Injuries to Running Backs and Wide Receivers in
the National Football League," Am J Sports Med, 34(12), pp. 1911-1917.
[17]
Majima, T., Lo, I. K., Marchuk, L. L., Shrive, N. G., and Frank, C. B., 2006,
"Effects of Ligament Repair on Laxity and Creep Behavior of an Early Healing
Ligament Scar," J Orthop Sci, 11(3), pp. 272-277.
[18]
Abramowitch, S. D., Woo, S. L., Clineff, T. D., and Debski, R. E., 2004, "An
Evaluation of the Quasi-linear Viscoelastic Properties of the Healing Medial
Collateral Ligament in a Goat Model," Ann Biomed Eng, 32(3), pp. 329-335.
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Indelicato, P. A., 1983, "Non-operative Treatment of Complete Tears of the
Medial Collateral Ligament of the Knee," J Bone Joint Surg (Am), 65(3), pp.
323-329.
8
[20]
Kannus, P., 1988, "Long-term Results of Conservatively Treated Medial
Collateral Ligament Injuries of the Knee Joint," Clinical Orthopaedics and
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[21]
Markolf, K. L., Mensch, J. S., and Amstutz, H. C., 1976, "Stiffness and Laxity of
the Knee--The Contributions of the Supporting Structures. A quantitative In
Vitro Study," J Bone Joint Surg Am, 58(5), pp. 583-594.
[22]
Yasuda, K., Erickson, A. R., Beynnon, B. D., Johnson, R. J., and Pope, M. H.,
1993, "Dynamic Elongation Behavior in the Medial Collateral and Anterior
Cruciate Ligaments During Lateral Impact Loading," J Orthop Res, 11(2), pp.
190-198.
[23]
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F., Rattner, J. B., Hart, D. A., and Frank, C. B., 2007, "Periarticular Ligament
Changes Following ACL/MCL Transection in an Ovine Stifle Joint Model of
Osteoarthritis," J Orthop Res, 25(8), pp. 997-1006.
[24]
Zhang, D., and Arola, D. D., 2004, "Applications of Digital Image Correlation to
Biological Tissues," J Biomed Opt, 9(4), pp. 691-699.
[25]
Elias, J. J., and Cosgarea, A. J., 2007, "Computational Modeling: an Alternative
Approach for Investigating Patellofemoral Mechanics," Sports Med Arthrosc,
15(2), pp. 89-94.
[26]
Gardiner, J. C., and Weiss, J. A., 2003, "Subject-specific Finite Element Analysis
of the Human Medial Collateral Ligament During Valgus Knee Loading," J
Orthop Res, 21(6), pp. 1098-1106.
[27]
Anderson, A. E., Peters, C. L., Tuttle, B. D., and Weiss, J. A., 2005, "Subjectspecific Finite Element Model of the Pelvis: Development, Validation and
Sensitivity Studies," J Biomech Eng, 127(3), pp. 364-373.
[28]
Robinson, P. S., Huang, T. F., Kazam, E., Iozzo, R. V., Birk, D. E., and
Soslowsky, L. J., 2005, "Influence of Decorin and Biglycan on Mechanical
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181-185.
[29]
Puxkandl, R., Zizak, I., Paris, O., Keckes, J., Tesch, W., Bernstorff, S., Purslow,
P., and Fratzl, P., 2002, "Viscoelastic Properties of Collagen: Synchrotron
Radiation Investigations and Structural Model," Philos Trans R Soc Lond B Biol
Sci, 357(1418), pp. 191-197.
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Lee, D. A., 2005, "The Influence of Noncollagenous Matrix Components on the
9
Micromechanical Environment of Tendon Fascicles," Ann Biomed Eng, 33(8),
pp. 1090-1099.
[31]
Kaeding, C. C., Pedroza, A. D., Parker, R. D., Spindler, K. P., McCarty, E. C.,
and Andrish, J. T., 2005, "Intra-articular Findings in the Reconstructed
Multiligament-injured Knee," Arthroscopy, 21(4), pp. 424-430.
CHAPTER 2
BACKGROUND
Knee Joint Overview
The knee is the most complex synovial joint in the human body and is welldesigned to withstand large (~7x body weight) and repetitive loads [3]. For example,
the knee of an average jogger will retain 16,000 impacts per week, equivalent to 2400
tons of force [4]. The knee offers mobility in all six degrees of freedom. However, the
joint primarily functions in the sagittal plane, where it has a flexion range of 100
degrees. At full extension, the knee is highly stable due to a “screw home mechanism”
that locks the joint over the final 20 degrees of extension [5]. Uniform articulating
contact during flexion is maintained by a smooth sliding and rolling mechanism between
the tibial and femoral surfaces [6].
Although the knee is best suited to transfer
compressive loads, it must also resist large torques due to forces that twist the lower
extremity. At full extension, the knee is capable of resisting 90 N-m of valgus and 120
N-m of tibial axial torque [7]. The mobility and strength that is axiomatic to proper
knee function is provided by the articulating surfaces, musculature and ligaments.
Articulations
Synovial, or diarthrodial, joints are classified as having a synovial filled capsule
that surrounds the articulating bones. The knee has two separate articulations: the
11
patellofemoral joint and the tibiofemoral joint (Figure 2.1). These joints are technically
condyloid joints, where convex condyles articulate with concave surfaces; however,
since they mainly support motion in one planar direction (sagittal), they act more like
modified hinges. The convex articulations of the patellofemoral joint are the oval
medial and lateral condyles, while the trey-shaped tibial plateau provides the concave
surface. Lying between the femoral condyles, the intercondyler notch forms the patellar
groove where the patella slides, or tracks, upon during flexion and extension. The
specific motion of articulating bones can be quantified through kinematic measurements,
such as those performed in Chapter 4 and 5 of this dissertation.
The bony interfaces of these articulations are mediated by a ~2 mm layer of
articular or hyaline cartilage [8]. Articular cartilage is 80% water and has qualities inbetween bone and dense connective tissue.
It is resilient, flexible, and in the
surrounding synovial fluid has a coefficient of friction 40x lower than Teflon [9, 10]. In
mature cartilage tissue, chondrocyte cells are sparsely distributed. The solid phase of
Patella
FemoroTibial
Joint
Femur
Tibia
FemoroPatellar
Joint
Figure 2.1: An x-ray picture of the medial knee at 30
deg flexion.
12
the extracellular matrix is predominantly type II collagen (~60%) and proteoglycans
(~30%). The collagen interfibrillar space contains approximately 30% of the water,
while the proteoglycan solution domain holds 70% of the water [11]. The cartilage
surfaces are surrounded by a vascularized dense membrane called a perichondrium. The
cartilage structure itself is avascular and aneural. Therefore, nutrients must diffuse from
the perichondrium through the cartilage matrix to reach the cellular network of
chondrocytes. The extracellular matrix is responsible for the mechanical properties of
cartilage.
Collagen provides tensile strength and grips aggregated proteoglycans
through physical entrapment and weak electrostatic and hydrogen-bonding [12].
Through dynamic and equilibrium factors, the keratin sulfate and chondrotin sulfate
glycosaminoglycans associated with the cartilage proteoglycans are able to retain water
under compression. The retention of nearly incompressible water provides a solid-fluid
dynamic modulus up to 50x higher than the equilibrium modulus [13-15], which is
measured after water exudation.
The load bearing and shock absorption capability of the tibiofemoral articulation
is augmented by the fibrocartilaginous semilunar menisci that sit on the medial and
lateral concavities of the tibial plateau (Figures 2.2, 2.3). The menisci transmit 50% and
85% of the joint load at full extension and 90 deg flexion, respectively [16]. The
menisci also increase the contact area of the proximal tibia by 50-70% [16] and attenuate
intermittent shock waves developed during gait [17]. Structurally, the menisci are fiber
reinforced composites (74% water by wet weight) that primarily insert into the anterior
and posterior meniscal horns (i.e., the points of the crescent shaped menisci). The solid
phase consists mostly of dense Type I collagen fibers with small amounts of
13
Figure 2.2: Illustration of the anterior knee
joint. Adapted from [1].
14
Anterior
cruciate ligament
Popliteus tendon
Lateral meniscus
Lateral collateral
ligament
Figure 2.3: Illustration of the posterior knee
joint. Adapted from [1].
Ligament of
Wrisberg
Medial meniscus
Medial collateral
ligament
15
proteoglycans (<1% wet weight). The collagen has a circumferential orientation in the
interior and a radial orientation on the surface.
These orientations are adept at
supporting the enormous hoop stresses that develop during joint compression [18, 19].
Muscles
The musculature provides passive (muscle tone) and dynamic support to the knee
joint via forces applied to the tendenous inserts from muscular contraction. Muscle
consists of a network of nerves and vessels, a muscle-specific extracellular matrix and
muscle cells that contain multiple nuclei. Two major muscle groups cross the knee and
enhance joint stability: the quadriceps muscle group (Figure 2.4A) and the hamstring
muscle group (Figure 2.4B). Coactivation of these muscle groups during gait aids the
ligaments in maintaining stability and equalizes the distribution of stress on the articular
cartilage [20]. Valgus and varus stability of the knee is increased by 28-35% when the
medial and lateral compartments are actively contracted [21], and Hughston et al. [22]
found that medial knee stability was 25% stiffer with contraction of the
semimembranosous muscle (Figure 2.4B). The hamstring group also reduces anterior
tibial translation during gait [23]. The joint position sense [24] that contributes to joint
stability and function is improved by stretching the muscles that surround the knee [25].
When muscle hypertrophies, the knee ligaments are at an increased risk of damage [20].
Ligaments
Ligaments are short bands of dense fibrous tissue that bind bone to bone. There
are four major ligaments of the knee that play a primary role in joint stability: the
anterior cruciate ligament (ACL), the posterior cruciate ligament (PCL), the medial
16
Semimembranosus
Muscle
Semitendinosus
Muscle
Quadriceps
and Patellar
Tendons
Biceps
Femoris
Muscle
Iliotibial Band
A
B
Gastrocnemious Muscle
Figure 2.4: Illustration of the A) quadricep and
B) hamstring muscle groups of the knee.
17
collateral ligament (MCL) or tibial collateral ligament, and the lateral collateral ligament
(LCL) or fibular collateral ligament (Figures 2.2, 2.3). The joint capsule and other knee
ligaments (e.g., posterior medial corner, medial and lateral retinacullum, the popliteal
ligament and the meniscofemoral and meniscotibial ligament) provide additional
stability. Individually, knee ligaments act as primary and/or secondary restraints to
specific motions, while collectively they limit and passively guide all six degrees of
freedom in the knee joint. This function is supported by an elastic modulus that is over
300 times greater than skin [26-28].
The MCL has an elastic modulus and maximum
stress of 332 MPa and 38 MPa [27], respectively, while the ACL has an elastic modulus
and maximum stress of 345 MPa and 36 MPa [29], respectively. These mechanical
characteristics of ligament are a function of their composition and molecular
organization.
Ligament Composition and Molecular Organization
The structural organization of ligament follows a hierarchy. In general, ligament
can be described as a fiber reinforced matrix, where fibrous collagen fascicles provide
structural integrity to the extrafibrillar matrix. Fibroblasts are regularly distributed in
the extrafibrillar matrix and are responsible for maintaining the integrity of the collagen
fascicles. The orientation of the fascicles is tissue dependent and can be parallel,
oblique, and helical relative to the primary loading direction. When unloaded, collagen
fibers form a sinusoidal crimp pattern that gradually straightens when strained, which
forms the upwardly concave stress-strain behavior of ligament under tension [30].
Ligament is highly hydrated and 70% water by weight [31]. Of the dry weight fraction,
18
collagen accounts for 65-80% of the total solid phase [32]. The remaining constituents
include extracellular matrix proteins (e.g., elastin) and proteoglycans [33, 34].
The insertion sites of ligament have a dramatically different composition that has
adapted to reduce stress concentrations that occur between the ligament-bone interface.
Two types of insertions sites exist: direct insertions and indirect insertions. Direct
insertions are found at the femoral insertion in the MCL, and have fibrocartilage and
mineralized fibrocartilage zones between deep ligament fibers and bone.
Indirect
insertions are found at the tibial insertion of the MCL, and are characterized by
superficial fiber fixation directly to the bone without fibrocartilagenous transitional
zones. At both the insertions and midsubstance, collagen is the molecular constituent
most responsible for resisting tensile loads [35-37].
Collagen
Collagen proteins provide the “connective” ability of connective tissue and
therefore, not surprisingly, constitute 25% of the total protein mass in mammals. Type I
collagen represents the largest dry-weight fraction in ligament (~70%)[32].
The
assembly of collagen fascicles in ligament begins with the secretion of Type I collagen
molecules by fibroblast cells. A collagen molecule, or tropocollagen unit (300 nm in
length and 1.5 nm in diameter), is a tightly wound triple helix of polypeptide chains, or
alpha chains, that contain about 1000 amino acids each. In the extracellular matrix,
tropocollagens covalently cross-link into a quarter stagger pattern to make collagen
microfibrils (10-300 nm in diameter)( Figure 2.5). Structural stability of collagen is a
product of the covalent crosslinks formed between alpha chains and between adjacent
19
Figure 2.5: Schematic of the structural hierarchy of tendon and ligament.
Adapted from Kasterlic [2].
tropocollagen units. Microfibrils amass to form collagen fibrils, and fibrils aggregate to
form collagen fibers. Round or spindle shaped fibroblasts sparsely occupy the spaces
between these fibers. Fibers group to form fiber bundles, or fascicles, which measure
many micrometers in diameter and can be seen without a microscope.
Other types of collagen in ligament, such as types XII, are fibril-associated
collagens. These collagens are formed similarly to type I collagen, but do not aggregate
to form fibrils in the extracellular matrix. Instead, they bind periodically to the existing
fibrils. It is hypothesized that these collagens mediate the interfibril interactions along
with fibril-macromolecular interactions [38]. However, this subgroup of collagen has
not been specifically identified with any tissue-level mechanical function.
20
Extrafibrillar Matrix
The extrafibrillar matrix represents the noncollagenous components of the
extracellular matrix. These components include proteins such as elastin and fibronectin,
along with proteoglycans and water. Elastin is a highly extensible protein that is a
composite of single elastin molecules covalently cross-linked.
In ligament, elastin
accounts for less than 1% of ligament dry weight [39] and is found in abundance as
longitudinal oriented fibers that run in parallel to collagen fibrils. Elastin mechanically
contributes to the toe-region of the stress response at low strains when the collagen
fibers are still partially crimped [40]. The glycoprotein fibronectin constitutes 1-2
μg/mg of dry tissue in the major knee ligaments [41]. Fibronectins function by coupling
normal and growing tissue elements, and are required in vitro for collagen organization
and fibroblast deposition [42].
Proteoglycans account for less than 1% of ligament dry weight and consist of a
protein core covalently bonded to one or numerous glycosaminoglycans. Proteoglycans
expressed in ligament include the small leucine-rich proteoglycans decorin and biglycan,
and the hyalectan proteoglycans versican and aggrecan [43]. Decorin accounts for 90%
of total proteoglycan content in ligament [43]. The high presence of decorin is likely
due to its role in controlling the diameter and integrity of Type I collagen [43]. Decorin
is secreted in the fibroblasts and “decorates” the collagen fibrils by attaching to gap
spaces between longitudinally adjacent tropocollagen units [44]. The number of GAG
side chains depends on the proteoglycan.
Decorin has a single GAG side chain,
dermatan sulfate, and biglycan has two GAG side chains that are chiefly dermatan
21
sulfate [45]. Versican binds 10-30 GAGs and aggrecan 100 GAGs of chondroitin and
keratan sulfate [45].
Glycosaminoglycans play a significant role in the association of water with the
extrafibrillar matrix and may contribute to the mechanical response of ligament [46, 47].
Biochemically, GAGs are sulfated polysaccharides, which mediate tissue hydration
through electrostatic and nonelectrostatic interactions.
Hydration from electrostatic
interactions has been termed “charge effect,” and it occurs when the negative fixed
charge density of sulfated GAGs attract cations, and thus create a concentration
imbalance that swells the tissue through osmotic gradients [48]. This charge effect is
reduced as the negative fixed charges become neutralized in hypertonic solution. Nonelectrostatic swelling is represented by configurational entropy of the GAG chains in
solution. When the solution is hypotonic, the negative charges within the GAGs repulse
each other, leading to stiff GAG formations that have low entropy. In hypertonic
solution (i.e., 1 M of NaCl,), the charge effect is neutralized and increased flexibility of
GAG chain configurations leads to higher entropy and water intake [49]. The charge
and configuration effects have an inverse relationship that oppositely influences swelling
depending on the tonicity of the solution [49]. These water retention mechanisms of
GAGs enhance the ability of articular cartilage to bear substantial loads in compression
[50, 51], and may similarly affect the mechanics of ligament. Further, GAG to GAG
interactions may act as a mechanical link between discontinuous collagen fibrils [46, 52]
and are a principal topic of Chapters 6 and 7.
22
Ligament Contribution to Knee Joint Pathology
Although the characteristic crimp pattern and molecular organization of ligament
is highly adapted to control tension, irreversible damage occurs when the mechanical
limits of the tissue is exceeded [53]. In 2004, there were 14.2 million visits to the
hospital due to knee pain [54], and an estimated 45% of those visits were related to
ligament injury [55].
The prevalence of knee ligament injuries is linked to the
nonconforming articular surfaces of the knee. For example, a healthy hip is primarily
stabilized from the deep “ball and socket” geometry of the femoral head and acetabulum
cup, whereas the knee is minimally stabilized from the shallow “modified hinge”
geometry of the tibial plateau and femoral condyles. Therefore, the connective tissues
of the knee are burdened with stabilizing a highly mobile joint.
The MCL and ACL are the most commonly injured ligaments of the knee [55].
The MCL primarily restricts medial joint opening, and overstretching of the MCL
typically occurs during participation in sports such as football and soccer due to a direct
blow to the lateral side of the knee [56]. The MCL also restrains external rotation.
Sports that rigidly-fix elongated equipment on the foot, such as alpine skiing, are
exceptionally well suited to applying large external rotational torques on the knee. For
this reason, the MCL is the most common injury during alpine skiing and accounts for
18% of all ski related injuries [57].
The ACL primarily restricts anterior tibial
translation and internal tibial rotation. Injury to the ACL occurs in pivot sports and
nearly 70% of all ACL injuries are noncontact [58]. In skiing, ACL tears account for
16% of injuries [57], and frequently occur when the skier falls back, a motion that places
tremendous stress on the ACL as the tibia translates in the anterior direction. Females
23
have a higher incidence of ACL injuries compared to males, which has been related to
measurably lower yield strength and modulus of elasticity in the female ACL [59] along
with biochemical changes related to hormone fluctuations [60]. Combined MCL and
ACL injuries are also common and account for 70% of all multiligament injuries [61].
The interdependent functions of the ACL and MCL relate to their frequent and
associated injuries. Consequently, the relationship between these ligaments is the focus
of Chapters 4 and 5.
When ligaments are torn, the failure typically occurs through the tissue
substance, but can also occur by bony avulsion and in rare occurrences at the insertion
junction [62, 63]. In general, bony avulsion and junction failures have better outcomes
than when the tissue substance is disrupted [64]. Tissue substance failures in the MCL
most frequently occur near the femoral or superior insertion [65]. The extra-articular
MCL receives adequate blood supply, and after an initial inflammatory phase, matrix
and cellular proliferation begins. By the time remodeling and maturation is completed
after 12 months, however, the MCL has only 50-70% of its original strength [66, 67].
The two synergistic bundles of the ACL most commonly tear near the femoral insertion,
although the rupture pattern of the two bundles differs in 44% of patients [68]. The
intraarticular ACL receives significantly less blood than the MCL [69] and therefore,
unlike the MCL, a midsubstance rupture cannot heal [70].
The dissimilar healing mechanisms of the MCL and ACL have warranted
different treatment regiments. The widespread opinion with MCL injuries is that nonoperative treatment is equally as effective as surgical intervention and that exercise
accelerates recovery [71].
Biomechanical analysis has shown that conservative
24
treatment of transected canine MCLs yields better biomechanical properties than
surgical treatment [67]. Avulsion injuries to the MCL, when minimally displaced, can
be treated nonsurgically, but otherwise are recommended for surgical reattachment of
the soft tissue to the denuded bone with sutures or screws [72].
While MCL surgery is uncommon, ACL surgery is extremely prevalent.
Reconstructive ACL surgery is performed on 75,000 – 100,000 people each year in the
United States [73]. Complete ACL ruptures do not heal spontaneously and can cause
gross instabililty due to knee subluxation. The ACL reconstruction procedure typically
involves removing the ruptured ACL and replacing the ligament with an autograft or
allograft from the semitendinousus tendon or patellar ligament by fixing the grafts in
reamed femoral and tibial channels. There are also strong advocates of nonsurgical
treatment, which focuses on improving joint stability through muscular conditioning
[74]. However, reports have revealed that 60-80% of patients treated nonsurgically
experience unsatisfactory outcomes in high-risk populations [75, 76]. Unfortunately,
even ACL reconstruction often does not completely restore pre-injury gait [77] or joint
stability and studies have shown a large percentage of knees with reconstructed ACLs
have unsatisfactory athletic performance [78-80].
In the short-term, knee instability due to ligament injury limits participation in
certain sports and activities [81], but in the long-term, these injuries can lead to the more
severe and debilitating pathology of osteoarthritis. Osteoarthritis is a joint disease that
results in the breakdown and eventual loss of the protective layer of hyaline cartilage
between the articulating bones. This condition affects 21 million people in the United
States and accounts for 25% of visits to the primary care physicians [82]. The only
25
treatment for this painful disease is total knee replacement. In 2002, 350,000 primary
total knee replacements were performed in the United States [83].
Although this
operation has been extremely successful at relieving painful osteoarthritic symptoms,
there is an extensive recovery period and normal joint motion and function is
permanently restricted. Therefore, considerable research has focused on preventing the
necessity of knee replacement.
Along with developing technology to regenerate
cartilage through tissue-engineering methods [84, 85], an emphasis has been placed on
identifying the etiology of this disease. Osteoarthritis has been connected to age-related
water loss in articular cartilage [86, 87] and recent studies have shown that ligament
injury can result in early progression of this disease [88].
The link between ligament pathology and osteoarthritis resides with altered gait
and biological adaptations that occur after ligament trauma. For example, the movement
patterns in the ACL deficient knee will exhibit increased flexion during the swing phase
and a greater hip extensor moment to reduce anterior tibial translation [77, 80] . These
gait changes are identified with redistribution of stresses on the articular cartilage. A
study by Andriacchi et al. [88] correlated a shift in the normal load bearing regions of
the knee joint after ACL rupture with increased cartilage thinning and Maffulli et al.
[89] found that 42% of ACL-deficient patients had a lesion on weight bearing regions of
the articular cartilage.
The mechanoreceptors that protect the knee through
proprioreceptive mechanisms are also damaged during ligament rupture [90].
Mechanoreceptors give afferent signals of the spacial limb position to dynamically
control muscle and are most active at the limits of motion where joint supports are
susceptible to injury [91]. Elimination of these receptors disrupts ligament-muscle
26
communication. Further, injury to one ligament in the knee can result in histological,
biochemical and molecular changes to the other knee ligaments, even if they do not
share functional roles [92]. One can conclude that joints behave like an integrated organ
system, and it is not surprising that ligament damage and cartilage wear are related. It is
therefore imperative to restore ligament mechanical function for long-term health of the
knee. Proof of ligament restoration in meaningful terms of joint structural stability and
ligament material properties is cardinal, and thus biomechanical experimentation is an
indispensable tool for evaluating the efficacy of repair and reconstruction techniques.
Experimental Biomechanics of Ligament
The function of joint stabilizers is mechanical in nature and understanding the
mechanical characteristics of ligament has many clinical impacts.
Biomechanical
studies have improved the prevention of ligament injury through intelligent design of
athletic equipment [7], playing surface interfaces [93] and prophylactic braces [94].
Quantifying ligament mechanical behavior is necessary to assess surgical techniques and
verify the efficacy of harvested or engineered grafts [95, 96]. The mechanisms of tissue
repair and structural integrity can be elucidated to improve wound healing modalities
and tissue engineering constructs [97, 98]. Finally, biomechanical studies of ligament
can be used to define constitutive models that mathematically describe experimental
observations. Constitutive models are essential in using computational techniques to
comprehensively analyze the complex relationships of joints and provide treatment
strategies [99].
Experimental mechanic studies in ligament can be grouped into
structural and material tests.
27
Structural tests involve the physiological manipulation of whole or partial joints
while kinematic and force data are collected. These experiments determine joint motion
limits and the role of and interaction between joint stabilizers. The first structural
experiments on cadaveric knees were performed by Brantigan in 1941 [100]. Brantigan
fixed the femur and applied torques and forces to the tibia at various flexion angles. The
simplification of these early experiments has evolved into modern systems that better
mimic physiological loading configurations. One such system is the Oxford Rig, which
permits six degrees of freedom (DOF) by simulating a flexed knee stance through
tension of the quadircieps tendon [101]. Another recent system is a robotic arm that can
operate in force and displacement mode in up to six DOF by finding the passive knee
flexion path in joints [102]. These and other structural testing systems have been used to
discern the primary and secondary ligamentous restraints in knees and the forces that
ligaments transfer from bone-to-bone.
Limitations of structural knee tests include
problems inherent to in vitro experiments as well as misrepresentation of passive
residual forces in muscle. Structural tests are also unable to characterize the behavior of
a specific tissue substance; however, this can be accomplished with material tests.
Material tests are used to characterize the fundamental relationship between
stress, strain and other state variables. This relationship may behave in an elastic or
viscoelastic manner and may depend on material symmetries. Like all biological tissue,
ligaments have elastic and viscous attributes. Elastic materials lose no energy during
deformation and behave independent of time, while viscoelastic materials dissipate
energy during deformation and have time dependent characteristics such as relaxation,
28
creep, and strain-rate sensitivity. Material symmetry classifies whether a certain tissue
has any preferred directions for a given loading configuration.
To perform a material test the tissue must be isolated and inserted into a material
testing system where tensile, compressive or shear loads are applied. Loads are applied
along one axis, or along multiple axes to better represent physiological loading patterns.
Ligaments can be isolated at their bony insertions [103] or be punched from the tissue
substance [27]. Material symmetry is determined by testing a material in various cutting
planes. In ligament, when tensile testing is aligned with visible collagen fascicles the
material is much stiffer than when tensile testing is aligned transverse to these fibers
[27].
