Relationship Between CYP2A6 and CHRNA5-CHRNA3-CHRNB4 Variation and Smoking Behaviors and Lung Cancer Risk
Bibliographic record
Abstract
Genetic variations in the CYP2A6 nicotine metabolic gene and the CHRNA5-CHRNA3-CHRNB4 (CHRNA5-A3-B4) nicotinic gene cluster have been independently associated with lung cancer. With genotype data from ever-smokers of European ancestry (417 lung cancer patients and 443 control subjects), we investigated the relative and combined associations of polymorphisms in these two genes with smoking behavior and lung cancer risk. Kruskal-Wallis tests were used to compare smoking variables among the different genotype groups, and odds ratios (ORs) for cancer risk were estimated using logistic regression analysis. All statistical tests were two-sided. Cigarette consumption (P < .001) and nicotine dependence (P = .036) were the highest in the combined CYP2A6 normal metabolizers and CHRNA5-A3-B4 AA (tag single-nucleotide polymorphism rs1051730 G>A) risk group. The combined risk group also exhibited the greatest lung cancer risk (OR = 2.03; 95% confidence interval [CI] = 1.21 to 3.40), which was even higher among those who smoked 20 or fewer cigarettes per day (OR = 3.03; 95% CI = 1.38 to 6.66). Variation in CYP2A6 and CHRNA5-A3-B4 was independently and additively associated with increased cigarette consumption, nicotine dependence, and lung cancer risk. CYP2A6 and CHRNA5-A3-B4 appear to be more strongly associated with smoking behaviors and lung cancer risk, respectively.
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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 | 0.001 |
| 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 | 0.000 |
| 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".