Abstract
Osteoarthritis (OA) is the most common chronic degenerative joint disease which causes substantial joint pain, deformity and loss of activities of daily living. Currently, there are over 500 million OA cases worldwide, and there is an urgent need to identify biomarkers for early detection, and monitoring disease progression in patients without obvious radiographic damage to the joint. We have used regression modelling to describe the association of 19 of the currently available biomarkers (predictors) with key radiographic and clinical features of OA (outcomes) in one of the largest and best characterised OA cohort (NIH Osteoarthritis Initiative). We demonstrate that of the 19 currently available biomarkers only 4 (serum Coll2-1 NO2, CS846, COMP and urinary CTXII) were consistently associated with established radiographic and/or clinical features of OA. These biomarkers are independent of one another and provide additional predictive power over, and above established predictors of OA such as age, gender, BMI and race. We also show that that urinary CTXII had the strongest and consistent associations with clinical symptoms of OA as well as radiographic evidence of joint damage. Accordingly, urinary CTXII may aid in early diagnosis of OA in symptomatic patients without radiographic evidence of OA.
Highlights
Osteoarthritis (OA) is the most common chronic degenerative joint disease which causes substantial joint pain, deformity and loss of activities of daily living
In the univariable model baseline Kellgren and Lawrence (K&L) grade was significantly associated with serum C2C, CPII, Coll[] NO2, hyaluronic acid (HA) and urinary CTXII, while the 12 months K&L grades correlated with serum CTXI and HA only
The correlation between urinary CTXII and K&L grade were essentially similar at baseline and 24 months the actual observed odd ratio was slightly stronger at 24 months
Summary
Osteoarthritis (OA) is the most common chronic degenerative joint disease which causes substantial joint pain, deformity and loss of activities of daily living. There are over 500 million OA cases worldwide, and there is an urgent need to identify biomarkers for early detection, and monitoring disease progression in patients without obvious radiographic damage to the joint. We demonstrate that of the 19 currently available biomarkers only 4 (serum Coll[] NO2, CS846, COMP and urinary CTXII) were consistently associated with established radiographic and/or clinical features of OA These biomarkers are independent of one another and provide additional predictive power over, and above established predictors of OA such as age, gender, BMI and race. Diagnosis is based on symptoms, and usually confirmed with X-ray at late-stage disease when there is already irreparable damage to the joints Other imaging methods such as Magnetic Resonance Imaging (MRI) and Dual-energy X-ray absorptiometry (DXA) may be used for early diagnosis and monitoring OA but widespread routine use of these imaging techniques is limited by their availability and/or costs.
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