Evidence map›Paper›PMID 26176225›Full record

ArticlePloS one2015

Pretreatment Prediction of Individual Rheumatoid Arthritis Patients' Response to Anti-Cytokine Therapy Using Serum Cytokine/Chemokine/Soluble Receptor Biomarkers.

Kazuko Uno, Kazuyuki Yoshizaki, Mitsuhiro Iwahashi, Jiro Yamana, Seizo Yamana, Miki Tanigawa, Katsumi Yagi

Open access · goldAbstract read
In one paragraph

Article in PloS one, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.

0numbers the graph read from it
0cells of the map it votes in
16citing papers in PubMed
3.0field-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

16 citing papers in PubMed, 37 citations in OpenAlex.

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  3. Towards Personalized Medicine in Rheumatoid Arthritis.Open access rheumatology : research and reviews · 2024
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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

7 authors at 2 institutions in 1 country.

Kazuko UnoDepartment of Organic Fine Chemicals, Division of Biological and Molecular Sciences, The Institute of Scientific and Industrial Research, Osaka University, Suita, Osaka, Japan; Louis Pasteur Center for Medical Research, Kyoto, Kyoto, Japan.
Kazuyuki YoshizakiDepartment of Organic Fine Chemicals, Division of Biological and Molecular Sciences, The Institute of Scientific and Industrial Research, Osaka University, Suita, Osaka, Japan; Tokushukai Medical Corporation, Tokyo, Japan.
Mitsuhiro IwahashiHigashi Hiroshima Memorial Hospital, Higashi Hiroshima, Hiroshima, Japan.
Jiro YamanaHigashi Hiroshima Memorial Hospital, Higashi Hiroshima, Hiroshima, Japan.
Seizo YamanaHigashi Hiroshima Memorial Hospital, Higashi Hiroshima, Hiroshima, Japan.
Miki TanigawaDepartment of Organic Fine Chemicals, Division of Biological and Molecular Sciences, The Institute of Scientific and Industrial Research, Osaka University, Suita, Osaka, Japan.
Katsumi YagiLouis Pasteur Center for Medical Research, Kyoto, Kyoto, Japan.
Osaka University · JPLouis Pasteur Center for Medical Research · JP

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

unlabelledThe inability to match rheumatoid arthritis (RA) patients with the anti-cytokine agent most efficacious for them is a major hindrance to patients’ speedy recovery and to the clinical use of anti-cytokine therapy. Identifying predictive biomarkers that can assist in matching RA patients with more suitable anti-cytokine treatment was our aim in this report. The sample consisted of 138 RA patients (naïve and non-naïve) who were administered tocilizumab or etanercept for a minimum of 16 weeks as a prescribed RA treatment. Pretreatment serum samples were obtained from patients and clinical measures of their disease activity were evaluated at baseline and 16 weeks after treatment commenced. Using patients’ pretreatment serum, we measured 31 cytokines/chemokines/soluble receptors and used multiple linear regression analysis to identify biomarkers that correlated with patients’ symptom levels (DAS28-CRP score) at week 16 and multiple logistic analyses for biomarkers that correlated with patients’ final outcome. The results revealed that sgp130, logIL-6, logIL-8, logEotaxin, logIP-10, logVEGF, logsTNFR-I and logsTNFR-II pretreatment serum levels were predictive of the week 16 DAS28-CRP score in naïve tocilizumab patients while sgp130, logGM-CSF and logIP-10 were predictive in non-naïve patients. Additionally, we found logIL-9, logVEGF and logTNF-α to be less reliable at predicting the week 16 DAS28-CRP score in naïve etanercept patients. Multiple linear regression and multiple logistic regression analyses identified biomarkers that were predictive of remission/non-remission in tocilizumab and etanercept therapy. Although less reliable than those for tocilizumab, we identified a few possible biomarkers for etanercept therapy. The biomarkers for these two therapies differ suggesting that their efficacy will vary for individual patients. We discovered biomarkers in RA pretreatment serum that predicted their week 16 DAS28-CRP score and clinical outcome to tocilizumab therapy. Most of these biomarkers, especially sgp130, are involved in RA pathogenesis and IL-6 signal transduction, which further suggests that they are highly reliable.

trial registrationUMIN-CTR Clinical Trial UMIN000016298.

Indexed as

Antibodies, Monoclonal, HumanizedAntirheumatic AgentsArthritis, RheumatoidBiomarkersChemokinesCytokine Receptor gp130EtanerceptFemaleHumansMaleMiddle AgedReceptors, ChemokineRegression AnalysisRemission InductionROC CurveSolubilityAntibodies, Monoclonal, HumanizedAntirheumatic AgentsBiomarkersChemokinesCytokine Receptor gp130EtanerceptReceptors, Chemokinetocilizumab

Identifiers

PMID26176225
PMCPMC4503565
OpenAlexW1517711090

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.