Evidence map›Paper›PMID 30830401›Full record

ArticleMetabolomics : Official journal of the Metabolomic Society2018

Identification of a select metabolite panel for measuring metabolic perturbation in response to heavy exertion.

David C Nieman, Nicholas D Gillitt, Wei Sha

Abstract read
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In one paragraph

Article in Metabolomics : Official journal of the Metabolomic Society, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed
0.9field-weighted citation impact, top 27% of its field
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

11 citing papers in PubMed, 14 citations in OpenAlex.

  1. Article
  2. Physical Exercise After Solid Organ Transplantation: A Cautionary Tale.Transplant international : official journal of the European Society for Organ Transplantation · 2024
    Review
  3. Review
  4. Article
  5. Review
  6. Article
  7. Review
  8. Exercise immunology: Future directions.Journal of sport and health science · 2020
    Review
  9. Article
  10. Review
  11. 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

3 authors at 3 institutions in 1 country.

David C NiemanHuman Performance Laboratory, Appalachian State University, North Carolina Research Campus, Kannapolis, NC, 28081, USA. niemandc@appstate.edu.ORCID 0000-0002-8305-1860
Nicholas D GillittDole Nutrition Research Laboratory, North Carolina Research Campus, Kannapolis, NC, USA.
Wei ShaBioinformatics Services Division, University of North Carolina at Charlotte, North Carolina Research Campus, Kannapolis, NC, 28081, USA. wsha@uncc.edu.
Appalachian State University · USDavid H. Murdock Research Institute · USUniversity of North Carolina at Charlotte · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

INTRODUCTION AND

objectiveDatabases from three global metabolomics-based studies (N = 59) (PMID: 25409020, 26561314, 29566095) were evaluated for metabolite shifts following heavy exertion (75-km cycling) to generate a representative, select panel of metabolites identified by variable importance in projection (VIP) scores. METHODS AND

resultsOPLS-DA was used to separate samples at pre- and post-exercise during the water-only trial in one of the studies (PMID: 26561314), and of 590 metabolites, 26 (all but one from the lipid pathway) had a VIP > 2 and were selected for the panel. A second OPLS-DA based on the 26 metabolites was performed to separate pre- and post-exercise samples, and this model performed as well as the one with 590 metabolites (Q2Y = 0.923, 0.925 respectively); this model also showed a complete separation using OPLS-DA plots between pre- and post-exercise samples for the other two studies. A latent variable t1 (a linear combination of the 26 metabolites), was generated and the metabolite data at each time point were projected to t1 with the relative distance on t1 and area under the curve (AUC) determined from the three databases. Acute carbohydrate compared to water-only ingestion was linked to a 28-47% reduction in AUCs following exercise depending on the carbohydrate source and recovery time period.

conclusionsThese data support that a panel of 26 metabolites can be used to represent global metabolite increases induced by prolonged, intensive exercise. This select panel includes metabolites primarily from the lipid super pathway, and exercise-induced increases are sensitive to the moderating effect of acute carbohydrate ingestion.

Indexed as

MetabolomePhysical ExertionAdultBiomarkersCarbohydrate MetabolismDietHumansLipid MetabolismMalePhysical Conditioning, HumanBiomarkersCarbohydrateExerciseLipidsMetabolites

Identifiers

PMID30830401
OpenAlexW2899195076

What OpenQuestion holds

Textmetadata
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.