Phenol Antioxidant Quantity and Quality in Foods: Beers and the Effect of Two Types of Beer on an Animal Model of Atherosclerosis
Bibliographic record
Abstract
The free phenols have been measured in 15 lagers, 6 porters and ales, and 11 light and nonalcoholic beers. Phenols were measured colorimetrically using an oxidation-reduction reaction with Folin-Ciocalteu reagent and catechin as the standard. The order of phenol concentration was ales > lagers > low calorie > nonalcoholic. The quality of antioxidants of the major phenols in beers and the quality of beer antioxidants were measured by (1) dose-response inhibition of lower density lipoprotein oxidation and (2) concentration of phenols in the beers at which 50% of the peroxide was destroyed in a luminescent assay for antioxidant activity. The beers' lipoprotein antioxidant quality was clearly superior to that of vitamin antioxidants and to that of the phenol ingredients, suggesting synergism among the antioxidants in the mixture. The average per capita consumption of beer in the United States in 2000 was 225 mL/day, equivalent to 42 mg/day of catechin equivalents. Beer provides more antioxidants per day than wine in the U.S. diet. A dark beer and a lager beer were given at two concentrations to cholesterol-fed hamsters, an animal model of atherosclerosis. At the high dose ((1)/(2)-diluted beer) both lager and dark beer significantly inhibited atherosclerosis compared to a control of 2% alcohol. At the high dose, lager significantly decreased cholesterol and triglycerides, and both beers acted as in vivo antioxidants by decreasing the oxidizability of lower density lipoproteins. At the low dose ((1)/(10)-diluted beer) only the lager beer significantly decreased atherosclerosis compared to the 0.4% alcohol control. The polyphenols in the beers appear to be responsible for the benefits of beer in this model. Lager beer inhibited atherosclerosis at a human equivalent dose in this hamster model of atherosclerosis.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".