Brief Report: HIV Infection Does Not Explain Higher Nicotine Metabolism in People Living With HIV
Bibliographic record
Abstract
BACKGROUND: Smoking contributes to significant morbidity and mortality in people with HIV. People with HIV have relatively high nicotine metabolism rates, as measured by the nicotine metabolite ratio (NMR, 3-hydroxycotinine/cotinine). A higher NMR is associated with difficulty quitting smoking. We hypothesized that HIV infection might upregulate nicotine metabolism. SETTING: A retrospective study of male current smokers in the Multicenter AIDS Cohort Study who HIV seroconverted between 1985 and 1993. METHODS: Eligibility included having plasma stored before and after confirmed HIV seroconversion and current tobacco use. Samples were selected from the closest available visits before (median 3.3 months) and after (median 9.4 months) seroconversion. Antiretroviral therapy use was exclusionary. Cotinine and 3-hydroxycotinine were measured using liquid chromatography-tandem mass spectrometry. We compared NMR from plasma pre-HIV and post-HIV infection using signed-rank tests. We targeted a sample size of 71 pairs to achieve 80% power to detect a 0.1 unit increase in NMR with P = 0.05. RESULTS: We analyzed paired samples from 78 participants; the median age was 34.5 years [interquartile range (IQR 29-40 years)]. The median NMR pre-HIV and post-HIV was 0.45 (IQR 0.32-0.54) and 0.46 (IQR 0.34-0.56), respectively. The median change in NMR postseroconversion was +0.01 (IQR -0.05, +0.09), P = 0.25. Stratification of median change in NMR by timing between samples or time since HIV seroconversion did not alter this finding. CONCLUSIONS: Acquiring HIV had no measurable effect on NMR. We postulate that upregulation of the NMR may be due to direct pharmacologic effects of HIV medications or metabolic changes in response to HIV infection.
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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 | 0.001 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 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".