SIRT1 Mediates the Effect of GLP-1 Receptor Agonist Exenatide on Ameliorating Hepatic Steatosis
Bibliographic record
Abstract
GLP-1 and incretin mimetics, such as exenatide, have been shown to attenuate hepatocyte steatosis in vivo and in vitro, but the specific underlying mechanism is unclear. SIRT1, an NAD(+)-dependent protein deacetylase, has been considered as a crucial regulator in hepatic lipid homeostasis by accumulated studies. Here, we speculate that SIRT1 might mediate the effect of the GLP-1 receptor agonist exenatide (exendin-4) on ameliorating hepatic steatosis. After 8 weeks of exenatide treatment in male SIRT1(+/-) mice challenged with a high-fat diet and their wild-type (WT) littermates, we found that lipid deposition and inflammation in the liver, which were improved dramatically in the WT group, diminished in SIRT1(+/-) mice. In addition, the protein expression of SIRT1 and phosphorylated AMPK was upregulated, whereas lipogenic-related protein, including SREBP-1c and PNPLA3, was downregulated in the WT group after exenatide treatment. However, none of these changes were observed in SIRT1(+/-) mice. In HepG2 cells, exendin-4-reversed lipid deposition induced by palmitate was hampered when SIRT1 was silenced by SIRT1 RNA interference. Our data demonstrate that SIRT1 mediates the effect of exenatide on ameliorating hepatic steatosis, suggesting the GLP-1 receptor agonist could serve as a potential drug for nonalcoholic fatty liver disease (NAFLD), especially in type 2 diabetes combined with NAFLD, and SIRT1 could be a therapeutic target of NAFLD.
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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) | 0.001 | 0.001 |
| 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 | 0.001 |
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".