CYP2A6 reduced activity gene variants confer reduction in lung cancer risk in African American smokers—findings from two independent populations
Bibliographic record
Abstract
We investigated genetic variation in CYP2A6 in relation to lung cancer risk among African American smokers, a high-risk population. Previously, we found that CYP2A6, a nicotine/nitrosamine metabolism gene, was associated with lung cancer risk in European Americans, but smoking habits, lung cancer risk and CYP2A6 gene variants differ significantly between European and African ancestry populations. Herein, African American ever-smokers, drawn from two independent lung cancer case-control studies, were genotyped for reduced activity CYP2A6 alleles and grouped by predicted metabolic activity. Lung cancer risk in the Southern Community Cohort Study (n = 494) was lower among CYP2A6 reduced versus normal metabolizers, as estimated by multivariate conditional logistic regression [odds ratio (OR) = 0.44; 95% confidence interval (CI) = 0.26-0.73] and by unconditional logistic regression (OR = 0.62; 95% CI = 0.41-0.94). The association was replicated in an independent study from MD Anderson Cancer Center (n = 407) (OR = 0.64; 95% CI = 0.42-0.98), and pooling the studies yielded an OR of 0.64 (95% CI = 0.48-0.86). Exploratory analyses revealed a significant interaction between CYP2A6 genotype and sex on the risk for lung cancer (Southern Community Cohort Study: P = 0.04; MD Anderson: P = 0.03; Pooled studies: P = 0.002) with a CYP2A6 effect in men only. These findings support a contribution of genetic variation in CYP2A6 to lung cancer risk among African American smokers, particularly men, whereby CYP2A6 genotypes associated with reduced metabolic activity confer a lower risk of developing lung cancer.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".