Bioactive peptides in the management of lifestyle-related diseases: Current trends and future perspectives
Bibliographic record
Abstract
Lifestyle-related diseases constitute a major concern in the twenty-first century, with millions dying worldwide each year due to chosen lifestyles and associated complications such as obesity, type 2 diabetes, hypertension, and hypercholesterolemia. Although synthetic drugs have been shown to be quite effective in the treatment of these conditions, safety of these compounds remains a concern. Natural alternatives to drugs include food-derived peptides are now being explored for the prevention and treatment of lifestyle-related complications. Peptides are fragments nascent in the primary protein sequences and could impart health benefits beyond basic nutritional advantages. Evidence suggests that by controlling adipocyte differentiation and lipase activities, bioactive peptides may be able to prevent obesity. Bioactive peptides act as agents against type 2 diabetes because of their ability to inhibit enzymatic activities of DPP-IV, α-amylase, and α-glucosidase. Moreover, bioactive peptides can act as competitive inhibitors of angiotensin-converting enzyme, thus eliciting an antihypertensive effect. Bioactive peptides may have a hypocholesterolemic effect by inhibiting cholesterol metabolism pathways and cholesterol synthesis. This review addresses current knowledge of the impact of food-derived bioactive peptides on lifestyle diseases. In addition, future insights on the clinical trials, allergenicity, cytotoxicity, gastrointestinal stability, and regulatory approvals have also been considered.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".