Keele Research Repository

Background: Biomarkers are needed to predict clinical outcome for microfracture and osteotomy surgeries to ensure patients can be better stratified to receive the most appropriate treatment.
Hypothesis/purpose: To identify novel biomarker candidates and to investigate the potential of a panel of protein biomarkers for the prediction of clinical outcome following treatment with microfracture or osteotomy.
Study Design: Descriptive Laboratory Study
Methods: Label-free quantitation following liquid chromatography-tandem mass-spectrometry of dynamic range compressed synovial fluids (SF) from individuals who responded excellently or poorly (based on the change in Lysholm score) to microfracture (n=6) or osteotomy (n=7) was used to identify novel candidate biomarker proteins. Biomarkers which were (a) identified in this proteomic analysis, (b) which relate to OA severity or (c) with predictive value in another early OA therapy ( autologous cell implantation) were measured in the SF of 19 and 13 patients prior to microfracture or osteotomy, respectively, using commercial immunoassays and normalised to urea. These were aggrecanase-1 (ADAMTS-4), cartilage oligomeric matrix protein (COMP), hyaluronan (HA), Lymphatic Vessel Endothelial Hyaluronan Receptor-1 (LYVE-1), matrix metalloproteinase (MMP)-1 and -3, soluble CD14 (sCD14), S100 calcium binding protein A13 (S100A13) and 14-3-3 protein theta (YWHAQ). Levels of COMP and HA were also measured in the plasma of these patients. To find predictors of postoperative function multivariable regression analyses were performed.
Results: Proteomic analyses highlighted YWHAQ and LYVE-1 as being differentially abundant between the clinical responders/improvers and non-responders following microfracture. A linear regression model following backward variable selection could relate pre-operative concentrations of SF proteins (HA, YWHAQ, LYVE-1), activity of ADAMTS-4 and patient demographics (smoker status and gender) with 12 month post-microfracture Lysholm score. Further a generalized linear model