Differences in pharmacogenetics of nicotine and alcohol metabolism: Review and recommendations for future research
Bibliographic record
Abstract
Genetic variations in the enzymes involved in alcohol and nicotine metabolism can profoundly affect the rates of metabolism of these drugs, which in turn can influence drug-taking behaviors. The frequency of these genetic variants differs substantially among ethnic groups, resulting in differing genetic influences, or degrees of risk, among different populations. For many of these enzymatic pathways, the genetic variations that affect the rates of metabolism are very different in Asians and Caucasians and may contribute to population differences in alcohol- and tobacco-related behaviors and diseases. Understanding how variations in alcohol- and nicotine-metabolizing enzymes alter these drug responses and behaviors, with special focus on genetic variations, is the subject of this review. Each section describes genetic variations in a particular enzymatic pathway and reviews what is known about population variation and the resulting impact on alcohol- and/or nicotine-related disorders. Each section concludes with some thoughts on future research priorities. Sections 1 and 2 review the primary alcohol- and acetaldehyde-metabolizing enzymes and provide a working model for how the genetic variations in these enzymes may affect alcohol consumption and related disorders. Section 1 focuses on the alcohol and acetaldehyde dehydrogenases, while section 2 focuses on CYP2E1 and its possible role in alcohol and tobacco dependence. Sections 3 and 4 describe enzymes involved in nicotine metabolism, with section 3 focusing on the major nicotine-to-cotinine metabolizing enzyme, CYP2A6, and how genetically differing rates of metabolic inactivation of nicotine alter smoking. Section 4 describes data on the many additional enzymes involved in the metabolism of nicotine and its metabolites and summarizes our current understanding of the influence these enzymes may have on metabolism and addiction in this relatively new research area.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".