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Record W2068212056 · doi:10.1097/fpc.0b013e3283539062

DRD1 associations with smoking abstinence across slow and normal nicotine metabolizers

2012· article· en· W2068212056 on OpenAlexaff
Won‐Ho Lee, Riju Ray, Andrew W. Bergen, Gary E. Swan, Paul D. Thomas, Rachel F. Tyndale, Neal L. Benowitz, Caryn Lerman, David V. Conti

Bibliographic record

VenuePharmacogenetics and Genomics · 2012
Typearticle
Languageen
FieldMedicine
TopicSmoking Behavior and Cessation
Canadian institutionsCentre for Addiction and Mental HealthUniversity of Toronto
FundersNational Cancer InstituteNational Institute on Drug Abuse
KeywordsNicotineSmoking cessationAbstinenceOdds ratioCYP2A6Nicotinic agonistBonferroni correctionMedicineInternal medicinePharmacologyPsychologyPsychiatryReceptorMetabolismPathology

Abstract

fetched live from OpenAlex

Nicotine metabolism and genetic variation have an impact on nicotine addiction and smoking abstinence; however, further research is required. The nicotine metabolite ratio (NMR) is a robust biomarker of nicotine metabolism used to categorize slow and normal nicotine metabolizers (lower 25th quartile cut off). In two randomized clinical trials of smoking abstinence treatments, we conducted NMR-stratified analyses on smoking abstinence across 13 regions coding for nicotinic acetylcholine receptors and proteins involved in the dopamine reward system. Gene×NMR interaction P-values were adjusted for multiple correlated tests, and we used a Bonferroni-corrected α-level of 0.004 to determine system-wide significance. Three single-nucleotide polymorphisms in DRD1 (rs11746641, rs2168631, and rs11749035) had significant interactions (0.001 ≤ adjusted P-values ≤ 0.004) with increased odds of abstinence within slow metabolizers (odds ratios=3.1-3.5, 95% confidence interval 1.7-6.7). Our findings support the role of DRD1 in nicotine dependence, and identify genetic and nicotine metabolism profiles that may interact to impact nicotine dependence.

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 score0.018
Threshold uncertainty score0.435

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 head0.030
GPT teacher head0.322
Teacher spread0.292 · 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

Citations10
Published2012
Admission routes1
Has abstractyes

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