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Metabolomics in epidemiology: from metabolite concentrations to integrative reaction networks
Metabolomics is becoming feasible for population-scale studies of human disease. In this review, we survey epidemiological studies that leverage metabolomics and multi-omics to gain insight into disease mechanisms. We outline key practical, technological and analytical limitations while also highlig...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2016
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5100607/ https://www.ncbi.nlm.nih.gov/pubmed/27118561 http://dx.doi.org/10.1093/ije/dyw046 |
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author | Fearnley, Liam G Inouye, Michael |
author_facet | Fearnley, Liam G Inouye, Michael |
author_sort | Fearnley, Liam G |
collection | PubMed |
description | Metabolomics is becoming feasible for population-scale studies of human disease. In this review, we survey epidemiological studies that leverage metabolomics and multi-omics to gain insight into disease mechanisms. We outline key practical, technological and analytical limitations while also highlighting recent successes in integrating these data. The use of multi-omics to infer reaction rates is discussed as a potential future direction for metabolomics research, as a means of identifying biomarkers as well as inferring causality. Furthermore, we highlight established analysis approaches as well as simulation-based methods currently used in single- and multi-cell levels in systems biology. |
format | Online Article Text |
id | pubmed-5100607 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-51006072016-11-10 Metabolomics in epidemiology: from metabolite concentrations to integrative reaction networks Fearnley, Liam G Inouye, Michael Int J Epidemiol Reviews Metabolomics is becoming feasible for population-scale studies of human disease. In this review, we survey epidemiological studies that leverage metabolomics and multi-omics to gain insight into disease mechanisms. We outline key practical, technological and analytical limitations while also highlighting recent successes in integrating these data. The use of multi-omics to infer reaction rates is discussed as a potential future direction for metabolomics research, as a means of identifying biomarkers as well as inferring causality. Furthermore, we highlight established analysis approaches as well as simulation-based methods currently used in single- and multi-cell levels in systems biology. Oxford University Press 2016-10 2016-04-26 /pmc/articles/PMC5100607/ /pubmed/27118561 http://dx.doi.org/10.1093/ije/dyw046 Text en © The Author 2016. Published by Oxford University Press on behalf of the International Epidemiological Association. http://creativecommons.org/licenses/by/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Reviews Fearnley, Liam G Inouye, Michael Metabolomics in epidemiology: from metabolite concentrations to integrative reaction networks |
title | Metabolomics in epidemiology: from metabolite concentrations to integrative reaction networks |
title_full | Metabolomics in epidemiology: from metabolite concentrations to integrative reaction networks |
title_fullStr | Metabolomics in epidemiology: from metabolite concentrations to integrative reaction networks |
title_full_unstemmed | Metabolomics in epidemiology: from metabolite concentrations to integrative reaction networks |
title_short | Metabolomics in epidemiology: from metabolite concentrations to integrative reaction networks |
title_sort | metabolomics in epidemiology: from metabolite concentrations to integrative reaction networks |
topic | Reviews |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5100607/ https://www.ncbi.nlm.nih.gov/pubmed/27118561 http://dx.doi.org/10.1093/ije/dyw046 |
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