State-dependent central synaptic regulation by GLP-1 is essential for energy homeostasis
Bibliographic record
Abstract
Central glucagon-like peptide-1 (GLP-1), secreted by a distinct population of nucleus tractus solitarius neurons, suppresses feeding but the exact mechanisms of action in the brain remain unclear. Here, we investigate a descending circuit formed by GLP-1 receptor (GLP-1R) neurons in the paraventricular hypothalamic nucleus (PVNGLP-1R) projecting to the dorsal vagal complex (DVC) of the brain stem in mice. PVNGLP-1R→DVC synapses release glutamate and are augmented by GLP-1. Chemogenetic activation of PVNGLP-1R→DVC suppresses feeding. Under an energy deficit (that is, hunger) state, synaptic strength is weaker but is more profoundly augmented by GLP-1R activation than under energy-replete state. In an obese condition, the dynamic synaptic changes in this circuit are disrupted. Optogenetic activation of PVNGLP-1R→DVC projections suppresses food intake energy state dependently, and blocking its synaptic release or ablating GLP-1Rs in the presynaptic neurons impairs metabolic health. These findings indicate that the state-dependent synaptic regulation by GLP-1 in PVNGLP-1R→DVC descending circuit is important for energy homeostasis. In this paper, the authors describe the energy state-dependent regulation of the PVNGLP-1R to DVC circuit, resulting in altered food intake and metabolic health, mediated by GLP-1 receptor signalling.
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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".