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Machine learning-assisted optimization of dietary intervention against dementia risk

Nature Human Behaviour, 2025

Chen S., Chen H., You J., Chen S., Fu Y., Zhang W., Huang L., Feng J., Gao X., Cheng W., Yuan C., Yu J.

Disease areaApplication areaSample typeProducts
Neurology
Nutritional Science
Pathophysiology
Plasma
Olink Explore 3072/384

Olink Explore 3072/384

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.

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