Predicting the heterogeneous chemo-mechano-biological degeneration of cartilage using 3-D biphasic finite elements

Background and Objective:

Osteoarthritis (OA), a debilitating joint disease, involves progressive cartilage degeneration and altered biomechanics. We established a novel chemo-mechano-biological (CMB) modeling framework that integrates biphasic mechanics with biochemical and biological processes to predict cartilage degeneration (i.e. loss of masses of constituents presenting as loss of thickness) under pathological conditions. Our framework captures time-dependent remodeling of cartilage constituents in 3-D driven by mechanical loading, biochemical signaling, and cellular metabolism.

Methods:

We formulated a nonlinear, large-strain biphasic constitutive model coupled with a biochemical model of signaling pathways. Our framework incorporates depth-dependent metabolic activity, explicitly linking availability of oxygen to chondrocyte behavior and extracellular matrix (ECM) remodeling. We included interactions among mechanical stimuli, growth factors, pro-inflammatory cytokines, enzymes (collagenases and aggrecanases), and inhibitors (TIMP). We conducted nonlinear, biphasic finite element (FE) simulations in 3-D, allowing for realistic representations of intra-cartilage heterogeneity. We simulated cyclic, confined compression of full-thickness cartilage, a scenario mimicking conditions in vivo during walking or running.

Results:

Our simulations spanning 24 months presented realistic patterns of cartilage degeneration including zonal variations in matrix composition and thickness loss. In healthy cartilage, interstitial fluid pressure resisted mechanical loading, maintaining ECM integrity. However, in degenerative overloading conditions, enzymatic activity and altered metabolic functions led to increased porosity, reduced fluid pressure, and heterogeneous degradation of ECM. Incorporating depth-dependent metabolic activity revealed pronounced degeneration in the superficial zone (SZ) and progressively reduced loss toward the deep zone (DZ). This outcome aligns with experimental evidence on progression of OA. Oxygen availability played a critical role, with higher levels exacerbating degradation, consistent with findings linking oxidative stress to cartilage degeneration.

Conclusion:

Our nonlinear, biphasic FE framework offers a robust tool for investigating mechanisms of cartilage degeneration and OA, and advancing therapeutic strategies. It uniquely integrates biphasic mechanics, signaling pathways, and metabolic activity in 3-D, providing insights into patterns of cartilage degeneration. We previously developed automated and publicly available tools to generate patient-specific knee models from MR Images, altogether enabling personalized diagnostics/prognostics and pre-/post-operative planning. Our CMB framework is also publicly available as a plugin for FEBio at https://github.uconn.edu/imLab/FEVGnR-Plugin, supporting broader research on OA and cartilage biomechanics.