Nicotine Metabolite Ratio (NMR) Prospectively Predicts Smoking Relapse: Longitudinal Findings From ITC Surveys in Five Countries
Bibliographic record
Abstract
INTRODUCTION: The ratio of trans 3'-hydroxycotinine (3HC) to cotinine (nicotine metabolite ratio [NMR]) is a biomarker of the rate of nicotine metabolism, with higher NMR indicating faster metabolism. Higher NMR has been found to be associated with higher daily cigarette consumption and less success stopping smoking in cessation trials. This study examines differences in NMR among population-based samples of smokers in the five countries and explores the relationship between NMR and smoking abstinence. METHODS: Participants (N = 874) provided saliva samples during International Tobacco Control (ITC) surveys in the United States, United Kingdom, Mauritius, Mexico, and Thailand conducted in 2010/2011 with follow-up surveys in 2012/2013. When all samples were received, they were sent to a common laboratory for analysis using liquid chromatography and tandem mass spectroscopy. RESULTS: There was significant variation in NMR across countries (F = 15.49, p < .001). Those who reported smoking at follow-up had a mean NMR of 0.32, compared to a mean NMR of 0.42 in participants who reported that they had stopped (F = 8.93; p = .003). Higher mean NMR values were also associated with longer quit duration (p = .007). There was no substantial difference in NMR between current smokers who made a failed quit attempt and those who made no attempt-both had significantly lower NMR compared to those who quit and remained abstinent. Smokers with a higher NMR were more likely to report that they stopped smoking compared to those with a lower NMR (odds ratio = 2.67; 95% confidence interval: 1.25 to 5.68). CONCLUSIONS: These results suggest faster nicotine metabolizers may be less likely to relapse following a quit attempt. This finding differs from results of clinical trials testing stop smoking medications, where slower metabolizers have been found to be more likely to maintain abstinence from smoking. IMPLICATIONS: Results of this study suggest faster nicotine metabolizers may be less likely to relapse following a quit attempt. This finding differs from results of clinical trials testing stop smoking medications, where slower metabolizers have been found to be more likely to maintain abstinence from smoking.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".