{"title":"机器学习辅助优化饮食干预对抗痴呆风险","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","doi":"10.1038/s41562-025-02255-w","DOIUrl":null,"url":null,"abstract":"<p>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>","PeriodicalId":19074,"journal":{"name":"Nature Human Behaviour","volume":"137 1","pages":""},"PeriodicalIF":15.9000,"publicationDate":"2025-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Machine learning-assisted optimization of dietary intervention against dementia risk\",\"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\",\"doi\":\"10.1038/s41562-025-02255-w\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>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>\",\"PeriodicalId\":19074,\"journal\":{\"name\":\"Nature Human Behaviour\",\"volume\":\"137 1\",\"pages\":\"\"},\"PeriodicalIF\":15.9000,\"publicationDate\":\"2025-07-02\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Nature Human Behaviour\",\"FirstCategoryId\":\"102\",\"ListUrlMain\":\"https://doi.org/10.1038/s41562-025-02255-w\",\"RegionNum\":1,\"RegionCategory\":\"心理学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"MULTIDISCIPLINARY SCIENCES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Nature Human Behaviour","FirstCategoryId":"102","ListUrlMain":"https://doi.org/10.1038/s41562-025-02255-w","RegionNum":1,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MULTIDISCIPLINARY SCIENCES","Score":null,"Total":0}
Machine learning-assisted optimization of dietary intervention against dementia risk
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.
期刊介绍:
Nature Human Behaviour is a journal that focuses on publishing research of outstanding significance into any aspect of human behavior.The research can cover various areas such as psychological, biological, and social bases of human behavior.It also includes the study of origins, development, and disorders related to human behavior.The primary aim of the journal is to increase the visibility of research in the field and enhance its societal reach and impact.