Glucagon-Like Peptide-1, But Not Glucose-Dependent Insulinotropic Peptide, Regulates Fasting Glycemia and Nonenteral Glucose Clearance in Mice
Bibliographic record
Abstract
Glucagon-like peptide-1 (GLP-1) and glucose-dependent insulinotropic peptide (GIP) potentiate glucose-stimulated insulin secretion after enteral nutrient ingestion. We compared the relative incretin and nonincretin actions of GLP-1 and GIP in +/+ and GLP-1R−/− mice using exendin(9–39) and immunopurified anti-GIP receptor antisera (GIPR Ab) to antagonize GLP-1 and GIP action, respectively. Both antagonists produced a significant increase in glycemic excursion after oral glucose loading of +/+ mice (P < 0.05 for antagonists vs. controls). Exendin(9–39) also increased blood glucose and decreased glucose-stimulated insulin in +/+ mice after ip glucose loading[ 0.58 ± 0.02 vs. 0.47 ± 0.02 ng/ml in saline- vs. exendin(9–39)-treated mice, respectively, P < 0.05]. In contrast, GIPR Ab had no effect on glucose excursion or insulin secretion, after ip glucose challenge, in+ /+ or GLP-1R−/− mice. Repeated administration of exendin(9–39) significantly increased blood glucose and reduced circulating insulin levels but had no effect on levels of pancreatic insulin or insulin messenger RNA transcripts. In contrast, no changes in plasma glucose, circulating insulin, pancreatic insulin content, or insulin messenger RNA were observed in mice, 18 h after administration of GIPR Ab. These findings demonstrate that GLP-1, but not GIP, plays an essential role in regulating glycemia, independent of enteral nutrient ingestion in mice in vivo.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".