Glucagon-like Peptide-1 Receptor-based Therapeutics for Metabolic Liver Disease
Bibliographic record
Abstract
Glucagon-like peptide-1 (GLP-1) controls islet hormone secretion, gut motility, and body weight, supporting development of GLP-1 receptor agonists (GLP-1RA) for the treatment of type 2 diabetes (T2D) and obesity. GLP-1RA exhibit a favorable safety profile and reduce the incidence of major adverse cardiovascular events in people with T2D. Considerable preclinical data, supported by the results of clinical trials, link therapy with GLP-RA to reduction of hepatic inflammation, steatosis, and fibrosis. Mechanistically, the actions of GLP-1 on the liver are primarily indirect, as hepatocytes, Kupffer cells, and stellate cells do not express the canonical GLP-1R. GLP-1RA reduce appetite and body weight, decrease postprandial lipoprotein secretion, and attenuate systemic and tissue inflammation, actions that may contribute to attenuation of metabolic-associated fatty liver disease (MAFLD). Here we discuss evolving concepts of GLP-1 action that improve liver health and highlight evidence that links sustained GLP-1R activation in distinct cell types to control of hepatic glucose and lipid metabolism, and reduction of experimental and clinical nonalcoholic steatohepatitis (NASH). The therapeutic potential of GLP-1RA alone, or in combination with peptide agonists, or new small molecule therapeutics is discussed in the context of potential efficacy and safety. Ongoing trials in people with obesity will further clarify the safety of GLP-1RA, and pivotal studies underway in people with NASH will define whether GLP-1-based medicines represent effective and safe therapies for people with MAFLD.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".