Evidence map›Paper›PMID 32647218›Full record

ArticleScientific reports2020

Multivariable logistic and linear regression models for identification of clinically useful biomarkers for osteoarthritis.

Yulia Liem, Andrew Judge, John Kirwan, Khadija Ourradi, Yunfei Li, Mohammed Sharif

Open access · goldAbstract read
In one paragraph

Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.

0numbers the graph read from it
0cells of the map it votes in
23citing papers in PubMed
5.3field-weighted citation impact, top 3% 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

23 citing papers in PubMed, 46 citations in OpenAlex.

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  8. Methods in DNA methylation array dataset analysis: A review.Computational and structural biotechnology journal · 2024
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  17. Clinical monitoring in osteoarthritis: Biomarkers.Osteoarthritis and cartilage · 2022
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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

6 authors at 2 institutions in 1 country.

Yulia LiemTranslational Health Sciences, Bristol Medical School, Musculoskeletal Research Unit, Southmead Hospital, University of Bristol, Learning and Research Building (Level 2), Bristol, BS10 5NB, UK.
Andrew JudgeTranslational Health Sciences, Bristol Medical School, Musculoskeletal Research Unit, Southmead Hospital, University of Bristol, Learning and Research Building (Level 2), Bristol, BS10 5NB, UK.
John KirwanUniversity of Bristol, Biomedical Sciences Building, University Walk, Bristol, UK.
Khadija OurradiTranslational Health Sciences, Bristol Medical School, Musculoskeletal Research Unit, Southmead Hospital, University of Bristol, Learning and Research Building (Level 2), Bristol, BS10 5NB, UK.
Yunfei LiTranslational Health Sciences, Bristol Medical School, Musculoskeletal Research Unit, Southmead Hospital, University of Bristol, Learning and Research Building (Level 2), Bristol, BS10 5NB, UK.
Mohammed SharifTranslational Health Sciences, Bristol Medical School, Musculoskeletal Research Unit, Southmead Hospital, University of Bristol, Learning and Research Building (Level 2), Bristol, BS10 5NB, UK. mo.sharif@bristol.ac.uk.
Southmead Hospital · GBUniversity of Bristol · GB

Funding

Versus Arthritis
6 · The paper itself

Abstract

Osteoarthritis (OA) is the most common chronic degenerative joint disease which causes substantial joint pain, deformity and loss of activities of daily living. Currently, there are over 500 million OA cases worldwide, and there is an urgent need to identify biomarkers for early detection, and monitoring disease progression in patients without obvious radiographic damage to the joint. We have used regression modelling to describe the association of 19 of the currently available biomarkers (predictors) with key radiographic and clinical features of OA (outcomes) in one of the largest and best characterised OA cohort (NIH Osteoarthritis Initiative). We demonstrate that of the 19 currently available biomarkers only 4 (serum Coll2-1 NO2, CS846, COMP and urinary CTXII) were consistently associated with established radiographic and/or clinical features of OA. These biomarkers are independent of one another and provide additional predictive power over, and above established predictors of OA such as age, gender, BMI and race. We also show that that urinary CTXII had the strongest and consistent associations with clinical symptoms of OA as well as radiographic evidence of joint damage. Accordingly, urinary CTXII may aid in early diagnosis of OA in symptomatic patients without radiographic evidence of OA.

Indexed as

AgedBiomarkersCartilage Oligomeric Matrix ProteinChondroitin SulfatesCollagen Type IIDisease ProgressionEarly DiagnosisFemaleHumansLinear ModelsLogistic ModelsMaleMiddle AgedMultivariate AnalysisOsteoarthritis, KneePeptide FragmentsBiomarkersCartilage Oligomeric Matrix ProteinChondroitin SulfatesCollagen Type IIcollagen type II (108-116), (NO2)Tyr(111)-C-terminal cross-linking telopeptide of type II collagen, humanPeptide FragmentsTSP5 protein, human

Identifiers

PMID32647218
PMCPMC7347626
OpenAlexW3041082121

What OpenQuestion holds

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LicenceCC BY
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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.