Exposome-wide patterns predict brain health in aging.

Humans; Aging/physiology; Brain/physiology/diagnostic imaging; Exposome; Female; Aged; Male; UK Biobank; Gray Matter/physiology/diagnostic imaging; Middle Aged; Machine Learning; Life Style

Abstract :

[en] Promoting brain health is vital for well-being and reducing healthcare burdens. Brain health as measured with the Brain Age Gap (BAG) - the difference between chronological and predicted brain age- relates to many factors. However, a holistic view, integrating the range of factors an individual brain is exposed to, is missing for understanding how the exposome shapes brain health. After computing BAG as an indicator of grey matter (GM) health, we predicted it using machine learning based on 261 exposome variables (spanning biomedical, environmental, lifestyle, socio-affective, and early life domains) in UK Biobank participants. Exposome data can predict GM health with factors pertaining to cardiovascular and bone health, along with alcohol and smoking, nutrition and diabetes showing greater contribution to the prediction. In such domains, life period and duration of exposure appeared crucial. These findings call for early prevention in cardiovascular and metabolic health to promote life-long brain health.

Disciplines :

Neurosciences & behavior

Author, co-author :

Mahdipour, Mostafa ; Institute of Neuroscience and Medicine (INM-7: Brain and Behaviour), Research Centre Jülich, Jülich, Germany. m.mahdipour@fz-juelich.de. ; Institute of Systems Neuroscience, Heinrich Heine University Düsseldorf, Düsseldorf, Germany. m.mahdipour@fz-juelich.de.

Maleki Balajoo, Somayeh ; Institute of Neuroscience and Medicine (INM-7: Brain and Behaviour), Research Centre Jülich, Jülich, Germany. ; Institute of Systems Neuroscience, Heinrich Heine University Düsseldorf, Düsseldorf, Germany.

Raimondo, Federico ; Université de Liège - ULiège > GIGA > GIGA Neurosciences - Physiology of Cognition ; Institute of Neuroscience and Medicine (INM-7: Brain and Behaviour), Research Centre Jülich, Jülich, Germany. ; Institute of Systems Neuroscience, Heinrich Heine University Düsseldorf, Düsseldorf, Germany.

Wu, Jianxiao ; Institute of Neuroscience and Medicine (INM-7: Brain and Behaviour), Research Centre Jülich, Jülich, Germany. ; Institute of Systems Neuroscience, Heinrich Heine University Düsseldorf, Düsseldorf, Germany.

Nicolaisen-Sobesky, Eliana ; Institute of Neuroscience and Medicine (INM-7: Brain and Behaviour), Research Centre Jülich, Jülich, Germany. ; Institute of Systems Neuroscience, Heinrich Heine University Düsseldorf, Düsseldorf, Germany.

More, Shammi ; Institute of Neuroscience and Medicine (INM-7: Brain and Behaviour), Research Centre Jülich, Jülich, Germany. ; Institute of Systems Neuroscience, Heinrich Heine University Düsseldorf, Düsseldorf, Germany. ; Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Sankt Augustin, Germany.

Hoffstaedter, Felix ; Institute of Neuroscience and Medicine (INM-7: Brain and Behaviour), Research Centre Jülich, Jülich, Germany. ; Institute of Systems Neuroscience, Heinrich Heine University Düsseldorf, Düsseldorf, Germany.

Schwender, Holger ; Mathematical Institute, Heinrich Heine University Düsseldorf, Düsseldorf, Germany.

Tahmasian, Masoud ; Institute of Neuroscience and Medicine (INM-7: Brain and Behaviour), Research Centre Jülich, Jülich, Germany. ; Institute of Systems Neuroscience, Heinrich Heine University Düsseldorf, Düsseldorf, Germany. ; Department of Nuclear Medicine, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany.

Eickhoff, Simon B ; Institute of Neuroscience and Medicine (INM-7: Brain and Behaviour), Research Centre Jülich, Jülich, Germany. ; Institute of Systems Neuroscience, Heinrich Heine University Düsseldorf, Düsseldorf, Germany.

Genon, Sarah ; Université de Liège - ULiège > Département des sciences cliniques ; Institute of Neuroscience and Medicine (INM-7: Brain and Behaviour), Research Centre Jülich, Jülich, Germany. s.genon@fz-juelich.de. ; Institute of Systems Neuroscience, Heinrich Heine University Düsseldorf, Düsseldorf, Germany. s.genon@fz-juelich.de.

Language :

English

Title :

Exposome-wide patterns predict brain health in aging.

Publication date :

10 April 2026

Journal title :

Nature Communications

eISSN :

2041-1723

Publisher :

Nature Publishing Group, London, Gb

Volume :

Issue :

Peer reviewed :

Peer Reviewed verified by ORBi

Funding number :

GE 2835/2-1/Deutsche Forschungsgemeinschaft (German Research Foundation)/; GE 2835/9-1/Deutsche Forschungsgemeinschaft (German Research Foundation)/; SFB 1451 - Project-ID 431549029/Deutsche Forschungsgemeinschaft (German Research Foundation)/

Commentary :

Available on ORBi :

since 03 July 2026

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