An analysis of 14 molecular markers for monitoring osteoarthritis. Relationship of the markers to clinical end-points.
OBJECTIVE: To investigate whether any of 14 serum and urine molecular markers (MMs) used to monitor osteoarthritis (OA) would be associated with particular clinical end-points. DESIGN: Thirty-nine OA patients were bled and urine collected at five time points: at baseline visit and at visits 1, 3, 6 and 12 months later. Twelve clinical measurements were made and the concentrations of each of 14 MMs were determined. Principal component analysis, stepwise linear regression with backward elimination, and logistic regression were used to determine the correlations between MMs and clinical measures. RESULTS: Principal component analysis was used to reduce the 12 clinical measurements into three independent clinical clusters: baseline clinical assessments, changes in clinical assessments and signal joint measurements. The 14 MMs were similarly reduced to five MM clusters. Each of the three clinical clusters was correlated with a single but different MM cluster. Baseline clinical assessments were correlated with bone markers typified by hydroxylysyl pyridinoline (HP) crosslinks, swelling of the signal joint was correlated with inflammation markers, especially CRP, and the change in clinical assessments over the 1 year evaluation was correlated with TGFbeta1. There was no correlation between any of the skeletal markers and the clinical measures, a situation which draws attention to the need for a direct assessment of cartilage damage in OA to validate the use of cartilage markers. CONCLUSIONS: This study demonstrates statistical methodology for analysis of clinical trials using multiple MMs and clinical end-points. The patient numbers are sufficient to test hypotheses of relationships of single MMs such as CRP, TGFbeta1 and HP to clinical measures, but larger clinical trials are needed to validate hypotheses.