Ethnic variation in CYP2A6 and association of genetically slow nicotine metabolism and smoking in adult Caucasians
Bibliographic record
Abstract
Genetically variable CYP2A6 is the primary enzyme that inactivates nicotine to cotinine. Our objective was to investigate allele frequencies among five ethnic groups and to investigate the relationship between genetically slow nicotine metabolic inactivation and smoking status, cigarette consumption, age of first smoking and duration of smoking. Chinese, Japanese, Canadian Native Indian, African-North American and Caucasian DNA samples were assessed for CYP2A6 allelic frequencies (CYP2A6*1B-*12,*1x2). Adult Caucasian non-smokers (n = 224) (1-99 cigarettes/lifetime) and smokers (n = 375) (> or = 100 cigarettes/lifetime) were assessed for demographics, tobacco/drug use history and DSM-IV dependence and genotyped for CYP2A6 alleles associated with decreased nicotine metabolism (CYP2A6*2, CYP2A6*4, CYP2A6*9, CYP2A6*12). CYP2A6 allele frequencies varied substantially among the ethnic groups. The proportion of Caucasian slow nicotine inactivators was significantly lower in current, DSM-IV dependent smokers compared to non-smokers [7.0% and 12.5%, respectively, P = 0.03, odds ratio (OR) = 0.52; 95% confidence interval (CI) 0.29-0.95]; non-dependent smokers showed similar results. Daily cigarette consumption (cigarettes/day) was significantly (P = 0.003) lower for slow (21.3; 95% CI 17.4-25.2) compared to normal inactivators (28.2; 95% CI 26.4-29.9); this was observed only in DSM-IV dependent smokers. Slow inactivators had a significantly (P = 0.03) lower age of first smoking compared to normal inactivators (13.0 years of age; 95% CI 12.1-14.0 versus 14.2; 95% CI 13.8-14.6), and a trend towards smoking for a shorter duration. This study demonstrates that slow nicotine inactivators are less likely to be adult smokers (dependent or non-dependent). Slow inactivators also smoked fewer cigarettes per day and had an earlier age of first smoking (only dependent smokers).
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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 | 0.002 |
| 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) | 0.002 | 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".