---
title: "Impact of GLP-1 Receptor Agonists on Alcohol-Related Hospitalizations in Adults with AUD"
id: "bmj-open-0-association-between-glp-1-receptor-agonists-and-alcohol-related"
canonical_url: "https://medichelpline.com/clinical-feed/bmj-open-0-association-between-glp-1-receptor-agonists-and-alcohol-related"
content_type: "clinical_feed_article"
specialty: "General"
source_name: "BMJ Open"
source_url: "http://bmjopen.bmj.com/cgi/content/short/16/7/e109259?rss=1"
published_at: "2026-07-21T22:32:17.000Z"
evidence_level: "Journal Feed"
license: "CC-BY-NC-4.0 / Informational Use"
---
# Impact of GLP-1 Receptor Agonists on Alcohol-Related Hospitalizations in Adults with AUD
## Provenance & Clinical Metadata
- **Canonical URL:** https://medichelpline.com/clinical-feed/bmj-open-0-association-between-glp-1-receptor-agonists-and-alcohol-related
- **Specialty:** [General](https://medichelpline.com/clinical-feed/general.md)
- **Primary Source:** BMJ Open
- **Source URL:** [Original Journal Publication](http://bmjopen.bmj.com/cgi/content/short/16/7/e109259?rss=1)
- **Published At:** 2026-07-21T22:32:17.000Z
- **Evidence Rating:** Journal Feed
## Executive GIST (TL;DR)
- Study evaluated the **association** between GLP-1 receptor agonists (semaglutide, tirzepatide) and **alcohol-related hospitalizations** in adults with alcohol use disorder (AUD) and either type 2 diabetes (T2D) or obesity. - Utilized a **retrospective cohort study** design with electronic health records from multiple US healthcare systems (2018-2024). - Participants included adults with AUD initiating treatment with GLP-1 RAs or active comparators. - Four target trials were conducted: anti-diabetic medication (ADM), anti-obesity medication (AOM), medications for AUD with T2D (MAUD-T2D), and medications for AUD with obesity (MAUD-obesity). - Results demonstrated that starting a GLP-1 RA correlated with a **lower risk** of alcohol-related hospitalizations, showing hazard ratios of 0.74 to 0.35 across the trials compared to various comparators. - Study findings suggest that GLP-1 RAs may influence outcomes related to excessive alcohol use, marking a significant correlation with reduced alcohol-related admissions.
## Clinical Analysis & Structured Key Points
Skip to main content Intended for healthcare professionals Log In Basket Search for this keyword Advanced search Latest content Archive For authors About Browse by collection You are here Home Archive Volume 16, Issue 7 Email alerts Article Text Article info Citation Tools Share Rapid Responses Article metrics Alerts PDF General practice / Family practice Original research Association between GLP-1 receptor agonists and alcohol-related hospitalisations among adults with alcohol use disorder: multi-target trial emulation study http://orcid.org/0000-0003-3026-2450Patricia J Rodriguez1, Jay B Lusk2,3, http://orcid.org/0000-0001-9134-6370Hemalkumar B Mehta4, Joseph F Levy5, Andreas P Kalogeropoulos6, Samir Soneji7, Duy Do1, Emma Holler1, Emily Webber1, Ty Gluckman8, http://orcid.org/0000-0002-6610-5353Nicholas Stucky1 Correspondence to Dr Hemalkumar B Mehta; hbmehta@jhu.edu Abstract Objective To evaluate the association between use of newer glucagon-like peptide-1 receptor agonists (GLP-1 RAs; semaglutide, tirzepatide) and alcohol-related hospitalisations among adults with alcohol use disorder (AUD) and type 2 diabetes (T2D) or obesity. Retrospective cohort study using target trial emulation. Setting Electronic health record data from a collective of US healthcare systems. Participants Adults with AUD and T2D or obesity who started a newer GLP-1 RA (semaglutide or tirzepatide) or a relevant active comparator between 1 January 2018 and 31 December 2024. Interventions Initiation of a newer GLP-1 RA compared with an active comparator across four target trials: (1) anti-diabetic medication (ADM) trial, (2) anti-obesity medication (AOM) trial, (3) medications for alcohol use disorder with T2D (MAUD-T2D) trial, and (4) medications for alcohol use disorder with obesity (MAUD-obesity) trial. Main outcome measures Time to first alcohol-related emergency department visits or hospitalisation within 1 year of treatment initiation. Non-alcohol-related hospitalisation was assessed as a negative control outcome. Propensity score based methods (weighting and matching) were used to control confounding and Cox proportional hazards models were used to estimate treatment effects. Results A total of 40 703 adults met study criteria, including 18 676 in the ADM trial, 9391 in the AOM trial, 8942 in the MAUD-T2D trial and 11 198 in the MAUD-obesity trial. Initiation of a newer GLP-1 RA was associated with a lower hazard of alcohol-related hospitalisation in the ADM trial (HR 0.74, 95% CI 0.62 to 0.89 vs sulfonylureas; HR 0.78, 95% CI 0.65 to 0.92 vs other ADMs), the AOM trial (HR 0.68, 95% CI 0.54 to 0.85 vs other AOMs), the MAUD-T2D trial (HR 0.37, 95% CI 0.29 to 0.46) and the MAUD-obesity trial (HR 0.35, 95% CI 0.26 to 0.47). Conclusions Among adults with AUD and T2D or obesity, initiation of newer GLP-1 RAs was associated with a lower observed risk of alcohol-related hospitalisation. Data availability statement Data may be obtained from a third party and are not publicly available. Data used in this study are not publicly available and can be obtained from Truveta. https://creativecommons.org/licenses/by-nc/4.0/ This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: https://creativecommons.org/licenses/by-nc/4.0/. https://doi.org/10.1136/bmjopen-2025-109259 Statistics from Altmetric.com See more details Picked up by 1 news outlets Request Permissions If you wish to reuse any or all of this article please use the link below which will take you to the Copyright Clearance Center’s RightsLink service. You will be able to get a quick price and instant permission to reuse the content in many different ways. Request permissions STRENGTHS AND LIMITATIONS OF THIS STUDY Conducted four separate target trials in clinically distinct target populations by employing a target trial emulation framework with a new-user active comparator design to reduce bias and confounding. Use of large, multi-system US electronic health record dataset to enhance generalisability. Use of propensity score based methods and negative control outcomes analysis. Potential for residual confounding due to unmeasured socioeconomic and lifestyle factors. Introduction Excessive alcohol use is a leading cause of preventable mortality in the US, with more than 178 000 attributable deaths annually and an estimated economic burden exceeding $200 billion.1–4 Alcohol use disorder (AUD) occurs in 10% of US adults,5 yet only 2% of adults with AUD receive medication-assisted treatment.5–7 While efficacious,8 current US Food and Drug Administration (FDA)-approved medications for AUD (MAUD)—acamprosate, disulfiram and naltrexone—have tolerability and adherence challenges that limit their real-world effectiveness.9–11 Glucagon-like peptide-1 receptor agonists (GLP-1 RAs) are used primarily for the treatment of type 2 diabetes mellitus (T2D) and obesity. A growing body of observational evidence suggests that GLP-1 RAs may also reduce alcohol consumption and related adverse outcomes.12 13 However, evidence from randomised clinical trials remains limited and mixed. In a recent randomised trial of semaglutide, reductions were observed in drinks per drinking day and alcohol craving, but there was no significant effect on overall alcohol consumption or number of drinking days.14 These findings may reflect differences in study design and populations, including the inclusion of non-treatment-seeking individuals. Observational studies of varying quality have also reported reductions in AUD-related clinical outcomes, including alcohol-related hospitalisations and recurrent AUD diagnoses, although these studies may lack generalisability.12 13 15 16 Important gaps remain in understanding the potential impact of GLP-1 RAs on AUD. These include a lack of high-quality observational studies using rigorous and consistently defined populations. In addition, given its recent approval, few studies have included tirzepatide, a dual glucose-dependent insulinotropic polypeptide (GIP) and GLP-1 RA. Tirzepatide has demonstrated greater effectiveness on glycaemic and weight-related outcomes compared with semaglutide.17–19 While these metabolic effects are not directly related to alcohol use, they may reflect broader effects on reward pathways and appetite regulation that could also influence alcohol consumption and related outcomes. Accordingly, we employed a target trial emulation framework to estimate the effect of newer GLP-1 RAs (semaglutide and tirzepatide) on alcohol-related hospitalisations for adults with AUD, using four target trials involving clinically distinct populations. Methods Data This study used a subset of Truveta Data.20 Truveta provides access to daily updated and linked electronic health record (EHR) data from a collective of 30 US healthcare systems, representing care delivered to more than 120 million patients across geographically and demographically diverse populations. The dataset reflects provider-based clinical care rather than a closed or fully enumerated population (eg, a health maintenance organisation). Individuals are included based on healthcare utilisation (ie, documented encounters), irrespective of insurance status; thus, patients with any form of coverage, as well as those without insurance (eg, self-pay), are represented. Data are captured across inpatient, outpatient and emergency settings, reflecting care delivered across the continuum, including both primary and secondary care depending on documentation within participating systems. The Truveta database includes data related to demographics, encounters, diagnoses, vital signs (eg, weight, body mass index, blood pressure), medication requests (prescriptions) and laboratory tests and results (eg, haemoglobin A1c, blood alcohol concentration). In addition to EHR data, medication dispensing data (via e-prescribing) include fills for prescriptions written both within and outside Truveta constituent healthcare systems, resulting in greater observability of patients’ medication history. Medication dispense histories are updated at the time of the encounter and include fill dates, NDC or RxNorm codes, quantity dispensed and days of medication supplied. Truveta Data are normalised into a common data model through syntactic and semantic normalisation.20 Truveta Data are then de-identified by expert determination under the Health Insurance Portability and Accountability Act Privacy Rule. Once de-identified, data are available for analysis in R or Python using Truveta Studio. Data for this study were accessed on 1 April 2025. As this study used only de-identified data, it did not require institutional review board approval under US