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A reference map of potential determinants for the human serum metabolome

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    Publikation: Bidrag til tidsskriftTidsskriftartikelForskningpeer review

  2. Combinatorial, additive and dose-dependent drug–microbiome associations

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  • 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
  • Henrik Vestergaard
  • Manimozhiyan Arumugam
  • Torben Hansen
  • Kristine Allin
  • Tue Hansen
  • Mun Gwan Hong
  • Jochen Schwenk
  • Ragna Haussler
  • Matilda Dale
  • Toni Giorgino
  • Marianne Rodriquez
  • Mandy Perry
  • Rachel Nice
  • Timothy McDonald
  • Andrew Hattersley
  • Angus Jones
  • Ulrike Graefe-Mody
  • Patrick Baum
  • Rolf Grempler
  • Cecilia Engel Thomas
  • Federico De Masi
  • Caroline Anna Brorsson
  • Gianluca Mazzoni
  • Rosa Allesøe
  • Simon Rasmussen
  • Valborg Gudmundsdóttir
  • Agnes Martine Nielsen
  • Karina Banasik
  • Konstantinos Tsirigos
  • Birgitte Nilsson
  • Helle Pedersen
  • Søren Brunak
  • Tugce Karaderi
  • Agnete Troen Lundgaard
  • Martin Ridderstråle
  • Line Engelbrechtsen
  • The IMI DIRECT consortium
Vis graf over relationer

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.

OriginalsprogEngelsk
TidsskriftNature
Vol/bind588
Udgave nummer7836
Sider (fra-til)135-140
Antal sider6
ISSN0028-0836
DOI
StatusUdgivet - 3 dec. 2020

Bibliografisk note

Funding Information:
Acknowledgements We thank past and present members of the Segal group for discussions. N.B. received a PhD scholarship for Data Science by the Israeli Council for Higher Education (CHE) via the Weizmann Data Science Research Center and is supported by a research grant from Madame Olga Klein Astrachan. T.K. is a CIFAR Azrieli Global Scholar in the Humans & the Microbiome Program. E.S. is supported by the Crown Human Genome Center, by D. L. Schwarz, J. N. Halpern and L. Steinberg, and by grants funded by the European Research Council and the Israel Science Foundation. The work leading to this publication has received support from the Innovative Medicines Initiative Joint Undertaking under grant agreement no.115317 (DIRECT), resources of which are composed of financial contribution from the European Union’s Seventh Framework Programme (FP7/2007-2013) and in-kind contribution from EFPIA companies. We thank A. Dutta for introducing us to the DIRECT consortium dataset.

Publisher Copyright:
© 2020, The Author(s), under exclusive licence to Springer Nature Limited.

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