Mutual Links between the Endocannabinoidome and the Gut Microbiome, with Special Reference to Companion Animals: A Nutritional Viewpoint
Bibliographic record
Abstract
There is growing evidence that perturbation of the gut microbiome, known as "dysbiosis", is associated with the pathogenesis of human and veterinary diseases that are not restricted to the gastrointestinal tract. In this regard, recent studies have demonstrated that dysbiosis is linked to the pathogenesis of central neuroinflammatory disorders, supporting the existence of the so-called microbiome-gut-brain axis. The endocannabinoid system is a recently recognized lipid signaling system and termed endocannabinoidome monitoring a variety of body responses. Accumulating evidence demonstrates that a profound link exists between the gut microbiome and the endocannabinoidome, with mutual interactions controlling intestinal homeostasis, energy metabolism and neuroinflammatory responses during physiological conditions. In the present review, we summarize the latest data on the microbiome-endocannabinoidome mutual link in health and disease, focalizing the attention on gut dysbiosis and/or altered endocannabinoidome tone that may distort the bidirectional crosstalk between these two complex systems, thus leading to gastrointestinal and metabolic diseases (e.g., idiopathic inflammation, chronic enteropathies and obesity) as well as neuroinflammatory disorders (e.g., neuropathic pain and depression). We also briefly discuss the novel possible dietary interventions based not only on probiotics and/or prebiotics, but also, and most importantly, on endocannabinoid-like modulators (e.g., palmitoylethanolamide) for intestinal health and beyond.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".