Genetic Variation in CYP2A6-Mediated Nicotine Metabolism Alters Smoking Behavior
Bibliographic record
Abstract
Approximately 50% of the initiation of tobacco dependence is genetically influenced, whereas maintenance of dependent smoking behavior and amount smoked have approximately 70% genetic contribution (1-5). Determining the variation in nicotine's inactivation is important because of nicotine's role in producing tobacco dependence and regulating smoking patterns (6-11). The genetically polymorphic CYP2A6 enzyme is responsible for the majority of the metabolic inactivation of nicotine to cotinine (12-14). Both in vitro and in vivo studies have demonstrated considerable interindividual variation in CYP2A6 activity (15-17). CYP2A6 is genetically polymorphic, individuals carrying inactive CYP2A6 alleles have decreased nicotine metabolism, are less likely to become smokers and if they do, they smoke fewer cigarettes per day (13,18,19). The decrease in smoking behavior was confirmed by measuring carbon monoxide (CO, a measure of smoke inhalation) levels, plasma and urine nicotine and cotinine levels, and cigarette counts (13,18,19). A duplication variant in the CYP2A6 gene locus has been identified which increases nicotine inactivation and increases smoking (19). CYP2A6 can also activate tobacco smoke procarcinogens (e.g. NNK, 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanone); current studies are investigating the role of CYP2A6 in risk for lung cancer. Based on these epidemiologic data it was postulated that inhibition of CYP2A6 activity might be useful in a therapeutic context. Kinetic studies in humans indicated that selective CYP2A6 inhibitors decrease the metabolic removal of nicotine. It was also shown that inhibiting CYP2A6 in vivo (phenocopying, or mimicking the genetic defect) in smokers results in decreased smoking, making nicotine orally bioavailable, and the rerouting of procarcinogens to detoxifying pathways (20-22).
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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) | 0.004 | 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".