This type of material symmetry has one preferred direction and is termed
transverse isotropy. The elastic material properties can be tested by 1) loading the
material to different strain-levels and allowing equilibration and 2) loading in a quasistatic manner to reduce inertial effects and time-dependent viscoelastic effects. The
viscoelastic relaxation and creep properties are determined by deforming the tissue to a
constant displacement or force, respectively. Strain-rate sensitivity is measured by
varying loading rates. Finally, damping or energy dissipation can be measured by
determining the hysteresis or energy dissipation during a loading-unloading cycle. The
phase shift that occurs between sinusoidal displacements and the oscillatory force
response also reflects this damping behavior [104].
Numerical Modeling of Ligament
Constitutive Models
Once the material properties of a tissue are determined, it is useful to represent
the observed response mathematically for predictive modeling.
The mathematical
29
interpretation of the material is a constitutive relationship that objectively describes the
interaction of stress, strain and other state variables such as strain rate and time.
Constitutive models contain coefficients that are fit to the experimental results through
numerical methods such as curve fitting [105], which can be supplemented with inverse
finite element methods [106]. Soft tissues experience large nonlinear deformations during
normal activity and therefore the constitutive relationship must be a function of variables
that are independent of rotation and have a nonlinear response to strain.
One constitutive model that can accommodate this tissue response is a
hyperelastic strain energy model. The strain energy W represents the stored energy in
the system, and the hyperelastic model permits large deformations by being a function of
the right Cauchy-Green deformation tensor C, which is unaffected by rigid body
rotation. For ligament under tension, the material behavior can be described reasonably
well by a transversely isotropic strain energy [107]:
W = F1 ( I1 ) + F2 (λ ) +
K
(ln( J )) 2
2
(1)
Here F1 is the matrix strain energy and F2 is the collagen fiber strain energy. These two
terms represent the stored energy due to deviatoric deformation. The matrix strain
energy is a function of the first deviatoric invariant, I1. The collagen fiber strain energy
is a function of the deviatoric part of the stretch ratio along the fiber direction, λ. The
dilational, or pressure response, is a function of the determinant of the deformation
gradient (i.e., volume change), J, and the bulk modulus, K. The mathematical functions
for each of these components are selected to best describe the tissue being modeled. In
ligament, a neo-Hookean model is commonly chosen to represent the matrix strain
30
energy [107]. With this simple model, the matrix function is only a dependent on the I1
invariant. The collagen fibers are given piecewise continuous functions for stress that
have physical interpretations [108]. Under compression, the fiber strain energy equation
is zero, because collagen structures tend to buckle under small compressive forces.
During initial tension, ligament will exhibit nonlinear toe region as collagen fibers
uncrimp until the fibers are uniformly straightened at some stretch threshold, λ* This is
mathematically represented by including an exponential function from the start of tensile
stretch to λ*. After the tissue is stretched past this threshold, the fiber strain energy is
linearly governed by the modulus of the straightened fibers.
The stress is derived from the strain energy (energy per unit volume):
S=2
∂W
,
∂C
(2)
where S is the 2nd Piola-Kirchhoff stress and C is the right Cauchy-Green deformation
tensor. The 2nd Piola-Kirchhoff stress can easily be converted to other stress forms such
as engineering stress (1st Piola-Kirchhoff Stress) and Cauchy stress.
A limitation of this hyperelastic strain energy material is that it does not
represent the innate viscoelasticity in ligament. Y-C Fung introduced the quasilinear
viscoelastic (QLV) model in 1973 [109] to better represent the strain-history dependent
characteristics of biological tissue. In the QLV theory, the tensile stress is the product of
a reduced relaxation function (3A) and an elastic stress function (3B):
t
T (t ) =
∫ G(t − τ )
−∞
(A)
∂T ( e ) [λ (τ )] ∂λ (τ )
dτ ,
∂λ
∂τ
(B)
(3)
31
where T is the Cauchy stress, G is the reduced relaxation function, τ denotes time, t
denotes time limit, and λ is stretch. The mechanism of this equation is that the reduced
relaxation function (3A) decays for increasing values of t-τ. This decaying function
introduces time-history dependence and acts to scale the elastic stress. The chain rule is
used to express the instantaneous change in elastic stress (3B). Thus, these two terms
scale stress at an instant in time, and the total stress T is calculated by superposing the
instantaneous stresses over the entire stress history of the response.
The QLV model can accommodate any well posed elastic (e.g., hyperelasticity)
and reduced relaxation function (e.g., continuous spectrum of kelvin models).
Limitations of the QLV model include the nonphysical meaning of the parameter inputs
and its inability to model the experimentally observed dependence of damping on strainlevel [110]. Poroelasticity theory is an alternative constitutive model that has physically
meaningful inputs and can possibly support strain-dependent damping in ligament.
The poroelastic constitutive model was first implemented in biological tissue by
Mow et al. [111] to account for the deformation of the solid matrix and interstitial fluid
flow in articular cartilage. The force interface between these two phases is a function of
fluid pressure and solid/fluid velocity, therefore, a transitory response exists and the
theory captures viscoelastic attributes.
The poroelastic model is formulated from
fundamental principles, which includes the equations of motion for the solid and fluid
phase:
π +div(Ts ) = 0
(4)
-π+div(Tf ) = 0
(5)
32
where Tf and Ts are the stress tensors of the solid and fluid phase, respectively. An extra
reaction force, π, is present in both equations from the phase interaction.
The
constitutive model specified for the solid-phase stress tensor can be any well constructed
constitutive model, including equations from hyperelastic strain energy and QLV
theories. The constitutive model specified for the fluid-phase can be a Newtonian
viscous fluid, however, an ideal non-viscous fluid is often assumed and hence the stress
tensor for the fluid phase equates to the fluid pressure. A constitutive relation is also
specified for the frictional force vector, π. This drag force is typically a function of
relative phase velocity, fluid pressure and permeability (constant or strain dependent).
The conservation of mass for the mixture requires:
div(φs v s + φ f v f ) = 0
(6)
where vs and vf are the respective solid-phase and fluid-phase velocities, and φs and φf
are the respective solid-phase and fluid-phase volume fractions. The coefficient inputs
to the poroelastic model include parameters (e.g., permeability) that can be
experimentally measured [112]. The physical meaning of the coefficients has inherent
advantage over more phenomenological based models such as the QLV theory. The
poroelastic model has traditionally been used to describe compression-loaded tissues
such as cartilage [113], but theoretically it can also predict material behavior under any
loading configuration. The ability of the biphasic model to predict stress-relaxation and
harmonic oscillation behavior in ligament is briefly explored in the final chapter.
33
Finite Element Models
The background of this dissertation concludes with a brief dialogue on the finite
element method. The finite element method was developed to calculate how force and
displacements influence the stress and strain continuum in single or multiple bodies. For
3D finite element analysis, the stress and strain continuum is solved over the volume of
the object(s) being analyzed.
The basic rationale is that calculations are greatly
simplified for repeated geometric shapes, such as tetrahedrals and hexahedrals.
Therefore, complex geometries are first discretized into a large group of simplified
shapes that can be aggregated to solve for the system. After discretization, boundary
conditions are applied (preprocessing) and the analysis is initiated.
The vector
displacements are determined at each node by minimizing the systems potential energy
(internal strain energy + external forces) with a quasi-Newton method.
Specified
constitutive models and the associated coefficients are necessary for this calculation.
Since the potential energy needs to be integrated over the volume of the system,
discontinuous nodal displacements can not be used and it is necessary to evaluate
differentiable shape functions. Once the total potential energy has been minimized at a
load step, the process starts again for the next load step until the analysis is complete.
Postprocessing is performed to visualize predictions.
Finite element methods are well established in traditional engineering disciplines
and are becoming increasingly pervasive in biological sciences as their utility becomes
recognized [99, 114]. To bridge the gap between clinician and engineer, however, it is
critical to rigorously validate an FE model by coupling experimental and theoretical
work [115]. Strong advances by Gardiner et al. [108] in validating FE models of knee
34
ligaments have paved the way for a clinically relevant injury model of the knee to be
simulated in this dissertation. A validated FE model grants access to a full continuum of
mechanical data and represents a successful synergy between experimental
biomechanics, mathematical constitutive relations, and numerical methods.
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CHAPTER 3
SIMULTANEOUS MEASUREMENT OF THREE-DIMENSIONAL
JOINT KINEMATICS AND LIGAMENT STRAINS WITH
OPTICAL METHODS1
Abstract
The objective of this study was to assess the precision and accuracy of a nonproprietary, optical three-dimensional (3D) motion analysis system for the simultaneous
measurement of soft tissue strains and joint kinematics. The system consisted of two
high-resolution digital cameras and software for calculating the 3D coordinates of
contrast markers. System precision was assessed by examining the variation in the
coordinates of static markers over time. Accuracy of 3D strain was assessed by moving
contrast markers fixed distances in the field of view and calculating the error in
predicted strain. For kinematic measurements 3D accuracy was assessed by simulating
the measurements that are required for recording joint kinematics. The field of view
(190 mm) was chosen to allow simultaneous recording of markers for soft tissue strain
measurement and knee joint kinematics. Average system precision was between ±0.004
mm and ±0.035 mm, depending on marker size and camera angle.
Absolute
___________________
1
Reprinted from Journal of Biomechanical Engineering, Vol. 127, No. 1, Trevor J.
Lujan, Spencer P. Lake, Timothy A. Plaizier, Benjamin J. Ellis, Jeffrey A. Weiss,
“Simultaneous Measurement of Three-dimensional Joint Kinematics and Ligament
Strains with Optical Methods,” pp: 193-197, 2005, with permission from the ASME
46
error in strain measurement varied from a minimum of ±0.025% to a maximum of
±0.142%, depending on the angle between cameras and the direction of strain with
respect to the camera axes. Kinematic accuracy for translations was between ±0.008 and
±0.034 mm, while rotational accuracy was ±0.082 to ±0.160 deg.
These results
demonstrate that simultaneous measurement of 3D soft tissue strain and 3D joint
kinematics can be performed while achieving excellent accuracy for both sets of
measurements.
Introduction
The measurement of strain is of fundamental interest in the study of soft tissue
mechanics. In studies of musculoskeletal joint mechanics, the accurate measurement of
three-dimensional joint kinematics is equally important. By simultaneously quantifying
the strains in soft tissues such as ligaments and the joint kinematics in response to
externally applied loads, it is possible to elucidate the role of these structures in guiding
and restraining joint motion and to identify potential injury methods and clinical
treatments [1-3]. Further, simultaneous acquisition of joint kinematics and strain fields
can be used to drive and validate subject-specific models of ligament and joint
mechanics [4].
The simultaneous measurement of joint kinematics and soft tissue strain is
typically accomplished by using a combination of two or more different technologies.
Joint kinematics are commonly quantified using video-based techniques [5,6],
instrumented spatial linkages (ISLs) [7,8] or electromagnetic tracking systems [9-11].
ISL systems require the attachment of a bulky mechanical linkage across the joint, while
electromagnetic tracking systems are often plagued by interference from ferrous
47
materials, limiting their applicability. In contrast, there are relatively few techniques
that are capable of measurement of 3D soft tissue strains. Alternatives include the use of
1D measurements from contact devices such as DVRTs [12, 13]. Optical methods are
currently considered to be the best option for 3D strain measurement on visible soft
tissues.
These methods use the direct linear transformation to calculate 3D strain
measurements from two or more cameras [4,14].
Previous optical systems were
primarily based on super VHS video, yielding an effective vertical resolution of 400
lines. This limited resolution requires the use of extremely small fields of view to
achieve accuracies of ±0.1-0.5% error in percent strain [14].
This precludes the
simultaneous tracking of markers for kinematic measurements, since a larger field of
view is needed to see both the strain and kinematic markers.
Currently available
systems based on digital cameras typically use vendor-supplied proprietary cameras
and/or framegrabbers. These systems primarily use digital cameras that have much
better resolution and sensitivity than video-based systems.
However, the use of
proprietary vendor-supplied hardware is often costly and ties the support and upgrade of
the system to a particular vendor or system integrator.
Due to ongoing improvements in the sensitivity and resolution of charge-coupled
devices (CCDs), modern progressive-scan digital cameras can provide images with very
high quality and resolution.
The improved spatial resolution (typically at least
1024x1024) opens up the possibility of using a field of view that is large enough to track
markers for both soft tissue strain and joint kinematics.
The use of cameras and
framegrabbers from individual vendors is especially attractive since it eliminates the
need for proprietary, vendor-specific hardware and software. The objective of this study
48
was to develop a methodology for simultaneous measurement of 3D soft tissue strain
and joint kinematics using a nonproprietary digital camera system, and to quantify the
errors associated with these measurements in a test setup that mimicked the study of
knee ligament biomechanics.
Materials and Methods
Measurement Systems
The measurement system consisted of two high-resolution digital cameras
(Pulnix TM-1040, 1024x1024x30 frames per second (fps), Sunnyvale, CA) equipped
with 50 mm 1:1.8 lenses and extension tubes, two frame grabbers (Bitflow, Woburn,
MA) and Digital Motion Analysis Software (DMAS, Spica Technology Corporation,
Maui, HI). The cameras were configured to record 6 fps directly to computer memory,
requiring 2.1 MB of memory per frame. The cameras were focused at a target with a
190 mm diagonal field of view (FOV). The DMAS software tracked marker centroids
in both camera views automatically and applied the modified direct linear
transformation (DLT) to calculate the 3D centroid coordinates [15]. Preliminary tests
demonstrated that black markers against a white background provided superior contrast
and therefore system accuracy in comparison to markers covered with reflective tape,
while two 100 W incandescent lights provided better contrast than halogen or
fluorescent lighting. In the following sections, all instrument accuracy values are per the
manufacturer.
A 3D calibration frame was manufactured. Twenty-seven white Delrin spherical
markers (4.75 mm dia.) were arranged in three horizontal planes, with a 3x3 grid pattern
on each plane and 60 mm marker spacing. The exact coordinates of each marker
49
centroid were determined with a coordinate measuring machine (Zeiss Eclipse 4040,
accuracy ±0.0004 mm). These coordinates were used for DLT calibration.
Precision
The precision was determined by examining the variation of the 3D positions of
stationary markers over time. After calibration, two different frames with twelve 4.75
and 2.38 mm dia. spherical markers were recorded for 25 seconds. The dimensions of
these markers were chosen to be the exact same size as the kinematic markers (4.75 mm
dia) and the strain markers (2.38 mm dia) used during actual biomechanical testing in
our laboratory. The variation in marker position was determined by computing two
standard deviations of the length of their position vector and the individual x, y and z
coordinates over time. Experiments were repeated at camera angles of 30, 60 and 90
deg (Figure 3.1). To evaluate the precision of the system in actual test conditions, the
variation of kinematic (4.75 mm dia) and strain (2.38 mm dia) marker positions were
determined for four-sets of three-second passive recordings taken at a 30° camera angle
during biomechanical testing of a human medial collateral ligament (MCL) (Figure 3.2).
Complete details of this test configuration and marker placement can be found in our
previous publications [4,14].
Accuracy of Simulated Strain Measurement
Accuracy tests were performed dynamically to determine the ability of the
system to measure simulated 3D changes in strain. The effects of strain magnitude and
camera angle were assessed. A 2.38 mm dia. marker was adhered to a fixed location,
while a similar marker was adhered 13.5 mm apart (Linitial) to a linear actuator (Tol-O-
50
Y-Axis
Objective
X-Axis
Camera
Angle (θ)
Z-Axis
Figure 3.1. Plan view of the camera setup. The z-axis is directed out of the page. To
assess system sensitivity to camera angle, angles of 30, 60 and 90 deg were used during
testing.
Medial Meniscus
Distal
Femur
Proximal
Tibia
Tibial
Kinematic Block
Femoral
Kinematic
Block
Figure 3.2. Photograph of test setup for simultaneous measurement of MCL strain and
knee joint kinematics. Eighteen markers (2.38 mm diameter) were adhered to the MCL
for strain measurement. Femoral and tibial kinematic blocks, each with three kinematic
markers (4.75 mm diameter), were affixed to the cortical bone.
51
Matic, Inc, Hamel, MN, accuracy ±0.0025 mm). The Linitial was chosen to replicate the
spacing between markers used to calculate 3D strains in the human MCL [4,14]. In two
separate tests, the actuator was translated (ΔL) along either the z- or x-axis in Figure 3.1
to simulate strains of 1, 2, 5 and 20%. Tests were performed four times for each
displacement.
Accuracy was calculated as the difference between the predicted
displacement and the known actuator displacement.
The error in simulated strain
measurement was computed by determining the absolute difference between actuator
strain and DMAS strain (ΔL/Linitial).
Accuracy of Kinematic Measurements
When tracking joint kinematics, it is desirable to establish “embedded”
coordinate systems within the bones using a convention such as the one described by
Grood and Suntay [16].
The transformation matrix between embedded coordinate
systems is established by tracking markers on the bones that define separate “marker”
coordinate systems. The transformation between one marker coordinate system and the
corresponding embedded coordinate system on a bone does not change during testing.
By establishing these transformations before testing and then tracking the transformation
between marker coordinate systems during testing, the transformation between
embedded coordinate systems can be determined [4,14].
To assess kinematic
measurement accuracy, the setup and calculations necessary to record knee joint
kinematics were simulated. Two L-shaped white blocks (the “kinematic blocks,” Figure
3.2) with three 4.75 mm dia. black markers that formed a 90 deg angle were used to
establish marker coordinate systems. The following tests were repeated four times for
each translation or rotation, at camera angles of 30, 60 and 90 deg.
52
To measure accuracy of translations along the z-axis in Figure 3.1 from
kinematic calculations, one kinematic block was adhered to a static fixture and a second
one was attached to the linear actuator. An Inscribe 3D Digitizer (Immersion Corp, San
Jose, CA accuracy ±0.085 mm) was used to determine the centroids of the markers by
digitizing points on the marker surface and then fitting the coordinates to the equation of
a sphere. To simulate the use of embedded coordinate systems, three points on both the
static fixture and the actuator were digitized and used to establish orthonormal
coordinate systems. The transformation matrices were calculated between the kinematic
blocks and their respective embedded coordinate systems. The actuator was displaced
0.500, 1.000, 5.000 and 50.000 mm and an overall transformation matrix between the
embedded systems was calculated by concatenation.
The ratio of the calculated
translations to the known translations was computed.
To measure accuracy of translation measurements along the x-axis in Figure 3.1
from kinematic calculations, a kinematic block was adhered to an x-y table and the table
was moved 0.50, 1.00, 5.00, and 50.00 mm, measured with digital calipers (Mitutoyo,
San Jose, accuracy ±0.02 mm). Error was calculated as the ratio of translation predicted
from the motion analysis data to the known translation.
To determine accuracy of rotations about the z-axis in Figure 3.1, a rotational
actuator (Tol-O-Matic, Inc, Hamel, MN, accuracy ±0.002°) was used to rotate one of the
kinematic blocks through angles of 2.00° and 20.00°.
The transformation matrix
between the two embedded coordinate systems was calculated and the rotation between
the two systems was resolved using the method of Grood and Suntay [16]. The ratio of
the rotation angle from the motion analysis data to the known angle was determined.
53
Statistical Analysis
The effects of camera angle and marker size on 3D precision were assessed using
a two-way ANOVA with repeated measures. The effects of camera angle and strain
magnitudes on x-axis and z-axis strain accuracy were assessed using two separate twoway ANOVAs with repeated measures. The effects of camera angle on x-axis and zaxis translational kinematic accuracy and z-axis rotational kinematic accuracy were
assessed using two separate two-way ANOVAs with repeated measures. Statistical
significance was set at p<0.05 for all analyses.
Results
Precision
Results for precision were excellent for both marker sizes (Table 3.1). The
larger markers exhibited significantly better precision than the smaller markers
(p=0.005). There was no effect of camera angle on marker precision (p=0.089). The
best results (±0.004 mm, 0.0020 % FOV) were obtained for the larger markers using a
30-deg camera angle. Precision did not vary considerably between the x, y and z
coordinates of the markers. For example, at a 60 deg camera angle, the larger markers
had x, y and z coordinate precisions of 0.019, 0.019 and 0.012 mm,
respectively. The precisions for both marker sizes obtained with a MCL biomechanical
test setup were comparable to precisions for the controlled tests (Table 3.1, 4th row).
54
Table 3.1. Results for measurement of 3D precision. The first three rows
of data were obtained by recording stationary markers for 25 seconds with
a FOV of 190 mm. The fourth row was obtained from passive recording
acquired during biomechanical testing of a human MCL. Each passive
recording was approximately 3 seconds long, with a camera angle of 30°,
and a FOV of 190 mm. Absolute precision was calculated as two standard
deviations of the position measurement, while Percent FOV was
calculated as the precision divided by the FOV multiplied by 100.
4.75 mm dia. Markers
2.38 mm dia. Markers
Camera Angle (θ)
Precision
(mm)
Percent FOV
(%)
Precision
(mm)
Percent FOV
(%)
30°
0.004
0.0020%
0.035
0.0163%
60°
0.011
0.0049%
0.025
0.0117%
90°
0.006
0.0026%
0.012
0.0048%
MCL Study (30°)
.009
0.0044%
0.011
0.0054%
55
Accuracy of Simulated Strain Measurement
The optical system delivered excellent results for strain error (Table 3.2). There
was a significant effect of camera angle on accuracy for z- and x-axis strain accuracy
(p=0.004 and p<0.001, respectively, Figure 3.3). The most accurate camera angle for
strains along the z-axis was 30 deg, having an average accuracy of ±0.005 mm with a
strain error of ±0.035%. Conversely, the most accurate camera angle when strains were
measured along the x-axis was 90 deg, having average accuracies of ±0.003 resulting in
a strain error of ±0.025%.
There was a significant effect of strain magnitude on
accuracy for z- and x-axis strain accuracy (p=0.008 and p<0.001, respectively). The
condition conferring the least accuracy occurred for both the z-axis and x-axis cases at
90 deg and 30 deg, respectively, when a 20% strain was applied.
Accuracy of Kinematic Measurements
The optical system delivered very good results for kinematic accuracy (Table
3.3).
Data for z-axis kinematic accuracy are shown as an example (Figure 3.4). The
average x- and z-axis translational accuracies across all three camera angles and all four
actuator displacements were ±0.025 and ±0.016 mm, respectively. Average accuracy for
rotation was ±0.124 deg (Table 3.3). There was a significant effect of camera angle on
accuracy of kinematic measurements of translation along the z- and x-axes (p=0.029 and
p<0.001, respectively), but there was no effect of camera angle on kinematic rotational
accuracy (p=0.378).
The effect of camera angle on the kinematic translational
accuracies was similar to that for the strain accuracies. There was a significant effect of
the magnitude of translation/rotation on accuracy of kinematic measurements of
Table 3.2. Accuracy of 3D simulated strain measurement along the z- and x-axes for all four strain levels. Accuracy (mm)
was calculated as the difference between the actuator-based value and the value calculated from the optical system data.
Strain error (%) is the accuracy divided by the gauge length (13.5 mm) multiplied by 100.
Camera
Angle (θ)
30 deg
z
axis
60 deg
90 deg
30 deg
x
axis
60 deg
90 deg
1.0%
Strain
2.0%
Strain
5.0%
Strain
20.0%
Strain
Averages across
all Strains
Accuracy (mm)
0.004 ± 0.007
0.011 ± 0.003
0.003 ± 0.004
0.001 ± 0.007
0.005 ± 0.005
Strain Error (%)
0.028 ± 0.052
0.084 ± 0.018
0.019 ± 0.030
0.010 ± 0.052
0.035 ± 0.033
Accuracy (mm)
0.002 ± 0.001
0.013 ± 0.005
0.005 ± 0.005
0.009 ± 0.012
0.007 ± 0.006
Strain Error (%)
0.014 ± 0.009
0.099 ± 0.037
0.040 ± 0.035
0.067 ± 0.091
0.055 ± 0.037
Accuracy (mm)
0.009 ± 0.001
0.016 ± 0.003
0.011 ± 0.004
0.024 ± 0.005
0.015 ± 0.003
Strain Error (%)
0.065 ± 0.006
0.115 ± 0.025
0.081 ± 0.027
0.175 ± 0.034
0.109 ± 0.049
Accuracy (mm)
0.011 ± 0.002
0.011 ± 0.002
0.019 ± 0.002
0.036 ± 0.003
0.019 ± 0.012
Strain Error (%)
0.081 ± 0.018
0.082 ± 0.014
0.138 ± 0.017
0.267 ± 0.024
0.142 ± 0.088
Accuracy (mm)
0.002 ± 0.002
0.018 ± 0.002
0.006 ± 0.011
0.003 ± 0.002
0.007 ± 0.007
Strain Error (%)
0.015 ± 0.015
0.130 ± 0.017
0.045 ± 0.082
0.021 ± 0.011
0.053 ± 0.053
Accuracy (mm)
0.002 ± 0.001
0.006 ± 0.001
0.003 ± 0.007
0.003 ± 0.001
0.003 ± 0.002
Strain Error (%)
0.017 ± 0.008
0.045 ± 0.007
0.022 ± 0.049
0.018 ± 0.006
0.025 ± 0.013
56
Absolute Strain Error (% strain)
57
A
30° Camera Angle
0.25
60° Camera Angle
90° Camera Angle
0.20
0.15
0.10
0.05
1.0
2.0
5.0
20.0
Absolute Strain Error (% strain)
Z-Axis Strain (%)
B
30° Camera Angle
60° Camera Angle
90° Camera Angle
0.25
0.20
0.15
0.10
0.05
1.0
2.0
5.0
20.0
X-Axis Strain (%)
Figure 3.3. A - Simulated 3D strain error along the z-axis. B – Simulated 3D strain
error along the x-axes. Strain Error was computed as the difference between the
actuator-based strain and the strain calculated by the motion analysis system, divided by
the gauge length.
58
Table 3.3. Accuracy of 3D kinematic measurements. Accuracy is the difference
between the actuator translation/rotation and the value calculated from the optical
system data. Accuracy in terms of Percent FOV is the accuracy divided by FOV
multiplied by 100. Results are the average across all translations/rotations descibed
in the Methods section.
