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      Human postprandial responses to food and potential for precision nutrition

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          Abstract

          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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          Author and article information

          Journal
          Nature Medicine
          Nat Med
          Springer Science and Business Media LLC
          1078-8956
          1546-170X
          June 11 2020
          Article
          10.1038/s41591-020-0934-0
          8265154
          32528151
          d53520c7-3d31-4869-be33-5305353d3d6d
          © 2020

          http://www.springer.com/tdm

          http://www.springer.com/tdm

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