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Bar, N.* ; Korem, T.* ; Weissbrod, O.* ; Zeevi, D.* ; Rothschild, D.* ; Leviatan, S.* ; Kosower, N.* ; Lotan-Pompan, M.* ; Weinberger, A.* ; Le Roy, C.I.* ; Menni, C.* ; Visconti, A.* ; Falchi, M.* ; Spector, T.D.* ; Adamski, J. ; Franks, P.W.* ; Pedersen, O.* ; Segal, E.* ; IMI DIRECT Consortium (Thorand, B. ; Troll, M. ; Grallert, H. ; Adam, J. ; Sharma, S. ; Haid, M.)

A reference map of potential determinants for the human serum metabolome.

Nature 588, 135–140 (2020)
Postprint DOI PMC
Open Access Green
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.The levels of 1,251 metabolites are measured in 475 phenotyped individuals, and machine-learning algorithms reveal that diet and the microbiome are the determinants with the strongest predictive power for the levels of these metabolites.
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Publication type Article: Journal article
Document type Scientific Article
Corresponding Author
Keywords Uremic Toxins; Disease; Supplementation; Environment; Alignment; Betaine
ISSN (print) / ISBN 0028-0836
e-ISSN 1476-4687
Journal Nature
Quellenangaben Volume: 588, Issue: , Pages: 135–140 Article Number: , Supplement: ,
Publisher Nature Publishing Group
Publishing Place London
Non-patent literature Publications
Reviewing status Peer reviewed
Institute(s) Molekulare Endokrinologie und Metabolismus (MEM)
Institute of Epidemiology II (EPI2)
Grants EFPIA
European Union
Innovative Medicines Initiative Joint Undertaking
Israel Science Foundation
European Research Council
Crown Human Genome Center
Madame Olga Klein Astrachan
Israeli Council for Higher Education (CHE) via the Weizmann Data Science Research Center