Exenatide, a Glucagon-like Peptide-1 Receptor Agonist, Acutely Inhibits Intestinal Lipoprotein Production in Healthy Humans
Bibliographic record
Abstract
OBJECTIVE: Incretin-based therapies for the treatment of type 2 diabetes mellitus improve plasma lipid profiles and postprandial lipemia, but their exact mechanism of action remains unclear. Here, we examined the acute effect of the glucagon-like peptide-1 receptor agonist, exenatide, on intestinal and hepatic triglyceride-rich lipoprotein production and clearance in healthy humans. METHODS AND RESULTS: Fifteen normolipidemic, normoglycemic men underwent 2 studies each (SC 10 μg exenatide versus placebo), 4 to 6 weeks apart, in random order, in which triglyceride-rich lipoprotein particle kinetics were examined with a primed, constant infusion of deuterated leucine and analyzed by multicompartmental modeling under pancreatic clamp conditions. A fed state was maintained during each study by infusing a high-fat, mixed macronutrient, liquid formula at a constant rate directly into the duodenum via a nasoduodenal tube. Exenatide significantly suppressed the plasma concentration and production rate of triglyceride-rich lipoprotein-apolipoprotein B-48, but not of triglyceride-rich lipoprotein-apolipoprotein B-100. CONCLUSIONS: These results suggest a possible direct effect of exenatide on intestinal lipoprotein particle production, independent of changes in weight gain and satiety as seen in long-term studies and independent of changes in gastric emptying. This finding expands our understanding of the effects of exenatide in metabolic regulation beyond its primary therapeutic role in regulation of glucose homeostasis. Clinical Trial Registration- URL: http://www.clinicaltrials.gov, NCT01056549.
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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 | 0.000 |
| 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".