GLP-1 (Glucagon-Like Peptide-1) Is Physiologically Relevant for Chylomicron Secretion Beyond Its Known Pharmacological Role
Bibliographic record
Abstract
Objective: GLP-1R (glucagon-like peptide-1 receptor) agonists are increasingly used for the treatment of hyperglycemia in type 2 diabetes, with additional body weight reducing effects. Long-term administration of GLP-1R agonists has demonstrated cardioprotective effects, but the mechanism of cardiovascular protection is not currently known. Several studies in humans and animal models have shown suppression of intestinal CM (chylomicron) secretion and plasma TG (triglyceride) levels by pharmacological doses of GLP-1R agonists. The objective of this study was to assess the physiological role of endogenously secreted GLP-1 on CM secretion in rats. Approach and Results: Lymph flow, TG concentration, and TG output were assessed in mesenteric lymph duct-cannulated rats in response to an intraduodenal lipid bolus, preceded by an intraperitoneal injection of GLP-1R antagonist Ex (9–39; exendin 9–39) or vehicle. TG output was significantly enhanced in the presence of Ex (9–39) compared with vehicle over a 4-hour period post-lipid bolus ( P =0.007). Total lymph volume ( P =0.005) and TG mass ( P <0.0001) cumulatively collected by the end of the 4-hour period were significantly increased by GLP-1R antagonist. Conclusions: GLP-1R antagonism enhanced intestinal TG output in rats through stimulation of lymph flow and increased lymph TG concentration. Endogenously secreted GLP-1 after a lipid bolus is sufficient to modulate CM secretion in the rat, with GLP-1 physiologically restraining CM secretion through the GLP-1R. It remains to be determined whether the lipid lowering actions of GLP-1R agonists play a role in the cardiovascular protective effects of these therapeutic agents.
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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 | 0.001 |
| 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".