Complementary and antagonistic effects of combined glucagon‐like peptide‐2 and glucagon‐like peptide‐1 receptor agonist administration on parameters relevant to short bowel syndrome
Bibliographic record
Abstract
BACKGROUND: Short bowel syndrome (SBS) is characterized by malabsorption requiring parenteral nutrition. The intestinotrophic glucagon-like peptide (GLP)-2 receptor agonist, h[Gly2]GLP2, is used to treat patients with SBS. Evidence suggests that GLP-1 receptor agonists such as exendin-4 (Ex4) may be beneficial in SBS given their ability to increase intestinal growth and delay gastric emptying (GE). METHODS: Intestinal growth, body weight (BW), food intake (FI), GE, gastrointestinal (GI) transit, intestinal permeability, and glucose tolerance were investigated in male and female C57/BL6 mice following vehicle, h[Gly2]GLP2, or Ex4 treatment, alone or in combination at "low," "medium," and "high" doses (0.1, 0.5, 1.0 and 0.01, 0.05, 0.1 μg/g, respectively). RESULTS: Only the h[Gly2]GLP2 low/Ex4 high-dose combination additively increased small intestinal (SI) weight compared with vehicle and both monoagonists (P < 0.01-0.001), via increased villus height (P < 0.01) and SI length (P < 0.05). This combination had no effects on BW; FI; and fat, liver, spleen, heart, and kidney weights but reduced GI transit (P < 0.001) versus low-dose h[Gly2]GLP2 monotreatment and abrogated the inhibitory effects of high-dose Ex4 on GE (P < 0.01) and of low-dose h[Gly2]GLP2 on intestinal permeability (P < 0.05). Ex4-induced improvements in glucose homeostasis were maintained upon combination with h[Gly2]GLP2 (P < 0.001). CONCLUSIONS: These findings suggest that combining specific doses of GLP-2- and GLP-1 receptor agonists additively improves SI growth and GI transit without detrimental effects on BW, FI, GE, and glucose homeostasis, and may be useful for the treatment of patients with SBS.
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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) | 0.001 | 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) | 0.002 | 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".