Characterizing correlations among disease severity measures in osteochondral tissues from osteoarthritic knees.

Osteoarthritis (OA) is a complex disease with biologic, biomechanical, and clinical heterogeneity among patients. Relationships among OA tissue metabolism, histopathology, and extracellular matrix (ECM) composition have not been well characterized. It was hypothesized that moderate (r = .4-.69) to strong (r > .7) correlations exist among these different measures of disease severity in osteochondral tissues from OA knees. Joint surfaces were obtained from patients (n = 6) undergoing total knee arthroplasty. Osteochondral explants (n = 136) were created and cultured for 3 days. Culture media were collected for biomarker analyses, and tissue was assessed for viability, histological scoring, and ECM composition. Correlations among media biomarker concentrations, histological scoring, ECM composition, and viability were determined using a Spearman correlation. GRO-α, IL-6, IL-8, and MCP-1 showed strong positive correlations to each other, and moderate positive correlations to NO, PGE2, and MMP-2. Total MMP activity, MMP-9, and MMP-13 had strong positive correlations to each other, and moderate positive correlations to MMP-1. MMP-2 had a moderate to strong positive correlations to histological scores (total and cartilage structure) and collagen content. MMP-2, IL-6, IL-8, and MCP-1 had moderate negative correlations, and MMP-9 had a moderate positive correlation, to viability. GRO-α, IL-6, IL-8, and MCP-1 had moderate positive correlations to collagen content. MMP-9, MMP-13, and total MMP activity had moderate negative correlations to tissue GAG. The data suggest links among proinflammatory and degradative pathways are present in OA osteochondral tissues. Further characterization of these links have the potential to delineate mechanisms of disease and diagnostic and therapeutic targets for knee OA.

Investigating the relationship between proteomic, compositional, and histologic biomarkers and cartilage biomechanics using artificial neural networks.

A thorough understanding of the relationship between the biological and mechanical functions of articular cartilage is necessary to develop diagnostics and treatments for arthritic diseases. A key step in developing this understanding is the establishment of models which utilize large numbers of biomarkers to create comprehensive models of the interplay between cartilage biology and biomechanics, which will more accurately demonstrate the complex etiology and progression of tissue adaptation and degradation. It is the goal of this study to demonstrate the ability of artificial neural networks (ANNs) to utilize biomarkers to create predictive models of articular cartilage biomechanics, which will provide a basis for more sophisticated research in the future. Osteochondral plugs were collected from patients undergoing total knee arthroplasty, cultured, then analyzed to collect proteomic, compositional, and histologic biomarker data. Samples were subjected to stress relaxation testing as well as computational simulations using finite element analysis (FEA) modeling and optimization to determine key mechanical properties. The acquired data was fed into an ANN to generate a model which predicts the biomechanical properties of cartilage from given biomarkers. Using all significant inputs, the developed neural network predicted the ground substance modulus with a moderate degree of accuracy, but had difficulty predicting the collagen fiber modulus and cartilage permeability. Using only clinically attainable biomarkers, the best-performing model produced comparably accurate and more consistent predictions of all three mechanical properties. These models demonstrate the potential for ANNs to be included in clinical studies of articular cartilage.