The Role of<i>CYP2A6</i>in the Emergence of Nicotine Dependence in Adolescents
Bibliographic record
Abstract
OBJECTIVES: The objectives of our study were to evaluate whether genetic variation in nicotine metabolic inactivation accounted for the emergence of nicotine dependence from mid- to late adolescence and whether initial smoking experiences mediated this effect. METHODS: Participants were 222 adolescents of European ancestry who participated in a longitudinal cohort study of the biobehavioral determinants of adolescent smoking. Survey data were collected annually from grade 9 to the end of grade 12. Self-report measures included nicotine dependence, smoking, age first smoked, initial smoking experiences, peer and household member smoking, and alcohol and marijuana use. DNA collected via buccal swabs was assessed for CYP2A6 alleles that are common in white people and are demonstrated to decrease enzymatic function (CYP2A6*2, *4, *9, *12). RESULTS: Latent growth-curve modeling indicated that normal metabolizers (individuals with no detected CYP2A6 variants) progressed in nicotine dependence at a faster rate and that these increases in nicotine dependence leveled off more slowly compared with slower metabolizers (individuals with CYP2A6 variants). Initial smoking experiences did not account for how CYP2A6 genetic variation impacts nicotine dependence. CONCLUSIONS: These findings may help to promote a better understanding of the biology of smoking behavior and the emergence of nicotine dependence in adolescents and inform future work aimed at understanding the complex interplay between genetic, social, and psychological factors in adolescent smoking behavior.
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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 | 0.001 | 0.004 |
| 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 | 0.001 | 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".