Cartilage mechanical responses during gait as in silico biomarkers for medial knee OA progression

Osteoarthritis (OA) is a common degenerative joint disorder affecting the whole joint, particularly characterized by articular cartilage breakdown, mostly affecting the knee’s medial compartment. Its prevalence is high with aging as an important risk factor. With a global aging population, understanding, preventing, and managing OA becomes increasingly important. Progression of structural knee OA is multifactorial, including biomechanical stressors, inflammatory responses, and genetic predispositions. Traditional attempts to identify biomarkers predicting structural OA progression focus on wet biochemical markers from blood, synovia, or urine. This study assesses in silico loading-related parameters of the cartilage mechanical response as promising predictors of OA progression. A novel MSK-FE workflow relating knee movement to contact pressures and cartilage tissue response was developed. Subjects presenting OA progression over 2 years exhibited elevated medial compartment loading magnitude and posterolateral location shift at baseline. Unsupervised k-means clustering, using strain histograms, successfully differentiated progressors from non-progressors and controls when combining contact pressure and cartilage tissue mechanical responses. This study demonstrates the potential of computationally efficient, in silico mechanical biomarkers to identify personalized OA progression risk after 2 years. This approach offers promising clinical benefit by identifying patients at risk of OA progression, making them eligible for preventative treatment strategies.