Beneficial effect of low ethanol intake on the cardiovascular system: possible biochemical mechanisms
Bibliographic record
Abstract
Low ethanol intake is known to have a beneficial effect on cardiovascular disease. In cardiovascular disease, insulin resistance leads to altered glucose and lipid metabolism resulting in an increased production of aldehydes, including methylglyoxal. Aldehydes react non-enzymatically with sulfhydryl and amino groups of proteins forming advanced glycation end products (AGEs), altering protein structure and function. These alterations cause endothelial dysfunction with increased cytosolic free calcium, peripheral vascular resistance, and blood pressure. AGEs produce atherogenic effects including oxidative stress, platelet adhesion, inflammation, smooth muscle cell proliferation and modification of lipoproteins. Low ethanol intake attenuates hypertension and atherosclerosis but the mechanism of this effect is not clear. Ethanol at low concentrations is metabolized by low Km alcohol dehydrogenase and aldehyde dehydrogenase, both reactions resulting in the production of reduced nicotinamide adenine dinucleotide (NADH). This creates a reductive environment, decreasing oxidative stress and secondary production of aldehydes through lipid peroxidation. NADH may also increase the tissue levels of the antioxidants cysteine and glutathione, which bind aldehydes and stimulate methylglyoxal catabolism. Low ethanol improves insulin resistance, increases high-density lipoprotein and stimulates activity of the antioxidant enzyme, paraoxonase. In conclusion, we suggest that chronic low ethanol intake confers its beneficial effect mainly through its ability to increase antioxidant capacity and lower AGEs.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".