CYP2A6 and CYP2B6 genetic variation and its association with nicotine metabolism in South Western Alaska Native people
Bibliographic record
Abstract
OBJECTIVES: Alaska Native (AN) people have a high prevalence of tobacco use and associated morbidity and mortality when compared with the general USA population. Variations in the CYP2A6 and CYP2B6 genes, encoding enzymes responsible for nicotine metabolic inactivation and procarcinogen activation, have not been characterized in AN and may contribute toward the increased risk. METHODS: AN people (n=400) residing in the Bristol Bay region of South Western Alaska were recruited for a cross-sectional study on tobacco use. They were genotyped for CYP2A6*1X2A, *1X2B, *1B, *2, *4, *7, *8, *9, *10, *12, *17, *35 and CYP2B6*4, *6, *9 and provided plasma and urine samples for the measurement of nicotine and metabolites. RESULTS: CYP2A6 and CYP2B6 variant frequencies among the AN Yupik people (n=361) were significantly different from those in other ethnicities. Nicotine metabolism [as measured by the plasma and urinary ratio of metabolites trans-3'-hydroxycotinine to cotinine (3HC/COT)] was significantly associated with CYP2A6 (P<0.001), but not CYP2B6 genotype (P=0.95) when controlling for known covariates. It was noteworthy that the plasma 3HC/COT ratios were high in the entire Yupik people, and among the Yupik CYP2A6 wild-type participants, they were substantially higher than those in previously characterized racial/ethnic groups (P<0.001 vs. Caucasians and African Americans). CONCLUSION: Yupik AN people have a unique CYP2A6 genetic profile that associated strongly with in-vivo nicotine metabolism. More rapid CYP2A6-mediated nicotine and nitrosamine metabolism in the Yupik people may modulate the risk of tobacco-related diseases.
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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 | 0.000 |
| 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 | 0.000 |
| 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".