Involvement of the Endocannabinoid System in the Control of Pain and Obesity by Exercise in Rodents: A Systematic Review
Bibliographic record
Abstract
Studies in recent years have shown that the endocannabinoid (eCB) system is activated by exercise and modulates several physiological processes. Thus, the present review aimed to summarize the literature about the involvement of the eCB system in the control of pain, obesity, and metabolism by exercise. MEDLINE, EMBASE, and Web of Science were searched for experimental studies that investigated the presence of the eCB system in animal models of pain and obesity, in which the animals were subjected to different exercise modalities. The primary outcomes were pain, obesity, and metabolism. The databases were searched for articles from their inception up until March 2020. Two independent reviewers extracted the data and assessed the methodological quality of the included studies. Thirteen studies were considered eligible for this review. The results indicated that there was increased expression and levels of cannabinoid receptors and eCBs, respectively, after aerobic and resistance exercise, and that this effect was associated with antinociception. The eCB system was modulated by exercise in obese rats, confirming that it may also be involved in the control of obesity and metabolism when these are modulated by aerobic training. Exercise can be effective in controlling pain, partly through the involvement of the eCB system. In addition, exercise can modulate the imbalance of the eCB system in obesity and metabolic disorders, thus also controlling these pathologies through this signaling system.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".