Entheseal tissue signature in response to IL-17A inhibition in psoriatic arthritis: results from the EBIO entheseal biopsy study
Bibliographic record
Abstract
Enthesitis, a hallmark of psoriatic arthritis (PsA), reflects the interplay between mechanical stress and immune dysregulation at tendon-bone interfaces. This study investigates the cellular and molecular responses in entheseal tissues following interleukin (IL)-17A inhibition in patients with active PsA. In this prospective, interventional phase 4 trial, we enrolled 10 patients with enthesitis of the lateral epicondyle, performed entheseal biopsies, and analysed tissues by imaging mass cytometry (IMC) and spatial transcriptomics before and after treatment. Following 24 weeks of IL-17A inhibition, 9/10 patients of the cohort clinically responded to treatment as assessed by Disease Activity in Psoriatic Arthritis (DAPSA), Spondyloarthritis research consortium of canada (SPARCC) score, and power Doppler sonography. IMC analysis showed significant reductions of enthesitis-related immune cell populations, in particular IL-17-producing CD4, CD8, CD4 neg /CD8 neg T cells, granulocytes, and innate lymphoid cells type 3. Spatial transcriptomics revealed that CD200+DKK3+ fibroblasts and innate lymphoid cells type 2 expanded and formed an anti-inflammatory niche upon treatment. Notably, IL-17A inhibition led to decreased osteoblast differentiation markers, suggesting a potential mechanism to inhibit pathological bone formation. These findings underline the pivotal role of IL-17A in enthesitis, showing that IL-17A inhibition profoundly modulates the tissue microenvironment of entheses in PsA.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| 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".