Glucagon-like Peptide-1 Receptor Agonists Improve Liver and Metabolic Health Outcomes in Type 2 Diabetes and Obesity Regardless of Prior Liver Disease

Glucagon-like Peptide-1 Receptor Agonists Improve Liver and Metabolic Health Outcomes in Type 2 Diabetes and Obesity Regardless of Prior Liver Disease | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Glucagon-like Peptide-1 Receptor Agonists Improve Liver and Metabolic Health Outcomes in Type 2 Diabetes and Obesity Regardless of Prior Liver Disease Arun Sanyal, Kavin Parmar, Ana Beatriz Souza de Oliveira, Vinay Jahagirdar, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8342646/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted You are reading this latest preprint version Abstract Metabolic dysfunction- associated steatotic liver disease (MASLD) is a common comorbidity in obesity and type 2 diabetes mellitus (T2DM), which are treated with glucagon-like peptide-1 receptor agonists (GLP-1 RA). The benefits of GLP-1 RA on major adverse liver outcomes (MALO) in this population are not well established. We retrospectively evaluated the impact of GLP-1 RA, compared to propensity-matched controls, on mortality, MALO, major adverse cardiac events (MACE), and obesity- related cancers in obese adults with T2DM using the Tri NetX global collaborative database. GLP-1 RA decreased all-cause mortality (1.7 vs 5.4% H.R. (95% CI) 0.38, 0.37–0.39 at 2 years), MACE (4.4 vs 8.2% H.R. 0.66, 0.65–0.68), MALO (0.7 vs 1.8% H.R. 0.5, 0.49–0.55) and obesity-related cancers (1.5 vs 2.2%, H.R. 0.85, 0.81–0.89) and at all durations of exposure studied. GLP-1 RA improved the rates of each of the MACE components (p < 0.0001 for each). GLP-1 RA reduced ascites, hepatic encephalopathy, variceal hemorrhage, and hepatocellular cancer (p < 0.01 for all outcomes). These benefits were confirmed in those who also had a diagnosis of either MASLD or cirrhosis prior to the initiation of GLP-1 RA. These data demonstrate the liver and metabolic health benefits of GLP-RAs and support access to these agents. Word count 199 Health sciences/Gastroenterology/Hepatology/Liver diseases/Non-alcoholic fatty liver disease Health sciences/Gastroenterology/Hepatology/Liver diseases/Non-alcoholic steatohepatitis nonalcoholic fatty liver disease metabolic dysfunction associated steatotic liver disease metabolic dysfunction associated steatohepatitis cirrhosis type 2 diabetes mellitus obesity GLP-1 receptor agonists Semaglutide Tirzepatide macrovascular microangiopathic renal major adverse liver outcomes mortality cardio-renal-metabolic outcomes real-world data Figures Figure 1 Figure 2 Figure 3 Figure 4 INTRODUCTION Noncommunicable diseases (NCDs) account for about forty million deaths and seventy percent of premature deaths each year globally 1 . Cardiovascular disease, type 2 diabetes mellitus (T2DM), hypertension, and cancers are well-recognized NCDs. It is, however, now recognized that NCDs also include additional conditions that are interlinked with these classical NCDs and impact the course of these conditions, such as metabolic dysfunction-associated steatotic liver disease (MASLD) 2 . Multiple NCDs like T2DM and MASLD are mechanistically linked by insulin resistance and are often concomitantly present in the same individual 3 , 4 . There is a collinear progression of these conditions; for example, those with more advanced MASLD with cirrhosis are more likely to both develop and have type 2 diabetes than those with early stages of MASLD 5 , 6 . These conditions also impact each other, and those with MASLD experience higher cardio-renal-metabolic outcomes and cancers, while those with a greater number of metabolic disorders are more likely to experience adverse liver-related outcomes 2 , 7 . Glucagon-like peptide-1 receptor agonists (GLP-1 RA) have transformed the care of T2DM and demonstrated improvement in mortality, glycemic control, and related cardio-renal-metabolic outcomes 8 – 10 . There are currently limited data on major adverse liver outcomes (MALO) 11 , inclusive of ascites, overt encephalopathy, and variceal hemorrhage in broad populations with T2DM and the impact of GLP-1 RA on MALO in this population. In a study of US veterans, GLP-1 RA reduced mortality compared to those on DPP inhibitors, but no association with MALO was noted 12 . Further, the regulatory approval of GLP-1 RA was based on trials in populations enriched to have the outcome of interest and often excluded comorbidities that could impact outcomes 9 , 13 , 14 , thus limiting the generalizability of the data. In order to fill these data gaps, we interrogated the TriN etX Global collaborative database, which includes individual patient-level data on over 200 million individuals longitudinally 15 . We evaluated the long-term outcomes in individuals with T2DM and obesity who received a GLP-1 RA and compared them to a propensity-matched control group that did not receive a GLP-1 RA. These provide a comprehensive assessment of the benefits versus risks of GLP-1 RA in a real-world clinical practice setting. RESULTS A total of 3,307,931 adult individuals with T2DM and obesity were identified in the TriN etX database (Figure 1) . Of these, 795,273 individuals received one of several GLP-1 RAs, including Lixisenatide, Albiglutide, Exenatide, Liraglutide, Dulaglutide, Semaglutide, and Tirzepatide. Those who remained on the same or a different GLP-1 RA for varying time intervals from 2-8 years were evaluated for the major outcomes of interest, including all-cause mortality, MACE, MALO, and cancers. For the all-cause mortality analysis (Table 1) , the population included 66.2% Caucasians (n=198,634), 19.6% African Americans (n=59,059), 10.3% Hispanics (n=31,161), and 2.4% Asians (n=7,489). 