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A reference map of potential determinants for the human serum metabolome

机译：人血清代谢物的潜在决定簇的参考图

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摘要

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 environment~(1). The origins of specific compounds are known, including metabolites that are highly heritable~(2,3), or those that are influenced by the gut microbiome~(4), by lifestyle choices such as smoking~(5), or by diet~(6). 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 cohorts~(7,8)that were not available to us when we trained the algorithms. We used feature attribution analysis~(9)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.
机译：血清代谢物含有血清的生物标志物和各种疾病的致病剂，其中一些是内源性产生的，并且一些已从环境中吸收的一些〜（1）。特异性化合物的起源是已知的，包括高度遗传〜（2,3）的代谢物，或受到肠道微生物组的影响的代谢物〜（4），例如吸烟〜（5），或饮食〜 （6）。然而，大多数代谢物的关键决定因素仍然很清楚。在这里，我们从491个个体的独特和深度表型健康人群中测量了血清样品中1,251种代谢物的水平。我们应用了机器学习算法，在宿主遗传学，肠道微生物组，临床参数，饮食，生活方式和人体测量测量的基础上预测了举行的个体中的代谢物水平，并获得了超过76％的成熟代谢物的统计学意义的预测。饮食和微生物组具有最强的预测能力，每次解释数百个代谢物 - 在某些情况下，解释了超过50％的观察方差。我们通过在我们培训算法时，通过在我们培训的两个地理上独立的队列中显示出高复制率，通过显示出在两个地理上独立的队列〜（7,8）中的高复制率进行了验证了与微生物组相关的预测。我们使用特征归因分析〜（9）来揭示特定的膳食和细菌相互作用。我们进一步证明，这些相互作用中的一些可能是因果的，因为我们预计在面包干预的随机临床试验后，我们预计的代谢产物被发现增加。总体而言，我们的结果揭示了800多个代谢物的潜在决定因素，朝着在不同条件下的代谢物改变和设计用于操纵循环代谢物水平的方法的机械理解。

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  • 来源
    《Nature》 |2020年第7836期|135-140|共6页
  • 作者

    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;

  • 作者单位

    Department of Computer Science and Applied Mathematics Weizmann Institute of Science|Department of Molecular Cell Biology Weizmann Institute of Science;

    Department of Computer Science and Applied Mathematics Weizmann Institute of Science|Department of Molecular Cell Biology Weizmann Institute of Science|Department of Systems Biology Columbia University Irving Medical Center|Department of Obstetrics and Gynecology Columbia University Irving Medical Center|CIFAR Azrieli Global Scholars Program CIFAR;

    Department of Computer Science and Applied Mathematics Weizmann Institute of Science|Department of Molecular Cell Biology Weizmann Institute of Science|Department of Epidemiology Harvard T.H. Chan School of Public Health;

    Department of Computer Science and Applied Mathematics Weizmann Institute of Science|Department of Molecular Cell Biology Weizmann Institute of Science|Center for Studies in Physics and Biology The Rockefeller University;

    Department of Computer Science and Applied Mathematics Weizmann Institute of Science|Department of Molecular Cell Biology Weizmann Institute of Science;

    Department of Computer Science and Applied Mathematics Weizmann Institute of Science|Department of Molecular Cell Biology Weizmann Institute of Science;

    Department of Computer Science and Applied Mathematics Weizmann Institute of Science|Department of Molecular Cell Biology Weizmann Institute of Science;

    Department of Computer Science and Applied Mathematics Weizmann Institute of Science|Department of Molecular Cell Biology Weizmann Institute of Science;

    Department of Computer Science and Applied Mathematics Weizmann Institute of Science|Department of Molecular Cell Biology Weizmann Institute of Science;

    Department for Twin Research & Genetic Epidemiology King's College London;

    Department for Twin Research & Genetic Epidemiology King's College London;

    Department for Twin Research & Genetic Epidemiology King's College London;

    Department for Twin Research & Genetic Epidemiology King's College London;

    Department for Twin Research & Genetic Epidemiology King's College London;

    Research Unit Molecular Endocrinology and Metabolism Genome Analysis Center Helmholtz Zentrum München German Research Center for Environmental Health Neuherberg Germany|Lehrstuhl für Experimentelle Genetik Technische Universität München Freising-Weihenstephan Germany|Department of Biochemistry Yong Loo Lin School of Medicine National University of Singapore;

    Lund University Diabetes Center Department of Clinical Sciences Lund University|Harvard T.H. Chan School of Public Health;

    The Novo Nordisk Foundation Center for Basic Metabolic Research Faculty of Health and Medical Sciences University of Copenhagen;

    Department of Computer Science and Applied Mathematics Weizmann Institute of Science|Department of Molecular Cell Biology Weizmann Institute of Science;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);美国《生物学医学文摘》(MEDLINE);美国《化学文摘》(CA);
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