<scp>HLA</scp>‐<scp>DQA</scp>1‐<scp>HLA</scp>‐<scp>DRB</scp>1 polymorphism is a major predictor of azathioprine‐induced pancreatitis in patients with inflammatory bowel disease
Bibliographic record
Abstract
BACKGROUND: Azathioprine (AZA)-induced pancreatitis is an unpredictable and dose-independent adverse event affecting 2%-7% of patients with inflammatory bowel disease (IBD) patients treated with AZA. There are no tools in clinical practice to identify at-risk individuals; however, a genome wide association study (GWAS) identified a strong association between the Class II HLA gene region polymorphism (rs2647087) and thiopurine-induced pancreatitis. AIM: To independently confirm the findings of the GWAS in an IBD cohort, to evaluate its utility in clinical practice and to offer a novel AZA treatment algorithm for IBD based on pharmacogenomic principles. METHODS: A retrospective cohort study evaluated 373 AZA-exposed IBD patients from a tertiary care academic centre in London, Canada. Due to the limited number of patients taking mercaptopurine (MP), such patients were not included this cohort. All subjects underwent screening for the single nucleotide polymorphism (SNP) rs2647087 mapped to the HLA-DQA1*02:01-HLA-DRB1*07:01 haplotype and were sub-divided based on the presence (n = 13) or absence (n = 360) of an AZA-induced pancreatitis diagnosis. The risk of AZA-induced pancreatitis was assessed based on rs2647087 genotype. RESULTS: The risk of pancreatitis during AZA-therapy was highly predictable and genotype dependent: 0.53% for wild type (A/A), 4.25% (OR = 4.19, 95% CI 1.02-36.45, P = 0.044) for heterozygous (A/C), and 14.63% (OR = 15.83, 95% CI 3.80-145.26, P = 0.0001) for homozygous variant (C/C) patients. CONCLUSIONS: The class II HLA region (at rs2647087) is an important marker of AZA-induced pancreatitis risk. We propose a simple and clinically implementable algorithm based on rs2647087 and TPMT genotypes for AZA selection and dosing for patients with IBD.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".