Translation (x-axis)
Translation (z-axis)
Rotation (z-axis)
Camera
Angle (θ)
Accuracy
(mm)
% FOV
Accuracy
(mm)
% FOV
Accuracy
(deg)
% FOV
30 deg
0.034 ± 0.031
0.012 ± 0.004
0.008 ± 0.011
0.005 ± 0.006
0.132 ± 0.162
0.074 ± 0.091
60 deg
0.018 ± 0.005
0.009 ± 0.006
0.019 ± 0.023
0.009 ± 0.010
0.082 ± 0.069
0.043 ± 0.036
90 deg
0.021 ± 0.002
0.009 ± 0.008
0.020 ± 0.017
0.006 ± 0.005
0.160 ± 0.218
0.074 ± 0.101
0.05
30° Camera Angle
60° Camera Angle
90° Camera Angle
Accuracy (mm)
0.04
0.03
0.02
0.01
0.5
1.0
5.0
Z-Axis Translation (mm)
Figure 3.4. Results for the measurement of 3D kinematics along the z-axis direction.
Accuracy was measured as the difference between the actuator based translation and the
value calculated by the motion analysis system, divided by the actuator translation.
59
translation along the z- and x-axes and rotation about the z-axis (p<0.001, p<0.001 and
p=0.033, respectively). Larger translations/rotations reduced kinematic accuracy.
Discussion
This study demonstrated that the 3D system can accurately measure simulated
strain and kinematics using physical and optical conditions that accommodate
simultaneous tracking of markers for both measurements. A reduced camera angle
significantly improved accuracy for frontal plane (z-axis) displacements, while an
increased camera angle significantly improved accuracies of displacements along the
intersection of the saggital and transverse planes (x-axis). Moreover, in comparison to
similar systems using proprietary vendor-specific hardware, this system is a small
fraction of the cost.
Accuracy and precision of the system were determined using a testing
environment that was specifically designed to mimic the physical and optical conditions
for experiments on the human MCL in intact knees. For actual testing of the human
MCL, only precision was determined. It is not possible to determine strain accuracy
during an actual biomechanical test since a gold standard for strain measurements is
difficult if not impossible to establish. However, by setting all testing variables (i.e.,
FOV, marker size, marker spacing, lighting) appropriately, the controlled tests faithfully
reproduced the physical and optical conditions of actual biomechanical tests for the
MCL. Results for precision from the controlled tests and from actual measurements on
the MCL were similar (Table 3.1), supporting the notion that the controlled tests
provided a good surrogate for the physical and optical conditions that are encountered
during actual tests on the MCL.
60
System precision (Table 3.1) was calculated using the length of the position
vectors of the markers, and thus these measurements should be considered average
errors that take into account the precision in all three spatial directions.
For the
simulated strain measurements, the major spatial directions were accounted for by
testing along the x- and z-axes. By positioning the cameras at an angle that permitted
the most marker motion perpendicular to the cameras, therefore reducing the
incremental distances that each pixel in the video system represents, the strain error was
significantly decreased. For z-axis strains this occurred at a 30 deg camera angle, and
for x-axis strains this occurred at a 90 deg camera angle. Based upon the accuracy
results (Figure 3.3), it is recommended that these camera angles be optimized
accordingly, especially if large strains are predicted.
The measurements of strain
accuracy should be considered a best case when considering general measurements in
3D using comparable camera angles.
Although a similar argument applies to the
kinematic measurements, the “gold standard” for these measurements was based on a
combination of digitizer, actuator encoder, or digital caliper measurements. Because of
differences in the accuracy of these measurement techniques and the propagation of
errors in the kinematic measurements, the results for translational and rotational
kinematic accuracies likely represent worst cases.
This error propagation is likely
responsible for the reduction of accuracy with increased axial translation (Figure 3.4).
As with any system based on video or digital cameras, the most important
determinants of precision and accuracy are the resolution of the CCD and the FOV used
for measurements. This assumes that an accurate DLT calibration has been performed
and that this calibration has taken into account errors associated with lens distortion. In
61
this study, the FOV was chosen to allow simultaneous tracking of markers for strain and
kinematic measurements in the context of studying the human MCL [4,14]. Limitations
on the rate of data transfer from the camera to the framegrabber cards and then to
computer memory, primarily imposed by the bandwidth of the computer system’s bus,
result in a tradeoff between the frame rate and spatial resolution of the CCD. Cameras
with higher resolution CCDs typically have slower frame rates. This limitation will
likely be eliminated with improvements in computer architecture. Marker contrast is
also very important, with improved contrast yielding better system precision and thus
accuracy.
During actual biomechanical testing, contrast may become reduced by
extraneous objects in the foreground and background. Draping the testing backdrop and
fixtures with white material, and applying white gauze to any uninvolved tissue that may
darken the captured image will alleviate this problem. Extreme specimen discoloration
could similarly reduce marker contrast. Affixing the strain markers to the tissue with
adhesives may cause local strain abnormalities; therefore a minimal amount of adhesive
should be applied. Finally, the physical size of a CCD affects the sensitivity through its
responsivity and dynamic range, with larger CCDs yielding better sensitivity and thus
better image quality (see, e.g., [17]). The cameras used in this study had 1” CCDs, the
largest size that was available.
In summary, the 3D measurement system provided excellent accuracy for
simulated strain measurement and very good accuracy for kinematic measurements. The
absolute and percent errors are considered to be more than acceptable for simultaneous
3D measurements of ligament strain and joint kinematics.
62
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CHAPTER 4
EFFECT OF ACL DEFICIENCY ON MCL STRAINS
AND JOINT KINEMATICS1
Abstract
The knee joint is partially stabilized by the interaction of multiple ligament
structures.
This study tested the interdependent functions of the anterior cruciate
ligament (ACL) and the medial collateral ligament (MCL) by evaluating the effects of
ACL deficiency on local MCL strain while simultaneously measuring joint kinematics
under specific loading scenarios.
A structural testing machine applied anterior
translation and valgus rotation (limits 100 N and 10 N m, respectively) to the tibia of 10
human cadaveric knees with the ACL intact or severed. A three-dimensional motion
analysis system measured joint kinematics and MCL tissue strain in 18 regions of the
superficial MCL.
ACL deficiency significantly increased MCL strains by 1.8%
(p<0.05) during anterior translation, bringing ligament fibers to strain levels
characteristic of microtrauma. In contrast, ACL transection had no effect on MCL
strains during valgus rotation (increase of only 0.1%).
Therefore, isolated valgus
________________________
1
Reprinted from Journal of Biomechanical Engineering, Vol. 129, No. 3, Trevor J.
Lujan, Michelle S. Dalton, Brent M. Thompson, Benjamin J. Ellis, Jeffrey A. Weiss,
“Effect of ACL Deficiency on MCL Strains and Joint Kinematics,” pp: 386-92, 2007,
with kind permission from the ASME.
65
rotation in the ACL-deficient knee was nondetrimental to the MCL. The ACL was also
found to promote internal tibial rotation during anterior translation, which in turn
decreased strains near the femoral insertion of the MCL. These data advance the basic
structure-function understanding of the MCL, and may benefit the treatment of ACL
injuries by improving the knowledge of ACL function and clarifying motions that are
potentially harmful to secondary stabilizers.
Introduction
The mechanical functions of knee ligaments are interrelated, with multiple soft
tissue structures contributing to joint stability under externally applied loading
conditions [1, 2]. The overlapping function of the anterior cruciate ligament (ACL) and
medial collateral ligament (MCL) is a prime example of this concept, as these ligaments
share responsibility in stabilizing anterior translation of the tibia and valgus joint
opening [3]. Injuries to the ACL and MCL account for 26% of knee trauma [4], with
combined ACL/MCL injuries comprising 70% of all multiligament knee injuries [5].
Isolated MCL injuries often adequately heal without surgical intervention. However,
conservatively treated ACL injuries have a high incidence of unsatisfactory outcomes [6,
7]. Even ACL reconstructed knees exhibit abnormal kinematics [8-10] that may lead to
cartilage degeneration [11].
Due to the relationship between the ACL and MCL,
treatment of combined or isolated ACL injuries may be improved by an understanding
of the mechanical effects of ACL deficiency on MCL function.
The current knowledge of ligament function in the knee joint is largely based on
ligament cutting studies that measured changes in laxity after dissecting a specific
structure.
Experimental studies in cadaveric knees have demonstrated that the
66
superficial MCL is the primary restraint to valgus rotation, and a secondary restraint to
anterior translation [3, 12-16], while the ACL is the primary restraint to anterior
translation, and a secondary restraint to valgus rotation [3, 13, 14, 17-19]. In addition,
the MCL and ACL both resist internal tibial rotation [20-22], with the MCL also
resisting external tibial rotation [22, 23]. Recent experiments have investigated local
tissue strains and overall force in the ligament during applied loading conditions. Local
MCL strains have been measured for single or combined loading conditions, and with
the exception of studies by Fischer et al. [24] and Yasuda et al. [25], all MCL strain
studies have focused on intact knees [26-31].
Fischer utilized strain measurement
techniques to determine if function of the superficial MCL was affected when the
posterior aspect of the longitudinal parallel fibers was severed. Significant changes in
strain were only seen in an ACL deficient knee, prompting future research to look into
the interaction between the superficial MCL and the ACL. Yasuda found that the ACL
has minimal effect on the dynamic strain behavior of the MCL when a lateral impact
load is applied to the knee, and kinematic studies determined that when the MCL is
intact, the ACL has only a small influence on valgus laxity near full knee extension [12,
22]. Nevertheless, force measurement studies found that when the MCL is intact, ACL
tension significantly increases with the application of a valgus load over a range of
flexion angles [20, 32]. These results leave the role of the ACL in resisting valgus
rotation in an MCL-intact knee unclear; moreover, it is unknown how ACL deficiency
quantitatively affects regional MCL strain under specific loading conditions.
Interpretation of these interactions may be aided by investigating how ACL
deficiency alters localized MCL strains and joint kinematics. Local measurement of
67
ligament strain provides insight into regional function and the values of strain directly
relate to the propensity of the tissue to damage, tear or rupture [33]. Further, local strain
measurements on heterogeneous tissue structures are necessary to understand how
externally applied kinematic motions are resisted by specific regions [26, 34]. This
information would provide a broad visualization of MCL structural behavior and would
identify the loading configurations that the MCL resists actively. Finally, studying MCL
strain patterns in normal and ACL-deficient knees can afford a physiological baseline to
compare the in vitro efficacy of ACL reconstruction techniques. The objective of this
research was to quantify regional MCL strains and joint kinematics in the normal and
ACL deficient knee during anterior translation and valgus rotation at varying flexion
angles and tibial axial constraint. Two hypotheses were tested: (1) Strains in the MCL
increase following ACL transection during application of anterior translation, and (2)
strains in the MCL increase following ACL transection during application of valgus
rotation.
Materials and Methods
Experimental Design
Kinematic tests were performed on human knees before and after ACL
transection. Briefly, the tibia of each knee was subjected to cyclic anterior-posterior (AP) translation and varus-valgus (V-V) rotation at flexion angles of 0, 30, 60, and 90 deg
with tibial axial rotation constrained or unconstrained. MCL tissue strains and joint
kinematics were recorded during the entire application of anterior translation and valgus
rotation to the tibia.
Following testing, the MCL was dissected free from the joint to
68
measure the stress-free strain pattern of the MCL. All tissues were kept moist with 0.9%
saline solution throughout dissection and testing.
Specimen Preparation
Ten cadaveric right knees were acquired fresh-frozen from male donors (donor
age = 56±7 y, range 18-65). Each knee was from midtibia to midfemur and was allowed
to thaw for 16 h prior to dissection. All skin, fascia, muscle, and other periarticular soft
tissue surrounding the knee joint was removed, including the patella and patellar tendon.
One knee was eliminated from testing due to the absence of a medial meniscus.
Otherwise all knees showed no sign of arthritis or previous soft tissue injury. The fibula
was secured to the tibia with a stainless steel screw to ensure an anatomical position was
maintained. The femur and tibia were potted in mounting tubes using catalyzed polymer
resin (Bondo, Mar-Hyde, Atlanta, GA). Two L-shaped white blocks (the “kinematic
blocks”) with three black acrylic markers (4.75 mm dia.) were fastened to the anterior
femoral condyle and the posterior aspect of the tibia using nylon screws. Kinematic
blocks were used to record the three-dimensional kinematic motions of the tibia and
femur during testing.
A 3 x 7 grid of markers (2.3 mm dia.) was adhered to the MCL using
cynoacrylate (Figure 4.1).
These markers formed 18 gauge lengths for strain
measurement, with each gauge length spanning approximately 15 mm along the collagen
fiber direction. The markers were teased with tweezers after adhesion to verify that they
were attached to the superficial MCL fibers and not to the fascia. The markers in the
first and second rows were arranged along the anterior and posterior longitudinal parallel
fibers of the superficial MCL, respectively (Figure 4.1). Distal to the joint line, the
69
Meniscus
Femur
Tibia
1
2
3
1
2
3
1
2
1
1
1
2
1
2
2
2
3
3
Marker in Row 1
Marker in Row 2
Marker in Row 3
1
2
3
3
3
3
Figure 4.1. Location of fiducial markers. Twenty-one markers defined 18 regions for
strain measurement. The markers in row 1 and row 2 were affixed to the anterior and
posterior longitudinal fibers of the superficial MCL. Markers in row 3 inferior to the
joint line were considered affixed to the distal oblique fibers of the superficial MCL.
Markers in row 3 superior to the joint line were considered affixed to the anterior
posteromedial corner.
markers in the third row were affixed to the distal oblique fibers of the superficial MCL.
Proximal to the joint line, the markers in the third row were affixed to the anterior
portion of the posteromedial corner. These naming conventions are consistent with
Robinson et al. [35] and Warren and Marshall [36].
Testing Procedure
Each knee was mounted in fixtures on a custom testing machine. The machine
and fixtures allowed up to four degrees of freedom (DOF) through a combination of
linear and rotary bearings and actuators (Figure 4.2). Flexion was fixed, and either A-P
displacement during V-V rotation or V-V rotation during A-P displacement was fixed.
The tibial fixture permitted tibial axial rotation to be either constrained or unconstrained.
70
F
A
B
Femoral Kinematic Block
D
CC
Tibial Kinematic Block
E
Figure 4.2. Schematic of the loading apparatus, depicting a medial view of the knee at 0
deg flexion. Kinematic blocks are rigidly attached to the tibia and femur for 3-D motion
measurement. A – applied anterior-posterior tibial translation. B - applied varus-valgus
rotation. C – adjustable flexion angle. D - constrained or unconstrained tibial axial
rotation. E - unconstrained medial-lateral translation and joint distraction. F –
load/torque cell.
Thirty-two tests were performed on each knee. A-P displacements were applied to a set
force limit and V-V rotations were applied to a set torque limit (limits of ±100 N and
±10 N m, respectively [22, 26, 37]). Both A-P and V-V tests were performed at four
flexion angles (0, 30, 60, and 90 deg), with tibial rotation either unconstrained or
constrained, and the ACL either intact or deficient. Ten cycles were run for each test to
precondition the soft tissue structures of the knee. Data were analyzed at the tenth cycle
during anterior translation and valgus rotation. Linear and angular velocities (1.5 mm/s
and 1 deg/s, respectively) were selected to achieve quasi-static test conditions, thus
minimizing tissue viscoelastic and inertial effects. A bus cable (RTSI, Plano, TX) was
integrated with LabView software to enable real-time capture of both the loading data
from the multiaxial load cell (Futek T5105, Irvine, CA, accuracy ±2.2 N and ±0.056 N
71
m) and the positional data from the linear or rotary actuators (Tol-O-Matic, Inc, Hamel,
MN, linear accuracy ±0.0025 mm, rotational accuracy ±0.002 deg).
MCL strains and joint kinematics were measured simultaneously using a 3D
motion analysis system that tracked the centroids of the markers attached to the MCL
and kinematic blocks (Figure 4.2) [34]. The associated software used the modified
direct linear transformation method to calculate the 3D spatial coordinates of the
markers [34]. The 3D motion analysis system consisted of two high-resolution digital
cameras (Pulnix TM-1040, 1024x1024x30 fps, Sunnyvale, CA) equipped with 50 mm
1:1.8 lenses and extension tubes, two frame grabbers (Bitflow, Woburn, MA) and digital
motion analysis software (DMAS, Spica Technology Corp, Maui, HI).
The extra-
capsular location of the MCL and its planar geometry facilitated the use of this motion
analysis system for strain measurement. Unconstrained tibial axial rotation of the knee
was calculated using the established kinematic conventions of Grood and Suntay [38].
Prior to testing, a mechanical digitizer (Immersion Corp, San Jose, CA accuracy ±0.085
mm) was used to create “embedded” coordinate systems based on anatomical landmarks
[39, 40]. The centroids of the markers on the kinematic blocks were determined by
averaging four digitized points around the circumference of each marker.
These
centroids were used to create marker coordinate systems. The transformation matrix
between the femur and tibia could then be calculated by using the transformation
matrices formed between the embedded and marker coordinate systems and the videotracked kinematic block systems [34].
A testing methodology was developed to initiate ACL-deficient tests from the
ACL-intact neutral position. This neutral position was defined for each flexion angle by
72
finding the inflection point of the force response resulting from small cyclic A-P and VV displacements, with tibial axial rotation unconstrained.
Actuator translation and
rotation positions were logged so that the original neutral positions could be restored
after ACL transection. To mimic ACL deficiency, the ACL was transected through its
midsubstance without removing the knee from the fixture. Care was taken to avoid
damage to the PCL. To verify that the ACL-intact testing position was reproduced for
the ACL-deficient knee, kinematic block positions were measured in relation to each
other and the multiaxial test frame for each flexion angle. After ACL transection,
positional information was compared at each flexion angle and adjustments were made
if necessary.
Establishment of Reference Configuration for Strain Measurement
Following testing, the MCL was dissected from its femoral and tibial
attachments to measure the stress-free reference length (lo) between all 18 marker pairs.
Using validated procedures [26, 37], the motion analysis system measured the stress-free
configuration after the isolated ligament relaxed for 10 min on a saline covered glass
plate. This was an important step for the calculation of absolute strain, as force exists in
the ligament when it is attached to its insertion sites. Material properties of ligament,
including ultimate and substructural failure limits, have been quantified in the literature
using stress-free configurations [33, 41]. Accurate interpretation of strain data therefore
required the use of stress-free reference lengths. In this study, it was found that basing
strain results on in situ gauge lengths measured at 0 and 30 deg passive knee flexion, on
average significantly underpredicted strain by 2.7±0.1% (p<0.001) and 1.1±0.1%
(p<0.001), respectively.
73
Data and Statistical Analysis
The lengths between marker pairs were measured in the previously described
stress-free reference state (lo) and during kinematic tests (l) at peak valgus rotation, peak
anterior translation, and in the neutral position. Tensile strain along the fiber direction
was calculated as ε = (l - lo) / lo. Repeated measures ANOVA analysis with three
within-subject factors (ACL state, knee flexion angle, tibial axial constraint) was used in
conjunction with Bonferroni adjusted pair-wise comparisons to measure significance of
factors, factor interactions and between factor levels.
If significance was found
(p≤0.05), adjusted paired t-tests were used for case by case comparisons. A similar
analysis was performed for the kinematic data. A power analysis demonstrated that a
sample size of 10 was sufficient to obtain a power of 0.8 when detecting a 1.0% change
in the strain, a 1.0 deg kinematic rotation, and 1.5 mm kinematic displacement. Data are
reported as mean±standard error, unless otherwise stated.
To represent MCL strains graphically, mean values of regional fiber strain were
applied to a finite element mesh of a MCL constructed from one of the specimens [37].
This mesh was input to TOPAZ3D (LLNL, Livermore, CA) to perform a least squares
interpolation of fiber strain values between discrete measurement locations, which
yielded a continuous spatial representation of the results.
Results
Effect of ACL Transection
ACL deficiency significantly increased anterior translation by an average 10.0 ±
1.1 mm (p<0.001, Figure 4.3A), and MCL strains were significantly greater for ACLdeficient cases at peak anterior translation (Figure 4.3B). ACL deficiency did not
B
MCL Strains at Peak Anterior-Tibial Translation
90
Insert
Tib
Insert
Fem
0
0
30
60
Flexion Angle (Deg)
Fem
Tib
Fem
Insert
TibInsert
Tib
Tib
Fem
Insert
Fem
Insert
Insert
Fem
Fe
Insert
Insert
Fe
Fem
Fe
Insert
TibInsert
Tib
Tib
Fem
Tib
Insert
Tib
Insert
TibInsert
Tib
Insert
Insert
Fem
Fe
Insert
TibInsert
Tib
Tib
5
60
Insert
Fem
Fem
10
Insert
Tib
Fem
15
30
Tib
*
ACL Deficient
Insert
Fem
Tib
*
*
0
Insert
Tib
Tib
AT Translation (mm)
20
ACL Intact
ACL Deficient
*
25
Flexion Angle (Degrees)
ACL Intact
30
Fem
Anterior-Tibial Translation
Fem
A
Insert
Insert
Fem
Fe
90
Buckled
0%
2%
4%
6%
8%
10%
Figure 4.3. Experimental results from anterior-posterior translation. A) Anterior tibial translation at all flexion angles, with
unconstrained tibial axial rotation, before and after ACL transection. B) Average MCL strains at ATT as a function of knee flexion
angle, with unconstrained tibial axial rotation, before and after ACL transection. ACL transection had no significant effect on valgus
laxity or MCL strains. Error bars = SD.
74
75
significantly affect valgus rotation (p=0.12, Figure 4.4A), and MCL strains were not
significantly affected by ACL deficiency at peak valgus rotation (Figure 4.4B). ACL
transection increased MCL strains by an average 1.8 ± 0.5% at peak anterior translation.
In contrast, ACL transection increased MCL strains by only 0.1 ± 0.1% at peak valgus
rotation (Figure 4.5). The significant strain increases at peak anterior translation (p<0.05)
occurred along every region of the superficial longitudinal MCL and the region
representing the anterior fibers of the posteromedial corner.
ACL transection caused the largest increase in MCL strain during anterior
translation at 30 deg of knee flexion (2.0 ± 1.5%), corresponding with the greatest
increase in anterior laxity (12.4 ± 1.3 mm). During anterior translation, the lowest
aggregate strain increases due to ACL transection occurred at 0 deg of knee flexion (1.4 ±
0.7%); however, even with these lower strain increases, 0 deg flexion had the greatest
absolute strain in both ACL intact and ACL-deficient cases. For all anterior translation
cases, the region with the largest overall strain increase due to ACL transection was near
the femoral insertion (3.8 ± 1.1%), while the region with the least overall increase was
along the distal oblique fibers of the superficial MCL (0.3 ± 0.4%) (Figure 4.5).
Effect of Knee Flexion Angle
Knee flexion angle had a significant effect on both anterior translation and valgus
rotation (p<0.001 and p=0.01, respectively). Flexing or extending the knee to 30 deg
from all other flexion angles significantly increased anterior translation (average of 3.1 ±
0.5 mm for all cases, Figure 4.3A). Extending the knee to 0 deg from all other angles
significantly decreased valgus rotation (average of 1.5 ± 0.3 deg for all cases,
B
MCL Strains at Peak Valgus Rotation
ACL Intact
Fem
Fem
Fem
Fem
Insert
Fem
Fem
Inser
Tibt
90
Tib
Insert
Fem
Insert
Tib
Tib
Inser
Tibt
Insert
Insert
Fem
Insert
Tib
Tib
60
Tib
Insert
Fem
Insert
Tib
Insert
Fem
Insert
Tib
Tib
2
Inser
Tib
t
Fem
4
30
Tib
6
Insert
Fem
0
Tib
ACL Intact
ACL Deficient
ACL Deficient
Inser
Tib
t
Tib
8
Flexion Angle (Degrees)
V-V Rotation (Deg)
10
Fem
Valgus Rotation
Fem
Fem
A
rt m
Insert
Fem
0
0
30
60
Flexion Angle (Deg)
90
Buckled
0%
2%
4%
6%
8%
10%
Figure 4.4. Experimental results from varus-valgus rotation. A) Valgus rotation at all flexion angles, with unconstrained tibial axial
rotation, before and after ACL transection. B) Average MCL strains at peak valgus rotation as a function of knee flexion angle, with
unconstrained tibial axial rotation, before and after ACL transection. ACL transection had no significant effect on valgus laxity or
MCL strains. Error bars = SD.
76
77
*
*
*
*
*
*
*
*
*
*
*
*
*
*
Fem
Insert
Peak Anterior
Translation
Tib
Insert
Overall Differences in % Strain from ACL Transection
4%
3%
Fem
Insert
Peak Valgus
Rotation
Tib
Insert
2%
1%
0%
Figure 4.5. MCL strain changes due to ACL transection at peak anterior translation and
valgus rotation, averaged over all cases. Transection significantly increased MCL
strains during anterior translation, but had no effect on MCL strains during valgus
rotation. * p < 0.05 (within a region).
78
Figure 4.4A). Medial collateral ligament strains in most measurement regions were also
significantly affected by flexion angle for all test cases. Interestingly, MCL strain
patterns were changed in a nearly uniform manner with each successive 30 deg flexion,
for both loading configurations. This uniform change in strain followed a pattern of
small yet significant strain increases along the most anterior row distal to the joint line
(0.3 ± 0.2%), coupled with larger and significant decreases in change around the
posteromedial corner (-3.5 ± 0.6%). Both ACL-intact knees and ACL-deficient knees
exhibited this trend in MCL strain behavior.
Effect of Tibial Axial Constraint
Unconstraining tibial axial rotation significantly increased anterior translation by
an average of 0.6 ± 0.1 mm and valgus rotation by an average of 0.7 ± 0.2 deg (p<0.001
and p=0.001, respectively), under all test conditions.
Overall increases in laxity
corresponded with overall decreases in MCL strains of 0.45 ± 0.24% during anterior
translation and 0.10 ± 0.17% during valgus rotation.
These strain decreases were
significant across the majority of longitudinal parallel fibers during anterior translation
and near the femoral insertion during valgus rotation.
When tibial axial rotation was unconstrained for the ACL-intact cases, an
average internal tibial rotation (ITR) of 9.3 ± 3.8 deg occurred during anterior
translation. Transecting the ACL significantly reduced ITR during anterior translation at
30, 60 and 90 deg flexion by an average 6.9 ± 3.8 deg (Figure 4.6A). When tibial
rotation was unconstrained, the larger ITR in knees with an intact ACL resulted in
significantly lower MCL strains in the longitudinal fibers near the femoral insertion (2.5
79
± 0.4%, Figure 4.6B). In contrast, for ACL deficient knees, ITR was reduced and the
decreases in strain near the femoral insertion were insignificant (0.6 ± 0.2%, Figure
4.6B). This illustrates that decreased ITR after ACL transection results in increased
MCL strains in the longitudinal fibers near the femoral insertion. Statistical analysis
further supported this observation, as there was a significant interaction between tibial
axial constraint and ACL transection along strain regions near the femoral insertion at
peak anterior translation (p<0.05).
