Peripheral Exendin-4 and Peptide YY3–36 Synergistically Reduce Food Intake through Different Mechanisms in Mice
Bibliographic record
Abstract
Glucagon-like peptide-1(7-36NH2) (GLP-1) and peptide YY(3-36NH2) (PYY(3-36NH2)) are cosecreted from the intestine in response to nutrient ingestion. Peripheral administration of GLP-1 or PYY(3-36NH2) decreases food intake (FI) in rodents and humans; however, the exact mechanisms by which these peptides regulate FI remain unclear. Male C57BL/6 mice were injected (ip) with exendin-4(1-39) (Ex4, a GLP-1 receptor agonist) and/or PYY(3-36NH2) (0.03-3 microg), and FI was determined for up to 24 h. Ex4 and PYY(3-36NH2) alone decreased FI by up to 83 and 26%, respectively (P < 0.05-0.001), whereas a combination of the two peptides (0.06 microg Ex4 plus 3 microg PYY(3-36NH2)) further reduced FI for up to 8 h in a synergistic manner (P < 0.05-0.001). Ex4 and/or PYY(3-36NH2) delayed gastric emptying by a maximum of 19% (P < 0.01-0.001); however, there was no significant effect on locomotor activity nor was there induction of taste aversion. Capsaicin pretreatment prevented the inhibitory effect of Ex4 on FI (P < 0.05), but had no effect on the anorexigenic actions of PYY(3-36NH2). Similarly, exendin-4(9-39) (a GLP-1 receptor antagonist) partially abolished Ex4-induced anorexia (P < 0.05), but did not affect the satiation produced by PYY(3-36NH2). Conversely, BIIE0246 (a Y2 receptor antagonist) completely blocked the anorexigenic effects of PYY(3-36NH2) (P < 0.001), but had no effect on Ex4-induced satiety. Thus, Ex4 and PYY(3-36NH2) suppress FI via independent mechanisms involving a GLP-1 receptor-dependent, sensory afferent pathway (Ex4) and a Y2-receptor mediated pathway (PYY(3-36NH2)). These findings suggest that administration of low doses of Ex4 together with PYY(3-36NH2) may increase the suppression of FI without inducing significant side effects.
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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 | 0.001 |
| 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".