Variation in CYP2A6 and nicotine metabolism among two American Indian tribal groups differing in smoking patterns and risk for tobacco-related cancer
Bibliographic record
Abstract
OBJECTIVE: The Northern Plains (NP) and Southwest (SW) American Indian populations differ in their smoking patterns and lung cancer incidence. We aimed to compare CYP2A6 genetic variation and CYP2A6 enzyme activity (representative of the rate of nicotine metabolism) between the two tribal populations as these have previously been associated with differences in smoking, quitting, and lung cancer risk. PARTICIPANTS AND METHODS: American Indians (N=636) were recruited from two different tribal populations (NP in South Dakota, SW in Arizona) as part of a study carried out as part of the Collaborative to Improve Native Cancer Outcomes P50 Project. A questionnaire assessed smoking-related traits and demographics. Participants were genotyped for CYP2A6 genetic variants *1B, *2, *4, *7, *9, *12, *17, and *35. Plasma and/or saliva samples were used to measure nicotine's metabolites cotinine and 3'-hydroxycotinine and determine CYP2A6 activity (3'-hydroxycotinine/cotinine, i.e. the nicotine metabolite ratio, NMR). RESULTS: The overall frequency of genetically reduced nicotine metabolizers, those with CYP2A6 decrease-of-function or loss-of-function alleles, was lower in the NP compared with the SW (P=0.0006). The CYP2A6 genotype was associated with NMR in both tribal groups (NP, P<0.0001; SW, P=0.04). Notably, the rate of nicotine metabolism was higher in NP compared with SW smokers (P=0.03), and in comparison with other ethnic groups in the USA. Of the variables studied, the CYP2A6 genotype was the only variable to significantly independently influence NMR among smokers in both tribal populations (NP, P<0.001; SW, P=0.05). CONCLUSION: Unique CYP2A6 allelic patterns and rates of nicotine metabolism among these American Indian populations suggest different risks for smoking, and tobacco-related disease.
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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 | 0.000 | 0.001 |
| 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".