A review of the efficacy of egg-derived bioactive peptides and hydrolysates on glycemic regulation
Bibliographic record
Abstract
Type 2 diabetes is a chronic metabolic disorder that may lead to serious health complications, including cardiovascular disease, kidney failure, and nerve damage. Furthermore, type 2 diabetes prevalence is projected to exceed 592 million people worldwide by 2035. Bioactive peptides are biological molecules that modulate physiological pathways, exhibiting antidiabetic, antioxidant, anti-inflammatory, antihypertensive, and immunomodulatory activities. Recently egg white has gained attention for developing bioactive peptides. It is hypothesized that consuming these peptides either individually or as part of unpurified or minimally purified hydrolysates may benefit individuals with type 2 diabetes when incorporated into the diet. Although human trials investigating the effects of egg white hydrolysate remain limited, several in vitro and rodent model studies have demonstrated beneficial effects of egg white hydrolysate and other egg-derived bioactive peptides on glucose homeostasis and glycemic control. However, the underlying mechanisms by which egg-derived hydrolysates and peptides affect glucose regulation appear to be diverse. This review summarizes evidence from preclinical and clinical studies investigating the glucoregulatory effects of egg white hydrolysate and egg-derived bioactive peptides in obesity and/or type 2 diabetes. Findings suggest potential benefits through mechanisms such as modulation of body weight, regulation of glucose absorption and uptake, and enhancement of insulin secretion and signaling. Nonetheless, further robust animal studies and clinical trials are needed to enhance our knowledge in this field and advance the use of egg peptides in the management of type 2 diabetes.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| 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) | 0.003 | 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".