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.

译文

食物代谢反应影响心脏代谢疾病的风险,但缺乏大规模的高分辨率研究。我们在英国招募了n = 1002的双胞胎和不相关的健康成年人参加PREDICT 1研究,并在临床和在家中评估了餐后代谢反应。我们观察到在相同餐后血液中甘油三酸酯(103%),葡萄糖(68%)和胰岛素(59%)的餐后反应中存在较大的个体间差异(通过群体变异系数(s.d./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)的影响(膳食中的大量营养素(3.6%))比餐后营养素(3.6%)的影响更大,餐后血糖的影响则不大(分别为6.0%和15.4%);遗传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独立验证(n = 100人)。我们开发了一种机器学习模型,该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。 ClinicalTrials.gov的注册标识符为NCT03479866。

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