Abstract
INTRODUCTION:
Randomized clinical trials indicate that glucagon-like peptide-1 receptor agonist (GLP-1 RA) treatment improves histologic endpoints in steatotic liver disease, but its effects on long-term clinical outcomes remain uncertain. We examined the association between GLP-1 RA use and hepatic complications in individuals with metabolic dysfunction-associated steatotic liver disease (MASLD) and type 2 diabetes mellitus (T2DM).
METHODS:
We conducted a retrospective, new-user cohort study emulating sequential monthly target trials using All of Us Research Program data from 2010 to 2023 to compare GLP-1 RA users with propensity score-matched controls. Eligible participants had MASLD and T2DM. We excluded participants with previous hepatic complications or other chronic liver diseases. The primary outcome was a composite of incident cirrhosis, hepatic decompensation, hepatocellular carcinoma, or liver transplantation. We performed intention-to-treat and per-protocol analyses.
RESULTS:
A total of 2,110 GLP-1 RA users were matched to 2,110 nonusers. Over a median follow-up of 2.7 years, 187 hepatic complication events occurred: 74 among GLP-1 RA users (13.5 per 1,000 person-years) and 113 among nonusers (21.9 per 1,000 person-years). GLP-1 RA use was associated with a 38% lower risk in the intention-to-treat analysis (hazard ratio, 0.62; 95% confidence interval, 0.46–0.83; P = 0.003) and a 42% lower risk in the per-protocol analysis (hazard ratio, 0.58; 95% confidence interval, 0.42–0.80; P = 0.001). Subgroup analyses by baseline fibrosis-4 and body mass index demonstrated consistent directional trends. Landmark analyses at 6 months were consistent with the primary findings. A negative control outcome, incident fracture, showed no association.
DISCUSSION:
In a nationwide, diverse cohort of individuals with MASLD and T2DM, GLP-1 RA use was associated with a reduction in hepatic complication events, supporting a potential hepatoprotective effect in real-world clinical practice.
Keywords: GLP-1 RA, MASLD, fibrosis
Graphical Abstract
INTRODUCTION
Metabolic dysfunction-associated steatotic liver disease (MASLD), previously known as nonalcoholic fatty liver disease, is the most common cause of chronic liver disease and cirrhosis in the United States (US) (1–3). In the past 3 decades, MASLD prevalence has increased in parallel with obesity and type 2 diabetes (T2DM) and is projected to continue rising, contributing to a growing burden of hepatocellular carcinoma (HCC), liver transplantation (LT), and liver-related mortality (1,4). In addition to its clinical impact, MASLD imposes substantial economic costs with annual medical costs exceeding $100 billion, underscoring the need for effective preventive and therapeutic strategies (5).
Glucagon-like peptide-1 receptor agonist (GLP-1 RA) treatment has demonstrated efficacy and potential benefits in MASLD (6). Although GLP-1 RAs as a class are not approved by the U.S. Food and Drug Administration (FDA) for this indication, the FDA’s approval of semaglutide for metabolic dysfunction-associated steatohepatitis (MASH) reflects emerging evidence supporting their role in steatotic liver disease (7). Randomized controlled trials (RCTs) have demonstrated improvements in histologic steatohepatitis and fibrosis among patients with MASH treated with GLP-1 RAs (8–10). Meta-analyses, inclusive of studies of patients with MASLD, corroborate these findings noting the potential benefits in improving steatosis and inflammatory profiles (11,12). However, although RCTs have characterized histologic endpoints well, they have not clearly described long-term clinical outcomes given limited follow-up time. Observational studies have sought to fill this gap and report potential reductions in cirrhosis, major adverse liver outcomes, and mortality, although many have limited generalizability to the US population (13–16).
To address the limited data on long-term clinical outcomes in people with MASLD and T2DM treated with GLP-1 RAs in the United States, we conducted a retrospective cohort study using a target-trial emulation design. We leveraged the All of Us Research Program (AoU), a large national database, to examine the association of GLP-1 RA treatment and hepatic complications in this population. We performed both intention-to-treat (ITT) and per-protocol (PP) analyses, the latter to approximate real-world adherence.
METHODS
Study design and data source
We conducted a retrospective cohort study using a sequential target trial emulation (TTE) design to examine the effects of GLP-1 RA initiation on hepatic complications among individuals with MASLD and T2DM (Table 1). The TTE approach mimics the design and analysis of RCTs using observational data and helps minimize common biases such as immortal time bias and confounding by indication (17).
Table 1.
Target trial specification and emulation
| Protocol component | Target trial | Emulated trial using observational data |
|---|---|---|
| Eligibility criteria | Age ≥18 Known diagnosis of MASLD and T2DM No previous diagnosis of HCC No history of liver transplantation or bariatric surgery No history of cirrhosis No previous hepatic decompensation FIB-4 score ≤2.67 |
Same as the target trial Additional exclusions:
|
| Treatment strategies | Initiation of GLP-1 RA vs no GLP-1 RA initiation | Same as for the target trial |
| Treatment assignment | Patients are randomly assigned to a strategy at baseline | Patients who initiated GLP-1 RA treatment during each sequential monthly trial period. We emulated random assignments by matching patients who started using GLP-1 RA in a 1:1 ratio to control patients during the same calendar month, based on propensity scores derived from 46 covariates measured at baseline:
|
| Outcome | Primary outcome: Hepatic complications (composite outcome) Components include:
|
Same as the target trial |
| Follow-up | For each patient, follow-up starts on the date of the treatment initiation (index date) and ends on the day of the outcome of interest, death, or the end of the study period, whichever occurs first. | Same as the target trial |
| Causal contrasts | Intention-to-treat effect Per-protocol effect (the effect if all patients had received the treatment they were assigned to at baseline) |
Observational analogues to the intention-to-treat effect Observational analogues of per-protocol effects. For the per-protocol effect, follow-up began on the date of start of GLP-1 RA and ended on the day of the outcome of interest, death, administrative censoring (2023-10-01), or GLP-1 RA discontinuation, whichever occurs first. |
| Data analysis plan | Cumulative incidence curves and estimate of risk, risk differences, and hazard ratios comparing the treatment groups. Subgroup analyses by baseline FIB-4 group (Low/Intermediate), BMI categories, HbA1c levels, and concomitant medication use. |
Same as for the target trial 165 sequential monthly trials from January 2010 to September 2023. |
BMI, body mass index; DPP-4, dipeptidyl peptidase-4; FIB-4, fibrosis-4 index; GLP-1 RA, glucagon-like peptide-1 receptor agonist; HbA1c, glycated hemoglobin A1c; HBV, hepatitis B virus; HCC, hepatocellular carcinoma; HCV, hepatitis C virus; MASLD, metabolic dysfunction-associated steatotic liver disease; T2DM, type 2 diabetes mellitus.