Discussion
Understanding the interdependent functions of the ACL and MCL can clarify the
structure-function relationship of both ligaments. This study found that ACL deficiency
significantly increased MCL strains during anterior translation, but had no effect on
MCL strains during valgus rotation. Joint kinematics measured simultaneously with
MCL strains were consistent with comparable studies [19, 22, 26]. The results support
our hypothesis that ACL deficiency increases MCL strain during anterior translation,
which is logical considering the respective primary and secondary roles of the ACL and
MCL in restraining anterior translation. Conversely, our hypothesis that ACL deficiency
would increase MCL strain during valgus rotation was rejected.
This means that
application of a valgus rotation to 10 N m in the ACL-deficient knee was nondetrimental to the MCL.
The finding that strains in the superficial MCL are insensitive to ACL transection
during valgus rotation was surprising considering that the ACL has been shown to be an
active stabilizer to valgus rotation when the MCL is healthy [20, 32]. Studies by Fukuda
0
ACL Intact
ACL Deficient
4%
6%
Fem
Fem
Insert
TibInsert
Tib
Insert
Ti Insert
Ti
bb
Insert
Insert
Buckled
0%
2%
Fem
Fem
Tib
Insert
Insert
Fem Tib
Fem
Tib
Uncnst
Tib
4
Fixed
ACL Deficient
Fem
8
ACL Intact
Insert
Ti Insert
Ti
bb
Fem
12
MCL Strains at Peak Anterior Translation (30° Flexion)
Tib
*
16
B
Tib
Internal Tibial Rotation from
Neutral to Peak Anterior
Translation (30° Flexion)
Tibial Axial Rotation
Internal Tibial Rotation
(degrees)
A
Insert
Insert
8%
10%
Figure 4.6. Interdependence of the ACL and tibial axial rotation. A) Internal tibial axial rotation from neutral to peak anterior
translation, 30 deg knee flexion, before and after ACL transection. B) Average MCL strains at peak anterior translation, 30 deg knee
flexion, with fixed and unconstrained tibial axial rotation, before and after ACL transection. In the ACL-intact knee, unconstraining
tibia axial rotation significantly reduced strain along the anterior MCL. After ACL transection, internal tibial rotation was
significantly decreased and MCL strain was unaffected when tibial axial rotation was unconstrained. Thus, in the intact knee, the
ACL promoted internal tibial rotation during anterior translation, which relieved strain in the MCL. This also occurred at 60 and 90
deg flexion. * p < 0.05
80
81
et al. [32] and Miyasaka et al. [20] used force superimposition techniques and strain
gauges, respectively, determining that the ACL resists valgus rotation from full
extension to 90 deg flexion. Our results showed that ACL transection produced small,
insignificant increases in valgus laxity, yet this increased valgus rotation minimally
impacted MCL strains at all flexion angles (average increase was 0.1%, average
p=0.64). A few explanations on this discrepancy are offered. First, it is possible that the
reported ACL force contributions during valgus rotation in an intact knee are easily
accommodated by the MCL after ACL transection. Therefore, MCL strain changes are
imperceptible and the integrity of the MCL is unaltered. Another possibility is that other
secondary stabilizers might increase their contribution to resisting valgus rotation after
ACL transection, allowing the MCL to continue to function normally. Yet, the most
likely explanation involves differences in degrees of freedom between testing systems.
The testing machine and fixtures in this study permitted up to 4 DOF, while experiments
by Fukuda et al. [32] used a 5 DOF system.
The 5 DOF system permitted A-P
translation during V-V rotation, and demonstrated that coupled A-P translation during
V-V rotation increases after ACL transection. Therefore, in an intact knee, the function
of the ACL during valgus rotation may be to resist coupled anterior translation, and the
ACL only resists pure valgus rotation after the MCL is compromised.
To make clinical interpretations, it was necessary to identify loading conditions
that generate damaging strains, which was feasible since a stress-free reference was used
for strain calculation. A stress-free reference allows direct comparison with material
properties reported in the literature. Ligament rupture typically occurs at ~18% strain
[41], and the onset of microtrauma or substructural failure in ligament occurs at 5.2%
82
strain [33]. During valgus rotation, maximum absolute strains in the midlongitudinal
MCL fibers remained around 4.4% in both the intact and ACL-deficient knee, below the
microtrauma threshold.
During anterior translation, ACL transection significantly
increased maximum absolute strains along the mid-longitudinal fibers from 2.9% to
5.7%, a strain level that could induce microtrauma. These results show evidence that
longitudinal MCL fibers in ACL-deficient knees are initially predisposed to damage
from anterior translation. This finding is useful in interpreting results from a study by
Tashman et al. [42] who measured kinematic gait changes over two years in ACLdeficient and ACL-intact canines. Consistent with our results and the literature [19, 22,
43], ACL transection immediately caused large translational increases during anterior
translation and small rotational increases during valgus rotation. In the ACL-deficient
knee, anterior translation significantly escalated with time. Our data suggest that the
MCL initially assisted in stabilizing anterior translation; however, the MCL became
strained over time leading to increased anterior tibial displacements. This potential
increase in MCL laxity may be one factor in the unsatisfactory outcomes characteristic
of conservatively treated ACL injuries [44].
Interestingly, the strains of around 10% in the anterior posteromedial corner
during both loading conditions greatly exceed the reported substructure failure threshold.
However, these results are deceiving. The material tests that defined damaging strains
[33, 41] were tested along the midlongitudinal MCL fibers and therefore are not directly
comparable with regions of the posteromedial corner.
Considering that the
posteromedial corner has been shown to play a limited role in resisting valgus rotation
83
[22], this tissue is likely less stiff with greater failure strain thresholds than the adjoining
longitudinal fibers.
Relating joint kinematics and local strains has enabled a better understanding of
functional MCL regions and is applicable to clinical diagnosis.
Following ACL
transection, increased anterior laxity was resisted by fibers near the femoral insertion
and along the midsubstance of the parallel longitudinal fibers. The greatest average
increase in MCL strain following ACL transection occurred at 30 deg flexion, consistent
with the largest increase in anterior translation. Yet, 0 deg flexion held the distinguished
position of having the greatest absolute strains both before and after ACL transection.
Increasing the flexion angle, for both loading conditions, slightly stretched the fibers
distal to the joint line along the anterior superficial MCL. Meanwhile, the posterior
regions of the superficial MCL and anterior posteromedial corner uniformly slackened
with this increased flexion. This behavioral pattern and the corresponding magnitude of
the strain changes were unaffected by ACL transection, and were insensitive to the
discrepancies between anterior translation and valgus subluxation patterns observed at
different flexion angles. Therefore, regardless of directional loading or ACL condition,
progressive flexing of the knee will reduce overall MCL strain. These findings support
using a knee flexion angle of 15-30 deg when administering the Lachman test rather
than performing the test at full extension [45]. At a slightly flexed angle, the MCL will
not be overstressed, and deviations in joint laxity with the contralateral knee are
maximized.
The relationship between tibial axial rotation and the MCL and ACL was further
developed in this study.
When tibial axial rotation was constrained, knee laxity
84
decreased under both loading conditions. This was at least partially due to increased
resistance along the longitudinal fibers of the MCL, which experienced significantly
higher strains, particularly during anterior translation.
Unconstraining tibial axial
rotation permitted internal tibial rotation, which in turn decreased MCL strains. Internal
tibial rotation during anterior translation was reduced after the ACL was transected.
Thus, the ACL encourages internal rotation, perhaps by “unwinding” during anterior
translation [46].
In summary, in an intact knee, the ACL promotes internal tibial
rotation, which in turn reduces MCL strains along the longitudinal parallel fibers of the
superficial MCL near the femoral insertion.
The specific limitations of the methods used in this study deserve discussion.
Joint kinematics may have been altered due to the dissection necessary for strain
measurement or because joint compressive forces and stabilizing muscle activity were
not represented. Muscle activity has been shown to reduce knee laxity [47]. Therefore,
to reproduce the magnitudes of strains from this study in vivo, greater force and torque
limits would likely be required. Removing the patella may have also influenced MCL
strain patterns and joint kinematics. Tests were performed in a controlled environment,
and did not undergo high speed motions or combined loading configurations that would
have been more analogous to injury causing mechanisms. Strain measurement was
based on changes to gauge length between marker pairs. This assumed that strain was
homogeneous over the length of these discrete regions. For graphical representation,
MCL strain values were interpolated between marker rows, which may not accurately
account for inhomogeneities orthogonal to the fiber direction. Finally, strains in the
deep MCL were not measured, although previous studies have shown that the deep MCL
85
is half as stiff as the superficial MCL [48] and has a minimal contribution to valgus
restraint when the superficial MCL is intact [49].
Through measurement of tissue strain and joint kinematics, this research has
improved the understanding of how the ACL and MCL interact. Additionally, results
from this study can be used to validate finite element models and improve the governing
constitutive equations. The present results and methods can also serve as a baseline to
verify that a specific ACL reconstruction technique not only returns the knee to regular
joint kinematics, but is capable of returning normal functionality to the intact MCL.
References
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Sakane, M., Livesay, G. A., Fox, R. J., Rudy, T. W., Runco, T. J., and Woo, S.
L., 1999, "Relative Contribution of the ACL, MCL, and Bony Contact to the
Anterior Stability of the Knee," Knee Surg Sports Traumatol Arthrosc, 7(2), pp.
93-97.
[2]
Smillie, I. S., 1969, "Knee Injuries in Athletes," Proc R Soc Med, 62(9), pp. 937939.
[3]
Markolf, K. L., Mensch, J. S., and Amstutz, H. C., 1976, "Stiffness and Laxity of
the Knee--The Contributions of the Supporting Structures. A Quantitative In
Vitro Study," J Bone Joint Surg Am, 58(5), pp. 583-594.
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CHAPTER 5
MCL INSERTION SITE AND CONTACT FORCES
IN THE ACL-DEFICIENT KNEE
Abstract
The objectives of this research were to determine the effects of ACL deficiency
on MCL insertion site and contact forces during anterior tibial loading and valgus
loading using a combined experimental-finite element (FE) approach. Our hypothesis
was that ACL deficiency would increase MCL insertion site forces at its attachment to
the tibia and femur and increase contact forces between the MCL and these bones. Six
male knees were subjected to varus-valgus and anterior-posterior loading at flexion
angles of 0 and 30 deg. Three-dimensional joint kinematics and MCL strains were
recorded during kinematic testing.
Following testing, the MCL of each knee was
removed to establish a stress-free reference configuration. A FE model of the femurMCL-tibia complex was constructed for each knee to simulate valgus rotation and
anterior translation at 0 and 30 deg, using subject-specific bone and ligament geometry
and joint kinematics. A transversely isotropic hyperelastic material model with average
material coefficients taken from a previous study was used to represent the MCL.
___________________
Reprinted by kind permission of Wiley-Liss Inc. a subsidiary of John Wiley & Sons Inc.
Journal of Orthopaedic Research, Vol. 24, No. 4, Benjamin J. Ellis, Trevor J. Lujan,
Michelle S. Dalton, Jeffrey A. Weiss, “MCL Insertion Site and Contact Forces in the
ACL-Deficient Knee”, pp: 800-10, 2007.
91
Subject-specific MCL in situ strain distributions were used in each model. Insertion site
and contact forces were determined from the FE analyses.
FE predictions were
validated by comparing MCL fiber strains to experimental measurements. The subjectspecific FE predictions of MCL fiber stretch correlated well with the experimentally
measured values (R2 = 0.953). ACL deficiency caused a significant increase in MCL
insertion site and contact forces in response to anterior tibial loading. In contrast, ACL
deficiency did not significantly increase MCL insertion site and contact forces in
response to valgus loading, demonstrating that the ACL is not a restraint to valgus
rotation in knees that have an intact MCL. When evaluating valgus laxity in the ACLdeficient knee, increased valgus laxity indicates a compromised MCL.
Introduction
The effect of anterior cruciate ligament (ACL) deficiency on the mechanical
function of other knee ligaments remains unclear, although it is known that even knees
with reconstructed ACLs often exhibit abnormal knee kinematics [1]. The ACL is a
primary restraint to anterior tibial translation and a secondary restraint to valgus rotation
[2-12], while the medial collateral ligament (MCL) is a primary restraint to valgus
rotation [3-14] and a secondary restraint to anterior tibial translation [2, 4-6, 11, 14-18].
The MCL is involved in approximately 40% of all severe knee injuries [19], while
approximately 50% of partial MCL tears and 80% of complete MCL tears occur in
conjunction with injury to other knee ligaments [20]. In alpine skiing, the most common
ligament that is injured in conjunction with the MCL is the ACL [21].
Animal studies have shown that MCL healing is substantially poorer in the case
of a combined MCL/ACL injury than for an isolated MCL injury [2, 5-7, 11, 12]. After
92
12 weeks of healing, MCLs from knees with combined MCL/ACL injuries had a tensile
strength of only 10% of control values [12]. It has been proposed that the healing MCL
in the ACL-deficient knee is subjected to increased strains and forces as a result of ACL
deficiency [6]. An ACL graft acts as a stabilizer initially, but as it heals, forces are
transferred to the MCL that hinder healing and result in a hypertrophy of the MCL with
tissue of lower quality. As long as two years after injury, healing MCLs still had
“significantly different biological composition, biomechanical properties, and matrix
organization” [11].
Although animal studies have shown that the MCL may be at risk for injury in an
ACL deficient knee, conclusions in the literature as to the exact contributions of the
MCL and ACL to valgus stability vary within and between studies of ligament healing
in animal models and joint kinematics in cadaver models.
In animal models, the
variation in results is confounded by the variation in the type of injury model used.
Results from a rabbit healing study showed that valgus rotation does not increase over
time in response to healing of the ACL graft after an O'Donoghue triad injury (rupture of
the medial collateral ligament with removal of the anterior cruciate ligament and part of
the medial meniscus) although anterior translation did significantly increase over the
same healing period [2].
The conclusions of this animal study that created an
O'Donoghue triad injury are in contrast to other animal studies that have shown that
there are higher ACL forces and increased valgus laxity in response to a valgus load in a
MCL deficient knee [3-6, 9, 10]. Two previous cadaver studies concluded that valgus
laxity is relatively unaffected by ACL deficiency [13, 14] and Mazzocca et al. concluded
that, “the ACL can be compromised in isolated grade III MCL injuries” caused by a
93
valgus load [10]. The actual insertion site and contact forces in the MCL in response to
a valgus torque in the intact and ACL-deficient knee, which arguably are the most
relevant data for interpretation of ligament contribution to joint function, are unknown.
The aim of this study was to examine the effects of ACL deficiency on MCL
insertion site and contact forces when the knee is subjected to anterior tibial loading and
valgus torque. It was hypothesized that ACL deficiency would cause an increase in
MCL insertion site and contact forces in response to both loading conditions.
Materials and Methods
Overview
This study combined experimental and computational methods to determine the
effect of ACL injury on MCL insertion site and contact forces during anterior tibial
loading and valgus loading. Computed tomography (CT) images were used to obtain the
subject-specific geometry of the femur, tibia and MCL in a series of six cadaveric knees.
Each knee was tested with the ACL intact and the ACL completely severed. For each
injury state the knee was subjected to anterior-posterior (A-P) translation and varusvalgus (V-V) rotation at two flexion angles (0 and 30 deg) with tibial rotation
constrained and unconstrained while knee kinematics and MCL strains were recorded.
Polygonal surfaces were extracted from the CT data and were used to generate subjectspecific FE models of each knee.
The FE models were analyzed under the
experimentally measured kinematics to determine MCL strains, contact forces, and
insertion site forces. FE predicted fiber stretches were compared to experimental values
as a means of validation and the effect of injury state, flexion angle, and tibial constraint
on MCL insertion site and contact forces was determined.
94
Specimen Preparation and CT Scan
Six intact human male cadaver knees were used (donor age 60 ± 8.3 years).
Preparation for testing followed the same protocol as Gardiner et al. [22], with the
exception that additional contrast markers were used to define gauge lengths for
measurement of MCL fiber strain. Markers were distributed along the visible fiber
direction of the MCL in a 3x7 grid pattern, forming 18 gauge lengths (Figure 4.1). Each
gauge length was approximately 15 mm long. The position of the markers was chosen
based on anatomical landmarks. The boundaries of the MCL insertion sites on the femur
and tibia were marked with copper wires to aid with identification of their geometry in
the volumetric CT images. Nylon kinematic blocks were fastened to the distal femur
and proximal tibia (shown in Figure 3.2), while positioning blocks with three beveled
cavities (not shown in figure), forming a right angle, were fastened to the proximal
femur and distal tibia [23]. After dissection, a volumetric CT scan was obtained for each
knee at 0 deg flexion (slice thickness = 1.3 mm with 1.0 mm overlap, 142-168 mm field
of view, 512 × 512 acquisition matrix).
Kinematic Testing
Following the CT scan, each knee was mounted in fixtures on a custom materials
testing machine, which allowed both A-P translation and V-V rotation to be applied at
fixed flexion angles with constrained or unconstrained tibial axial rotation and
unconstrained medial-lateral translation and joint distraction (Figure 4.2).
During
testing, 10 cycles of A-P translation (load limits of ±100 N at 1.5 mm/sec) and 10 cycles
of V-V rotation (torque limits of ±10 N-m at 1 deg/sec) were independently applied to
the tibia. The A-P load and V-V torque limits were established so that they were large
95
enough to achieve the terminal stiffness of the ligament without inflicting injury to the
tissue and thereby allowing multiple tests with the same specimen [24]. A-P and V-V
loading were conducted at 0° and 30° flexion. The tests were repeated with tibial axial
rotation constrained and unconstrained at each flexion angle. The load and torque were
measured with a multi-axis load cell (Futek T5105, Irvine, CA, accuracy ±2.2 N and
±0.056 N-m). Following the eight ACL intact tests, the ACL was transected through the
midsubstance without damage to the PCL or removal of the knee from the fixture and all
the tests were repeated. Finally, following ACL transection, the attachment of the
medial meniscus to the MCL was transected. This test was performed to verify that the
attachment did not influence joint kinematics and MCL strains under A-P and V-V
loading; a similar conclusion was reached for the effect of the meniscus attachment on
MCL strains in the intact knee in our previous study [22]. To minimize hysteresis
effects, data from the 10th cycle of loading were analyzed for all tests.
Care was taken to ensure that the relative kinematic positions of the bones were
duplicated for a given flexion angle and tibial axial rotation constraint for both injury
states. When testing the intact knee, a neutral A-P and V-V position was determined at
each flexion angle with tibial axial rotation unconstrained. The neutral A-P and V-V
positions were determined by iteratively adjusting the starting position and running A-P
and V-V motion cycles until the given load (±100 N) and torque (±10 N-m) limits
produced equal anterior and posterior translation and equal varus and valgus rotations,
respectively. Once these reference positions were established, actuator translation and
rotation positions were logged so the positions could be restored after ACL transection.
The three-dimensional kinematic position of the femur relative to the tibia was verified
96
through the use of a Microscribe digitizer (Immersion Corp, San Jose, CA, accuracy
±0.085 mm) in combination with the positioning blocks. The digitizer and positioning
blocks were used to precisely determine the relative three-dimensional kinematics of the
femur and tibia [25]. In this manner, positional repeatability between the different injury
states was insured.
Measurement of Joint Kinematics and Ligament Strains
A digital motion analysis system consisting of two high-resolution digital
cameras (Pulnix TM-1040, 1024x1024x30 fps, Sunnyvale, CA) and Digital Motion
Analysis Software (DMAS, Spica Technology Corporation, Maui, HI) was used to
record MCL strain in the 18 measurement regions and joint kinematics simultaneously
(strain measurement accuracy: ±0.035 percent; joint kinematic translational accuracy:
±0.025 mm; joint kinematic rotational accuracy: ±0.124 deg) [23].
In Situ Strain
At the conclusion of testing, the MCL was dissected from the bones and placed
in a buffered saline bath for 10 minutes to allow the ligament to achieve a stress-free
reference configuration. The 3D coordinates of the fiducial markers on the MCL were
determined using the digital motion analysis system. This provided reference (zeroload) lengths for each strain region, l0 [12, 22, 26]. These values were combined with
length measurements taken during the kinematic testing to calculate in situ fiber strain
between marker pairs. These data were used as input to the subject-specific FE models
[22, 27].
97
CT Scan, Surface Reconstruction and FE Mesh Generation
Using the copper insertion site wires and MCL strain contrast markers as guides,
cross-sectional contours of the MCL, femur, and tibia were extracted from the CT
dataset (SurfDriver, Kailua, Hawaii). Polygonal surfaces were generated by stacking
and lacing together the contours [28] and smoothing was applied [29]. The polygons
composing the surfaces of the femur and tibia were converted directly to shell elements
and used to represent the bones as rigid bodies [30]. The MCL surface was imported
into FE preprocessing software (TrueGrid, XYZ Scientific, Livermore, CA) and a
hexahedral mesh was created.
Constitutive Model
The MCL was represented as transversely isotropic hyperelastic, with the strain
energy (W) [22]:
( )
()
~ K
~
2
W = F1 I 1 + F2 λ + (ln ( J )) .
2
(1)
Here, I1 is the first deviatoric invariant, λ is the deviatoric part of the stretch
ratio along the local fiber direction, and J is the determinant of the deformation gradient,
~
F. The matrix strain energy F1 ( I1 ) was chosen so that ∂F1 ∂I 1 = C1 , yielding the neo-
( )
Hookean constitutive model. The derivatives of the fiber strain energy function F2 λ
were defined as a function of the fiber stretch:
~ ∂F
λ 2 =0,
∂λ
λ ≤ 1 ;
98
[ ( (
)) ]
~ ∂F
~
λ 2 = C 3 exp C 4 λ − 1 − 1 ,
∂λ
~
1 < λ < λ* ;
~ ∂F
~
λ 2 = C5 λ + C6 ,
∂λ
λ ≥ λ* .
(2)
~
C3 scales the exponential stress, C4 specifies the rate of collagen uncrimping, C5 is the
modulus of straightened collagen fibers, and λ* is the stretch at which the collagen is
straightened. The third term in Eq (1) represents the bulk (volumetric) response, with
the bulk modulus K controlling the entire volumetric response of the material. The
population-average material coefficients from Gardiner et al. were used [22]:
C1 = 1.44 MPa ,
λ * = 1.062 (no units) ,
C3 = 0.57 MPa ,
C4 = 48.0 (no units) ,
C5 = 467.1 MPa . Population average material coefficients were used based on the
finding that using average coefficients versus subject specific coefficients yielded no
significant difference in the accuracy of FE strain predictions [31]. Due to a lack of
experimental data describing ligament bulk behavior, the bulk modulus was specified to
be two orders of magnitude greater than C1, yielding nearly incompressible material
behavior [22].
Boundary Conditions
The experimentally measured kinematic dataset was used to prescribe the motion
of the tibia relative to the femur in the FE analyses [22]. The coordinates of the
kinematic blocks in both the CT and kinematic datasets allowed for correlation of the
two datasets. The entire FE model was transformed so that the global coordinate system
was aligned with the coordinate system of the femur kinematic block. Motion of the
tibia was described using incremental translations and rotations referenced to the femur
99
kinematic block [30, 32]. The MCL mesh was attached to the bones by defining node
sets, based on the area within the copper wires, at the proximal and distal ends of the
MCL as the same rigid material as the femur and tibia, respectively. Contact was
enforced using the penalty method.
Finite Element Analysis
The implicitly integrated FE code NIKE3D was used for all analyses [32]. An
automatic time stepping strategy was employed, with iterations based on a quasi-Newton
method. Each analysis was performed in three parts. In the first part, the knee was
moved from the position in which it was placed at the time of the CT scan to the initial
testing position (either 0 or 30 deg of flexion).
During the second part, the
experimentally measured in situ strains for a given flexion angle and injury state were
applied to the MCL. During the third part the experimental kinematic motion was
applied (either anterior translation or valgus rotation). FE results were analyzed with
GRIZ [33].
Regional Strains, Insertion Site and Contact Forces
FE predicted fiber stretches for nodes within each measurement region were
averaged. Average FE predicted fiber stretches were compared to the experimentally
measured values.
The magnitude of ligament forces at the insertion sites and the
magnitude of the resultant forces due to MCL-bone contact were obtained from the
NIKE3D output.
100
Statistical Analysis
Regression analyses were used to evaluate the ability of the FE models to predict
experimentally measured values of MCL fiber stretch. FE predictions of regional fiber
stretch were determined as a function of location along the length of the MCL. The
predicted stretches were calculated and tabulated for all six knees according to test case
and compared to experimental results. Coefficients of determination (R2), regression
lines, and p-values were determined.
The effect of tibial axial rotation constraint on insertion site and contact forces
was assessed with a paired t-test using all the force data (insertion site and contact forces
for both injury states at both angles and both loading conditions). The effects of withinsubject treatment (injury state and flexion angle) in response to anterior and valgus
loading on insertion site and contact forces were assessed using 2-way repeated
measures ANOVAs. The results of the paired t-test showed no significant effect of tibial
constraint on insertion site and contact forces (see results section below), so only the
force data for the tests with constrained tibial axial rotation were used in the 2-way
ANOVAs. In cases when significance was found (p<0.05), multiple comparisons were
performed using the Tukey procedure.
Results
Experimental Kinematics
Before ACL resection, the average anterior displacements at 0 and 30 deg knee
flexion in response to a 100 N anterior tibial load were 6.8 ± 2.6 mm and 6.7 ± 2.2 mm,
respectively. ACL transection significantly increased anterior displacement in response
to a 100 N anterior tibial load (16.5 ± 6.1 mm and 20.8 ± 4.4 mm at 0 and 30 deg,
101
respectively) (p<0.001 for both flexion angles). Before ACL transection, the average
valgus rotation at 0 and 30 deg knee flexion in response to a 10 N-m valgus torque were
3.6 ± 1.8 deg and 5.1 ± 2.0 deg, respectively. ACL deficiency did not significantly
change valgus rotation in response to a 10 N-m valgus torque (4.3 ± 1.9 deg and 5.3 ±
1.7 deg at 0 and 30 deg, respectively). Subsequent separation of the medial meniscus
attachment had no significant effect on knee joint kinematics for both A-P and V-V
loading (data not shown). Because there was no change in joint kinematics following
separation of the medial meniscus from the MCL in the ACL-deficient knee, these data
were not subsequently analyzed via FE analysis.
