Activation of N-Methyl-d-aspartate (NMDA) Receptors in the Dorsal Vagal Complex Lowers Glucose Production
Bibliographic record
Abstract
Diabetes is characterized by hyperglycemia due partly to increased hepatic glucose production. The hypothalamus regulates hepatic glucose production in rodents. However, it is currently unknown whether other regions of the brain are sufficient in glucose production regulation. The N-methyl-D-aspartate (NMDA) receptor is composed of NR1 and NR2 subunits, which are activated by co-agonist glycine and glutamate or aspartate, respectively. Here we report that direct administration of either co-agonist glycine or NMDA into the dorsal vagal complex (DVC), targeting the nucleus of the solitary tract, lowered glucose production in vivo. Direct infusion of the NMDA receptor blocker MK-801 into the DVC negated the metabolic effect of glycine. To evaluate whether NR1 subunit of the NMDA receptor mediates the effect of glycine, NR1 in the DVC was inhibited by DVC NR1 antagonist 7-chlorokynurenic acid or DVC shRNA-NR1. Pharmacological and molecular inhibition of DVC NR1 negated the metabolic effect of glycine. To evaluate whether the NMDA receptors mediate the effects of NR2 agonist NMDA, DVC NMDA receptors were inhibited by antagonist D-2-amino-5-phosphonovaleric acid (D-APV). DVC D-APV fully negated the ability of DVC NMDA to lower glucose production. Finally, hepatic vagotomy negated the DVC glycine ability to lower glucose production. These findings demonstrate that activation of NR1 and NR2 subunits of the NMDA receptors in the DVC is sufficient to trigger a brain-liver axis to lower glucose production, and suggest that DVC NMDA receptors serve as a therapeutic target for diabetes and obesity.
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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 | 0.001 |
| 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".