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Machine learning-assisted optimization of dietary intervention against dementia risk

Nature Human Behaviour (2025)Cite this article

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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.

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Fig. 1: Design of the study.
Fig. 2: The associations of 34 food groups with incident ACD.
Fig. 3: The MODERN diet score construction and its associations with dementia risk.
Fig. 4: The associations of MODERN diet with other health-related outcomes.
Fig. 5: The potential pathways between the MODERN diet and ACD linking brain structure, metabolism, inflammation and proteomic signature.

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Data availability

The main dataset supporting the conclusions of this article is available in the UK Biobank (UKB) repository (https://www.ukbiobank.ac.uk/). The disease and death outcomes in UKB can be obtained from the following restricted access national healthcare databases: the Hospital Episode Statistics (https://digital.nhs.uk/services/hospital-episode-statistics), the Scottish Morbidity Records (https://www.ndc.scot.nhs.uk/National-Datasets/data-dictionary-smr01/), the National Health Service Information Center (https://digital.nhs.uk) and Central Register Scotland (https://www.nrscotland.gov.uk). This study utilized the UKB Resource under application number 19542. The Health and Retirement Study dataset is publicly available through its website (https://hrsdata.isr.umich.edu/data-products/2013-health-care-and-nutrition-study-hcns). The data from the Framingham Heart Study Offspring Cohort can be applied for at http://www.framinghamheartstudy.org/researchers/index.php. This study utilized the FOS data under application number 11068. The National Health and Nutrition Examination Survey data are publicly available through the CDC/NCHS website (https://www.cdc.gov/nchs/nhanes/). The summary statistics of the AD GWAS can be accessed at https://gwas.mrcieu.ac.uk/datasets/ieu-b-2/.

Code availability

The analysis programmes can be accessed on GitHub at https://github.com/Happychrischen/Diet_ML_Dementia (ref. 70).

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Acknowledgements

We thank all the participants and professionals contributing to the UK Biobank and the Health and Retirement Study. This study was supported by grants from the Science and Technology Innovation 2030 Major Projects (2022ZD0211600 to J.-T.Y.), the National Natural Science Foundation of China (82071201, 82271471 and 92249305 to J.-T.Y.; 82071997 and 82472055 to W.C.; 8210120183 to C.Y.; 82402381 and 82471940 to J.Y.), the Shanghai Municipal Science and Technology Major Project (2023SHZDZX02 to J.-T.Y., 2018SHZDZX01 to J.-F.F.), the National Nutrition Science Research Grant (CNS-NNSRG2021-61 to C.Y.), the Zhejiang University Global Partnership Fund, Shanghai Pujiang Talent Program (23PJD006 to J.Y.), a Research Start-up Fund of Huashan Hospital (2022QD002 to J.-T.Y.), the Program of Shanghai Academic Research Leader (23XD1420400 to J.-T.Y.), the Excellence 2025 Talent Cultivation Program at Fudan University (3030277001 to J.-T.Y.), Shanghai Talent Development Funding for The Project (2019074), Shanghai Rising-Star Program (21QA1408700 to W.C.), 111 Project (B18015 to J.-F.F.), the National Postdoctoral Program for Innovative Talents (BX20230087 to S.-D.C.), the China Postdoctoral Science Foundation (2023M740672 to S.-D.C.), and ZHANGJIANG LAB, Tianqiao and Chrissy Chen Institute, the State Key Laboratory of Neurobiology and Frontiers Center for Brain Science of the Ministry of Education, and Shanghai Center for Brain Science and Brain-Inspired Technology, Fudan University. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the paper.

