Interleukin 10 and Tumor Necrosis Factor-α Genotypes in Rheumatoid Arthritis — Association with Clinical Response to Glucocorticoids
Bibliographic record
Abstract
OBJECTIVE: There are dysregulated levels of interleukin 10 (IL-10) and tumor necrosis factor-alpha (TNF-alpha) in rheumatoid arthritis (RA), and their role in the disease is controversial. We analyzed the association of functional polymorphisms of IL-10 and TNF-alpha with susceptibility and disease characteristics at the time of diagnosis, and we also evaluated their possible use as predictors of clinical response to treatments. METHODS: Patients with recent-onset RA (n = 162) and healthy controls (n = 373) were genotyped for -1082 IL-10 and -308 TNF-alpha polymorphisms and data were related to clinical and immunological measurements of patients at the time of diagnosis. Response to treatment after 6 months was determined in 125 patients by the absolute change in Disease Activity Score (DAS28) and the American College of Rheumatology criteria for improvement. RESULTS: We found a reduced frequency of the low IL-10 producer genotype (-1082AA) in patients with RA compared to controls (26.5% vs 38.9%; p = 0.006), while it is a risk factor for anticyclic citrullinated peptide antibodies (anti-CCP) positivity (p = 0.028). Evaluation of clinical response to treatments indicated that carriage of the high IL-10 genotype was associated with a favorable outcome (p = 0.009), specifically to prednisone therapy (p = 0.0003). No significant effects were observed with TNF-alpha polymorphism alone; however, in combination with the IL-10 genotype, it increased the strength of these associations. CONCLUSION: Results show an association between the low IL-10 producer genotype and protection from RA; nevertheless, when other specific genetic and/or environmental factors trigger onset of RA, this genotype may predispose to development of anti-CCP+ RA disease with reduced response to prednisone treatment.
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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 | 0.002 |
| 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) | 0.002 | 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".