A synovial fluid fingerprint for end-stage knee osteoarthritis
Keywords: Biomarkers
Synovial Fluid
Osteoarthritis

Osteoarthritis (OA) is a common and debilitating condition with no cure. The lack of disease- modifying treatments is linked to a deficiency of suitable biomarkers. This thesis focuses on synovial fluid (SF) as a source of combination biomarkers in knee OA. The thesis aims to demonstrate the utility of wide-spectrum SF analysis by defining a diagnostic "fingerprint" for end-stage knee OA from a diverse range of soluble molecules with pathophysiological relevance.
SF is a difficult matrix to study and volumes from OA joints are often insufficient for multiple marker analysis by conventional assays. This thesis begins by optimising, validating and comprehensively comparing SF analysis on two leading multiplex platforms, Luminex xMAP and MesoScale Discovery (MSD). Hyaluronidase treatment of SF permitted better intra-assay precision and higher assay signals compared to neat or diluted samples on the Luminex platform, but not conclusively on the MSD platform. Both platforms demonstrated acceptable precision and accuracy, but important differences were highlighted. Limited agreement in concentration measurements across platforms suggests that biomarker analysis is at best only quasi-quantitative.
Important practical considerations of SF sampling were addressed using multiplex analysis of 22 different markers in end-stage knee OA. The anatomical site of sampling was shown not to affect assay measurements even when there were significant compartmental differences in structural burden of disease. A longitudinal study demonstrated strong to excellent agreement in analyte measurements taken ∼2 weeks apart, during which there had been no change in clinical and radiographic severity. These studies suggest biological equilibrium in SF composition between knee joint compartments and short-term temporal stability in the SF biological profile in end-stage knee OA.
Building on these analytical and practical considerations, the SF biology of end-stage knee OA was explored with a view to biomarker discovery. Wide-spectrum SF analysis of a diverse range of 35 cytokines, chemokines, growth factors, matrix enzymes and markers of matrix turnover was conducted in 98 well-characterised patients with end-stage knee OA, knee injury and inflammatory arthritis. The data was analysed using both classical and multivariate statistical approaches.
Surprisingly, patients with cumulatively more severe structural phenotypes of knee OA demon- strated no differences in the concentration or variability of markers despite significant differences in structural burden of disease. This suggests end-stage knee OA has a constant biology and may be considered a useful terminal benchmark for OA biomarker research.
Partial least squares discriminant analysis produced detailed and streamlined multivariate models with excellent (>95%) diagnostic accuracy, reliability and specificity for patients with end-stage knee OA, knee injury and inflammatory arthritis. The models were successfully validated with 10 new end-stage knee OA test samples. The streamlined 8-marker model included a combination biomarker fingerprint for end-stage knee OA comprising "ridges" of PIIANP, TIMP-1, ADAMTS-4 and MCP-1 with "troughs" of IP-10 and TGFb-1. These findings represent a new breed of biomarker that could qualify as a "virtual knee replacement". The results and multivariate approach used in the thesis serve as a proof-of-concept to stimulate a new era in OA biomarker discovery.