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Author notes

  1. These authors contributed equally: Si-Jia Chen, Hui Chen, Jia You, Shi-Dong Chen.

Authors and Affiliations

  1. Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China

    Si-Jia Chen, Shi-Dong Chen, Wei Cheng & Jin-Tai Yu

  2. School of Public Health, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China

    Hui Chen, Liyan Huang & Changzheng Yuan

  3. Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China

    Jia You, Wei Zhang, Jian-Feng Feng & Wei Cheng

  4. Department of Neurology, Qingdao Municipal Hospital, Qingdao University, Qingdao, China

    Yan Fu

  5. Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, Shanghai, China

    Jian-Feng Feng & Wei Cheng

  6. Fudan ISTBI—ZJNU Algorithm Centre for Brain-Inspired Intelligence, Zhejiang Normal University, Jinhua, China

    Jian-Feng Feng & Wei Cheng

  7. MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China

    Jian-Feng Feng

  8. Zhangjiang Fudan International Innovation Center, Shanghai, China

    Jian-Feng Feng

  9. Department of Nutrition and Food Hygiene, School of Public Health, Institute of Nutrition, Fudan University, Shanghai, China

    Xiang Gao

  10. Department of Nutrition, Harvard T H Chan School of Public Health, Boston, MA, USA

    Changzheng Yuan

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Contributions

J.-T.Y. and C.Y. had full access to all of the data in the study and took responsibility for the integrity of the data and the accuracy of the data analysis. J.-T.Y. conceptualized and designed the project. All authors acquired, analysed or interpreted data. S.-J.C., S.-D.C., H.C., J.Y., C.Y. and J.-T.Y. drafted the paper. S.-J.C., H.C., J.Y., S.-D.C., L.H., X.G., W.C., C.Y. and J.-T.Y. critically revised the paper for important intellectual content. S.-J.C., J.Y., H.C., Y.F., W.Z. and W.C. performed statistical analysis. J.Y., S.-D.C., J.-F.F., W.C., C.Y. and J.-T.Y. obtained funding. J.-F.F., W.C., C.Y. and J.-T.Y. provided administrative, technical or material support. All authors read and approved the final paper.

Corresponding authors

Correspondence to Changzheng Yuan or Jin-Tai Yu.

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Extended data

Extended Data Fig. 1 The associations between the components of the MODERN diet and ACD risk in 185,012 UKB participants.

a. The Kaplan-Meier curves of ACD-free survival in participants receiving 0 vs. 1 point for each food component. Shaded areas showed 95% confidence intervals. b. The HR for incident ACD comparing 1 vs. 0 point was estimated using the Cox proportional hazard regression model. A two-sided Wald test was performed to assess statistical significance. The significant results (P value < 0.05) were denoted in red in the forest plot. The analyses were adjusted for total energy intake, age, sex, ethnicity, TDI, educational attainment, smoking status, physical activity, BMI, ApoE-ε4 gene, and history of hypertension, diabetes, cerebrovascular disease, and other CVDs. ACD: all-cause dementia; TDI: Townsend deprivation index; BMI: body mass index; CVDs: cardiovascular diseases.

Extended Data Fig. 2 The associations between the MODERN diet and ACD risk in 185,012 UKB participants.

a. The associations of MODERN and MIND diet with incident ACD using the Cox proportional hazard regression model. b. The nonlinear associations between MODERN and MIND diet score and incident dementia using the RCS. A two-sided Wald test was performed to assess statistical significance. The significant results (P value < 0.05) were denoted in red in the forest plot. The dashed lines showed 95% confidence intervals. The analyses were adjusted for total energy intake, age, sex, ethnicity, TDI, educational attainment, smoking status, physical activity, BMI, ApoE-ε4 gene, and history of hypertension, diabetes, cerebrovascular disease, and other CVDs. MIND: Mediterranean-DASH Intervention for Neurodegenerative Delay; ACD: all-cause dementia; RCS: restricted cubic spline; TDI: Townsend deprivation index; BMI: body mass index; CVDs: cardiovascular diseases.

Extended Data Fig. 3 The inclusion and exclusion criteria of participants in external cohorts.

HRS: Health and Retirement Study. FOS: Framingham Heart Study Offspring Cohort. NHANES: National Health and Nutrition Examination Survey.

Supplementary information

Supplementary Information

Supplementary Figs. 1–5.

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Supplementary Table 1

Supplementary Tables 1–23.

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Chen, SJ., Chen, H., You, J. et al. Machine learning-assisted optimization of dietary intervention against dementia risk. Nat Hum Behav (2025). https://doi.org/10.1038/s41562-025-02255-w

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