Impaired IL-6-induced JAK-STAT signaling in CD4+ T cells associates with longer treatment duration in giant cell arteritis
Bibliographic record
Abstract
INTRODUCTION: The IL-12-IFNγ-Th1 and the IL-6-IL-23-Th17 axes are considered the dominant pathogenic pathways in Giant Cell Arteritis (GCA). Both pathways signal via activation of the downstream JAK/STAT proteins. We hypothesized that phosphorylated STAT (pSTAT) signatures in circulating immune cells may aid to stratify GCA-patients for personalized treatment. METHODS: To investigate pSTAT expression, PBMCs from treatment-naive GCA-patients (n = 18), infection controls (INF, n = 11) and age-matched healthy controls (HC, n = 15) were stimulated in vitro with IL-6, IL-2, IL-10, IFN-γ, M-CSF or GM-CSF, and stained with CD3, CD4, CD19, CD45RO, pSTAT1, pSTAT3, pSTAT5 antibodies, and analyzed by flow cytometry. Serum IL-6, sIL-6-receptor and gp130 were measured by Luminex. The change in percentages of pSTAT3+CD4+T-cells was evaluated at diagnosis and at 3 months and 1-year of follow-up. Kaplan-Meier analyses was used to asses prognostic accuracy. RESULTS: Analysis of IL-6 stimulated immune cell subsets revealed a significant decrease in percentages of pSTAT3+CD4+T-cells of GCA-patients and INF-controls compared to HCs. Following patient stratification according to high (median>1.5 pg/mL) and low (median<1.5 pg/mL) IL-6 levels, we observed a reduction in the pSTAT3 response in GCA-patients with high serum IL-6. Percentages of pSTAT3+CD4+T-cells in patients with high serum IL-6 levels at diagnosis normalized after glucocorticoid (GC) treatment. Importantly, we found that patients with low percentages of pSTAT3+CD4+T-cells at baseline require longer GC-treatment. CONCLUSION: Overall, in GCA, the percentages of in vitro IL-6-induced pSTAT3+CD4+T-cells likely reflect prior in vivo exposure to high IL-6 and may serve as a prognostic marker for GC-treatment duration and may assist improving personalized treatment options in the future.
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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 | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".