Detecting change-points in preclinical rheumatoid arthritis biomarkers using Bayesian multivariate segmented regression
- 1Colorado School of Public Health
Abstract
BackgroundRheumatoid 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.
MethodsUsing 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 assays 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.
ResultsIn 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.
ConclusionsThis 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.
Key MessagesO_LITreating autoantibody concentrations at the upper limit of detection as right-censored and modeling the residual correlation among biomarkers allow joint estimation of when and how steeply each autoantibody begins to rise before RA diagnosis. C_LIO_LIRF-IgA, RF-IgM, and ACPA-IgG exhibited the earliest pre-diagnostic trajectory acceleration (7.8 to 9.2 years before diagnosis), and all eight markers in a second panel of anti-citrullinated protein antibodies shifted before diagnosis, with IgG isotypes changing earlier than IgA. C_LIO_LIA composite time integrating the timing and magnitude of autoantibody change provides a different ranking of biomarkers than change-point estimation alone, which may inform future screening and prevention strategies. C_LI
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BibTeX
@misc{wolde2026detecting,
title = {{Detecting change-points in preclinical rheumatoid arthritis biomarkers using Bayesian multivariate segmented regression}},
author = {Y. F. Wolde and A. M. Jensen and B. D. Wagner and J. D. Edison and M. L. Feser and M. Mahler and K. D. Deane and K. P. Josey},
howpublished = {medRxiv},
year = {2026},
month = may,
doi = {10.64898/2026.05.22.26353892},
url = {https://www.medrxiv.org/content/10.64898/2026.05.22.26353892v2},
note = {CC BY},
}
RIS
TY - UNPB
TI - Detecting change-points in preclinical rheumatoid arthritis biomarkers using Bayesian multivariate segmented regression
AU - Y. F. Wolde
AU - A. M. Jensen
AU - B. D. Wagner
AU - J. D. Edison
AU - M. L. Feser
AU - M. Mahler
AU - K. D. Deane
AU - K. P. Josey
PY - 2026
DA - 2026/05/22
PB - medRxiv
DO - 10.64898/2026.05.22.26353892
UR - https://www.medrxiv.org/content/10.64898/2026.05.22.26353892v2
AB - BackgroundRheumatoid 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. MethodsUsing 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 assays 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. ResultsIn 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. ConclusionsThis 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. Key MessagesO_LITreating autoantibody concentrations at the upper limit of detection as right-censored and modeling the residual correlation among biomarkers allow joint estimation of when and how steeply each autoantibody begins to rise before RA diagnosis. C_LIO_LIRF-IgA, RF-IgM, and ACPA-IgG exhibited the earliest pre-diagnostic trajectory acceleration (7.8 to 9.2 years before diagnosis), and all eight markers in a second panel of anti-citrullinated protein antibodies shifted before diagnosis, with IgG isotypes changing earlier than IgA. C_LIO_LIA composite time integrating the timing and magnitude of autoantibody change provides a different ranking of biomarkers than change-point estimation alone, which may inform future screening and prevention strategies. C_LI
ER -