Body composition and cognitive function in Chinese rural adults: an exploratory factor analysis and network analysis.

Background: Growing evidence suggests that body composition has a significant influence on cognitive function. However, their relationship remains controversial. This study investigated the association between body composition and cognitive function. Methods: This multicenter cross-sectional study recruited participants from 38 rural townships in Beizhen from July to August 2023. We included participants who completed both cognitive function assessments and body composition measurements. Exploratory factor analysis was employed for dimensionality reduction and classification of body composition. A logistic regression model was utilized to evaluate the association between primary body composition and cognitive decline. Network analysis was performed using R software to construct network models of body composition and cognitive function, to identify key variables and their interconnections. Results: Exploratory factor analysis classified 27 body composition variables into 6 factors. Among the 6 factors, "muscle mass" (OR = 0.393), "central obesity" (OR = 1.69), and "leg-dominant fat distribution" (OR = 0.473) are associated with cognitive function. "Muscle mass," "central obesity," and "leg-dominant fat distribution" were used to construct network models related to cognitive function. In these three models, the most central domains are all language, attention, and registration. Conclusion: This study found that "central obesity" increased the risk of cognitive decline, while "muscle mass" and "leg-dominant fat distribution" had protective effects. Interventions targeting language, attention, and registration domains might help address cognitive decline caused by changes in body composition.