Release of Lipids Stored in the Intestine by Glucagon-Like Peptide-2 Involves a Gut-Brain Neural Pathway
Bibliographic record
Abstract
BACKGROUND: The gut hormone GLP-2 (glucagon-like peptide-2) plays important roles in lipid handling in the intestine. During postabsorptive stage, it releases preformed chylomicrons stored in the intestine, the underlying mechanisms of which are not well understood. Previous studies implicate the involvement of neural pathways in GLP-2's actions on lipid absorption in the intestine, but the role of such mechanisms in releasing postabsorptive lipid storage has not been established. METHODS: Here, in mesenteric lymph duct cannulated rats, we directly tested whether gut-brain neural communication mediates GLP-2's effects on postabsorptive lipid mobilization in the intestine. We performed total subdiaphragmatic vagotomy to disrupt the gut-brain neural communication and analyzed lipid output 5 hours after a lipid load in response to intraperitoneal GLP-2 or saline. RESULTS: Peripheral GLP-2 administration led to increased lymph lipid output and activation of proopiomelanocortin neurons in the arcuate nucleus of hypothalamus. Disruption of gut-brain neural communication via vagotomy blunted GLP-2's effects on promoting lipid release in the intestine. CONCLUSIONS: These results, for the first time, demonstrate a novel mechanism in which postabsorptive mobilization of intestinal lipid storage by GLP-2 enlists a gut-brain neural pathway.
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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) | 0.001 | 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".