Peripheral Biomarker Signatures in Rheumatoid Arthritis–Associated Interstitial Lung Disease
Austin M. Wheeler, Joshua F. Baker, Thomas R. Riley, Tate Johnson, Yangyuna Yang, Punyasha Roul, Katherine D. Wysham, Grant W. Cannon, Gary Kunkel, Gail S. Kerr, Dana P. Ascherman, Paul A. Monach, Andreas Reimold, Scott M. Matson, Jill A. Poole, Michael J. Duryee, Geoffrey M. Thiele, Ted R. Mikuls, Bryant R. England
Abstract
OBJECTIVE: Rheumatoid arthritis-associated interstitial lung disease (RA-ILD) is a significant cause of morbidity and mortality among patients with RA, yet effective risk stratification for RA-ILD is lacking. We sought to characterize unique peripheral blood biomarker signatures in RA that could improve RA-ILD discrimination beyond clinical and genetic risk factors. METHODS: We performed a cross-sectional study of participants in the Veterans Affairs Rheumatoid Arthritis Registry. ILD was validated through systematic record review. Autoantibodies (n = 14), proinflammatory cytokines/chemokines (n = 36), adipokines (n = 3), alarmins (n = 3), and matrix metalloproteinases (n = 9) were measured from banked serum or plasma. MUC5B rs35705950 genotyping was obtained via microarray. Principal component (PC) analysis was performed to derive unique biomarker signatures. Logistic regression was used to compare models predicting RA-ILD presence using combinations of clinical, peripheral biomarker, and genetic variables. RESULTS: Among 2,001 participants with RA (88.7% male, mean age 63.7 years), 121 (6.4%) had RA-ILD. A total of 15 PCs were identified, with 8 significantly associated with RA-ILD after adjusting for clinical factors. These eight included biomarker themes of innate and allergic responses, autoantibodies (anti-cyclic citrullinated peptide, rheumatoid factor, anti-malondialdehyde-acetaldehyde adduct), adipokines, alarmins, tissue remodeling, and neutrophil chemotaxis. Models with PCs (area under the receiver operating characteristic curve [AUC] 0.739) and PCs with MUC5B (AUC 0.751) outperformed clinical risk factors alone (AUC 0.630; P < 0.001). CONCLUSION: Peripheral biomarker signatures are associated with RA-ILD and improve RA-ILD identification beyond clinical risk factors. In addition to demonstrating the potential for peripheral biomarkers to aid RA-ILD risk stratification, these findings suggest diverse pathways are involved in RA-ILD pathogenesis.