Nature子刊:多因素机器学习模型预测餐后代谢反应
创作:mildbreeze 审核:mildbreeze 07月01日
  • 纳入1002名英国受试者进行试验,探究多种因素与餐后甘油三酯(TG)、血糖和胰岛素反应的关系,并在另一队列中验证;
  • 餐后代谢反应有明显的个体差异,TG、血糖和胰岛素的变异系数分别为103%、68%和59%;
  • 饮食成分、用餐背景和遗传因素(SNP)对餐后血糖反应有较高的解释度(15.4%、~7.6%和9.5%),但对TG和胰岛素的解释度较低;
  • 肠道菌群对餐后TG、血糖和胰岛素反应的解释度为7.5%、6.4%和5.8%;
  • 建立机器学习模型可预测餐后TG和血糖。
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mildbreeze
对食物的代谢反应可影响心血管代谢疾病的风险,但目前仍缺少大规模高分辨率的相关研究。《Nature Medicine》发表的一项最新研究,纳入了英国PREDICT1研究中的1002名健康受试者(包括几百名双胞胎),分析了他们的餐后代谢反应情况(甘油三酯、血糖、胰岛素),及其与膳食成分、日常饮食、用餐背景(之前的饮食、进餐顺序/时间、睡眠、运动等)、人体和临床生化指标、遗传、肠道菌群等因素的关系,并在一个独立的美国队列中进行了验证。该研究发现,遗传因素(基于SNP分析)对于餐后的代谢反应(特别是甘油三酯)似乎并没有起到主导作用,而一些可变因素(如用餐背景)的作用则比预期的要大。研究者使用机器学习开发了一个预测个体餐后反应的模型,在血糖和甘油三酯预测方面有相对较好的表现。这些发现对于进一步开发个体化的饮食营养干预策略有参考意义。
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Nature Medicine [IF:36.13]

Human postprandial responses to food and potential for precision nutrition

对食物的人体餐后反应和精准营养的潜力

10.1038/s41591-020-0934-0

06-11, Article

Abstract & Authors:展开

Abstract:收起
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.

First Authors:
Sarah E Berry,Ana M Valdes

Correspondence Authors:
Ana M Valdes,Tim D Spector

All Authors:
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

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Science Translational Medicine期刊

Personalized dietary advice on the horizon

2020-06-24

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中国生物技术网微信公众号

同样是饱餐一顿,但餐后炎症在不同健康个体之间存在显著差异

生物技术君,2020-06-14

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Nature Reviews Endocrinology期刊

Towards precision nutrition

2020-06-26

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