Associations of<i>CYP2A6</i>genotype with smoking behaviors in southern China
Bibliographic record
Abstract
AIMS: To investigate the association of CYP2A6 genetic polymorphisms with smoking-related phenotypes in Chinese smokers. DESIGN: Case-only genetic association study. SETTING: Southern China. PARTICIPANTS: A total of 1328 Han Chinese smokers who participated in a community-based chronic disease screening project in Guangzhou and Zhuhai from 2006 to 2007. MEASUREMENTS: All participants answered a structured questionnaire about socio-demographic status and smoking behaviors and informative alleles were genotyped for the cytochrome P450 2A6 (CYP2A6) gene (CYP2A6*4,*5,*7,*9 and *10). FINDINGS: The frequencies of CYP2A6*4, *5, *7, *9 and *10 alleles were 8.5, 1.2, 6.3, 13.5 and 2.4%, which corresponded to 48.9, 15.4, 24.2 and 11.5% of participants being classified as normal, intermediate, slow and poor metabolizers, respectively. Multivariate analyses in male smokers demonstrated that compared with normal metabolizers, poor metabolizers reported smoking fewer cigarettes per day [adjusted odds ratio (OR) = 0.49; 95% confidence interval (CI): 0.32-0.76], started smoking regularly later in life (adjusted OR = 1.55; 95% CI: 1.06-2.26) and, among former smokers, reported smoking for a shorter duration prior to quitting (adjusted OR = 0.33; 95% CI: 0.12-0.94). However, poor metabolizers were less likely to quit smoking and remain abstinent than normal metabolizers (adjusted OR = 0.54; 95% CI: 0.34-0.86). CONCLUSIONS: Reduced metabolism function of cytochrome P450 2A6 in smokers appears to be associated with fewer cigarettes smoked, later initiation of smoking regularly, shorter smoking duration and lower likelihood of smoking cessation.
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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 | 0.000 | 0.000 |
| 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 | 0.000 | 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 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".