Development and validation of machine learning models for predicting low muscle mass in patients with obesity and diabetes.

Background And Aims: Low muscle mass (LMM) is a critical complication in patients with obesity and diabetes, exacerbating metabolic and cardiovascular risks. Novel obesity indices, such as the body roundness index (BRI), conicity index, and relative fat mass, have shown promise for assessing body composition. This study aimed to investigate the associations of these indices with LMM and to develop machine learning models for accurate and accessible LMM prediction.

Method: Data from NHANES 2011-2018 (n = 2,176) were analyzed. Obesity was defined by body fat percentage, and LMM was determined using skeletal muscle mass index thresholds adjusted for BMI. Predictive models were developed using logistic regression, random forest, and other algorithms, with feature selection via LASSO regression. Validation included NHANES 2005-2006 data (n = 310). Model performance was evaluated using AUROC, Brier scores, and SHapley Additive exPlanations (SHAP) for feature importance.

Results: BRI was independently associated with LMM (odds ratio 1.39, 95% confidence interval 1.22-1.58; P < 0.001). Eight features were included in the random forest model, which achieved excellent discrimination (AUROC = 0.721 in the validation set) and calibration (Brier score = 0.184). Feature importance analysis highlighted BRI, creatinine, race, age, and HbA1c as key contributors to the model's predictive performance. SHAP analysis emphasized BRI's role in predicting LMM. An online prediction tool was developed.

Conclusions: BRI is a significant predictor of LMM in patients with obesity and diabetes. The random forest model demonstrated strong performance and offers a practical tool for early LMM detection, supporting clinical decision-making and personalized interventions.