This study used data from the AoU, a precision-medicine initiative sponsored by the National Institutes of Health. AoU includes comprehensive data from adult volunteer participants residing in the United States at enrollment. Details on the data curation process are available at https://allofus.nih.gov/. Although data are derived from multiple sources, our study used data from the electronic health record, participant provided information, and US Census American Community Survey available in the All of Us Controlled Tier Dataset v8, which collected information on participants enrolled between May 2018 and October 2023.
We constructed 165 sequential monthly trials from January 1, 2010, to September 1, 2023. Each monthly trial emulated a RCT in which eligible patients who initiated GLP-1 RA therapy during that month, or GLP-1 RA users, were comparedmatched control patients who had not initiated GLP-1 RA therapy, or nonusers. For GLP-1 RA users, the index date was the first prescription date. For nonusers, we applied fair index date assignment such that each nonuser’s index date was set so that the time from the first MASLD diagnosis to that date equaled the MASLD duration of the matched GLP-1 RA user at the user’s index date. This approach allowed for dynamic cohort entry while maintaining the temporal sequence necessary to minimize immortal time bias (17).
Definitions
Diagnoses for medical conditions were defined by the presence of corresponding International Statistical Classification of Diseases and Related Health Problems, Ninth Revision (ICD-9) and Tenth Revision (ICD-10) codes in the database (Supplementary Table 1, http://links.lww.com/AJG/D935). MASLD was defined as hepatic steatosis and a concurrent metabolic risk factor. In this study, MASLD required diagnostic codes for both fatty liver and T2DM. Hepatic decompensation events were defined as diagnoses of ascites, hepatic encephalopathy, hepatorenal syndrome, spontaneous bacterial peritonitis, or variceal bleeding. Hepatic complications were defined as cirrhosis, HCC, LT, or hepatic decompensation events. Fibrosis-4 (FIB-4) scores were calculated according to the established formula (18). FIB-4 score groups were classified as low if <1.45, intermediate if ≥1.45 and ≤2.67, and high if >2.67.
Study population
Participants 18 years of age or older with available age, race, and sex data and with diagnoses of T2DM and MASLD were included. Participants with previous cirrhosis, HCC, LT, hepatic decompensation events, or other chronic liver diseases including hepatitis B virus, hepatitis C virus, or alcohol-associated liver disease were excluded (Figure 1). Participants were also excluded if they had previous exposure to GLP-1 RAs before the trial start period, GLP-1 RA use less than 30 days, GLP-1 RA initiation before MASLD diagnosis, lack of follow-up, or high-risk FIB-4 scores (>2.67) at baseline.
Figure 1.
Cohort selection for included participants. Adults (18 years and older) with MASLD and T2DM in the All of Us Research Program were identified (n = 16,208). Exclusions were made for HBV infection, HCV infection, alcohol-related liver disease, history of HCC, history of LT, history of hepatic decompensation, history of cirrhosis, MASLD diagnosis after trial start, GLP-1 RA use <30 days, baseline FIB-4 >2.67, and no follow-up period. The unadjusted cohort included GLP-1 RA users (n = 2,438) and nonusers (n = 9,637). The propensity score-matched cohort (n = 4,220) included GLP-1 RA users (n = 2,110) and nonusers (n = 2,110). FIB-4, Fibrosis-4; GLP-1 RA, glucagon-like peptide-1 receptor agonist; HBV, hepatitis B virus; HCC, hepatocellular carcinoma; HCV, hepatitis C virus; LT, liver transplantation; MASLD, metabolic dysfunction-associated steatotic liver disease; T2DM, type 2 diabetes mellitus.
Outcome, exposures, and covariates
Exposure was defined as the initiation of a GLP-1 RA agent, which included semaglutide, liraglutide, dulaglutide, exenatide, albiglutide, lixisenatide, and tirzepatide (Supplementary Table 2, http://links.lww.com/AJG/D935). The index date for the GLP-1 RA users was the date of the first prescription. Nonusers were assigned a matched index date as defined above. The primary outcome was a composite of hepatic complications, defined as the new development relative to the index date of cirrhosis, hepatic decompensation, HCC, or LT. For patients experiencing multiple events, the earliest event was considered the primary outcome.
Baseline covariates assessed at each trial start included demographics (age, sex, race, body mass index [BMI] [kg/m2]), comorbidities (MASLD duration, type 2 diabetes, diabetes complications, hypertension, coronary artery disease, dyslipidemia, chronic kidney disease, peripheral vascular disease, cerebrovascular disease, heart failure, chronic obstructive pulmonary disease, hypothyroidism, hyperthyroidism, Supplementary Table 1, http://links.lww.com/AJG/D935), co-medications (metformin, insulin, sulfonylurea, sodium-glucose cotransporter 2 inhibitor, thiazolidinedione, dipeptidyl peptidase-4 inhibitor, statin, aspirin, fibrate, niacin, angiotensin-converting-enzyme inhibitor, angiotensin receptor blocker, calcium channel blocker, beta blocker, diuretics, Supplementary Table 2, http://links.lww.com/AJG/D935), laboratory parameters (aspartate aminotransferase, alanine aminotransferase, albumin, creatinine, platelet count), FIB-4 score, FIB-4 score groups, number of abdominal imaging studies in the 3 years before trial initiation, and socioeconomic status variables. Laboratory values used to calculate the FIB-4 score were defined as the closest available measurements within ±365 days of the trial start date. Socioeconomic status data, sourced from the U.S. Census American Community Survey through three-digit zone improvement plan code linkage, included zone improvement plan code assisted income fraction, high school education fraction, median income, uninsured fraction, poverty fraction, vacant housing fraction, and area deprivation index. In addition, healthcare utilization variables derived from the All of Us Personal and Family Health History survey were included. These comprised self-reported frequency of general practitioner and specialist visits in the past 12 months and financial barriers to prescription medications and specialist care.
Statistical analyses
For each monthly trial, GLP-1 RA users were matched one to one to nonusers using propensity score (PS) matching with goal standardized mean difference <0.1 criteria across covariates. Propensity scores were estimated using logistic regression incorporating 44 baseline covariates, including demographics, comorbidities, co-medications, laboratory parameters, and socioeconomic status, and healthcare utilization (Supplementary Figure 3, http://links.lww.com/AJG/D935). Importantly, MASLD duration was included as a matching variable to support fair comparison. Nonusers were selected without replacement, which meant that once matched, they could not participate in subsequent trials.
