Multidimensional Body Composition Modalities and Their Associations with Brain Aging in Chinese Adults

OBJECTIVE: This study examined the associations between multidimensional body composition modalities and brain aging in Chinese adults. METHODS: Brain age was estimated using ridge regression based on 24 head computed tomography-derived neuroanatomical indicators in a Chinese cohort ( n = 557). Brain age gap (BAG), the deviation between the predicted brain age and chronological age (CA), was categorized into brain age acceleration (BAG > 0) and deceleration (BAG < 0) groups. Principal component analysis of 22 correlation-independent body composition indicators identified different body composition modalities. Logistic regression was used to examine the associations between these modalities and the BAG groups. RESULTS: The mean absolute error of brain age in predicting CA was 6.41 years. Three body composition modalities were identified: fat mass dominant (characterized by high loading coefficients of body fat mass, fat mass index, visceral fat level, and fat-to-lean mass ratio); fat-free mass dominant; and trunk-leg contrast distribution. The fat mass dominant modality was significantly associated with brain age acceleration (odds ratio [ OR] = 1.40, 95% confidence interval [ CI]: 1.15‒1.71), and the association was robust in sensitivity analyses. CONCLUSION: The fat mass dominant modality was significantly associated with accelerated brain aging. This study suggests integrating deep body composition indicators into clinical and community health screening could aid in targeted prevention of brain aging.