81.1% of individuals had hypertension, and 21.8% of individuals already had a diagnosis of a prior MACE event, while 17.5% had a prior history of CKD, and 2% had a prior history of a liver-related event. The ICD-10 codes to identify individual events are provided in Supplemental Table 1 . Impact on all-cause mortality In order to generate biologically plausible data and to assess the impact of varying durations of GLP-1 RA exposure on outcomes, we pre-specified various minimum durations of exposure (2-8 years) and measured outcomes over a 3-year period after that when continued GLP-1 use was not formally tracked. The 3-year time frame was chosen because third-party payers often assess benefits of therapy within this time frame to make policy decisions related to access to care. In separate analyses, outcomes were measured during the time course of documented exposure only. The data from both analyses were qualitatively similar and all data are presented using the former approach. The use of any GLP-1 RA, inclusive of all of the individual agents noted above, was associated with a decrease in all-cause mortality following a minimum period of exposure of 2 years (Figure 1) . The absolute risk was 1.7 vs 5.4 % (Hazard Ratio (H.R.) with 95% CI: 0.38 (0.37-0.39), p< 0.0001). The mortality rates remained relatively lower in those on a GLP-1 RA compared to controls, 2% vs 6.4% (H.R. 0.38, 0.34-0.42) even after 8 years of minimum exposure. The differences were statistically significant at all minimum durations of exposure studied. These changes were further reflected in Kaplan-Meier analyses of time to death (Supplemental Figure 1) . Impact on major adverse cardiac events (MACE): For those receiving any GLP-1 RA, 4.4% experienced MACE compared to 8.2% of controls after 2 years of minimum exposure (H.R. 0.66 (0.65-0.68)) (Figure 2, panel B) . GLP-1 RA reduced the risk of MACE at all durations of exposure studied. The rates of MACE (GLP-1 vs control) were 4.9 vs 8.8%, 4.9 vs 9.2% and 4.9 vs 9.2% after 4, 6, and 8 years of minimum exposure, respectively, with a hazard ratio of 0.65-0.67 (P< 0.0001 at all time-points studied). Time-to-event analysis further demonstrated a prolongation of time to MACE with GLP-1 RA ( supplemental Figure 2 ) . Figure 3 panels A-F demonstrate the effects of GLP-1 RA on individual cardiovascular outcomes. Those receiving any GLP-1 RA had a lower incidence of acute myocardial infarction, non-ST-elevated MI, cerebral infarction, unstable angina, heart failure, and coronary revascularization procedures. As with all-cause mortality, the differences were statistically significant with 2 years of exposure and persisted at all durations of exposure studied. Of note, those on GLP-1 RA had lower rates of both heart failure with preserved ejection fraction (2% vs 3.6%, (HR 0.7, 0.65-0.75) with 2 years of minimum exposure and 2.4% vs 4.8% (H.R. 0.61 0.54-0.69) with 8 years of minimum exposure) and with reduced ejection fraction (1.4% vs 2.5% (H.R. 0.7 (0.67-0.74) and 1.9% vs 3.3% (H.R. 0.68 (0.59-0.78) with at least 2 and 8 years of exposure respectively. These benefits were also reflected in Kaplan-Meier time to event analyses (Supplemental Figure 2) . Impact of major adverse liver outcomes (MALO): The use of GLP-1 RA in this broad population with obesity and T2DM without well-characterized underlying liver disease but without pre-existing hepatitis C or B or alcohol use disorder diagnoses was associated with a decrease in MALO at all time points (Figure 2, panel C) . The absolute risk and related H.R. with minimum exposure for at least 2 and 8 years were 0.77% vs 1.82 % (H.R: 0.51 (0.49-0.55)) and 1.08% vs 2.17 % (H.R.: 0.61 (0.52-0.72)) (p< 0.0001 at all time-points). The time to MALO was also longer in those on GLP-1 RA ( supplemental Figure 3 ) . Figure 3 panels G-L demonstrate the impact of GLP-1 RA on individual liver-related events. GLP-1 RA reduced the incidence of ascites by approximately 50% with 2 years of minimum exposure (0.64% vs 1.56 % H.R. 0.5 (0.47-0.53)). These benefits were maintained up to 8 years of minimum drug exposure (0.85% vs 1.93 %, H.R. 0.54 (0.45-0.64)). These benefits were further reflected in the reduced incidence of spontaneous bacterial peritonitis and hepatorenal syndrome. The incidence of hepatic encephalopathy was similarly reduced by GLP-1 RA, with an absolute risk of 0.15% vs 0.29% with at least 2 years of exposure and 0.23% vs 0.4% with a minimum exposure of 8 years. The risk of hepatocellular cancer was also decreased in those receiving GLP-1 RA, even with a minimum exposure for 2 years (absolute risk: 0.14% vs 0.22%, H.R.: 0.83 (0.71-0.96)). The risks of variceal hemorrhage were also reduced in those with 2-6 years of GLP-1 RA exposure; however, there were too few events in the group with 8 years or more of GLP-1 RA exposure to compute meaningfully. Impact on the development of cancers: There was a numerical decrease in overall obesity- related cancers (Figure 2, panel D) . After four years of minimum exposure, the decreases in rates of ovarian, pancreatic, and rectal cancer were most pronounced. However, in those with 8 years of minimum exposure, many of these effects were less pronounced and non-significant. GLP-1 RA also reduced the incidence of prostate cancer. Importantly, the rates of thyroid cancer were not increased in those exposed to GLP-1 RA. Impact on other metabolic and renal outcomes: GLP-1 RA reduced the incidence of hypertension ( Supplemental Table 2 ) . Those receiving GLP-1 RA had a higher rate of receiving a diagnosis of a diabetic ophthalmic complication (ICD10 E11.3, E11.51, E11.39, E11.311); however, overall blindness (ICD10 H54) was decreased (0.7 vs 1.05% H.R. 0.68 (0.62-0.76) at year 4 and 0.76% vs 1.1% H.R. 0.8 (0.68-1.04) after 8 years of minimum exposure). A new diagnosis of CKD also decreased with a cumulative incidence of 5.8% in those on GLP-1 RA versus 7.9% in controls after 8 years of minimum exposure. Neuropathy was slightly reduced in patients using GLP-1 RA (5.39% vs 6.54%, H.R. 0.98 (0.94-1.03), and 4.91% vs 6.95%, H.R. 0.88 (0.8-0.97) after 4 and 8 years of minimum exposure, respectively). Differential effects of individual GLP-1 RA: The biological activity of individual GLP-1 RAs is variable, with more recently approved drugs having more data to support their cardio-renal-metabolic benefits. Since these agents