University of Oulu

Peter Würtz, Antti J Kangas, Pasi Soininen, Debbie A Lawlor, George Davey Smith, Mika Ala-Korpela; Quantitative Serum Nuclear Magnetic Resonance Metabolomics in Large-Scale Epidemiology: A Primer on -Omic Technologies, American Journal of Epidemiology, Volume 186, Issue 9, 1 November 2017, Pages 1084–1096, https://doi.org/10.1093/aje/kwx016

Quantitative serum nuclear magnetic resonance metabolomics in large-scale epidemiology : a primer on -omic technologies

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Author: Würtz, Peter1,2,3; Kangas, Antti J.1,2,3; Soininen, Pasi1,2,4;
Organizations: 1Computational Medicine, Faculty of Medicine, University of Oulu
2Biocenter Oulu, University of Oulu
3Brainshake Ltd.
4NMR Metabolomics Laboratory, School of Pharmacy, University of Eastern Finland
5School of Social and Community Medicine, University of Bristol
6Medical Research Council Integrative Epidemiology Unit at the University of Bristol
Format: article
Version: published version
Access: open
Online Access: PDF Full Text (PDF, 0.7 MB)
Persistent link: http://urn.fi/urn:nbn:fi-fe201801182109
Language: English
Published: Oxford University Press, 2017
Publish Date: 2018-01-18
Description:

Abstract

Detailed metabolic profiling in large-scale epidemiologic studies has uncovered novel biomarkers for cardiometabolic diseases and clarified the molecular associations of established risk factors. A quantitative metabolomics platform based on nuclear magnetic resonance spectroscopy has found widespread use, already profiling over 400,000 blood samples. Over 200 metabolic measures are quantified per sample; in addition to many biomarkers routinely used in epidemiology, the method simultaneously provides fine-grained lipoprotein subclass profiling and quantification of circulating fatty acids, amino acids, gluconeogenesis-related metabolites, and many other molecules from multiple metabolic pathways. Here we focus on applications of magnetic resonance metabolomics for quantifying circulating biomarkers in large-scale epidemiology. We highlight the molecular characterization of risk factors, use of Mendelian randomization, and the key issues of study design and analyses of metabolic profiling for epidemiology. We also detail how integration of metabolic profiling data with genetics can enhance drug development. We discuss why quantitative metabolic profiling is becoming widespread in epidemiology and biobanking. Although large-scale applications of metabolic profiling are still novel, it seems likely that comprehensive biomarker data will contribute to etiologic understanding of various diseases and abilities to predict disease risks, with the potential to translate into multiple clinical settings.

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Series: American journal of epidemiology
ISSN: 0002-9262
ISSN-E: 1476-6256
ISSN-L: 0002-9262
Volume: 186
Issue: 9
Pages: 1084 - 1096
DOI: 10.1093/aje/kwx016
OADOI: https://oadoi.org/10.1093/aje/kwx016
Type of Publication: A1 Journal article – refereed
Field of Science: 3142 Public health care science, environmental and occupational health
Subjects:
Funding: The scientific development and epidemiologic applications of the quantitative serum NMR metabolomics platform has been supported by the Academy of Finland, TEKES–the Finnish Funding Agency for Technology and Innovation, Sigrid Juselius Foundation, Novo Nordisk Foundation, Finnish Diabetes Research Foundation, Paavo Nurmi Foundation, and strategic and infrastructural research funding from the University of Oulu, Finland, as well as by the British Heart Foundation, Wellcome Trust, and Medical Research Council, UK. P.W. is supported by the Academy of Finland. D.A.L., G.D.S., and M.A.K. work in a Unit that receives funds from the University of Bristol and UK Medical Research Council (MC_UU_12013/1, MC_UU_12013/5). D.A.L. is a UK National Institute of Health Senior Investigator (NF-SI-0611–10196).
Copyright information: © The Author(s) 2017. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. 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.
  https://creativecommons.org/licenses/by/4.0/