| Title: | A machine learning-derived dietary pattern for aging |
| Journal: | npj Science of Food |
| Published: | 7 Aug 2026 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/42660934/ |
| DOI: | https://doi.org/10.1038/s41538-026-01048-3 |
| URL: | https://www.nature.com/articles/s41538-026-01048-3_reference.pdf |
Publication 21047
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Abstract
Diet is a modifiable determinant of aging. We develop and validate the Machine-learning YouTHful (MYTH) Diet, a dietary pattern associated with reduced aging-related mortality. Using data from 191,689 participants in the UK Biobank, we conducted a food-wide association analysis and identified 18 food groups significantly associated with aging-related mortality. A Light Gradient Boosting Machine (LightGBM) model was used to rank food importance, leading to the construction of a 10-component MYTH Diet score (range: 0-10). Higher MYTH scores were consistently associated with reduced aging-related mortality in both internal (Quartile 4 vs. 1: hazard ratio [HR] = 0.79; 95% CI: 0.75-0.84) and external (Q4 vs. Q1: HR = 0.68; 95% CI: 0.58-0.80) validation cohorts. Multi-omics analyses revealed that the diet's protective effects were partly mediated through proteomic, metabolic, and inflammatory pathways, with mediators including TNFRSF4, the proportion of polyunsaturated fatty acids (PUFA%), and lipid-related metabolites such as medium very-low-density lipoprotein phospholipids (M-VLDL-PL). Higher MYTH scores were also linked to slower biological aging in the lungs, liver, pancreas, as well as lower risks for 15 aging-related diseases. These findings suggest that the MYTH Diet may offer a biologically informed, scalable framework for developing personalized nutrition strategies aimed at supporting healthy aging and longevity.</p>
5 Authors
- Yating Miao
- Zhirong Li
- Xinyao Zhang
- Zuyun Liu
- Yanan Ma
2 Applications
| Application ID | Title |
|---|---|
| 61856 | The role of biological aging in the effect of genetic and environmental factors on health outcomes |
| 769193 | Multifactorial Analysis of Brain Function, Structure, and Disease Risk: Investigating Genetic, Lifestyle, and Social Determinants of Neurological and Mental Health Using UK Biobank Data |
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