Changes in peripheral endocannabinoid levels in substance use disorders: a review of clinical evidence
Bibliographic record
Abstract
Background: The endocannabinoid (eCB) system is a key modulator of stress and reward and is impacted by alcohol and drug use. Recently, the eCB system has been highlighted as a potential novel target in the treatment of substance use disorders (SUDs).Objectives: Understanding how chronic substance use impacts the function of the eCB system can provide a mechanistic rationale for targeting this system in the treatment of SUDs.Methods: A comprehensive review of studies assessing concentrations of eCB ligands N-arachidonoyl ethanolamine (anandamide; AEA) and 2-arachidonoylglycerol (2-AG) in individuals with a SUD diagnosis was performed using all EBSCO databases, PubMed, and Google Scholar. Methods and results related to eCB concentrations, diagnosis, and other factors (e.g. treatment status) were extracted from papers written in English and published in peer-reviewed journals before May 22, 2024.Results: Fifteen studies were reviewed; three in alcohol use disorder (AUD), three in cannabis use disorder (CUD), four in cocaine use disorder, one in opioid use disorder (OUD) and four across SUDs. Generally, AEA concentrations were usually, but not always, increased in AUD, CUD, OUD, and cocaine use disorder. 2-AG concentrations were measured less often but were increased in CUD and decreased in cocaine use disorder.Conclusions: Studies generally support the hypothesis that chronic substance use can impact eCB levels, most often with increased AEA and decreased (or not quantified) 2-AG concentrations, though results were often conflicting. Variability in methodology and study design may limit generalizability across studies.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.011 | 0.012 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 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".