have only been used for a shorter time frame, the effects of individual agents in use in routine practice on all-cause mortality, MACE, MALO, and cancer incidence with data available after 2 years of minimum exposure are shown in Supplemental Figure 4 . Tirzepatide, a GLP-1/GIP co-agonist, had the greatest numerical reduction of each of these at the end of 2 years of exposure, followed by semaglutide, while the effects of liraglutide, dulaglutide, and exenatide were similar but numerically lower than that noted for semaglutide and tirzepatide. Exenatide did not significantly impact MACE or cancer risk. Impact of GLP-1 in those with T2DM and MASLD: The long-term benefits of GLP-1 RA on MASLD-related outcomes are not well-established. We therefore evaluated the effects of GLP-1 RA in those who also had a diagnosis of MASLD in addition to T2DM and obesity at the time of initiation of therapy and compared them to controls who did not receive GLP-1 RA ( Supplemental Figure 5 ) . GLP-1 RA had a substantial benefit on all-cause mortality (2.2% vs 6.24% with 8 years of minimum exposure, H.R.: 0.39, (0.31-0.5)). It also improved MACE (5.32% vs 7.14%, H.R. 0.91 (0.74-1.11), MALO (1.5% vs 2.84%, H.R. 0.63 (0.46-0.85), and cancer (1.84% vs 3%, H.R. 0.71 (0.53-0.96)) with 8 years of minimum exposure. The effects on individual MACE and MALO components are provided in supplemental Tables 3 and 4, respectively . Impact on those with T2DM and cirrhosis: A phase 2 trial of semaglutide in those with cirrhosis due to MASLD demonstrated its safety but was unable to demonstrate histological benefit 16 . This study had a small sample size and was of short duration. The potential effects of GLP-1 RA in those who already have cirrhosis remain to be fully established. To address this knowledge gap, we evaluated the effects of GLP-1 RA in those with T2DM and Obesity who also had a diagnosis of cirrhosis but did not have hepatitis C, hepatitis B, Alcohol related liver disease, or Alcohol Use Disorder (Figure 4) . The mortality in those with cirrhosis was higher than for the overall population. GLP-1 RA had a major impact on all-cause mortality (6% vs 14% H.R. 0.45 (0.4-0.5) after 2 years of exposure. It also had a major impact on MALO (6.33% vs 10.37%, H.R.:0.66 (0.58-0.76) with a minimum exposure of 2 years to the drug). Benefits were also seen for MACE and all-cause cancer. The impact on individual MALO components is provided in supplemental Table 5 . Importantly, in this high-risk population, the rate of hepatocellular cancer was also lower in those receiving GLP-1 RA after 2 years of exposure. There were too few individuals with cirrhosis and past 2 years of exposure to allow statistical comparisons. DISCUSSION NCDs are the principal causes of mortality worldwide 1 . GLP-1 RAs are a major class of drugs that have established benefits for mortality, MACE, glycemic control, and renal dysfunction in highly selected trial populations 8-10,17 . The current study focused on a broad population of individuals receiving GLP-1 RA for T2DM and obesity. It demonstrated clinically meaningful benefit in real-world clinical practice, arguably a highly relevant setting for assessing the real value of drugs. The benefits range from mortality, cardiovascular disease, CKD, MALO, and cancers in this population. They further provide data on the safety of these agents with data on ophthalmic complications of T2DM and blindness, thyroid cancer, and pancreatitis. These data have several important implications for the field. For clinicians, the broad benefits across multiple end-organ related outcomes in a large diabetic population not specifically selected to be enriched with one particular end-organ disease, such as heart failure or MASH, are both reassuring and clinically valuable. These benefits across multiple disease states that are likely to be concomitantly present further strengthen the utility of GLP-1 RA as anchor-therapy for multiple key NCDs and have the potential to “bend the curve” of NCD-associated premature morbidity and mortality. A key and novel finding is the robust reduction of MALO in the current study. The ongoing ESSENCE trial of patients with histologically-defined MASH with stage 2 or 3 fibrosis will capture the effects of Semaglutide on liver-related events in this population 14 . However, this is likely to take several years to complete, and prior experience from the REGENERATE trial indicates that even after five years of follow-up, reduced progression to cirrhosis is the only outcome that occurs in enough numbers to allow assessment of drug benefits. Also, phase 3 trials of tirzepatide, survodutide, and retatrutide are all early in their life-cycle, and outcomes data will not be available for several years. In this setting, the current data are particularly relevant and support the use of GLP-1 RA for MASH, especially given the recent approval of Semaglutide. It is however important to note that these data do not permit response or benefit assessment at an individual patient level. Therefore, it should not be construed to support the use of GLP-1 RA without baseline assessment of severity of liver disease and monitoring its course in those on treatment. The observed benefits of GLP-1 RA on MALO in those with cirrhosis are also important in the context of failure to demonstrate histological regression of cirrhosis or MASH resolution in a small phase 2 trial of Semaglutide, although it was generally well tolerated 16 . This likely reflects the much larger population evaluated in the current study. Many patients with MASH-associated cirrhosis have T2DM 5,18 . The all-cause mortality, MACE, MALO, and HCC benefits in those with cirrhosis support their use for the treatment of T2DM in this population, which is also expected to reduce the use of insulin for glycemic control, which carries the potential for hypoglycemia and also weight gain 19,20 . The current study also confirms the observed improvement in the risk of coronary events and stroke noted in prior trials 21,22 . The current study further demonstrated