construction and evaluation of sarcopenia risk prediction model for non-obese older adults.

INTRODUCTION: Sarcopenia is common in older adults, with distinct features in non-obese individuals. This study developed and validated a nomogram to predict sarcopenia risk specifically in non-obese older adults. METHODS: Data from 1,654 CHARLS participants were used for model development and internal validation, and 263 NHANES participants for external validation. LASSO regression selected predictors. A nomogram was constructed and assessed using AUC (discrimination), calibration curves (accuracy), and decision curve analysis (clinical utility). RESULTS: Seven variables were included in the final nomogram: age, sex, BMI, hemoglobin (Hb), difficulty rising from a chair, history of falls, and basic living ability. The model demonstrated excellent discrimination, with an AUC of 0.88 (95% CI: 0.86-0.90) in the training set. Performance remained robust in the internal (AUC 0.89, 95% CI: 0.85-0.92) and external (AUC 0.94, 95% CI: 0.91-0.97) validation sets. In the sex-stratified analysis, age, BMI, BUN, liver diseases, and basic living ability were included in the male model (AUC: 0.95, 95% CI: 0.93-0.97). For females, age, BMI, creatinine (Cr), difficulty running 1 km, and basic living ability were included (AUC: 0.83, 95% CI: 0.79-0.87). All models demonstrated good calibration and clinical utility. CONCLUSION: This study established an effective and simple prediction model for sarcopenia in non-obese older adults. This nomogram can serve as a valuable clinical tool for the early identification and proactive management of sarcopenia in this specific population.