“Human Postprandial Responses to Food and Potential for Precision Nutrition”, 2020-06-11 (; similar):
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, n%) 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.
…The heritability of postprandial responses in the UK cohort was examined using classical twin methods (variance components analyses) to establish the upper bound of what might be predicted by directly measured genetic variation. Two-thirds of the cohort was recruited from the TwinsUK registry16, of which 230 twin pairs (n = 460; 183 monozygotic and 47 dizygotic) were studied for heritability. Additive genetic factors explained 48% of the variance in glucoseiAUC0–2h, whereas 0% of the variance in triglyceride6h-rise and 9% of the variance in insulin2h-rise were explained in this way (Figure 3b). The estimated genetic variances in insulin1h-rise and C-peptide1h-rise were close to 0 (Supplementary Table 4).