ObjectiveTo explore risk factors for sarcopenia after radical gastrectomy for gastric cancer in older patients. MethodsOlder patients who underwent radical gastrectomy for gastric cancer at Tangshan People’s Hospital from January 2022 to June 2023 were retrospectively collected. The occurrence of sarcopenia was recorded, and factors influencing its development were analyzed. Factors with statistical significance in univariate analysis and clinical relevance were included in a multivariate binary logistic regression model. Receiver operating characteristic (ROC) curve analysis was used to evaluate the discriminatory ability of significant predictors for sarcopenia occurrence. ResultsA total of 300 older patients underwent radical gastrectomy for gastric cancer were enrolled. Sarcopenia occurred in 74 patients (incidence rate: 24.7%). Multivariate binary logistic regression analysis identified the following independent risk factors for sarcopenia (all P<0.05): American Society of Anesthesiologists (ASA) class Ⅲ, postoperative chemotherapy, geriatric nutritional risk index (GNRI) <89, body mass index (BMI) <18.5 kg/m2, lack of exercise habits, and lower levels of serum total protein, grip strength, skeletal muscle mass index (SMI), 6-meter walking speed, and short physical performance battery (SPPB) score. Among these, factors with an area under the ROC curve (AUC) >0.7 were serum total protein, SMI, and 6-meter walking speed. The combined model integrating these three factors achieved an AUC of 0.937 (sensitivity 96.65%; specificity 89.71%; Youden index 0.844). ConclusionsThis study reveals a high incidence of sarcopenia after radical gastrectomy in older gastric cancer patients. The risk of sarcopenia is multifactorial, involving surgical tolerance (ASA classification), postoperative therapy (chemotherapy), nutritional status (GNRI and BMI), exercise habits, and various muscle-related functional indicators (serum total protein, grip strength, SMI, walking speed, and physical performance score). The combined predictive model shows potential for early identification of high-risk patients and timely preventive interventions.