A predictive nomogram based on triglyceride glucose index to body mass index ratio for low appendicular skeletal muscle mass.

The aim of this study was to investigate risk factors, develop, and assess the predictive nomogram for low appendicular skeletal muscle mass index (ASMI) in middle-aged and elderly populations. A total of 3,209 inpatients were divided into a Training Set (n = 2,407) and a Validation Set (n = 802). A nomogram was developed using R software for internal validation, and external validation was performed using the Validation Set. Gender (male), age, height, weight, triglyceride levels, alanine aminotransferase levels, alcohol consumption, and the triglyceride-glucose index to body-mass index ratio (TyG/BMI) were identified as predictors for the nomogram of low ASMI. In the Training Set, Q1-Q4 subgroups were performed for TyG/BMI, and logistic regression analysis showed that a TyG/BMI ratio greater than 0.37 was significantly associated with an increased risk of developing low ASMI (P < 0.001), with an area under the receiver operating characteristic curve (AUC) of 0.879 for the nomogram. In the Validation Set, the nomogram also demonstrated excellent calibration and discrimination, with an AUC of 0.881. Decision curve analysis (DCA) indicated excellent clinical utility of the nomogram. The study innovatively used TyG/BMI to predict low ASMI, which can reduce the impact of obesity on the diagnosis of sarcopenia. The nomogram can be effectively used to screen for possible sarcopenia in community settings. Due to the cross-sectional study design and unable to obtain complete data on the assessment of muscle strength, the predictive efficacy of our nomogram model requires further confirmation through external validation by large, multicenter prospective studies on sarcopenia population.