Effect of race and glucuronidation rates on the relationship between nicotine metabolite ratio and nicotine clearance
Bibliographic record
Abstract
OBJECTIVES: To investigate if the nicotine metabolite ratio (NMR, the ratio of nicotine metabolites 3'-hydroxycotinine/cotinine) is a reliable phenotypic biomarker for nicotine clearance across races, and as a function of differences in the rate of nicotine, cotinine and 3'-hydroxycotinine glucuronidation and UGT genotypes. METHODS: Participants [Caucasians (Whites), African Americans (Blacks) and Asian-Americans (Asians)] received an oral solution of deuterium-labeled nicotine and its metabolite cotinine. Plasma and saliva concentrations of nicotine and cotinine were used to determine oral clearances. Rates of glucuronidation were assessed from urine glucuronide/parent ratios, and UGT2B10 and UGT2B17 genotypes from DNA. RESULTS: Among the 227 participants, 96 (42%) were White, 67 (30%) Asian and 64 (28%) Black. Compared to the other two races, Whites had higher nicotine and cotinine total oral clearance, Blacks had lower nicotine and cotinine glucuronidation rates and Asians had lower 3'-hydroxycotinine glucuronidation rates. A strong positive correlation (correlations coefficients 0.77-0.84; P < 0.001) between NMR and nicotine oral clearance was found for all three races, and NMR remained a strong predictor for the nicotine oral clearance while adjusting for race, sex and age. Neither the metabolite glucuronidation ratios nor the UGT genotypes had significant effects on the ability of NMR to predict nicotine oral clearance. CONCLUSION: NMR appears to be a reliable phenotypic biomarker for nicotine clearance across races, glucuronidation phenotypes and genotypes. Racial differences in the relationships between NMR, smoking behaviors and addiction are unlikely to be related to an inadequate estimation of nicotine clearance on the basis of NMR.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.003 | 0.001 |
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".