Multivariable logistic and linear regression models for identification of clinically useful biomarkers for osteoarthritis.

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 associa

SUBMITTER: Liem Y 

PROVIDER: S-EPMC7347626 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature