Immunological Profiling in Knee Osteoarthritis: Treg Dysfunction as Key Driver of Pain
Bibliographic record
Abstract
ABSTRACT Pain is the hallmark symptom of osteoarthritis (OA) and its biological drivers remain poorly understood. While the role of innate immunity in OA has been extensively studied, the involvement of adaptive immunity, in particular regulatory T cells (Tregs), is not well understood. Using a comprehensive multi-omic approach on the peripheral blood from 46 knee OA patients with similar radiographic stage, including deep immunophenotyping, cytokine profiling, transcriptomic and T-cell receptor analysis on sorted CD4 Tregs and effector T cells (Teff), we identified an immunological signature associated with OA-related pain. Cytokines promoting Treg expansion and activation (with increases of sIL2-RA, sTNFR1, sTNFR2) were correlated with the Western Ontario and McMaster Universities Arthritis Index (WOMAC) pain subscore, suggesting a potential Treg dysfunction. Nineteen T cell subsets were correlated with WOMAC pain. Notably, we found a negative correlation of cell subsets associated with Treg expansion and activation (FoxP3+CTLA4+, CD4+CD57+, Treg CD95+, CD4 Treg CD45RA-). Differential gene expression analysis between patients with low and high WOMAC pain intensity (threshold ≥ 40/100) revealed an upregulation of inflammasome-related genes such as IL1RL1, IL31RA, IFITM3, NLRP3, IFNG in Tregs. Functional enrichment analysis highlighted an overrepresentation of innate immune response, IL-8, and interferon activation pathways suggesting a pro-inflammatory state in Tregs of patients with high pain intensity. Collectively, our systems immunology approach highlights multiple associations between Treg dysfunctionality and OA-related pain, providing new insights into the adaptive immune system’s contribution to OA-related pain.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".