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Predictive validity of biochemical biomarkers in knee osteoarthritis: data from the FNIH OA Biomarkers Consortium
  1. Virginia Byers Kraus1,
  2. Jamie E Collins2,
  3. David Hargrove3,
  4. Elena Losina2,
  5. Michael Nevitt4,
  6. Jeffrey N Katz4,
  7. Susanne X Wang5,
  8. Linda J Sandell6,
  9. Steven C Hoffmann7,
  10. David J Hunter8
  11. for the OA Biomarkers Consortium
  1. 1Duke Molecular Physiology Institute and Division of Rheumatology, Duke University School of Medicine, Durham, North Carolina, USA
  2. 2Brigham and Women's Hospital, Boston, Massachusetts, USA
  3. 3LabCorp Clinical Trials, San Leandro, California, USA
  4. 4Department of Epidemiology and Biostatistics, University of California San Francisco, San Francisco, California, USA
  5. 5AbbVie, North Chicago, Illinois, USA
  6. 6Department of Orthopaedic Surgery, Musculoskeletal Research Center, Washington University in St. Louis, St Louis, Missouri, USA
  7. 7Foundation for the National Institutes of Health, Bethesda, Maryland, USA
  8. 8Rheumatology Department, Royal North Shore Hospital and Institute of Bone and Joint Research, Kolling Institute, University of Sydney, Sydney, New South Wales, Australia
  1. Correspondence to Dr Virginia Byers Kraus, Box 104775, Duke Molecular Physiology Institute, 300 North Duke St, Durham, NC 27701, USA; vbk{at}


Objective To investigate a targeted set of biochemical biomarkers as predictors of clinically relevant osteoarthritis (OA) progression.

Methods Eighteen biomarkers were measured at baseline, 12 months (M) and 24 M in serum (s) and/or urine (u) of cases (n=194) from the OA initiative cohort with knee OA and radiographic and persistent pain worsening from 24 to 48 M and controls (n=406) not meeting both end point criteria. Primary analyses used multivariable regression models to evaluate the association between biomarkers (baseline and time-integrated concentrations (TICs) over 12 and 24 M, transposed to z values) and case status, adjusted for age, sex, body mass index, race, baseline radiographic joint space width, Kellgren-Lawrence grade, pain and pain medication use. For biomarkers with adjusted p<0.1, the c-statistic (area under the curve (AUC)), net reclassification index and the integrated discrimination improvement index were used to further select for hierarchical multivariable discriminative analysis and to determine the most predictive and parsimonious model.

Results The 24 M TIC of eight biomarkers significantly predicted case status (ORs per 1 SD change in biomarker): sCTXI 1.28, sHA 1.22, sNTXI 1.25, uC2C-HUSA 1.27, uCTXII, 1.37, uNTXI 1.29, uCTXIα 1.32, uCTXIβ 1.27. 24 M TIC of uCTXII (1.47–1.72) and uC2C-Human Urine Sandwich Assay (HUSA) (1.36–1.50) both predicted individual group status (pain worsening, joint space loss and their combination). The most predictive and parsimonious combinatorial model for case status consisted of 24 M TIC uCTXII, sHA and sNTXI (AUC 0.667 adjusted). Baseline uCTXII and uCTXIα both significantly predicted case status (OR 1.29 and 1.20, respectively).

Conclusions Several systemic candidate biomarkers hold promise as predictors of pain and structural worsening of OA.

  • Knee Osteoarthritis
  • Osteoarthritis
  • Disease Activity

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