Abstract
Metabolic responses to food influence cardiometabolic disease risk, but large-scale high-resolution studies are lacking. We recruited n = 1,002 twins and unrelated healthy adults in the UK into the PREDICT1 study and assessed postprandial metabolic responses in a clinic setting and at home. We observed large inter-individual variability (population coefficient of variation [SD/mean]%) in postprandial blood triglyceride (103%), glucose (68%), and insulin (59%) responses to identical meals. Person-specific factors, such as the gut microbiome, had a greater influence (7.1% of variance) than 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). 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. ClinicalTrials.gov registration: NCT03479866.