<i>N</i>-acetyltransferase 2 Slow Acetylator Genotype Associated with Adverse Effects of Sulfasalazine in the Treatment of Inflammatory Bowel Disease
Bibliographic record
Abstract
AIM: N-acetyltransferase 2 (NAT2) is an important enzyme catalyzing N-acetylation of sulfasalazine (SASP). The aim of the present study was to investigate associations of the genotypes of NAT2 with inflammatory bowel disease (IBD), and with adverse effects of SASP, which is used as the first-line treatment of IBD. PATIENTS AND METHODS: The wildtype allele (NAT2*4) and three variant alleles (NAT2*5B, NAT2*6A and NAT*7B) of the NAT2 gene were determined in 101 patients with IBD (84 patients with ulcerative colitis and 17 patients with Crohn's disease) and 109 healthy controls by the polymerase chain reaction-restriction fragment length polymorphism method. Sixty-eight patients with IBD treated with SASP were followed, and their adverse reactions were recorded. RESULTS: Eleven patients (16%) experienced adverse effects from SASP, including nine cases of sulfapyridine (SP) dose-related adverse effects and two cases of hypersensitivity (skin rash). Patients with the slow acetylator genotypes without the NAT2*4 allele experienced adverse effects more frequently (36%) than those with the fast acetylator genotypes with at least one NAT2*4 allele (11%), but the results were not significantly different (OR of 0.26, 95% CI 0.065 to 1.004; P=0.051). However, those with the slow acetylator genotypes experienced more SP dose-related adverse effects than those with the fast acetylator genotypes (36% versus 8%, OR of 0.17, 95% CI 0.039 to 0.749; P=0.019). CONCLUSIONS: The NAT2 gene polymorphism was not associated with susceptibility to IBD in Chinese populations, but the NAT2 slow acetylator genotypes were significantly associated with SP dose-related adverse effects of SASP in the treatment of IBD.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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".