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. 2025 Mar;10(3):278-285.
doi: 10.1016/j.bpsc.2024.11.017. Epub 2024 Nov 29.

Metabolic Status Modulates Global and Local Brain Age Estimates in Overweight and Obese Adults

Shalaila S Haas  1 , Fahim Abbasi  2 , Kathleen Watson  2 , Thalia Robakis  1 , Alison Myoraku  2 , Sophia Frangou  3 , Natalie Rasgon  4
Affiliations

Metabolic Status Modulates Global and Local Brain Age Estimates in Overweight and Obese Adults

Shalaila S Haas et al. Biol Psychiatry Cogn Neurosci Neuroimaging. 2025 Mar.

Abstract

Background: As people live longer, maintaining brain health becomes essential for extending health span and preserving independence. Brain degeneration and cognitive decline are major contributors to disability. In this study, we investigated how metabolic health influences the 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 body mass index of 114 physically and cognitively healthy adults. The homeostatic model assessment for insulin resistance served as a reference. T1-weighted brain magnetic resonance imaging was used to calculate voxel-level and global brainAGE. Longitudinal data were available for 53 participants over a 3-year interval.

Results: K-means clustering divided the sample into 2 groups, those with favorable (n = 58) and those with suboptimal (n = 56) metabolic health. The suboptimal group showed signs of insulin resistance and dyslipidemia (false discovery rate-corrected p < .05) and had older global brainAGE and local brainAGE, with deviations most prominent in cerebellar, ventromedial prefrontal, and medial temporal regions (familywise error-corrected p < .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 health span.

Keywords: Brain aging; Clustering; Insulin resistance; Machine learning; Metabolic health; brainAGE.

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Conflict of interest statement

The authors report no biomedical financial interests or potential conflicts of interest.

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