Objective Anti–tumor necrosis factor α (anti-TNF) therapy is a mainstay of treatment in rheumatoid arthritis (RA). The aim of the present study was to test established RA genetic risk factors to determine whether the same alleles also influence the response to anti-TNF therapy. Methods A total of 1,283 RA patients receiving etanercept, infliximab, or adalimumab therapy were studied from among an international collaborative consortium of 9 different RA cohorts. The primary end point compared RA patients with a good treatment response according to the European League Against Rheumatism (EULAR) response criteria (n = 505) with RA patients considered to be nonresponders (n = 316). The secondary end point was the change from baseline in the level of disease activity according to the Disease Activity Score in 28 joints (ΔDAS28). Clinical factors such as age, sex, and concomitant medications were tested as possible correlates of treatment response. Thirty-one single-nucleotide polymorphisms (SNPs) associated with the risk of RA were genotyped and tested for any association with treatment response, using univariate and multivariate logistic regression models. Results Of the 31 RA-associated risk alleles, a SNP at the PTPRC (also known as CD45) gene locus (rs10919563) was associated with the primary end point, a EULAR good response versus no response (odds ratio [OR] 0.55, P = 0.0001 in the multivariate model). Similar results were obtained using the secondary end point, the ΔDAS28 (P = 0.0002). There was suggestive evidence of a stronger association in autoantibody-positive patients with RA (OR 0.55, 95% confidence interval [95% CI] 0.39–0.76) as compared with autoantibody-negative patients (OR 0.90, 95% CI 0.41–1.99). Conclusion Statistically significant associations were observed between the response to anti-TNF therapy and an RA risk allele at the PTPRC gene locus. Additional studies will be required to replicate this finding in additional patient collections.

Two new pathways, T cell receptor signaling and a hematopoietic-specific signal transduction pathway, have been discovered that allow future pharmacologic interventions. The description of the new genetic risk factors and the interaction with environmental triggers as well as phenotypic features are gradually expanding the ability to predict disease susceptibility and course.

Background Based on different genetic and environmental risk factors and histology, it has been proposed that rheumatoid arthritis (RA) consists of 2 types: autoantibody-positive and autoantibody-negative RA. However, until now, this remained hypothetical. To assess this hypothesis, we studied whether the long-term outcomes differed for these 2 groups of RA patients. Methods and findings In the Leiden Early Arthritis Clinic cohort, 1,285 consecutive RA patients were included between 1993 and 2016 and followed yearly. Treatment protocols in routine care improved over time, irrespective of autoantibody status, and 5 inclusion periods were used as instrumental variables: 1993-1996, delayed mild disease-modifying antirheumatic drug (DMARD) initiation (reference period); 1997-2000, early mild DMARDs; 2001-2005, early methotrexate; 2006-2010, early methotrexate followed by treat-to-target adjustments; 2011-2016, similar to 2006-2010 plus additional efforts for very early referral. Three longterm outcomes were studied: sustained DMARD-free remission (SDFR) (persistent absence of clinical synovitis after DMARD cessation), mortality, and functional disability measured by yearly Health Assessment Questionnaire (HAQ). Treatment response in the short term (disease activity) was measured by Disease Activity Score-28 with erythrocyte sedimentation rate (DAS28-ESR). Linear mixed models and Cox regression were used, stratified for autoantibody positivity, defined as IgG anti-CCP2 and/or IgM rheumatoid factor positivity. In total, 823 patients had autoantibody-positive RA (mean age 55 years, 67% female); 462 patients had autoantibody-negative RA (age 60 years, 64% female). Age, gender, and percentage of autoantibody-positive patients were stable throughout the inclusion periods. Disease activity significantly decreased over time within both groups. SDFR rates

Our findings indicate that determination of RF level is subject to large variation; high RF level has limited additive prognostic value compared to ACPA positivity. Thus, omitting RF level and using RF presence, ACPA presence, and ACPA level may improve the 2010 criteria for RA.

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A 1-year course of MTX delayed and prevented RA development in high-risk UA patients. This emphasizes the importance of adequate risk prediction in trials that aim to prevent RA.

Treatment of patients with rheumatoid arthritis (RA) is rarely personalized, since predictors of disease course are lacking. The severity of RA can be measured objectively by radiographic progression. The most reliable way to measure radiographic progression is in a longitudinal cohort with serial time points, scoring on a quantitative scale, with a validated scoring method and trained readers. Current models used to predict radiographic progression are based on C-reactive protein and anti-citrullinated protein antibodies. Other biomarkers could increase the prognostic ability of these models. In this review, we evaluated the published (and partly nonpublished) data on genetic, serologic, and imaging biomarkers for the severity of joint destruction in RA. We evaluated variants in 10 genes (CD40, IL2RA, IL4R, IL15, OPG, DKK1, SOST, GRZB, MMP9, and SPAG16). In 5 variants (IL2RA, DKK1, GRZB, MMP9, and SPAG16), we found evidence of an association at the functional level. We evaluated several serological biomarkers, namely, autoantibodies (RF, ACPA, anti-CarP), markers related to inflammation (ESR, CRP), and proteinases or components of the extracellular matrix of bone and cartilage (MMP3, CTX-I, CTX-II, COMP, TIMP1, PYD, RANKL/OPG, CXCL13). Finally, we evaluated markers that can be visualized by ultrasound or MRI, including erosions, bone marrow edema, synovitis, and tenosynovitis. Several studies showed that bone marrow edema and synovitis on MRI are robust predictors of radiographic progression. Some studies showed that inflammation detected with ultrasound predicted radiographic progression. Future studies will reveal whether adding and combining all these different biomarkers will increase the accuracy of risk models predicting radiographic progression in RA.

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