We performed ITT and PP analyses. In the ITT analysis, follow-up began on the index dates for users and on the assigned matched index date for nonusers, and ended at the earliest occurrence of hepatic complications, death, or administrative censoring on October 1, 2023. In the PP analysis, follow-up additionally ended at GLP-1 RA discontinuation or censoring if there was a gap in GLP-1 RA prescription of 180 days or more. Missing values for covariates were handled using multiple imputation by chained equations with linear regression models, limited to variables with <15% missingness. Imputation included key laboratory and socioeconomic variables. For variables with ≥30% missingness, such as BMI and weight, not available categories were created and treated as separate groups to preserve missingness information and minimize bias. Baseline characteristics were compared using standardized mean differences for matched cohorts. Cumulative incidence functions were estimated using the Aalen-Johansen estimator, with death treated as a competing event. Cause-specific Cox proportional hazards models with robust standard errors clustered by patient identification number were used to estimate hazard ratios [HRs] with 95% confidence intervals (CIs). Fine-Gray subdistribution hazard models were fitted as a sensitivity analysis.
Sensitivity analyses included stratification by baseline FIB-4 score groups (low vs intermediate) and BMI categories (<35, 35–40, and ≥40 kg/m2). Each analysis used the same methodology as the primary analysis, with separate PS-matched cohorts within each stratum. Negative control analysis using incident fractures was performed to assess for residual confounding. To evaluate the potential influence of early outcome ascertainment, additional landmark analyses were conducted at 6, 12, and 18 months after trial enrollment. Longitudinal analysis of clinical parameters (BMI, weight, HbA1c [glycated hemoglobin A1c]) was conducted using available post-trial data, calculating mean changes from baseline every 6 months. All analyses were conducted in Python 3 (Python Software Foundation, Wilmington, DE). Two-sided P values <0.05 were considered statistically significant. The study adhered to the Strengthening the Reporting of Observational Studies in Epidemiology guidelines.
RESULTS
Baseline characteristics
The sequential TTE identified 4,220 patients, with 2,110 GLP-1 RA users matched one to one to 2,110 nonusers after PS matching. Baseline characteristics were generally well balanced, although modest imbalance remained for sodium-glucose cotransporter 2 inhibitor use, dipeptidyl peptidase-4 inhibitor use, and alanine aminotransferase levels (Table 2). PS distributions, annual enrollment patterns, and covariate balance demonstrated good balance (Supplementary Figures 1–3, http://links.lww.com/AJG/D935).
Table 2.
Baseline characteristics of the study population after propensity score matching
| Characteristic | GLP-1 RA (n = 2,110) | Nonusers (n = 2,110) | SMD |
|---|---|---|---|
| Demographics | |||
| Age (yr), mean ± SD | 55.81 ± 12.01 | 56.85 ± 13.39 | 0.082 |
| Male sex, n (%) | 0.038 | ||
| Female | 1,460 (69.2) | 1,423 (67.4) | |
| Male | 650 (30.8) | 687 (32.6) | |
| Race/ethnicity, n (%) | 0.061 | ||
| Asian | 25 (1.2) | 41 (1.9) | |
| Black | 346 (16.4) | 341 (16.2) | |
| Others | 624 (29.6) | 622 (29.5) | |
| White | 1,115 (52.8) | 1,106 (52.4) | |
| BMI category, n (%) | 0.042 | ||
| <35 kg/m2 | 460 (21.8) | 441 (20.9) | |
| 35–39.9 kg/m2 | 433 (20.5) | 441 (20.9) | |
| ≥40 kg/m2 | 538 (25.5) | 500 (23.7) | |
| Not available | 452 (21.4) | 423 (20.0) | |
| Comorbidities | |||
| MASLD duration (yr), mean ± SD | 3.90 ± 4.32 | 3.69 ± 4.23 | 0.049 |
| Diabetes complications, n (%) | 1,315 (62.3) | 1,339 (63.5) | 0.024 |
| Hypertension, n (%) | 1,755 (83.2) | 1,787 (84.7) | 0.041 |
| Coronary artery disease, n (%) | 614 (29.1) | 629 (29.8) | 0.016 |
| Dyslipidemia, n (%) | 1,700 (80.6) | 1,711 (81.1) | 0.013 |
| Chronic kidney disease, n (%) | 373 (17.7) | 396 (18.8) | 0.028 |
| Peripheral vascular disease, n (%) | 222 (10.5) | 258 (12.2) | 0.054 |
| Cerebrovascular disease, n (%) | 259 (12.3) | 271 (12.8) | 0.017 |
| Heart failure, n (%) | 322 (15.3) | 333 (15.8) | 0.014 |
| COPD, n (%) | 291 (13.8) | 289 (13.7) | 0.003 |
| Hypothyroidism, n (%) | 529 (25.1) | 545 (25.8) | 0.017 |
| Hyperthyroidism, n (%) | 56 (2.7) | 75 (3.6) | 0.052 |
| Medications | |||
| Metformin, n (%) | 1,596 (75.6) | 1,559 (73.9) | 0.040 |
| Insulin, n (%) | 1,101 (52.2) | 1,103 (52.3) | 0.002 |
| Sulfonylurea, n (%) | 677 (32.1) | 739 (35.0) | 0.062 |
| SGLT-2 inhibitor, n (%) | 302 (14.3) | 429 (20.3) | 0.119 |
| Thiazolidinedione, n (%) | 240 (11.4) | 290 (13.7) | 0.072 |
| DPP-4 inhibitor, n (%) | 378 (17.9) | 483 (22.9) | 0.123 |
| Statin, n (%) | 1,435 (68.0) | 1,457 (69.1) | 0.022 |
| Aspirin, n (%) | 1,124 (53.3) | 1,158 (54.9) | 0.032 |
| Fibrate, n (%) | 233 (11.0) | 288 (13.6) | 0.079 |
| Niacin, n (%) | 147 (7.0) | 170 (8.1) | 0.041 |
| ACE inhibitor, n (%) | 1,092 (51.8) | 1,054 (50.0) | 0.036 |
| ARB, n (%) | 657 (31.1) | 614 (29.1) | 0.044 |
| Calcium channel blocker, n (%) | 893 (42.3) | 881 (41.8) | 0.012 |
| Beta blocker, n (%) | 1,112 (52.7) | 1,121 (53.1) | 0.009 |
| Diuretics, n (%) | 1,007 (47.7) | 1,001 (47.4) | 0.006 |
| Laboratory values | |||
| AST (U/L), mean ± SD | 26.84 ± 16.82 | 25.48 ± 15.88 | 0.083 |
