<i>CYP2A6</i> Genetic Variation and Variable Nicotine Metabolism Among Two Distinct American Indian Tribal Groups With Different Levels of Smoking and Risk For Tobacco‐Related Cancer
Bibliographic record
Abstract
CYP2A6 variability, resulting in altered rates of nicotine metabolic inactivation, is associated with variation in smoking behaviors. Distinct patterns of smoking and disease prevalence of two American Indian (AI) tribes (Northern Plains, NP; Southwestern, SW) prompted our investigation of CYP2A6 genetic variability and nicotine metabolism in NP and SW AIs. Both tribes (NP n=426; SW n=210) were genotyped for multiple CYP2A6 variants representative of prevalent loss of function alleles from different ethnicities. Using 3'‐hydroxycotinine to cotinine (3HC/COT) ratio as a phenotype of nicotine metabolism, association between CYP2A6 genotype and rate of nicotine metabolism was examined. CYP2A6 genotype was associated with the rate of nicotine metabolism in both tribes (P<0.02), confirming that CYP2A6 genotype predicts rate of nicotine metabolism for these populations. The rate of nicotine metabolism was higher in NP compared to SW AIs when controlling for genotype ( P <0.01). The frequency of reduced metabolizers, those with loss of function alleles, was higher in the SW compared to the NP tribe ( P <0.01). Faster nicotine metabolism, and fewer reduced function variants, which was observed in the NP tribe, has been associated with higher levels of smoking and dependence, more difficulty quitting, and reduced response to some cessation pharmacotherapies in other populations. Funding: CIHR and the Collaborative to Improve Native Cancer Outcomes (NCI grant P50CA148110)
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".