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Mixing omics: combining genetics and metabolomics to study rheumatic diseases

Key Points

  • Although large-scale genome-wide association studies have identified many rheumatic disease-associated genetic variants, effect sizes are small (with the exception of HLA loci)

  • Metabolomics is a promising field for investigating the molecular pathogenesis of rheumatic diseases and for monitoring response to therapy, but only a few modest-sized studies have been conducted to date

  • The contribution made by genetic factors to the production of metabolites and to disease processes in general needs to be investigated if metabolomics studies are to reach their full potential

  • Integrating data from different omics studies, including metagenomics and proteomics, will help to increase our knowledge of pathways and diseases

Abstract

Metabolomics is an exciting field in systems biology that provides a direct readout of the biochemical activities taking place within an individual at a particular point in time. Metabolite levels are influenced by many factors, including disease status, environment, medications, diet and, importantly, genetics. Thanks to their dynamic nature, metabolites are useful for diagnosis and prognosis, as well as for predicting and monitoring the efficacy of treatments. At the same time, the strong links between an individual's metabolic and genetic profiles enable the investigation of pathways that underlie changes in metabolite levels. Thus, for the field of metabolomics to yield its full potential, researchers need to take into account the genetic factors underlying the production of metabolites, and the potential role of these metabolites in disease processes. In this Review, the methodological aspects related to metabolomic profiling and any potential links between metabolomics and the genetics of some of the most common rheumatic diseases are described. Links between metabolomics, genetics and emerging fields such as the gut microbiome and proteomics are also discussed.

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Figure 1: Metabolic profiling as a tool for studying rheumatic diseases.
Figure 2: Targeted versus untargeted metabolomics approaches.
Figure 3: Statistical approaches for the analysis of metabolomic data.

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Acknowledgements

TwinsUK was funded by the Wellcome Trust; European Community's Seventh Framework Programme (FP7/2007-2013). The study also receives support from the National Institute for Health Research (NIHR) Clinical Research Facility at Guy's & St Thomas' NHS Foundation Trust and NIHR Biomedical Research Centre based at Guy's and St Thomas' NHS Foundation Trust and King's College London. T.D.S. is an NIHR Senior Investigator and is holder of a European Research Council Advanced Principal Investigator award. The work of C.M., A.M.V. and T.D.S. is supported by a grant from the Medical Research Council AimHY programme (MR/M016560/1). The work of A.M.V. was supported by a grant from the Arthritis Research UK Pain Centre (18769).

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C.M., J.Z. and A.M.V. researched the data for the article. C.M. and A.M.V. wrote the article. All authors provided a substantial contribution to discussions of the content and contributed equally to review and/or editing of the manuscript before submission.

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Correspondence to Tim D. Spector.

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Examples of metabolomics studies investigating rheumatic diseases. (PDF 231 kb)

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Genetic basis of rheumatic diseases. (PDF 849 kb)

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Menni, C., Zierer, J., Valdes, A. et al. Mixing omics: combining genetics and metabolomics to study rheumatic diseases. Nat Rev Rheumatol 13, 174–181 (2017). https://doi.org/10.1038/nrrheum.2017.5

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