| ALT (U/L), mean ± SD | 33.96 ± 23.78 | 31.00 ± 23.27 | 0.106 |
| Platelet count (×103/μL), mean ± SD | 262.46 ± 69.14 | 260.25 ± 74.17 | 0.031 |
| Albumin (g/dL), mean ± SD | 4.08 ± 0.45 | 4.07 ± 0.47 | 0.020 |
| Creatinine (mg/dL), mean ± SD | 0.92 ± 0.54 | 0.95 ± 0.68 | 0.056 |
| FIB-4 score, mean ± SD | 1.06 ± 0.49 | 1.10 ± 0.51 | 0.076 |
| FIB-4 category, n (%) | 0.074 | ||
| Low (<1.45) | 1,713 (81.2) | 1,650 (78.2) | |
| Intermediate (1.45–2.67) | 397 (18.8) | 460 (21.8) | |
| Social determinants | |||
| Assisted income fraction, mean ± SD | 15.38 ± 5.90 | 15.43 ± 6.80 | 0.008 |
| High school education fraction, mean ± SD | 87.72 ± 4.97 | 87.52 ± 5.52 | 0.037 |
| Median income, mean ± SD | 63,531 ± 14,655 | 63,846 ± 15,485 | 0.021 |
| No health insurance fraction, mean ± SD | 8.69 ± 4.06 | 8.64 ± 3.82 | 0.012 |
| Poverty fraction, mean ± SD | 15.62 ± 4.80 | 15.66 ± 5.37 | 0.009 |
| Vacant housing fraction, mean ± SD | 10.11 ± 3.99 | 9.96 ± 4.48 | 0.036 |
| Deprivation index, mean ± SD | 0.32 ± 0.06 | 0.32 ± 0.06 | 0.002 |
| Number of radiologic examinations, mean ± SD | 1.48 ± 4.53 | 1.49 ± 3.49 | 0.003 |
| Healthcare utilization | |||
| General doctor visits (past 12 mo), n (%) | 0.075 | ||
| 1–3 visits | 432 (20.5) | 370 (17.5) | |
| 4–9 visits | 470 (22.3) | 464 (22.0) | |
| ≥10 visits | 163 (7.7) | 182 (8.6) | |
| Not surveyed | 1,045 (49.5) | 1,094 (51.8) | |
| Specialist visits (past 12 mo), n (%) | 0.075 | ||
| 1–3 visits | 379 (18.0) | 320 (15.2) | |
| 4–9 visits | 231 (10.9) | 232 (11.0) | |
| ≥10 visits | 63 (3.0) | 79 (3.7) | |
| Not surveyed | 1,437 (68.1) | 1,479 (70.1) | |
| Financial barrier to prescription medications, n (%) | 0.032 | ||
| No | 908 (43.0) | 875 (41.5) | |
| Yes | 237 (11.2) | 249 (11.8) | |
| Not surveyed | 965 (45.7) | 986 (46.7) | |
| Financial barrier to specialist care visits, n (%) | 0.041 | ||
| No | 914 (43.3) | 871 (41.3) | |
| Yes | 119 (5.6) | 130 (6.2) | |
| Not surveyed | 1,077 (51.0) | 1,109 (52.6) | |
ACE, angiotensin-converting enzyme; ALT, alanine aminotransferase; ARB, angiotensin receptor blocker; AST, aspartate aminotransferase; BMI, body mass index; COPD, chronic obstructive pulmonary disease; DPP-4, dipeptidyl peptidase-4; FIB-4, fibrosis-4 index; GLP-1 RA, glucagon-like peptide-1 receptor agonist; MASLD, metabolic dysfunction-associated steatotic liver disease; SGLT-2, sodium-glucose cotransporter-2; SMD, standardized mean difference.
The mean age was 55.8 years in GLP-1 RA users and 56.9 years in nonusers. Most participants were women and White in both groups. Mean MASLD duration, diabetes complications, hypertension, and dyslipidemia were similar between groups. Metformin, statin, and insulin use were also comparable. Mean FIB-4 scores were 1.06 and 1.10, and 81.2% vs 78.2% were classified as low-risk for advanced fibrosis. Social determinants of health, radiologic examination frequency, and healthcare utilization variables were comparable after matching (Table 2).
Primary outcome
During a median follow-up of 2.7 years, 187 patients developed hepatic complications. In the ITT analysis, 74 events occurred among GLP-1 RA users (incidence rate, 13.49 per 1,000 person-years; 95% CI, 10.59–16.94) and 113 events among nonusers (incidence rate, 21.87 per 1,000 person-years; 95% CI, 18.02–26.29). This finding corresponded to a 38% lower relative risk of hepatic complications for GLP-1 RA users (HR, 0.62; 95% CI, 0.46–0.83; P = 0.003) (Table 3; Figure 2a).
Table 3.
Association of GLP-1 RA use on outcomes among patients with MASLD and T2DM
| Exposure | No. of events | Person-years | Incidence per 1,000 person-years (95% CI) | Hazard ratio (95% CI) |
|---|---|---|---|---|
| Intention-to-treat | ||||
| Primary outcome | ||||
| GLP-1 RA users | 74 | 5,485 | 13.49 (10.59–16.94) | 0.62 (0.46–0.83) |
| Nonusers | 113 | 5,167 | 21.87 (18.02–26.29) | Reference |
| Per-protocol | ||||
| Primary outcome | ||||
| GLP-1 RA users | 56 | 4,326 | 12.94 (9.78–16.81) | 0.58 (0.42–0.80) |
| Nonusers | 113 | 5,167 | 21.87 (18.02–26.29) | Reference |
CI, confidence interval; GLP-1 RA, glucagon-like peptide-1 receptor agonist; MASLD, metabolic dysfunction-associated steatotic liver disease; T2DM, type 2 diabetes mellitus.
Figure 2.
(a) Cumulative incidence of hepatic complications among GLP-1 RA users and nonusers in the intention-to-treat analysis. Kaplan-Meier curves depict the cumulative incidence of the primary composite outcome among GLP-1 RA users and nonusers in the ITT analysis. The composite included incident cirrhosis, HCC, LT, or hepatic decompensation events. Hepatic decompensation events were defined as ascites, hepatic encephalopathy, hepatorenal syndrome, spontaneous bacterial peritonitis, or variceal bleeding. (b) Cumulative incidence of hepatic complications among GLP-1 RA users and nonusers in the per-protocol analysis. Kaplan-Meier curves depict the cumulative incidence of the primary composite outcome among GLP-1 RA users and nonusers in the PP analysis. The composite included incident cirrhosis, HCC, LT, or hepatic decompensation events. Hepatic decompensation events were defined as ascites, hepatic encephalopathy, hepatorenal syndrome, spontaneous bacterial peritonitis, or variceal bleeding. GLP-1 RA, glucagon-like peptide-1 receptor agonist; HCC, hepatocellular carcinoma; ITT, intention-to-treat; LT, liver transplantation; PP, per-protocol.
