The osteoarthritis inflammatory ecosystem: a node-edge-boundary-emergent framework toward precision stratification and therapeutic hypothesis generation

Abstract

Osteoarthritis (OA) is a heterogeneous whole-joint disorder in which pain, structural progression and treatment response often diverge from radiographic severity. This mismatch has limited disease-modifying OA drug development and highlights the need for biologically informed patient stratification. Chronic low-grade inflammation contributes to OA progression, but it is embedded within mechanical loading, metabolic dysfunction, synovitis, osteochondral remodeling, cellular senescence and pain sensitization rather than acting as a single-cytokine process. Here, we propose a Node-Edge-Boundary-Emergent (NEBE) inflammatory ecosystem framework that organizes current OA evidence around cellular nodes, communication edges, boundary conditions and emergent disease states. We summarize evidence from human tissue studies, imaging-linked biomarker work, single-cell and spatial analyses, preclinical models and clinical trials. Mechanisms are organized around three translational processes: damage sensing, multicellular network amplification and failed inflammatory resolution. We then discuss candidate biological states, including inflammatory-dominant, metabolic, senescence-associated, osteochondral-remodeling and mechanical-structural OA, and map therapeutic strategies to the ecosystem level they primarily target. Prior failures of cytokine blockade and several disease-modifying approaches may reflect ecosystem mismatch involving the wrong target level, patient population, disease stage or endpoint rather than simple irrelevance of inflammation. Routine phenotyping and MRI may provide first-level stratification, biomarkers may support exploratory trial enrichment and high-cost omics should be reserved for discovery, validation and selected complex cases. Prospective human cohorts and biomarker-guided trials are required to determine whether NEBE-informed stratification improves target engagement, progression prediction or treatment selection beyond existing OA frameworks.