055 Can interleukin IL-17A levels predict response to biologic treatment in patients with rheumatoid arthritis?
Bibliographic record
Abstract
Background: The cytokine interleukin IL-17A has an important pro-inflammatory role; it stimulates IL-1 and tumour necrosis factor (TNF) production, induces IL-6 secretion and upregulates RANK-ligand, which promotes bone erosion. We hypothesised that patients whose disease activity is not adequately controlled by TNF therapies in rheumatoid arthritis (RA) may have IL-17 driven disease. Our aim was to determine if pre-treatment or 3 month IL-17A concentrations correlate with treatment response to anti-TNF drugs by 6 months of treatment. Methods: Patient data was collected as part of the Biologics in Rheumatoid Arthritis Genetics and Genomics Study Syndicate (BRAGGSS). Patients were followed up at pre-treatment (baseline), 3 months, 6 months and 12 months with blood samples, patient questionnaires and clinical data obtained. RA patients were eligible for inclusion if they were commencing on adalimumab or etanercept. Patients were designated good or poor EULAR responder status at 6-month follow-up. Wilcoxon rank sum was used to compare IL-17A levels at pre-treatment and 3 months- according to EULAR classification by 6 months. A logistic regression analysis was carried out with regards to gender, baseline DMARD use and disease activity scores (DAS-28). Results: 152 patients were included, with 80 patients treated with adalimumab and 72 with etanercept. There was no statistically significant difference between mean IL-17A levels at baseline and 3-month follow-up in the group as a whole (p = 0.77). Nor was there any statistically significant difference between mean IL-17A levels at baseline and 3 months in either the adalimumab (p = 0.21) or etanercept treatment groups (0.22). In good responders, the mean serum levels of IL-17A increased from 1.06 pg/ml at baseline to 1.23pg/ml at 3 months. This came close to statistical significance (p = 0.07). In poor responders, the mean IL-17A levels actually decreased from 1.29pg/ml to 1.18pg/ml (p = 0.49). Adjusting for gender, baseline DMARD use and DAS-28 scores did not alter our findings. Conclusion: An increase in IL17-A serum levels between baseline and 3 months may be associated with a good EULAR response status by 6 months but larger sample sizes are required to confirm this. Disclosures: S. McDonald: None. R. Reed: None. I. Baricevic-Jones: None. S. Ling: None. D. Plant: None. A. Barton: None.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".