The PP analysis demonstrated consistent results, with 56 events among GLP-1 RA users (incidence rate, 12.94 per 1,000 person-years; 95% CI, 9.78–16.81) and 113 events among nonusers (incidence rate, 21.87 per 1,000 person-years; 95% CI, 18.02–26.29). This finding corresponded to a 42% lower relative risk of hepatic complications for GLP-1 RA users (HR, 0.58; 95% CI, 0.42–0.80; P = 0.001) (Table 3; Figure 2b).
The composite outcome was primarily driven by new cirrhosis diagnoses, which comprised most of the events in both groups. Decompensation events, including ascites, hepatic encephalopathy, and variceal bleeding, represented the second most common component, whereas HCC and LT were rare during the follow-up period.
Subgroup analysis
In subgroup analyses stratified by baseline FIB-4 score, GLP-1 RA use was associated with lower hazard estimates in the low FIB-4 group in both the ITT analysis (HR, 0.74; 95% CI, 0.52–1.05; P = 0.091) and the PP analysis (HR, 0.70; 95% CI, 0.47–1.03; P = 0.070), whereas no statistically significant association was observed in the intermediate FIB-4 group (ITT: HR, 0.88; 95% CI, 0.43–1.77; P = 0.712; PP: HR, 0.70; 95% CI, 0.31–1.55; P = 0.377) (Supplementary Table 3; Supplementary Figure 4, http://links.lww.com/AJG/D935).
When stratified by baseline BMI, in patients with BMI <35 kg/m2, the HRs were 0.74 (95% CI, 0.37–1.49; P = 0.400) in the ITT analysis and 0.62 (95% CI, 0.29–1.36; P = 0.234) in the PP analysis. In patients with BMI 35–40 kg/m2, the corresponding HRs were 0.78 (95% CI, 0.29–2.09; P = 0.627) and 0.69 (95% CI, 0.24–2.02; P = 0.503), respectively. In patients with BMI ≥ 40 kg/m2, the HRs were 0.53 (95% CI, 0.18–1.57; P = 0.253) in the ITT analysis and 0.38 (95% CI, 0.10–1.44; P = 0.155) in the PP analysis (Supplementary Table 4; Supplementary Figure 5, http://links.lww.com/AJG/D935).
Sensitivity analysis
Negative control analysis using incident fracture showed no association with GLP-1 RA use in either the ITT analysis (HR 1.01; 95% CI, 0.82–1.25; P = 0.944) or the PP analysis (HR 1.01; 95% CI, 0.81–1.27; P = 0.903), supporting minimal residual confounding (Supplementary Table 5, Supplementary Figure 6, http://links.lww.com/AJG/D935). Landmark analyses at 6, 12, and 18 months after treatment initiation yielded results consistent with the primary analysis at the 6-month landmark, whereas later landmarks demonstrated a similar direction of association but did not reach statistical significance (Supplementary Table 6; Supplementary Figure 7, http://links.lww.com/AJG/D935).
Longitudinal analyses of metabolic parameters suggested beneficial effects of GLP-1 RA therapy. Compared with nonusers, GLP-1 RA users demonstrated sustained reductions in BMI and weight over the five-year follow-up period, with mean BMI decreasing by approximately 1–2 kg/m2 and weight by 3–8 kg (Supplementary Figures 8 and 9, http://links.lww.com/AJG/D935). HbA1c levels initially improved in GLP-1 RA users with mean reductions of 0.7%–0.9% maintained through 2 years of follow-up (Supplementary Table 7, Supplementary Figure 10, http://links.lww.com/AJG/D935).
DISCUSSION
In this large retrospective cohort study with a TTE design, GLP-1 RA use was associated with a significantly reduced risk of hepatic complications in participants with T2DM and MASLD in both ITT and PP analyses. The reduction was driven primarily by fewer cases of incident cirrhosis. GLP-1 RA users also demonstrated improvements in metabolic parameters, such as BMI and weight, during follow-up.
Our findings support previous observational studies reporting fewer adverse liver outcomes among patients with MASLD exposed to GLP-1 RAs (13–16). Improvement in metabolic dysfunction, a key driver of MASLD, likely contributes substantially to the observed reduction in hepatic complications, consistent with patterns observed following bariatric surgery (19,20). This interpretation aligns with the established effects of GLP-1 RAs on caloric intake and glycemic control, as well as experimental data suggesting increased fatty acid oxidation and reduced lipogenesis (10,21,22). Results from the phase 3 semaglutide trial in MASH are also supportive, with histologic resolution of steatohepatitis coinciding with reductions in body weight and glycemia (8). Although baseline diabetes severity and healthcare utilization were well balanced after matching, postbaseline antihyperglycemic medication use may still differ between groups because of dynamic treatment intensification and combination therapy in routine clinical care. Consequently, HbA1c trajectories alone may not fully capture the complexity of diabetes management over time.
GLP-1 RA effects on inflammation may represent an additional mechanism contributing to improved clinical outcomes in MASLD. RCTs have described decreased lobular inflammation and hepatocellular ballooning with GLP-1 RA treatment, suggesting local hepatic anti-inflammatory effect (11). Reductions in C-reactive protein and chemokine (C-C motif) ligand 2 (CCL2) with GLP-1 RA treatment also suggest that changes in systemic inflammation can influence disease activity in MASLD (22,23). CCL2 is implicated in monocyte and macrophage recruitment to the liver, activation of hepatic stellate cells, myofibroblast differentiation, and extracellular matrix deposition, all of which may amplify local inflammation and promote fibrosis (24–26). However, our study cannot determine whether these anti-inflammatory effects reflect direct drug effects or downstream consequences of metabolic status (21).
Understanding the effects of GLP-1 RA treatment according to baseline MASLD severity is important for the development of effective prevention strategies. In our study, participants with low baseline FIB-4 scores exhibited a trend toward lower rates of hepatic complications, whereas no clear difference was observed in the intermediate FIB-4 group. However, these subgroup analyses should be interpreted cautiously. The relatively small number of participants in the intermediate FIB-4 group resulted in wide CIs and limited statistical power to detect meaningful differences between treatment groups. Therefore, these findings should be considered exploratory. From a biological perspective, GLP-1 RAs primarily target metabolic dysfunction, hepatic steatosis, and inflammatory pathways, which may be more modifiable in earlier stages of MASLD before advanced fibrosis develops (13). Consequently, the observed trend in the low FIB-4 group may reflect greater potential for disease modification in earlier fibrosis stages. Nevertheless, these findings remain exploratory and should be confirmed in larger studies with adequate power across fibrosis risk strata.
