英国伦敦国王学院Tim D. Spector研究团队揭示了人群对饮食的餐后代谢反应。这一研究成果于2020年6月11日在线发表在国际学术期刊《自然—医学》上。
Title: Human postprandial responses to food and potential for precision nutrition
Author: Sarah E. Berry, Ana M. 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 M. Ordovas, Christopher D. Gardner, Linda M. Delahanty, Andrew T. Chan, Nicola Segata, Paul W. Franks, Tim D. Spector
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.