Proof of concept for quantitative urine NMR metabolomics pipeline for large-scale epidemiology and genetics.

Autor:	Tynkkynen T; NMR Metabolomics Laboratory, School of Pharmacy, University of Eastern Finland, Kuopio, Finland.; Computational Medicine, Faculty of Medicine, University of Oulu and Biocenter Oulu, Oulu, Finland., Wang Q; Computational Medicine, Faculty of Medicine, University of Oulu and Biocenter Oulu, Oulu, Finland.; Center for Life Course Health Research, Faculty of Medicine, University of Oulu, Oulu, Finland.; Biocenter Oulu, University of Oulu, Oulu, Finland.; Systems Epidemiology, Baker Heart and Diabetes Institute, Melbourne, VIC, Australia., Ekholm J; Computational Medicine, Faculty of Medicine, University of Oulu and Biocenter Oulu, Oulu, Finland.; Center for Life Course Health Research, Faculty of Medicine, University of Oulu, Oulu, Finland.; Biocenter Oulu, University of Oulu, Oulu, Finland., Anufrieva O; Computational Medicine, Faculty of Medicine, University of Oulu and Biocenter Oulu, Oulu, Finland.; Center for Life Course Health Research, Faculty of Medicine, University of Oulu, Oulu, Finland.; Biocenter Oulu, University of Oulu, Oulu, Finland., Ohukainen P; Computational Medicine, Faculty of Medicine, University of Oulu and Biocenter Oulu, Oulu, Finland.; Center for Life Course Health Research, Faculty of Medicine, University of Oulu, Oulu, Finland.; Biocenter Oulu, University of Oulu, Oulu, Finland., Vepsäläinen J; NMR Metabolomics Laboratory, School of Pharmacy, University of Eastern Finland, Kuopio, Finland., Männikkö M; Center for Life Course Health Research, Faculty of Medicine, University of Oulu, Oulu, Finland.; Biocenter Oulu, University of Oulu, Oulu, Finland.; Northern Finland Birth Cohorts, Faculty of Medicine, University of Oulu, Oulu, Finland., Keinänen-Kiukaanniemi S; Center for Life Course Health Research, Faculty of Medicine, University of Oulu, Oulu, Finland.; Unit of Primary Health Care, Oulu University Hospital, OYS, Oulu, Finland.; Medical Research Center Oulu, Oulu University Hospital, University of Oulu, Oulu, Finland., Holmes MV; Medical Research Council Population Health Research Unit (MRC PHRU), University of Oxford, Oxford, UK.; Nuffield Department of Population Health, Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), University of Oxford, Oxford, UK.; National Institute for Health Research, Oxford Biomedical Research Centre, Oxford University Hospital, Oxford, UK.; Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, UK., Goodwin M; Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, UK.; Population Health Science, University of Bristol, Bristol, UK., Ring S; Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, UK.; Population Health Science, University of Bristol, Bristol, UK., Chambers JC; Department of Epidemiology and Biostatistics, MRC-PHE Centre for Environment and Health, Imperial College London, London, UK.; Ealing Hospital NHS Trust, Middlesex, UK.; Imperial College Healthcare NHS Trust, London, UK.; Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore., Kooner J; Ealing Hospital NHS Trust, Middlesex, UK.; Imperial College Healthcare NHS Trust, London, UK.; National Heart and Lung Institute, Imperial College London, London, UK., Järvelin MR; Center for Life Course Health Research, Faculty of Medicine, University of Oulu, Oulu, Finland.; Biocenter Oulu, University of Oulu, Oulu, Finland.; Unit of Primary Health Care, Oulu University Hospital, OYS, Oulu, Finland.; Department of Epidemiology and Biostatistics, MRC-PHE Centre for Environment and Health, Imperial College London, London, UK., Kettunen J; Computational Medicine, Faculty of Medicine, University of Oulu and Biocenter Oulu, Oulu, Finland.; Center for Life Course Health Research, Faculty of Medicine, University of Oulu, Oulu, Finland.; Biocenter Oulu, University of Oulu, Oulu, Finland.; THL: National Institute for Health and Welfare, Helsinki, Finland., Hill M; Medical Research Council Population Health Research Unit (MRC PHRU), University of Oxford, Oxford, UK.; Nuffield Department of Population Health, Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), University of Oxford, Oxford, UK., Davey Smith G; Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, UK.; Population Health Science, University of Bristol, Bristol, UK., Ala-Korpela M; NMR Metabolomics Laboratory, School of Pharmacy, University of Eastern Finland, Kuopio, Finland.; Computational Medicine, Faculty of Medicine, University of Oulu and Biocenter Oulu, Oulu, Finland.; Center for Life Course Health Research, Faculty of Medicine, University of Oulu, Oulu, Finland.; Biocenter Oulu, University of Oulu, Oulu, Finland.; Systems Epidemiology, Baker Heart and Diabetes Institute, Melbourne, VIC, Australia.; Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, UK.; Population Health Science, University of Bristol, Bristol, UK.; Department 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.
Jazyk:	angličtina
Zdroj:	International journal of epidemiology [Int J Epidemiol] 2019 Jun 01; Vol. 48 (3), pp. 978-993.
DOI:	10.1093/ije/dyy287
Abstrakt:	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. Conclusion: Quantitative urine metabolomics data suggest broad novelty for systems epidemiology. A roadmap for an open access methodology is provided. (© The Author(s) 2019. Published by Oxford University Press on behalf of the International Epidemiological Association.)
Databáze:	MEDLINE
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