001050149 001__ 1050149
001050149 005__ 20260107202519.0
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001050149 0247_ $$2datacite_doi$$a10.34734/FZJ-2025-05845
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001050149 1001_ $$0P:(DE-Juel1)187159$$aMahdipour, Mostafa$$b0$$eCorresponding author
001050149 245__ $$aWhat predicts individual brain health?: a machine learning study spanning the exposome
001050149 260__ $$c2025
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001050149 520__ $$aPromoting brain health is vital for well-being and reducing healthcare burdens. Individual brain health asmeasured with the Brain Age Gap (BAG) - the difference between chronological and predicted brain age-relates to many factors. However, an holistic view, integrating the range of factors an individual brain isexposed to, is missing for understanding how the exposome shapes brain health. After computing BAGas an indicator of individual grey matter (GM) health, we predicted it using machine learning based on261 exposome variables (spanning biomedical, environmental, lifestyle, socio-affective, and early lifedomains) in UK Biobank participants. Exposome data can predict GM health with factors pertaining tocardiovascular and bone health, along with alcohol and smoking, nutrition and diabetes showing greatercontribution to the prediction. In such domains, life period and duration of exposure appeared crucial.This calls for early prevention in cardiovascular and metabolic health to promote life-long brain health.
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001050149 7001_ $$0P:(DE-Juel1)178767$$aMaleki Balajoo, Somayeh$$b1
001050149 7001_ $$0P:(DE-Juel1)185083$$aRaimondo, Federico$$b2
001050149 7001_ $$0P:(DE-Juel1)177058$$aWu, Jianxiao$$b3
001050149 7001_ $$0P:(DE-Juel1)180537$$aNicolaisen, Eliana$$b4
001050149 7001_ $$0P:(DE-HGF)0$$aShammi, More$$b5
001050149 7001_ $$0P:(DE-Juel1)131684$$aHoffstaedter, Felix$$b6
001050149 7001_ $$0P:(DE-Juel1)188400$$aTahmasian, Masoud$$b7
001050149 7001_ $$0P:(DE-Juel1)131678$$aEickhoff, Simon$$b8
001050149 7001_ $$0P:(DE-Juel1)161225$$aGenon, Sarah$$b9$$eCorresponding author
001050149 773__ $$a10.21203/rs.3.rs-6410523/v1
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