Brief Report: Rate of Nicotine Metabolism and Tobacco Use Among Persons With HIV: Implications for Treatment and Research
Bibliographic record
Abstract
BACKGROUND: The nicotine metabolite ratio (NMR) is a biomarker that represents individual variation in the speed that nicotine is metabolized. The rate of nicotine metabolism alters smoking behavior (eg, amount) and has been validated for personalizing tobacco dependence medication selection to increase treatment efficacy and reduce treatment side effects in the general population of smokers. Although smoking rates are extremely high among those with HIV, the NMR has not been evaluated in this underserved population. METHODS: We used baseline data from a smoking cessation clinical trial for smokers with HIV (N = 131) to examine associations between NMR and demographic, smoking, disease-related, and psychological characteristics. Pearson r and analysis of variance were used to identify univariate correlates of NMR, which were then entered into a multiple linear regression model. RESULTS: In univariate analyses, a higher NMR (faster nicotine metabolism) was associated with being white, and more cigarettes per day, nicotine dependence, exhaled carbon monoxide, and symptoms of depression and anxiety, and using efavirenz. In a multiple regression model, a higher NMR was associated with more cigarettes per day, higher anxiety symptoms, and efavirenz use. CONCLUSIONS: As in other populations, faster nicotine metabolism was associated with the use of more cigarettes and higher anxiety symptoms. Notably, efavirenz use was associated with faster metabolism, which might make it harder to quit smoking for people with HIV treated with that medication. These findings could help guide further study and the clinical use of the NMR to personalize nicotine dependence treatment in this underserved population.
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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 | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".