From cultivation to cancer: formation of <i>N</i> -nitrosamines and other carcinogens in smokeless tobacco and their mutagenic implications
Bibliographic record
Abstract
Tobacco use is a major cause of preventable morbidity and mortality globally. Tobacco products, including smokeless tobacco (ST), generally contain tobacco-specific N-nitrosamines (TSNAs), such as N′-nitrosonornicotine (NNN) and 4-(methylnitrosamino)-1-(3-pyridyl)-butanone (NNK), which are potent carcinogens that cause mutations in critical genes in human DNA. This review covers the series of biochemical and chemical transformations, related to TSNAs, leading from tobacco cultivation to cancer initiation. A key aim of this review is to provide a greater understanding of TSNAs: their precursors, the microbial and chemical mechanisms that contribute to their formation in ST, their mutagenicity leading to cancer due to ST use, and potential means of lowering TSNA levels in tobacco products. TSNAs are not present in harvested tobacco but can form due to nitrosating agents reacting with tobacco alkaloids present in tobacco during certain types of curing. TSNAs can also form during or following ST production when certain microorganisms perform nitrate metabolism, with dissimilatory nitrate reductases converting nitrate to nitrite that is then released into tobacco and reacts chemically with tobacco alkaloids. When ST usage occurs, TSNAs are absorbed and metabolized to reactive compounds that form DNA adducts leading to mutations in critical target genes, including the RAS oncogenes and the p53 tumor suppressor gene. DNA repair mechanisms remove most adducts induced by carcinogens, thus preventing many but not all mutations. Lastly, because TSNAs and other agents cause cancer, previously documented strategies for lowering their levels in ST products are discussed, including using tobacco with lower nornicotine levels, pasteurization and other means of eliminating microorganisms, omitting fermentation and fire-curing, refrigerating ST products, and including nitrite scavenging chemicals as ST ingredients.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".