Fibroblast growth factor-21 is required for weight loss induced by 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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".