Sex and Age Differences in Lower Limb Muscle Strength: A Critical Review of Musculoskeletal Models and Experimental Data

Core Concepts

Current musculoskeletal (MSK) models, particularly those used in OpenSim, fail to accurately represent age- and sex-related differences in lower limb muscle mass distribution and physiological cross-sectional area (PCSA), impacting their accuracy and limiting their applicability in research and clinical settings.

Abstract

• Bibliographic Information: Maarleveld, R., Veeger, H.E.J., van der Helm, F.C.T., Son, J., Lieber, R.L., & van der Kruk, E. (Year). What the %PCSA? Addressing Diversity in Lower-Limb Musculoskeletal Models: Age- and Sex-related Differences in PCSA and Muscle Mass.
• Research Objective: This review investigates the impact of age and sex on lower limb muscle PCSA and muscle mass (Mm), comparing experimental data with parameters used in popular open-source MSK models to assess their accuracy in representing diverse populations.
• Methodology: The researchers conducted a comprehensive literature review, extracting PCSA and Mm data from 57 studies dating back to 1884. They analyzed sex and age differences in absolute and relative muscle strength, comparing these findings to muscle parameters used in five commonly used OpenSim lower limb models (Models D, G, A, L, and R).
• Key Findings: Significant sex differences were found in both absolute and relative muscle mass, with males exhibiting greater Mm in most lower limb muscles. Notably, males had a higher proportion of muscle mass in biarticular muscles like the rectus femoris and semimembranosus, while females showed greater relative mass in pelvic (gluteus maximus and medius) and ankle muscles (tibialis anterior, tibialis posterior, extensor digitorum longus). Age-related differences were primarily observed in absolute muscle mass, with the elderly exhibiting lower Mm than younger groups. However, relative muscle mass remained largely consistent across age groups, except for specific muscles like the gastrocnemius (higher in younger individuals) and gluteus medius (higher in the elderly). The analysis revealed that none of the OpenSim models accurately reflected female muscle mass distribution. Models L and R best represented male Mm, while Model A fell between male and female averages. Concerning age, Model A best represented the elderly, Model R aligned best with young adults, and Model L represented young adults for Mm but the elderly for %Mm. Models G and D demonstrated inconsistencies in representing both sex and age groups.
• Main Conclusions: The study highlights the significant limitations of current MSK models in reflecting the diversity of human musculature across age and sex. The authors emphasize the need for developing more inclusive models that incorporate these differences to improve the accuracy and reliability of biomechanical simulations, particularly in clinical applications and research focusing on specific populations.
• Significance: This research underscores the importance of considering age and sex as critical factors in musculoskeletal modeling. The findings have significant implications for improving the design and implementation of MSK models, ultimately leading to more accurate biomechanical analyses and personalized clinical applications.
• Limitations and Future Research: The study acknowledges limitations regarding the availability of comprehensive PCSA data across all age and sex groups, particularly for children and adults. Future research should focus on generating more comprehensive datasets, exploring muscle architecture variations, and developing standardized scaling methods that account for age and sex differences to enhance the inclusivity and accuracy of musculoskeletal models.

Stats

Males have a higher proportion of muscle mass in the rectus femoris (5.5%) and semimembranosus (6%) muscles compared to females (4.9% and 5.2%, respectively).
Females have a greater relative muscle mass in the gluteus maximus (26% vs. 21.7% in males), gluteus medius (12.6% vs. 9.7%), tibialis anterior (3.7% vs. 3.2%), tibialis posterior (2.7% vs. 2.3%), and extensor digitorum longus (1.9% vs. 1.6%).
Younger individuals have a higher relative muscle mass in the rectus femoris (6.1%), gastrocnemius (10.2%), and medial gastrocnemius (7.9%) compared to adults (5.2%, 8.5%, and 5.5% respectively) and the elderly (5%, 7.8%, and 4.9% respectively).
The elderly have a higher relative muscle mass in the gluteus medius (12%) compared to young individuals (7.6%).

Quotes

Key Insights Distilled From

What the %PCSA? Addressing Diversity in Lower-Limb Musculoskeletal Models: Age- and Sex-related Differences in PCSA and Muscle Mass

by R. Maarlevel... at arxiv.org 11-04-2024

https://arxiv.org/pdf/2411.00071.pdf

Deeper Inquiries

How can the development of more inclusive and representative MSK models influence the design and development of assistive devices and rehabilitation programs for diverse populations?

Developing more inclusive and representative Musculoskeletal (MSK) models stands to revolutionize the landscape of assistive devices and rehabilitation programs, ushering in an era of personalized and effective care for diverse populations. Here's how:

Personalized Assistive Device Design: Current assistive devices, designed based on generic models, often lack the nuance to cater to individual needs, leading to discomfort, inefficiency, and even injury. Inclusive MSK models, accounting for variations in PCSA (physiological cross-sectional area), muscle mass distribution, and other parameters across age, sex, and body types, will enable the creation of devices tailored to the unique biomechanics of each user. Imagine prosthetic limbs that move with the natural grace and strength of a biological limb, or exoskeletons that provide just the right amount of support, enhancing mobility and independence without compromising comfort.

Targeted Rehabilitation Programs: Rehabilitation after injury or surgery often follows a one-size-fits-all approach, failing to address the specific needs and recovery patterns of individuals. Inclusive MSK models can change this by providing insights into how different populations utilize their muscles during movement. This knowledge will empower clinicians to design personalized rehabilitation programs that optimize muscle activation, improve coordination, and accelerate recovery, leading to better long-term outcomes.

