Evidence map›Paper›PMID 36474953›Full record

ArticleOsteoarthritis and cartilage open2022

Identification of a differential metabolite-based signature in patients with late-stage knee osteoarthritis.

Jason S Rockel, Mehdi Layeghifard, Y Raja Rampersaud, Anthony V Perruccio, Nizar N Mahomed, J Roderick Davey, Khalid Syed, Rajiv Gandhi, Mohit Kapoor

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Osteoarthritis and cartilage open, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed, 1 pooled it
–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

6 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Osteoarthritis as a systemic disease.Nature reviews. Rheumatology · 2026
    Review
  4. Review
  5. Review
  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

9 authors.

Jason S RockelSchroeder Arthritis Institute, University Health Network, Toronto, ON, Canada.
Mehdi LayeghifardSchroeder Arthritis Institute, University Health Network, Toronto, ON, Canada.
Y Raja RampersaudSchroeder Arthritis Institute, University Health Network, Toronto, ON, Canada.
Anthony V PerruccioSchroeder Arthritis Institute, University Health Network, Toronto, ON, Canada.
Nizar N MahomedSchroeder Arthritis Institute, University Health Network, Toronto, ON, Canada.
J Roderick DaveySchroeder Arthritis Institute, University Health Network, Toronto, ON, Canada.
Khalid SyedSchroeder Arthritis Institute, University Health Network, Toronto, ON, Canada.
Rajiv GandhiSchroeder Arthritis Institute, University Health Network, Toronto, ON, Canada.
Mohit KapoorSchroeder Arthritis Institute, University Health Network, Toronto, ON, Canada.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: Multiple disease phenotypes have been identified in knee osteoarthritis (OA) patients based on anthropometric, sociodemographic and clinical factors; however, differential systemic metabolite-based signatures in OA patients are not well understood. We sought to identify differential plasma metabolome signatures in a cross-sectional sample of late-stage knee OA patients. Methods: Plasma from 214 (56.5% female; mean age ​= ​67.58 years) non-diabetic, non-obese (BMI <30 ​kg/m Results: Two patient clusters were determined based on the levels of 151 metabolites identified. Metabolite signature refinement found 24 metabolites could accurately predict cluster classification within the sample (AUC ​= ​0.921). Fifty-six genes were linked to at least 2 ​KEGG annotated metabolites. Pathway analysis found 26/56 genes were linked to enriched pathways including tRNA acylation and B-vitamin metabolism. Conclusion: This study demonstrates systemic metabolites can classify a cross-sectional cohort of OA patients into distinct clusters. Links between metabolites, genes and pathways can help determine biological differences between OA patients, potentially improving precision medicine and decision-making.

Indexed as

Cross-sectionalKnee osteoarthritisMetabolomicsPathway enrichmentSignature

Identifiers

PMID36474953
PMCPMC9718221

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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.