Biomarkers of Toxicant Exposure among Youth in Canada, England, and the United States Who Vape and/or Smoke Tobacco or Do Neither
Bibliographic record
Abstract
BACKGROUND: Few studies examine biomarkers of exposure to vaping and tobacco products among youth. We compared biomarkers for toxicants between youth who vape, smoke, "dual-use", or neither. METHODS: Participants ages 16 to 19 years in Canada, England, and the United States completed surveys and self-collected urine samples between September 2019 and January 2022 (N = 364). Urine was tested for metabolites of tobacco-specific nitrosamine NNK (4-(methylnitrosamino)-1-(3-pyridyl)-1-butanone) and six volatile organic compounds. Regression models examined differences in biomarker concentrations by past-week tobacco smoking and vaping, adjusting for creatinine, age, sex, country, and cannabis use. RESULTS: Compared with no vaping/smoking, exclusive vaping was associated with similar exposure to acrolein and acrylonitrile but higher exposure to toluene (P = 0.04) and acrylamide (P = 0.034, only in sensitivity analysis using past 24-hour measure). Compared with dual use or exclusive smoking, exclusive vaping was associated with lower exposure to NNK, acrolein, acrylamide, and acrylonitrile (P ≤ 0.01) but higher toluene exposure than dual use (P = 0.012). Exposure was similar for dual-use and exclusive smoking. Benzene and xylene biomarkers were detected in <5% and not compared. Among those smoking, NNK exposure was higher in the United States (geometric mean = 25.4 pg/mg creatinine) versus Canada (16.1 pg/mg; P = 0.006) and England (14.1 pg/mg; P = 0.018). CONCLUSIONS: Youth exclusively vaping had similar exposure as no vaping/smoking except for two volatile organic compounds and lower exposure than smoking or dual use except toluene. Higher NNK levels among US youth who smoke likely reflect differences in tobacco blend. IMPACT: Findings are generally consistent with literature indicating lower toxicant exposure from vaping versus smoking but elevated exposure versus no use for some.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".