Development of a sarcopenia identification model for older adults with multimorbidity in Xinjiang communities, Northwest China: a cross-sectional study

Aging populations around the world are experiencing increasing levels of multimorbidity. Older adults with multimorbidity are at an elevated risk for sarcopenia, making it imperative to identify individuals at a higher likelihood of sarcopenia to facilitate early prevention. This study aimed to develop a novel identification model for sarcopenia in multimorbid older adults to allow for timely identification and intervention. From April 2023 to August 2024, we conducted a cross-sectional study in Northern, Eastern, and Southern Xinjiang using multistage random sampling (random, stratified, and cluster sampling). We recruited 1,523 participants aged ≥ 60 years with multimorbidity. We developed the sarcopenia identification model with conventional multivariate logistic regression. 14.84% of multimorbid older adults had sarcopenia. Participants all had at least two chronic conditions, confirming multimorbidity. we identified body mass index, phase angle (PhA) generated from bioimpedance, diabetes, COPD, and education as independent factors associated with sarcopenia (P < 0.05). The model demonstrated good discrimination in the training set (area under the curve (AUC) = 0.886, 95% CI: 0.860–0.911), and was validated in the validation set (n = 457) (AUC = 0.887, 95% CI: 0.850–0.924). Within multimorbid older adults in Xinjiang, sarcopenia prevalence is relatively high. Significant associations with sarcopenia were found for older adults with lower BMI, lower PhA, lower educational level, diabetes, and COPD. This study highlights the need for targeted prevention and community-based intervention for these individuals.