A peripheral endocannabinoid mechanism contributes to glucocorticoid-mediated metabolic syndrome
Bibliographic record
Abstract
Glucocorticoids are known to promote the development of metabolic syndrome through the modulation of both feeding pathways and metabolic processes; however, the precise mechanisms of these effects are not well-understood. Recent evidence shows that glucocorticoids possess the ability to increase endocannabinoid signaling, which is known to regulate appetite, energy balance, and metabolic processes through both central and peripheral pathways. The aim of this study was to determine the role of endocannabinoid signaling in glucocorticoid-mediated obesity and metabolic syndrome. Using a mouse model of excess corticosterone exposure, we found that the ability of glucocorticoids to increase adiposity, weight gain, hormonal dysregulation, hepatic steatosis, and dyslipidemia was reduced or reversed in mice lacking the cannabinoid CB1 receptor as well as mice treated with the global CB1 receptor antagonist AM251. Similarly, a neutral, peripherally restricted CB1 receptor antagonist (AM6545) was able to attenuate the metabolic phenotype caused by chronic corticosterone, suggesting a peripheral mechanism for these effects. Biochemical analyses showed that chronic excess glucocorticoid exposure produced a significant increase in hepatic and circulating levels of the endocannabinoid anandamide, whereas no effect was observed in the hypothalamus. To test the role of the liver, specific and exclusive deletion of hepatic CB1 receptor resulted in a rescue of the dyslipidemic effects of glucocorticoid exposure, while not affecting the obesity phenotype or the elevations in insulin and leptin. Together, these data indicate that glucocorticoids recruit peripheral endocannabinoid signaling to promote metabolic dysregulation, with hepatic endocannabinoid signaling being especially important for changes in lipid metabolism.
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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 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".