Glucagon-like Peptide-2 Acutely Enhances Chylomicron Secretion in Humans Without Mobilizing Cytoplasmic Lipid Droplets
Bibliographic record
Abstract
CONTEXT: A portion of ingested fats are retained in the intestine for many hours before they are mobilized and secreted in chylomicron (CM) particles. Factors such as glucagon-like peptide-2 (GLP-2) and glucose can mobilize these stored intestinal lipids and enhance CM secretion. We have recently demonstrated in rodents that GLP-2 acutely enhances CM secretion by mechanisms that do not involve the canonical CM synthetic assembly and secretory pathways. OBJECTIVE: To further investigate the mechanism of GLP-2's potent intestinal lipid mobilizing effect, we examined intracellular cytoplasmic lipid droplets (CLDs) in intestinal biopsies of humans administered GLP-2 or placebo. DESIGN, SETTING, PATIENTS, AND INTERVENTIONS: A single dose of placebo or GLP-2 was administered subcutaneously 5 hours after ingesting a high-fat bolus. In 1 subset of participants, plasma samples were collected to quantify lipid and lipoprotein concentrations for 3 hours after placebo or GLP-2. In another subset, a duodenal biopsy was obtained 1-hour after placebo or GLP-2 administration for transmission electron microscopy and proteomic analysis. RESULTS: GLP-2 significantly increased plasma triglycerides by 46% (P = 0.009), mainly in CM-sized particles by 133% (P = 0.003), without reducing duodenal CLD size or number. Several proteins of interest were identified that require further investigation to elucidate their potential role in GLP-2-mediated CM secretion. CONCLUSIONS: Unlike glucose that mobilizes enterocyte CLDs and enhances CM secretion, GLP-2 acutely increased plasma CMs without significant mobilization of CLDs, supporting our previous findings that GLP-2 does not act directly on enterocytes to enhance CM secretion and most likely mobilizes secreted CMs in the lamina propria and lymphatics.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".