FE Predictions of Regional Fiber Stretch
The FE values for fiber stretch were excellent predictors of experimental fiber
stretch.
Regression analysis of FE predicted fiber stretch versus experimentally
measured fiber stretch for all regions, knees and test cases of intact and ACL-deficient
knees yielded a coefficient of determination of R2 = 0.953 (p=0.001) (Figure 5.1).
Fringe plots of the FE fiber strain illustrate the MCL strain patterns and increases in
strain caused by ACL-deficiency in response to anterior and valgus loading (Figure 5.2).
In response to both types of load and regardless of injury state, the highest MCL strains
were found in the posterior-proximal region [34]. MCL strain increased significantly in
response to anterior loading when the ACL was injured, but the increase was not
significantly different in response to a valgus load. Higher MCL strains tended to be
more distributed in response to valgus loading than anterior loading regardless of injury
102
1.25
y = 0.919x + 0.084
2
(R = 0.953)
1.20
FE Stretch
1.15
1.10
1.05
1.00
0.95
0.95
1.00
1.05
1.10
1.15
1.20
1.25
Experimental Stretch
Figure 5.1. FE predicted vs. experimental
fiber stretch for all knees, test conditions, and
measurement regions (N=1632).
100 N anterior tibial load, ACL intact
100 N anterior tibial load, ACL deficient
8%
10 N-m valgus torque, ACL intact
10 N-m valgus torque, ACL deficient
0%
Figure 5.2. Representative fringe plots of FE predicted fiber strain for a 100 N anterior load (top row) and a 10 N-m valgus load
(bottom row) for the uninjured knee and the ACL-deficient knee. MCL strains increased in response to anterior tibial loading when
the ACL was injured. MCL strains also increased locally in the ACL-deficient knee in response to a valgus torque, but these local
increases did not result in significant changes in insertion site or contact forces.
103
104
state. A comprehensive set of MCL strain data as well as the kinematic data from this
testing can be found in Lujan et al. [34].
Tibial Axial Rotation Constraint
A paired t-test using all insertion site and contact forces for both flexion angles
and loading conditions showed that there was no effect of tibial axial rotation constraint
on the predicted forces (p = 0.154).
Insertion Site Force
ACL deficiency caused significant increases in MCL insertion site forces at both
the femur and tibia during anterior tibial translation. The forces at both insertion sites
were significantly higher at 0 than at 30 deg in ACL-deficient knees in response to the
100 N anterior tibial load. The MCL femoral insertion site forces corresponding to the
in situ strains in the intact knee (before application of the experimental kinematics) at 0
and 30 deg were 39.7 ± 38.1 N and 6.0 ± 5.4 N, respectively. The MCL tibial insertion
site forces due to in situ strain in the intact knee at 0 and 30 deg were 42.9 ± 43.1 N and
6.0 ± 5.5 N, respectively.
Before ACL resection, the MCL femoral insertion site forces
during anterior translation at 0 and 30 deg were 55.9 ± 38.2 N and 8.3 ± 5.4 N,
respectively. Before ACL resection, the MCL tibial insertion site force during anterior
tibial translation at 0 and 30 deg were 58.7 ± 42.0 N and 8.3 ± 5.5 N, respectively
(Figure 5.3, left panel). ACL-deficiency significantly increased MCL insertion site
forces at the femur (126.6 ± 84.8 N and 74.1 ± 57.8 N at 0 and 30 deg, respectively) and
tibia (133.9 ± 88.5 N and 80.9 ± 62.4 N at 0 and 30 deg, respectively) during anterior
105
tibial translation (p<0.05 for all cases). Insertion site forces were significantly higher at
0 than at 30 deg during anterior tibial translation (p=0.012 for both insertions).
In contrast to the anterior loading results, ACL deficiency did not significantly
affect insertion site forces during application of valgus torque (Figure 5.3, right panel).
This was true at both the femoral and tibial insertion sites and for both flexion angles.
Although the increases caused by ACL deficiency were not statistically significant in
response to a valgus load, the results followed the same trend as the anterior loading
results, with higher forces and increases in forces at the tibial insertion and at 0 deg
flexion. Before ACL resection, the MCL femoral insertion site forces during valgus
rotation at 0 and 30 deg were 72.8 ± 49.9 N and 46.0 ± 34.4 N, respectively. Before
ACL resection, the MCL tibial insertion site forces during valgus rotation at 0 and 30
deg were 77.8 ± 55.6 N and 47.9 ± 37.9 N, respectively. After the ACL was transected,
Anterior Loading
Valgus Loading
250
*
*
200
*
*
150
100
50
0
Insertion Site Force (N)
Insertion Site Force (N)
250
ACL intact, femur
ACL cut, femur
ACL intact, tibia
ACL cut, tibia
200
150
100
50
0
0
30
Flexion Angle (Degrees)
0
30
Flexion Angle (Degrees)
Figure 5.3. FE predictions of insertion site forces for femoral and tibial insertion sites as
a function of flexion angle and ACL state. Left panel - anterior tibial translation. Right
panel – valgus rotation. Asterisks indicate statistically significant comparisons. There
was a significant increase in MCL insertion site forces at the femur and tibia during
anterior tibial translation after ACL injury at both 0 and 30 deg. In contrast, there was
no significant effect of ACL deficiency on MCL insertion site forces in response to
valgus loading. Both tibial and femoral insertion site forces were significantly higher at
0 deg in the ACL-deficient knee during anterior tibial translation. (mean ± stdev).
106
the MCL femoral insertion site forces during valgus rotation at 0 and 30 deg were 92.0 ±
64.0 N and 57.5 ± 45.0 N, respectively. After the ACL was transected, the MCL tibial
insertion site forces during valgus rotation at 0 and 30 deg were 99.0 ± 72.2 N and 60.7
± 49.3 N, respectively.
Contact Forces
ACL deficiency resulted in significantly increased MCL contact forces on the
tibia during anterior tibial translation at both flexion angles, and MCL contact forces on
the tibia were significantly higher at 0 than at 30 deg in the ACL-deficient knee. Before
ACL resection, the MCL contact forces on the tibia during anterior translation at 0 and
30 deg were 11.6 ± 11.9 N and 0.7 ± 0.9 N, respectively (Figure 5.4, left panel). ACL
deficiency significantly increased MCL contact forces on the tibia (28.4 ± 18.9 N and
21.1 ± 15.8 N at 0 and 30 deg, respectively) during anterior translation (p=0.001 at both
angles). MCL contact forces on the tibia in the ACL-deficient knee were significantly
higher at 0 than at 30 deg in response to anterior tibial loading (p=0.044). ACL
deficiency did not significantly affect contact forces during application of valgus torque
(Figure 5.4, right panel).
Discussion
The hypothesis of this research was that ACL deficiency would increase MCL
insertion site forces at the femur and tibia and increase contact forces between the MCL
and the bones in response to both anterior and valgus loading. This hypothesis was
partially disproved. In the ACL-deficient knee, the MCL is indeed subjected to higher
insertion site and contact forces in response to an anterior load. However, MCL forces
107
Anterior Loading
50
*
*
40
30
20
10
0
Contact Force (N)
Contact Force (N)
50
Valgus Loading
ACL intact, femur
ACL cut, femur
ACL intact, tibia
ACL cut, tibia
40
30
20
10
0
0
30
Flexion Angle (Degrees)
0
30
Flexion Angle (Degrees)
Figure 5.4. FE predictions of contact forces between the MCL and femur and between
the MCL and tibia as a function of flexion angle and ACL injury state. Left - anterior
tibial translation. Right – valgus rotation. Asterisks indicate statistically significant
comparisons. ACL deficiency significantly increased tibial contact forces at both
flexion angles during anterior tibial translation. Further, tibial contact forces in the
ACL-deficient knee at 0 deg were significantly higher than at 30 deg during anterior
loading. In contrast, there was no significant effect of ACL deficiency on MCL contact
forces during valgus loading (mean ± standard deviation).
due to a valgus torque are not significantly increased in the ACL-deficient knee. It
follows that the MCL resists anterior tibial translation in knees with intact ACLs, but the
ACL is not a restraint to valgus rotation when a healthy MCL is present.
ACL deficiency caused a significant increase in MCL insertion site and contact
forces in response to anterior tibial loading. This result is supported by an FE study that
examined MCL insertion site and contact forces in the ACL-deficient knee [35] and by
cadaver studies that have utilized a robotic/universal force-moment sensor system to
calculate MCL insertion site forces in the ACL-deficient knee [16, 17]. Moglo et al.
created a single FE model of the knee including the MCL, ACL, posterior cruciate
ligament, lateral collateral ligament, menisci, and cartilage [35]. A 100 N posterior load
108
was applied to the femur at a range of flexion angles from 0 to 90 deg to study the forces
in the remaining structures after removing the ACL.
At full extension, forces in
collateral ligaments increased in the ACL-deficient knee. Better support for our findings
can be found in two studies that used a robotic/universal force-moment sensor system to
calculate MCL insertion site forces in the intact and ACL-deficient knee [16, 17]. In
each study, anterior loads were applied to intact and ACL-deficient cadaver knees and
the resulting MCL insertion site forces were measured. Both studies found a significant
increase in MCL insertion site forces during anterior tibial loading in the ACL-deficient
knee.
In vivo studies of MCL healing have demonstrated that MCL healing is inferior
when injured in conjunction with the ACL [2, 5, 6, 12]. Each of these studies found
increased knee laxity and decreased MCL material properties when the MCL is injured
in conjunction with the ACL as compared to MCL injury with intact ACL, but only one
of these studies measured MCL forces. Using a goat model, the insertion site forces in
healing MCLs in response to an anterior tibial load in knees with reconstructed ACLs
were measured by Abramowitch et al. using a robotic/universal force-moment sensor
system [6]. It was their conclusion that “the healing MCL may have been required to
take on excessive loads and was unable to heal sufficiently as compared to an isolated
MCL injury.” Although these conclusions were reached based on healing studies in
animal models, application of the results of the present study suggest that differences in
MCL healing between knees with intact ACLs and those with transected ACLs could be
due to either anterior or valgus loading. The insertion site forces in response to a valgus
torque were generally of the same magnitude for a given flexion angle as the insertion
109
site forces in response to an anterior tibial load, although they did not significantly
increase with ACL deficiency, and the types and magnitudes of loads that hinder MCL
healing are unknown.
The ACL is not a restraint to valgus rotation if the MCL is intact (Figures 5.4 and
5.5, right panels). At first this may seem contradictory to the widely held notion that the
ACL is a secondary restraint to valgus rotation [3-8, 11].
However, upon closer
examination, these studies reached this conclusion based on the results of MCL
transection.
Specifically, when the MCL was injured or transected, the ACL
experienced increased loading during application of a valgus torque. Although our
conclusion has not been reported previously in the literature, the results of other studies
support the conclusions indirectly. Engle et al. examined the effect of ACL repair and
graft restructuring on MCL healing after an O'Donoghue triad injury [2]. At 0, 6, and 12
weeks postoperatively, the anterior translation and valgus rotation of the knees were
tested. From time point zero to 12 weeks anterior laxity significantly increased, but
valgus laxity did not.
Markolf et al. found that valgus knee laxity was relatively
unaffected by sectioning of the cruciate ligaments [14]. This idea is further supported by
a study looking at medial and lateral laxity in intact cadaver knees [13]. Using a six
degree of freedom linkage attached to six different knees with applied varus-valgus
loading, Grood et al. found that the ACL and PCL combined accounted for only 14.8%
of the medial restraining moment at five degrees knee flexion and only 13.4% of the
restraining moment at 25 degrees knee flexion. Thus, when evaluating valgus laxity in
the ACL-injured knee, any increase in valgus laxity indicates a compromised MCL.
110
Applying the in situ strain to the MCL during the second part of the FE analysis
creates insertion site forces. These forces represent the contribution of the MCL to knee
stability when there is little or no muscle activation or external loading. The insertion
site forces caused by the in situ strain were smaller than the insertion site forces present
after anterior or valgus loading in the intact knee, although not always significantly
smaller. This result is reflective of the relatively low load limits used in this study. In
the ACL-deficient knee, insertion site forces significantly increased in response to an
anterior tibial load, but not a valgus load, from the insertion site forces caused by the in
situ strain. This followed the trend of the results comparing the insertion site forces in
the ACL-deficient knee to the intact knee.
Changes in MCL contact forces followed the trend of MCL insertion site forces
in that there was a significant increase in contact forces between the MCL and tibia
following ACL transection in response to an anterior tibial load, but not a valgus torque.
Contact forces were generated between the MCL and tibia during anterior tibial
translation as the MCL slid over the convex surface of the tibia. These forces were
relatively small in knees with intact ACL. Contact forces increased when the ACL was
transected, and on average anterior tibial translation was more than doubled, forcing the
MCL to slide over parts of the bone that have increased curvature. To our knowledge
this is the first study to examine ligament contact forces using subject-specific FE
modeling.
The attachment of the medial meniscus to the MCL was not represented in the
FE analyses. This approach was justified by the results of our previous study, which
demonstrated that transection of the attachment had no effect on knee kinematics under
111
valgus loading in the intact knee [22]. In the present study, it was confirmed that
separation of the attachment of the medial meniscus to the MCL had no significant
effect on joint kinematics for both A-P and V-V loading in the ACL-deficient knee. Of
course, it is possible that other soft tissue structures that were dissected away from the
knees may contribute to knee stability under A-P and V-V loading, and thus as with any
cadaveric study, caution should be taken when extrapolating results to other situations.
Improvements in the experimental methods that were used in the present study
resulted in substantially better agreement between FE predictions and experimental
measurements of fiber stretch than was obtained in our previous study [22].
Improvements included the use of a more accurate digital motion analysis system,
placement of wires around the MCL insertion sites to aid in identifying their locations in
the CT images, and the placement of additional strain markers along and across the
MCL [23]. The excellent correlation between experimental and FE predicted fiber
strains (R2 = 0.953) provides confidence in the fidelity of the subject-specific FE model
predictions. Data such as insertion site forces and contact forces, which elucidate other
injury mechanisms and risks, can be evaluated using subject-specific FE methods.
Further, the combination of results available through a combined experimental and
computational protocol can be used to determine the likely location of injury and to what
extent it may occur.
Several assumptions were made in the constitutive model used for the MCL to
decrease both the experimental and computational time it took to conduct the study.
Average MCL material coefficients from a previous study [22] were used. In the
previous study, results from FE simulations using subject-specific material properties
112
were compared to those using average material properties and no statistical differences
were found. The MCL was assumed to have homogenous material properties. This
assumption was used in our previous study [22] that yielded good correlations between
experimental and FE strain results so the assumption was used again for this study which
yielded an even better correlation.
It should be noted that the A-P and V-V mechanical testing performed for this
research simulated an ideal clinical exam for knee laxity and no attempt was made to
simulate weight bearing or muscle forces. Caution should be use when extrapolating the
results reported here to a knee under muscle activation forces and/or ground contact
forces. The anterior load and valgus torque limits for this research were specifically
chosen to allow multiple tests on a single knee. Future research examining other loading
conditions including muscle and body weight forces during regular daily activities is still
needed.
In summary, ACL deficiency significantly increases MCL insertion site and
contact forces in response to an anterior tibial load, and the largest increases occur at full
extension. In contrast, ACL deficiency does not significantly increase MCL insertion
site and contact forces in response to a valgus torque. Since it was demonstrated that the
ACL is not a restraint to valgus rotation if the MCL is intact, increased valgus laxity in
the ACL-deficient knee indicates a compromised MCL.
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CHAPTER 6
EFFECT OF DERMATAN SULFATE GLYCOSAMINOGLYCANS ON
THE QUASI-STATIC MATERIAL PROPERTIES OF THE
HUMAN MEDIAL COLLATERAL LIGAMENT
Abstract
The glycosaminoglycan of decorin, dermatan sulfate (DS), has been suggested to
contribute to the mechanical properties of soft connective tissues such as ligaments and
tendons.
This study investigated the mechanical function of DS in human medial
collateral ligaments (MCL) using nondestructive shear and tensile material tests
performed before and after targeted removal of DS with Chondroitinase B (ChB). The
quasi-static elastic material properties of human MCL were unchanged after DS
removal. At peak deformation, tensile and shear stresses in ChB treated tissue were
within 0.5% (p>0.70) and 2.0% (p>0.30) of pretreatment values, respectively. From
pre- to post-ChB treatment under tensile loading, the tensile tangent modulus went from
242 ± 64 to 233 ± 57 MPa (p=0.44), and tissue strain went from 4.3 ± 0.3% to 4.4 ±
___________________
1
Reprinted by kind permission of Wiley-Liss Inc. a subsidiary of John Wiley & Sons
Inc. Journal of Orthopaedic Research, Vol. 25, No. 7, Trevor J. Lujan, Clayton J.
Underwood, Heath B. Henninger, Brent M. Thompson, Jeffrey A. Weiss, “Effect of
Dermatan Sulfate Glycosaminoglycans on the Quasi-Static Material Properties of the
Human Medial Collateral Ligament,” pp: 894-903, 2007.
117
0.3% (p=0.54). Tissue hysteresis was unaffected by DS removal for both tensile and
shear loading. Biochemical analysis confirmed that 90% of DS was removed by ChB
treatment when compared to control samples, and TEM imaging further verified the
degradation
of
DS
by
showing
an
88%
reduction
(p<.001)
of
sulfated
glycosaminoglycans in ChB treated tissue. These results demonstrate that DS in mature
knee MCL tissue does not resist tensile or shear deformation under quasi-static loading
conditions, challenging the theory that decorin proteoglycans contribute to the material
behavior of ligament.
Introduction
The mechanical characteristics of ligament and tendon are a function of their
composition and molecular organization. The major solid phase constituent of ligament
and tendon is type I collagen, which represents over 70% of the total solid phase [1, 2].
The remaining constituents include other collagens (III,V,VI,XI,XIV), extracellular
matrix proteins (e.g., elastin) and proteoglycans [3, 4]. The hierarchical organization of
type I collagen in ligament has been extensively studied; type I collagen fibrils form
parallel arrays of fibers that are the main contributors to ligament material properties [5].
However, there are very limited data on the mechanical influence of the noncollagenous
components of ligament.
Understanding the contributions of these components to
connective tissue mechanics can clarify their roles and aid efforts to engineer
replacement tissues.
The primary proteoglycan in ligament [6, 7], decorin, and its single sulfated
glycosaminoglycan (GAG) have been the subject of numerous studies due to their
respective proven and theoretical roles in tissue-level organization and mechanics at the
118
molecular level. Dermatan sulfate (DS) is the GAG that associates with the decorin core
protein in nearly all mature tissues [8, 9](Figure 6.1, A). The concave face of decorin
has been modeled to straddle the D-period binding site of a single collagen triple helix
[10, 11] (Figure 6.1, B). Decorin appears to be excluded from the tightly tropocollagen
packed fibril interior [12], localizing to the surface of the collagen fibrils (Figure 6.1, C).
The highly charged DS GAG projects away from the decorin core protein [13], generally
orthogonally aligned to the fibril direction [8].
Scanning electron and atomic force microscopy of connective tissues
demonstrate that the majority of DS GAGs span the space between neighboring fibrils
[12, 15]. Several studies report that collagen fibrils in various species are short and
discontinuous [16-18], suggesting that a secondary microstructure could be involved in
transferring axial force between contiguous fibrils. This finding led to an appealing
hypothesis that decorin based DS GAGs on adjacent fibril surfaces interact, creating
functional “cross-links” that transfer forces between discontinuous fibrils [8, 15, 17, 1924] (Figure 6.1, C). Supporting this hypothesis, molecular dynamics predict that the
summation of GAG interaction forces in collagen-decorin networks are capable of
transferring inter-fibril stress [15, 25], and experiments confirm that DS does indeed
self-associate [26].
The proposed fibril-fibril link would prevent fibril sliding and
potentially contribute to quasi-static mechanical properties under both shear and tensile
loading.
Using knockout mice and competitive peptides, previous studies have
investigated the possible mechanical role of decorin indirectly. Knockout studies have
B
A
C
D = 67nm
G
Micro
Fibril
I
LO
L C
NH2
Decorin Core
Protein
Fibril
L L L
L
L
L
Dermatan
Sulfate
GAG
L
L
y
y
Tropocollagen
x
z
CL
L
L
Figure 6.1. Illustration of interfibrillar GAG interaction. (A) Magnified schematic of decorin proteoglycan. The boxed domains “L”
represent the Leucine-rich repeats of the protein core. “C” represents the cysteine rich domain. The dermatan sulfate GAG is
attached to the protein core near the amino (NH2) terminus through a serine linked oligosaccharide (“LO”). On the macroscopic
level, dermatan sulfate is made up of iduronic acid-containing regions “I” and glucuronic acid-containing regions “G”.
Chondroitinase B will degrade the iduronic acid regions mainly into disaccharides. (B) Frontal view of collagen fibril assembly.
Five quarter staggered tropocollagen units construct a microfibril. Decorin core proteins likely bind to tropocollagen triple helix units
at the D-period gap region of the microfibril. Although decorin binds to collagen, the exact mechanism of this attachment is debated
[14]. (C) Cross-sectional view of collagen fibril assembly. A fibril consists of a variable number of microfibrils, however decorin is
only able to bind to tropocollagen units on the outer surface of the fibril. The proposed interaction between collagen fibrils involves
the association of at least two dermatan sulfate GAG side-chains bonded to decorin core proteins on adjacent collagen fibrils.
119
120
reported that decorin deficiency decreases the tensile strength in the dermis [27], does
not affect tensile strength or modulus in tail tendon fascicles [28, 29], and increases
modulus in the patellar tendon [28, 29]. NKISK, a pentapeptide that inhibits the ability
of decorin to bind to collagen, caused fibril to fibril disassociation [18], and ultimately
greater tissue laxity without reduction in material strength [30].
The mechanical
behavior of connective tissues from knockout mice may be influenced by changes in
tissue development due to the absence of decorin, such as irregular collagen structure
[27] and regulatory activity that increases biglycan production as a compensatory
mechanism [31-33]. Lack of biochemical analyses in the NKISK studies has left the
molecular alterations in question.
If NKISK specifically inhibits decorin binding,
observed changes may be due to the absence of the decorin core protein and not
necessarily the associated GAG. Altogether these studies have left the mechanical role
of DS under elastic tensile deformation unclear and controversial.
In this study, a new experimental model was developed to quantify the effects of
DS GAGs on the elastic material behavior of mature ligament tissue. The objective of
this study was to assess the role of DS in resisting quasi-static tensile and shear
deformation along the fiber direction in human ligament by targeted removal of DS
crosslinks with enzymatic degradation. Understanding the elastic contribution of DS
will clarify the structure-function relationships in ligament and address the validity of
the decorin cross-linking theory.
121
Materials and Methods
Experimental Design
The medial collateral ligament (MCL) of the human knee was chosen for study
due to the extensive prior mechanical test data available [34-37] and our well-developed
experimental protocols for mechanical characterization [34, 36, 38].
Knees were
acquired fresh-frozen and were allowed 16 h to thaw prior to dissection. The freezethaw cycle does not influence the quasi-static material properties of ligament [39, 40].
Knees with surgical scars, ligament injury or cartilage degeneration characteristic of
osteoarthritis were eliminated. To account for material symmetry characteristics of
ligament, two types of mechanical tests were performed – uniaxial tensile testing [34]
and shear testing [36]. For tensile testing, 16 specimens were harvested from 4 unpaired
human MCLs (donor age = 57±5 yrs). For shear testing, 16 specimens were harvested
from 5 unpaired human MCLs (donor age = 55±8 yrs). The specimens were divided
between two treatment groups: a control treatment group and a Chondroitinase B (ChB)
treatment group. ChB specifically degrades DS side chains of proteoglycans [41].
Mechanical testing was performed pre- and posttreatment on the same specimen.
Uniaxial Tensile Testing
Four unpaired human MCLs were used for tensile testing. A hardened steel
punch [34] was used to extract four samples from different locations in each superficial
MCL between the tibial and femoral insertions, for a total of 16 tested samples. The
punch shape included beveled ends for gripping and was oriented so that its long axis
was aligned with visible fiber bundles. Sample geometry met ASTM requirements for
fiber reinforced composite materials [42]. Samples were randomly divided between the
122
control and ChB treatment groups and loaded in a clamp assembly. A 0.1 N preload was
applied to establish a consistent reference length. Vertical position of the actuator (TolO-Matic, Hamel, MN, accuracy ±1.0 µm) and high-resolution micrometer measurements
(Newport, Irvine, CA, accuracy ±0.5 µm) of the x-y table were logged so that the
reference configuration could be reproduced after treatment.
Sample position was
further measured by tracking 4.75 mm dia acrylic white spheres adhered to each clamp
and to a fixed reference with a digital camera (Pulnix TM-1040, 1024x1024x30 fps,
Sunnyvale, CA) and digital motion analysis software (DMAS, Spica Technology Corp,
Maui, HI, accuracy ± 0.005 mm) [43]. This protocol verified that posttreatment clamp
positions were within 0.04 ± 0.01 mm of pretreatment clamp positions.
Specimen dimensions were measured using digital calipers (Mitutoyo, San Jose,
accuracy ±0.02 mm) by taking an average of three measurements (Table 6.1).
Following five minutes of relaxation, a triangular displacement profile was applied for
10 cycles at a strain rate of 1.0%/sec with a clamp-to-clamp strain amplitude of 8% of
the specimen length. Clamp-to-clamp strain was based on substructural failure limits of
ligament, reported to occur after 5% tissue strain [44].
Tissue tissue strain is
approximately half the clamp-to-clamp strain [37], so an 8% clamp-to-clamp strain was
chosen to avoid structural damage. The strain rate was selected to minimize viscoelastic
and inertial effects [34]. Tissue strain was measured by tracking 1 mm diameter contrast
Table 6.1. Physical dimensions of the MCL test specimens (n=16).
Specimen
type
Tensile
Shear
Mean clamp-toclamp length ± SD
15.06 ± 1.84
6.93 ± 0.91
Mean
width ± SD
1.81 ± 0.38
9.81 ± 0.93
Mean
thickness ± SD
1.48 ± 0.58
1.84 ± 0.42
123
markers on the specimen midsubstance using the DMAS system.
