Glycomacropeptide Prevents Iron/Ascorbate-Induced Oxidative Stress, Inflammation and Insulin Sensitivity with an Impact on Lipoprotein Production in Intestinal Caco-2/15 Cells
Bibliographic record
Abstract
BACKGROUND: Metabolic Syndrome (MetS), a major worldwide concern for the public health system, refers to a cluster of key metabolic components, and represents a risk factor for diabetes and cardiovascular diseases. As oxidative stress (OxS) and inflammation are the major triggers of insulin sensitivity (IS), a cardinal MetS feature, the principal aim of the present work is to determine whether glycomacropeptide (GMP), a milk-derived bioactive peptide, exerts beneficial effects on their expression. METHODS: Fully differentiated intestinal Caco-2/15 cells are used to evaluate the preventive action of 2 mg/mL GMP against OxS and inflammation induced by the mixture iron-ascorbate (Fe/Asc) (200 μM:2 mM). The potency of GMP of decreasing the production of lipoproteins, including chylomicrons (CM), very-low-density lipoproteins (VLDL) and low-density lipoproteins (LDL) is also assessed. RESULTS: The administration of GMP significantly reduces malondialdehyde, a biomarker of lipid peroxidation, and raises superoxide dismutase 2 and glutathione peroxidase via the induction of the nuclear factor erythroid 2-related factor 2, a transcription factor, which orchestrates cellular antioxidant defenses. Similarly, GMP markedly lowers the inflammatory agents tumor necrosis factor-α and cyclooxygenase-2 via abrogation of the nuclear transcription factor-kB. Moreover, GMP-treated cells show a down-regulation of Fe/Asc-induced mitogen activated protein kinase pathway, suggesting greater IS. Finally, GMP decreases the production of CM, VLDL, and LDL. CONCLUSIONS: Our results highlight the effectiveness of GMP in attenuating OxS, inflammation and lipoprotein biogenesis, as well as improving IS, the key components of MetS. Further investigation is needed to elucidate the mechanisms mediating the preventive action of GMP.
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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".