Nature:血液代谢物,饮食和菌群是关键源头?
创作:mildbreeze 审核:mildbreeze 11月12日
  • 测量491名健康人的1251个血清代谢物,通过机器学习构建基于遗传、肠道菌群、临床参数、饮食、生活方式和人体测量数据等的模型,能显著预测76%的代谢物;
  • 饮食、临床参数和菌群的预测力较强,分别能显著解释335、337和182个代谢物的水平,大部分基于菌群的代谢物预测在另两个独立队列中得到验证;
  • 饮食和菌群对代谢物的预测存在相互重叠和彼此独立之处;
  • 在一项小型临床试验中,验证了吃全麦面包与血液胞嘧啶和甜菜碱升高的因果关系。
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mildbreeze
血液中的代谢物与很多疾病存在关联,哪些因素能预测甚至决定这些代谢物的水平? Nature最新发表了来自以色列魏茨曼科学研究所Eran Segal团队主导的研究,通过机器学习算法,研究了遗传、肠道菌群、临床参数、饮食、生活方式和人体测量学等因素与1251种血液代谢物的关系,表明饮食和菌群是这些代谢物的关键预测因子。这些发现对于深入探究血液代谢物的决定因素和机制,以及开发操纵特定代谢物以改善健康的干预方法,有重要参考价值。
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Nature [IF:42.778]

A reference map of potential determinants for the human serum metabolome

人类血清代谢组潜在决定因素的参考图

10.1038/s41586-020-2896-2

11-11, Article

Abstract & Authors:展开

Abstract:收起
The serum metabolome contains a plethora of biomarkers and causative agents of various diseases, some of which are endogenously produced and some that have been taken up from the environment1. The origins of specific compounds are known, including metabolites that are highly heritable2,3, or those that are influenced by the gut microbiome4, by lifestyle choices such as smoking5, or by diet6. However, the key determinants of most metabolites are still poorly understood. Here we measured the levels of 1,251 metabolites in serum samples from a unique and deeply phenotyped healthy human cohort of 491 individuals. We applied machine-learning algorithms to predict metabolite levels in held-out individuals on the basis of host genetics, gut microbiome, clinical parameters, diet, lifestyle and anthropometric measurements, and obtained statistically significant predictions for more than 76% of the profiled metabolites. Diet and microbiome had the strongest predictive power, and each explained hundreds of metabolites—in some cases, explaining more than 50% of the observed variance. We further validated microbiome-related predictions by showing a high replication rate in two geographically independent cohorts7,8 that were not available to us when we trained the algorithms. We used feature attribution analysis9 to reveal specific dietary and bacterial interactions. We further demonstrate that some of these interactions might be causal, as some metabolites that we predicted to be positively associated with bread were found to increase after a randomized clinical trial of bread intervention. Overall, our results reveal potential determinants of more than 800 metabolites, paving the way towards a mechanistic understanding of alterations in metabolites under different conditions and to designing interventions for manipulating the levels of circulating metabolites.

First Authors:
Noam Bar,Tal Korem

Correspondence Authors:
Eran Segal

All Authors:
Noam Bar,Tal Korem,Omer Weissbrod,David Zeevi,Daphna Rothschild,Sigal Leviatan,Noa Kosower,Maya Lotan-Pompan,Adina Weinberger,Caroline I Le Roy,Cristina Menni,Alessia Visconti,Mario Falchi,Tim D Spector,The IMI DIRECT consortium,Jerzy Adamski,Paul W Franks,Oluf Pedersen,Eran Segal

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