Comparing the Rate of Nicotine Metabolism Among Smokers With Current or Past Major Depressive Disorder
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Persons with current or past major depressive disorder (MDD) vs those without have higher smoking rates. The nicotine metabolite ratio (NMR) represents variation in the rate of nicotine metabolism and has been associated with smoking behaviors and response to tobacco treatments. We compared NMR between smokers with current or past MDD (MDD+) vs smokers without MDD (MDD-). We also assessed correlates of NMR and compared withdrawal and craving between MDD+ and MDD- smokers. METHODS: Using baseline data from two clinical trials and propensity score weighting based on sex, race, body mass index, and smoking rate, we compared NMR between MDD+ (N = 279) and MDD- (N = 1575) smokers. We also compared groups on and nicotine withdrawal and craving. RESULTS: Mean NMR (β = -.02, 95% confidence interval [CI]: -0.05 to 0.01, P = .13) and the distribution of smokers across NMR quartiles (odds ratio [OR] = 0.76, 95% CI: 0.50 to 1.16, P = .21) were similar between MDD+ and MDD- samples. This relationship was not affected by antidepressant medication. In the MDD+ sample, African Americans had significantly lower mean NMR, while older smokers and smokers with lower education had higher mean NMR (Ps < .05). MDD+ smokers had significantly higher withdrawal and craving than MDD- smokers (Ps < .05). DISCUSSION AND CONCLUSIONS: While variability in NMR may not explain differences in smoking rates between MDD+ and MDD- smokers, MDD+ smokers report increased withdrawal and craving. SCIENTIFIC SIGNIFICANCE: In this first study to assess NMR among MDD+ smokers, the findings underscore the need to address withdrawal and craving within smoking cessation treatments for those with MDD. (Am J Addict 2021;00:00-00).
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".