Cancer risk assessment of ethyl carbamate in alcoholic beverages from Brazil with special consideration to the spirits cachaça and tiquira
Bibliographic record
Abstract
BACKGROUND: Ethyl carbamate (EC) is a multi-site carcinogen in experimental animals and probably carcinogenic to humans (IARC group 2A). Traces of EC below health-relevant ranges naturally occur in several fermented foods and beverages, while higher concentrations above 1 mg/l are regularly detected in only certain spirits derived from cyanogenic plants. In Brazil this concerns the sugarcane spirit cachaça and the manioc (cassava) spirit tiquira, which both regularly exceed the national EC limit of 0.15 mg/l. This study aims to estimate human exposure in Brazil and provide a quantitative risk assessment. METHODS: The human dietary intake of EC via alcoholic beverages was estimated based on WHO alcohol consumption data in combination with own surveys and literature data. This data comprises the EC contents of the different beverage groups cachaça, tiquira, other spirits, beer, wine, and unrecorded alcohol (as defined by the WHO; including alcohol which is not captured in routine government statistics nor taxed). The risk assessment was conducted using the margin of exposure (MOE) approach with benchmark doses obtained from dose-response modelling of animal experiments. Lifetime cancer risk was calculated using the T25 dose descriptor. RESULTS: Considering differences between pot-still and column-still cachaça, its average EC content would be 0.38 mg/l. Tiquira contained a considerably higher average EC content of 2.34 mg/l. The whole population exposure from all alcoholic beverages was calculated to be around 100 to 200 ng/kg bw/day, with cachaça and unrecorded alcohol as the major contributing factors. The MOE was calculated to range between 400 and 2,466, with the lifetime cancer risk at approximately 3 cases in 10,000. An even higher risk may exist for binge-drinkers of cachaça and tiquira with MOEs of up to 80 and 15, respectively. CONCLUSIONS: According to our risk assessment, EC poses a significant cancer risk for the alcohol-drinking population in Brazil, in addition to that of alcohol alone. Model calculations show that the implementation of the 0.15 mg/l limit for cachaça would be beneficial, including an increase of the MOE by a factor between 3 to 6. The implementation of policy measures for tiquira and unrecorded alcohol also appears to be advisable.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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".