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Record W7073938585

Influence of CYP2A6 genetic variation, nicotine dependence severity, and treatment on smoking cessation success

2023· article· en· W7073938585 on OpenAlexfundno aff

Bibliographic record

VenueLancaster EPrints (Lancaster University) · 2023
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCell Image Analysis Techniques
Canadian institutionsnot available
FundersCanadian Institutes of Health ResearchCampbell Family Mental Health Research InstituteNational Institutes of Health
KeywordsNicotinePlaceboVareniclinePopulationSmoking cessation
DOInot available

Abstract

fetched live from OpenAlex

Introduction Genetic variation in Cytochrome P450 2A6 (CYP2A6), the major nicotine metabolizing enzyme, is associated with nicotine dependence and smoking cessation. Nicotine dependence severity also predicts smoking cessation. Our goals were to determine how CYP2A6 variation and nicotine dependence alter smoking cessation, and whether dependence could refine CYP2A6-based treatment recommendations. Aims and Methods Adult smokers treated for 12 weeks with placebo, nicotine patch, or varenicline (NCT01314001) were grouped as CYP2A6 normal (n = 567) or slow (n = 432) nicotine metabolizers based on a CYP2A6 weighted genetic risk score. Fagerström test for nicotine dependence scores were measured at baseline and biochemically verified smoking cessation was assessed at end of treatment. Results Dependence neither mediated nor moderated an association between CYP2A6 variation and smoking cessation overall, within any treatment arm, or after stratifying by ancestry (n = 591 European, n = 408 African ancestry) or sex (n = 444 women, n = 555 men). In within-treatment analyses, the mediation effect odds ratio (OR) ranged from 0.95 to 1.00 and the bias-corrected 95% confidence interval contained 1. Moderation (i.e. interaction) effect ORs ranged from 0.88 to 1.61 (p = .397–.828). For CYP2A6 normal metabolizers, quit rates on varenicline were similar for those with high (41.1%) and low (43.4%) dependence, while quit rates were lower for those with high versus low dependence on both patch (16.5 vs. 29.7%) and placebo (8.9 vs. 18.5%). CYP2A6 slow metabolizers with high versus low dependence had lower quit rates in all three treatment arms. Conclusions Although nicotine dependence severity neither mediated nor moderated CYP2A6 associations with smoking cessation, incorporating information on dependence may optimize the choice of smoking cessation treatment aid in CYP2A6 normal and slow metabolizers. Implications Variation in CYP2A6 and nicotine dependence severity alter smoking cessation success. Our findings suggest that while nicotine dependence severity is unlikely to mediate or moderate CYP2A6 associations with cessation, incorporating patient information on both CYP2A6 and nicotine dependence severity may lead to improved smoking cessation strategies.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.035
Threshold uncertainty score0.623

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.010
GPT teacher head0.232
Teacher spread0.222 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

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