Evidence map›Paper›PMID 38521833›Full record

ArticleScientific reports2024

Unbiased serum metabolomic analysis in cats with naturally occurring chronic enteropathies before and after medical intervention.

Maria Questa, Bart C Weimer, Oliver Fiehn, Betty Chow, Steve L Hill, Mark R Ackermann, Jonathan A Lidbury, Joerg M Steiner, Jan S Suchodolski, Sina Marsilio

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
1.7field-weighted citation impact, top 17% 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

6 citing papers in PubMed, 4 citations in OpenAlex.

  1. Review
  2. Review
  3. Review
  4. Article
  5. Article
  6. 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

10 authors at 5 institutions in 1 country.

Maria QuestaDepartment of Medicine and Epidemiology, School of Veterinary Medicine, University of California, Davis, One Shields Avenue, Davis, CA, 95616, USA.
Bart C WeimerDepartment of Population Health and Reproduction, 100K Pathogen Genome Project, University of California School of Veterinary Medicine, University of California, Davis, Davis, CA, USA.
Oliver FiehnWest Coast Metabolomics Center, University of California Davis, Davis, CA, USA.
Betty ChowVCA Animal Specialty & Emergency Center, Los Angeles, CA, USA.
Steve L HillVeterinary Specialty Hospital, San Diego, CA, USA.
Mark R AckermannUS Department of Agriculture, National Animal Disease Center, Ames, IA, USA.
Jonathan A LidburyGastrointestinal Laboratory, Texas A&M University, College Station, TX, USA.
Joerg M SteinerGastrointestinal Laboratory, Texas A&M University, College Station, TX, USA.
Jan S SuchodolskiGastrointestinal Laboratory, Texas A&M University, College Station, TX, USA.
Sina MarsilioDepartment of Medicine and Epidemiology, School of Veterinary Medicine, University of California, Davis, One Shields Avenue, Davis, CA, 95616, USA. smarsilio@ucdavis.edu.ORCID 0000-0002-0693-0669
University of California, Davis · USTexas A&M University · USCalifornia Animal Hospital · USNational Animal Disease Center · USOrthopedic Specialty Hospital · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Chronic enteropathies (CE) are common disorders in cats and the differentiation between the two main underlying diseases, inflammatory bowel disease (IBD) and low-grade intestinal T-cell lymphoma (LGITL), can be challenging. Characterization of the serum metabolome could provide further information on alterations of disease-associated metabolic pathways and may identify diagnostic or therapeutic targets. Unbiased metabolomics analysis of serum from 28 cats with CE (14 cats with IBD, 14 cats with LGITL) and 14 healthy controls identified 1,007 named metabolites, of which 129 were significantly different in cats with CE compared to healthy controls at baseline. Random Forest analysis revealed a predictive accuracy of 90% for differentiating controls from cats with chronic enteropathy. Metabolic pathways found to be significantly altered included phospholipids, amino acids, thiamine, and tryptophan metabolism. Several metabolites were found to be significantly different between cats with IBD versus LGITL, including several sphingolipids, phosphatidylcholine 40:7, uridine, pinitol, 3,4-dihydroxybenzoic acid, and glucuronic acid. However, random forest analysis revealed a poor group predictive accuracy of 60% for the differentiation of IBD from LGITL. Of 129 compounds found to be significantly different between healthy cats and cats with CE at baseline, 58 remained different following treatment.

Indexed as

Cat DiseasesInflammatory Bowel DiseasesAnimalsCatsMetabolomeMetabolomics

Identifiers

PMID38521833
PMCPMC10960826
OpenAlexW4393118419

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.