期刊
INTERNATIONAL JOURNAL OF EPIDEMIOLOGY
卷 48, 期 3, 页码 978-993出版社
OXFORD UNIV PRESS
DOI: 10.1093/ije/dyy287
关键词
Metabolomics; urine; serum; genome-wide analyses; open access; multicentre
资金
- UK Medical Research Council [MC_UU_12013/1]
- Academy of Finland [297338, 307247]
- Novo Nordisk Foundation [NNF17OC0026062, NNF17OC0027034]
- UK Medical Research Council Population Health Research Unit
- Sigrid Juselius Foundation, Finland
- UK Medical Research Council
- British Heart Foundation Intermediate Clinical Research Fellowship [FS/18/23/33512]
- National Institute for Health Research Oxford Biomedical Research Centre
- University of Bristol
- Academy of Finland (AKA) [307247, 297338, 297338, 307247] Funding Source: Academy of Finland (AKA)
- MRC [MC_U137686855, MC_UU_00011/1, MC_UU_12013/1] Funding Source: UKRI
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.
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