An Autoinflammatory Disease with Deficiency of the Interleukin-1–Receptor Antagonist
Bibliographic record
Abstract
BACKGROUND: Autoinflammatory diseases manifest inflammation without evidence of infection, high-titer autoantibodies, or autoreactive T cells. We report a disorder caused by mutations of IL1RN, which encodes the interleukin-1-receptor antagonist, with prominent involvement of skin and bone. METHODS: We studied nine children from six families who had neonatal onset of sterile multifocal osteomyelitis, periostitis, and pustulosis. Response to empirical treatment with the recombinant interleukin-1-receptor antagonist anakinra in the first patient prompted us to test for the presence of mutations and changes in proteins and their function in interleukin-1-pathway genes including IL1RN. RESULTS: We identified homozygous mutations of IL1RN in nine affected children, from one family from Newfoundland, Canada, three families from The Netherlands, and one consanguineous family from Lebanon. A nonconsanguineous patient from Puerto Rico was homozygous for a genomic deletion that includes IL1RN and five other interleukin-1-family members. At least three of the mutations are founder mutations; heterozygous carriers were asymptomatic, with no cytokine abnormalities in vitro. The IL1RN mutations resulted in a truncated protein that is not secreted, thereby rendering cells hyperresponsive to interleukin-1beta stimulation. Patients treated with anakinra responded rapidly. CONCLUSIONS: We propose the term deficiency of the interleukin-1-receptor antagonist, or DIRA, to denote this autosomal recessive autoinflammatory disease caused by mutations affecting IL1RN. The absence of interleukin-1-receptor antagonist allows unopposed action of interleukin-1, resulting in life-threatening systemic inflammation with skin and bone involvement. (ClinicalTrials.gov number, NCT00059748.)
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".