Journal
NATURE MEDICINE
Volume 26, Issue 6, Pages 964-+Publisher
NATURE PORTFOLIO
DOI: 10.1038/s41591-020-0934-0
Keywords
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Funding
- Abbott
- Zoe Global
- Wellcome Trust [212904/Z/18/Z]
- Medical Research Council (MRC)/British Heart Foundation Ancestry and Biological Informative Markers for Stratification of Hypertension (AIMHY) [MR/M016560/1]
- BBSRC [BB/NO12739/1]
- European Research Council [CoG-2015_681742]
- Swedish Research Council
- Novo Nordisk Foundation
- Swedish Foundation for Strategic Research (IRC award)
- National Institute for Health Research Nottingham Biomedical Research Centre
- Wellcome Trust
- Medical Research Council
- European Union
- Chronic Disease Research Foundation (CDRF)
- National Institute for Health Research (NIHR)
- Biomedical Research Centre based at Guy's and St Thomas' NHS Foundation Trust
- King's College London
- European Commission Horizon 2020 program (H2020-MSCA-IF-2015) [703787]
- National Institutes of Health [P30 DK40561]
- European Research Council (ERC-STG project MetaPG)
- European H2020 program (ONCOBIOME project) [825410]
- European H2020 program (MASTER project) [818368]
- National Cancer Institute of the National Institutes of Health [1U01CA230551]
- Marie Curie Actions (MSCA) [703787] Funding Source: Marie Curie Actions (MSCA)
- MRC [MR/M016560/1, MR/N01183X/1] Funding Source: UKRI
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Metabolic responses to food influence risk of cardiometabolic disease, but large-scale high-resolution studies are lacking. We recruitedn = 1,002 twins and unrelated healthy adults in the United Kingdom to the PREDICT 1 study and assessed postprandial metabolic responses in a clinical setting and at home. We observed large inter-individual variability (as measured by the population coefficient of variation (s.d./mean, %)) in postprandial responses of blood triglyceride (103%), glucose (68%) and insulin (59%) following identical meals. Person-specific factors, such as gut microbiome, had a greater influence (7.1% of variance) than did meal macronutrients (3.6%) for postprandial lipemia, but not for postprandial glycemia (6.0% and 15.4%, respectively); genetic variants had a modest impact on predictions (9.5% for glucose, 0.8% for triglyceride, 0.2% for C-peptide). Findings were independently validated in a US cohort (n = 100 people). We developed a machine-learning model that predicted both triglyceride (r = 0.47) and glycemic (r = 0.77) responses to food intake. These findings may be informative for developing personalized diet strategies. The ClinicalTrials.gov registration identifier is NCT03479866.
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