Osteoarthritis induces profound structural degeneration of articular cartilage, with existing treatments remaining largely ineffective. This study pioneers a mechanoregulatory model utilizing histology-based finite element analysis to predict depth-dependent glycosaminoglycan (GAG) adaptations in both intact and damaged human cartilage under mechanical loading. Uniquely calibrated through rigorous one-week longitudinal in vitro experiments in intact cartilage, our model correctly predicts depth-dependent GAG content adaptation, also in damaged cartilage. Notably, the model reveals potential effects of fluid velocity and dissipated energy on an increase in GAG content, while highlighting the degenerative effects of maximum shear strain under physiological loading conditions. Interestingly, it replicates enhanced GAG production in damaged cartilage, consistent with our experimental observations. Beyond advancing the fundamental understanding of mechanical loading in cartilage homeostasis, this innovative model offers a robust platform for in silico trials, enabling the development of personalized rehabilitation protocols to optimize mechanical loading strategies for degenerative joint diseases. Our work represents a significant leap forward in leveraging computational tools to address the challenges of osteoarthritis treatment. All findings are based on human explants from one donor and should be interpreted as preliminary proof-of-concept.
Elahi et al. (2025) studied this question.