Evidence map›Paper›PMID 32909363›Full record

ArticleArthritis & rheumatology (Hoboken, N.J.)2021

Multiomics and Machine Learning Accurately Predict Clinical Response to Adalimumab and Etanercept Therapy in Patients With Rheumatoid Arthritis.

Weiyang Tao, Arno N Concepcion, Marieke Vianen, Anne C A Marijnissen, Floris P G J Lafeber, Timothy R D J Radstake, Aridaman Pandit

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

Article in Arthritis & rheumatology (Hoboken, N.J.), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 86 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
86citing papers in PubMed, 3 pooled it
16.3field-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.

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

86 citing papers in PubMed, 3 syntheses or guidelines pooled it, 154 citations in OpenAlex.

  1. Pooled it
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  12. Real-World Data and Causal Machine Learning to Enhance Drug Development.Therapeutic innovation & regulatory science · 2025
    Review
  13. Rheumatoid Arthritis: Biomarkers and the Latest Breakthroughs.International journal of molecular sciences · 2025
    Review
  14. Review
  15. Article
  16. Omics in IgG4-related disease.Chinese medical journal · 2025
    Review
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  19. Article
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26 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

7 authors at 1 institution in 1 country.

Weiyang TaoUniversity Medical Center Utrecht and Utrecht University, The Netherlands.
Arno N ConcepcionUniversity Medical Center Utrecht and Utrecht University, The Netherlands.
Marieke VianenUniversity Medical Center Utrecht and Utrecht University, The Netherlands.
Anne C A MarijnissenUniversity Medical Center Utrecht and Utrecht University, The Netherlands.
Floris P G J LafeberUniversity Medical Center Utrecht and Utrecht University, The Netherlands.
Timothy R D J RadstakeUniversity Medical Center Utrecht and Utrecht University, The Netherlands.
Aridaman PanditUniversity Medical Center Utrecht and Utrecht University, The Netherlands.ORCID 0000-0003-2057-9737
Utrecht University · NL

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveTo predict response to anti-tumor necrosis factor (anti-TNF) prior to treatment in patients with rheumatoid arthritis (RA), and to comprehensively understand the mechanism of how different RA patients respond differently to anti-TNF treatment.

methodsGene expression and/or DNA methylation profiling on peripheral blood mononuclear cells (PBMCs), monocytes, and CD4+ T cells obtained from 80 RA patients before they began either adalimumab (ADA) or etanercept (ETN) therapy was studied. After 6 months, treatment response was evaluated according to the European League Against Rheumatism criteria for disease response. Differential expression and methylation analyses were performed to identify the response-associated transcription and epigenetic signatures. Using these signatures, machine learning models were built by random forest algorithm to predict response prior to anti-TNF treatment, and were further validated by a follow-up study.

resultsTranscription signatures in ADA and ETN responders were divergent in PBMCs, and this phenomenon was reproduced in monocytes and CD4+ T cells. The genes up-regulated in CD4+ T cells from ADA responders were enriched in the TNF signaling pathway, while very few pathways were differential in monocytes. Differentially methylated positions (DMPs) were strongly hypermethylated in responders to ETN but not to ADA. The machine learning models for the prediction of response to ADA and ETN using differential genes reached an overall accuracy of 85.9% and 79%, respectively. The models using DMPs reached an overall accuracy of 84.7% and 88% for ADA and ETN, respectively. A follow-up study validated the high performance of these models.

conclusionOur findings indicate that machine learning models based on molecular signatures accurately predict response before ADA and ETN treatment, paving the path toward personalized anti-TNF treatment.

Indexed as

DNA MethylationGene Expression ProfilingAdalimumabAdultAgedArthritis, RheumatoidCD4-Positive T-LymphocytesClinical Decision RulesEtanerceptFemaleHumansLeukocytes, MononuclearMachine LearningMaleMiddle AgedMonocytesAdalimumabEtanerceptTumor Necrosis Factor Inhibitors

Identifiers

PMID32909363
PMCPMC7898388
OpenAlexW3083830923

What OpenQuestion holds

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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.