Brain age gap from MRI brain scans:
| 1. | 18,701 brain MRI scans collected across 34 countries | |
| 2. | Machine learning model (LightGBM) predicts brain's biological age from scan | |
| 3. | Brain Age Gap = predicted age − actual age | |
| 4. | 73 environmental indicators mapped to brain aging via nested cross-validation | |
Key finding:
Environmental exposome explained 3.3–9.1× more variance in brain aging than clinical diagnoses (Alzheimer's, dementia, neurological conditions)
Legaz, A. et al. (2026). "The environmental exposome shapes brain aging beyond clinical diagnosis." Nature Medicine. doi:10.1038/s41591-026-04302-z
Exposome Score = weighted composite of five domains:
| 25% | Air Quality | PM2.5 µg/m³ |
| 25% | Social | Gini + Governance + Unemployment |
| 20% | Infrastructure | GHSL facility density |
| 15% | Green Space | NDVI + m²/capita |
| 15% | Water | Safe drinking water % |
Score 0–100 mapped to brain aging range:
Range inspired by Legaz et al. findings but not their model output. Population-level estimates, not validated against brain imaging.
Your environment shapes your brain more than you think.
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