Evidence map›Paper›PMID 36308549›Full record

ArticleHandbook of experimental pharmacology2023

Quantitative NMR Methods in Metabolomics.

G A Nagana Gowda, Daniel Raftery

Abstract read
In one paragraph

Article in Handbook of experimental pharmacology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing 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

13 citing papers in PubMed.

  1. Artificial intelligence in diabetic retinopathy: from automated screening to risk-stratified care.Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie · 2026
    Review
  2. Article
  3. Article
  4. Review
  5. Article
  6. Article
  7. Review
  8. Methods and protocols · 2025
    Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. NMR-based metabolomics: Where are we now and where are we going?Progress in nuclear magnetic resonance spectroscopy
    Review
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

2 authors.

G A Nagana GowdaNorthwest Metabolomics Research Center, University of Washington, Seattle, WA, USA. ngowda@uw.edu.
Daniel RafteryNorthwest Metabolomics Research Center, University of Washington, Seattle, WA, USA. draftery@uw.edu.

Funding

Modeling Homeostasis of Human Blood MetabolitesR01GM131491 · NIGMS · UNIVERSITY OF WASHINGTON · PI RAFTERY, DANIEL, ZHANG, DABAO · 2020 to 2023
$2.2M
Quantitative Analysis of Labile Metabolites in Biological SamplesR01GM138465 · NIGMS · UNIVERSITY OF WASHINGTON · PI GOWDA, G. A. NAGANA, RAFTERY, DANIEL · 2021 to 2024
$1.5M
NIGMS NIH HHS R01 GM131491NIGMS NIH HHS R01 GM138465
6 · The paper itself

Abstract

Nuclear Magnetic Resonance (NMR) spectroscopy is one of the two major analytical platforms in the field of metabolomics, the other being mass spectrometry (MS). NMR is less sensitive than MS and hence it detects a relatively small number of metabolites. However, NMR exhibits numerous unique characteristics including its high reproducibility and non-destructive nature, its ability to identify unknown metabolites definitively, and its capabilities to obtain absolute concentrations of all detected metabolites, sometimes even without an internal standard. These characteristics outweigh the relatively low sensitivity and resolution of NMR in metabolomics applications. Since biological mixtures are highly complex, increased demand for new methods to improve detection, better identify unknown metabolites, and provide more accurate quantitation continues unabated. Technological and methodological advances to date have helped to improve the resolution and sensitivity and detection of a larger number of metabolite signals. Efforts focused on measuring unknown metabolite signals have resulted in the identification and quantitation of an expanded pool of metabolites including labile metabolites such as cellular redox coenzymes, energy coenzymes, and antioxidants. This chapter describes quantitative NMR methods in metabolomics with an emphasis on recent methodological developments, while highlighting the benefits and challenges of NMR-based metabolomics.

Indexed as

Magnetic Resonance ImagingMetabolomicsCoenzymesHumansMagnetic Resonance SpectroscopyReproducibility of ResultsCoenzymesFast NMR methodsIsotope taggingMetabolomicsNuclear magnetic resonance (NMR)Quantitation

Identifiers

PMID36308549
PMCPMC9992124

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

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.