GLP-2 Dysregulates Hepatic Lipoprotein Metabolism, Inducing Fatty Liver and VLDL Overproduction in Male Hamsters and Mice
Bibliographic record
Abstract
Fundamental complications of insulin resistance and type 2 diabetes include the development of nonalcoholic fatty liver disease and an atherogenic fasting dyslipidemic profile, primarily due to increases in hepatic very-low-density lipoprotein (VLDL) production. Recently, central glucagon-like peptide-2 receptor (GLP2R) signaling has been implicated in regulating hepatic insulin sensitivity; however, its role in hepatic lipid and lipoprotein metabolism is unknown. We investigated the role of glucagon-like peptide-2 (GLP-2) in regulating hepatic lipid and lipoprotein metabolism in Syrian golden hamsters, C57BL/6J mice, and Glp2r-/- mice consuming either a normal chow or high-fat diet (HFD). In the chow-fed hamsters, IP GLP-2 administration significantly increased fasting dyslipidemia, hepatic VLDL production, and the expression of key genes involved in hepatic de novo lipogenesis. In HFD-fed hamsters and chow-fed mice, GLP-2 administration exacerbated or induced hepatic lipid accumulation. HFD-fed Glp2r-/- mice displayed reduced glucose tolerance, VLDL secretion, and microsomal transfer protein lipid transfer activity, as well as exacerbated fatty liver. Thus, we conclude that GLP-2 plays a lipogenic role in the liver by increasing lipogenic gene expression and inducing hepatic steatosis, fasting dyslipidemia, and VLDL overproduction. In contrast, the lack of Glp2r appears to interfere with VLDL secretion, resulting in enhanced hepatic lipid accumulation. These studies have uncovered a role for GLP-2 in maintaining hepatic lipid and lipoprotein homeostasis.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".