Carcinogenicity of acetaldehyde in alcoholic beverages: risk assessment outside ethanol metabolism
Why this work is in the frame
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Bibliographic record
Abstract
AIMS: In addition to being produced in ethanol metabolism, acetaldehyde occurs naturally in alcoholic beverages. Limited epidemiological evidence points to acetaldehyde as an independent risk factor for cancer during alcohol consumption, in addition to the effects of ethanol. This study aims to estimate human exposure to acetaldehyde from alcoholic beverages and provide a quantitative risk assessment. METHODS: The human dietary intake of acetaldehyde via alcoholic beverages was estimated based on World Health Organization (WHO) consumption data and literature on the acetaldehyde contents of different beverage groups (beer, wine, spirits and unrecorded alcohol). The risk assessment was conducted using the European Food Safety Authority's margin of exposure (MOE) approach with benchmark doses obtained from dose-response modelling of animal experiments. Life-time cancer risk was calculated using the T25 dose descriptor. RESULTS: The average exposure to acetaldehyde from alcoholic beverages was estimated at 0.112 mg/kg body weight/day. The MOE was calculated to be 498, and the life-time cancer risk at 7.6 in 10,000. Higher risk may exist for people exposed to high acetaldehyde contaminations, as we have found in certain unrecorded alcohol beverages in Guatemala and Russia, for which we have demonstrated possible exposure scenarios, with risks in the range of 1 in 1000. CONCLUSIONS: The life-time cancer risks for acetaldehyde from alcoholic beverages greatly exceed the usual limits for cancer risks from the environment set between 1 : 10,000 and 1 : 1,000,000. Alcohol consumption has thus been identified as a direct source of acetaldehyde exposure, which in conjunction with other sources (food flavourings, tobacco) results in a magnitude of risk requiring intervention. An initial public health measure could be to reduce the acetaldehyde content in alcoholic beverages as low as technologically possible, and to restrict its use as a food flavour additive.
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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) | 0.003 | 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 | 0.001 | 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 it