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Nicotine Exposure From Smoking Tobacco and Vaping Among Adolescents

2025· article· en· W4408345187 on OpenAlexafffundabout
David Hammond, Jessica L. Reid, Maciej Ł. Goniewicz, Ann McNeill, Richard J. O’Connor, Danielle Corsetti, Ashleigh C Block, Leonie S. Brose, Deborah Robson

Bibliographic record

VenueJAMA Network Open · 2025
Typearticle
Languageen
FieldMedicine
TopicSmoking Behavior and Cessation
Canadian institutionsUniversity of Waterloo
FundersPublic Health Research ProgrammeNational Cancer InstituteNational Institutes of HealthKing's College LondonNational Institute for Health and Care ResearchHealth CanadaAmerican Association for Cancer Research
KeywordsCotinineNicotineCreatinineMedicineUrineTobacco smokePopulationCross-sectional studyEnvironmental healthInternal medicinePathology

Abstract

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Importance: It remains unknown whether nicotine intake among youths who vape is lower, comparable, or higher than among youths who smoke. Objective: To examine potential differences in biomarkers of exposure to nicotine (1) between adolescents who smoke tobacco, vape, both vape and smoke (dual use), or do not use; (2) between adolescents in 3 countries; and (3) by nicotine content and form in the vaping product last used among adolescents who exclusively vaped. Design, Setting, and Participants: This population-based, observational cross-sectional study invited adolescents aged 16 to 19 years in Canada, England, and the US who had previously completed national surveys to participate in a biomarker study based on their vaping and smoking status. Participants completed questionnaires and self-collected urine samples between September 2019 and January 2022. Analyses were conducted in February 2023 and between January and June 2024. Exposures: Vaping, tobacco smoking, dual use, or no use in the past 7 days. Main Outcomes and Measures: Urine concentration of cotinine, trans-3'-hydroxycotinine (3OH-cotinine), and total nicotine equivalents (TNE-2; molar sum of cotinine and 3OH-cotinine), normalized for creatinine concentration. Results: Among the 364 participants (mean [SD] age, 17.6 [1.1] years; 203 females [55.8%]) who provided usable urine samples and completed questionnaires, no differences in TNE-2 concentration were observed between adolescents who exclusively vaped (n = 73; geometric mean [SD], 3.10 [16.69] nmol/mg creatinine), exclusively smoked (n = 68; geometric mean [SD], 3.78 [18.00] nmol/mg creatinine), or both vaped and smoked (n = 77; geometric mean [SD], 6.07 [19.08] nmol/mg creatinine) in the past week, adjusting for creatinine concentration, age, sex, country, and cannabis use. All vaping and/or smoking groups had higher concentrations of TNE-2 than no use (n = 146; geometric mean [SD], 0.19 [1.14] nmol/mg creatinine; P < .001 for all contrasts). Among adolescents who exclusively vaped (n = 73), TNE-2 concentrations were not significantly different between those who reported using products containing more than 20 mg/mL nicotine (n = 33; geometric mean [SD], 4.35 [18.25] nmol/mg creatinine) and containing 20 mg/mL nicotine or less (n = 28; geometric mean [SD], 5.13 [15.64] nmol/mg creatinine). Reported use of vaping products containing nicotine salts (n = 23) was associated with higher concentration of TNE-2 (geometric mean [SD], 10.78 [18.03] nmol/mg creatinine) than reported use of products without nicotine salts (n = 29; geometric mean [SD], 2.72 [15.42] nmol/ng creatinine; P = .03) or reporting "don't know" (n = 14; geometric mean [SD], 1.55 [15.01] nmol/ng creatinine; P = .009). Similar patterns of exposure were observed for cotinine and 3OH-cotinine. Conclusions and Relevance: This cross-sectional study found that vaping was associated with similar nicotine exposure as smoking among adolescents. Reported use of a nicotine salt product was associated with higher nicotine exposure among those who exclusively vaped, consistent with findings from laboratory and population studies indicating greater dependence for nicotine salt e-cigarettes.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.038
Threshold uncertainty score0.489

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.021
GPT teacher head0.294
Teacher spread0.274 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations18
Published2025
Admission routes3
Has abstractyes

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