Our study is, to our knowledge, the first to evaluate GLP-1 RA use in MASLD and T2DM using the AoU data. The AoU data set allows examination of sociodemographic factors, includes populations underrepresented in medical research, and aggregates data from multiple sites across the United States (27). Accounting for these factors is critically important given their impact on MASLD burden and outcomes (28). Adherence to GLP-1 RAs in real-world clinical practice is suboptimal (29). Cost and tolerability of adverse effects may contribute and be exacerbated in populations with lower socioeconomic status or with limited health literacy, with important implications for persistence and effectiveness of this treatment approach in the general population (30–32). Our findings in ITT and PP analyses, which controlled for sociodemographic factors, support a clinical benefit of GLP-1 RA treatment in settings where access is achievable. Our results complement the limited data on GLP-1 RA use effects on long-term clinical outcomes in MASLD specific to the US population and add information on the socioeconomic context that is relevant for understanding practical aspects in the implementation of GLP-1 RA treatment of this population (13,33,34). The AoU intentionally oversamples historically underrepresented populations with a higher burden of chronic disease. These includes racial and ethnic minorities as well as individuals of lower-income socioeconomic status, demographic profiles that closely reflect the patients at highest risk for MASLD-related complications (35). Because both treatment groups were drawn from the same healthcare provider organization-based cohort, the relative effect estimates within our matched TTE framework are likely to be internally valid. However, generalizability may be limited for lean patients with MASLD, individuals with lower metabolic disease burden, and those facing substantial barriers to healthcare engagement who remain underrepresented even within AoU.
Strengths
Our study has multiple strengths. This study emulated a target trial within a large, national, multi-site cohort enhancing demographic and geographic diversity and extending generalizability beyond more selected populations in RCTs (36). We also designed our study to minimize bias. A new-user design addressed prevalent-user bias, and fair index assignment created comparable MASLD duration between GLP-1 RA users and nonusers, which reduces immortal time bias. PS matching on demographics, comorbidities, medications, laboratory markers, and FIB-4 helped address confounding by indication and created comparable groups regarding baseline disease severity and diagnostic intensity. Sequential monthly emulations mitigated secular trends in overall care delivery, including prescribing and disease surveillance, which may have been impacted by FDA approval of new GLP-1 RAs and the COVID-19 pandemic, respectively. Finally, we reported incidence rates and time-to-event estimates and performed both ITT and PP analyses, the latter censoring follow-up 180 days after the last GLP-1 RA prescription to address potential loss of exposure from discontinuation.
Limitations
Our study has some limitations. Because this was an observational study, residual and time-varying confounding may persist despite the use of a new-user design, fair index assignment, and PSmatching. Although multiple metabolic comorbidities and diabetes-related complications were adjusted for, some indicators of diabetes severity were incompletely captured. Alcohol-related liver disease was excluded using ICD diagnosis codes; however, alcohol consumption may not be fully captured in electronic health record data, and residual misclassification of alcohol use cannot be excluded. Cirrhosis and liver-related outcomes were identified using ICD diagnosis codes, which may introduce misclassification inherent to administrative data. Because imaging or histologic confirmation was not consistently available in the AoU data set, outcome validation was not feasible. Consequently, absolute incidence estimates should be interpreted with caution, whereas relative comparisons between treatment groups within a TTE framework remain informative. Medication exposure was inferred from prescription initiation dates, as detailed information on dosing, duration, and adherence was not consistently available. This limitation could result in exposure misclassification and may attenuate observed associations if treatment effects are dose-dependent (6). In addition, although we matched on FIB-4, liver enzymes, platelets, and BMI which likely improved comparability between groups and reduced differential detection, coding variability could not be excluded and studies incorporating imaging and histologic data are necessary to confirm our results. Another limitation is that our study had a low number of component events, which limited power for analyses on specific outcomes. In addition, follow-up duration was limited, and later landmark analyses resulted in substantial reductions in sample size, potentially reducing statistical power to detect differences. Finally, AoU relies on volunteer participants, who may have greater engagement with the healthcare system, and recruits from populations historically underrepresented in medical research, factors that can introduce selection and surveillance bias and limit broader generalizability.
In conclusion, in a large US cohort with T2DM and MASLD, GLP-1 RA use was associated with a significant reduction in hepatic complications. These findings complement existing clinical trial data and suggest potential benefits of GLP-1 RA treatment in MASLD on liver-related outcomes after accounting for sociodemographic factors relevant to treatment implementation. However, a longer follow-up is required to confirm the durability of these associations. Our findings encourage further examination of GLP-1 RA effectiveness across fibrosis risk groups and further highlight the importance of investigating factors that may influence treatment adherence.
Supplementary Material
SUPPLEMENTARY MATERIAL accompanies this paper at http://links.lww.com/AJG/D935
Study Highlights.
WHAT IS KNOWN
Glucagon-like peptide-1 receptor agonists (GLP-1 RA) improve histologic endpoints in steatotic liver disease.
The effects of GLP-1 RA on long-term hepatic complications remain uncertain.
WHAT IS NEW HERE
GLP-1 RA use reduces the risk of hepatic complications in metabolic dysfunction-associated steatotic liver disease and type 2 diabetes mellitus.
Benefits observed across subgroups defined by fibrosis-4 and body mass index.
Findings support a potential real-world hepatoprotective effect of GLP-1 RA.
ACKNOWLEDGEMENTS
We gratefully acknowledge All of Us participants for their contributions that made this research possible. We also thank the National Institutes of Health’s All of Us Research Program for collecting and making available the data for this study.
Financial support:
The All of Us Research Program is supported by the National Institutes of Health (NIH), Office of the Director: Regional MedicalCenters: 1OT2OD026549;1OT2OD026554; 1OT2OD026557; 1 OT2 OD026556; 1 OT2 OD026550; 1 OT2 OD 026552; 1 OT2 OD026553; 1 OT2 OD026548; 1 OT2 OD026551; 1 OT2 OD026555; IAA #: AOD 16037; Federally Qualified Health Centers: HHSN 263201600085U; Data and Research Center: 5 U2C OD023196; Biobank: 1 U24 OD023121; The Participant Center: U24 OD023176; Participant Technology Systems Center: 1 U24 OD023163; Communications and Engagement: 3 OT2 OD023205; 3 OT2 OD023206; and Community Partners: 1 OT2 OD025277; 3 OT2 OD025315; 1 OT2 OD025337; 1 OT2 OD025276. This research was also supported by NIH R01 CA255621 and U01 CA288375. Role of funder/sponsor statement: This funding source had no role in the design of this study or its execution, analyses, interpretation of the data, or decision to submit results.
