Background: As people live longer, maintaining brain health becomes essential for extending healthspan and preserving independence. Brain degeneration and cognitive decline are major contributors to disability. This study investigates how metabolic health influences brain-age-gap-estimate (brainAGE), which measures the difference between neuroimaging-predicted brain age and chronological age.
Methods: K-means clustering was applied to fasting metabolic markers including insulin, glucose, leptin, cortisol, triglycerides, high-density lipoprotein cholesterol and low-density lipoprotein cholesterol, steady-state-plasma glucose and of body mass index of 114 physically and cognitively healthy adults. The Homeostatic Model Assessment for Insulin Resistance served as a reference. T1-weighted brain MRIs were used to calculate voxel-level and global (G-brainAGE). Longitudinal data were available for 53 participants over a 3-year interval.
Results: K-mean clustering divided the sample into two groups: those with favorable (N=56) and suboptimal metabolic health (N=58). The suboptimal group showed signs of insulin resistance and dyslipidemia (PFDR<0.05) and had older G-brainAGE and L-brainAGE, with deviations most prominent in cerebellar, ventromedial prefrontal, and medial temporal regions (PFWE<0.05). Longitudinal analysis revealed group differences but no significant time or interaction effects on brainAGE measures.
Conclusions: Suboptimal metabolic status is linked to accelerated brain aging, particularly in brain regions rich in insulin receptors. These findings highlight the importance of metabolic health in maintaining brain function and suggest that promoting metabolic well-being may help extend healthspan.
Keywords: brain aging; brainAGE; clustering; insulin resistance; machine learning; metabolic health.
Copyright © 2024. Published by Elsevier Inc.