Force and
displacement were monitored continuously, with peak stress calculations based on the
10th cycle. Tangent modulus was calculated as the slope of the linear region of the
stress-strain curve using local tissue strain, and was standardized for each case by using
linear regression to fit the stress-strain data for the final 1% strain region. Hysteresis
was determined from the area enclosed by the loading and unloading stress-strain curves
from the last loading cycle.
Shear Testing
Five unpaired human MCLs were used. A rectangular punch (10 x 25 mm) was
used to extract up to four samples from each superficial, for a total of 16 tested samples.
Samples were randomly divided between the control and ChB treatment groups and
loaded in a clamp assembly so that visible fibers were along the direction of
displacement [36]. Load cells (Sensotec Inc, Columbus, OH, 1000 g, accuracy ±0.1 g)
monitored both transverse and longitudinal clamp reaction forces. After the specimen
was loaded and aligned in an unstrained state, a 1 g preload was applied in the transverse
direction. The vertical displacement was then set at a neutral position, defined as the
inflection point of the force response resulting from small cyclic up-down clamp
displacements. Clamp position and physical dimensions were recorded (Table 6.1)
using methods identical to the tensile mechanical protocol. Following a five-minute
equilibration period, a triangular displacement profile was applied for 10 cycles at a
strain rate of 6.5%/sec, with peak displacement based on tan(θ) = (peak displacement) /
(clamp-to-clamp length) = 0.4 [36]. Strain rate was chosen to minimize viscoelastic and
inertial effects and peak displacement was selected to maximize loading without
124
damaging the tissue. Transverse force, longitudinal force and vertical displacement
were monitored continuously, with peak stress calculations based on the 10th cycle.
Shear modulus was defined as the slope of the stress-strain curve over the final 0.1 shear
strain region. Hysteresis effect was measured using the previously mentioned methods.
Chondroitinase B Treatment Protocol
Following initial shear and tensile testing, each specimen was removed from the
test machine while still mounted in the clamps.
The entire specimen and clamp
assembly was bathed for 1 h at room temperature in 15 ml of buffer solution (15 ml of
20 mM Tris pH 7.5, 150 mM NaCl, 5 mM CaCl2) with protease inhibitors (1 tablet of
mini-complete per 10 ml of buffer). Samples were then bathed in 15 ml of either the
control buffer solution or the ChB (ChB, 1.0 IU/mL) for 6 h at room temperature with
gentle agitation using an orbital shaker. Preliminary tests confirmed that all DS was
completely degraded using 0.25 IU/mL for 6 h (data not shown). Immediately after
treatment, the clamp assembly was reattached to the test machine and returned to the
original testing position. The sample was allowed to equilibrate for five minutes. Shear
or tensile posttreatment testing was then performed with the same test parameters. For
the tensile test specimens, failure tests were performed subsequently using our published
protocol [34]. The sample was then removed from the clamps and placed in a stop
buffer (20 mM Tris, pH 7.5, 150 mM Sodium Chloride, and 10 mM EDTA) to inhibit
any further ChB activity. Samples were kept continuously moist by applying 0.9%
saline solution through a nozzle.
125
Specificity of Chondroitinase B
A large quantity of ChB was required to treat all ligament specimens. ChB was
cloned into a prokaryotic expression vector as previously described [45]. ChB degrades
DS into disaccharides by cleaving regions that contain iduronic acid (Figure 6.1, A).
One international unit (IU) of ChB activity corresponds to the amount of enzyme
required to liberate 1 μM of unsaturated uronic acid from DS per minute at 30oC (pH
7.5, [DS] = 2 mg/mL).
The specificity of ChB was determined by incubating ChB with various sulfated
GAGs and then determining the GAG concentration remaining after digestion using the
dimethylmethylene blue (DMB) assay [46]. Individual reactions (30 μl, n=6 for each
condition) were set up containing 1.0 IU/mL ChB and 500 μg/mL of sulfated GAG (DS,
chondroitin sulfate A and C, heparan sulfate, or keratan sulfate). Control reactions
contained GAGs and buffer only: 20 mM Tris-HCl (pH 7.5), 150 mM NaCl, and 5 mM
CaCl2. The reaction was allowed to proceed for 6 h at RT. Five μl of each reaction
(diluted 2 fold) was transferred to a 96 well plate in duplicate, along with GAG
standards. Two hundred μl of dimethylmethylene blue reagent were added to each well,
and the plate was immediately read in a plate reader at 530 and 590 nm.
GAG
concentrations were expressed as a percentage of control reactions.
Verification of ChB Activity and Removal of DS
Following mechanical testing, the ligament tissue between the clamps was
isolated with a scalpel and cut into three pieces for GAG quantification, decorin Western
blot analysis and transmission electron microscopy (TEM).
GAG quantification
followed established guidelines [47], which involved taking DMB assays from digested
126
papain extracts. These guidelines permitted DS content in control and ChB treated
samples to be calculated by subtracting the amount of GAG present in the papain extract
treated with additional ChB from the papain extract that was treated with additional
buffer only.
Western blot analysis examined the glycosylation state of decorin from extracted
proteoglycans. Wet weights were obtained and samples were then frozen in liquid
nitrogen and pulverized. Proteoglycans were extracted twice over 24 h at 4oC in a total
of 25 volumes of 4 M GuHCl , 50 mM acetate buffer pH 6.0 plus protease inhibitors.
Two-hundred microliters of extract were precipitated overnight by the addition of 1 ml
of 100% ethanol. Following centrifugation at 14,100 g, the protein pellet was washed
with 70% ethanol and resuspended in 20 μl of 8 M urea, followed by an additional 140
μl of 10 mM Tris pH 7.4. Twenty-five microliters of this extract were treated with and
without additional ChB, as described above. Fifteen microliters of each reaction was
resolved by size on 4-16% gradient SDS-polyacrylamide gels.
Proteins were
electrophoretically transferred to nitrocellulose membranes and blocked in 3% BSA in
1X TBST (Tris-buffered saline plus 0.1% Tween 20) for 1 hr at RT. Blots were
incubated with 0.2 μg/ml biotinylated anti-human decorin antibody (BAF140, R&D
Systems, Minneapolis, MN) for 3 h at RT on a nutator.
Blots were washed and
incubated with 0.8 μg/ml with Neutravidin-HRP (Pierce, Rockford, IL) for 1 h at RT.
Blots were developed using a chemiluminescent substrate (Bio-Rad, Hercules, CA) and
imaged with a gel documentation system with an integrated 12 bit camera.
TEM was used to investigate the presence of sulfated GAGs in the five control
and ChB treated MCL specimens that were tested in shear. Ligament sections were
127
fixed in 4% paraformaldehyde / 2% glutaraldehyde at 4°C overnight. Samples were
sectioned to 20 µm (cryostat, Leica CM3050S, Exton PA), mounted on slides, and
stained with Cupromeronic Blue [8, 48]. Ultrathin sections, approximately 70 nm, were
obtained via ultramicrotome (Leica Ultracut UCT, Exton, PA) and viewed using a
Hitachi H7100 TEM with a LAB6 filament. Digital images of a minimum of four fields
of view were obtained from each examined MCL. An image processing program was
used to determine the number of stained GAGs per image. All images from a sample
were averaged to produce a representative number of detected objects for each sample.
Statistical Analysis
The effects of control and ChB treatment on tensile and shear peak stresses,
tensile and shear tangent moduli and peak tissue strain were assessed using paired t-tests
to measure significance between pre- and posttreatment. Control versus ChB treatment
effect upon hysteresis was assessed using ANCOVA, controlling for pretreatment
results, with Bonferroni adjustment for multiple comparisons. TEM and biochemistry
results were assessed using independent t-tests to measure control versus ChB treatment
effect. A power analysis demonstrated that a sample size of 8 was sufficient to detect a
10% change with 80% confidence for all measured quasi-static mechanical results
(Power = 0.80). Unless otherwise noted, all results are reported with standard error.
Results
ChB and control treatments had no significant effect on tensile (Figure 6.2) or
shear (Figure 6.3) stress-strain response. At peak deformation, there were no significant
differences in the tensile peak stress due to control or ChB treatments (percent decreases
128
Control Treatment
6
Pretreatment
Pretreatment
Posttreatment
2
2
4
Posttreatment
4
ChB Treatment
B
Tensile Stress (MPa)
6
Tensile Stress (MPa)
A
0
2
4
6
8
0
2
4
6
8
Clamp Strain (%)
Clamp Strain (%)
Figure 6.2. Stress-Strain curves for tensile tests of control (n=8) and chondroitinase B
(n=8) treated ligament samples. (A) Tensile mechanical response immediately after
dissection (pretreatment) and after 6 h of control treatment (posttreatment). (B) Tensile
mechanical response immediately after dissection (pretreatment) and after 6 h of
chondroitinase B treatment (posttreatment). Bars = standard error.
Control Treatment
Pretreatment
24
Pretreatment
Posttreatment
8
8
16
16
Posttreatment
0
ChB Treatment
B
Shear Stress (KPa)
24
Shear Stress (KPa)
A
0.1
0.2
tan (θ)
0.3
0.4
0
0.1
0.2
tan (θ)
0.3
0.4
Figure 6.3. Stress-Strain curves for shear tests of control (n=8) and chondroitinase B
(n=8) treated ligament samples. (A) Shear mechanical response immediately after
dissection (pretreatment) and after 6 h of control treatment (posttreatment). (B) Shear
mechanical response immediately after dissection (pretreatment) and after 6 h of
chondroitinase B treatment (posttreatment). Bars = standard error.
129
of 0.4 ± 1.3% (p=0.75) and 0.4 ± 0.9% (p=0.718) due to control and ChB treatments,
respectively). Similarly, there were no significant differences in the shear peak stress
due to control or ChB treatments (percent increases of 5.6 ± 3.3% (p=0.13) and 1.7 ±
1.6% (p=0.32) due to control and ChB treatments, respectively).
Tensile tangent modulus, shear modulus and tissue strain were unaffected by
ChB and control treatments. For tensile tests, an 8.0% clamp strain equated to 5.1 ±
0.4% tissue strain. Between time stages, tensile control specimens went from 5.8 ±
0.5% to 5.7 ± 0.6% (p=0.58) tissue strain, while tensile ChB treated specimens went
from 4.3 ± 0.3% to 4.4 ± 0.3% (p=0.54) tissue strain. The pre- and posttreatment tensile
moduli from the 8% cyclic tests were, respectively, 160 ± 56 and 159 ± 55 MPa for the
control (p=0.79), 242 ± 64 and 233 ± 57 MPa for the ChB treated (p=0.37). The tensile
tangent modulus data calculated from the 8% cyclic strain tests (198 ± 28 MPa) had a
high correlation with the tensile tangent modulus data calculated from the failure tests
(202 ± 37 MPa) (r2=0.8). The pre- and posttreatment shear moduli were, respectively,
133 ± 11 and 142 ± 14 KPa for the control (p=0.08), 146 ± 39 and 151 ± 41 KPa for the
ChB treated (p=0.08).
There were no significant differences in percent hysteresis between control and
ChB treatments for tensile (p=0.16) and shear (p=0.79) material tests. The pre- and
posttreatment percent hysteresis for tensile specimens were, respectively, 18.8 ± 0.9%
and 20.4 ± 0.9% for control treated, and 20.0 ± 0.7% and 20.8 ± 0.9% for ChB treated.
The pre- and posttreatment percent hysteresis for shear specimens were, respectively,
21.2 ± 1.8% and 27.5 ± 1.5% for control, and 21.2 ± 1.1% and 28.0 ± 1.9% for ChB
treated.
130
ChB reduced the concentration of the DS standard by 78% (Figure 6.4).
Concentrations of Chondroitin sulfates A and C, heparan sulfate and keratan sulfate
were unaffected by ChB treatment. A likely reason that the specificity test did not show
a complete degradation of DS is due to the presence of DMB detectable glucuronic acidcontaining regions in DS, which are not digested by ChB [49, 50]. Biochemical analysis
of the mechanically tested samples found that over 90% of the DS in ChB treated
ligament samples (0.1 ± 0.2 μg DS / mg dry tissue) were eliminated when compared to
control specimens (1.5 ± 0.5 μg DS / mg dry tissue) (p < 0.001). DS accounted for 44 ±
4% of all sulfated GAGs (3.6 ± 0.6 μg sulfated GAGs / mg dry tissue) in the
mechanically tested controls. Western blot results for three control and three treated
specimens are shown in Figure 6.5, but all shear samples and random tensile samples
were tested and yielded similar results. Control specimens (lanes 1, 3, and 5) had two
predominant species, a band at ~43 kDa and a smear from 50-100 kDa. When extracts
Percent of Control
120
100
80
60
40
20
0
DS
CS
HS
KS
Figure 6.4. Specificity of Chondroitinase B. Chondroitinase B (1 U/ml) was incubated
with glycosaminoglycans (500 μg/ml) for 6 h. GAG concentration after 6 h was
determined using the DMB assay. Concentrations were normalized to control reactions
which did not contain chondroitinase B. DS = dermatan sulfate, CS = equal mixture of
chondroitin sulfates A and C, HS = heparan sulfate, KS = keratan sulfate. N = 6. Error
bars = SD.
131
Additional
ChB
-
+
-
+
-
+
-
+
-
+
-
+
2
3
4
5
6
7
8
9
10
11
12
Molecular Weight (KDa)
182
115
82
64
49
37
Lane
1
3 Paired Samples
3 Paired Samples
Control Treatment
ChB Treatment
Figure 6.5. Decorin western blot of proteins extracted from 3 control and 3 ChB treated
ligament samples. Each lane represents protein extracted from approximately 375 µg of
wet tissue. Two tests were performed on each sample. They were either given an
additional control buffer treatment (-) or given an additional treatment of ChB (+).
Decorin core protein migrated as a doublet at ~45 kDa. Decorin containing its dermatan
sulfate side-chain migrated as a smear from ~55 to 120 kDa.
were treated with ChB (lanes 2, 4, and 6) the smear was eliminated and there were two
major bands at 45 and 90 kDa. The species are consistent with published results in the
literature - the 45 kDa band, which appears as a doublet with shorter exposure times,
represents decorin that has lost its glycosaminoglycan side chain, and the 90 kDa band is
a dimeric form of decorin [51, 52]. Protein extracts obtained from ChB treated ligament
samples (lanes 7, 9, and 11) did not show the smear pattern between 50 and 100 kDa,
suggesting that decorin obtained from these samples had already lost its DS side chain.
Additional ChB treatment did not cause any further changes (lanes 8, 10, and 12).
132
TEM images further verified degradation of sulfated GAGs.
ChB treated
samples showed a significant reduction in the number of sulfated GAGs when compared
to controls (88% reduction, p<0.001). The quantity of stained GAGs was reasonably
uniform within groups having 47 ± 13 and 350 ± 40 stained GAGs per field for ChB
treated and control images, respectively. Stained GAGs remaining after ChB treatment
were preferentially aligned with the local collagen fibrils and generally larger than the
GAGs that were removed (Figure 6.6).
Discussion
The DS GAG of decorin has been implicated as a contributor to the material
behavior of connective tissue [27-29]. Our present findings do not support the theory
that sulfated GAG crosslinks influence continuum-level mechanical behavior during
Figure 6.6. Representative TEM images of tissue stained with Cupromeroneic Blue after
mechanical testing. Large arrows indicate collagen fibril direction, small arrows
indicate darkly stained sulfated GAGs. (A) Control treated specimen. (B) ChB treated
specimen with 88% reduction in sulfated GAGs. Note the decrease in the overall
number of fibril spanning GAGs and the preferred alignment of remaining sulfated
GAGs along the collagen fibril direction. (Bar = 200 nm).
133
quasi-static tensile loading [8, 15, 17, 19-23]. Shear testing was conducted to determine
whether DS contributes to the mechanical behavior of mature human MCL when fibrils
are sheared relative to each other [29, 53]. This hypothesis was similarly rejected.
Therefore, DS in mature human medial collateral ligament does not resist quasi-static
tensile or shear loads.
Mathematical studies have hypothesized that GAG crosslinks contribute to the
resistance of loads along the fiber direction by transferring force between discontinuous
fibrils. For instance, Redaelli et al. [22] used a molecular dynamics model to explore the
ability of decorin GAG crosslinks to prevent sliding between discontinuous 100 µm
fibrils. Results suggested that interfibrillar GAGs could transfer force between adjacent
fibrils, with maximum load transfer and GAG strain occurring at 5% tissue strain. The
current study demonstrated that even near 5% tissue strain, interfibrillar GAG crosslinks
did not contribute to the tensile material response of mature human MCL. This result
does not negate the theory that DS may resist fibril motion, but it does confirm that any
interfibrillar resistance from DS interaction does not affect quasi-static mechanics at the
macro scale. Past studies support this conclusion. Cribb and Scott [8] demonstrated that
the orientation of
sulfated GAGs does not change under tensile stretch along the
direction of the fibrils, indicating that there was no relative sliding of the fibrils.
Further, the mathematical model used by Radaelli [22] assumed fibril length to be
discontinuous in 100 µm segments. Fibril discontinuity is debated in the literature, with
many investigators reporting that mature fibrils are continuous structures [53-56], with
tapered ends only present on embryonic and in vitro fibrils [21, 57, 58]. It should be
noted that non-DS molecules, including other sulfated GAGs, may transfer forces
134
between shorter discontinuous fibrils or contribute to the mechanical properties of
ligaments in other test configurations.
Our findings aid the interpretation of results from studies using knockout mice
and competitive peptide techniques.
Results of this study suggest that material
alterations reported for tendons from decorin knockouts [27] are likely due to genetic
compensation or developmental abnormalities inherent in decorin deficiency. In another
study, pentapeptide NKISK treatment [30] disrupted the decorin-collagen interaction in
mouse tendon, resulting in increased strain behavior. Since the current study found that
DS degradation did not influence strain behavior, another factor may have influenced
tissue mechanics in the NKISK study. Biochemical analyses are needed to understand
how NKISK alters molecular composition.
Since negatively charged GAGs have an affinity for water molecules, we
investigated hysteresis during both shear and tensile tests. There were no significant
changes in hysteresis following the control and ChB treatments. However, DS may still
contribute to the viscoelastic response when other loading rates or protocols are used. In
studies of decorin-deficient knockout mice, decorin content correlated with strain-rate
sensitivity [28] and stress-relaxation behavior [59]. As stated previously, knockout
models are unable to directly measure DS contribution. Thus to completely characterize
the possible role of DS in ligament mechanical behavior, viscoelastic testing in both
tensile and compressive configurations is necessary.
To have confidence in our results, the experiments needed to be performed in a
reproducible manner that provided data consistent with the literature.
Ligament
mechanical behavior can vary between donors, and this can be especially problematic in
135
studies using cadaveric tissues. Measured mechanical response can also be influenced
by tissue heterogeneity, clamping, and specimen alignment in the material testing
system. These variables were controlled with the repeated measures design of the
experiments. Measured mechanical properties were also in good agreement with past
research. Peak shear stress for this experiment was 22 KPa, while published peak shear
stress using similar shear strains and loading configurations was 25 KPa [36]. Past
studies measured an average tangent modulus of 332 MPa in human MCL specimens
taken along the anterior portion of the superficial MCL between the tibial insertion and
the meniscal attachment [34]. Samples taken from this same region in the current study
had a similar average tangent modulus of 330 MPa.
Biochemical and TEM analysis verified selective degradation of DS and
elimination of interfibrillar crosslinks. Samples treated with ChB had 90% less DS than
control samples and an absence of the DS GAG side chain commonly associated with
decorin. TEM results demonstrated that ChB treatment removed the vast majority of
GAGs. Since the sulfated GAGs that remained after ChB treatment were oriented along
the fibrils, DS represented the majority of fibril-spanning sulfated GAGs in the mature
human MCL.
Since ChB degrades all DS chains, it likely degraded DS side chains on biglycan,
a small leucine rich proteoglycan that accounts for less than 10% of all proteoglycans in
ligament [7]. Biglycan has two associated GAG side chains and a core protein that
binds to collagen [60] and is highly homologous to decorin.
Biglycan is another
proteoglycan that could contribute to the mechanical properties of tendon [29].
However, since biglycan side chains are predominantly DS, and DS was eliminated via
136
ChB treatment, biglycan proteoglycans similarly did not contribute to quasi-static
mechanical behavior of the human MCL.
Several limitations of this research must be mentioned. A quasi-static loading
rate was used and thus DS depletion could have an effect if mechanical testing was
performed using faster loading rates.
Further, DS may contribute to ligament
mechanical properties in other test configurations such as compressive loading. Results
from this study are based on in vitro tests and long-term effects of DS depletion were not
considered. The human MCL tissue used in this study came from relatively old donors
with several different causes of death.
The exact time from death to postmortem
freezing was unknown but less than 24 h. Although the experimental design utilized
pre-and posttreatment testing of the same sample (repeated measures design), which
controls for the effect of initial differences between samples, the posmortem conditions
and differences in cause of death may have affected the starting concentration of DS in
the tissues. Similar to previous studies that used a repeated measures design to assess
treatment [59, 61], multiple samples were taken from each donor. However, it should be
noted that the measured quasi-static mechanical parameters and GAG content had as
much variability between samples from a specific donor as between donors.
Had
mechanical test data from each donor been averaged (n=4) to focus on variability
between donors, an acceptable change of 20% would be detected with 80% confidence
for all measured quasi-static mechanical results.
In summary, mechanical tests on the same mature human MCL specimens before
and after targeted DS removal demonstrated that DS does not contribute to the quasistatic tensile or shear material properties of mature human MCL. Future studies should
137
examine how DS contributes to viscoelasticity, and if other GAGs influence tissue
mechanics.
By continuing to examine microstructural influence on mechanical
properties of tissue, the origins of tissue material response can be elucidated and applied
to the improvement of tissue engineering and wound healing methods. Studies seeking
to engineer replacement tissues can use the results of this study and others like it to
understand the contribution of specific molecules to the mechanics of the tissue that is
being replaced.
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CHAPTER 7
VISCOELASTIC TENSILE BEHAVIOR OF HUMAN LIGAMENT AFTER
INTERFIBRILLAR GLYCOSAMINOGLYCAN DIGESTION
Abstract
The viscoelastic properties of human ligament potentially guard against structural
failure, yet the microstructural origins of these transient behaviors are unknown.
Glycosaminoglycans (GAGs) are widely suspected to affect ligament viscoelasticity by
forming molecular bridges between neighboring collagen fibrils. This study investigated
whether GAGs directly affect viscoelastic material behavior in human medial collateral
ligament (MCL) by using nondestructive tensile tests before and after degradation of
glycosaminoglycans with Chondroitinase ABC (ChABC). Control and ChABC treatment
(83% GAG removal) produced similar alterations to ligament viscoelasticity.
This
finding was consistent at different levels of collagen fiber stretch and tissue hydration.
On average, stress relaxation increased after incubation by 2.2% (control) and 2.1%
(ChABC), dynamic modulus increased after incubation by 3.6% (control) and 3.8%
(ChABC), and phase shift increased after incubation by 8.5% (control) and 8.4%
(ChABC). The changes in viscoelastic behavior after treatment were significantly more
pronounced at lower clamp-to-clamp strain levels. A 10% difference in the water content
of tested specimens had minor influence on ligament viscoelastic properties. The major
finding of this study was that mechanical interactions between collagen fibrils and GAGs
144
do not affect tissue-level viscoelastic mechanics in mature human MCL. These findings
narrow the number of extracellular matrix proteins that have a direct contribution to
ligament viscoelasticity.
Introduction
The intrinsic viscoelastic behavior of ligament originates from molecular
realignments that are neither infinitely slow (elastic) nor instantaneous (viscous). This
transient response may guard against structural failure. In particular, ligament stiffness
and damping properties increase at greater rates of strain [1, 2], and ligament stress
reduces with time during cyclic loading [3, 4]. These mechanisms may brake excessive
joint loads and protect against ligament fatigue failure [5]. Although such functionally
significant behaviors in ligament are well-defined, the microstructural genesis of these
transient behaviors is unknown.
Determination of microstructural influence on tissue-
scale viscoelasticity could improve the innovation and evaluation of ligament treatment
modalities (e.g. tissue engineering, drug therapy).
The origins of viscoelastic material behavior in ligament are certainly related to
its organization and composition. Ligament can be described as a hydrated (~70% water)
fiber-reinforced matrix, where collagen fibers provide structural integrity to the
extrafibrillar matrix [6, 7]. Collagen fibers are formed by arrays of fibrils that in turn are
composed of aggregating tropocollagen monomers. These tropocollagen units are tightly
wound Type I collagen helices, which represent 70% of the solid phase weight [8]. The
remaining constituents include other collagens (III, V, X, XII, and XIV), extracellular
matrix proteins and proteoglycans. Theories on the viscoelastic origin in ligament and
tendon include inherent viscoelasticy of the collagen fibers [6, 9], interaction of collagen
145
fibers with the extracellular constituents [10, 11] and fluid movement through the solid
phase [10, 11].
One theory that has received considerable attention concerns the
interaction between collagen fibrils and sulfated glycosaminoglycans [6, 12, 13].
Sulfated glycosaminoglycans (sGAGs) are negatively-charged polysaccharide
macromolecules that covalently bind to proteoglycan core proteins.
The sGAGs in
ligament include dermatan sulfate, chondroitin sulfate (A & C), and keratin sulfate.
Dermatan and chondroitin sulfate make up over 95% of all sGAGs in ligament [14], and
are the exclusive sGAG chains of the interfibrillar proteoglycans biglycan and decorin.
Biglycan attaches to collagen fibrils through its sGAG chains [15], while decorin, which
represents ~90% of all ligament proteoglycans [16], attaches to collagen fibrils through
the regular binding of its core protein on D-period sites [17]. The sGAG chains of
biglycan and decorin span fibrils [17, 18] and are preferentially distributed orthogonal to
the fibril direction [19].
Since sGAGs have been shown to self-associate [20],
interactions between sGAGs on neighboring fibrils have the capacity to influence the
fibril sliding that is attributed with tensile stretch [6]. Although it has been shown that
interfibrillar sGAGs do not appear to influence quasi-static ligaments mechanics [21],
sGAGs may still resist transient tensile loads [13, 22]. Further, sGAGs are highly
negatively charged and through collagen fibril interactions create a fixed charge density
that influences tissue hydration [23], and potentially viscoelastic behavior [24, 25].
