Association Between <scp><i>HLA</i></scp><i>–<scp>DPB1</scp></i> and Antineutrophil Cytoplasmic Antibody–Associated Vasculitis in Children
Bibliographic record
Abstract
Objective Antineutrophil cytoplasmic antibody (ANCA)–associated vasculitis (AAV) is a rare, life‐threatening inflammation of blood vessels that can affect both adults and children. Compared to adult‐onset disease, AAV is especially rare in children, with an annual prevalence of 0.5–6.4 cases per million children. The etiology of AAV remains largely unknown, and both environmental and genetic factors are likely involved. The present study was undertaken to explore the genetic susceptibility factors recently identified in adult patients, including HLA–DP and HLA–DQ, in pediatric patients. Methods We performed a genome‐wide association study of pediatric AAV in patients of European ancestry (n = 63 AAV cases, n = 315 population‐matched controls). Results We identified a significant genetic association between pediatric AAV and the HLA–DPB1*04:01 allele (P = 1.5 × 10−8, odds ratio [OR] 3.5), with a stronger association observed in children with proteinase 3–ANCA positivity than in children with myeloperoxidase−ANCA positivity. Among the HLA alleles, the HLA–DPB1*04:01 allele was the most highly associated with AAV, although not significantly, in a follow‐up adult AAV cohort (P = 2.6 × 10−4, OR 0.4). T cell receptor and interferon signaling pathways were also shown to be enriched in the pediatric AAV cohort. Conclusion The HLA–DPB1 locus showed an association with pediatric AAV, as similarly shown previously in adult AAV. Despite the difference in the age of onset, these findings suggest that childhood‐ and adult‐onset vasculitis share a common genetic predisposition. The identification of genetic variants contributing to AAV is an important step to improved classification tools and treatment strategies.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".