Brief Report: Nicotine Metabolism Ratio Increases in HIV-Positive Smokers on Effective Antiretroviral Therapy: A Cohort Study
Bibliographic record
Abstract
BACKGROUND: People with HIV (PWH) smoke tobacco at much higher rates than the general population. Previous research has shown that PWH have faster nicotine metabolism than HIV-uninfected individuals, which may underlie this disparity, but the cause is unknown. We investigated whether higher nicotine metabolite ratio (NMR; 3-hydroxycotinine:cotinine), a validated biomarker of nicotine metabolism through CYP2A6, was associated with antiretroviral use among HIV-infected smokers. METHODS: We conducted a retrospective cohort study of HIV-positive smokers in the University of Pennsylvania Center for AIDS Research cohort. We compared the NMR before viral suppression (>10,000 copies/mL) and after viral suppression on antiretroviral therapy (<200 copies/mL). We used mixed-effects linear regression to analyze the change in NMR after viral suppression and assessed for effect modification by efavirenz use. RESULTS: Eighty-nine individuals were included in the study. We observed effect modification by efavirenz use (interaction term for efavirenz use, P < 0.001). Among those on nonefavirenz regimens, the mean NMR increased by 0.14 (95% confidence interval: 0.05 to 0.23, P = 0.002). Among those on efavirenz-containing regimens, the mean NMR increased by 0.53 (95% confidence interval: 0.39-0.66, P < 0.001). CONCLUSIONS: We observed a clinically and statistically significant increase in NMR after viral suppression among smokers with HIV, which more than doubled among those on efavirenz-based regimens. Higher NMR among HIV-positive smokers on antiretroviral therapy may help explain the higher rates of tobacco use and lower quit rates among PWH in care. These findings suggest that regimen choice and other modifiable factors may be targets for future attempts to increase success rates for tobacco cessation among PWH.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".