Experimental and theoretical studies have investigated the viscoelastic mechanical
influence of sulfated GAGs during tensile deformation. Research by Robinson et al. [26]
and Elliot et al. [7] found that tendon tail fascicles in decorin-deficient mice were less
strain-rate sensitive and experienced faster stress relaxation than those of wild type mice,
146
while tendon tail fascicles in young wild type mice with elevated levels of decorin were
more strain-rate sensitive and experienced slower stress relaxation than those of wild type
mice. These results indicate that decorin deficiency is strongly associated with altered
tensile viscoelastic properties in tendon.
Further, degradation of sulfated GAGs
significantly altered tensile viscoelastic properties in lung parenchymal strips [27] and in
bovine cartilage [13, 22]. No studies have directly measured the influence of sGAGs in
ligament, but theoretical studies did successfully predict viscoelastic behavior of
structurally similar tendon with models that account for sGAG-fibril interactions [6, 13].
Collectively, these previous studies have identified sGAGs as a macromolecule
likely connected to viscoelastic tensile properties in ligament.
Yet, the mechanical
alterations observed in decorin-deficient mice [7, 28] were not specific to sGAGs and
experimental results in parenchymal lung and cartilage tissue may not translate to
ligament, where sGAGs are more sparsely distributed [8, 29]. Therefore, the aim of this
study was to specifically determine whether interfibrilar sGAGs influence the viscoelastic
material behavior of ligament under tension. To meet this objective, it was necessary to
control for tissue hydration and collagen fiber stretch, which have been demonstrated to
independently impact ligament viscoelasticity [2, 24, 25].
Materials and Methods
Experimental Design
The medial collateral ligament (MCL) of the human knee was chosen as a model
ligament due to a relatively high distribution of sulfated GAGs [8, 14, 30] and our well
established experimental protocol for mechanical characterization [1, 31].
Samples
extracted from MCLs were subjected to tensile loading along the fiber direction on a
147
material testing system.
This configuration best represents the principal loading
orientation in ligament [32]. After tensile testing was completed, samples were removed
from the material testing system and incubated in either a control or chondroitinase ABC
(ChABC) treatment. ChABC degrades chondroitin sulfate (A+C) and dermatan sulfate
without affecting other proteoglycan macromolecules [33, 34] and has been reported to
remove 98% of interfibrillar sGAGss [14]. Mechanical testing was then repeated on each
specimen to determine the treatment effect. To measure whether this treatment effect
was affected by testing parameters, strain level and buffer solution were varied to make
eight groups, with n=5 samples in each group, for a total of 40 samples.
Viscoelastic Testing
Eleven unpaired human MCLs were used for this experiment (donor age = 54±10 yrs).
Six MCLs were used for viscoelastic testing at a 4% strain level and five MCLs were
used for viscoelastic testing at a 6% strain level. Knees were acquired fresh-frozen and
were allowed 16 h to thaw prior to dissection. Knees with ligament lacerations, surgical
scars, or cartilage wear characteristic of osteoarthritis were eliminated.
During
dissection, the tissue was kept moist by applying a physiological 0.9% saline buffer
solution through a nozzle. A hardened steel punch was used to extract four tensile
samples from different locations in each superficial MCL between the tibial and femoral
insertions (Figure 7.1, A) [31]. Two strips of tissue immediately adjacent to the punched
tensile sample were retained to monitor the tissue hyrdration in an unloaded stated
(Figure 7.1, B). The punch shape included beveled ends for gripping and was oriented so
that its long axis was aligned with visible fiber bundles. Black optical markers were
adhered to the narrow central third of the tensile sample using cyanoacrylate. Samples
148
Tibia
Insert
A
MCL
Femur
Insert
B
Figure 7.1: Specimen acquisition. A) Up to four specimen sets were extracted from each
MCL, with three samples to each set. Each set included one punched sample for
viscoelastic tensile testing (dark grey) and two adjacent strips to monitor tissue hydration
(light grey). B) Photograph of the specimen set.
were loaded in the clamping assembly and width and thickness dimensions were
determined by taking an average of three measurements with digital calipers (Mitutoyo,
San Jose, CA; accuracy ±0.02 mm). Mechanical testing was performed in a custom built
testing chamber that interfaced with a heating recirculator (PolyScience, Niles, IL;
accuracy ± 0.2°C) to maintain a solution temperature of 37°C [3, 35].
The testing protocol involved a series of preconditioning loads followed by a
period of recovery and then viscoelastic testing (Figure 7.2).
This testing protocol
allowed the measurement of multiple viscoelastic characteristics while not permanently
altering the elastic tissue properties. To establish a consistent reference position for all
samples, a 0.1 N preload was applied (Sensotec Inc, Columbus, OH; 1,000 g, max range
2,900 grams, accuracy 0.3g). At this reference position, clamp-to-clamp specimen length
was determined. This was calculated by averaging three measurements (Mitutoyo, San
Jose, accuracy ±0.02 mm) of an inside calipers prong-to-prong distance after insertion
between the testing clamps. To precondition the samples, the tissue was first ramped at
1.0%/sec to 8% of the reference length, and held for 5 minutes. This was immediately
followed by a triangular displacement profile applied to 8% of the reference length for
149
Precondition
Viscoelastic Test
Rest
5 min
8%
Strain
6% or 4%
10 min
10 min
0.1, 1, 5, 10, and 15 Hz
Time
Figure 7.2: Diagram of the viscoelastic protocol. After preloading to 0.1 N, specimens
were preconditioned. Peak stress from 10th cycle (bold) was compared after incubation to
verify that protocol did not cause elastic damage. Specimens were allowed to recover for
10 minutes prior to stress relaxation and sinusoidal displacements (amplitude = 0.125%
strain).
10 cycles at a strain rate of 1%/sec. The 8% strain amplitude was selected to exceed the
highest strain level experienced during the testing protocol [36], while not elastically
damaging the tissue [21]. To verify that no elastic damage occurred between pre- and
posttreatment, the peak stress of the final preconditioning cycle was recorded (Figure
7.2).
After preconditioning, the sample was allowed to recover for 10 minutes at its
reference length. The specimen was then ramped at 1%/sec to a prescribed strain level of
4% or 6% clamp-to-clamp strain and allowed to stress-relax for 10 minutes (Figure 7.2).
At a 4% strain level, the stress-strain response is still in the toe region [21] and the
collagen fibers are not completely uncrimped [37]. At a 6% strain, the stress-strain
response is in the linear region and the collagen fibers are collectively straightened [37,
38].
Testing these two strain levels permitted the assessment of sulfated GAGs at
different states of collagen and extrafibrillar matrix contribution.
Following stress
relaxation, sinusoidal displacement waves (0.1, 1, 5, 10, and 15 Hz) were applied to a
0.125% strain amplitude (Figure 7.2). The 15 HZ sinusoidal oscillation corresponds to a
150
max strain-rate of ~12%/s. Since the optical tracking system recorded at a maximum of
30 HZ, the local tissue strain during 0.1 HZ oscillations was used to predict the local
strain for all sinusoidal frequencies [1]. There was an average drop of 0.6% ± 0.4% (SD)
in the equilibrium stress level between the start and completion of the entire set of
oscillations. This small drop verified that 10 minutes of stress relaxation was sufficient
to allow equilibrium to be reached.
After the test protocol was completed, each sample was removed from the
material testing system while still mounted in the clamps. The entire specimen and
clamp assembly was bathed for 1 h at room temperature in the buffer solution (detailed in
the next section) with protease inhibitors (one tablet of minicomplete per 10 ml of
buffer). This was followed by 6 h of incubation in the buffer solution with or without
ChABC treatment (1.0 IU/mL, Sigma-Aldrich, Buchs, Switzerland).
Following
incubation, the clamp assembly was reattached to the material testing system and
returned to the original testing position. Mechanical tests were then repeated using
parameters identical to the pretreatment mechanical tests. Treatment and repeatability
protocols are described in greater detail in our previous publications [14, 21](Chapter. 6).
Tissue Swelling
To control tissue swelling, two testing solutions were used throughout the
experiment. One solution consisted of a physiological buffer mixture (15 ml of 20 mM
Tris pH 7.5, 150 mM NaCl, 5 mM CaCl2), and was designated the standard buffer. A
second solution was this buffer plus 7.5% polyethylene glycol (10 kDa; Sigma-Aldrich,
Buchs, Switzerland), and was designated the PEG buffer. Polyethylene glycol has been
previously used to control swelling in biological tissue [39, 40] and this concentration has
151
been shown to maintain wet weight in ligament during prolonged immersion [41].
Samples were equilibrated in their respective buffer solutions for at least 2 h prior to
pretreatment mechanical tests. All mechanical tests and incubations were performed in
their respective buffer solutions.
Multiple techniques were employed to quantify tissue swelling during the course
of the experiment. Adjacent to each tensile sample, two strips were harvested to monitor
tissue hydration (Figure 7.1, B). One strip was weighed and immediately frozen to serve
as the time-zero control for water content.
The second strip shadowed the testing
protocol of the mechanically tested specimen in an unloaded state. For example, this
sample was immersed in solution from the testing chamber during mechanical tests and
was included in the same incubation compartment as the mechanically tested sample. It
was therefore possible to assess whether the wet weight of the tissue had been altered
during mechanical testing and incubation. After the posttreatment mechanical tests,
tensile samples were excised from the clamps. Tensile samples and their adjacent strips
were then weighed, rinsed, frozen and lyophilized. Water content was calculated from
the ratio of dry weight to final wet weight (APX-60, Denver Instruments, Denver;
readability ±0.1 mg).
Verification of Sulfated GAG Removal
The
efficacy
of
the
ChABC
treatment
was
determined
by
using
dimethylmethylene blue assays to first measure the concentration of sulfated GAGs in
papain digested samples before and after retreatment with ChABC [21, 27].
The
percentage of nondegraded sGAGs in ChABC incubated samples was estimated by
subtracting the amount of sGAGs (μg of sGAG / mg of dry weight) in the papain digested
152
sample from the sGAGs in the same sample after additional ChABC treatment. This
value, which represents the amount of sGAGs unsuccessfully digested during incubation,
was divided by the amount of sGAGs native to the sample. Native sGAG estimates were
calculated by averaging the amount of sGAGs in all the time-zero control specimens.
Since ChABC does not degrade keratin or heparin sulfate, this native sGAG estimate was
first reduced by the amount of sGAGs in the time-zero control after ChABC treatment.
An approximation of the efficacy of ChABC could thus be attained, along with the
percentage of sGAGs that could not be degraded by ChABC.
Data Reduction and Statistical Analysis
To generate stress-strain curves, the first Piola-Kirchhoff stress was determined
with tissue engineering strains approximated from the digital camera data ((l-lo) / lo,
where l is the current length and lo is the reference length). The stress–time curves from
the stress relaxation tests were normalized by the peak stress to obtain reduced relaxation
curves [42]. The cyclic strain–time and stress–time data from the final four cycles of
each frequency were fit to a four-parameter sine function in Matlab:
⎛ 2π t
⎞
y = y0 + A sin ⎜
+φ ⎟
⎝ b
⎠
(1)
Here, y and t represent the strain (or stress) and time data, respectively, y0 represents the
equilibrium strain (or stress) level and A denotes the amplitude of the sine wave. φ
represents the phase and f denotes the frequency (Hz). The final four cycles of each
frequency were chosen as input to give a sufficient number of preconditioning cycles,
while maintaining an excellent fit (average r2 = 0.998 ± 0.002). The fit parameters were
not significantly altered when the final two cycles were substituted as input (data not
153
shown). Dynamic modulus M (MPa) and phase shift φ (radians) were calculated for all
frequencies:
M=
Aσ
;
Aε
φ = φσ − φε
(2)
Aσ and Aε denote amplitudes of the cyclic stress–time and strain– time data, respectively,
while φσ and φε denote the corresponding phase angles [43].
Statistical analysis was performed in four parts. First, a repeated measures design
was used to determine the effect of incubation and frequency on material properties for
each of the eight test groups. The effect of incubation on peak stress, stress relaxation,
average dynamic modulus, and average phase shift was assessed using paired t-tests with
pre- and posttreatment data. The effect of frequency on dynamic modulus and phase
shift, along with factor interaction between frequency and time, was assessed using
repeated measures ANOVA with two within-subject factors (frequency, time), with
Bonferoni adjusted pair-wise comparisons to measure significance between levels.
Second, the effect of treatment, buffer solution and strain-level on mechanical changes
due to incubation was assessed using ANCOVA, by controlling for pretreatment results,
with Bonferroni adjustment for multiple comparisons. Third, to determine whether the
pretreatment material properties were different between the eight test groups, ANOVA
analysis was used with factors of treatment, buffer solution and strain-level. Lastly,
biochemistry results were assessed using independent t-tests to measure treatment effect
on sGAG content. A power analysis demonstrated that a sample size of 5 was sufficient
to minimally detect a 20% change in all measured mechanical properties with 80%
154
confidence (Power = 0.80). Significance was set to p≤0.05, and all results are reported
with standard error unless otherwise noted.
Results
Reduced Relaxation Data
The reduced relaxation curves were unaffected by incubation for all combinations
of treatments, buffer solutions and strain-levels (Table 7.1, Fig. 7.3).
The percent
relaxation for all tests was 30.0 ± 1.0% before incubation and 30.6 ± 1.0% after
incubation. The reduced relaxation of all samples tested at the lower strain-level, 31.8 ±
1.0%, was significantly greater than at the higher strain-level, 28.1 ± 1.0% (p=0.01). The
Table 7.1: Mechanical properties for all eight test groups (n=5) before and after
treatment. SEM – “Standard error of the mean” for paired differences. Bolded SEM
values represent statistically significant differences between related pre- and
posttreatment mechanical properties (p<0.05).
4% Strain Level
Standard buffer
PEG Buffer
6% Strain Level
Standard buffer
PEG Buffer
Pretreat
Posttreat
SEM
Pretreat
Posttreat
SEM
Pretreat
Posttreat
SEM
Pretreat
Posttreat
SEM
Avg. Peak
Stress (MPa)
5.3
5.4
(0.05)
5.5
5.8
(0.11)
6.8
6.7
(0.11)
9.6
9.9
(0.15)
Avg. Stress
Relax (%)
36.1
36.6
(0.9)
29.4
31.1
(1.0)
28.1
28.4
(0.3)
31.0
31.2
(0.6)
Avg. Dynamic
Mod (MPa)
176
181
(4.6)
264
280
(8.5)
485
501
(2.3)
758
786
(11.7 )
0.104
0.118
(0.004)
0.124
0.133
(0.004)
0.089
0.098
(0.002)
0.101
0.104
(0.002)
Avg. Peak
Stress (MPa)
8.3
8.4
(0.11)
7.0
7.0
(0.11)
7.9
7.8
(0.06)
11.6
11.9
(0.09)
Avg. Stress
Relax (%)
31.6
32.4
(0.8)
30.0
32.4
(1.8)
28.0
27.7
(0.4)
25.4
25.1
(0.3)
Avg. Dynamic
Mod (MPa)
374
361
(24.8)
361
369
(9.9)
671
726
(23.1)
627
655
(7.4)
0.106
0.120
(0.006)
0.116
0.126
(0.002)
0.091
0.098
(0.003)
0.099
0.104
(0.001)
Control Treat
Avg. Phase
Shift (rad)
ChABC Treat
Avg. Phase
Shift (rad)
155
4% Pretreatment
C
6% Pretreatment
6% Posttreatment
4% Posttreatment
Ctrl Treatment – Standard Buffer
Ctrl Treatment – PEG Buffer
Normalized Stress
Normalized Stress
1.0
0.9
0.8
0.7
0.6
0.01
0.1
1
10
100
1.0
0.9
0.8
0.7
0.6
1000
0.01
Time (s)
ChABC Treatment – Standard Buffer
0.1
1
10
Time (s)
100
1000
ChABC Treatment – PEG Buffer
1.0
Normalized Stress
Normalized Stress
1.0
0.9
0.8
0.7
0.6
0.01
0.1
1
Time (s)
10
100
1000
0.9
0.8
0.7
0.6
0.01
0.1
1
10
TIme (s)
100
1000
Figure 7.3: Reduced relaxation as a function of oscillatory frequency for control
treated in either standard buffer (top, right), and ChABC treated in either standard
buffer (bottom, left) or PEG buffer (bottom, right). Legends indicate applied
clamp-to-clamp strain for pre- and posttreatment results. Mean ± standard error.
156
type of treatment and buffer solution did not affect the change in relaxation after
incubation (p=0.69 and p=0.41, respectively).
However, the strain-level did indeed
influence the change in relaxation after incubation (p=0.02). The percent increase in
percent relaxation after incubation varied by 0.2% due to treatment, 3.2% due to buffer
solution (greater increase with PEG buffer), and 4.1% due to strain-level (greater increase
at a 4% strain level).
Dynamic Modulus
The average dynamic modulus (averaged over all frequencies) was significantly
affected by incubation in only two of the eight test groups (Fig. 7.4, Table 7.1). These
groups were both at a 6% clamp-to-clamp strain and had an average increase in dynamic
modulus of 4.0 ± 1.1% after treatment. The average dynamic modulus for all specimens
at 0.1 Hz was 424 ± 48 MPa before incubation and 434 ± 50 after incubation, while
average dynamic modulus at 15 Hz was 501 ± 55 before incubation and 522 ± 59 after
incubation. The samples tested at the higher strain level had a dynamic modulus 226%
greater than the lower strain level (p<0.001). Frequency only had a significant effect on
dynamic modulus for the two test groups control treated in standard buffer.
The
frequency effect on dynamic modulus was not influenced by incubation. The type of
treatment, buffer solution, and strain-level did not affect the change in dynamic modulus
between pre- and posttreatment (p=0.86, p=0.20 and p=0.96, respectively). The percent
increase in dynamic modulus after incubation only varied by 0.3% due to treatment, 0.1%
due to buffer solution, and 4.2% due to strain-level (greater increase at the 6% strainlevel).
157
6% Pretreatment
6% Posttreatment
4% Pretreatment
C
4% Posttreatment
Ctrl Treatment - Standard Buffer
1000
800
600
400
200
1200
Dynamic Modulus (MPa)
Dynamic Modulus (MPs)
1200
1000
800
600
400
200
0
Dynamic Modulus (MPa)
1200
1000
1
Frequency (Hz)
ChABC Treatment – Standard Buffer
800
600
400
200
0
0.1
1
Frequency (Hz)
0
10
10
0.1
1200
Dynamic Modulus (MPa)
0.1
Ctrl Treatment - PEG Buffer
1
Frequency (Hz)
10
ChABC Treatment - PEG Buffer
1000
800
600
400
200
0
0.1
1
Frequency (Hz)
10
Figure 7.4: Dynamic modulus as a function of oscillatory frequency for control treated in
either standard buffer (top, left) or PEG buffer (top, right), and ChABC treated in either
standard buffer (bottom, left) or PEG buffer (bottom, right). Legends indicate applied
clamp-to-clamp strain for pre- and posttreatment results. Mean ± standard error.
158
Phase Shift
The average phase shift (averaged over all frequencies) was significantly affected
by incubation in four of the eight test groups (Fig. 7.5, Table 7.1). Groups significantly
affected had an increase in phase shift of 9.4 ± 2.6%, while groups not significantly
affected had an increase in phase shift of 7.5 ± 0.8%. The average phase shift for all
specimens at 0.1 Hz was 0.062 ± 0.004 radians before incubation and 0.069 ± 0.004
radians after incubation, while at 15 Hz was 0.155 ± 0.004 radians before incubation and
0.169 ± 0.005 radians after incubation. The average phase shift at the 4% strain-level,
0.114 ± 0.003 radians, was significantly greater than at the 6% strain-level, 0.095 ± 0.003
radians (p<0.001). Additionally, the average phase shift in PEG buffer, 0.111 ± 0.003,
was significantly greater than in standard buffer, 0.098 ± 0.003 radians (p=0.004).
Frequency had a significant effect on phase shift for all eight test groups. The frequency
effect on phase shift for all eight groups was not influenced by incubation. The type of
treatment and buffer solution did not affect the change in phase shift after incubation
(p=0.98 and p=0.32, respectively). The strain-level, however, did significantly influence
the change in phase shift after incubation (p=0.02). The percent increase in phase shift
after incubation varied by 0.1% due to treatment, 5.2% due to buffer solution (greater
increase in standard buffer), and 5.3% due to strain-level (greater increase at a 4% strainlevel).
Tissue Swelling
The wet weights of the unloaded samples were normalized to their initial weight
and graphed with respect to discrete measurement points during testing (Figure 7.6). On
average, the wet weight of unloaded samples increased by 18 ± 2% when tested in
159
6% Pretreatment
6% Posttreatment
4% Pretreatment
C
0.20
Control Treatment – Standard Buffer
Phase Shift (radians)
Phase Shift (radians)
4% Posttreatment
0.15
0.10
0.05
1
Frequency (Hz)
0.15
0.10
10
0.1
ChABC Treatment – Standard Buffer
Phase Shift (radians)
Phase Shift (Radians)
Control Treatment – PEG Buffer
0.05
0.1
0.20
0.20
0.15
0.10
0.05
0.20
1
Frequency (Hz)
10
ChABC Treatment – PEG Buffer
0.15
0.10
0.05
0.1
1
Frequency (Hz)
10
0.1
1
Frequency (Hz)
10
Figure 7.5: Phase shift as a function of oscillatory frequency for control treated in either
standard buffer (top, left) or PEG buffer (top, right), and ChABC treated in either
standard buffer (bottom, left) or PEG buffer (bottom, right). Legends indicate applied
clamp-to-clamp strain for pre- and posttreatment results. Mean ± standard error.
160
standard buffer and decreased by 11 ± 2% when tested in PEG buffer. The unloaded
samples tested in standard buffer had a significantly greater wet weight than those tested
in PEG buffer (p<0.001). There was no effect of treatment on the overall normalized wet
weight (p=0.68). During the incubation period, the average wet weight in the unloaded
samples decreased by 1.2 ± 1.7% (p=0.48) in the standard buffer and increased by 3.2 ±
1.4% (p=0.08) in the PEG buffers. The greatest change during the incubation period was
a 4.5 ± 2.8% decrease in wet weight in samples treated with ChABC in standard buffer
(p=0.15).
The native water content was consistent between treatment and buffer solution
groups, with an average range from 71.6% to 73.1% (Figure 7.7). All tested samples had
significantly different water content than the native samples, with the exception of the
mechanically loaded samples tested in standard buffer (average water content = 72.3,
p=0.24). Compared to the native samples, unloaded samples incubated in the PEG buffer
had a significantly decreased water content, and unloaded samples incubated in standard
buffer had a significantly increased water content (p<0.001 and p<0.001, respectively).
There was no effect of treatment or strain-level on water content in incubated samples
(p=0.29 and p=0.52, respectively), although in samples incubated in standard buffer, the
ChABC treated samples had 2.2% less water content than control treated samples. The
mechanically loaded samples had 2.5% less water content than the unloaded samples
(p=0.002).
161
Normalized Wet Weight
1.4
Standard Buffer
Ctrl
ChABC
1.3
PEG Buffer
Ctrl
ChABC
1.2
1.1
1.0
0.9
0.8
Pre-trt
Test
Incubation
Post-trt
Test
Wet Weight Measurement Points
Figure 7.6: Time history of the unloaded samples wet weight through the course of the
experiment. Wet weight was normalized to the initial weight of the unloaded sample
immediately after dissection. All samples equilibrated in buffer solution for at least 2 h
before pretreatment test. The 4% and 6% strain-level groups were combined.
Water Content (%)
85
80
Ctrl – Standard
Ctrl – PEG
ChABC – Standard
ChABC – PEG
75
70
65
Native
Unloaded
Loaded
Figure 7.7: Water content of the time-zero reference samples (native), the incubated
samples that were not mechanically tested (unloaded), and the incubated samples that
were mechanically tested (loaded). Data from 4% and 6% strain-levels were combined.
162
Experimental Repeatability and Efficacy
Repeatability of the pretreatment mechanical tests was confirmed by optically
measuring posttreatment clamp positions to be within 0.03 ± 0.01 mm of the pretreatment
clamp positions.
To verify that the mechanical testing protocol did not elastically
damage the specimens, a comparison was made between pretreatment and posttreatment
peak stress during the last preconditioning cycle (Table 7.1). For all treatment, buffer
solution and strain level combinations, incubation affected the average peak stress by less
than 5%. All changes to the peak stress after incubation were not significant, with the
exception of the group treated with ChABC in PEG buffer solution at a 6% strain-level,
which had a significant increase of only 2.7 ± 0.8% (Table 7.1)(p=0.03). A qualitative
comparison validated that the 4% and 6% clamp-to-clamp strains were indeed in the toe
and linear stress-strain regions, respectively (data not shown). This comparison was
made by superimposing the 4% and 6% clamp-to-clamp strains on the final cycle of their
respective 8% clamp-to-clamp preconditioning loads.
The efficiency of ChABC digestion of sGAGs was dependent on the buffer
solution. ChABC treatment of the time-zero reference samples removed 90 ± 6% (SD) of
sulfated GAGs. The time-zero reference samples had 4.2 ± 0.4 and 4.4 ± 0.5 μg sulfated
gag / mg dry tissue in standard and PEG buffer groups, respectively. Samples that were
mechanically tested in the standard buffer and treated with ChABC had an average of 0.7
μg sulfated gag / mg dry tissue at the end of testing, a 91.8% reduction from the native
reference. Samples that were mechanically tested in the PEG buffer and treated with
ChABC had an average of 1.6 μg sulfated gag / mg dry tissue at the end of testing, a
68.4% reduction from the native reference. The sulfated GAG reduction in mechanically
163
tested samples treated with ChABC was significantly less in the PEG buffer compared to
the standard buffer (p=0.03). The native samples had on average 20% more sulfated
GAGs than the unloaded samples that endured the lengthy testing protocol (p=0.23).
Discussion
The aim of this research was to determine the specific influence of interfibrillar
sGAGs on ligament viscoelasticity. Viscoelastic properties were measured before and
after incubation in either control or ChABC treatments. Control and ChABC treatments
had no effect on stress relaxation and strain-rate dependent behavior, and moderately
increased dynamic modulus and phase shift.
Testing in either control or ChABC
treatment did not influence any of the observed viscoelastic changes after incubation.
This was consistent at different levels of collagen fiber stretch and tissue hydration.
