Beta-lactam-induced immediate hypersensitivity reactions: A genome-wide association study of a deeply phenotyped cohort
Bibliographic record
Abstract
Background β-lactam antibiotics are associated with a variety of immune-mediated or hypersensitivity reactions, including immediate (type I) reactions mediated by antigen-specific IgE. Objective We sought to identify genetic predisposing factors for immediate reactions to β-lactam antibiotics. Methods Patients with a clinical history of immediate hypersensitivity reactions to either penicillins or cephalosporins, which were immunologically confirmed, were recruited from allergy clinics. A genome-wide association study was conducted on 662 patients (the discovery cohort) with a diagnosis of immediate hypersensitivity and the main finding was replicated in a cohort of 98 Spanish cases, recruited using the same diagnostic criteria as the discovery cohort. Results Genome-wide association study identified rs71542416 within the Class II HLA region as the top hit ( P = 2 × 10 −14 ); this was in linkage disequilibrium with HLA-DRB1∗10:01 (odds ratio, 2.93; P = 5.4 × 10 −7 ) and HLA-DQA1∗01:05 (odds ratio, 2.93, P = 5.4 × 10 −7 ). Haplotype analysis identified that HLA-DRB1∗10:01 was a risk factor even without the HLA-DQA1∗01:05 allele. The association with HLA-DRB1∗10:01 was replicated in another cohort, with the meta-analysis of the discovery and replication cohorts showing that HLA-DRB1∗10:01 increased the risk of immediate hypersensitivity at a genome-wide level (odds ratio, 2.96; P = 4.1 × 10 −9 ). No association with HLA-DRB1∗10:01 was identified in 268 patients with delayed hypersensitivity reactions to β-lactams. Conclusions HLA-DRB1∗10:01 predisposed to immediate hypersensitivity reactions to penicillins. Further work to identify other predisposing HLA and non-HLA loci is required.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".