a statistically significant decrease in heart failure with either reduced ejection fraction or preserved ejection fraction. This could reflect the size and varying populations for this analysis versus prior clinical trials. Hypertension is a major contributor to cardiovascular and renal outcomes and disability-adjusted life-years lost 23 . Another important finding of this study is the decrease in new-onset hypertension, which is likely to have contributed to the observed decrease in cardiovascular events and development of CKD. The current study demonstrated a decrease in the number of obesity-associated cancers, particularly in the gastrointestinal tract and liver especially up to four years of minimum exposure. Recently, colorectal cancers have been diagnosed at earlier ages, leading guidelines for screening to be modified to start at age 45 years, even in normal risk individuals 24,25 . The reduction of such cancers as well as esophageal, stomach, and gallbladder cancers is of significant public health relevance. The dilution of the benefits on cancer incidence at time points past the 4 year mark may reflect the lower potency of older approved agents for whom long-term data were available only. For those designing clinical trials, particularly for MASH, these data provide a general estimate of the rates of outcomes that can be expected within 2-4 years in populations with the characteristics of the current study population and help in the design of such trials. Also, the reporting of outcomes related to many of the competing threats to life allows consideration of benefits and risks, which will facilitate the development of novel approaches for drug development targeting multiple NCDs, such as heart disease, CKD, and MASH, simultaneously, as is being considered by the MOSAIC group and reported previously 26 . Another key implication of the current data is support for greater access to GLP-1 RA in those with T2DM. Third-party payers often expect a return on investment for payment for drugs within 3 years. The current study demonstrates that benefits accrue within 2 years of exposure, and the data on benefits across multiple end-organ outcomes should allow estimation of the actuarial benefits per patient treated and generate the evidence needed to support broader and easier access to GLP-1 RA. It is also hoped that the current data will further fuel conversations between governmental and health policy agencies about the designation of GLP-1 RA as essential drugs to contain the current global pandemic of NCD-related morbidity and mortality. The current study has several strengths and some weaknesses. The principal strengths are the large sample size, data from real-world practice, a broad population of individuals with T2DM, and reporting of outcomes across multiple organ systems, as well as safety reporting. The principal weakness is the nature of real-world data which does not allow granular interrogation of outcomes at an individual level. Further outcomes are assessed from coding data, which can be unreliable; however, as with other real-world data, this weakness is compensated for by the large amount of data and the tight confidence limits around the observed risk differences. In summary, the current study robustly demonstrates the multifaceted benefits and safety of GLP-1 RA in patients with T2DM and obesity. It provides novel data on the benefits of GLP-1 RA on liver-related outcomes and extends the benefit profile from those already known from clinical trials to include both macrovascular and microvascular complications of T2DM. These data are expected to assist in clinical decision-making, future trial design and innovation, and to support greater access to such agents for those with T2DM and obesity. MATERIALS AND METHODS A retrospective analysis of the TriNetX Global collaborative database was performed to evaluate the long-term outcomes of individuals with T2DM and obesity who received GLP-1 RA therapy. The analysis covered the period from 2010 to 2025. The study was considered exempt from IRB review because it involved existing de-identified data for retrospective analysis. The analyses were conducted from April to October 2025 by the investigators who are fully responsible for the veracity and integrity of the data and the contents of this manuscript. While the work was supported by intramural resources of the Stravitz-Sanyal Institute at Virginia Commonwealth University School of Medicine, neither the institute nor the School of Medicine had any involvement in the design, conduct, data analysis, and interpretation of this study. The study followed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines (provided in supplemental materials). The TriNetX Data Resource: The TriNetX platform is a data resource that includes anonymized individual patient-level data from multiple institutions, including over 212 million individuals across 120 major health care organizations, of which 117 million are from the US 15 . For participating institutions, it provides access via secure portals and has built-in analytic capabilities that can be used to interrogate the data from these individuals. These data analysis capabilities allow individual patient-level data analysis to inform population-level data reports. Study population: The population of interest (test-group) included adults with T2DM and obesity who received a GLP-1 RA (ICD-10/RxNorm codes- E11 + TNX 9083 + ATC A10BJ or RxNorm 2601723 or 60548 or 1991302 or 475968 or 1551291). Patients with a history of bariatric surgery (CPT- 1007385) were excluded. (Figure 1) A detailed list of ICD-10 codes used for this study to define the populations and outcomes is provided in Supplemental Table 1 . For those who received a GLP-1 RA, ongoing GLP-1 RA use was identified by continued prescriptions for a GLP-1 RA with varying pre-specified durations of drug exposure from 2 to 8 years. Outcomes were further analyzed over an additional 3 years from the last date of the prescription period. The rationale for a minimum duration of exposure