Glycine Normalizes Hepatic Triglyceride-Rich VLDL Secretion by Triggering the CNS in High-Fat Fed Rats
Bibliographic record
Abstract
RATIONALE: Dysregulation of hepatic triglyceride (TG)-rich very low-density lipoproteins (VLDL-TG) in obesity and type 2 diabetes contributes to the dyslipidemia that leads to cardiovascular morbidity. The central nervous system (CNS), particularly the hypothalamus, regulates hepatic lipid metabolism. Although the underlying neurocircuitry remains elusive, glycine has been documented to enhance CNS N-methyl-d-aspartate (NMDA) receptor-mediated transmission. OBJECTIVE: We tested the hypothesis that glycine regulates hepatic VLDL-TG secretion by potentiating NMDA receptor-mediated transmission in the CNS. METHODS AND RESULTS: Using 10-hour fasted male Sprague-Dawley rats implanted with stereotaxic cannulae into an extrahypothalamic region termed the dorsal vagal complex (DVC) and vascular catheters to enable direct DVC infusion and blood sampling, respectively, the rate of hepatic VLDL-TG secretion was measured following tyloxapol (an inhibitor of lipoprotein lipase) injection. Direct DVC infusion of glycine lowered VLDL-TG secretion, whereas NMDA receptor blocker MK-801 fully negated glycine's effect. NR1 subunit of NMDA receptor antagonist 7-chlorokynurenic acid, adenoviral injection of NR1 short hairpin RNA (shRNA), and hepatic vagotomy also nullified glycine's effect. Finally, DVC glycine normalized the hypersecretion of VLDL-TG induced by high-fat feeding. CONCLUSIONS: Molecular and pharmacological inhibition of the NR1-containing NMDA receptors in the DVC negated the ability of glycine to inhibit hepatic secretion of VLDL-TG in vivo. Importantly, the hypersecretion of VLDL-TG from the liver induced by a model of high-fat feeding was restored by the hepatic lipid control of CNS glycine sensing. These findings collectively suggest that glycine or glycine analogues may have therapeutic benefits in lowering plasma lipid levels in diabetes and obesity by triggering the CNS.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 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".