Objectives To study long-term (up to 20-year) mortality of two treat-to-target trial cohorts in undifferentiated arthritis (UA) and early rheumatoid arthritis (RA).Methods The BeSt ... Show moreObjectives To study long-term (up to 20-year) mortality of two treat-to-target trial cohorts in undifferentiated arthritis (UA) and early rheumatoid arthritis (RA).Methods The BeSt (BehandelStrategieen) study (n=508, early RA) was performed between 2000 and 2012. For 10 years, patients were treated-to-target disease activity score (DAS)<= 2.4.The Induction therapy with Methotrexate and Prednisone in Rheumatoid Or Very Early arthritic Disease (IMPROVED) study (n=610, early RA/UA) was performed between 2007 and 2015. For 5 years, patients were treated-to-target DAS<1.6.Vital status of BeSt/IMPROVED participants was assessed up to and including 31 December 2021. Standardised mortality ratios (SMRs) were calculated. Stratified analyses for anticitrullinated protein antibody (ACPA) and smoking status were performed. Death causes and the potential effect of disease activity during the trial period on late mortality were assessed.Results Excess mortality was found in both BeSt (SMR 1.32, 95% CI 1.14 to 1.53) and IMPROVED (SMR 1.33, 95% CI 1.10 to 1.63) and became manifest after 10 years. Excess mortality was statistically significant in ACPA+ patients who smoked (BeSt: SMR 2.80, 95% CI 2.16 to 3.64; IMPROVED: 2.14, 95% CI 1.33 to 3.45). Mean survival time was 10 (95% CI 5 to 16) months shorter than expected in BeSt and 13 (95% CI 11 to 16) months in IMPROVED. The HR for mortality was 1.34 (95% CI 0.96 to 1.86; BeSt)/1.13 (95% CI 0.67 to 1.91; IMPROVED) per 1 point increase in mean DAS during the trial. The main cause of death was malignancy.Conclusions After long-term treatment-to-target, excess mortality occurred in patients with RA after>10 years since treatment start, with smoking as an important risk factor. Show less
Ouwerkerk, L. van; Meulen-de Jong, A.E. van de; Ninaber, M.K.; Teng, Y.K.O.; Huizinga, T.W.; Allaart, C.F. 2021
Objective. Rheumatoid arthritis (RA) is characterized by inflammation and joint destruction, with the degree of damage varying greatly among patients. Prediction of disease severity using known... Show moreObjective. Rheumatoid arthritis (RA) is characterized by inflammation and joint destruction, with the degree of damage varying greatly among patients. Prediction of disease severity using known clinical and serologic risk factors is inaccurate. This study was undertaken to identify new serologic markers for RA severity using an in silico model of the rheumatic joint. Methods. An in silico model of a prototypical rheumatic joint was used to predict candidate markers associated with erosiveness. The following 4 markers were chosen for validation: tartrate-resistant acid phosphatase 5b (TRAP-5b), N-telopeptide of type I collagen (NTX), angiopoietin 2 (Ang-2), and CXCL13. Serum from 74 RA patients was used to study whether radiologic joint destruction (total erosion score and total Sharp/van der Heijde score [SHS]) after 4 years of disease was associated with serum levels at the time of diagnosis. Serum marker levels were determined using enzyme-linked immunosorbent assays. For confirmation, baseline serum levels were analyzed for an association with progression of joint damage over 7 years of followup in a cohort of 155 patients with early RA. Results. Comparison of high and low quartiles of erosion score and SHS at 4 years showed a difference in baseline serum CXCL13 level (P = 0.011 and P = 0.018, respectively). In the confirmation cohort, elevated baseline CXCL13 levels were associated with increased rates of joint destruction during 7 years of followup (P < 0.001 unadjusted and P <= 0.004 with adjustment for C-reactive protein level). Analyzing anti-CCP-2-positive and anti-CCP-2-negative RA separately yielded a significant result only in the anti-CCP2- negative group (P <= 0.001). Conclusion. Our findings indicate that CXCL13 is a novel serologic marker predictive of RA severity. This marker was identified with the help of an in silico model of the RA joint. Show less
