Development and Validation of a Risk Prediction Model for Sarcopenia in Chinese Older Patients with Type 2 Diabetes Mellitus.

Abstract

Sarcopenia is a common prevalent age-related disorder among older patients with type 2 diabetes mellitus (T2DM). This study aimed to develop and validate a nomogram model to assess the risk of incident sarcopenia among older patients with T2DM. A total of 1434 older patients (≥ 60 years) diagnosed with T2DM between May 2020 and November 2023 were recruited. The study cohort was randomly divided into a training set (n = 1006) and a validation set (n = 428) at the ratio of 7:3. The best-matching predictors of sarcopenia were incorporated into the nomogram model. The accuracy and applicability of the nomogram model were measured by using the area under the receiver operating characteristic curve (AUC), calibration curve, Hosmer-Lemeshow test, and decision curve analysis (DCA). 571 out of 1434 participants (39.8%) had sarcopenia. Nine best-matching factors, including age, body mass index (BMI), diabetic duration, glycated hemoglobin A1c (HbA1c), 25 (OH)Vitamin D, nephropathy, neuropathy, nutrition status, and osteoporosis were selected to construct the nomogram prediction model. The AUC values for training and validation sets were 0.800 (95% CI = 0.773-0.828) and 0.796 (95% CI = 0.755-0.838), respectively. Furthermore, the agreement between predicted and actual clinical probability of sarcopenia was demonstrated by calibration curves, the Hosmer-Lemeshow test (P > 0.05), and DCA. Sarcopenia was prevalent among older patients with T2DM. A visual nomogram prediction model was verified effectively to evaluate incident sarcopenia in older patients with T2DM, allowing targeted interventions to be implemented timely to combat sarcopenia in geriatric population with T2DM.