Effect of Milk Protein–Polyphenol Conjugate on the Regulation of GLP-1 Hormone
Bibliographic record
Abstract
Modern functional foods are designed to provide health benefits beyond basic nutrition. They are enriched with bioactive ingredients like probiotics, vitamins, minerals, and antioxidants. These foods support overall health, enhance immune function, and help prevent chronic diseases. Milk proteins and tea are known to influence satiety and regulate body weight. Studies have shown that green tea polyphenols, namely, (-)-epigallocatechin gallate (EGCG), and whey proteins, predominantly lactoferrin (LF) from milk, play a role in regulating satiety. This study aims to investigate the effect of conjugating EGCG with apo-lactoferrin (Apo-LF) and assessing these effects on satiety through monitoring glucagon-like peptide-1 (GLP-1) regulation in a human colon (NCI-H716) cell line. Apo-LF-EGCG conjugates were synthesized and characterized in terms of structural and functional properties. The effect on GLP-1 regulation was assessed by real-time quantitative reverse-transcription polymerase chain reaction (qRT-PCR) and enzyme-linked immunosorbent assay (ELISA) to monitor gene and protein expressions, respectively. The results revealed that the protein-polyphenol interaction occurs through the complex formation of hydrogen bonds at the O-H and carbonyl groups of EGCG. The conjugates also showed a significant up-regulation of gene and protein expression levels of GLP-1 while also preventing EGCG from degradation, thereby preserving its antioxidant properties. The Apo-LF-EGCG conjugates increase satiety via increasing GLP-1 secretion in human colon cells while simultaneously retaining the antioxidant properties of EGCG. Therefore, these conjugates show potential for use as dietary supplements to enhance satiety.
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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 | 0.000 |
| 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".