Cannabis and Endometriosis: The Roles of the Gut Microbiota and the Endocannabinoid System
Bibliographic record
Abstract
Endometriosis, a chronic condition affecting around 10–14% of women, is challenging to manage, due to its complex pathogenesis and limited treatment options. Research has suggested a potential role of the gut microbiota and the endocannabinoid system in the development and progression of endometriosis. This narrative review aims to explore the role of, and any potential interactions between, the endocannabinoid system (ECS) and the gut microbiota in endometriosis. This review found that both the ECS and microbiota influence endometriosis, with the former regulating inflammation and pain perception and the latter influencing immune responses and hormonal balance. There is evidence that a dysregulation of the endocannabinoid system and the gut microbiota influence endometriosis symptoms and progression via changes in CB1 receptor expression and increased circulating levels of endocannabinoids. Microbial imbalances in the gut, such as increases in Prevotella, have been directly correlated to increased bloating, a common endometriosis symptom, while increases in E. coli have supported the bacterial contamination hypothesis as a potential pathway for endometriosis pathogenesis. These microbial imbalances have been correlated with increases in inflammatory markers such as TNF-α and IL-6, both often raised in those with endometriosis. Protective effects of the ECS on the gut were observed by increases in endocannabinoids, including 2-AG, resulting in decreased inflammation and improved gut permeability. Given these findings, both the ECS and the gut microbiota may be targets for therapeutic interventions for endometriosis; however, clinical studies are required to determine effectiveness.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".