Dorrain Y. Low, Theresia H. Mina, Nilanjana Sadhu, Kari E. Wong, Pritesh Rajesh Jain, Rinkoo Dalan, Hong Kiat Ng, Wubin Xie, Benjamin Lam, Darwin Tay, Xiaoyan Wang, Yik Weng Yew, James D. Best, Rangaprasad Sarangarajan, Paul Elliott, Elio Riboli, Jimmy Lee, Eng Sing Lee, Joanne Ngeow, Patricia A. Sheridan, Xue Li Guan, Gregory A. Michelotti, Marie Loh, John C. Chambers
{"title":"Metabolic variation reflects dietary exposure in a multi-ethnic Asian population","authors":"Dorrain Y. Low, Theresia H. Mina, Nilanjana Sadhu, Kari E. Wong, Pritesh Rajesh Jain, Rinkoo Dalan, Hong Kiat Ng, Wubin Xie, Benjamin Lam, Darwin Tay, Xiaoyan Wang, Yik Weng Yew, James D. Best, Rangaprasad Sarangarajan, Paul Elliott, Elio Riboli, Jimmy Lee, Eng Sing Lee, Joanne Ngeow, Patricia A. Sheridan, Xue Li Guan, Gregory A. Michelotti, Marie Loh, John C. Chambers","doi":"10.1038/s42255-025-01359-x","DOIUrl":null,"url":null,"abstract":"Understanding how diet shapes metabolism across diverse populations is essential to improving nutrition and health. Biomarkers reflecting diet are explored largely in European and American populations, but the food metabolome is highly complex and varies across region and culture. We assessed 1,055 plasma metabolites and 169 foods/beverages in 8,391 multi-ethnic Asian individuals and carried out diet–metabolite association analyses. Using machine learning, we developed multi-biomarker panels and composite scores for key foods, beverages and overall diet quality. Here we show these biomarker panels can be used to objectively assess dietary intakes in the Asian multi-ethnic population and can explain variances in intake prediction models better than single biomarkers. The identified diet–metabolite relationships are reproducible over time and improve prediction of clinical outcomes (insulin resistance, diabetes, body mass index, carotid intima-media thickness and hypertension), compared to self-reports. Our findings show insights into multi-ethnic diet-related metabolic variations and an opportunity to link exposure to population health outcomes. In a large multi-ethnic Asian cohort, associations between over 1,000 plasma metabolites and specific foods and beverages are made. These diet–metabolite relationships were used to accurately predict clinical phenotypes such as diabetes and hypertension.","PeriodicalId":19038,"journal":{"name":"Nature metabolism","volume":"7 9","pages":"1939-1954"},"PeriodicalIF":20.8000,"publicationDate":"2025-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Nature metabolism","FirstCategoryId":"3","ListUrlMain":"https://www.nature.com/articles/s42255-025-01359-x","RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENDOCRINOLOGY & METABOLISM","Score":null,"Total":0}
引用次数: 0
Abstract
Understanding how diet shapes metabolism across diverse populations is essential to improving nutrition and health. Biomarkers reflecting diet are explored largely in European and American populations, but the food metabolome is highly complex and varies across region and culture. We assessed 1,055 plasma metabolites and 169 foods/beverages in 8,391 multi-ethnic Asian individuals and carried out diet–metabolite association analyses. Using machine learning, we developed multi-biomarker panels and composite scores for key foods, beverages and overall diet quality. Here we show these biomarker panels can be used to objectively assess dietary intakes in the Asian multi-ethnic population and can explain variances in intake prediction models better than single biomarkers. The identified diet–metabolite relationships are reproducible over time and improve prediction of clinical outcomes (insulin resistance, diabetes, body mass index, carotid intima-media thickness and hypertension), compared to self-reports. Our findings show insights into multi-ethnic diet-related metabolic variations and an opportunity to link exposure to population health outcomes. In a large multi-ethnic Asian cohort, associations between over 1,000 plasma metabolites and specific foods and beverages are made. These diet–metabolite relationships were used to accurately predict clinical phenotypes such as diabetes and hypertension.
期刊介绍:
Nature Metabolism is a peer-reviewed scientific journal that covers a broad range of topics in metabolism research. It aims to advance the understanding of metabolic and homeostatic processes at a cellular and physiological level. The journal publishes research from various fields, including fundamental cell biology, basic biomedical and translational research, and integrative physiology. It focuses on how cellular metabolism affects cellular function, the physiology and homeostasis of organs and tissues, and the regulation of organismal energy homeostasis. It also investigates the molecular pathophysiology of metabolic diseases such as diabetes and obesity, as well as their treatment. Nature Metabolism follows the standards of other Nature-branded journals, with a dedicated team of professional editors, rigorous peer-review process, high standards of copy-editing and production, swift publication, and editorial independence. The journal has a high impact factor, has a certain influence in the international area, and is deeply concerned and cited by the majority of scholars.