Nicotine Metabolite Ratio Decreases After Switching Off Efavirenz‐Based Therapy in People With <scp>HIV</scp> Who Smoke
Bibliographic record
Abstract
Rates of cigarette smoking in people with HIV (PWH) are two to three times higher than in people without HIV. Nicotine is metabolized by CYP2A6 and the nicotine metabolite ratio (NMR; 3-hydroxycotinine/cotinine) is a measure of nicotine clearance. Higher NMR has been observed in PWH and is associated with lower quit rates. Efavirenz, a mainstay antiretroviral therapy (ART) globally, partially upregulates its own metabolism through CYP2A6. We hypothesized that efavirenz also upregulates nicotine metabolism by CYP2A6, resulting in a higher NMR, and switching to non-efavirenz ART would decrease the NMR, potentially leading to improved quit rates. We compared the NMR during and after efavirenz use among PWH in a longitudinal, multisite cohort. Eligibility criteria included: (i) active cigarette smoking, (ii) ART switched from efavirenz-based to non-efavirenz-based regimen, (iii) plasma available at pre- and post-ART switch, and (iv) viral suppression during study period. Plasma cotinine and 3-hydroxycotinine were measured by liquid chromatography-tandem mass spectrometry. T-tests compared the NMR on and off efavirenz. Samples were collected between 2010 and 2019 in 72 PWH. The mean NMR difference after switching to a non-efavirenz-based regimen was -0.24 (SD: 0.37, P < 0.001); 44 PWH had at least a 0.1 decrease in NMR. Effect modification by race was present; Black PWH had a larger mean decrease. Our findings suggest that previously observed higher NMR among PWH may be due to direct pharmacologic effects of ART. Assessing the effect of ART on the NMR suggests that avoiding nicotine metabolism inducers could potentially increase quit rates.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".