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

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 experi-ments 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 ¼ 995) were performed. In addition, confirmatory genome-wide analyses of urine metabolites were conducted ( n ¼ 578). The fully automated quantitative regression-based spectral analysis is demon-strated for creatinine and glucose ( n ¼ 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-in-dividual 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. intra-individual and inter-individual variation in urinary metabolites. In addition, we detail the characteristics of quantitative urine metabolite data in epidemiology, and present novel analyses regarding how the urine metabolites associate with circulating metabolites and lipids. Confirmative genome-wide analyses are also presented. All data domains substantiate the potential usefulness of quantitative molecular data on urine samples in systems epidemiology. energy and microbial metabolism, for example, 2-hydroxyisobutyrate, cis-aconitate, and pseudouridine. Multiple urinary metabolites, e.g. 3-hydroxyisobutyrate, lactate, pseudouridine and cis-aconitate associate with circulating amino acids, glucose and creatinine.