Hyperglycemia after protein ingestion concurrent with injection of a GLP-1 receptor agonist in rats: a possible role for dietary peptides
Bibliographic record
Abstract
Protein ingestion after injection of the glucagon-like peptide-1 receptor agonist Exendin-4 (Ex-4) causes hyperglycemia in rats. The objectives of this study were to determine the components of protein digestion responsible for this effect and to associate it with changes in the concentrations of other metabolites and hormones. Two experiments were conducted. In the first experiment, food-deprived rats were gavaged with intact whey (WP) or albumin protein, their hydrolysates, amino acid mixtures (1 g/2.5 ml), or water 5 min after injection of either PBS or Ex-4 (0.5 microg/rat). Tail vein blood was analyzed for glucose over 2 h. In the second experiment, food-deprived rats were gavaged with WP with or without Ex-4. Groups of conscious rats were killed by decapitation either before, or at selected times after gavage. Plasma concentrations of glucose, amino acids, free fatty acids (FFA), glycerol, insulin, glucagon, and leptin were measured. In experiment 1, blood glucose was higher when intact proteins and protein hydrolysates, but not amino acid mixtures, were given with than without Ex-4 (P < 0.05). In experiment 2, concentrations of glucose, FFA, and the ratio of tyrosine to branched-chain amino acid were higher (P < 0.01), but leptin and essential amino acid concentrations were lower (P < 0.05), and insulin, glucagon, and glycerol were similar when WP was given with or without Ex-4. We conclude that the hyperglycemia caused by the administration of Ex-4 concurrently with dietary protein arises from the action of peptides released during digestion and their interaction with Ex-4 in the regulation of glucose, fatty acid, and amino acid metabolism.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".