107-OR: Brain GLP-1R Is Likely Required for Peripheral Liraglutide Treatment on Stimulating Hepatic FGF21
Bibliographic record
Abstract
Glucagon-like peptide 1 (GLP-1) receptor agonists (GLP-1RAs) are therapeutic agents for Type 2 Diabetes (T2D) and chronic weight management. We reported previously that peripheral GLP-1RA (liraglutide, Lira) administration stimulates hepatic fibroblast growth factor 21 (FGF21) in wild type but not GLP-1R-/- mice, and GLP-1R is not expressed in the liver tissue. Here we aim to determine the underlying mechanisms for such stimulatory effect via organ-organ communications. Firstly, we asked whether acute intraduodenal (i.d.) GLP-1RA administration stimulates hepatic FGF21; and if so, whether the stimulation requires GLP-1R. We revealed that i.d. administration of GLP-1RA in WT mice significantly elevated hepatic Fgf21 levels by 5-fold compared to WT mice received i.d. PBS, whereas such effect was virtually lost in GLP-1R-/- mice. Secondly, we investigated the central role of GLP-1R. We antagonized brain GLP-1R activity via i.c.v. Ex9-39 injection after peripheral Lira treatment in HFD-challenged mice. Mice received peripheral Lira treatment and i.c.v. Exendin 9-39 treatment show impaired stimulatory effect on serum FGF21 compared to the i.c.v. PBS counterpart, suggesting that brain GLP-1R is likely required for peripheral GLP-1RA to stimulate hepatic FGF21. Together, we further demonstrated that peripheral GLP-1RA administration stimulates hepatic FGF21, and revealed that such beneficial effect likely requires brain GLP-1R. Our study will aid the understanding of metabolic hormones and pave the way for determining their underlying mechanisms in metabolic homeostasis Disclosure J. Feng: None. W. Shao: None. T. Jin: None. Funding Canadian Institutes of Health Research (PJT159735) Guarantor: Tianru Jin
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".