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Objectives. To determine whether molecular remission defined by a multi-biomarker disease activity (MBDA) score predicts a reduced risk of joint damage progression, and whether the MBDA score can augment existing classifications of remission. Methods. The study examined 271 visits for 163 RA patients in the Leiden Early Arthritis Cohort. The MBDA score and other variables from each visit were evaluated for prediction of progression [change in Sharp-van der Heijde Score (ΔSHS) >3] over the ensuing 12 months. Positive likelihood ratios (PLRs) for non-progression were calculated for remission based upon DAS based on 28-joint counts and CRP (DAS28-CRP <2.32), EULAR/ACR Boolean criteria and MBDA score (≤25). Results. Ninety-three per cent of patients in MBDA-defined remission did not experience progression, compared with 70% of patients not in MBDA remission (P = 0.001). There were no significant differences in the fraction of non-progressers between patients in remission and those not in remission using either DAS28-CRP or EULAR/ACR criteria. The PLR for non-progression over 12 months for MBDA remission was 4.73 (95% CI 1.67, 15.0). Among patients in DAS28-CRP remission, those with a high MBDA score were 2.3 times as likely (95% CI 1.1, 3.7) to have joint damage progression during the next year. Conclusion. MBDA-defined remission was an indicator of limited radiographic progression over the following 12 months. For patients in DAS28-CRP remission, high MBDA scores were a significant indicator of elevated risk of progression. MBDA results may provide a useful adjunct to clinical assessment to identify progression-free remission and assess subclinical disease. [ABSTRACT FROM AUTHOR]

Background: Disease activity measurement is a key component of rheumatoid arthritis (RA) management. Biomarkers that capture the complex and heterogeneous biology of RA have the potential to complement clinical disease activity assessment. Objectives: To develop a multi-biomarker disease activity (MBDA) test for rheumatoid arthritis. Methods: Candidate serum protein biomarkers were selected from extensive literature screens, bioinformatics databases, mRNA expression and protein microarray data. Quantitative assays were identified and optimized for measuring candidate biomarkers in RA patient sera. Biomarkers with qualifying assays were prioritized in a series of studies based on their correlations to RA clinical disease activity (e.g. the Disease Activity Score 28-C-Reactive Protein [DAS28-CRP], a validated metric commonly used in clinical trials) and their contributions to multivariate models. Prioritized biomarkers were used to train an algorithm to measure disease activity, assessed by correlation to DAS and area under the receiver operating characteristic curve for classification of low vs. moderate/high disease activity. The effect of comorbidities on the MBDA score was evaluated using linear models with adjustment for multiple hypothesis testing. Results: 130 candidate biomarkers were tested in feasibility studies and 25 were selected for algorithm training. Multi-biomarker statistical models outperformed individual biomarkers at estimating disease activity. Biomarker-based scores were significantly correlated with DAS28-CRP and could discriminate patients with low vs. moderate/high clinical disease activity. Such scores were also able to track changes in DAS28-CRP and were significantly associated with both joint inflammation measured by ultrasound and damage progression measured by radiography. The final MBDA algorithm uses 12 biomarkers to generate an MBDA score between 1 and 100. No significant effects on the MBDA score were found for common comorbidities. Conclusion: We followed a stepwise approach to develop a quantitative serum-based measure of RA disease activity, based on 12-biomarkers, which was consistently associated with clinical disease activity levels. [ABSTRACT FROM AUTHOR]

Abstract: Rheumatoid arthritis (RA) is a chronic inflammatory disorder that primarily involves the joints. Accurate and frequent assessment of RA disease activity is critical to optimal treatment planning. A novel algorithm has been developed to determine a multi-biomarker disease activity (MBDA) score based upon measurement of the concentrations of 12 serum biomarkers in multiplex format. Biomarker assays from several different platforms were used in feasibility studies to identify biomarkers of potential significance. These assays were adapted to a multiplex platform for training and validation of the algorithm. In this study, the analytical performance of the underlying biomarker assays and the MBDA score was evaluated. Quantification of 12 biomarkers was performed with multiplexed sandwich immunoassays in three panels. Biomarker-specific capture antibodies were bound to specific locations in each well; detection antibodies were labeled with electrochemiluminescent tags. Data were acquired with a Sector Imager 6000, and analyte concentrations were determined. Parallelism, dynamic range, cross-reactivity, and precision were established for each biomarker as well as for the MBDA score. Interference by serum proteins, heterophilic antibodies, and common RA therapies was also assessed. The individual biomarker assays had 3–4 orders of magnitude dynamic ranges, with good reproducibility across time, operators, and reagent lots; the MBDA score had a median coefficient of variation of <2% across the score range. Cross-reactivity as well as interference by serum rheumatoid factor (RF), human anti-mouse antibodies (HAMA), or common RA therapies, including disease-modifying antirheumatic drugs and biologics, was minimal. The same MBDA score was observed in different subjects despite having different biomarker profiles, supporting prior literature reports that multiple pathways contribute to RA. [Copyright &y& Elsevier]