Potential competing interests:
J. Choi received a Gilead Sciences research grant. No other authors have disclosures.
ABBREVIATIONS:
- ACE (ACEi)
Angiotensin-converting enzyme (inhibitor)
- ALD
Alcohol-associated liver disease
- ALT
Alanine aminotransferase
- AoU
All of Us Research Program
- ARB
Angiotensin receptor blocker
- AST
Aspartate aminotransferase
- BMI
Body mass index
- CCL2
Chemokine (C-C motif) ligand 2
- CI
Confidence interval
- COPD
Chronic obstructive pulmonary disease
- CRP
C-reactive protein
- DPP-4 (DPP-4i)
Dipeptidyl peptidase-4 (inhibitor)
- EHR
Electronic health record
- FDA
(U.S.) Food and Drug Administration
- FIB-4
Fibrosis-4 index
- GLP-1 RA
Glucagon-like peptide-1 receptor agonist
- HbA1c
Glycated hemoglobin A1c
- HBV
Hepatitis B virus
- HCC
Hepatocellular carcinoma
- HCV
Hepatitis C virus
- HR
Hazard ratio
- ICD-10
International Statistical Classification of Diseases and Related Health Problems; Tenth Revision
- ICD-9
International Statistical Classification of Diseases and Related Health Problems, Ninth Revision
- IRB
Institutional Review Board
- ITT
Intention-to-treat
- LT
Liver transplantation
- MASH
Metabolic dysfunction-associated steatohepatitis
- MASLD
Metabolic dysfunction-associated steatotic liver disease
- MICE
Multiple imputation by chained equations
- NAFLD
Nonalcoholic fatty liver disease
- NIH
National Institutes of Health
- PP
Per-protocol
- PPI
Participant provided information
- PS
Propensity score
- RCT
Randomized controlled trial
- SD
Standard deviation
- SGLT-2 (SGLT-2i)
Sodium-glucose cotransporter 2 (inhibitor)
- SMD
Standardized mean difference
- STROBE
Strengthening the Reporting of Observational Studies in Epidemiology
- T2DM
Type 2 diabetes mellitus
- TTE
Target trial emulation
- U.S.
United States
- ZIP
Zone improvement plan (code)
Footnotes
Ethical statement: The All of Us Research Program protocol was approved and overseen by the All of Us Institutional Review Board (IRB). Data access required the researcher’s institution to have a Data Use and Registration Agreement with the All of Us Research Program and for the researcher to complete the All of Us Responsible Conduct of Research training and accept the Data User Code of Conduct. Please visit researchallofus.org for more information.
Data sharing statement:
The data are not publicly available due to privacy policy. Data, analytic methods, and study materials will not be made available to other researchers due to the Data and Statistics Dissemination Policy of the All of Us Research Program. Please visit researchallofus.org for more information.
REFERENCES
- 1.Younossi ZM, Stepanova M, Younossi Y, et al. Epidemiology of chronic liver diseases in the USA in the past three decades. Gut 2020;69(3):564–8. [DOI] [PubMed] [Google Scholar]
- 2.Younossi ZM, de Avila L, Racila A, et al. Prevalence and predictors of cirrhosis and portal hypertension in the United States. Hepatology 2025; 82(5):1229–40. [DOI] [PubMed] [Google Scholar]
- 3.Paik JM, Hobbs K, Gupta A, et al. Prevalence of MASLD, Met-ALD, and ALD and associated fibrosis among US adults: Insights from NHANES 2017 to 2023. J Clin Gastroenterol. 2025. doi: 10.1097/MCG.0000000000002202. In press. [DOI] [PubMed] [Google Scholar]
- 4.Le P, Tatar M, Dasarathy S, et al. Estimated burden of metabolic dysfunction–associated steatotic liver disease in US adults, 2020 to 2050. JAMA Netw Open 2025;8(1):e2454707. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5.Younossi ZM, Blissett D, Blissett R, et al. The economic and clinical burden of nonalcoholic fatty liver disease in the United States and Europe. Hepatology 2016;64(5):1577–86. [DOI] [PubMed] [Google Scholar]
- 6.Targher G, Valenti L, Byrne CD. Metabolic dysfunction-associated steatotic liver disease. N Engl J Med 2025;393(7):683–98. [DOI] [PubMed] [Google Scholar]
- 7.U.S. Food and Drug Administration. FDA approves treatment for serious liver disease known as ‘MASH’. News & Events for Human Drugs 2025. [cited 2025 August 15]. https://www.fda.gov/drugs/news-events-human-drugs/fda-approves-treatment-serious-liver-disease-known-mash#:~:text5The%20U.S.%20Food%20and%20Drug,is%20a%20serious%20liver%20disease
- 8.Sanyal AJ, Newsome PN, Kliers I, et al. Phase 3 trial of semaglutide in metabolic dysfunction-associated steatohepatitis. N Engl J Med 2025; 392(21):2089–99. [DOI] [PubMed] [Google Scholar]
- 9.Armstrong MJ, Gaunt P, Aithal GP, et al. Liraglutide safety and efficacy in patients with non-alcoholic steatohepatitis (LEAN): A multicentre, double-blind, randomised, placebo-controlled phase 2 study. Lancet 2016;387(10019):679–90. [DOI] [PubMed] [Google Scholar]
- 10.Loomba R, Hartman ML, Lawitz EJ, et al. Tirzepatide for metabolic dysfunction-associated steatohepatitis with liver fibrosis. N Engl J Med 2024;391(4):299–310. [DOI] [PubMed] [Google Scholar]
- 11.Wang Y, Zhou Y, Wang Z, et al. Efficacy of GLP-1-based therapies on metabolic dysfunction-associated steatotic liver disease and metabolic dysfunction-associated steatohepatitis: A systematic review and meta-analysis. J Clin Endocrinol Metab 2025;110(10):2964–79. [DOI] [PubMed] [Google Scholar]
- 12.Ghosal S, Datta D, Sinha B. A meta-analysis of the effects of glucagon-like-peptide 1 receptor agonist (GLP1-RA) in nonalcoholic fatty liver disease (NAFLD) with type 2 diabetes (T2D). Sci Rep 2021;11:22063. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13.Kanwal F, Kramer JR, Li L, et al. GLP-1 receptor agonists and risk for cirrhosis and related complications in patients with metabolic dysfunction-associated steatotic liver disease. JAMA Intern Med 2024;184(11):1314–23. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14.Kuo CC, Chuang MH, Li CH, et al. Glucagon-like peptide-1 receptor agonists and liver outcomes in patients with MASLD and type 2 diabetes. Aliment Pharmacol Ther 2025;61(7):1163–74. [DOI] [PubMed] [Google Scholar]
