Proof of concept for quantitative urine NMR metabolomics pipeline for large-scale epidemiology and genetics
Tynkkynen, Tuulia; Wang, Qin; Ekholm, Jussi; Anufrieva, Olga; Ohukainen, Pauli; Vepsäläinen, Jouko; Männikko, Minna; Keinänen-Kiukaanniemi, Sirkka; Holmes, Michael V; Goodwin, Matthew; Ring, Susan; Chambers, John C; Kooner, Jaspal; Järvelin, Marjo-Riitta; Kettunen, Johannes; Hill, Michael; Smith, George Davey; Ala-Korpela, Mika (2019-01-29)
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
© 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.
https://creativecommons.org/licenses/by/4.0/
https://urn.fi/URN:NBN:fi-fe202003138149
Tiivistelmä
Abstract
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.
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