for 2 years was to have plausibility that the GLP-1 RA had time to affect underlying disease biology. Independent analyses were performed separately for those with T2DM and obesity without consideration of the presence of MASLD (K76.0 or K75.81) or cirrhosis (K74) and in those with T2DM who also carried a diagnosis of MASLD/MASH or cirrhosis before initiation of GLP-1 RA. For analysis of liver outcomes and individuals with concomitant MASLD/MASH or cirrhosis, those with hepatitis C, Hepatitis B, Alcohol Use Disorder, or a diagnosis of Alcohol-associated hepatitis or liver disease were excluded. Control populations Data from the test group were compared with a propensity-matched control group using 1:1 propensity scores generated by greedy nearest-neighbor algorithms with a caliper width of 0.1 in the TriN etX analytics systems. A standard mean difference of < 0.1 was considered a negligible difference 27 . The covariates included demographics (age, race, gender), known comorbidity profile (hypertension, known heart disease, CKD, stroke, cirrhosis), body mass index (BMI), laboratory data (hemoglobin, bilirubin, AST, platelets, INR, creatinine, LDL-cholesterol), smoking status, and use of statins, SGLT2 inhibitors, and angiotensin receptor blockers. Endpoints: The first endpoint measured was all-cause mortality. Cardiovascular outcomes included a 5-point MACE (acute myocardial infarction, cerebral infarction, unstable angina, heart failure, coronary revascularisation procedures) (I21, I21.0, I21.1, I21.2, I21.3, I21.29, I21.11, I21.21, I21.01, I21.4, I21.A9, I63, I50, I20.0). Additional outcomes included new diagnosis of heart failure with reduced ejection fraction as well as heart failure with preserved ejection fraction (I50.2 or I50.3). Major adverse liver outcomes (MALO) were defined as a composite of acute variceal hemorrhage (ICD-10 185.01), overt ascites (R18), and hepatic encephalopathy (K76.82). Hepatocellular cancer (C22) was measured separately. Additional outcomes included spontaneous bacterial peritonitis and hepatorenal syndrome (K65.2 and K76.7). Cancer-related outcomes included breast cancer, GI cancers (esophageal cancer, gastric cancer, gallbladder cancer, pancreatic cancer, colorectal cancer), ovarian and endometrial cancer, renal cell cancer, prostate cancer, and thyroid cancer (C50, C15, C16, C23, C25, C18, C20, C56, C54.1, C64, C65, C61, and C73). Microangiopathic outcomes, including diabetes-related ophthalmic complications (E11.3 or E11.51 or E11.39 or E11.311) and blindness (H54), and neuropathy (G62 or E11.40) were also included. Renal outcomes included CKD and renal replacement therapy (N18 and CPT- 1012740). Safety-related outcomes of interest included thyroid medullary cancer, acute pancreatitis, acute cholecystitis, or cholecystectomy (C73, K85, K81.0, CPT- 1014153 or 47562 or SNOMED- 45595009). Statistical plan: Those who received any GLP-1 RA were compared to those who did not. Also, a sensitivity analysis of data for the individual approved GLP-1 RA was performed. Outcomes were measured up to a fixed time point (3 years) after the minimum duration of exposure requirement was satisfied (2, 4, 6, and 8 years) and hazard ratios with 95% confidence limits were computed and reported. The absolute outcome rates were also reported alongside, since hazard ratios do not provide an estimate of absolute risk. For time-to-event analyses, proportional hazards models were used with censoring and accounting of confounding factors, and hazard ratios with 95% CI were provided. Groups were compared using the log-rank test. For all-cause mortality, the entire population, regardless of prior events, was included. For analysis of other outcomes, those who had experienced the outcome prior to initiation of the GLP-1 RA were excluded, and only new- onset of events were computed to provide data on the impact of GLP-1 RA on the incidence of these outcomes. Significance was set at p< 0.001 for all-cause mortality, MACE, MALO, any cancer, and CKD. This statistical bar was deliberately set at a high level to increase certainty, i.e., confidence in the interpretation of the data due to potential “noise” in EMR data. Also, we agreed internally at the initiation of data analysis that we would proceed with assessment of statistical significance for all-cause mortality, then sequentially to MACE, followed by MALO, and then any cancer with this fixed significance level if it met significance for mortality, followed by each sequential outcome. This was done to minimize over-interpretation due to multiple comparisons. If any outcome in this sequence did not meet our agreed-upon threshold, the remaining downstream data would only report significance nominally and provide OR with confidence limits. References unique to the methods section: 27. Austin P.C. Optimal caliper widths for propensity-score matching when estimating differences in means and differences in proportions in observational studies. Statistics in Medicine, 2011, 30.1:150-161. Abbreviations LDL low density lipoprotein BMI Body mass index MASLD Metabolic Dysfunction-Associated Steatotic Liver Disease CKD Chronic Kidney Disease INR=international normalized ratio AST aspartate aminotransferase ALT alanine aminotransferase ALP- alkaline phosphatase Declarations ACKNOWLEDGEMENTS N/A AUTHOR CONTRIBUTIONS Concept Design Implementation Analysis Interpretation Critical Review K.P. √ √ √ √ √ √ A.B. √ √ √ √ V.J. √ √ E.S. √ √ √ A.J.S. √ √ √ √ √ Conflicts of Interest: Arun J. Sanyal: AJS has stock options in Tiziana, Rivus, Durect, and NorthSea. He has served as a paid consultant to Intercept, Genfit, Boehringer Ingelhiem, Eli Lilly, Novo Nordisk, Glaxo Smith Kline, Madrigal, Amgen, Genentech, Merck, Zydus, Astra Zeneca, Alnylam, Regeneron, Altimmune, Surrozen, Poxel, Hanmi, Akero Therapeutics, Boston Pharma, 89 Bio, Pliant, Chemomab, Salix, TARGET-MASH, Path AI, Histoindex. His institution receives funding from Avant Sante for consultation with him and has received grants from Novo Nordisk, Hanmi, 89 Bio, Madrigal, Gilead, Akero, Merck, Takeda, Salix, Intercept, and