Preprint

FZJ-2025-05845

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png What predicts individual brain health?: a machine learning study spanning the exposome

Mahdipour, M. (Corresponding author)FZJ* ; Maleki Balajoo, S.FZJ* ; Raimondo, F.FZJ* ; Wu, J.FZJ* ; Nicolaisen, E.FZJ* ; Shammi, M. ; Hoffstaedter, F.FZJ* ; Tahmasian, M.FZJ* ; Eickhoff, S.FZJ* ; Genon, S. (Corresponding author)FZJ*

2025

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Please use a persistent id in citations: doi:10.21203/rs.3.rs-6410523/v1

Abstract: Promoting brain health is vital for well-being and reducing healthcare burdens. Individual brain health asmeasured with the Brain Age Gap (BAG) - the difference between chronological and predicted brain age-relates to many factors. However, an holistic view, integrating the range of factors an individual brain isexposed to, is missing for understanding how the exposome shapes brain health. After computing BAGas an indicator of individual grey matter (GM) health, we predicted it using machine learning based on261 exposome variables (spanning biomedical, environmental, lifestyle, socio-affective, and early lifedomains) in UK Biobank participants. Exposome data can predict GM health with factors pertaining tocardiovascular and bone health, along with alcohol and smoking, nutrition and diabetes showing greatercontribution to the prediction. In such domains, life period and duration of exposure appeared crucial.This calls for early prevention in cardiovascular and metabolic health to promote life-long brain health.

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Contributing Institute(s):

1. Gehirn & Verhalten (INM-7)

Research Program(s):

1. 5251 - Multilevel Brain Organization and Variability (POF4-525) (POF4-525)

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