Central Nervous System Regulation of Intestinal Lipoprotein Metabolism by Glucagon-Like Peptide-1 via a Brain–Gut Axis
Bibliographic record
Abstract
OBJECTIVE: Intestinal overproduction of atherogenic chylomicron particles postprandially is an important component of diabetic dyslipidemia in insulin-resistant states. In addition to enhancing insulin secretion, peripheral glucagon-like peptide-1 (GLP-1) receptor stimulation has the added benefit of reducing this chylomicron overproduction in patients with type 2 diabetes mellitus. Given the presence of central GLP-1 receptors and GLP-1-producing neurons, we assessed whether central GLP-1 exerts an integral layer of neuronal control during the production of these potentially atherogenic particles. APPROACH AND RESULTS: Postprandial production of triglyceride-rich lipoproteins was assessed in Syrian hamsters administered a single intracerebroventricular injection of the GLP-1 receptor agonist exendin-4. Intracerebroventricular exendin-4 reduced triglyceride-rich lipoprotein-triglyceride and -apolipoprotein B48 accumulation relative to vehicle-treated controls. This was mirrored by intracerebroventricular MK-0626, an inhibitor of endogenous GLP-1 degradation, and prevented by central exendin9-39, a GLP-1 receptor antagonist. The effects of intracerebroventricular exendin-4 were also lost during peripheral adrenergic receptor and central melanocortin-4 receptor inhibition, achieved using intravenous propranolol and phentolamine and intracerebroventricular HS014, respectively. However, central exendin9-39 did not preclude the effects of peripheral exendin-4 treatment on chylomicron output. CONCLUSIONS: Central GLP-1 is a novel regulator of chylomicron production via melanocortin-4 receptors. Our findings point to the relative importance of central accessibility of GLP-1-based therapies and compel further studies examining the status of this brain-gut axis in the development of diabetic dyslipidemia and chylomicron overproduction.
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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) | 0.000 | 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) | 0.003 | 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".