Sources of Variation in Nicotine Metabolism and Associations with Smoking Abstinence in Adolescents and Adults
Bibliographic record
Abstract
Smoking remains a major public health concern; worldwide, approximately one billion people smoke. Despite the fact that many smokers are motivated to quit, only a small minority of those making quit attempts successfully quit each year. Variation in the rate of nicotine metabolic inactivation influences a number of smoking behaviours, including cessation. Thus, we sought to characterize genetic, environmental, and demographic sources of variability in the rate of nicotine metabolism, and resultant influences on smoking behaviours. Variation in the activity of the major nicotine-metabolizing enzyme, cytochrome P450 2A6 (CYP2A6) changes nicotine clearance and is associated with altered smoking behaviours, including cessation, in adults. We demonstrate here that slow (versus normal) nicotine metabolizers are more likely to achieve prolonged abstinence in adolescence, as in adulthood. We further demonstrate that in clinical trials, adult slow (versus normal) nicotine metabolizers are more likely to achieve early abstinence. We also investigated additional sources of genetic variability in the rate of nicotine metabolism and their potential influences on smoking. We demonstrate that genetic variation in an additional nicotine-metabolizing enzyme (i.e., FMO3), and a cytochrome P450 co-enzyme (i.e., POR), does not substantially alter nicotine metabolism, CYP2A6 activity, or tobacco consumption. We further demonstrate that environmental and demographic sources of variability in CYP2A6 activity, such as gender and ethnicity, explain only a small proportion of the total variation in CYP2A6 activity; however, these factors may have unique impacts on smoking behaviours and thus should be further investigated in studies of smoking. Overall, our findings provide additional information regarding the role of variation in nicotine metabolism rate in cessation outcomes in both adolescents and adults. A greater understanding of the factors that influence smoking cessation will help optimize treatment outcomes and reduce the burden of tobacco-related disease.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".