Brain Age Gap Associations with Body Composition and Metabolic Indices in an Asian Cohort: An MRI-Based Study.
Authors
Affiliations (6)
Affiliations (6)
- Department of Radiology, Taipei Veterans General Hospital, Taipei 112, Taiwan; School of Medicine, National Yang Ming Chiao Tung University, Taipei 112, Taiwan.
- Department of Neurology, Neurological Institute, Taipei Veterans General Hospital, Taipei, Taiwan; Institute of Neuroscience, National Yang Ming Chiao Tung University, Taipei 112, Taiwan.
- Division of Occupational Medicine, Chang Gung Memorial Hospital, Taoyuan 333, Taiwan.
- Center for Healthy Longevity and Aging Sciences, National Yang Ming Chiao Tung University, Taipei 112, Taiwan.
- Institute of Neuroscience, National Yang Ming Chiao Tung University, Taipei 112, Taiwan; Brain Research Center, National Yang Ming Chiao Tung University, Taipei 112, Taiwan.
- Institute of Neuroscience, National Yang Ming Chiao Tung University, Taipei 112, Taiwan; Center for Healthy Longevity and Aging Sciences, National Yang Ming Chiao Tung University, Taipei 112, Taiwan; Brain Research Center, National Yang Ming Chiao Tung University, Taipei 112, Taiwan; Department of Education and Research, Taipei City Hospital, Taipei 112, Taiwan. Electronic address: [email protected].
Abstract
Global aging raises concerns about cognitive health, metabolic disorders, and sarcopenia. Prevention of reversible decline and diseases in middle-aged individuals is essential for promoting healthy aging. We hypothesize that changes in body composition, specifically muscle mass and visceral fat, and metabolic indices are associated with accelerated brain aging. To explore these relationships, we employed a brain age model to investigate the links between the brain age gap (BAG), body composition, and metabolic markers. Using T1-weighted anatomical brain MRIs, we developed a machine learning model to predict brain age from gray matter features, trained on 2,675 healthy individuals aged 18-92 years. This model was then applied to a separate cohort of 458 Taiwanese adults (57.8 years ± 11.6; 280 men) to assess associations between BAG, body composition quantified by MRI, and metabolic markers. Our model demonstrated reliable generalizability for predicting individual age in the clinical dataset (MAE = 6.11 years, r = 0.900). Key findings included significant correlations between larger BAG and reduced total abdominal muscle area (r = -0.146, p = 0.018), lower BMI-adjusted skeletal muscle indices, (r = -0.134, p = 0.030), increased systemic inflammation, as indicated by high-sensitivity C-reactive protein levels (r = 0.121, p = 0.048), and elevated fasting glucose levels (r = 0.149, p = 0.020). Our findings confirm that muscle mass and metabolic health decline are associated with accelerated brain aging. Interventions to improve muscle health and metabolic control may mitigate adverse effects of brain aging, supporting healthier aging trajectories.