Development, validation, and visualization of a machine learning-based predictive model for sarcopenia risk in patients with diabetes mellitus

Abstract

Background Sarcopenia is a common complication in patients with diabetes mellitus (DM), yet early screening remains challenging due to the lack of accessible tools. This study aimed to develop and externally validate a machine learning (ML) -based predictive model for sarcopenia risk in DM patients, with enhanced interpretability. Methods Data from 1,074 DM patients at a single center (training and internal validation) and two external datasets (NHANES, n = 1,078; CHARLS, n = 547) were used. Seven ML methods were evaluated. Feature selection used LASSO and Boruta. Model performance was assessed by AUC, calibration (Brier score), and decision curve analysis. SHAP was applied to explain the final model. Results Six predictors (age, sex, BMI, hemoglobin, uric acid, creatinine) were identified. In internal validation, LightGBM achieved the highest AUC (0.973). However, on both external datasets, logistic regression showed superior and stable performance (NHANES AUC = 0.967, CHARLS AUC = 0.949), with lower Brier scores (0.042 and 0.061) and favorable net benefit. LightGBM’s performance decreased externally (AUC 0.873 and 0.868). Therefore, logistic regression was selected as the final model. SHAP analysis revealed that low BMI, older age, and low creatinine were the strongest predictors of sarcopenia. Conclusion Using six readily available clinical features, the logistic regression model shows robust generalizability across populations. This clinically practical tool supports early risk identification in diabetic patients, enabling timely interventions to mitigate sarcopenia-related complications.