Food Peptides, Gut Microbiota Modulation, and Antihypertensive Effects
Bibliographic record
Abstract
The gut microbiota is increasingly important in the overall human health and as such, it is a target in the search of novel strategies for the management of metabolic disorders including blood pressure, and cardiovascular diseases. The link between microbiota and hypertension is complex and this review is intended to provide an overview of the mechanism including the production of postbiotics, mitigation of inflammation, and the integration of food biological molecules within this complex system. The focus is on hydrolyzed food proteins and peptides which are less commonly investigated for prebiotic properties. The analysis of available data showed that food peptides are multifunctional and can prevent gut dysbiosis by positively affecting the production of postbiotics or gut metabolites (short-chain fatty acids, polysaccharides, biogenic amines, bile acids). Peptides and the postbiotics then displayed antihypertensive effects via the renin-angiotensin system, the gut barrier, the endothelium, and reduction in inflammation and oxidative stress. Despite the promising antihypertensive effect of the food peptides via the modulation of the gut, there is a lack of human studies as most of the works have been conducted in animal models.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".