Development and validation of a machine learning-based sarcopenia prediction model using the triglyceride glucose–frailty index

Objective Sarcopenia is a progressive skeletal muscle disorder characterized by declines in muscle mass and function. This study developed a novel composite biomarker, the triglyceride glucose–frailty index, which integrates metabolic and frailty-related measures, and examined its association with sarcopenia in adults. Methods Data from 2334 participants in the National Health and Nutrition Examination Survey were analyzed. Multivariable logistic regression was used to evaluate the association between triglyceride glucose–frailty index and sarcopenia after adjustment for key covariates. Restricted cubic spline analyses and subgroup analyses were conducted to assess nonlinearity and consistency. Machine learning models were developed to predict sarcopenia, and model performance was evaluated using the area under the curve, accuracy, and F1-score. SHapley Additive exPlanations analysis was applied to improve model interpretability. Results After full adjustment, higher triglyceride glucose–frailty index was significantly associated with an increased risk of sarcopenia (odds ratio = 1.468, 95% confidence interval: 1.246–1.730, p < 0.0001), showing a clear dose–response relationship. Subgroup analyses demonstrated consistent associations across populations. Among the machine learning models, XGBoost showed the best predictive performance, with an area under the curve of 0.978. SHapley Additive exPlanations analysis identified triglyceride glucose–frailty index as the second most important predictor after race. Conclusions Triglyceride glucose–frailty index is a strong and independent predictor of sarcopenia. This composite biomarker may serve as a practical tool for improved risk stratification and early prevention in clinical settings.