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.

Human postprandial responses to food and potential for precision nutrition / Berry, S. E.; Valdes, A. M.; Drew, D. A.; Asnicar, F.; Mazidi, M.; Wolf, J.; Capdevila, J.; Hadjigeorgiou, G.; Davies, R.; Al Khatib, H.; Bonnett, C.; Ganesh, S.; Bakker, E.; Hart, D.; Mangino, M.; Merino, J.; Linenberg, I.; Wyatt, P.; Ordovas, J. M.; Gardner, C. D.; Delahanty, L. M.; Chan, A. T.; Segata, N.; Franks, P. W.; Spector, T. D.. - In: NATURE MEDICINE. - ISSN 1078-8956. - 26:6(2020), pp. 964-973. [10.1038/s41591-020-0934-0]

Human postprandial responses to food and potential for precision nutrition

Asnicar F.;Hart D.;Segata N.;
2020-01-01

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.
2020
6
Berry, S. E.; Valdes, A. M.; Drew, D. A.; Asnicar, F.; Mazidi, M.; Wolf, J.; Capdevila, J.; Hadjigeorgiou, G.; Davies, R.; Al Khatib, H.; Bonnett, C.; Ganesh, S.; Bakker, E.; Hart, D.; Mangino, M.; Merino, J.; Linenberg, I.; Wyatt, P.; Ordovas, J. M.; Gardner, C. D.; Delahanty, L. M.; Chan, A. T.; Segata, N.; Franks, P. W.; Spector, T. D.
Human postprandial responses to food and potential for precision nutrition / Berry, S. E.; Valdes, A. M.; Drew, D. A.; Asnicar, F.; Mazidi, M.; Wolf, J.; Capdevila, J.; Hadjigeorgiou, G.; Davies, R.; Al Khatib, H.; Bonnett, C.; Ganesh, S.; Bakker, E.; Hart, D.; Mangino, M.; Merino, J.; Linenberg, I.; Wyatt, P.; Ordovas, J. M.; Gardner, C. D.; Delahanty, L. M.; Chan, A. T.; Segata, N.; Franks, P. W.; Spector, T. D.. - In: NATURE MEDICINE. - ISSN 1078-8956. - 26:6(2020), pp. 964-973. [10.1038/s41591-020-0934-0]
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