Glycomacropeptide as an Efficient Agent to Fight Pathophysiological Mechanisms of Metabolic Syndrome
Bibliographic record
Abstract
There is currently a growing interest in the use of nutraceuticals as a means of preventing the development of complex diseases. Given the considerable health potential of milk-derived peptides, the aim of this study was to investigate the protective effects of glycomacropeptide (GMP) on metabolic syndrome. Particular emphasis was placed on the potential mechanisms mitigating cardiometabolic disorders in high-fat, high-fructose diet-fed mice in the presence of GMP or Bipro, an isocaloric control. The administration of GMP for 12 weeks reduced obesity, hyperglycemia and hyperinsulinemia caused by a high-fat, high-fructose diet, resulting in a decline in insulin resistance. GMP also lessened systemic inflammation, as indicated by decreased circulating inflammatory cytokines. In the intestinal and hepatic tissues, GMP improved homeostasis by increasing insulin sensitivity and attenuating high-fat, high-fructose-induced inflammation, oxidative stress and endoplasmic reticulum stress. Biochemical and histological analyses revealed improved hepatic steatosis and fatty acid composition in the livers of high-fat, high-fructose diet-fed mice treated with GMP compared to Bipro. A trend toward a decrease in bile acids without any marked changes in intestinal microbiota composition characterized GMP-treated animals compared to those administered Bipro. GMP offers considerable potential for fighting metabolic syndrome-related components and complications given its beneficial effects on risk factors such as inflammation, oxidative stress and endoplasmic reticulum stress without involving the intestinal microbiota.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".