Development and Validation of a Predictive Model for Sarcopenia Risk in Older Chinese Adults Based on Key Factors

Background Sarcopenia, characterized by progressive loss of skeletal muscle mass and function, poses a significant health risk to the aging population. This study aims to construct and validate a predictive model for sarcopenia in elderly Chinese individuals using data from the China Health and Retirement Longitudinal Study (CHARLS). Methods We observed participants aged 60 and above without a diagnosis of sarcopenia in 2011 and followed up in 2013 for the incidence of sarcopenia. After excluding participants with missing data, disabilities, cancer, and extreme values, a total of 2,197 individuals were included in the study. Sarcopenia was assessed based on the 2019 Asian Working Group for Sarcopenia (AWGS) criteria. The predictive factors analyzed included sociodemographic characteristics, health status, lifestyle habits, psychological status, pain-related information, and blood biochemical indicators. LASSO-logistic regression and XGBoost machine learning models were employed to identify key predictors and develop the predictive model. Results The study identified older age, lower BMI, female gender, memory-related diseases, arthritis or rheumatism, shorter night sleep duration, and lower education level as independent risk factors for sarcopenia. Both methods produced models with high predictive accuracy, though the XGBoost model had a slightly higher AUC than the logistic regression model (0.881 vs. 0.849). However, the difference in AUC between the two models was not statistically significant. The XGBoost model demonstrated higher sensitivity but lower specificity. Ultimately, the logistic regression model was considered the better choice for this study due to its interpretability and comparable performance. Conclusion This study identified key risk factors for sarcopenia using machine learning and traditional statistical methods, such as logistic regression, and developed robust predictive models. The findings provide valuable insights for early intervention and management of sarcopenia in the elderly Chinese population, highlighting the need for a multidisciplinary approach to improve health outcomes in this group.