Genfit. He receives royalties from Elsevier and Wolter Kluwers. Kavin Parmar: No conflicts to declare Ana Beatriz Oliveira: No conflicts to declare Vinay Jahagirdar: No conflicts to declare Ekaterina Smirnova: No conflicts to declare Data Availability Statement The analysis was performed on the TriN etX data analytics platform, and the results were downloaded from the platform. Upon acceptance of the manuscript, all of the relevant reports will be uploaded to GitHub. Code Availability Statement Statistical analytic codes used for the reported study are proprietary to Tri N etX and are publicly available to users of the platform. References Collaborators, N.C.D.C. NCD Countdown 2030: worldwide trends in non-communicable disease mortality and progress towards Sustainable Development Goal target 3.4. Lancet 392, 1072–1088 (2018). Chan, K.E., et al. The Spectrum and Impact of Metabolic Dysfunction in MAFLD: A Longitudinal Cohort Analysis of 32,683 Overweight and Obese Individuals. Clin Gastroenterol Hepatol 21, 2560–2569 e2515 (2023). Sanyal, A.J., et al. Nonalcoholic steatohepatitis: association of insulin resistance and mitochondrial abnormalities. Gastroenterology 120, 1183–1192 (2001). Sheka, A.C., et al. Nonalcoholic Steatohepatitis: A Review. Jama 323, 1175–1183 (2020). Cusi, K., et al. Metabolic Dysfunction-Associated Steatotic Liver Disease (MASLD) in People With Diabetes: The Need for Screening and Early Intervention. A Consensus Report of the American Diabetes Association. Diabetes care (2025). Sanyal, A.J., et al. Prospective Study of Outcomes in Adults with Nonalcoholic Fatty Liver Disease. The New England journal of medicine 385, 1559–1569 (2021). Shang, Y., et al. Metabolic Syndrome Traits Increase the Risk of Major Adverse Liver Outcomes in Type 2 Diabetes. Diabetes care 47, 978–985 (2024). Perkovic, V., et al. Effects of Semaglutide on Chronic Kidney Disease in Patients with Type 2 Diabetes. The New England journal of medicine 391, 109–121 (2024). Lincoff, A.M., et al. Semaglutide and Cardiovascular Outcomes in Obesity without Diabetes. The New England journal of medicine 389, 2221–2232 (2023). Jastreboff, A.M., et al. Tirzepatide for Obesity Treatment and Diabetes Prevention. The New England journal of medicine 392, 958–971 (2025). Abbott, B.P., et al. Prospects for observing and localizing gravitational-wave transients with Advanced LIGO, Advanced Virgo and KAGRA. Living reviews in relativity 21, 3 (2018). Kanwal, F., 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 184, 1314–1323 (2024). Sanyal, A.J., et al. A Phase 2 Randomized Trial of Survodutide in MASH and Fibrosis. The New England journal of medicine 391, 311–319 (2024). Sanyal, A.J., et al. Phase 3 Trial of Semaglutide in Metabolic Dysfunction-Associated Steatohepatitis. The New England journal of medicine 392, 2089–2099 (2025). Palchuk, M.B., et al. A global federated real-world data and analytics platform for research. JAMIA Open 6, ooad035 (2023). Loomba, R., et al. Semaglutide 2.4 mg once weekly in patients with non-alcoholic steatohepatitis-related cirrhosis: a randomised, placebo-controlled phase 2 trial. The lancet. Gastroenterology & hepatology 8, 511–522 (2023). Marso, S.P., et al. Liraglutide and Cardiovascular Outcomes in Type 2 Diabetes. The New England journal of medicine 375, 311–322 (2016). Arab, J.P., et al. High prevalence of undiagnosed liver cirrhosis and advanced fibrosis in type 2 diabetic patients. Ann Hepatol 15, 721–728 (2016). Yen, F.S., et al. The Risk of Severe Hypoglycemia and Mortality in Patients With Type 2 Diabetes and Discharged With Acute Liver Injury. Diabetes care 46, 20–27 (2023). Hodish, I. Insulin therapy, weight gain and prognosis. Diabetes, obesity & metabolism 20, 2085–2092 (2018). Marso, S.P., Holst, A.G. & Vilsboll, T. Semaglutide and Cardiovascular Outcomes in Patients with Type 2 Diabetes. The New England journal of medicine 376, 891–892 (2017). Strain, W.D., et al. Effects of Semaglutide on Stroke Subtypes in Type 2 Diabetes: Post Hoc Analysis of the Randomized SUSTAIN 6 and PIONEER 6. Stroke 53, 2749–2757 (2022). Chew, N.W.S., et al. The global burden of metabolic disease: Data from 2000 to 2019. Cell metabolism 35, 414–428 e413 (2023). Sung, H., et al. Colorectal cancer incidence trends in younger versus older adults: an analysis of population-based cancer registry data. The Lancet. Oncology 26, 51–63 (2025). Shaukat, A., et al. ACG Clinical Guidelines: Colorectal Cancer Screening 2021. The American journal of gastroenterology 116, 458–479 (2021). Zannad, F., Sanyal, A.J., Butler, J., Miller, V. & Harrison, S.A. Integrating liver endpoints in clinical trials of cardiovascular and kidney disease. Nature medicine 30, 2423–2431 (2024). Tables Table 1: Baseline characteristics for the study population for assessment of all-cause mortality Before propensity matching After propensity matching GLP-1 RA N= 305,328 Control N= 2,392,239 Standardized mean difference GLP-1 RA N= 215,108 Control N= 215,108 Standardized mean difference Age mean (SD) yrs 56 ± 10.8 53.6 ± 12 0.2086 55.5 ± 11.1 55.7 ± 11.1 0.0132 Sex (females) 174,739 (58.2%) 1,155,210 (50.6%) 0.1524 124,808 (58%) 123,727 (57.5%) 0.0102 Sex (males) 124,887 (41.6%) 1,122,428 (49.2%) 0.1534 90,104 (41.8%) 91,167 (42.3%) 0.0100 White 198,634 (66.2%) 1,381,692 (60.6%) 0.1163 140,297 (65.2%) 140,179 (65.1%) 0.0012 African-American 59,059 (19.6%) 491,015 (21.5%) 0.0459 43,667 (20.3%) 43,585 (20.2%) 0.0009 Hispanic 31,161 (10.3%) 279,743 (12.2%) 0.0595 21,733 (10.1%) 20,566 (9.5%) 0.0182 Asian 7,489 (2.4%) 67,256 (2.9%) 0.0279 5,337 (2.4%) 5,565 (2.5%) 0.0067 BMI, mean (SD) 37 ± 7.72 35.4 ± 7.35 0.2150 37.2 ± 7.81 34.7 ± 7.4 0.3364 Ischemic Heart Disease 70,473 (23.4%) 260,986 (11.4%) 0.3212 44,858 (20.8%) 54,334 (25.2%) 0.1047 CKD 52,556 (17.5%) 169,737 (7.4%) 0.3084 33,384 (15.5%) 33,637 (15.6%) 0.0032 Hypertension 243,524 (81.1%) 895,163 (39.2%) 0.9477 165,876 (77.1%) 165,603 (76.9%) 0.0030 Fatty liver Disease 57,733 (19.2%) 100,780 (4.4%) 0.4716 33,274 (15.4%) 31,101 (14.4%) 0.0283 