A State-of-the-Science Review of Alcoholic Beverages and Polycyclic Aromatic Hydrocarbons
Bibliographic record
Abstract
BACKGROUND: The association between alcohol and certain cancers is well established, yet beyond ethanol and its metabolite acetaldehyde, little is known about the presence of other carcinogenic compounds in alcoholic beverages, including polycyclic aromatic hydrocarbons (PAHs), such as benzo[a]pyrene (a Group I carcinogen). OBJECTIVES: We summarized the published literature on PAH levels in alcoholic beverages to identify potential gaps in knowledge to inform future research. METHODS: Medline and Scopus were searched for primary research published from January 1966 to November 2023 that quantified PAH levels among various types of alcoholic beverages, including whisky, rum, brandy, gin, vodka, wine, and beer. Studies that were not primary literature were excluded; only studies that quantified PAH content in the specified alcoholic beverages were included. RESULTS: products. Of the studies, 7 examined spirits; 3, beer; and 4, wines. Benzo[a]pyrene was most prevalent among spirit products, particularly whisky, with values generally exceeding acceptable levels for drinking water. Some beer and wine products also contained PAHs, albeit at lower levels and less frequently than spirit products. DISCUSSION: PAHs are found in some alcohol products and appear to vary by beverage type. However, there is an incomplete understanding of their presence and levels among large, representative samples from the range of currently available alcohol products. Addressing this gap could improve understanding of alcohol-cancer relationships and may have important implications for public health and the regulation of alcohol products. In addition, novel methods, such as direct mass spectroscopy, may facilitate more thorough testing of samples to further investigate this relationship. https://doi.org/10.1289/EHP13506.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".