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Berry, Sarah E; Valdes, Ana M; Drew, David A; Asnicar, Francesco; Mazidi, Mohsen; Wolf, Jonathan; Capdevila, Joan; Hadjigeorgiou, George; Davies, Richard; Al Khatib, Haya; Bonnett, Christopher; Ganesh, Sajaysurya; Bakker, Elco; Hart, Deborah; Mangino, Massimo; Merino, Jordi; Linenberg, Inbar; Wyatt, Patrick; Ordovas, Jose M; Gardner, Christopher D; Delahanty, Linda M; Chan, Andrew T; Segata, Nicola; Franks, Paul W; Spector, Tim D
Nature medicine, 06/2020, Volume: 26, Issue: 6Journal Article
Metabolic responses to food influence risk of cardiometabolic disease, but large-scale high-resolution studies are lacking. We recruited n = 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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