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@ARTICLE{Mahdipour:1050149,
      author       = {Mahdipour, Mostafa and Maleki Balajoo, Somayeh and
                      Raimondo, Federico and Wu, Jianxiao and Nicolaisen, Eliana
                      and Shammi, More and Hoffstaedter, Felix and Tahmasian,
                      Masoud and Eickhoff, Simon and Genon, Sarah},
      title        = {{W}hat predicts individual brain health?: a machine
                      learning study spanning the exposome},
      reportid     = {FZJ-2025-05845},
      year         = {2025},
      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.},
      cin          = {INM-7},
      cid          = {I:(DE-Juel1)INM-7-20090406},
      pnm          = {5251 - Multilevel Brain Organization and Variability
                      (POF4-525)},
      pid          = {G:(DE-HGF)POF4-5251},
      typ          = {PUB:(DE-HGF)25},
      doi          = {10.21203/rs.3.rs-6410523/v1},
      url          = {https://juser.fz-juelich.de/record/1050149},
}