TITLE:
Disentangling Brain Aging: A Multimodal Biomarker Framework to Separate Intrinsic Aging, Lifestyle Effects, and Neuropathology
AUTHORS:
Sundas Almas, Rana Muhammad Ali Washakh, Rana Muhammad Umar Waque, Nudrat Almas, María Bringas, Pedro Antonio Valdes Sosa
KEYWORDS:
Brain Age Prediction, Biological Aging, Disentangled Representation Learning, Lifestyle Confounding, Neuroethics, Multimodal Neuroimaging
JOURNAL NAME:
Journal of Biosciences and Medicines,
Vol.14 No.5,
May
15,
2026
ABSTRACT: Brain age prediction models estimate biological brain age from neuroimaging data, with the Brain Age Gap (BAG), the discrepancy between predicted and chronological age, widely used as a biomarker of accelerated or delayed aging. However, BAG tacitly pathologizes normal biological and socio-behavioral variation by conflating aging, lifestyle, and disease. To address this limitation, we propose a Multimodal Disentanglement Framework (MDF) that decomposes BAG into three orthogonal components: 1) intrinsic biological aging, 2) lifestyle-modulated brain changes, and 3) pathology-associated deviation. Using multimodal MRI and rich phenotypic data from 12,340 participants in the UK Biobank, we trained a hierarchical deep learning architecture to isolate these components. Our results show that lifestyle factors explain 38% of BAG variance, while only 12% is attributable to future neuropathology. Crucially, the pathology-specific component significantly outperforms standard BAG in predicting all-cause mortality (HR = 1.82 vs. 1.41) and incident Alzheimer’s disease (AUC = 0.84 vs. 0.71). These findings challenge the assumption that BAG reflects “pure” biological aging and underscore the ethical necessity of disentangling modifiable from pathological drivers in brain biomarker research.