Toxicant and Nicotine Exposure in Pregnant Smokers, Vapers, and Nicotine-Replacement Users: Cross-Sectional Study
Bibliographic record
Abstract
INTRODUCTION: Given the increasing usage of vaping during pregnancy and limited longitudinal health-related data, there is an urgent need to assess the potential risks of vaping. AIMS AND METHODS: A cross-sectional study was conducted among pregnant UK adults (n = 140). Five study groups were purposively recruited: exclusive-smokers (n = 38), exclusive-vapers (former smokers) (n = 35), dual users of smoking and vaping (n = 25), dual users of smoking and nicotine replacement therapy (n = 10), and "never-users" of nicotine or tobacco products (n = 32). Sociodemographic, smoking, and vaping characteristics were assessed. Participants' urine samples were analyzed for biomarkers of exposure to tobacco alkaloids, and toxicants, including 14 volatile organic compounds (VOCs), tobacco-specific nitrosamine 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanol (NNAL), heavy metals (cadmium, lead, chromium, nickel, copper, and tin) and a polycyclic aromatic hydrocarbon (2-naphthol). Regression analysis was used to compare biomarkers by group. RESULTS: Nicotine levels varied across product users, but not significantly. After controlling for confounders, for most VOCs, biomarker levels were similar for exclusive-vapers and never-users and significantly lower than for exclusive-smokers and any dual users. There were generally no significant differences between groups for 2-naphthol or heavy metals. For NNAL, cadmium and chromium, a high percentage of values were below the limit of detection, making analyses unreliable. CONCLUSIONS: During pregnancy, former smokers who are established exclusive vapers, but not dual users, had levels of selected VOCs that were substantially lower than those for exclusive smokers and comparable with those who have never used nicotine or tobacco products. IMPLICATIONS: Based on the biomarkers assessed in this study, during pregnancy, on average, exclusive-vapers are likely to have similar levels of exposure to selected VOCs as never-users and far lower levels than exclusive-smokers or dual-users (although dual-vaping and smoking may result in less exposure than exclusive-smoking). This provides preliminary information about exposure to vaping during pregnancy and suggests that, for some biomarkers, exclusive vaping is likely to result in lower exposures than exclusive smoking or dual-use. There may be exposure to other vaping toxicants that were not explored in this study. Studies are needed to assess pregnancy and birth outcomes as well as early life effects.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".