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Cross-sectional study reveals high rates of sarcopenia, malnutrition, or frailty in older adults, highlighting low body mass index and poor appetite as key risks.
Sarcopenia, malnutrition, and frailty (SMF) frequently co-occur in older adults and share overlapping clinical and pathophysiological features. This study aimed to examine the prevalence and overlap of sarcopenia, malnutrition, and frailty and to explore features associated with the presence of one or more SMF conditions using comprehensive geriatric assessment and machine learning. A cross-sectional study included 570 Chinese community-dwelling older adults. Standardized diagnostic criteria were applied: the Asian Working Group for Sarcopenia (AWGS) 2019 consensus for sarcopenia, the Global Leadership Initiative on Malnutrition (GLIM) criteria for malnutrition, and the FRAIL scale for frailty. Data included demographics, multimorbidity, appetite, oral and psychological condition, and 3-day, 24-hour dietary records. A Light Gradient Boosting Machine (LightGBM) model integrated with SHapley Additive exPlanation (SHAP) was applied to identify key predictors. Overall, 45.6% had at least one condition (SMF group), while 3.3% exhibited complete coexistence of all three conditions. The SMF group was characterized by advanced age, lower Body Mass Index (BMI), greater multimorbidity, and poorer appetite. Dietary analysis revealed significantly lower intakes of total energy, protein, carbohydrates, water, and niacin in the SMF group. SHAP analysis identified low BMI, multimorbidity, and poor appetite as the critical features. Furthermore, the machine learning models indicated contributions from specific dietary inadequacies, notably insufficient carbohydrate and antioxidant micronutrients intake, which were underestimated in conventional analyses. The presence of one or more SMF conditions was common among community-dwelling older adults, whereas complete coexistence of all three conditions was uncommon. BMI, multimorbidity, and poor appetite may provide readily available information for risk stratification in community settings. Prospective studies are needed to validate these findings and their clinical utility.
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Zhong et al. (2026) studied this question.
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