The Cannabinoid System as a Potential Novel Target for Alcohol‐Associated Liver Disease: A Propensity‐Matched Cohort Study
Bibliographic record
Abstract
BACKGROUND: Alcohol-associated liver disease (ALD) is a leading cause of liver-related morbidity and mortality, yet effective therapeutic options remain limited. Preclinical data suggest that modulation of the hepatic endocannabinoid system, particularly via cannabidiol (CBD), may reduce alcohol-induced liver injury. Due to CBD's limited clinical use, we sought to evaluate the association between cannabis use and ALD risk among patients with alcohol use disorder (AUD). METHODS: Using the TriNetX US Collaborative Network, we identified adult patients with AUD between 2010 and 2022. Three cohorts were constructed: cannabis use disorder (CUD), cannabis users without cannabis abuse or dependence (CU) and non-cannabis users (non-CU). Outcomes included ALD, hepatic decompensation and composite all-cause mortality over 3 years. Incidence and hazard ratios were calculated using Kaplan-Meier analysis and Cox regression. RESULTS: After matching, 33 114 patients were included in each of the CUD and non-CU groups. Compared to non-CU, CUD was associated with a lower risk of ALD (HR 0.60, 95% CI 0.53-0.67; p < 0.001), hepatic decompensation (HR 0.83, 95% CI 0.73-0.95; p =0.005) and all-cause mortality (HR 0.86, 95% CI 0.80-0.94; p < 0.001) among individuals with AUD. Although CU was associated with lower risks of ALD, its risks of hepatic decompensation and all-cause mortality were similar to those of the non-CU cohort with AUD. CONCLUSION: In this propensity-matched cohort study of patients with AUD, cannabis use was associated with a reduced risk of ALD, with the greatest risk reduction seen in patients with CUD compared to CU and non-CU. Our findings suggest that modulation of cannabinoid receptors may offer a new target for the development of pharmacological therapies for ALD.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".