Evidence map›Paper›PMID 30689875›Full record

ArticleInternational journal of epidemiology2019

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

Tuulia Tynkkynen, Qin Wang, Jussi Ekholm, Olga Anufrieva, Pauli Ohukainen, Jouko Vepsäläinen, Minna Männikkö, Sirkka Keinänen-Kiukaanniemi, Michael V Holmes, Matthew Goodwin and 8 more

Abstract read
In one paragraph

Article in International journal of epidemiology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers.

0numbers the graph read from it
0cells of the map it votes in
35citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

35 citing papers in PubMed.

  1. Article
  2. Exploring metabolic signatures in urine using NMR for improved prognosis of gliomas.Metabolomics : Official journal of the Metabolomic Society · 2026
    Article
  3. Article
  4. Article
  5. Review
  6. Article
  7. Review
  8. Article
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  10. Article
  11. Article
  12. Review
  13. A metabolic readout of the urine metabolome of COVID-19 patients.Metabolomics : Official journal of the Metabolomic Society · 2023
    Article
  14. Review
  15. Quantitative NMR Methods in Metabolomics.Handbook of experimental pharmacology · 2023
    Article
  16. Article
  17. Article
  18. Article
  19. A Real-Life Reproducibility Assessment for NMR Metabolomics.Diagnostics (Basel, Switzerland) · 2022
    Article
  20. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

18 authors.

Tuulia TynkkynenNMR Metabolomics Laboratory, School of Pharmacy, University of Eastern Finland, Kuopio, Finland.
Qin WangComputational Medicine, Faculty of Medicine, University of Oulu and Biocenter Oulu, Oulu, Finland.
Jussi EkholmComputational Medicine, Faculty of Medicine, University of Oulu and Biocenter Oulu, Oulu, Finland.
Olga AnufrievaComputational Medicine, Faculty of Medicine, University of Oulu and Biocenter Oulu, Oulu, Finland.
Pauli OhukainenComputational Medicine, Faculty of Medicine, University of Oulu and Biocenter Oulu, Oulu, Finland.
Jouko VepsäläinenNMR Metabolomics Laboratory, School of Pharmacy, University of Eastern Finland, Kuopio, Finland.
Minna MännikköCenter for Life Course Health Research, Faculty of Medicine, University of Oulu, Oulu, Finland.
Sirkka Keinänen-KiukaanniemiCenter for Life Course Health Research, Faculty of Medicine, University of Oulu, Oulu, Finland.
Michael V HolmesMedical Research Council Population Health Research Unit (MRC PHRU), University of Oxford, Oxford, UK.
Matthew GoodwinMedical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, UK.
Susan RingMedical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, UK.
John C ChambersDepartment of Epidemiology and Biostatistics, MRC-PHE Centre for Environment and Health, Imperial College London, London, UK.
Jaspal KoonerEaling Hospital NHS Trust, Middlesex, UK.
Marjo-Riitta JärvelinCenter for Life Course Health Research, Faculty of Medicine, University of Oulu, Oulu, Finland.
Johannes KettunenComputational Medicine, Faculty of Medicine, University of Oulu and Biocenter Oulu, Oulu, Finland.
Michael HillMedical Research Council Population Health Research Unit (MRC PHRU), University of Oxford, Oxford, UK.
George Davey SmithMedical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, UK.
Mika Ala-KorpelaNMR Metabolomics Laboratory, School of Pharmacy, University of Eastern Finland, Kuopio, Finland.

Funding

British Heart Foundation FS/18/23/33512Department of HealthMedical Research Council MC_U137686855Medical Research Council MC_UU_00011/1Medical Research Council MC_UU_12013/1Wellcome Trust 212945/Z/18/Z
6 · The paper itself

Abstract

backgroundQuantitative 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.

methodsWe 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).

resultsIntra-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.

conclusionQuantitative urine metabolomics data suggest broad novelty for systems epidemiology. A roadmap for an open access methodology is provided.

Indexed as

MetabolomicsEpidemiologic StudiesGenome-Wide Association StudyHumansMagnetic Resonance SpectroscopyProof of Concept StudyUrinegenome-wide analysesMetabolomicsmulticentreopen accessserumurine

Identifiers

PMID30689875
PMCPMC6659374

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.