NASH 10,472 (3.4%) 14,351 (0.6%) 0.2025 5,248 (2.4%) 4,699 (2.1%) 0.0170 Cirrhosis 10,368 (3.4%) 42,845 (1.8%) 0.0979 6,417 (2.9%) 5,868 (2.7%) 0.0153 Nicotine Dependence 33,008 (11%) 128,994 (5.6%) 0.1943 22,271 (10.3%) 22,420 (10.4%) 0.0023 Heart Failure 35,975 (11.9%) 140,262 (6.1%) 0.2043 23,943 (11.1%) 24,935 (11.5%) 0.0145 Stroke 14,724 (4.9%) 62,481 (2.7%) 0.1132 10,246 (4.7%) 10,134 (4.7%) 0.0025 Unstable Angina 7,850 (2.6%) 22,156 (0.9%) 0.1241 4,994 (2.3%) 4,993 (2.2%) 0.0019 Hemoglobin (gm/dl) Mean S.D. 13.6 ± 1.83 13.2 ± 2.19 0.2074 13.6 ± 1.82 13.2 ± 2.12 0.1635 Platelets (/mm3) 262 ± 77.8 252 ± 86.9 0.1177 263 ± 77.9 254 ± 83.9 0.1136 LDL-cholesterol (mg/dl) 87.4 ± 36.3 102 ± 38.7 0.3940 88.8 ± 35.8 101 ± 40.3 0.3082 Hb A1C (%) 7.52 ± 1.88 7.12 ± 1.91 0.2064 7.39 ± 1.89 6.96 ± 1.75 0.2330 AST (IU/l) 24.8 ± 18.5 31.4 ± 96.5 0.0951 24.8 ± 18 29.6 ± 74.9 0.0894 Bilirubin (mg/dl) 0.53 ± 0.38 0.65 ± 1.7 0.0964 0.53 ± 0.39 0.61 ± 1.41 0.0753 INR 1.11 ± 0.5 1.15 ± 0.472 0.0880 1.11 ± 0.474 1.15 ± 0.489 0.0707 Creatinine (mg/dl) 1.16 ± 5.37 1.28 ± 5.11 0.0222 1.16 ± 5.24 1.26 ± 4.73 0.0213 ALT (IU/l) 29.1 ± 25.3 35 ± 68.6 0.1135 29 ± 26.2 33.1 ± 53.6 0.0957 ALP (IU/l) 85.8 ± 38.8 91.8 ± 58 0.1198 85.4 ± 40.1 90.5 ± 52.2 0.1092 Abbreviations: LDL= low density lipoprotein , BMI= Body mass index, MASLD= Metabolic Dysfunction-Associated Steatotic Liver Disease, CKD= Chronic Kidney Disease INR= international normalized ratio, AST= aspartate aminotransferase , ALT= a lanine aminotransferase , ALP- a lkaline phosphatase SI conversion factors: To convert alanine aminotransferase to μkat/L, multiply by 0.0167; aspartate aminotransferase to μkat/L, multiply by 0.0167; bilirubin, total to μmol/L, multiply by 17.104; creatinine to μmol/L, multiply by 88.4; Data are presented as number and percent (%) unless otherwise indicated. Percentages may not sum up to 100% due to rounding. Also note that an individual may have more than one comorbidity. Table 2: Cancer rates in those who received a GLP-1 RA vs controls After 4 years of exposure After 8 years of exposure GLP-1 RA (%) N=93,413 Control (%) N=93,413 HR (95% CI) GLP-1 RA (%) N=20,661 Control (%) N=20,661 HR (95% CI) Breast cancer 0.4 0.5 0.8(0.7-0.92) 0.48 0.63 0.95(0.73-1.25) Ovarian cancer 0.05 0.11 0.56(0.39-0.79) 0.05 0.13 0.53(0.27-1.06) Endometrial cancer 0.13 0.2 0.8(0.63-1) 0.11 0.16 0.89(0.52-1.5) GI-Liver Cancers Esophageal cancer 0.03 0.05 0.64(0.4-1.02) - - - Gastric cancer 0.04 0.07 0.71(0.48-1.07) - - - Pancreatic cancer 0.09 0.23 0.49(0.38-0.63) 0.13 0.23 0.69(0.43-1.1) Colon cancer 0.2 0.28 0.82(0.68-1) 0.19 0.26 0.92(0.6-1.39) Rectal cancer 0.04 0.08 0.64(0.43-0.94) 0.04 0.08 0.74(0.34-1.64) Other Cancers Renal cell cancer 0.23 0.27 0.98(0.81-1.18) 0.2 0.3 0.81(0.54-1.23) Thyroid papillary cancer 0.11 0.14 0.98(0.75-1.26) 0.1 0.09 1.28(0.67-2.44) Prostate cancer 0.35 0.53 0.77(0.67-0.89) 0.28 0.63 0.56(0.41-0.78) Absolute rates of cumulative incidence are reported within two different time-frames (4 years and 8 years) of exposure. For g all bladder cancer at 4 and 8 years, the patient count was too small to report results, whereas for Esophageal and Gastric cancer at 8 years, the patient count was too small to report results. Additional Declarations Yes there is potential Competing Interest. Arun J. Sanyal: AJS has stock options in Tiziana, Rivus, Durect, and NorthSea. He has served as a paid consultant to Intercept, Genfit, Boehringer Ingelhiem, Eli Lilly, Novo Nordisk, Glaxo Smith Kline, Madrigal, Amgen, Genentech, Merck, Zydus, Astra Zeneca, Alnylam, Regeneron, Altimmune, Surrozen, Poxel, Hanmi, Akero Therapeutics, Boston Pharma, 89 Bio, Pliant, Chemomab, Salix, TARGET-MASH, Path AI, Histoindex. His institution receives funding from Avant Sante for consultation with him and has received grants from Novo Nordisk, Hanmi, 89 Bio, Madrigal, Gilead, Akero, Merck, Takeda, Salix, Intercept, and Genfit. He receives royalties from Elsevier and Wolter Kluwers. Kavin Parmar: No conflicts to declare Ana Beatriz Oliveira: No conflicts to declare Vinay Jahagirdar: No conflicts to declare Ekaterina Smirnova: No conflicts to declare Supplementary Files STROBEchecklistVJ.docx STROBE Statement—checklist of items that should be included in reports of observational studies SUPPLE1.docx Figure 1-5 Table 1-5 Cite Share Download PDF Status: Under Review Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8342646","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":574728552,"identity":"7a11f782-8a8e-4dc8-bfef-b2484e154b1e","order_by":0,"name":"Arun Sanyal","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABC0lEQVRIiWNgGAWjYHAD5sYHQDIByGhgJlILY7MBRAtjYzOxWtokiNLCP/uM2QOGP3Z5BscPtlXz1BzO42c/2P64gMEmX94BuxaJcznmBoxtycUGZxLbbvMcO1ws2ZPY2DyDIc1y4wEc1pzhMZNgbGBO3HAgse3mzIbDIEZjMw/DYQPDBuw65EFaGP7UJ244/7CtEKRl//mH+LUYgLWwAQ2/kdjG8BFkiwTUFnkc7jI8w1Ymkdh2PHHmjYfNEh+OpSfOuPGwcTaPQZqBAQ4tcmeYt0l8+FOd2Hc++eCHhBrrxP7+5AOfeSpsDORxOAwMEoBY4QCqg4HoABalyADTTLy2jIJRMApGwUgCAPm2Y0KycksBAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0001-8682-5748","institution":"Virginia Commonwealth University","correspondingAuthor":true,"prefix":"","firstName":"Arun","middleName":"","lastName":"Sanyal","suffix":""},{"id":574728553,"identity":"5c7f3bee-0903-4af1-8037-ca5853eedf67","order_by":1,"name":"Kavin Parmar","email":"","orcid":"https://orcid.org/0009-0001-3610-1343","institution":"Virginia Commonwealth University","correspondingAuthor":false,"prefix":"","firstName":"Kavin","middleName":"","lastName":"Parmar","suffix":""},{"id":574728554,"identity":"024bd2cc-c1a4-43f0-b1cf-e05399343e58","order_by":2,"name":"Ana Beatriz Souza de Oliveira","email":"","orcid":"https://orcid.org/0000-0003-2457-3439","institution":"Virginia Commonwealth University","correspondingAuthor":false,"prefix":"","firstName":"Ana","middleName":"Beatriz Souza","lastName":"de Oliveira","suffix":""},{"id":574728555,"identity":"16d3c65d-7fc0-4412-80fb-15233b41c3cb","order_by":3,"name":"Vinay Jahagirdar","email":"","orcid":"","institution":"Virginia Commonwealth