Carcinogen Exposure among Canadian Tobacco Users: Changes in NNK Exposure from 2007–2009 through 2012–2013
Bibliographic record
Abstract
Abstract Background: Tobacco-specific nitrosamines (TSNAs) are a class of carcinogens found in tobacco products, whose levels can vary considerably depending on tobacco blends and manufacturing processes. The current study examined whether recent increases in levels of the TSNA NNK [4-(methylnitrosamino-1-(3-pyridyl)-1-butanone] in Canadian cigarettes translated into differences in exposure among Canadian tobacco users. Methods: Nationally representative data from the Canadian Health Measures Survey (CHMS) were used to measure levels of total urinary NNAL [4-(methylnitrosamino)-1-(3-pyridyl)-1-butanol], a metabolite of the TSNA NNK, among tobacco users. Data from CHMS Cycle 3 (2012–13) were used to examine NNAL, and linear regression was used to examine predictors. Data from CHMS Cycle 1 (2007–09) and Cycle 3 (2012–13) were used to examine changes in NNAL over time. Results: From 2007–2009 through 2012–2013, levels of creatinine-corrected NNAL increased by 64% (P < 0.0001). Levels of NNAL in 2012–2013 were higher among older respondents (P = 0.04), among females (P = 0.03), among respondents identifying as “white” and “Aboriginal” (P < 0.0001), and among those with greater daily cigarette consumption (P < 0.001), as well as greater levels of urinary free cotinine (P < 0.0001) and urinary creatinine (P < 0.0001). Conclusions: The findings indicate that exposure to the TSNA NNK among Canadian tobacco users has increased considerably from 2007–2009 through 2012–2013, in parallel to changes in TSNA levels in Canadian cigarettes. In the absence of epidemiologic data, it is unclear whether this change translates into increased risk. Impact: The study findings have potential implications for tobacco manufacturers, who bear a responsibility to reduce levels of tobacco carcinogens to the full extent possible. Cancer Epidemiol Biomarkers Prev; 27(3); 262–7. ©2018 AACR.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".