Development and validation of a nomogram for identifying prevalent sarcopenia in Chinese patients with...

Sarcopenia is recognized as a significant comorbidity in patients with Cardiovascular-Kidney-Metabolic (CKM) Syndrome, yet validated prediction models for this population remain lacking. This study aimed to develop and validate a nomogram for predicting sarcopenia risk in Chinese patients with CKM syndrome.

Data were derived from the China Health and Retirement Longitudinal Study (CHARLS) and an independent hospital dataset. The CHARLS 2015 dataset was split into a training set and an internal validation set; the CHARLS 2011 dataset served as the external validation set; and inpatients from Guangdong Provincial Hospital of Chinese Medicine constituted the hospital validation set. Sarcopenia was diagnosed according to the 2025 Asian Working Group for Sarcopenia criteria. Least absolute shrinkage and selection operator (LASSO) regression combined with multivariable logistic regression was used for predictor selection and model development. Model performance was evaluated by discrimination, calibration, and decision curve analysis (DCA).

Nine predictors were identified: age, smoking status, high-density lipoprotein cholesterol, triglycerides, uric acid, C-reactive protein, hemoglobin, chronic obstructive pulmonary disease, and chronic liver disease. The model achieved area under the curve values of 0.817, 0.808, 0.800, and 0.834 in the training, internal validation, external validation, and hospital validation sets, respectively. Calibration was satisfactory in development cohorts (p > 0.05), with some calibration drift in external populations. DCA confirmed clinical utility across all datasets.

The developed nomogram incorporating nine accessible predictors demonstrated robust discrimination and clinical applicability for sarcopenia risk assessment in Chinese CKM patients, supporting its use in early screening and individualized intervention.