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研究揭示人群对饮食的餐后代谢反应
2020-06-12 23:38

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。

研究人员在英国招募了总人数为1022的双胞胎和无关健康成年人参加PREDICT 1研究,并在临床和在家中评估了餐后代谢反应。研究人员观察到,同一餐后血液中甘油三酸酯(103%)、葡萄糖(68%)和胰岛素(59%)的餐后反应存在较大的个体间差异,通过群体变异系数(标准偏差/平均值,%)衡量。
 
餐后脂肪血症的人特异性因素(如肠道微生物组),对餐后血脂的影响比餐前大量营养素(3.6%)的影响大(差异为7.1%),但对餐后血糖的影响则不大(分别为6.0%和15.4%)。 基因变异对预测的影响不大(葡萄糖为9.5%,甘油三酸酯为0.8%,C肽为0.2%)。
 
研究结果在美国队列中得到独立验证(n=100人)。研究人员开发了一种机器学习模型,可以预测甘油三酸酯(r=0.47)和血糖(r=0.77)对食物摄入的反应。这些发现可能有助于制定个性化的饮食策略。
 
据悉,食物代谢反应会影响心脏代谢疾病的风险,但缺乏大规模的高分辨率研究。
 
附:英文原文

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

Issue&Volume: 2020-06-11

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.

DOI: 10.1038/s41591-020-0934-0

Source: https://www.nature.com/articles/s41591-020-0934-0

Nature Medicine:《自然—医学》,创刊于1995年。隶属于施普林格·自然出版集团,最新IF:87.241
官方网址:https://www.nature.com/nm/
投稿链接:https://mts-nmed.nature.com/cgi-bin/main.plex


本期文章:《自然—医学》:Online/在线发表

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