Bioactive Natural Constituents from Food Sources—Potential Use in Hypertension Prevention and Treatment
Bibliographic record
Abstract
Prevention and management of hypertension are the major public health challenges worldwide. Uncontrolled high blood pressure may lead to a shortened life expectancy and a higher morbidity due to a high risk of cardiovascular complications such as coronary heart disease (which leads to heart attack) and stroke, congestive heart failure, heart rhythm irregularities, and kidney failure etc. In recent years, it has been recognized that many dietary constituents may contribute to human cardiovascular health. There has been an increased focus on identifying these natural components of foods, describing their physiological activities and mechanisms of actions. Grain, vegetables, fruits, milk, cheese, meat, chicken, egg, fish, soybean, tea, wine, mushrooms, and lactic acid bacteria are various food sources with potential antihypertensive effects. Their main bioactive constituents include angiotensin I-converting enzyme (ACE) inhibitory peptides, vitamins C and E, flavonoids, flavanols, cathecins, anthocyanins, phenolic acids, polyphenols, tannins, resveratrol, polysaccharides, fiber, saponin, sterols, as well as K, Ca, and P. They may reduce blood pressure by different mechanisms, such as ACE inhibition effect, antioxidant, vasodilatory, opiate-like, Ca(2+) channel blocking, and chymase inhibitory activities. These functional foods may provide new therapeutic applications for hypertension prevention and treatment, and contribute to a healthy cardiovascular population. The present review summarizes the antihypertensive food sources and their bioactive constituents, as well as physiological mechanisms of dietary products, especially focusing on ACE inhibitory activity.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".