regulations. Patients and the public were not involved in this study. Study design A target trial emulation framework was used to compare on-treatment alcohol-related hospitalisations for adults with AUD who newly initiated a GLP-1 RA (semaglutide or tirzepatide) or an active comparator medication. Newer GLP-1 RAs were compared with alternatives using four separate target trials (table 1), defined by a disease cohort (T2D vs obesity without T2D) and a treatment-related reason (related to T2D/obesity vs AUD). This yielded four trials: (1) anti-diabetic medication (ADM) trial, (2) anti-obesity medication (AOM) trial, (3) medications for alcohol use disorder with T2D (MAUD-T2D) trial, (4) medications for alcohol use disorder with T2D obesity (MAUD-obesity) trial. The four target trials reflect distinct clinical contexts for initiating GLP-1 RAs, enabling complementary inference about their impact on AUD. ADM (T2D) and AOM (obesity) trials include patients starting metabolic treatment and not actively seeking AUD care. They estimate associations between GLP-1 use and alcohol-related outcomes in routine care, assessing effects independent of AUD treatment-seeking. Because T2D and obesity are the primary FDA-approved indications, these trials mirror real-world prescribing. VIEW INLINE VIEW POPUP Table 1 Target trials and arms. Target trials are defined by the disease cohort (T2D or obesity without T2D) and (presumed) reason for seeking treatment (non-AUD or AUD) In contrast, MAUD trials include patients with markers of more severe AUD initiating AUD treatment, approximating a treatment-seeking population. They estimate associations between GLP-1 RAs and alcohol-related outcomes relative to approved AUD medications. Collectively, these designs evaluate GLP-1 RAs across clinically relevant populations with different treatment intent, baseline risk, health status and AUD severity. Each trial included a clinically distinct population with expected differences in underlying health status, AUD severity and treatment-seeking context. This retrospective observational cohort study follows the STROBE reporting guidelines.21 Study population Each trial included a new-user cohort of adults with an AUD diagnosis and a cohort-specific condition of interest (T2D or obesity, respectively) between 1 January 2018 and 31 December 2024. AUD was defined by the presence of an AUD-related diagnosis in the previous 2 years. Initiation of a trial-specific medication, defined by pharmacy dispensing, served as the study index event. New use was defined by a previous 2-year negative history of dispensing and administration of trial-specific medications. T2D was defined by the presence of T2D diagnostic codes within the previous 2 years. For inclusion in the T2D cohorts, a baseline haemoglobin A1c (HbA1c) was also required (up to 1 year before the index event), though no restrictions were made on the HbA1c value. Obesity without T2D was defined by a body mass index (BMI) ≥30 kg/m2 at baseline, using the most recent BMI in the 12 months prior to the index date. Patients with T2D were excluded from the obesity cohort. To improve observability of historical and follow-up information for this study, the population was restricted to patients with at least two outpatient office visits in the previous 2 years. All code lists are provided in online supplemental table S3. Supplemental material [bmjopen-2025-109259supp001.pdf] For the MAUD trials, additional restrictions were applied to emulate patients likely to seek treatment for AUD. History of AUD was defined more narrowly, excluding broader and less severe alcohol-related diagnostic codes (international classification of diseases, 10th revision (ICD-10) F10). In addition, history of AUD was required within the year prior to the index date, rather than within 2 years as used in other trials. As such, the MAUD cohorts represent more restricted subsets of the broader T2D and obesity populations defined for the ADM and AOM trials. Comorbidities, previous medication use and utilisation were assessed using a 2-year lookback window. Study design timelines and patient flow diagrams are depicted in online supplemental figure S1–S5. Outcomes Patients were followed from the index date (treatment initiation) for up to 1 year to identify alcohol-related emergency department (ED) visits or hospitalisations, defined as emergency department or inpatient encounters with either alcohol-related diagnosis (including AUD, alcohol withdrawal and/or other diagnoses related to acute alcohol use; codes in online supplemental table S3) or testing for blood alcohol, ethyl glucuronide or ethyl sulfate levels. Among those with AUD, alcohol-related testing in acute care settings was presumed to be suggestive of the potential involvement of alcohol in their visit. Test values themselves were not considered because values are highly sensitive to time elapsed relative to alcohol consumption.22 Unlike claims data, diagnosis positions are not consistently captured in the EHR, particularly for outpatient encounters, including ED visits. As such, some visits captured as outcomes in this study likely represent visits with alcohol-related reasons rather than visits specifically for alcohol-related reasons. To test specificity to alcohol-related hospitalisations, non-alcohol-related hospitalisations were also compared as a negative control outcome. Non-alcohol-related hospitalisations were defined as all ED