Known and Novel Sources of Variability in the Nicotine Metabolite Ratio in a Large Sample of Treatment-Seeking Smokers
Bibliographic record
Abstract
BACKGROUND: The ratio of 3'hydroxycotinine to cotinine, or nicotine metabolite ratio (NMR), is strongly associated with CYP2A6 genotype, CYP2A6-mediated nicotine and cotinine metabolism, and nicotine clearance. Higher NMR (faster nicotine clearance) is associated retrospectively with heavier smoking and lower cessation rates. METHODS: NMR as a predictive biomarker of cessation outcomes is being investigated (NCT01314001). In addition to strong CYP2A6 genetic influences on NMR, demographic and hormonal factors alter NMR. Here, we analyzed, for the first time together, these sources of variation on NMR in smokers screened for this clinical trial (N = 1,672). RESULTS: Participants (mean age = 45.9) were 65.1% Caucasian, 34.9% African American, and 54.8% male. Mean NMR (SD) was higher in Caucasians versus African Americans [0.41 (0.20) vs. 0.33 (0.21); P < 0.001], and in females versus males [0.41 (0.22) vs. 0.37 (0.20); P < 0.001]. Among females, birth control pill use (N = 17) and hormone replacement therapy (N = 14) were associated with 19.5% (P = 0.09) and 29.3% (P = 0.06) higher mean NMR, respectively, albeit nonsignificantly. BMI was negatively associated with NMR (Rho = -0.14; P < 0.001), whereas alcohol use (Rho = 0.11; P < 0.001) and cigarette consumption (Rho = 0.12; P < 0.001) were positively associated with NMR. NMR was 16% lower in mentholated cigarette users (P < 0.001). When analyzed together in a linear regression model, these predictors (each ≤2%) accounted for <8% of total NMR variation. CONCLUSIONS: Although these factors significantly affected NMR, they contributed little (together <8%; each ≤2%) to total NMR variation. IMPACT: Thus, when using NMR, for example, to prospectively guide smoking cessation therapy, these sources of variation are unlikely to cause NMR misclassification.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".