Abstract / Summary
Background: Rheumatoid arthritis (RA) has a preclinical period characterized by elevations in serum autoantibodies. Identifying the timing and magnitude of autoantibody trajectory changes may inform screening strategies and preventative interventions. Methods: Using a Bayesian multivariate segmented regression, we jointly modeled longitudinal autoantibody trajectories from two Department of Defense Serum Repository cohorts (Cohort A: 209 matched case-control pairs, 1566 samples, six autoantibody biomarkers; Cohort B: 309 cases with two matched controls each, 2758 samples, eight autoantibody biomarkers). Change-points and magnitudes of change were estimated simultaneously under a multivariate normal likelihood on log-transformed concentrations, treating values at each assay's upper limit of detection as right-censored through data augmentation and modeling an unstructured residual correlation among biomarkers; a diagonal-covariance sensitivity analysis is reported in the Supplement. Results: In Cohort A, 5/6 biomarkers shifted before diagnosis, with 95% highest posterior density intervals (HPDIs) excluding zero. RF-IgA changed earliest, 9.2 years before diagnosis (HPDI: -13.8, -4.2), followed by RF-IgM at 8.2 years (-11.7, -5.4) and ACPA-IgG at 7.8 years (-9.9, -6.1). In Cohort B, all eight biomarkers changed before diagnosis, anti-CCP3 (IgG) earliest at 7.2 years (-8.1, -6.4); IgG isotypes changed earlier and more steeply than IgA. Ranked by a composite time integrating timing and magnitude, ACPA-IgG and anti-CCP3 (IgG) came first. Conclusions: This Bayesian model estimates change-points and magnitudes simultaneously while accounting for the upper limits of detection and for correlation among biomarkers, and it characterizes the uncertainty in both. It complements prior divergence-based methods for understanding preclinical RA autoimmunity.