Prediction of sarcopenia at different time intervals: an interpretable machine learning analysis of modifiable factors.

Abstract

Objectives: This study aims to develop sarcopenia risk prediction models for Chinese older adults at different time intervals and to identify and compare modifiable factors contributing to sarcopenia development.

Methods: This study used data from 3,549 participants aged 60 and older in the China Health and Retirement Longitudinal Study (CHARLS). Sarcopenia status was evaluated by the AWGS2019 algorithm. Full models for 2- and 4-year sarcopenia risk, considering multifactorial baseline variables, were compared with modifiable models. Eight machine learning (ML) algorithms were used to build these models, with performance evaluated by the area under the receiver operating characteristic curve (AUC-ROC). SHapley Additive exPlanations (SHAP) was applied for model explanation.

Results: The average age of participants was 67.0 years (SD = 6.1), with 47.8% being female (1,696 participants). The ML models achieved moderate performance, and eXtreme Gradient Boosting (XGBoost) emerged as the best model for both the full and modifiable models in the 2-year prediction, with AUCs of 0.804 and 0.795, respectively (DeLong test, P = 0.665). In contrast, in the 4-year prediction, the Light Gradient Boosting Machine (LightGBM) performed best with AUCs of 0.795 and 0.769, respectively (P = 0.053). The SHAP analysis highlighted gender and estimated glomerular filtration rate (eGFR) as the most important predictors in both the full and modifiable models.

Conclusions: Prediction models based on modifiable factors at different time intervals can help identify older Chinese adults at high risk of sarcopenia. These findings highlight the importance of prioritizing functional capacity and psychosocial determinants in sarcopenia prevention strategies.