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
Glucagon-like peptide-1 receptor (GLP-1R) agonists (GLP-1RA) and fibroblast growth factor-21 (FGF21) confer similar metabolic benefits. GLP-1RA induce FGF21, leading us to investigate mechanisms engaged by the GLP-1RA liraglutide to increase FGF21 levels and the metabolic relevance of liraglutide-induced FGF21. Circulating FGF21 levels were measured in fasted male C57BL/6J, neuronal GLP-1R knockout, β-cell GLP-1R knockout, and liver peroxisome proliferator-activated receptor alpha knockout mice treated acutely with liraglutide. To test the metabolic relevance of liver FGF21 in response to liraglutide, chow-fed control and liver Fgf21 knockout (LivFgf21−/−) mice were treated with vehicle or liraglutide in metabolic chambers. Body weight and composition, food intake, and energy expenditure were measured. Since FGF21 reduces carbohydrate intake, we measured body weight in mice fed matched diets with low- (LC) or high-carbohydrate (HC) content and in mice fed a high-fat, high-sugar (HFHS) diet. This was done in control and LivFgf21−/− mice and in mice lacking neuronal β-klotho (Klb) expression to disrupt brain FGF21 signaling. Liraglutide increases FGF21 levels independently of decreased food intake via neuronal GLP-1R activation. Lack of liver Fgf21 expression confers resistance to liraglutide-induced weight loss due to attenuated reduction of food intake in chow-fed mice. Liraglutide-induced weight loss was impaired in LivFgf21−/− mice when fed HC and HFHS diets but not when fed a LC diet. Loss of neuronal Klb also attenuated liraglutide-induced weight loss in mice fed HC or HFHS diets. Our findings support a novel role for a GLP-1R-FGF21 axis in regulating body weight in a dietary carbohydrate-dependent manner.
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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 | 0.001 | 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".