Evidence map›Paper›PMID 37418094›Full record

ArticleMetabolomics : Official journal of the Metabolomic Society2023

NMR metabolite quantification of a synthetic urine sample: an inter-laboratory comparison of processing workflows.

Cécile Canlet, Catherine Deborde, Edern Cahoreau, Grégory Da Costa, Roselyne Gautier, Daniel Jacob, Cyril Jousse, Mélia Lacaze, Inès Le Mao, Estelle Martineau and 6 more

Abstract read
In one paragraph

Article in Metabolomics : Official journal of the Metabolomic Society, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

  1. Article
  2. HarmonizedAnalytical chemistry · 2026
    Article
  3. Article
  4. NMR-Based Stable Isotope Tracing of Cancer Metabolism.Methods in molecular biology (Clifton, N.J.) · 2025
    Article
  5. 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

16 authors.

Cécile Canlet *Toxalim (Research Centre in Food Toxicology), Toulouse University, INRAE UMR 1331, ENVT, INP-Purpan, UPS, MetaToul-AXIOM Platform, National Infrastructure of Metabolomics and Fluxomics: MetaboHUB, INRAE, 31027, Toulouse, France.
Catherine Deborde *INRAE, Univ. Bordeaux, Biologie du Fruit et Pathologie, UMR1332, Bordeaux Metabolome - MetaboHUB, Centre INRAE de Nouvelle-Aquitaine Bordeaux, 33140, Villenave d'Ornon, France.
Edern CahoreauTBI, Université de Toulouse, CNRS, INRAE, INSA, MetaboHUB - MetaToul, National Infrastructure of Metabolomics and Fluxomics, 31077, Toulouse, France.
Grégory Da CostaUniv. Bordeaux, Bordeaux INP, INRAE, OENO, UMR 1366, ISVV, Bordeaux Metabolome - MetaboHUB, 33140, Villenave d'Ornon, France.
Roselyne GautierToxalim (Research Centre in Food Toxicology), Toulouse University, INRAE UMR 1331, ENVT, INP-Purpan, UPS, MetaToul-AXIOM Platform, National Infrastructure of Metabolomics and Fluxomics: MetaboHUB, INRAE, 31027, Toulouse, France.
Daniel JacobINRAE, Univ. Bordeaux, Biologie du Fruit et Pathologie, UMR1332, Bordeaux Metabolome - MetaboHUB, Centre INRAE de Nouvelle-Aquitaine Bordeaux, 33140, Villenave d'Ornon, France.
Cyril JousseUniversité Clermont Auvergne, Clermont Auvergne INP, CNRS, Institut de Chimie de Clermont-Ferrand. Université Clermont Auvergne, INRAE, UNH, Plateforme d'Exploration du Métabolisme, MetaboHUB Clermont, 63000, Clermont-Ferrand, France.
Mélia LacazeToxalim (Research Centre in Food Toxicology), Toulouse University, INRAE UMR 1331, ENVT, INP-Purpan, UPS, MetaToul-AXIOM Platform, National Infrastructure of Metabolomics and Fluxomics: MetaboHUB, INRAE, 31027, Toulouse, France.
Inès Le MaoUniv. Bordeaux, Bordeaux INP, INRAE, OENO, UMR 1366, ISVV, Bordeaux Metabolome - MetaboHUB, 33140, Villenave d'Ornon, France.
Estelle MartineauNantes Université, CNRS, CEISAM UMR 6230, 44000, Nantes, France.
Lindsay PeyrigaTBI, Université de Toulouse, CNRS, INRAE, INSA, MetaboHUB - MetaToul, National Infrastructure of Metabolomics and Fluxomics, 31077, Toulouse, France.
Tristan RichardUniv. Bordeaux, Bordeaux INP, INRAE, OENO, UMR 1366, ISVV, Bordeaux Metabolome - MetaboHUB, 33140, Villenave d'Ornon, France.
Virginie SilvestreNantes Université, CNRS, CEISAM UMR 6230, 44000, Nantes, France.
Mounir TraïkiaUniversité Clermont Auvergne, Clermont Auvergne INP, CNRS, Institut de Chimie de Clermont-Ferrand. Université Clermont Auvergne, INRAE, UNH, Plateforme d'Exploration du Métabolisme, MetaboHUB Clermont, 63000, Clermont-Ferrand, France.
Annick MoingINRAE, Univ. Bordeaux, Biologie du Fruit et Pathologie, UMR1332, Bordeaux Metabolome - MetaboHUB, Centre INRAE de Nouvelle-Aquitaine Bordeaux, 33140, Villenave d'Ornon, France. annick.moing@inrae.fr.ORCID 0000-0003-1144-3600
Patrick GiraudeauNantes Université, CNRS, CEISAM UMR 6230, 44000, Nantes, France. patrick.giraudeau@univ-nantes.fr.ORCID 0000-0001-9346-9147

Funding

Agence Nationale de la Recherche MetaboHUB ANR-11-INBS-0010HORIZON EUROPE European Research Council 814747/SUMMIT
6 · The paper itself

Abstract

introductionAbsolute quantification of individual metabolites in complex biological samples is crucial in targeted metabolomic profiling.

objectivesAn inter-laboratory test was performed to evaluate the impact of the NMR software, peak-area determination method (integration vs. deconvolution) and operator on quantification trueness and precision.

methodsA synthetic urine containing 32 compounds was prepared. One site prepared the urine and calibration samples, and performed NMR acquisition. NMR spectra were acquired with two pulse sequences including water suppression used in routine analyses. The pre-processed spectra were sent to the other sites where each operator quantified the metabolites using internal referencing or external calibration, and his/her favourite in-house, open-access or commercial NMR tool.

resultsFor 1D NMR measurements with solvent presaturation during the recovery delay (zgpr), 20 metabolites were successfully quantified by all processing strategies. Some metabolites could not be quantified by some methods. For internal referencing with TSP, only one half of the metabolites were quantified with a trueness below 5%. With peak integration and external calibration, about 90% of the metabolites were quantified with a trueness below 5%. The NMRProcFlow integration module allowed the quantification of several additional metabolites. The number of quantified metabolites and quantification trueness improved for some metabolites with deconvolution tools. Trueness and precision were not significantly different between zgpr- and NOESYpr-based spectra for about 70% of the variables.

conclusionExternal calibration performed better than TSP internal referencing. Inter-laboratory tests are useful when choosing to better rationalize the choice of quantification tools for NMR-based metabolomic profiling and confirm the value of spectra deconvolution tools.

Indexed as

Body FluidsMetabolomicsFemaleHumansMagnetic Resonance ImagingMagnetic Resonance SpectroscopyMaleWorkflowDeconvolutionMetabolomic profilingPeak integrationQuantitative NMRSynthetic urine

Identifiers

PMID37418094
PMCPMC10328857

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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.