| Title: | Exposome-wide patterns predict brain health in aging |
| Journal: | Nature Communications |
| Published: | 10 Apr 2026 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/41963296/ |
| DOI: | https://doi.org/10.1038/s41467-026-71271-9 |
| URL: | https://www.nature.com/articles/s41467-026-71271-9.pdf |
Publication 18036
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Abstract
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.</p>
13 Keywords
- Adult
- Aged
- Aging
- Brain
- Exposome
- Female
- Gray Matter
- Humans
- Life Style
- Machine Learning
- Male
- Middle Aged
- United Kingdom
11 Authors
- Mostafa Mahdipour
- Somayeh Maleki Balajoo
- Federico Raimondo
- Jianxiao Wu
- Eliana Nicolaisen-Sobesky
- Shammi More
- Felix Hoffstaedter
- Holger Schwender
- Masoud Tahmasian
- Simon B. Eickhoff
- Sarah Genon
1 Application
| Application ID | Title |
|---|---|
| 41655 | Characterizing brain networks and their inter-individual variability by high-throughput imaging and computational modelling |
Enabling scientific discoveries that improve human health