Effect of UGT2B10, UGT2B17, FMO3, and OCT2 genetic variation on nicotine and cotinine pharmacokinetics and smoking in African Americans
Bibliographic record
Abstract
OBJECTIVES: Nicotine metabolism rates differ considerably among individuals, even after controlling for variation in the major nicotine-metabolizing enzyme, CYP2A6. In this study, the impact of genetic variation in alternative metabolic enzymes and transporters on nicotine and cotinine (COT) pharmacokinetics and smoking was investigated. METHODS: We examined the impact of UGT2B10, UGT2B17, FMO3, NAT1, and OCT2 variation on pharmacokinetics and smoking (total nicotine equivalents and topography) before and after stratifying by CYP2A6 genotype in 60 African American (AA) smokers who received a simultaneous intravenous infusion of deuterium-labeled nicotine and COT. RESULTS: Variants in UGT2B10 and UGT2B17 were associated with urinary glucuronidation ratios (glucuronide/free substrate). UGT2B10 rs116294140 was associated with significant alterations in COT and modest alterations in nicotine pharmacokinetics. These alterations, however, were not sufficient to change nicotine intake or topography. Neither UGT2B10 rs61750900, UGT2B17*2, FMO3 rs2266782, nor NAT1 rs13253389 altered nicotine or COT pharmacokinetics among all individuals (n=60) or among individuals with reduced CYP2A6 activity (n=23). The organic cation transporter OCT2 rs316019 significantly increased nicotine and COT Cmax (P=0.005, 0.02, respectively) and decreased nicotine clearance (P=0.05). UGT2B10 rs116294140 had no significant impact on the plasma or urinary trans-3'-hydroxycotinine/COT ratio, commonly used as a biomarker of CYP2A6 activity. CONCLUSION: We found that polymorphisms in genes other than CYP2A6 represent minor sources of variation in nicotine pharmacokinetics, insufficient to alter smoking in AAs. The change in COT pharmacokinetics with UGT2B10 rs116294140 highlights the UGT2B10 gene as a source of variability in COT as a biomarker of tobacco exposure among AA smokers.
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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 | 0.001 | 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) | 0.002 | 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".