Nicotine metabolism: the impact of CYP2A6 on estimates of additive genetic influence
Bibliographic record
Abstract
To conduct a pharmacogenetic investigation of nicotine metabolism in twins. One hundred and thirty nine twin pairs [110 monozygotic (MZ) and 29 dizygotic (DZ)] underwent a 30-min infusion of stable isotope-labelled nicotine and its major metabolite, cotinine, followed by an 8-h in-hospital stay. Blood and urine samples were taken at regular intervals for analysis of nicotine, cotinine and metabolites by gas chromatography-mass spectrometry or liquid chromatography-mass spectrometry and subsequent characterization of pharmacokinetic and metabolism phenotypes. DNA was genotyped to confirm zygosity and for variation in the gene for the primary enzyme involved in nicotine metabolism, CYP2A6 (alleles tested: *1, *1x2, *2, *4, *7, *9 and *12). Univariate biometric analyses quantified genetic and environmental influences on each pharmacokinetic measure in the presence and absence of covariates, including measured CYP2A6 genotype. The best-fitting model identified a substantial amount of variation in the weight-adjusted rate of total clearance of nicotine attributable to additive genetic influences [59.4%, 95% confidence interval (CI)=44.7-70.7]. The majority of variation in the clearance of nicotine via the cotinine pathway was similarly genetically influenced (60.8%, 95% CI=46.9-71.5). Heritability estimates were reduced to 54.2% and 51.8%, respectively, but remained substantial after taking into account the effect of variation in CYP2A6 genotype. These results suggest the involvement of additional genetic factors (e.g. uncharacterized or novel CYP2A6 alleles as well as other genes in the metabolic pathway) that remain to be identified.
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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 | 0.014 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".