The Ability of Plasma Cotinine to Predict Nicotine and Carcinogen Exposure is Altered by Differences in CYP2A6: the Influence of Genetics, Race, and Sex
Bibliographic record
Abstract
BACKGROUND: Cotinine, a nicotine metabolite, is a biomarker of tobacco, nicotine, and carcinogen exposure. However, a given cotinine level may not represent the same tobacco exposure; for example, African-Americans have higher cotinine levels than Caucasians after controlling for exposure. METHODS: Cotinine levels are determined by the amount of cotinine formation and the rate of cotinine removal, which are both mediated by the enzyme CYP2A6. Because CYP2A6 activity differs by sex (estrogen induces CYP2A6) and genotype, their effect on cotinine formation and removal was measured in nonsmoking Caucasians (Study 1, n = 181) infused with labeled nicotine and cotinine. The findings were then extended to ad libitum smokers (Study 2, n = 163). RESULTS: Study 1: Reduced CYP2A6 activity altered cotinine formation less than cotinine removal resulting in ratios of formation to removal of 1.31 and 1.12 in CYP2A6 reduced and normal metabolizers (P = 0.01), or 1.39 and 1.12 in males and females (P = 0.001), suggesting an overestimation of tobacco exposure in slower metabolizers. Study 2: Cotinine again overestimated tobacco and carcinogen exposure by 25% or more in CYP2A6 reduced metabolizers (≈2-fold between some genotypes) and in males. CONCLUSIONS: In people with slower relative to faster CYP2A6 activity, cotinine accumulates resulting in substantial differences in cotinine levels for a given tobacco exposure. IMPACT: Cotinine levels may be misleading when comparing those with differing CYP2A6 genotypes within a race, between races with differing frequencies of CYP2A6 gene variants (i.e., African-Americans have higher frequencies of reduced function variants contributing to their higher cotinine levels), or between the sexes.
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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 | 0.005 |
| 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 | 0.001 | 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".