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Ann Rheum Dis. 2017 Jan;76(1):186-195. doi: 10.1136/annrheumdis-2016-209252. Epub 2016 Jun 13.

Predictive validity of biochemical biomarkers in knee osteoarthritis: data from the FNIH OA Biomarkers Consortium.

Author information

1
Duke Molecular Physiology Institute and Division of Rheumatology, Duke University School of Medicine, Durham, North Carolina, USA.
2
Brigham and Women's Hospital, Boston, Massachusetts, USA.
3
LabCorp Clinical Trials, San Leandro, California, USA.
4
Department of Epidemiology and Biostatistics, University of California San Francisco, San Francisco, California, USA.
5
AbbVie, North Chicago, Illinois, USA.
6
Department of Orthopaedic Surgery, Musculoskeletal Research Center, Washington University in St. Louis, St Louis, Missouri, USA.
7
Foundation for the National Institutes of Health, Bethesda, Maryland, USA.
8
Rheumatology Department, Royal North Shore Hospital and Institute of Bone and Joint Research, Kolling Institute, University of Sydney, Sydney, New South Wales, Australia.

Abstract

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.

KEYWORDS:

Disease Activity; Knee Osteoarthritis; Osteoarthritis

PMID:
27296323
PMCID:
PMC5851287
DOI:
10.1136/annrheumdis-2016-209252
[Indexed for MEDLINE]
Free PMC Article

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