GLP-1 Receptor Activation Indirectly Reduces Hepatic Lipid Accumulation But Does Not Attenuate Development of Atherosclerosis in Diabetic Male ApoE−/− Mice
Bibliographic record
Abstract
Glucagon-like peptide-1 receptor (GLP-1R) agonists reduce lipid accumulation in peripheral tissues, attenuating atherosclerosis and hepatic steatosis in preclinical studies. We examined whether GLP-1R activation decreases atherosclerosis progression in high-fat diet-fed male ApoE(-/-) mice after administration of streptozotocin and treatment with the long-acting GLP-1R agonist taspoglutide administered once monthly vs. metformin in the drinking water for 12 wk. Taspoglutide did not reduce plaque area or lipid content in the aortic arch or abdominal aorta, and no significant change in aortic macrophage accumulation was detected after taspoglutide or metformin. In contrast, hepatic triglyceride levels were significantly reduced in livers from taspoglutide-treated mice. Both peripheral and intracerebroventricular administration of exendin-4 rapidly decreased plasma triglyceride levels in fasted mice, and taspoglutide therapy in ApoE(-/-) mice modulated the expression of hepatic genes controlling fatty acid uptake and oxidation. We were unable to detect expression of the entire Glp1r coding sequence in macrophages isolated from ApoE(-/-), C57BL/6, and IL10(-/-) mice. Similarly, Glp1r mRNA transcripts were not detected in RNA from isolated murine hepatocytes. Using Western blotting and tissue extracts from Glp1r(+/+) and Glp1r(-/-) mice, and cells transfected with a tagged murine GLP-1R cDNA, we could not validate the sensitivity and specificity of three different GLP-1R antisera commonly used for the detection of GLP-1R protein. Taken together, these findings illustrate divergent actions of GLP-1R agonists on atherosclerosis progression and accumulation of ectopic lipid in ApoE(-/-) mice and highlight the importance of indirect GLP-1R actions for the control of hepatic lipid accumulation.
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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 | 0.002 |
| 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".