<i>CYP2A6</i>Genotype, Phenotype, and the Use of Nicotine Metabolites as Biomarkers during<i>Ad libitum</i>Smoking
Bibliographic record
Abstract
CYP2A6 inactivates nicotine to cotinine and cotinine to 3-hydroxycotinine. We investigated which of plasma nicotine and metabolites were most related to CYP2A6 genotype and smoking levels. We assessed demographic and smoking histories in 152 Caucasian ad libitum smokers, measured breath carbon monoxide (CO) levels, and determined plasma nicotine, cotinine, and 3-hydroxycotinine by high-performance liquid chromatography and CYP2A6 genotypes by PCR. Cigarettes per day was most closely related to CO (r = 0.60, P < 0.001) followed by plasma cotinine (r = 0.53, P < 0.001), whereas plasma cotinine was most strongly correlated with CO levels (r = 0.74, P < 0.001), confirming that cotinine is a good indicator of smoking levels; this was not limited by CYP2A6 variants. 3-Hydroxycotinine/cotinine is reported to be a good marker of CYP2A6 activity, and we found that the 3-hydroxycotinine/(cotinine + nicotine) ratio was most correlated with CYP2A6 genotype (r = 0.38, P < 0.001). Inclusion of the CYP2A6*12A allele strengthened the correlation (r = 0.46, P < 0.001), suggesting that the identification of novel alleles will continue to improve this relationship. Nicotine metabolism is slower in smokers, and we have shown that CYP2A6 is reduced by nicotine treatment in monkeys. Here, we found that plasma nicotine levels were inversely correlated with CYP2A6 activity (3-hydroxycotinine/cotinine, r = -0.41, P < 0.001) among those without CYP2A6 variants, suggesting a reduction in metabolism with higher nicotine levels. Together, these findings (a) confirm the use of plasma cotinine and CO as indicators of Caucasians' smoking levels, and that this is not limited by CYP2A6 genetic variation; (b) indicate that 3-hydroxycotinine/cotinine and 3-hydroxycotinine/(cotinine + nicotine) are moderately good indicators of the CYP2A6 genotype; and (c) support that nicotine exposure may reduce its own metabolism.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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) | 0.001 | 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".