Wang Zongjie, Wu Yafei, Zhu Junmin, Fang Ya
School of Public Health, Xiamen University, Xiamen, China.
Key Laboratory of Health Technology Assessment of Fujian Province, Xiamen University, Xiamen, China.
Psychogeriatrics. 2025 Jan;25(1):e13205. doi: 10.1111/psyg.13205. Epub 2024 Oct 23.
Sarcopenia is a prominent issue among aging populations and associated with poor health outcomes. This study aimed to examine the predictive value of questionnaire and biomarker data for sarcopenia, and to further develop a user-friendly calculator for community-dwelling middle-aged and older adults.
We used two waves (2011 and 2013) of the China Health and Retirement Longitudinal Study (CHARLS) to predict sarcopenia, defined by the Asian Working Group for Sarcopenia 2019 criteria. We restricted the analytical sample to adults aged 45 or above (N = 2934). Five machine learning models were used to construct Q-based (only questionnaire variables), Bio-based (only biomarker variables), and combined (questionnaire plus biomarker variables) models. Area under the receiver operating characteristic curve (AUROC) was used for performance assessment. Temporal external validation was performed based on two datasets from CHARLS. Important predictors were identified by Shapley values and coefficients.
Extreme gradient boosting (XGBoost), considering both questionnaire and biomarker characteristics, emerged as the optimal model, and its AUROC was 0.759 (95% CI: 0.747-0.771) at a decision threshold of 0.20 on the test set. Models also performed well on the external datasets. We found that cognitive function was the most important predictor in both Q-based and combined models, and blood urea nitrogen was the most important predictor in the Bio-based model. Other key predictors included education, haematocrit, total cholesterol, drinking, number of chronic diseases, and instrumental activities of daily living score.
Our findings offer a potential for early screening and targeted prevention of sarcopenia among middle-aged and older adults in the community setting.
肌肉减少症是老年人群中的一个突出问题，与不良健康结局相关。本研究旨在探讨问卷和生物标志物数据对肌肉减少症的预测价值，并进一步为社区居住的中老年人开发一个用户友好的计算器。
我们使用中国健康与养老追踪调查（CHARLS）的两期数据（2011年和2013年）来预测肌肉减少症，肌肉减少症根据亚洲肌肉减少症工作组2019年标准定义。我们将分析样本限制为45岁及以上的成年人（N = 2934）。使用五个机器学习模型构建基于问卷的（仅问卷变量）、基于生物标志物的（仅生物标志物变量）和组合的（问卷加生物标志物变量）模型。采用受试者工作特征曲线下面积（AUROC）进行性能评估。基于CHARLS的两个数据集进行时间外部验证。通过Shapley值和系数确定重要预测因素。
考虑问卷和生物标志物特征的极端梯度提升（XGBoost）模型成为最优模型，在测试集上决策阈值为0.20时其AUROC为0.759（95% CI：0.747 - 0.771）。模型在外部数据集上也表现良好。我们发现认知功能在基于问卷的模型和组合模型中都是最重要的预测因素，而血尿素氮在基于生物标志物的模型中是最重要的预测因素。其他关键预测因素包括教育程度、血细胞比容、总胆固醇、饮酒情况、慢性病数量和日常生活活动能力得分。
我们的研究结果为在社区环境中对中老年人进行肌肉减少症的早期筛查和针对性预防提供了可能性。
