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Background <p>Sarcopenia, characterized by progressive loss of skeletal muscle mass and function, is a growing public health concern, particularly among aging populations in China. However, comprehensive predictive models for sarcopenia risk remain scarce. This study aims to develop and validate an accurate, interpretable predictive model for sarcopenia risk in elderly Chinese adults, identifying independent risk factors and offering insights for targeted interventions.</p> Methods <p>This cohort study utilized data from the China Health and Retirement Longitudinal Study (CHARLS). Participants aged 60 years and older, free of sarcopenia at baseline in 2011, were followed through 2013. After excluding individuals with missing data, extreme values, or confounding conditions (e.g., cancer, disabilities), 2,197 participants were included. Sarcopenia was diagnosed according to the 2019 Asian Working Group for Sarcopenia (AWGS) criteria, which assess appendicular skeletal muscle mass, muscle strength, and physical performance. A range of sociodemographic, health, lifestyle, psychological, and biochemical factors were analyzed using multivariable logistic regression. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC), calibration, and decision curve analysis.</p> Results <p>Over a two-year period, the incidence of sarcopenia was 10.29%, with higher rates observed in women (13.20%) compared to men (7.99%). Independent risk factors included older age, lower BMI, female, memory-related diseases, arthritis or rheumatism, shorter sleep duration, and lower education levels. The logistic regression model demonstrated robust performance with an AUC of 0.849 (95% CI: 0.821–0.878) and consistent calibration. Restricted cubic spline analyses revealed protective effects of BMI (21.3–26.0) and sleep duration (5–8 h). The model provides clinical utility, emphasizing modifiable risk factors and enhancing interpretability.</p> Conclusions <p>This study offers a practical and interpretable predictive model for sarcopenia, highlighting key modifiable risk factors such as BMI and sleep duration. The findings underscore the critical need for evidence-based, individualized prevention strategies and a multidisciplinary approach to mitigate sarcopenia in aging populations.</p>

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Development and validation of a predictive model for sarcopenia risk in older Chinese adults based on key factors

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年7月2日

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  • Qianwei Sun,
  • Lei Shen,
  • Huamin Liu,
  • Zhangqun Lou,
  • Qi Kong

摘要

Background

Sarcopenia, characterized by progressive loss of skeletal muscle mass and function, is a growing public health concern, particularly among aging populations in China. However, comprehensive predictive models for sarcopenia risk remain scarce. This study aims to develop and validate an accurate, interpretable predictive model for sarcopenia risk in elderly Chinese adults, identifying independent risk factors and offering insights for targeted interventions.

Methods

This cohort study utilized data from the China Health and Retirement Longitudinal Study (CHARLS). Participants aged 60 years and older, free of sarcopenia at baseline in 2011, were followed through 2013. After excluding individuals with missing data, extreme values, or confounding conditions (e.g., cancer, disabilities), 2,197 participants were included. Sarcopenia was diagnosed according to the 2019 Asian Working Group for Sarcopenia (AWGS) criteria, which assess appendicular skeletal muscle mass, muscle strength, and physical performance. A range of sociodemographic, health, lifestyle, psychological, and biochemical factors were analyzed using multivariable logistic regression. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC), calibration, and decision curve analysis.

Results

Over a two-year period, the incidence of sarcopenia was 10.29%, with higher rates observed in women (13.20%) compared to men (7.99%). Independent risk factors included older age, lower BMI, female, memory-related diseases, arthritis or rheumatism, shorter sleep duration, and lower education levels. The logistic regression model demonstrated robust performance with an AUC of 0.849 (95% CI: 0.821–0.878) and consistent calibration. Restricted cubic spline analyses revealed protective effects of BMI (21.3–26.0) and sleep duration (5–8 h). The model provides clinical utility, emphasizing modifiable risk factors and enhancing interpretability.

Conclusions

This study offers a practical and interpretable predictive model for sarcopenia, highlighting key modifiable risk factors such as BMI and sleep duration. The findings underscore the critical need for evidence-based, individualized prevention strategies and a multidisciplinary approach to mitigate sarcopenia in aging populations.