Evidence map›Paper›PMID 40190030›Full record

ArticleJournal of internal medicine2025

Common genetic variants do not impact clinical prediction of methotrexate treatment outcomes in early rheumatoid arthritis.

Anton Öberg Sysojev, Bénédicte Delcoigne, Thomas Frisell, Lars Alfredsson, Lars Klareskog, SRQ biobank group, Saedis Saevarsdottir, Magnus Boman, Leonid Padyukov, Johan Askling and 1 more

Abstract read
In one paragraph

Article in Journal of internal medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
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

1 citing paper in PubMed.

  1. Article
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.

Anton Öberg SysojevClinical Epidemiology Division, Department of Medicine Solna, Karolinska Institutet, Stockholm, Sweden.ORCID 0000-0003-3221-7979
Bénédicte DelcoigneClinical Epidemiology Division, Department of Medicine Solna, Karolinska Institutet, Stockholm, Sweden.
Thomas FrisellClinical Epidemiology Division, Department of Medicine Solna, Karolinska Institutet, Stockholm, Sweden.
Lars AlfredssonInstitute of Environmental Medicine (IMM), Karolinska Institutet, Stockholm, Sweden.
Lars KlareskogDivision of Rheumatology, Department of Medicine Solna, Karolinska Institutet and Karolinska University Hospital, Stockholm, Sweden.ORCID 0000-0001-9601-6186
SRQ biobank group
Saedis SaevarsdottirClinical Epidemiology Division, Department of Medicine Solna, Karolinska Institutet, Stockholm, Sweden.
Magnus BomanClinical Epidemiology Division, Department of Medicine Solna, Karolinska Institutet, Stockholm, Sweden.
Leonid PadyukovDivision of Rheumatology, Department of Medicine Solna, Karolinska Institutet and Karolinska University Hospital, Stockholm, Sweden.
Johan AsklingClinical Epidemiology Division, Department of Medicine Solna, Karolinska Institutet, Stockholm, Sweden.
Helga WesterlindClinical Epidemiology Division, Department of Medicine Solna, Karolinska Institutet, Stockholm, Sweden.ORCID 0000-0003-3380-5342

Funding

CancerfondenEuropean Research CouncilHjärt-LungfondenKarolinska InstitutetNordforskReumatikerförbundetStiftelsen Konung Gustaf V:s 80-årsfondStiftelsen Professor Nanna Svartz FondVinnova
6 · The paper itself

Abstract

backgroundMethotrexate (MTX) is the mainstay initial treatment of rheumatoid arthritis (RA), but individual response varies and remains difficult to predict. The role of genetics remains unclear, but studies suggest its importance.

methodsIncident RA patients starting MTX-monotherapy were identified through a large-scale Swedish register linkage. Demographic, clinical, medical, and drug history features were combined with fully imputed genotype data and used to train and evaluate multiple learning models to predict key MTX treatment outcomes.

resultsAmong 2432 patients, we consistently observed an estimated area under the curve (AUC) of ∼0.62, outperforming models trained on sex and age. The best performance was observed for EULAR primary response (AUC = 0.67), whereas models struggled the most with predicting discontinuation. Genetics provided negligible improvements to prediction quality.

conclusionsDespite an extensive study population with broad multi-modal data, predicting MTX treatment outcomes remains a challenge. Common genetic variants added minimal predictive power over clinical features.

Indexed as

Antirheumatic AgentsArthritis, RheumatoidGenetic VariationMethotrexateAdultAgedFemaleGenotypeHumansMaleMiddle AgedRegistriesSwedenTreatment OutcomeAntirheumatic AgentsMethotrexatemachine learningmethotrexatepharmacogeneticspolymorphismrheumatoid arthritissingle nucleotide

Identifiers

PMID40190030
PMCPMC12087826

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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.