Elsevier

Journal of Biomechanics

Volume 47, Issue 3, 7 February 2014, Pages 631-638
Journal of Biomechanics

Relationships of 35 lower limb muscles to height and body mass quantified using MRI

https://doi.org/10.1016/j.jbiomech.2013.12.002Get rights and content

Abstract

Skeletal muscle is the most abundant tissue in the body and serves various physiological functions including the generation of movement and support. Whole body motor function requires adequate quantity, geometry, and distribution of muscle. This raises the question: how do muscles scale with subject size in order to achieve similar function across humans? While much of the current knowledge of human muscle architecture is based on cadaver dissection, modern medical imaging avoids limitations of old age, poor health, and limited subject pool, allowing for muscle architecture data to be obtained in vivo from healthy subjects ranging in size. The purpose of this study was to use novel fast-acquisition MRI to quantify volumes and lengths of 35 major lower limb muscles in 24 young, healthy subjects and to determine if muscle size correlates with bone geometry and subject parameters of mass and height. It was found that total lower limb muscle volume scales with mass (R2=0.85) and with the height–mass product (R2=0.92). Furthermore, individual muscle volumes scale with total muscle volume (median R2=0.66), with the height–mass product (median R2=0.61), and with mass (median R2=0.52). Muscle volume scales with bone volume (R2=0.75), and muscle length relative to bone length is conserved (median s.d.=2.1% of limb length). These relationships allow for an arbitrary subject's individual muscle volumes to be estimated from mass or mass and height while muscle lengths may be estimated from limb length. The dataset presented here can further be used as a normative standard to compare populations with musculoskeletal pathologies.

Introduction

Skeletal muscle is the most abundant tissue in the human body and is essential for movement. Muscle volume is an important determinant of muscle functional capacity. For example, physiological cross sectional area (PCSA) correlates with peak isometric force (Gans, 1982, Sacks and Roy, 1982) and can be computed from muscle volume and optimal fiber length (Brand et al., 1986, Lieber and Friden, 2000). Additionally, muscle volume has been related to the energetic capacity of muscle (Roberts et al., 1998) and to its associated joint torque-generating capacity (Fukunaga et al., 2001, Holzbaur et al., 2007a, Trappe et al., 2001). These relationships between muscle PCSA and peak muscle force, and between muscle volume and peak joint torque motivate the study of muscle volumes and lengths across healthy humans varying in size.

To date, the most comprehensive muscle volume data in humans has been based on cadaveric measurements (Friederich and Brand, 1990, Ward et al., 2009, Wickiewicz et al., 1983). While cadaver dissection is attractive in its directness of measurement, old age and poor health among cadaver donors confounds applying cadaver data to young, healthy populations (Doherty, 2003, Narici et al., 2003, Vidt et al., 2012). Furthermore, other limitations of cadaver studies have generally prevented muscle scaling relationships from being assessed.

Imaging modalities, on the other hand, have been used in the upper limb to obtain in vivo muscle architectures in subjects ranging in age and health condition (Holzbaur et al., 2007b, Vidt et al., 2012) and in the lower limb to quantify volumes of large muscles and muscle groups in subject populations of interest (Correa and Pandy, 2011, Fukunaga et al., 1992, Gopalakrishnan et al., 2010, Kawakami et al., 2000, LeBlanc et al., 2000). Prolonged imaging times have impeded the scanning of large limbs with high resolution. In this study, we use advanced high-speed MRI, which makes it possible to quickly scan the entire lower limb in high resolution, facilitating a thorough assessment of the individual muscles in the lower limb.

Quantifying healthy muscle volumes with in vivo approaches enables several important questions to be answered. First: are relative muscle volumes and lengths preserved across differently sized healthy individuals? Subject-specificity of anatomy has been discussed in biomechanical modeling (Duda et al., 1996, White et al., 1989). A finding of consistent scaling for muscle volumes and lengths would suggest that anatomical variability is small in healthy individuals and would also provide a normative basis of comparison for individuals with musculoskeletal impairments. Second: how do bones and muscles scale with each other? Muscles and bones are mechanically linked in structure and function. Previous authors have investigated how bone strength scales with size in general and in various animal species and humans (Alexander et al., 1979, Biewener, 1989, Ferretti et al., 2001, Frost, 1997). The relationship between muscle and bone volumes and lengths has not been explored in healthy humans in vivo. A finding that muscle lengths scale with bone lengths would lend further confidence to anthropometric scaling of the musculoskeletal system (Brand et al., 1982, White et al., 1989). A finding of a volumetric relationship between muscle and bone may also serve as a normative standard for evaluating gross age- and activity-related bone loss such as osteoporosis or bone resorption during space flight.

A comprehensive in vivo assessment of muscles from differently sized subjects would also allow for the exploration of how muscle volumes scale with body size. Geometric similarity has been used as a principle for muscle scaling but has been questioned by previous authors studying muscles of animals (Alexander et al., 1981) and humans (Nevill et al., 2004). Other authors have used many subject parameters (e.g. limb girth, age, gender, body weight, and waist size) to predict muscle volume (Chen et al., 2011, Lee et al., 2000). It may be that a two-component parameter, the height–mass product, can predict human muscle volume variation with high accuracy.

In this study, we used a fast non-Cartesian MRI sequence to rapidly scan the entire lower limb and comprehensively assess 35 lower limb muscles of 24 healthy males and females ranging in height by 28 cm and in mass by 51 kg. The goals of this study were to (i) determine relative volumes and lengths of lower limb muscles in this population, (ii) determine how muscles and bones scale with each other, and (iii) determine how lower limb muscle volume scales with the subject parameters of mass and height.

Section snippets

Methods

Twenty-four active, healthy subjects (8 females and 16 males with the following subject characteristics (mean±s.d. [range]): age: 25.5±11.1 [12–51] years, height: 171±10 [145–188] cm, body mass: 71.8±14.6 [47.5–107.0] kg, body mass index: 24.3±4.0 [18.9–35.1] kg/m2) all with no history of lower limb injury, were provided informed consent and selected for this study (Table 1). Subject selection and study protocol were approved by the University of Virginia's Institutional Review Board. Subjects

Results

Within narrow standard deviations, muscle volume fractions for the 35 muscles included in this study are conserved for this population (Fig. 2A). Standard deviations are on the order of 1% of total lower limb musculature. The ratio of muscle belly length to bone length in the lower limb is conserved for this population (Fig. 2B). The average standard deviation of muscle belly length is 2.2% of bone length.

Volumes of individual muscles scale linearly with total limb muscle volume (Fig. 3 and

Discussion

The purpose of this study was to determine muscle volumes and lengths for a cohort of healthy subjects in vivo in order to determine how muscles, bones, and subject parameters scale together. Our results revealed that: (i) muscle volumes scale relative to total muscle volume, and muscle lengths scale relative to bone length for healthy individuals varying in size and age; (ii) bone volume and muscle volume in the lower limb scale together, and (iii) total lower limb muscle volume scales with

Conflict of interest statement

The authors wish to report a patent application in their names for the technique described in this article.

Acknowledgments

Funding for this work was provided by the UVA-Coulter Foundation Translational Research Partnership. We thank John Christopher, Drew Gilliam, Lindsey Sauer, Katherine Read, Kelly Anderson, Ayodeji Bode-Oke, Mary Boyles, Adriana Irvine, Emily Kehne, Colin Maloney, Natalie Powers, Clara Tran, An Truong, and Diana Webber for their help with this study.

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