Autoantibodies to joint-related peptides as predictive markers in early rheumatoid arthritis

Abstract

Objective: For better management of RA, new biomarkers are needed to predict the development of different disease courses. This study aims to identify autoantibodies against epitopes on proteins in the joints and to predict disease outcome in patients with new onset RA.

Methods: Sera from new-onset RA patients from the Swedish BARFOT (Better Anti Rheumatic PharmacOTherapy) and TIRA-2 (Swedish acronym for 'tidiga insatser vid reumatoid artrit') cohorts (n = 1986) were screened for autoantibodies to selected peptides (JointIDs) in a bead-based multiplex flow immunoassay. Disease outcomes included Boolean remission 1.0, swollen joint count and radiographic destruction. Multivariate logistic regression and zero-inflated negative binomial models that accounted for clinical factors were used to identify JointIDs with the strongest potential to predict prognosis.

Results: Boolean remission was predicted with 42% sensitivity and 75% specificity in male patients positive for antibodies to a non-modified collagen type II (COL2) peptide at 12 months. When antibodies to a specific citrullinated cartilage oligomeric protein (COMP) peptide were absent and the patient was in Boolean remission at 6 months, the sensitivity was 13% and the specificity 99%. Positivity for the non-modified COL2 peptide also reduced the frequency of swollen joints by 41% and 33% at 6 and 12 months, respectively. Antibodies to CCP predicted joint destruction with low specificity (58%). Positivity for a COL2 and a glucose-6-phosphate dehydrogenase peptide in citrullinated forms increased specificity (86%) at the expense of sensitivity (39%).

Conclusion: Autoantibodies against joint-related proteins at RA diagnosis predict remission with high specificity and, in combination with clinical factors, may guide future treatment decisions.

Keywords: autoantibodies; joint destruction; prognosis; remission; rheumatoid arthritis.

Figure 1.

The predictive ability of the pattern of JointIDs and clinical factors for the outcomes. Sensitivity, specificity and area under the curve (AUC) presented as percentage (%) for the multivariate models for predicting Boolean or DAS28 remission or high erosion at 6 or 12 months. The tick marks the factors taken into account for each model. The number (No) of factors varies between one and three and is the requirement for being overall positive for predicting the outcome. Anti-CCP (aCCP) does not predict remission but is included as comparison. At 12 months, the AUC [receiver operating characteristic (ROC)] for Boolean remission is 80% when only Boolean remission at 6 months is in the model, and AUC (ROC) for DAS28 remission is 72%, when only DAS28 remission at 6 months is in the model