A novel multi-biomarker combination predicting relapse from long-term remission after discontinuation of biological drugs in rheumatoid arthritis.
A novel multi-biomarker combination predicting relapse from long-term remission after discontinuation of biological drugs in rheumatoid arthritis.
复制标题
DOI：
10.1038/s41598-021-00357-9
复制
发表时间：
2021-10-21
影响因子：
4.6
通讯作者：
Minota S
中科院分区：
文献类型：
--
作者：
Nagatani K;Sakashita E;Endo H;Minota S
生物疾病修饰抗风湿药物（bDMARDs）在类风湿关节炎（RA）中显示出显著的治疗效果。然而，长期使用bDMARDs具有成本高和感染风险等缺点。因此，需要一种方法来预测任何未来的RA复发。在此，我们报告了一种新的多生物标志物组合，可预测缓解期患者停用bDMARDs后的复发。入选40例缓解期超过12个月的RA患者。停用bDMARD，并在接下来的24个月内每月随访一次。队列中40例患者中有14例（35%）在24个月时保持缓解，而26例（65%）在不同时间点复发。通过探索性多变量ROC分析，对从缓解期患者纵向获得的血清样本进行复发预测生物标志物和来自73种细胞因子的指数的评估。根据5种细胞因子（IL-34、CCL 1、IL-1β、IL-2和IL-19）计算的复发预测指数可强烈区分复发患者和保持缓解的患者。这些发现可能有助于临床决策，即何时停止bDMARD治疗RA。
Biological disease modifying anti-rheumatic drugs (bDMARDs) show dramatic treatment efficacy in rheumatoid arthritis (RA). Long-term use of bDMARDs, however, has disadvantages such as high costs and infection risk. Therefore, a methodology is needed to predict any future RA relapse. Herein, we report a novel multi-biomarker combination which predicts relapse after bDMARDs-withdrawal in patients in remission. Forty patients with RA in remission for more than 12 months were enrolled. bDMARDs were withdrawn and they were followed monthly for the next 24 months. Fourteen patients (35%) of 40 in the cohort remained in remission at 24 months, whereas 26 (65%) relapsed at various time-points. Serum samples obtained longitudinally from patients in remission were assessed for the relapse-prediction biomarkers and index from 73 cytokines by the exploratory multivariate ROC analysis. The relapse-prediction index calculated from the 5 cytokines, IL-34, CCL1, IL-1β, IL-2 and IL-19, strongly discriminated between patients who relapsed and those who stayed in remission. These findings could contribute to clinical decision-making as to the timing of when to discontinue bDMARDs in RA treatment.
登录
查看更多内容
影响因子：
3.7
作者：
Centola M;Cavet G;Shen Y;Ramanujan S;Knowlton N;Swan KA;Turner M;Sutton C;Smith DR;Haney DJ;Chernoff D;Hesterberg LK;Carulli JP;Taylor PC;Shadick NA;Weinblatt ME;Curtis JR
通讯作者：
Curtis JR
影响因子：
4.9
作者：
Aletaha D;Nell VP;Stamm T;Uffmann M;Pflugbeil S;Machold K;Smolen JS
通讯作者：
Smolen JS
对医疗统计信息的自由使用的易于使用的软件“ EZR”的调查。
影响因子：
4.8
作者：
Kanda Y
通讯作者：
Kanda Y
影响因子：
4.1
作者：
Ichinose K;Shimizu T;Umeda M;Fukui S;Nishino A;Koga T;Kawashiri SY;Iwamoto N;Tamai M;Nakamura H;Sato S;Origuchi T;Kawakami A
通讯作者：
Kawakami A
影响因子：
2.7
作者：
Benjamini, Yoav;Krieger, Abba M.;Yekutieli, Daniel
通讯作者：
Yekutieli, Daniel