TY  - EJOUR
AU  - Mahdipour, Mostafa
AU  - Maleki Balajoo, Somayeh
AU  - Raimondo, Federico
AU  - Wu, Jianxiao
AU  - Nicolaisen, Eliana
AU  - Shammi, More
AU  - Hoffstaedter, Felix
AU  - Tahmasian, Masoud
AU  - Eickhoff, Simon
AU  - Genon, Sarah
TI  - What predicts individual brain health?: a machine learning study spanning the exposome
M1  - FZJ-2025-05845
PY  - 2025
AB  - 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.
LB  - PUB:(DE-HGF)25
DO  - DOI:10.21203/rs.3.rs-6410523/v1
UR  - https://juser.fz-juelich.de/record/1050149
ER  -