Glucagon Receptor Signaling Regulates Energy Metabolism via Hepatic Farnesoid X Receptor and Fibroblast Growth Factor 21
Bibliographic record
Abstract
Glucagon, an essential regulator of glucose and lipid metabolism, also promotes weight loss, in part through potentiation of fibroblast growth factor 21 (FGF21) secretion. However, FGF21 is only a partial mediator of metabolic actions ensuing from glucagon receptor (GCGR) activation, prompting us to search for additional pathways. Intriguingly, chronic GCGR agonism increases plasma bile acid levels. We hypothesized that GCGR agonism regulates energy metabolism, at least in part, through farnesoid X receptor (FXR). To test this hypothesis, we studied whole-body and liver-specific FXR-knockout (Fxr∆liver) mice. Chronic GCGR agonist (IUB288) administration in diet-induced obese (DIO) Gcgr, Fgf21, and Fxr whole-body or liver-specific knockout (∆liver) mice failed to reduce body weight when compared with wild-type (WT) mice. IUB288 increased energy expenditure and respiration in DIO WT mice, but not Fxr∆liver mice. GCGR agonism increased [14C]palmitate oxidation in hepatocytes isolated from WT mice in a dose-dependent manner, an effect blunted in hepatocytes from Fxr∆liver mice. Our data clearly demonstrate that control of whole-body energy expenditure by GCGR agonism requires intact FXR signaling in the liver. This heretofore-unappreciated aspect of glucagon biology has implications for the use of GCGR agonism in the therapy of metabolic disorders.
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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 | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".