Development of a multi-biomarker disease activity test for rheumatoid arthritis

• Published in: PloS one 2013-04, Vol.8 (4), p.e60635-e60635
• Main Authors: Centola, Michael, Cavet, Guy, Shen, Yijing, Ramanujan, Saroja, Knowlton, Nicholas, Swan, Kathryn A, Turner, Mary, Sutton, Chris, Smith, Dustin R, Haney, Douglas J, Chernoff, David, Hesterberg, Lyndal K, Carulli, John P, Taylor, Peter C, Shadick, Nancy A, Weinblatt, Michael E, Curtis, Jeffrey R
• Format: Article
• Language: English
• Subjects: Adult; Aged; Algorithms; Arthritis; Arthritis, Rheumatoid - blood; Arthritis, Rheumatoid - diagnosis; Arthritis, Rheumatoid - genetics; Bioindicators; Bioinformatics; Biology; Biomarkers; Biomarkers - blood; Blood proteins; Breast cancer; C-reactive protein; C-Reactive Protein - analysis; Clinical trials; Comorbidity; Correlation; Correlation analysis; Diabetes; Diagnosis; Diagnostic imaging; Disease control; Engineering; Feasibility studies; Female; Gene Expression; Gene Expression Profiling; Growth factors; Health risk assessment; Humans; Immunology; Informatics; Laboratories; Mathematical models; Medical research; Medical tests; Medicine; Middle Aged; Models, Statistical; Patient assessment; Protein arrays; Proteins; Radiography; Rheumatic diseases; Rheumatoid arthritis; Rheumatoid factor; Rheumatology; RNA; ROC Curve; Screens; Severity of Illness Index; Statistical analysis; Statistical models; Studies; Test procedures; Tumor necrosis factor-TNF; Ultrasound; Womens health
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0060635

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. To develop a multi-biomarker disease activity (MBDA) test for rheumatoid arthritis. 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. 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. 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.