Liver Fibroblast Growth Factor 21 (FGF21) is Required for the Full Anorectic Effect of the Glucagon-Like Peptide-1 Receptor Agonist Liraglutide in Male Mice fed High Carbohydrate Diets
Bibliographic record
Abstract
Abstract Glucagon-like peptide-1 receptor (GLP-1R) agonists and fibroblast growth factor 21 (FGF21) confer similar metabolic benefits. Studies report that GLP-1RA induce FGF21. Here, we investigated the mechanisms engaged by the GLP-1R agonist liraglutide to increase FGF21 levels and the metabolic relevance of liraglutide-induced FGF21. We show that liraglutide increases FGF21 levels via neuronal GLP-1R activation. We also demonstrate that lack of liver Fgf21 expression confers partial resistance to liraglutide-induced weight loss. Since FGF21 reduces carbohydrate intake, we tested whether the contribution of FGF21 to liraglutide-induced weight loss is dependent on dietary carbohydrate content. In control and liver Fgf21 knockout (Liv Fgf21 -/- ) mice fed calorically matched diets with low- (LC) or high-carbohydrate (HC) content, we found that only HC-fed Liv Fgf21 -/- mice were resistant to liraglutide-induced weight loss. Similarly, liraglutide-induced weight loss was partially impaired in Liv Fgf21 -/- mice fed a high-fat, high-sugar (HFHS) diet. Lastly, we show that loss of neuronal β-klotho expression also diminishes liraglutide-induced weight loss in mice fed a HC or HFHS diet, indicating that FGF21 mediates liraglutide-induced weight loss via neuronal FGF21 action. Our findings support a novel role for a GLP-1R-FGF21 axis in regulating body weight in the presence of high dietary carbohydrate content.
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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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".