Coagonist of glucagon-like peptide-1 and glucagon receptors ameliorates nonalcoholic fatty liver disease
Bibliographic record
Abstract
Nonalcoholic fatty liver disease (NAFLD) is often associated with obesity and type 2 diabetes. Coagonists of glucagon-like peptide-1 receptor (GLP-1R) and glucagon receptor (GCGR) are under clinical investigation for the treatment of obesity and type 2 diabetes. In this study, we have demonstrated the effect of a balanced coagonist in the treatment of NAFLD using mouse models. GLP-1R agonist exendin-4, glucagon, and coagonist (Aib2 C24 chimera2) were administered to C57BL6/J mice, in which NAFLD was induced by carbon tetrachloride (CCl 4 ) treatment after high-fat diet (HFD) feeding, and choline-deficient, L-amino-acid-defined HFD (CDAHFD) feeding. Repeated dose administration of coagonist significantly attenuated liver inflammation and steatosis induced by acute and long-term treatment with CCl 4 in HFD-fed mice. Coagonist markedly attenuated the CDAHFD-induced expression of TIMP-1, MMP-9, TNF-α, MCP-1, COL1A1, and α-SMA. It also inhibited progression of hepatic steatosis and fibrosis in mice. Exendin-4 was better than glucagon, but coagonist was most effective in reduction of hepatic inflammation as well as steatosis. Coagonist of GLP-1R and GCGR improved NAFLD in C57BL6/J mice. This effect is mediated by reduction in lipotoxicity and inflammation in liver.
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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 | 0.000 |
| 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".