An association of CYP2A6 genotype and smoking topography
Bibliographic record
Abstract
Nicotine is metabolized into biologically inactive cotinine primarily by the cytochrome P450 enzyme CYP2A6. CYP2A6 genetic variations have been phenotypically grouped as slow, intermediate, and normal metabolizers. Slow metabolizers smoke fewer cigarettes daily and weekly, and have lower carbon monoxide (CO) and plasma nicotine levels, suggesting a reduced smoking rate compared with normal metabolizers. CYP2A6 also is involved in the metabolic activation of tobacco-specific procarcinogenic nitrosamines, such as 4-(methyl-nitrosamino)-1-(3-pyridyl)-1-butanone (NNK) and 4-(methyl-nitrosamino)-1-(3-pyridyl)-1-butanol (NNAL). NNK is one of the most abundant and potent procarcinogens in cigarette smoke. The present study investigated the association of CYP2A6 genotype with smoking topography, a quantifiable measure of smoking behavior, in a sample of treatment-seeking smokers prior to treatment. Smoking topography measures indicative of quantity of smoke exposure--number of puffs, mean puff volume, and total puff volume--were the outcome variables. Covariates associated with smoking topography were included in the analyses. Results indicated that CYP2A6 genotype group had a significant effect on mean puff volume and total puff volume, but not number of puffs, such that slow metabolizers exhibited reduced puffing compared with others. Smokers having CYP2A6 variants resulting in low activity metabolize nicotine more slowly, and convert procarcinogen nitrosamines to carcinogens more slowly, than do normal metabolizers. The results from this study also suggest a behavioral mechanism that may account for reduced cancer risk in slow metabolizers.
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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 | 0.004 | 0.000 |
| 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) | 0.000 | 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".