Tuulia Tynkkynen, Qin Wang, Jussi Ekholm, Olga Anufrieva, Pauli Ohukainen, Jouko Vepsäläinen, Minna Männikkö, Sirkka Keinänen-Kiukaanniemi, Michael V Holmes, Matthew Goodwin, Susan Ring, John C Chambers, Jaspal Kooner, Marjo-Riitta Järvelin, Johannes Kettunen, Michael Hill, George Davey Smith, Mika Ala-Korpela, Proof of concept for quantitative urine NMR metabolomics pipeline for large-scale epidemiology and genetics, International Journal of Epidemiology, Volume 48, Issue 3, June 2019, Pages 978–993, https://doi.org/10.1093/ije/dyy287
Proof of concept for quantitative urine NMR metabolomics pipeline for large-scale epidemiology and genetics
|Author:||Tynkkynen, Tuulia1,2; Wang, Qin2,3,4,5; Ekholm, Jussi2,3,4;|
1NMR Metabolomics Laboratory, School of Pharmacy, University of Eastern Finland, Kuopio, Finland
2Computational Medicine, Faculty of Medicine, University of Oulu and Biocenter Oulu, Oulu, Finland
3Center for Life Course Health Research, Faculty of Medicine, University of Oulu, Oulu, Finland
4Biocenter Oulu, University of Oulu, Oulu, Finland
5Systems Epidemiology, Baker Heart and Diabetes Institute, Melbourne, VIC, Australia
6Northern Finland Birth Cohorts, Faculty of Medicine, University of Oulu, Oulu, Finland
7Unit of Primary Health Care, Oulu University Hospital, OYS, Oulu, Finland
8Medical Research Center Oulu, Oulu University Hospital, University of Oulu, Oulu, Finland
9Medical Research Council Population Health Research Unit (MRC PHRU), University of Oxford, Oxford, UK
10Nuffield Department of Population Health, Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), University of Oxford, Oxford, UK
11National Institute for Health Research, Oxford Biomedical Research Centre, Oxford University Hospital, Oxford, UK
12Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, UK
13Population Health Science, University of Bristol, Bristol, UK
14Department of Epidemiology and Biostatistics, MRC-PHE Centre for Environment and Health, Imperial College London, London, UK
15Ealing Hospital NHS Trust, Middlesex, UK
16Imperial College Healthcare NHS Trust, London, UK
17Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore
18National Heart and Lung Institute, Imperial College London, London, UK
19THL: National Institute for Health and Welfare, Helsinki, Finland
20Center for Life Course Health Research, Faculty of Medicine, University of Oulu, Oulu, Finland Biocenter Oulu, University of Oulu, Oulu, Finland
21Department of Epidemiology and Preventive Medicine, School of Public Health and Preventive Medicine, Faculty of Medicine, Nursing and Health Sciences, Alfred Hospital, Monash University, Melbourne, VIC, Australia
22Corresponding author. Baker Heart and Diabetes Institute, Systems Epidemiology, 75 Commercial Road, Melbourne, VIC 3004, Australia
|Online Access:||PDF Full Text (PDF, 1.7 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe202003138149
Oxford University Press,
|Publish Date:|| 2020-03-13
Background: Quantitative molecular data from urine are rare in epidemiology and genetics. NMR spectroscopy could provide these data in high throughput, and it has already been applied in epidemiological settings to analyse urine samples. However, quantitative protocols for large-scale applications are not available.
Methods: We describe in detail how to prepare urine samples and perform NMR experiments to obtain quantitative metabolic information. Semi-automated quantitative line shape fitting analyses were set up for 43 metabolites and applied to data from various analytical test samples and from 1004 individuals from a population-based epidemiological cohort. Novel analyses on how urine metabolites associate with quantitative serum NMR metabolomics data (61 metabolic measures; n = 995) were performed. In addition, confirmatory genome-wide analyses of urine metabolites were conducted (n = 578). The fully automated quantitative regression-based spectral analysis is demonstrated for creatinine and glucose (n = 4548).
Results: Intra-assay metabolite variations were mostly <5%, indicating high robustness and accuracy of urine NMR spectroscopy methodology per se. Intra-individual metabolite variations were large, ranging from 6% to 194%. However, population-based inter-individual metabolite variations were even larger (from 14% to 1655%), providing a sound base for epidemiological applications. Metabolic associations between urine and serum were found to be clearly weaker than those within serum and within urine, indicating that urinary metabolomics data provide independent metabolic information. Two previous genome-wide hits for formate and 2-hydroxyisobutyrate were replicated at genome-wide significance.
Conclusions: Quantitative urine metabolomics data suggest broad novelty for systems epidemiology. A roadmap for an open access methodology is provided.
International journal of epidemiology
|Pages:||978 - 993|
|Type of Publication:||
A1 Journal article – refereed
|Field of Science:||
3121 General medicine, internal medicine and other clinical medicine
Q.W. was supported by a Novo Nordisk Foundation Postdoctoral Fellowship (grant number NNF17OC0027034). M.G., S.R., G.D.S. and M.A.K. work in a unit that is supported by the University of Bristol and UK Medical Research Council (MC_UU_12013/1). J.K. was supported through funds from the Academy of Finland (grant numbers 297338 and 307247) and Novo Nordisk Foundation (grant number NNF17OC0026062). M.Hi. was supported by the UK Medical Research Council Population Health Research Unit. M.A.K. was supported by the Sigrid Juselius Foundation, Finland. M.V.H. works in a unit that receives funding from the UK Medical Research Council and is supported by a British Heart Foundation Intermediate Clinical Research Fellowship (FS/18/23/33512) and the National Institute for Health Research Oxford Biomedical Research Centre.
|Academy of Finland Grant Number:||
297338 (Academy of Finland Funding decision)
307247 (Academy of Finland Funding decision)
© The Author(s) 2019. Published by Oxford University Press on behalf of the International Epidemiological Association. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permitsunrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.