Chen Xiaodong, Li Liping
School of Public Health, Shantou University, No. 243 Daxue Road, Shantou, 515063, Guangdong, China.
Injury Prevention Research Center, Shantou University Medical College, Shantou, 515041, China.
BMC Geriatr. 2025 Feb 27;25(1):133. doi: 10.1186/s12877-025-05792-1.
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.
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.
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.
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.
本研究旨在为中国老年人在不同时间间隔建立肌肉减少症风险预测模型，并识别和比较导致肌肉减少症发生的可改变因素。
本研究使用了中国健康与养老追踪调查（CHARLS）中3549名60岁及以上参与者的数据。采用AWGS2019算法评估肌肉减少症状态。将考虑多因素基线变量的2年和4年肌肉减少症风险的完整模型与可改变因素模型进行比较。使用八种机器学习（ML）算法构建这些模型，通过受试者工作特征曲线下面积（AUC-ROC）评估性能。应用SHapley加性解释（SHAP）进行模型解释。
参与者的平均年龄为67.0岁（标准差=6.1），女性占47.8%（1696名参与者）。ML模型表现中等，在2年预测中，极端梯度提升（XGBoost）成为完整模型和可改变因素模型的最佳模型，AUC分别为0.804和0.795（德龙检验，P=0.665）。相比之下，在4年预测中，轻梯度提升机（LightGBM）表现最佳，AUC分别为0.795和0.769（P=0.053）。SHAP分析突出显示性别和估计肾小球滤过率（eGFR）是完整模型和可改变因素模型中最重要的预测因素。
基于不同时间间隔可改变因素的预测模型有助于识别肌肉减少症高风险的中国老年人。这些发现突出了在肌肉减少症预防策略中优先考虑功能能力和心理社会决定因素的重要性。
