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 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".