Nicotine metabolism and its association with CYP2A6 genotype among Indigenous people in Alaska who smoke
Bibliographic record
Abstract
Abstract Prevalence of smoking is higher in Alaska Native and American Indian (ANAI) populations living in Alaska than the general US population. Genetic factors contribute to smoking and cessation rates. The objective of this study was to compare CYP2A6 genetic variation and CYP2A6 enzyme activity toward nicotine in an ANAI population. ANAI ( N = 151) people trying to quit smoking were recruited. DNA samples were genotyped for CYP2A6 variants *1X2A , *1B , *2 , *4 , *9 , *10 , *12 , and *35 . Multiple nicotine metabolites were measured in plasma and urine samples, including cotinine and 3′‐hydroxycotinine used to determine CYP2A6 activity (e.g., nicotine metabolite ratio [NMR]). We calculated summary statistics for all of the genotypes and metabolites and assigned CYP2A6 activity scores based on known information. We studied the association of CYP2A6 variants with the NMR and smoking histories. The overall frequency of the CYP2A6*1B gain of function allele was high in the ANAI versus non‐ANAI populations in other studies. Both *4 null and *9 decrease of function alleles had frequencies similar to previous studies of ANAI populations. In a multivariate analysis, the genotype‐inferred CYP2A6 activity score was associated with both plasma and urine NMR ( p value = 8.56E‐08 and 4.08E‐13, respectively). Plasma NMR was also associated with duration of smoking ( p value < 0.01) but not urinary total nicotine equivalents uncorrected for creatinine (TNE9 uc ) or biological sex. Urine NMR was significantly associated ( p value < 0.01) with TNE9 uc . Variation in NMR in this ANAI population is explained in part by CYP2A6 genetic variation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".