{
  "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.\n\nMethodsUsing 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.\n\nResultsIn 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.\n\nConclusionsThis 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.\n\nKey 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",
  "abstract_shown": true,
  "affiliations": [
    "Colorado School of Public Health"
  ],
  "article_type": "PUBLISHAHEADOFPRINT",
  "arxiv": null,
  "author_total": 8,
  "authors": [
    {
      "name": "Y. F. Wolde"
    },
    {
      "name": "A. M. Jensen"
    },
    {
      "name": "B. D. Wagner"
    },
    {
      "name": "J. D. Edison"
    },
    {
      "name": "M. L. Feser"
    },
    {
      "name": "M. Mahler"
    },
    {
      "name": "K. D. Deane"
    },
    {
      "affs": [
        0
      ],
      "name": "K. P. Josey"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/8kas5n69g4ne1.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.05.22.26353892",
  "fulltext_url": "https://www.medrxiv.org/content/10.64898/2026.05.22.26353892v2.full",
  "id": "8kas5n69g4ne1",
  "issns": [],
  "landing_url": "https://www.medrxiv.org/content/10.64898/2026.05.22.26353892v2",
  "language": null,
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "medrxiv",
      "rheumatology"
    ],
    [
      "openalex",
      "2745|Rheumatoid Arthritis Research and Therapies"
    ]
  ],
  "openalex": "W7162294937",
  "pdf_url": "https://www.medrxiv.org/content/10.64898/2026.05.22.26353892v2.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-05-22",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/8kas5n69g4ne1.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:20:07Z",
      "native_id": "10.64898/2026.05.22.26353892",
      "source": "medrxiv",
      "url": "https://www.medrxiv.org/content/10.64898/2026.05.22.26353892v2"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "medrxiv:rheumatology",
      "subfield": "general"
    }
  ],
  "title": "Detecting change-points in preclinical rheumatoid arthritis biomarkers using Bayesian multivariate segmented regression",
  "title_text": "Detecting change-points in preclinical rheumatoid arthritis biomarkers using Bayesian multivariate segmented regression",
  "type": "preprint",
  "updated_at": "2026-10-30T12:20:07Z",
  "url": "https://research.draconitic.com/paper/8kas5n69g4ne1",
  "venue": "medRxiv",
  "versions": []
}