Therefore, this study concludes that mechanical interactions between collagen fibrils and
interfibrillar sGAGss do not affect tissue-level viscoelastic mechanics in human MCL.
The mechanical property most impacted by incubation was phase shift. Phase
shift, which quantifies energy dissipation, increased by 8.4% in control treated samples
and 8.5% in ChABC samples. Although this increase was only significant in half the
tested groups (Table 7.1), it was observed in 35 of the 40 tested samples. A similar timedependent effect occurs in articular cartilage.
Cartilage tested after control-treated
incubation exhibited greater creep [22] and phase shift [44] than cartilage tested
immediately upon dissection. This effect may tie into the observed relationship between
strain-level and changes in viscoelastic properties after incubation. Samples tested at a
4% strain level were more apt to dissipate energy after incubation than samples tested at
the 6% strain level. This would indicate a time-dependent degradation of structures or
164
networks that provide elastic function at low-strain rates, such as elastin [45].
Interestingly, in our final preliminary studies with the same test procedure (Fig. 7.2), the
phase shift increased by only 1.4 ± 3.3% after incubation (n=4). The only deviation in
the preliminary protocol was that tests were conducted on specimens that had been
thawed and refrozen multiple times. Therefore, this ex vivo loss in energy storage
appears to equilibrate in time.
Although the specific contribution of sGAGss on viscoelastic properties has not
been tested previously in ligament or tendon, it has been examined in other connective
tissues.
Enyzmatic degradation with ChABC has affirmed that sGAGS moderately
control the kinetics of the viscoelastic response in cartilage through sGAG-collagen
interactions [22, 44, 46, 47]. Based on the negligible effect of ChABC treatment on
ligament viscoelasticity, it can be concluded that sGAGs do not physically provide the
functional integrity in ligament that they do in cartilage. This may be due to the low
concentrations of sGAGs in ligament (<1% dry weight) relative to cartilage (15-30% dry
weight), and the differences in anisotropy and collagen organization. It is important to
note that although sGAGs did affect viscoelasticity in cartilage under tension and
compression, collagen is a more significant determinant of cartilage viscoelastic
properties than sGAG concentration [46]. Al Jamal et al. [27] used a repeated measures
design with ChABC on parenchymal lung tissue to show that dermatan and chondroitin
sulfate removal resulted in increased hysteresis. Compositional and structural differences
between ligament and lung tissue may again account for the conflicting results.
Additionally, the paranchymal lung study was unable to reproduce material properties
with the control group, which complicates their data interpretations.
165
The primary function of ligament is to resist tensile loads. It is becoming clear
that interfibrillar sGAGs do not directly support this function, although they likely have
an indirect effect by modulating collagen fibrillogenesis [48, 49]. This inference is
supported by our previous study, which showed that sGAGs have no effect on ligament
quasi-static material behavior [14] and in other studies that examined decorin-deficiency.
Mice knockout studies identified decorin as a proteoglycan that influences elastic and
viscoelastic tensile behavior in tendon [7, 28]. Based on the results of the present study,
it appears that these mechanical alterations were due to compensatory or developmental
abnormalities intrinsic to decorin deficient mice. For instance, histological studies have
shown decorin-deficiency to cause irregularly sized and spaced fibrils, which is likely
caused by the absence of interfibrillar GAGs that normally inhibit lateral fibril fusion
[50].
Although tendon and ligament have unique histological and biochemical
characteristics, they also exhibit gross compositional similarities and ligaments have
higher GAG concentrations than tendons [8]. Therefore, it is appropriate to extend the
results of the present study to tendon. Finally, the results of the present study are only
applicable to tensile loads, and it is still possible that sGAGs physically resist
compression in mature human ligament. Sulfated GAG-fibril networks have been shown
to resist compressive stresses by retarding fluid exudation [51], and the sGAG
concentrations in ligament are highest near the insertion sites, which experience the
highest compressive stresses [14, 30, 52].
The experimental protocol provided reproducible results and material properties
consistent with the literature. The protocol incorporated a repeated measures design to
reduce the inter- and intraspecimen variability that has been observed mechanically and
166
chemically in ligament [21]. To test the reproducibility of the protocol, a quantitative
comparison of peak stress values was made to confirm that pretreatment mechanical tests
did not damage the sample and cause a reduction in posttreatment mechanical behaviors.
Pretreatment and posttreatment peak stress values were indeed verified to be within 5%
for all test groups. Several protocol revisions were required to achieve this consistency.
The viscoelastic results are consistent with previous research that defined the viscoelastic
properties of human MCL [1]. Variations in phase shift between these studies may be
due to different testing environments (fluid chamber versus humidity chamber [1]).
Since specimens were extracted from four distinct regions in the MCL, the material
properties and conclusions of this study are representative of the longitudinal (proximal
and distal) and oblique fiber zones of the superficial MCL.
Incubation in ChABC successfully removed the interfibriller sGAGs, chondroitin
and dermatan sulfate. In standard buffer, ChABC treated samples had a 92% reduction of
dermatan and chondroitin sulfates, while in PEG buffer, ChABC treated samples had a
69% reduction in chondroitin and dermatan sulfates. These degradation values are higher
or similar to those reported in other studies that used ChABC in connective tissue [27, 53,
54]. Our previous studies in human ligament used TEM imaging with cupromeronic blue
staining to determine that 80% removal of dermatan and chondroitin sulfate resulted in a
98% reduction in interfibrillar sGAGs [14]. Therefore, we are confident that the majority
of interfibrillar sGAGs were degraded.
The PEG buffer significantly lessened the
effectiveness of ChABC, which is likely due to the increased viscosity of the buffer
solution and the inverse relationship between viscosity and diffusion (Stokes-Einstein
relation). ChABC will also degrade hyaluronic acid, a macromolecule that forms non-
167
covalent bonds with aggrecan and is chemically similar to chondroitin sulfate. Based on
the effectiveness of ChABC in degrading hyaluronic acid [14], this acid does not appear
to influence viscoelasticity in human MCL. Biochemical assays showed that chondroitin
and dermatan sulfate represent 90% of total sGAGs in the MCL samples. The remaining
10% of sGAGs likely includes keratin sulfate and heparin sulfate. These sGAGs may
contribute to ligament viscoelasticity, but this is unlikely based on their low
concentrations and distribution near fibroblasts [55].
Tissue hydration was controlled by immersing samples in two types of treatment
buffer solutions throughout the entire testing protocol. One of the buffers included 7.5%
PEG to create a hypertonic solution that reduced tissue swelling. The philosophy behind
testing at varying states of hydration was to ensure that our results were not dependent on
a particular tissue hydration state or masked by tissue swelling.
Previous research
indicated that concentrations of 0.9% saline and 7.5% PEG maintained initial wet weight
in ligament, with a 5% decrease in water content [41]. This study found that tissue
dehydration was more pronounced with these same concentrations, likely from the
addition of 5mM calcium chloride or surrounding temperature variations .
The minor effect of hydration on mechanical properties has implications for other
theoretical explanations on the origins of viscoelasticity in ligaments and tendons. Fluid
flow can contribute to viscoelasticity through solid-fluid interactions that results in
frictional drag [56, 57]. These biphasic interactions are dependent on permeability,
which is a function of sGAG concentration and water content [58].
Since sGAG
extraction and water content fluctuations had no effect on viscoelastic characteristics,
fluid flow appears to not contribute to viscoelastic effects in organized structures such as
168
ligament that have low concentrations of sGAGs. Other studies have reached similar
conclusions [27, 59]. The present study, however, is limited in supporting this statement,
for two reasons. First, sulfate groups may have remained in the tissue after being cleaved
and continued to influence fluid permeability. Secondly, fluid flow mechanisms in
ligament may be insensitive to 10% fluctuations in gross water content. Future studies
that specifically examine fluid flow mechanisms in ligament through experimental or
theoretical approaches could better address this postulation.
A principal limitation of this research is that biochemical composition within the
ligaments may have been altered postmortem. This concern is justified by research that
found freezing rabbit ligament caused significant changes in hysteresis during tensile
tests [3, 60], yet, the measured change was small and its relevance has been questioned
[3]. It is unlikely that the in vivo conditions of the MCL were perfectly replicated in this
experiment, although every effort was made to provide a physiological testing
environment (e.g., temperature, hydration, protease inhibitors). Lastly, the prolonged
effect of sGAG deprivation was not investigated in this study.
In conclusion, this study demonstrated that mechanical interactions between
sGAGs and collagen fibrils insignificantly contribute to tissue-level viscoelastic behavior
in mature ligament. Although the origins of viscoelasticity in human ligament remains
unresolved, our findings have helped narrow the possible contributions of extracellular
matrix proteins. Future studies need to assess the inherent viscoelasticity of collagen by
determining the influence of inter- and intramolecular collagen bonds and fibrilassociating collagens (i.e. Type XII) on transient ligament behavior. The findings of this
169
study have progressed the structure-function knowledge of ligament, and are applicable
to research seeking to engineer and evaluate replacement tissues.
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CHAPTER 8
DISCUSSION
Summary
The objective of this dissertation was to strengthen the scientific knowledge of
mechanical relationships that impact ligament function in the human knee.
Two
mechanical relationships were studied in this dissertation: The first between the medial
collateral ligament (MCL) and the anterior cruciate ligament (ACL), the second between
sulfated glycosaminoglycans and collagen fibrils.
This discussion will focus on
conclusions from these studies. The principal conclusions are first summarized:
•
The MCL was more susceptible to damage after ACL transection. This finding
favors reconstruction of the ACL to minimize damage to interrelated ligaments.
•
The ACL does not directly resist valgus torque. However, the ACL indirectly resists
valgus torques by limiting coupled anterior tibial translation during valgus rotation.
•
The ACL promotes internal tibial rotation during anterior translation. This should be
considered for reconstructive surgery techniques that aim to restore ACL function.
•
Sulfated glycosaminoglycans had negligible influence on tissue level mechanics of
ligament during tensile loading, and therefore do not directly support ligaments’
primary function of resisting tensile loads.
•
Ligament viscoelasticity was insensitive to tissue hydration, which questions the
theory that interstitial fluid flow controls the tensile time-dependent mechanics.
176
The anterior cruciate ligament and medial collateral ligament have overlapping
functionality that results in a high incidence of multiligament injuries [1].
An
experimental and theoretical approach was used to study the interdependence between
these two primary knee stabilizers.
Cadaveric joint kinematics and local strain
distributions upon the MCL were measured with the ACL intact and deficient for
specific loading configurations. Finite element models of the experimental tests were
generated from subject-specific geometries and kinematic measurements. Ligament
strain and joint kinematics were measured with a newly validated method of optical
tracking. For a field of view typical of cadaveric knee experiments (~190 mm), this
optical system had an accuracy of 0.04% for measuring strain and an accuracy better
than 0.07% for measuring three-dimensional joint kinematics. This optical technique
demonstrated superior accuracy and flexibility to previous methods [2, 3], and was
likely responsible for this studies improved correlation of experimental-theoretical strain
measurements (r2 = 0.95) compared to previous subject-specific studies of human knee
ligaments (r2=0.75)[4]. This optical measurement system has now been implemented in
other national labs for biomechanical experiments [5-7].
This research clarified the specific and interdependent function of the MCL. As
hypothesized, the MCL actively resists anterior tibial translation (ATT) and this
resistance increases in the ACL-deficient knee. ACL transection significantly increased
peak strain in the MCLs midlongitudinal fibers from 2.9% to 5.7%. The 5.7% strain is
above the 5.2% threshold where microtrauma initiates [8]. This would indicate that the
MCL is sensitive to overuse in an ACL deficient knee. The FE model predicted peak
insertion site forces in the tibia and femur to increase after ACL transection by 54% and
177
56%, respectively. The associated force values at the insertions (max=140 N) are
difficult to measure experimentally and provide parameters to evaluate fatigue
propensity in MCL reattachment surgeries and engineered replacement tissues. The FE
model also helped resolve a query on the source of strain heterogeneity in the MCL [9].
High contact gradients coincided with strain heterogeneity, while regions with consistent
bone contact had uniform strain fields. For example, the MCL substance between the
medial condyle of the tibia (high contact) and distal to the femoral insertion (low
contact) experienced elevated strain heterogeneity. Regions of uniform contact proximal
to the tibial insertion had homogenous localized strain.
The observations of this research also explicitly defined ACL function. In an
intact knee, the ACL was found to promote internal tibial rotation during anterior
translation. This “unwinding” [10] in turn reduced MCL strains along the longitudinal
parallel fibers of the superficial MCL near the femoral insertion, which is the region that
experiences the highest strains and is most prone to rupture [11]. Additionally, it is well
known that the ACL is the primary restraint to ATT, but controversy exists as to whether
the ACL is an active secondary restraint to valgus rotation when the MCL is intact [1215]. This study found that ACL-deficiency was nondetrimental to the MCL’s ability to
restrain pure valgus rotation. The discrepancy between our results and previous reports,
in that the ACL actively resists valgus rotation [14, 15], can be logically explained. The
ACL and posterior cruciate ligament (PCL) counteract each other during valgus rotation
through respective application of posterior and anterior force on the tibia [16]. After
ACL transection, this force balance is disrupted and the tibia will slide anteriorly during
valgus rotation [15]. Since the tibia was constrained from anterior-posterior translation,
178
this kinematic alteration manifested as a 121 ± 49% increase in posterior force during
peak valgus load at full knee extension.
Coupled ATT thus likely explains the
previously measured ACL force response during valgus torque [15, 17]. Removing the
anterior-posterior constraint may have resulted in increased valgus rotation and MCL
strains in our study due to coupled ATT. However, based on the negligible increases in
MCL strains after ACL transection, it appears that the ACL does not directly resist
valgus torque, but serves its primary function of resisting ATT during valgus rotation.
These functional roles of the ACL should be considered when evaluating surgical
reconstructions, and may be helpful in diagnosing whether an ACL deficient patient has
a combined MCL injury.
The ability of the MCL and ACL to perform their functions of joint stabilization
is directly related to the molecular relationships that provide structural integrity. A
molecular relationship that has been widely implicated in contributing to tissue level
mechanics is between proteoglycans and collagen fibrils. This relationship has been
theoretically and experimentally shown to affect elastic and viscoelastic mechanical
behavior in ligament [18-22]. However, the sulfated glycosaminoglycans (GAG) that
would mediate this mechanical interaction have not been specifically studied.
Therefore, two experimental protocols were performed to test whether elastic and
viscoleastic material properties of ligament where affected by GAG degradation via
enzymatic treatments. A repeated measures design was utilized to afford a powerful
means of measuring treatment effect.
Successful execution of these experiments
required the design and fabrication of a new material test system that utilized detailed
protocols to control experimental repeatability.
179
The principal conclusion of these extensive mechanical tests was that sulfated
glycosaminoglycans have negligible influence on tissue level mechanics during tensile
loading. The degradation of interfibrillar GAGs had less than a 5% effect on almost all
mechanical properties. In comparison, removal of intermolecular collagen crosslinks in
tendon fascicles reduced peak stress by 90% [23], and removal of elastin in tendon and
aponeuroses significantly increased hysteresis [24].
Relative to these molecular
structures, it becomes quite clear that interfibrillar GAGs do not directly support
ligaments primary function of resisting tensile loads. Nonetheless, GAGs likely have an
indirect affect on tissue-level mechanics [19, 21] by modulating collagen fibrillogenesis
through inhibition of lateral fibril fusion [25].
The findings of this research are relevant to past studies. First, the results
contradict the popular theory that interfibrillar GAG interactions are responsible for
transmitting forces along discontinuous collagen fibrils [22, 26, 27], and support studies
that have predicted fibrils to be continuous [28, 29].
Additionally, the observed
insensitivity of viscoelastic material behavior to tissue hydration is inconsistent with the
theory that interstitial fluid flow [30] controls the time-dependent mechanical response
of ligament in tension. The removal of GAGs should have theoretically increased
permeability and therefore affected pressure gradients and time-dependent drag
interactions between the fluid and solid phases.
This inference can be confirmed
through future experiments that measure the affect of GAG extraction on permeability
[31]. Lastly, the role of GAGs in ligament and cartilage is drastically different. In
cartilage, GAGs retard fluid flow through the porous solid phase to partially control
elastic and viscoelastic material properties in both tension and compression [32-34].
180
The different mechanical roles of GAGs in ligament and cartilage may be due to the low
concentrations of GAGs in ligament (<1% dry weight) relative to cartilage (15-30% dry
weight), and the differences in anisotropy and collagen organization (Type I vs. Type
II). This body of work has expanded the structure-function knowledge of ligament and
the findings are applicable to research seeking to engineer and evaluate replacement
tissues.
Limitations and Future Work
An important limitation to the numerical analysis covered in Chapter 5 is the
inability of the material model to represent time-dependent effects. To negotiate this
limitation, it was necessary to apply quasi-static loads to the associated experimental
study, thus limiting the viscoelastic effects and permitting the finite element model to
make accurate predictions.
These slow strain-rates are justified in the context of
simulating diagnostic exams.
However, the applied strain-rates (1%/s) are not
appropriate for modeling ligament injury mechanisms, which occur at strain-rates in
excess of 28%/s [35]. To broaden the application of these subject-specific finite element
models, it is necessary to implement and validate a material that represents the intrinsic
viscoelastic behavior of ligament. The functionality of such a viscoelastic material
model would be enhanced if it was physical, as opposed to phenomenological. A
physical model would not only permit further investigation into the structural
mechanisms contributing to mechanical integrity, but it would permit the study of
compositional inhomogeneities and pathologies (e.g., Ehlers-Danlos syndrome) that
alter the physical structure of ligament [36]. The concurrent validation of functional
181
physical models with experiments examining structure-function relationships is a natural
expansion of the work covered in this dissertation.
Future work should immediately test the ability of a poroelastic material to
represent the time-dependent effects experimentally observed in Chapter 7. Poroelastic
constitutive equations are derived from first principles and physically represent water
transport through a porous solid medium [37]. Analytically, pressures at continuum
points are solved in addition to displacements. The pressure is a function of fluid flux
and tissue permeability. Through conservation of mass, fluid flux relates to the velocity
of the solid-fluid mixture. These relations provide a time-dependent material response.
Experimental studies support the supposition that water freely moves through the solid
phase of ligament. Hannafin and Arnoczky [38] determined that 6% of water in a canine
flexor tendon, which is compositionally similar to ligament [39], is exuded shortly after
application of small static loads. Additionally, MRI studies measured an increase in free
water molecules after tendon was exposed to axial stress, which suggests that previously
bound molecules were released [40]. In these studies, fluid transport was indicated by
higher concentrations of water molecules along the tissue periphery. The ability of
poroelastic theory to describe experimental behavior will allow conclusions to be drawn
about the solid-fluid mechanical relationship in ligament.
Preliminary studies in our lab have demonstrated that the poroelastic model is
able to accurately predict stress relaxation behavior using experimental permeability
coefficients [31] and validated bulk modulus values [4].
The poroelastic model,
however, was found to incorrectly predict how variations to strain-level and frequency
influence aspects of the mechanical response. For example, stress relaxation increased
182
with strain-level, which conflicts with experimental results (Figure 7.3). Furthermore,
the poroelastic FE model incorrectly predicted a decrease in phase shift as oscillation
frequency increased, while experimentally the phase shift significantly increased
progressively at greater frequencies (Figure 7.5).
This poroelastic behavior is in
agreement with a study by Ateshian et al. [41]. Ateshian et al. demonstrated that with
instantaneous loads the interstitial fluid pressure within a poroelastic model is nearly
identical to the hydrostatic pressure of an incompressible elastic material at every point
except near the fluid boundary. Hence, the poroelastic material will behave elastically.
It is important to note that the loading rate at which a poroelastic material begins to
behave elastically is dependent on permeability and bulk modulus. Alterations to these
parameters may result in model predictions that better represent experimental behavior.
This raises the question of whether the parameters used in this preliminary study
accurately represent ligament.
Experimental research supports a low bulk modulus by demonstrating that the
Poisson’s ratio (longitudinal vs. transverse) in ligament is greater than 1.5 [42]. This
high poisons ratio is indicative of significant volumetric deformation during tension,
which would require a value for bulk modulus lower than values commonly used in the
literature [4]. Furthermore, a Poissons ratio above 1.5 would indicate that fluid is
flowing out of the tissue during tension. Therefore, the model used in this preliminary
study, along with numerous studies in the literature [4, 43, 44], have been inaccurately
modeling the volumetric deformation of ligament during tensile loading. Although the
incorrect orientation of fluid flow will still model damping effects, it is not
183
physiological. In the future, hyperelastic constitutive models should be emulated that
utilize strain energy functions permitting Poisson’s ratios greater than 0.5 [45].
Other inconsistencies exist between physical ligament mechanics and the
implemented poroelastic material.
The permeability used in the current model is
isotropic and strain-independent. Experimentation has in fact shown that permeability is
anisotropic [30] and strain-dependent [31]. The isotropic permeability can be replaced
with an anisotropic tensor [50] that physiologically reduces permeability along the
collagen fiber direction [51]. This anisotropy should allow more realistic boundary
conditions to be applied at off-axis surfaces and will direct lateral fluid flow. The
permeability tensor can also be readily updated to be strain-dependent [52]. Due to the
high Poisson’s ratio for ligament, the permeability should decrease with tensile stress,
which could describe the experimental reduction in stress relaxation at higher strain
levels. Furthermore, based on the insensitivity of ligament viscoelastic behavior to
extraction of sulfated glycosaminoglycan macromolecules (Chapter 7), it would appear
that collagen networks mediate fluid flow and not constituents in the extrafibrillar
matrix.
Overall, poroelastic theory may be an effective tool to describe ligament
material behavior and future investigations offer significant challenges and rewards.
Another principal limitation to this body of work is the in vitro model used for
study. The structural cadaveric test employed for the MCL-ACL experiments have
inherent error associated with muscle excision, tissue swelling, chemical decay, and
motion constraints (i.e., limited degrees of freedom). For example, the removal of joint
stabilizing muscles would result in an over-estimation of the translations and rotations
associated with particular load limits. However, regardless of this over-estimation,
184
ligament strains are still valid for the measured kinematics. All limitations inherent in
cadaveric models could be eliminated by using image registration to noninvasively
measure strain distribution in vivo [49, 50]. For example, ligament strains and joint
kinematics in different population groups (e.g., normal, ACL-deficient) could be
measured with open MRI technology, which facilitates anatomical positions and joint
loads. The highly accurate data provided in this research would serve as an excellent
source for future studies that use such noninvasive methods.
In all experiments, ligament biochemical composition may have been altered
postmortem. Although efforts were made to provide physiological testing environments,
it is unlikely that the in vivo conditions of the MCL were perfectly replicated. To
remove this limitation, future studies of GAG interactions could be performed on
appropriate animal models [51, 52]. The observation made in Chapter 7 that viscoelastic
energy dissipation is dependent on exposure time could be controlled with this type of
study. An animal model could also be used to examine the effect of GAG degradation
over long time periods. It is feasible that interfibrillar space reductions from GAG
extraction [25] could eventually cause fibril damage due to interfibrillar friction. Lastly,
gene alterations could be used to propagate interfibrillar GAGs to determine if a certain
concentration of GAGs influenced tissue mechanics.
Supplementary experiments were performed to test certain aspects of
biochemical alterations.
One valid apprehension of the GAG studies was that
mechanical interaction of the GAGs is dependent on intermediary ions (e.g., calcium,
sodium) that neutralize the repulsion of negatively charged GAGs. To ensure that
calcium exudation was not masking the mechanical contributions from GAGs,
185
supplementary experiments were performed. In these pilot studies, ligament tissue was
rinsed during dissection and mechanical testing with a solution that included high
concentrations of calcium (0.9% NaCl + 2mM Ca). Upon completion of testing, these
samples were soaked in normal saline (1 hr, 0.9% NaCl), retested, soaked again in the
calcium solution (1 hr) and retested. This sequential treatment minimally affected peak
stress in tensile (~2%) and shear (~4%) loading configurations. Even though these
experiments did not implicate calcium exudation as a cause of mechanical defect,
physiological calcium concentrations were always included in the testing buffer (5 mM
CaCl2). Supplementary studies were also conducted with elastase to test the sensitivity
of the testing system. Elastin was expected to contribute to the tensile response at low
strains [53]. Compared to the control group elastase treatment doubled the phase shift
and decreased the peak stress at a 4% clamp-to-clamp strain level by 35%. It is logical
that elastin provides mechanical support when fibers are crimped and loss of elastin
would reduce energy storage mechanisms. Considering the small sample size (n=2) and
lack of chemical verification of elastase efficacy and specificity, these results should be
interpreted with caution.
Nonetheless, this pilot study confirmed that the testing
apparatus was sensitive to molecular alterations and it is recommended that elastin be
further investigated to quantify its contribution to the mechanical function of ligament.
Although the origins of elasticity and viscoelasticity in human ligament are
incomplete, future work can begin where this dissertation ends. First, to completely
characterize the mechanical effect of GAGs in ligament, compression and permeability
tests need to be performed. Theoretically, GAGs should contribute to compressive
stiffness [54] near insertions and articulations where compressive forces are innate. This
186
theory is supported by regionally high GAG concentrations in these zones [55].
Experiments can be synchronized with an investigation of GAG influence on
permeability transverse to the fiber direction. By identifying mechanisms responsible
for resisting fluid exudation, these findings would be applicable to the poroelastic
model. Next, mounting evidence from this study and others [28, 40] suggests that the
intrinsic viscoelasticity in collagen and the fluid exudation from collagen controls the
viscoelastic response when ligament is tensioned. The effect of intercollagen bonds on
viscoelastitcy can be examined in a rat model with β-APN [23] and the effect of intracollagen bonds on viscoelasticity may be tested with molecular dynamics models [56].
One postulation is that intercollagen bonds primarily control viscoelasticity in ligament
through molecular realignments that originate from fluid interaction and tropocollagen
sliding [23].
In conclusion, continued investigations in the structure-function relationships of
ligament will drive the long-term goal of representing material behavior with physical
models and be applicable to treatment strategies. Implementing physical constitutive
relationships into finite element models may improve adaptability of the model to
anatomically distinct regions [21, 57] and facilitate representation of viscoelastic
mechanisms that guard against structural failure. Connective tissue is composed of
similar constituents that have diverse concentrations and organizations. Understanding
the akin and dissimilar functions of these constituents in specific tissues will aid
interpretations of mechanical alterations that occur from injury repair mechanisms and
disease states, as well as spur innovations in clinical treatments.
187
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