Evidence map›Paper›PMID 19460157›Full record

ArticleArthritis research & therapy2009

Blood autoantibody and cytokine profiles predict response to anti-tumor necrosis factor therapy in rheumatoid arthritis.

Wolfgang Hueber, Beren H Tomooka, Franak Batliwalla, Wentian Li, Paul A Monach, Robert J Tibshirani, Ronald F Van Vollenhoven, Jon Lampa, Kazuyoshi Saito, Yoshiya Tanaka and 4 more

Registry-linked trialOpen access · goldAbstract readComparative Study
In one paragraph

Article in Arthritis research & therapy, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT02878161 (Screening Protein Predictive of Response to Tumor Necrosis Factor-α Inhibitors Treatment in Chinese Rheumatoid Arthritis From "Real World" and Investigating Its Mechanism Through Signal Pathway), which is not on this map. Cited by 50 papers.

0numbers the graph read from it
0cells of the map it votes in
50citing papers in PubMed
11.1field-weighted citation impact, top 1% 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.

NCT02878161 phase4unknown statusnot on this mapstarted 2016, after this paper: background citation

Screening Protein Predictive of Response to Tumor Necrosis Factor-α Inhibitors Treatment in Chinese Rheumatoid Arthritis From "Real World" and Investigating Its Mechanism Through Signal Pathway

TypeinterventionalSponsorFen LiRan2016 to 2019Enrolled240ConditionsRheumatoid ArthritisArmsmethotrexate(necessary), infliximab, etanercept, adalimumab, leflunomide (permitted, not necessary)
3 · Its place in the literature

Who cites it

50 citing papers in PubMed, 117 citations in OpenAlex.

  1. Trial
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  4. Towards Personalized Medicine in Rheumatoid Arthritis.Open access rheumatology : research and reviews · 2024
    Review
  5. Article
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  9. Review
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  11. Article
  12. Article
  13. Review
  14. Review
  15. Review
  16. Article
  17. Synovial tissue research: a state-of-the-art review.Nature reviews. Rheumatology · 2017
    Review
  18. Article
  19. Article
  20. Review
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

14 authors at 6 institutions in 3 countries.

Wolfgang HueberDepartment of Medicine, Division of Immunology & Rheumatology, Stanford University, Stanford, CA 94305, USA. whueber@stanford.edu
Beren H Tomooka
Franak Batliwalla
Wentian Li
Paul A Monach
Robert J Tibshirani
Ronald F Van Vollenhoven
Jon Lampa
Kazuyoshi Saito
Yoshiya Tanaka
Mark C Genovese
Lars Klareskog
Peter K Gregersen
William H Robinson
Feinstein Institute for Medical Research · USKarolinska Institutet · SEStanford University · USUniversity of Occupational and Environmental Health Japan · JPVA Palo Alto Health Care System · USBrigham and Women's Hospital · US

Funding

Anti-Fibrinogen Autoimmunity in Rheumatoid ArthritisR21AI069160 · NIAID · STANFORD UNIVERSITY · PI ROBINSON, WILLIAM H · 2007 to 2008
$454k
NHLBI PROTEOMICS INITIATIVE-N01HV28183N01HV028183 · HV · STANFORD UNIVERSITY · PI NOLAN, GARRY P · 2002 to 2006
–
NHLBI NIH HHS N01 HV028183NIAID NIH HHS AI069160NIAID NIH HHS R21 AI069160
6 · The paper itself

Abstract

introductionAnti-TNF therapies have revolutionized the treatment of rheumatoid arthritis (RA), a common systemic autoimmune disease involving destruction of the synovial joints. However, in the practice of rheumatology approximately one-third of patients demonstrate no clinical improvement in response to treatment with anti-TNF therapies, while another third demonstrate a partial response, and one-third an excellent and sustained response. Since no clinical or laboratory tests are available to predict response to anti-TNF therapies, great need exists for predictive biomarkers.

methodsHere we present a multi-step proteomics approach using arthritis antigen arrays, a multiplex cytokine assay, and conventional ELISA, with the objective to identify a biomarker signature in three ethnically diverse cohorts of RA patients treated with the anti-TNF therapy etanercept.

resultsWe identified a 24-biomarker signature that enabled prediction of a positive clinical response to etanercept in all three cohorts (positive predictive values 58 to 72%; negative predictive values 63 to 78%).

conclusionsWe identified a multi-parameter protein biomarker that enables pretreatment classification and prediction of etanercept responders, and tested this biomarker using three independent cohorts of RA patients. Although further validation in prospective and larger cohorts is needed, our observations demonstrate that multiplex characterization of autoantibodies and cytokines provides clinical utility for predicting response to the anti-TNF therapy etanercept in RA patients.

Indexed as

AdultAgedAntirheumatic AgentsArthritis, RheumatoidAutoantibodiesBiomarkersCohort StudiesCytokinesEtanerceptFemaleHumansImmunoglobulin GMaleMiddle AgedPredictive Value of TestsReceptors, Tumor Necrosis FactorAntirheumatic AgentsAutoantibodiesBiomarkersCytokinesEtanerceptImmunoglobulin GReceptors, Tumor Necrosis FactorTumor Necrosis Factor-alpha

Identifiers

PMID19460157
PMCPMC2714123
OpenAlexW2092829005

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

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.