Evidence map›Paper›PMID 29958292›Full record

ArticlePloS one2018

A classification modeling approach for determining metabolite signatures in osteoarthritis.

Jason S Rockel, Weidong Zhang, Konstantin Shestopaloff, Sergei Likhodii, Guang Sun, Andrew Furey, Edward Randell, Kala Sundararajan, Rajiv Gandhi, Guangju Zhai and 1 more

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

Article in PloS one, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed, 1 pooled it
2.8field-weighted citation impact, top 10% 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

13 citing papers in PubMed, 1 synthesis or guideline pooled it, 25 citations in OpenAlex.

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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

11 authors at 3 institutions in 3 countries.

Jason S RockelArthritis Program, Toronto Western Hospital, University Health Network, University of Toronto, Toronto, Ontario, Canada.
Weidong ZhangDiscipline of Genetics, Faculty of Medicine, Memorial University of Newfoundland, St John's, Newfoundland, Canada.
Konstantin ShestopaloffArthritis Program, Toronto Western Hospital, University Health Network, University of Toronto, Toronto, Ontario, Canada.
Sergei LikhodiiDepartment of Laboratory Medicine, Faculty of Medicine, Memorial University of Newfoundland, St John's, Newfoundland, Canada.
Guang SunDiscipline of Medicine, Faculty of Medicine, Memorial University of Newfoundland, St John's, Newfoundland, Canada.
Andrew FureyDepartment of Surgery, Faculty of Medicine, Memorial University of Newfoundland, St John's, Newfoundland, Canada.
Edward RandellDepartment of Laboratory Medicine, Faculty of Medicine, Memorial University of Newfoundland, St John's, Newfoundland, Canada.
Kala SundararajanArthritis Program, Toronto Western Hospital, University Health Network, University of Toronto, Toronto, Ontario, Canada.
Rajiv GandhiArthritis Program, Toronto Western Hospital, University Health Network, University of Toronto, Toronto, Ontario, Canada.
Guangju ZhaiDiscipline of Genetics, Faculty of Medicine, Memorial University of Newfoundland, St John's, Newfoundland, Canada.
Mohit KapoorArthritis Program, Toronto Western Hospital, University Health Network, University of Toronto, Toronto, Ontario, Canada.ORCID 0000-0001-9877-0772
Memorial University of Newfoundland · CAUniversity Health Network · CAUniversity of Tasmania · AU

Funding

Canadian Institute of Health Research FRN 132178Canadian Institute of Health Research FRN 143058Canadian Institute of Health Research FRN 153298Canadian Institute of Health Research MOP 126016
6 · The paper itself

Abstract

Multiple factors can help predict knee osteoarthritis (OA) patients from healthy individuals, including age, sex, and BMI, and possibly metabolite levels. Using plasma from individuals with primary OA undergoing total knee replacement and healthy volunteers, we measured lysophosphatidylcholine (lysoPC) and phosphatidylcholine (PC) analogues by metabolomics. Populations were stratified on demographic factors and lysoPC and PC analogue signatures were determined by univariate receiver-operator curve (AUC) analysis. Using signatures, multivariate classification modeling was performed using various algorithms to select the most consistent method as measured by AUC differences between resampled training and test sets. Lists of metabolites indicative of OA [AUC > 0.5] were identified for each stratum. The signature from males age > 50 years old encompassed the majority of identified metabolites, suggesting lysoPCs and PCs are dominant indicators of OA in older males. Principal component regression with logistic regression was the most consistent multivariate classification algorithm tested. Using this algorithm, classification of older males had fair power to classify OA patients from healthy individuals. Thus, individual levels of lysoPC and PC analogues may be indicative of individuals with OA in older populations, particularly males. Our metabolite signature modeling method is likely to increase classification power in validation cohorts.

Indexed as

Arthroplasty, Replacement, KneeBiomarkersCohort StudiesFemaleHumansMaleMetabolomicsMiddle AgedModels, BiologicalOsteoarthritis, KneeBiomarkers

Identifiers

PMID29958292
PMCPMC6025859
OpenAlexW2810354392

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.