- 15.Havranek B, Loh R, Torre B, et al. Glucagon-like peptide-1 receptor agonists improve metabolic dysfunction-associated steatotic liver disease outcomes. Sci Rep 2025;15(1):4947. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16.Liu BD, Aly M, Hsin-Ti Lin C, et al. Glucagon-like peptide-1 receptor agonists are associated with improved survival and reduced liver-related events in patients with type 2 diabetes and metabolic dysfunction-associated liver disease: A large real-world retrospective study. Endocr Pract 2025;31(8):1025–32. [DOI] [PubMed] [Google Scholar]
- 17.Hernan MA, Wang W, Leaf DE. Target trial emulation: A framework for causal inference from observational data. JAMA 2022;328(24):2446–7. [DOI] [PubMed] [Google Scholar]
- 18.Sterling RK, Lissen E, Clumeck N, et al. Development of a simple noninvasive index to predict significant fibrosis in patients with HIV/ HCV coinfection. Hepatology 2006;43(6):1317–25. [DOI] [PubMed] [Google Scholar]
- 19.Lassailly G, Caiazzo R, Ntandja-Wandji LC, et al. Bariatric surgery provides long-term resolution of nonalcoholic steatohepatitis and regression of fibrosis. Gastroenterology 2020;159(4):1290–301.e5. [DOI] [PubMed] [Google Scholar]
- 20.Aminian A, Al-Kurd A, Wilson R, et al. Association of bariatric surgery with major adverse liver and cardiovascular outcomes in patients with biopsy-proven nonalcoholic steatohepatitis. JAMA 2021;326(20): 2031–42. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21.Yabut JM, Drucker DJ. Glucagon-like peptide-1 receptor-based therapeutics for metabolic liver disease. Endocr Rev 2023;44(1):14–32. [DOI] [PubMed] [Google Scholar]
- 22.Armstrong MJ, Hull D, Guo K, et al. Glucagon-like peptide 1 decreases lipotoxicity in non-alcoholic steatohepatitis. J Hepatol 2016;64(2): 399–408. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23.Verma S, Bhatta M, Davies M, et al. Effects of once-weekly semaglutide 2.4 mg on C-reactive protein in adults with overweight or obesity (STEP 1, 2, and 3): Exploratory analyses of three randomised, double-blind, placebo-controlled, phase 3 trials. EClinicalMedicine 2023;55:101737. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 24.Marra F, Tacke F. Roles for chemokines in liver disease. Gastroenterology 2014;147(3):577–94.e1. [DOI] [PubMed] [Google Scholar]
- 25.Xi S, Zheng X, Li X, et al. Activated hepatic stellate cells induce infiltration and formation of CD163(+) macrophages via CCL2/CCR2 pathway. Front Med (Lausanne) 2021;8:627927. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26.Baeck C, Wei X, Bartneck M, et al. Pharmacological inhibition of the chemokine C-C motif chemokine ligand 2 (monocyte chemoattractant protein 1) accelerates liver fibrosis regression by suppressing Ly-6C(+) macrophage infiltration in mice. Hepatology 2014;59(3):1060–72. [DOI] [PubMed] [Google Scholar]
- 27.All of Us Research Program Investigators; Denny JC, Rutter JL, Goldstein DB, et al. The “All of Us” research program. N Engl J Med 2019;381(7):668–76. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28.Faulkner CS, Aboona MB, Surendra L, et al. Neighborhood social determinants of health are associated with metabolic dysfunction-associated steatotic liver disease outcomes. Clin Gastroenterol Hepatol 2025;23(9):1577–87.e10. [DOI] [PubMed] [Google Scholar]
- 29.Weiss T, Carr RD, Pal S, et al. Real-world adherence and discontinuation of glucagon-like peptide-1 receptor agonists therapy in type 2 diabetes mellitus patients in the United States. Patient Prefer Adherence 2020;14:2337–45. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 30.Rodriguez PJ, Zhang V, Gratzl S, et al. Discontinuation and reinitiation of dual-labeled GLP-1 receptor agonists among US adults with overweight or obesity. JAMA Netw Open 2025;8(1):e2457349. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 31.Essien UR, Singh B, Swabe G, et al. Association of prescription co-payment with adherence to glucagon-like peptide-1 receptor agonist and sodium-glucose cotransporter-2 inhibitor therapies in patients with heart failure and diabetes. JAMA Netw Open 2023;6:e2316290. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 32.Magnani JW, Mujahid MS, Aronow HD, et al. Health literacy and cardiovascular disease: Fundamental relevance to primary and secondary prevention: A scientific statement from the American Heart Association. Circulation 2018;138(2):e48–74. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 33.Elsaid MI, Li N, Firkins SA, et al. Impacts of glucagon-like peptide-1 receptor agonists on the risk of adverse liver outcomes in patients with metabolic dysfunction-associated steatotic liver disease cirrhosis and type 2 diabetes. Aliment Pharmacol Ther 2024;59(9):1096–110. [DOI] [PubMed] [Google Scholar]
- 34.Mao X, Zhang X, Lai R, et al. Glucagon-like peptide 1 receptor agonist and reduced liver and non-liver complications in adults with type 2 diabetes and metabolic dysfunction-associated steatotic liver disease: A target trial emulation study. Clin Mol Hepatol 2025;31(3):1084–99. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 35.Zeng C, Schlueter DJ, Tran TC, et al. Comparison of phenomic profiles in the All of Us Research Program against the US general population and the UK Biobank. J Am Med Inform Assoc 2024;31(4):846–54. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 36.Mantovani A, Morandin R, Fiorio V, et al. Glucagon-like peptide-1 receptor agonists improve MASH and liver fibrosis: A meta-analysis of randomised controlled trials. Liver Int 2025;45:e70256. [DOI] [PMC free article] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
The data are not publicly available due to privacy policy. Data, analytic methods, and study materials will not be made available to other researchers due to the Data and Statistics Dissemination Policy of the All of Us Research Program. Please visit researchallofus.org for more information.