| Title: | Machine learning-assisted optimization of dietary intervention against dementia risk |
| Journal: | Nature Human Behaviour |
| Published: | 2 Jul 2025 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/40603580/ |
| DOI: | https://doi.org/10.1038/s41562-025-02255-w |
Publication 16347
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Abstract
A healthy diet has been associated with a reduced risk of dementia. Here we devised a Machine learning-assisted Optimizing Dietary intERvention against demeNtia risk (MODERN) diet based on data from 185,012 UK Biobank participants, 1,987 of whom developed all-cause dementia over 10 years. We first identified 25 food groups associated with dementia in a food-wide association analysis. Second, we ranked their importance using machine learning and prioritized eight groups (for example, green leafy vegetables, berries and citrus fruits). Finally, we established and externally validated a MODERN score (0-7), which showed stronger associations with lower risk of dementia-related outcomes (hazard ratio comparing highest versus lowest tertiles: 0.64, 95% CI: 0.43-0.93) than the a priori-defined MIND diet (0.75, 0.61-0.92). Across 63 health-related outcomes, the MODERN diet showed particularly significant associations with mental/behavioural disorders. Multimodal neuroimaging, metabolomics, inflammation and proteomics analyses revealed potential pathways and further support the potential of MODERN diet for dementia prevention.</p>
12 Authors
- Si-Jia Chen
- Hui Chen
- Jia You
- Shi-Dong Chen
- Yan Fu
- Wei Zhang
- Liyan Huang
- Jian-Feng Feng
- Xiang Gao
- Wei Cheng
- Changzheng Yuan
- Jin-Tai Yu
1 Application
| Application ID | Title |
|---|---|
| 19542 | Identifying multi-level biomarkers and disease mechanisms for major mental disorders |
Enabling scientific discoveries that improve human health