<i>IL1RN</i> Variation Influences Both Disease Susceptibility and Response to Recombinant Human Interleukin‐1 Receptor Antagonist Therapy in Systemic Juvenile Idiopathic Arthritis
Bibliographic record
Abstract
OBJECTIVE: To determine whether systemic juvenile idiopathic arthritis (JIA) susceptibility loci that were identified by candidate gene studies demonstrate association with systemic JIA in the largest study population assembled to date. METHODS: Single-nucleotide polymorphisms (SNPs) from 11 previously reported systemic JIA risk loci were examined for association in 9 populations, including 770 patients with systemic JIA and 6,947 controls. The effect of systemic JIA-associated SNPs on gene expression was evaluated in silico in paired whole genome and RNA sequencing data from the lymphoblastoid cell lines (LCLs) of 373 European subjects from the 1000 Genomes Project. Responses of systemic JIA-associated SNPs to anakinra treatment were evaluated in 38 US patients for whom treatment response data were available. RESULTS: ). Systemic JIA-associated SNPs correlated with IL1RN expression in LCLs, with an inverse correlation between systemic JIA risk and IL1RN expression. The presence of homozygous IL1RN high expression alleles correlated strongly with a lack of response to anakinra therapy (odds ratio 28.7 [95% confidence interval 3.2-255.8]). CONCLUSION: In our study, IL1RN was the only candidate locus associated with systemic JIA. The implicated SNPs are among the strongest known determinants of IL1RN and interleukin-1 receptor antagonist levels, linking low expression with increased systemic JIA risk. Homozygous high expression alleles predicted nonresponsiveness to anakinra therapy, making them ideal candidate biomarkers to guide systemic JIA treatment. This study is an important first step toward the personalized treatment of systemic JIA.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".