Anton Öberg Sysojev, Bénédicte Delcoigne, Thomas Frisell, Lars Alfredsson, Lars Klareskog, The SRQ biobank group, Saedis Saevarsdottir, Magnus Boman, Leonid Padyukov, Johan Askling, Helga Westerlind
{"title":"常见的遗传变异不影响甲氨蝶呤治疗早期类风湿关节炎结果的临床预测。","authors":"Anton Öberg Sysojev, Bénédicte Delcoigne, Thomas Frisell, Lars Alfredsson, Lars Klareskog, The SRQ biobank group, Saedis Saevarsdottir, Magnus Boman, Leonid Padyukov, Johan Askling, Helga Westerlind","doi":"10.1111/joim.20087","DOIUrl":null,"url":null,"abstract":"<div>\n \n \n <section>\n \n <h3> Background</h3>\n \n <p>Methotrexate (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.</p>\n </section>\n \n <section>\n \n <h3> Methods</h3>\n \n <p>Incident 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.</p>\n </section>\n \n <section>\n \n <h3> Results</h3>\n \n <p>Among 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.</p>\n </section>\n \n <section>\n \n <h3> Conclusions</h3>\n \n <p>Despite 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.</p>\n </section>\n </div>","PeriodicalId":196,"journal":{"name":"Journal of Internal Medicine","volume":"297 6","pages":"693-701"},"PeriodicalIF":9.0000,"publicationDate":"2025-04-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/joim.20087","citationCount":"0","resultStr":"{\"title\":\"Common genetic variants do not impact clinical prediction of methotrexate treatment outcomes in early rheumatoid arthritis\",\"authors\":\"Anton Öberg Sysojev, Bénédicte Delcoigne, Thomas Frisell, Lars Alfredsson, Lars Klareskog, The SRQ biobank group, Saedis Saevarsdottir, Magnus Boman, Leonid Padyukov, Johan Askling, Helga Westerlind\",\"doi\":\"10.1111/joim.20087\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div>\\n \\n \\n <section>\\n \\n <h3> Background</h3>\\n \\n <p>Methotrexate (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.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Methods</h3>\\n \\n <p>Incident 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.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Results</h3>\\n \\n <p>Among 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.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Conclusions</h3>\\n \\n <p>Despite an extensive study population with broad multi-modal data, predicting MTX treatment outcomes remains a challenge. 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Common genetic variants do not impact clinical prediction of methotrexate treatment outcomes in early rheumatoid arthritis
Background
Methotrexate (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.
Methods
Incident 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.
Results
Among 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.
Conclusions
Despite 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.
期刊介绍:
JIM – The Journal of Internal Medicine, in continuous publication since 1863, is an international, peer-reviewed scientific journal. It publishes original work in clinical science, spanning from bench to bedside, encompassing a wide range of internal medicine and its subspecialties. JIM showcases original articles, reviews, brief reports, and research letters in the field of internal medicine.