Human postprandial responses to food and potential for precision nutrition

Sarah Berry, Ana Valdes, David A Drew, Francesco Asnicar, Mohsen Mazidi, Jonathan Wolf, Joan Capdevila, George Hadjigeorgiou, Richard Davies, Haya Al Khatib, Christopher Bonnett, Sajaysurya Ganesh, Elco Bakker, Deborah Hart, Massimo Mangino, Jordi Merino, Inbar Linenberg, Patrick Wyatt, Jose Ordovas, Christopher GardnerLinda Delahanty, Andrew Chan, Nicola Segata, Paul Franks, Tim Spector

Research output: Contribution to journalArticlepeer-review

398 Citations (Scopus)
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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 registration identifier is NCT03479866.

Original languageEnglish
Pages (from-to)964-973
Number of pages10
JournalNature Medicine
Issue number6
Early online date11 Jun 2020
Publication statusPublished - 11 Jun 2020


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