Association of Synovial Innate Immune Exhaustion With Worse Pain in Knee Osteoarthritis
Bibliographic record
Abstract
OBJECTIVE: Uncontrolled pain remains a major clinical challenge in the management of knee osteoarthritis (OA), the most common disabling joint disease. Worse pain is associated with synovial innate immune cell infiltration (synovitis), but the role of innate immune-regulatory cells in pain is unknown. Our objective was to identify synovial innate immune cell subsets and pathophysiologic mechanisms associated with worse pain in patients with knee OA. METHODS: Synovial tissue biopsies from 122 patients with mild-to-severe knee OA pain (Knee Injury and OA Outcome Score [KOOS]) were analyzed to identify associations between synovial histopathology and worse pain. We then used spatial transcriptomics and proteomics of synovial tissue microenvironments (n = 32), followed by single-cell RNA sequencing (n = 8), to identify synovial cell composition and cell-cell communication networks in patients with more severe OA pain. RESULTS: Histopathological signs of synovial microvascular dysfunction and perivascular edema were associated with worse KOOS pain (-10.76; 95% confidence interval [CI] -18.90 to -2.61). Patients with worse pain had fewer immune-regulatory macrophages, expanded fibroblast subsets, and enrichment in neurovascular remodeling pathways. Synovial macrophages from patients with worse pain expressed markers of immune exhaustion and decreased phagocytic function (-19.42%; 95% CI -35.96 to -2.89) and their conditioned media increased neuronal cell stress in dorsal root ganglia. CONCLUSION: Although synovitis increases during OA, our findings suggest that exhaustion, dysfunction, and loss of immune-regulatory macrophages is associated with worse pain and may be an important therapeutic target.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".