Cigarette smoking enhances the metabolic activation of the polycyclic aromatic hydrocarbon phenanthrene in humans
Bibliographic record
Abstract
Although it is well established that human cytochrome P450 1 family enzymes are induced by cigarette smoking through activation of the Ah receptor, it is not known whether this leads to increased metabolic activation or detoxification of carcinogenic polycyclic aromatic hydrocarbons (PAH), which are present in cigarette smoke and the general environment. We gave oral doses of deuterated phenanthrene ([D10]Phe), a non-carcinogenic surrogate of carcinogenic PAH such as benzo[a]pyrene, to smokers (N = 170, 1 or 10 μg doses) and non-smokers (N = 57, 1 μg dose). Bioactivation products (dihydrodiol and tetraol) and detoxification products (phenols) of [D10]Phe were determined in 6-h urine to obtain a comprehensive metabolic profile. Cigarette smoking increased the bioactivation of [D10]Phe and decreased its detoxification resulting in significantly different metabolic patterns between smokers and non-smokers (P < 0.01), consistent with increased cancer risk in smokers. The Phe bioactivation ratios ([D10]PheT/total [D9]OHPhe) were significantly higher (2.3 (P < 0.01) to 4.8 (P < 0.001) fold) in smokers than non-smokers. With solid human in vivo evidence, our results for the first time demonstrate that cigarette smoking enhances the metabolic activation of Phe, structurally representative of carcinogenic PAH, in humans, strongly supporting their causal role in cancers caused by smoking. The results suggest potential new methods for identifying smokers who could be at particularly high risk for cancer.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.001 | 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".