Sun Qianwei, Shen Lei, Liu Huamin, Lou Zhangqun, Kong Qi
The Affiliated Hospital of Xuzhou Medical University, Xuzhou, Jiangsu, China.
Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, China.
BMC Geriatr. 2025 Jul 2;25(1):464. doi: 10.1186/s12877-025-06104-3.
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.
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.
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.
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.
肌肉减少症以骨骼肌质量和功能的逐渐丧失为特征，是一个日益受到关注的公共卫生问题，在中国老年人群中尤为突出。然而，针对肌肉减少症风险的综合预测模型仍然匮乏。本研究旨在开发并验证一个针对中国老年成年人肌肉减少症风险的准确、可解释的预测模型，识别独立风险因素，并为针对性干预提供见解。
这项队列研究使用了中国健康与养老追踪调查（CHARLS）的数据。纳入了2011年基线时无肌肉减少症的60岁及以上参与者，并随访至2013年。在排除有缺失数据、极端值或混杂情况（如癌症、残疾）的个体后，共纳入2197名参与者。根据2019年亚洲肌肉减少症工作组（AWGS）标准诊断肌肉减少症，该标准评估四肢骨骼肌质量、肌肉力量和身体表现。使用多变量逻辑回归分析一系列社会人口学、健康、生活方式、心理和生化因素。使用受试者工作特征曲线下面积（AUC）、校准和决策曲线分析评估模型性能。
在两年期间，肌肉减少症的发病率为10.29%，女性（13.20%）的发病率高于男性（7.99%）。独立风险因素包括年龄较大、体重指数较低、女性、记忆相关疾病、关节炎或风湿病、睡眠时间较短以及教育水平较低。逻辑回归模型表现稳健，AUC为0.849（95%CI：0.821 - 0.878），校准一致。限制立方样条分析显示体重指数（21.3 - 26.0）和睡眠时间（5 - 8小时）具有保护作用。该模型具有临床实用性，强调了可改变的风险因素并增强了可解释性。
本研究提供了一个针对肌肉减少症的实用且可解释的预测模型，突出了体重指数和睡眠时间等关键可改变风险因素。研究结果强调了基于证据的个体化预防策略以及多学科方法对于减轻老年人群肌肉减少症的迫切需求。
