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。

IF 9 2区 医学 Q1 MEDICINE, GENERAL & INTERNAL
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,&nbsp;Bénédicte Delcoigne,&nbsp;Thomas Frisell,&nbsp;Lars Alfredsson,&nbsp;Lars Klareskog,&nbsp;The SRQ biobank group,&nbsp;Saedis Saevarsdottir,&nbsp;Magnus Boman,&nbsp;Leonid Padyukov,&nbsp;Johan Askling,&nbsp;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,&nbsp;Bénédicte Delcoigne,&nbsp;Thomas Frisell,&nbsp;Lars Alfredsson,&nbsp;Lars Klareskog,&nbsp;The SRQ biobank group,&nbsp;Saedis Saevarsdottir,&nbsp;Magnus Boman,&nbsp;Leonid Padyukov,&nbsp;Johan Askling,&nbsp;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\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Internal Medicine\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://onlinelibrary.wiley.com/doi/10.1111/joim.20087\",\"RegionNum\":2,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"MEDICINE, GENERAL & INTERNAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Internal Medicine","FirstCategoryId":"3","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1111/joim.20087","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MEDICINE, GENERAL & INTERNAL","Score":null,"Total":0}
引用次数: 0

摘要

背景:甲氨蝶呤(MTX)是类风湿关节炎(RA)的主要初始治疗,但个体反应不同,仍然难以预测。基因的作用尚不清楚,但研究表明它的重要性。方法:通过大规模瑞典登记联系确定开始mtx单药治疗的偶发性RA患者。将人口统计学、临床、医学和药物史特征与完全输入的基因型数据相结合,用于训练和评估多种学习模型,以预测关键的MTX治疗结果。结果:在2432例患者中,我们一致观察到估计曲线下面积(AUC)为~ 0.62,优于基于性别和年龄训练的模型。EULAR的主要反应(AUC = 0.67)表现最佳,而模型在预测停药方面表现最差。遗传学对预测质量的改善微不足道。结论:尽管有广泛的研究人群和广泛的多模式数据,但预测MTX治疗结果仍然是一个挑战。常见的基因变异对临床特征的预测能力微乎其微。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Journal of Internal Medicine
Journal of Internal Medicine 医学-医学:内科
CiteScore
22.00
自引率
0.90%
发文量
176
审稿时长
4-8 weeks
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.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信