Potential of Fatty Acid Amide Hydrolase (FAAH), Monoacylglycerol Lipase (MAGL), and Diacylglycerol Lipase (DAGL) Enzymes as Targets for Obesity Treatment: A Narrative Review
Bibliographic record
Abstract
The endocannabinoid system (ECS) plays an integral role in maintaining metabolic homeostasis and may affect hunger, caloric intake, and nutrient absorption. Obesity has been associated with higher levels of the endogenous cannabinoid transmitters (endocannabinoids). Therefore, the ECS is an important target in obesity treatment. Modulating the enzymes that synthesize and degrade endocannabinoids, namely fatty acid amide hydrolase (FAAH), monoacylglycerol lipase (MAGL), and diacylglycerol lipase (DAGL), may be a promising strategy to treat obesity. This review aims to synthesize all studies investigating pharmacological or genetic manipulation of FAAH, MAGL, or DAGL enzymes in association with obesity-related measures. Pharmacological inhibition or genetic deletion of FAAH tended to promote an obesogenic state in animal models, though the relationships between human FAAH polymorphisms and obesity-related outcomes were heterogeneous, which could be due to FAAH having both pro-appetitive and anti-appetitive substrates. Genetic deletion of Mgll and Dagla as well as pharmacological inhibition of DAGL tended to reduce body weight and improve metabolic state in animal studies, though the effects of Mgll manipulation were tissue-dependent. Monitoring changes in body weight in ongoing clinical trials of FAAH inhibitors may clarify whether FAAH inhibition is a potential therapeutic strategy for treatment obesity. More preclinical work is needed to characterize the role of MAGL and DAGL modulation in obesity-related outcomes.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".