Targeting the endocannabinoid/CB1 receptor system for treating obesity in Prader–Willi syndrome
Bibliographic record
Abstract
Extreme obesity is a core phenotypic feature of Prader–Willi syndrome (PWS). Among numerous metabolic regulators, the endocannabinoid (eCB) system is critically involved in controlling feeding, body weight, and energy metabolism, and a globally acting cannabinoid-1 receptor (CB1R) blockade reverses obesity both in animals and humans. The first-in-class CB1R antagonist rimonabant proved effective in inducing weight loss in adults with PWS. However, it is no longer available for clinical use because of its centrally mediated, neuropsychiatric, adverse effects. We studied eCB ‘tone’ in individuals with PWS and in the Magel2-null mouse model that recapitulates the major metabolic phenotypes of PWS and determined the efficacy of a peripherally restricted CB1R antagonist, JD5037 in treating obesity in these mice. Individuals with PWS had elevated circulating levels of 2-arachidonoylglycerol and its endogenous precursor and breakdown ligand, arachidonic acid. Increased hypothalamic eCB ‘tone’, manifested by increased eCBs and upregulated CB1R, was associated with increased fat mass, reduced energy expenditure, and decreased voluntary activity in Magel2-null mice. Daily chronic treatment of obese Magel2-null mice and their littermate wild-type controls with JD5037 (3 mg/kg/d for 28 days) reduced body weight, reversed hyperphagia, and improved metabolic parameters related to their obese phenotype. Dysregulation of the eCB/CB1R system may contribute to hyperphagia and obesity in Magel2-null mice and in individuals with PWS. Our results demonstrate that treatment with peripherally restricted CB1R antagonists may be an effective strategy for the management of severe obesity in PWS.
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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) | 0.001 | 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".