Reducing Gender and Age Bias in Treatment: Current MSK models, often based on limited datasets skewed towards certain demographics, can perpetuate biases in treatment. For instance, a model that underrepresents female muscle architecture might lead to misdiagnosis or ineffective treatment of knee injuries in women. Inclusive models will help address this disparity, ensuring that clinical decisions are based on accurate and representative data, leading to equitable and effective healthcare for all.

Enhancing Understanding of Musculoskeletal Health: By incorporating data from diverse populations, inclusive MSK models will provide a more comprehensive understanding of how age, sex, and other factors influence musculoskeletal health. This knowledge will be invaluable in developing targeted interventions, preventive measures, and public health initiatives that address the unique needs of different demographic groups.
In conclusion, the development of inclusive and representative MSK models is not just about improving the accuracy of simulations; it's about transforming the way we design assistive devices, deliver rehabilitation, and approach musculoskeletal health for everyone.

Could the observed differences in muscle mass distribution between sexes be attributed to factors beyond biological sex, such as physical activity levels and sociocultural influences on movement patterns?

While biological differences between sexes undoubtedly contribute to variations in muscle mass distribution, it's crucial to acknowledge the potential influence of other factors that extend beyond the realm of pure biology:

Physical Activity Levels and Types: Research has consistently shown that engaging in regular physical activity can significantly impact muscle mass and strength. However, the type of activity matters. For instance, activities that emphasize lower body strength, like running or dance, might contribute to the higher relative muscle mass observed in the pelvic and ankle muscles of females. Conversely, activities favored by males, such as weightlifting focusing on upper body strength, might explain the greater proportion of muscle mass in their upper body.

Sociocultural Influences on Movement Patterns: From a young age, societal norms and cultural practices can shape our movement patterns. For example, certain cultures may encourage activities that promote balance and flexibility in women, potentially influencing the development of specific muscle groups. Conversely, men might be encouraged towards activities that emphasize strength and power, leading to different muscle development patterns.

Occupational Demands: Jobs that require prolonged standing, walking, or lifting can lead to specific muscle adaptations. If certain occupations are more prevalent in one sex, it could contribute to the observed differences in muscle mass distribution. For instance, if more women are engaged in professions requiring prolonged standing, it might explain the greater relative muscle mass in their lower leg muscles.

Hormonal Fluctuations: While testosterone, often associated with muscle growth, is typically higher in males, hormonal fluctuations throughout the menstrual cycle and menopause can influence muscle protein synthesis and breakdown in females. These fluctuations, often overlooked in research, might contribute to the variations observed in muscle mass distribution.

Nutritional Intake: Adequate protein intake is essential for muscle growth and repair. Differences in dietary habits and nutritional intake between sexes, influenced by cultural norms or personal preferences, could contribute to variations in muscle mass.
It's important to note that disentangling the influence of these factors from biological sex is complex. Most studies control for biological sex but often lack the granularity to fully account for the interplay of these sociocultural and behavioral factors. Future research needs to adopt a more holistic approach, considering the complex interplay of biology, environment, and behavior to gain a more nuanced understanding of muscle mass distribution across populations.

If our understanding of human anatomy and physiology is constantly evolving, how can we ensure that the MSK models we use remain up-to-date and reflect the most accurate and current knowledge?

Maintaining the relevance and accuracy of MSK models in the face of ever-evolving anatomical and physiological knowledge requires a multi-pronged approach:

Fostering a Culture of Continuous Update and Refinement: Treating MSK models as static entities is a recipe for obsolescence. Instead, we need to cultivate a culture of continuous update and refinement, incorporating new data and insights as they emerge. This involves:

Regularly Reviewing and Integrating New Research: Staying abreast of the latest findings in muscle physiology, biomechanics, and imaging techniques is crucial. This allows for the identification of new data that can be incorporated into existing models, improving their accuracy and representativeness.
Developing Standardized Procedures for Data Incorporation: Establishing clear guidelines and protocols for integrating new data into MSK models ensures consistency and reliability. This might involve developing algorithms or software tools that automate the process, minimizing errors and biases.

Embracing Open-Source Development and Collaboration: Proprietary MSK models, often limited in scope and accessibility, hinder the collective advancement of the field. Open-source development, on the other hand, fosters transparency, collaboration, and rapid dissemination of knowledge. This allows researchers to build upon each other's work, share data, and collectively refine models, ensuring they remain at the forefront of scientific understanding.

Investing in Validation and Sensitivity Analyses: Blindly trusting MSK models without rigorous validation is a risky proposition. Regular validation studies, comparing model predictions to experimental data from diverse populations, are essential to assess accuracy and identify areas for improvement. Additionally, sensitivity analyses, examining how model outputs change in response to variations in input parameters, help understand the limitations and uncertainties associated with the model.

Leveraging Technological Advancements: The rapid pace of technological innovation, particularly in imaging and computational modeling, presents exciting opportunities to enhance MSK models.

Incorporating Data from Advanced Imaging Techniques: Techniques like diffusion tensor imaging (DTI) and high-resolution ultrasound provide unprecedented insights into muscle architecture and function. Integrating this data into MSK models can significantly improve their accuracy and predictive power.
Developing Machine Learning Algorithms: Machine learning algorithms can be trained on vast datasets of anatomical and physiological data to identify patterns and relationships that might not be apparent through traditional statistical methods. This can lead to the development of more sophisticated and personalized MSK models.
In conclusion, ensuring the long-term relevance of MSK models requires a dynamic and adaptive approach. By embracing continuous improvement, open collaboration, rigorous validation, and technological advancements, we can ensure these models remain powerful tools for understanding human movement and improving health outcomes.