ArticleThe Journal of international medical research2026
Development and validation of a machine learning-based sarcopenia prediction model using the triglyceride glucose-frailty index.
Article in The Journal of international medical research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
ObjectiveSarcopenia 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.MethodsData 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.ResultsAfter 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,
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