Wu Yanxuan, Li Fu, Chen Hao, Shi Liang, Yin Meng, Hu Fan, Yu Gongchang
Neck-Shoulder and Lumbocrural Pain Hospital of Shandong First Medical University, Shandong First Medical University & Shandong Academy of Medical Sciences, Jinan, China.
Shandong First Medical University & Shandong Academy of Medical Sciences, Jinan, China.
PLoS One. 2025 Sep 9;20(9):e0331925. doi: 10.1371/journal.pone.0331925. eCollection 2025.
Metabolic syndrome (MetS) and sarcopenia are major global public health problems, and their coexistence significantly increases the risk of death. In recent years, this trend has become increasingly prominent in younger populations, posing a major public health challenge. Numerous studies have regarded reduced muscle mass as a reliable indicator for identifying pre-sarcopenia. Nevertheless, there are currently no well-developed methods for identifying low muscle mass in individuals with MetS.
A total of 2,467 MetS patients (aged 18-59 years) with low muscle mass assessed by dual-energy X-ray absorptiometry (DXA) were included using data from the 2011-2018 National Health and Nutrition Examination Survey (NHANES). Least Absolute Shrinkage and Selection Operator (LASSO) regression was then used to screen for important features. A total of nine Machine learning (ML) models were constructed in this study. Area under the curve (AUC), F1 Score, Recall, Precision, Accuracy, Specificity, PPV, and NPV were used to evaluate the model's performance and explain important predictors using the Shapley Additive Explain (SHAP) values.
The Logistic Regression (LR) model performed the best overall, with an AUC of 0.925 (95% CI: 0.9043, 0.9443), alongside strong F1-score (0.87) and specificity (0.89). Five important predictors are displayed in the summary plot of SHAP values: height, gender, waist circumference, thigh length, and alkaline phosphatase (ALP).
This study developed an interpretable ML model based on SHAP methodology to identify risk factors for low muscle mass in a young population of MetS patients. Additionally, a web-based tool was implemented to facilitate sarcopenia screening.
代谢综合征（MetS）和肌肉减少症是全球主要的公共卫生问题，它们的共存显著增加了死亡风险。近年来，这种趋势在年轻人群中日益突出，构成了重大的公共卫生挑战。许多研究将肌肉量减少视为识别肌肉减少症前期的可靠指标。然而，目前尚无完善的方法来识别患有MetS个体的低肌肉量。
利用2011 - 2018年美国国家健康与营养检查调查（NHANES）的数据，纳入了2467例经双能X线吸收法（DXA）评估为低肌肉量的MetS患者（年龄18 - 59岁）。然后使用最小绝对收缩和选择算子（LASSO）回归筛选重要特征。本研究共构建了9个机器学习（ML）模型。使用曲线下面积（AUC）、F1分数、召回率、精确率、准确率、特异性、阳性预测值（PPV）和阴性预测值（NPV）来评估模型性能，并使用Shapley加性解释（SHAP）值解释重要预测因素。
逻辑回归（LR）模型总体表现最佳，AUC为0.925（95%置信区间：0.9043，0.9443），同时F1分数（0.87）和特异性（0.89）也很强。SHAP值的汇总图显示了五个重要预测因素：身高、性别、腰围、大腿长度和碱性磷酸酶（ALP）。
本研究基于SHAP方法开发了一个可解释的ML模型，以识别年轻MetS患者群体中低肌肉量的风险因素。此外，还实施了一个基于网络的工具来促进肌肉减少症的筛查。