and inpatient encounters other than those meeting the above criteria for an alcohol-related hospitalisation. We would expect no association between GLP-1 RAs and the negative control outcome; any observed association may indicate residual confounding. Additional details on negative control outcome selection are provided in the online supplement (page 4). In addition to hospitalisation outcomes, changes in hepatic biomarkers (aspartate aminotransferase (AST), alanine aminotransferase (ALT)) were explored at 9 months on treatment for patients with available values in the ADM trial. AST and ALT are indirect markers of excessive alcohol intake and can be elevated for up to 2–3 weeks after consumption22; however, they are notably imperfect markers of alcohol consumption, subject to both low sensitivity and specificity for heavy drinking.22 23 Additional methods and results are provided in the online supplement (pages 5–6 and 15–16). Patients were censored at medication discontinuation, initiation of a GLP-1 RA (or a newer GLP-1 RA for those starting on an older GLP-1 RA), the last encounter before 1 April 2025 or 1 year from the index event, whichever occurred first. Medication discontinuation was defined as 60 days without medication on hand, based on fill dates and days’ supply per fill. Statistical analysis Treatment selection Population balancing methods were used to address non-random treatment selection. Different balancing approaches were used for the ADM and AOM trials, compared with the MAUD trials, due to differences in the target population and estimand. For the ADM and AOM trials, the estimand of interest was the on-treatment average treatment effect (ATE), so that estimates would generalise to the full trial population. The propensity to initiate a newer GLP-1 RA, relative to other trial medications, was estimated using multinomial regression, inclusive of a variety of factors plausibly related to treatment selection, including demographics, clinical factors, comorbidities and utilisation (complete list provided in online supplement, page 5). We calculated stabilised inverse probability of treatment weights (IPTW), truncated at the 99th percentile to reduce the impact of extreme weights.24 25 For MAUD trials, the target population included patients actively seeking treatment for AUD. Therefore, the estimand of interest was the on-treatment, average treatment effect among those treated (ATT) with MAUD, with the expectation that effects generalise to the cohort of patients who would otherwise receive treatment with approved MAUD. Propensity scores were estimated as the likelihood of initiating MAUD, relative to newer GLP-1 RAs, using logistic regression. Additional covariates were included to adjust for differences in AUD severity and recency (complete list in online supplement, page 5). Because weighting approaches yielded poor balance between groups (likely due to the requirement that all patients receive some weight), 1:1 nearest neighbour propensity score matching was applied (with a calliper of 0.05), pairing a patient treated with MAUD to a similar patient treated with a newer GLP-1 RA.26 27 Unmatched patients were not included in the analysis. Informative censoring Inverse probability of censoring weights (IPCW) were applied to account for informative censoring, where patients remaining on treatment differed from those who were censored.28 The probability of artificial censoring (due to medication discontinuation, switching or loss to follow-up (last encounter)) before 365 days was first estimated using logistic regression. The model considered demographic, clinical and utilisation factors, as well as the exposure group and the index year. Stabilised and truncated IPCW were calculated as the inverse probability of artificial censoring. For ADM and AOM trials, combined weights were calculated as the product of IPCW and IPTW. For MAUD trials, IPCW was applied to the propensity score matched population. Outcomes models The probability of on-treatment ED visit or hospitalisation by 365 days was extracted from weighted Kaplan–Meier curves. Unweighted curves were also plotted for comparison (online supplemental figure S7). Cox proportional hazards models with robust standard errors were used to estimate the on-treatment hazard of alcohol-related hospitalisation between treatment groups. The same approach was used to estimate the hazard of non-alcohol-related hospitalisations between groups. For all Cox models, we assessed the proportional hazards assumption using Schoenfeld residuals and visual inspection of residual plots. These analyses did not indicate meaningful departures from the primary analysis. E-values were calculated to estimate the magnitude of unmeasured confounding required to negate the observed treatment effects.29 As a sensitivity analysis, we repeated the primary analysis in each trial using a more specific outcome definition restricted to alcohol-related ED visits and hospitalisations identified by diagnostic codes only (excluding events defined solely by laboratory testing). This analysis was conducted to evaluate the robustness of findings to potential misclassification of outcomes based on laboratory testing alone. Results In total, 40 703 patients met the criteria for at least one trial. This included 18 676 in the ADM trial (newer GLP-1 RA: 4051 (22%); older GLP-1 RA: 2205 (12%); sulf
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