Carnitine in Alcohol Use Disorders: A Scoping Review
Bibliographic record
Abstract
Recent studies in alcohol use disorders (AUDs) have demonstrated some connections between carnitine metabolism and the pathophysiology of the disease. In this scoping review, we aimed to collate and examine existing research available on carnitine metabolism and AUDs and develop hypotheses surrounding the role carnitine may play in AUD. A scoping review method was used to search electronic databases in September 2019. The database search terms used included "alcohol, alcoholism, alcohol abuse, alcohol consumption, alcohol drinking patterns, alcohol-induced disorders, alcoholic intoxication, alcohol-related disorders, binge drinking, Wernicke encephalopathy, acylcarnitine, acetyl-l-carnitine, acetylcarnitine, carnitine and palmitoylcarnitine." The inclusion criteria included English language, human-based, AUD diagnosis and measured blood or tissue carnitine or used carnitine as a treatment. Of 586 studies that were identified and screened, 65 underwent abstract review, and 41 were fully reviewed. Eighteen studies were ultimately included for analysis. Data were summarized in an electronic data extraction form. We found that there is limited literature available. Alcohol use appears to impact carnitine metabolism, most clearly in the setting of alcoholic cirrhosis. Six studies found carnitine to be increased in AUD, of which 5 were conducted in patients with alcoholic cirrhosis. Only 3 placebo-controlled trials were identified and provide some support for the use of carnitine in AUD to decrease cravings, anhedonia, and withdrawal and improve cognition. The increase in plasma carnitine in alcoholic cirrhosis may be related to disordered fatty acid metabolism and oxidative stress that occurs in AUD. The multiple possible therapeutic effects carnitine could have on ethanol metabolism and the early evidence available for carnitine supplementation as a treatment for AUD provide a foundation for future randomized control trials of carnitine for treating AUD.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".