GLP-1 Elicits an Intrinsic Gut–Liver Metabolic Signal to Ameliorate Diet-Induced VLDL Overproduction and Insulin Resistance
Bibliographic record
Abstract
OBJECTIVE: Perturbations in hepatic lipid and very-low-density lipoprotein (VLDL) metabolism are involved in the pathogenesis of obesity and hepatic insulin resistance. The objective of this study is to delineate the mechanism of subdiaphragmatic vagotomy in preventing obesity, hyperlipidemia, and insulin resistance. APPROACH AND RESULTS: By subjecting the complete subdiaphragmatic vagotomized mice to various nutritional conditions and investigating hepatic de novo lipogenesis pathway, we found that complete disruption of subdiaphragmatic vagal signaling resulted in a significant decrease of circulating VLDL-triglyceride compared with the mice obtained sham procedure. Vagotomy further prevented overproduction of VLDL-triglyceride induced by an acute fat load and a high-fat diet-induced obesity, hyperlipidemia, hepatic steatosis, and glucose intolerance. Mechanistic studies revealed that plasma glucagon-like peptide-1 was significantly raised in the vagotomized mice, which was associated with significant reductions in mRNA and protein expression of SREBP-1c (sterol regulatory element-binding protein 1c), SCD-1 (stearoyl-CoA desaturase-1), and FASN (fatty acid synthase), as well as enhanced hepatic insulin sensitivity. In vitro, treating mouse primary hepatocytes with a glucagon-like peptide-1 receptor agonist, exendin-4, for 48 hours inhibited free fatty acid, palmitic acid treatment induced de novo lipid synthesis, and VLDL secretion from hepatocytes. CONCLUSIONS: Elevation of glucagon-like peptide-1 in vagotomized mice may prevent VLDL overproduction and insulin resistance induced by high-fat diet. These novel findings, for the first time, delineate an intrinsic gut-liver regulatory circuit that is mediated by glucagon-like peptide-1 in regulating hepatic energy metabolism.
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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) | 0.002 | 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".