Brain GLP-1 Signaling Regulates Femoral Artery Blood Flow and Insulin Sensitivity Through Hypothalamic PKC-δ
Bibliographic record
Abstract
OBJECTIVE: Glucagon-like peptide 1 (GLP-1) is a gut-brain hormone that regulates food intake, energy metabolism, and cardiovascular functions. In the brain, through a currently unknown molecular mechanism, it simultaneously reduces femoral artery blood flow and muscle glucose uptake. By analogy to pancreatic β-cells where GLP-1 activates protein kinase C (PKC) to stimulate insulin secretion, we postulated that PKC enzymes would be molecular targets of brain GLP-1 signaling that regulate metabolic and vascular function. RESEARCH DESIGN AND METHODS: We used both genetic and pharmacological approaches to investigate the role of PKC isoforms in brain GLP-1 signaling in the conscious, free-moving mouse simultaneous with metabolic and vascular measurements. RESULTS: In normal wild-type (WT) mouse brain, the GLP-1 receptor (GLP-1R) agonist exendin-4 selectively promotes translocation of PKC-δ (but not -βII, -α, or -ε) to the plasma membrane. This translocation is blocked in Glp1r(-/-) mice and in WT mice infused in the brain with exendin-9, an antagonist of the GLP-1R. This mechanism coordinates both blood flow in the femoral artery and whole-body insulin sensitivity. Consequently, in hyperglycemic, high-fat diet-fed diabetic mice, hypothalamic PKC-δ activity was increased and its pharmacological inhibition improved both insulin-sensitive metabolic and vascular phenotypes. CONCLUSIONS: Our studies show that brain GLP-1 signaling activates hypothalamic glucose-dependent PKC-δ to regulate femoral artery blood flow and insulin sensitivity. This mechanism is attenuated during the development of experimental hyperglycemia and may contribute to the pathophysiology of type 2 diabetes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".