University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Vinay","middleName":"","lastName":"Jahagirdar","suffix":""},{"id":574728556,"identity":"22a8d4aa-f56b-4e49-b438-a58b0cf86323","order_by":4,"name":"Ekaterina Smirnova","email":"","orcid":"","institution":"Virginia Commonwealth University Health System","correspondingAuthor":false,"prefix":"","firstName":"Ekaterina","middleName":"","lastName":"Smirnova","suffix":""}],"badges":[],"createdAt":"2025-12-12 07:05:08","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8342646/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8342646/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":103739100,"identity":"30a2fabb-ea80-4542-a0f9-77dbb8721283","added_by":"auto","created_at":"2026-03-02 10:37:19","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":182193,"visible":true,"origin":"","legend":"\u003cp\u003eA diagram to demonstrate how the study cohorts for analysis were constructed from the Tri NetX database.\u003c/p\u003e","description":"","filename":"image1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8342646/v1/91ba4047956dc4c9bc22ef01.jpg"},{"id":104400450,"identity":"5e1975e7-9563-474a-8e4a-9677cbf4984b","added_by":"auto","created_at":"2026-03-11 12:10:00","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":148715,"visible":true,"origin":"","legend":"\u003cp\u003eThe percent of individuals with T2DM and obesity experiencing key outcomes of interest is plotted as a function of the minimum duration of exposure to a GLP-1 RA with three additional years of follow-up. The dotted line and shaded area below it reflect the risk profile for propensity-matched controls, whereas the solid line and shaded area in blue represent the risk profile for those who received a GLP-1 RA. GLP-1 RA use was associated with a significant decrease in risk of mortality (panel A), major adverse cardiac events (MACE) (panel B), major adverse liver outcome (MALO), and obesity-associated cancers (panel D) at all time points analyzed (minimum exposure 2,4, 6, and 8 years). The corresponding hazard ratio and 95% CI in those receiving a GLP-1 RA for a minimum duration of 2 years \u0026nbsp;are shown on each panel.\u003c/p\u003e","description":"","filename":"image2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8342646/v1/4e1f4cd41d8b9650ed68718f.jpg"},{"id":103739098,"identity":"1439e7c6-c290-4b23-aada-77a0a0fac074","added_by":"auto","created_at":"2026-03-02 10:37:19","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":118273,"visible":true,"origin":"","legend":"\u003cp\u003eThe percent of individuals experiencing key outcomes of interest is plotted as a function of the minimum duration of exposure to a GLP-1 RA with three additional years of follow-up. The dotted line and shaded area below it reflect the risk profile for propensity-matched controls, whereas the solid line and shaded area in blue represent the risk profile for those who received a GLP-1 RA. The key outcomes measured were: Panels (A): unstable angina, (B) acute myocardial infarction (MI), (C) ST- elevation \u0026nbsp;MI (STEMI), (D) cerebral infarction, (E) heart failure, (F) coronary revascularization, (G) ascites, (H) spontaneous bacterial peritonitis (SBP), (I) hepatorenal syndrome, (J) Hepatic encephalopathy, (K) esophageal variceal hemorrhage (EVH) and (L) hepatocellular cancer. GLP-1 RA use improved all of these outcomes at the time points for the minimum duration of exposure (2, 4, 6, and 8 years). The corresponding hazard ratio and 95% CI in those receiving a GLP-1 RA for a minimum duration of 2 years are shown on each panel. Data for EVH are only plotted for up to 6 years due to the low number of actual events.\u003c/p\u003e","description":"","filename":"image3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8342646/v1/28180aa66c56cd2d2227bb04.jpg"},{"id":103739101,"identity":"5c4bfc20-8fd9-467d-850d-2d2177b13f1a","added_by":"auto","created_at":"2026-03-02 10:37:19","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":94658,"visible":true,"origin":"","legend":"\u003cp\u003eThe percent of individuals with T2DM and obesity who also had a diagnosis of cirrhosis prior to initiation of GLP-1 RA, experiencing key outcomes of interest, is plotted as a function of the minimum duration of exposure to a GLP-1 RA with three additional years of follow-up. The dotted line and shaded area below it reflect the risk profile for propensity-matched controls, whereas the solid line and shaded area in blue represent the risk profile for those who received a GLP-1 RA. GLP-1 RA use was associated with a significant decrease in risk of mortality (panel A), major adverse cardiac events (MACE) (panel B), major adverse liver outcomes (MALO), and obesity-associated cancers (panel D) at all time points analyzed (minimum exposure 2,4, 6, and 8 years). The corresponding hazard ratio and 95% CI in those receiving a GLP-1 RA for a minimum duration of 2 years are shown on each panel.\u003c/p\u003e","description":"","filename":"image4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8342646/v1/8b41c019584b6b52abef027e.jpg"},{"id":104407840,"identity":"2c8745b0-765b-4627-b7da-7519b4f37a43","added_by":"auto","created_at":"2026-03-11 12:40:23","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1753610,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8342646/v1/ef785679-bc89-4b69-a407-a27b640d9c57.pdf"},{"id":103739103,"identity":"572e116d-3d91-49cd-8376-dfa6e5a70372","added_by":"auto","created_at":"2026-03-02 10:37:20","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":88232,"visible":true,"origin":"","legend":"STROBE Statement\u0026#x2014;checklist of items that should be included in reports of observational studies","description":"","filename":"STROBEchecklistVJ.docx","url":"https://assets-eu.researchsquare.com/files/rs-8342646/v1/d5932dd9622e9db76b4e05f3.docx"},{"id":103739102,"identity":"d7c31271-9471-4247-a4f1-dca2fa687116","added_by":"auto","created_at":"2026-03-02 10:37:19","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":889316,"visible":true,"origin":"","legend":"Figure 1-5 Table 1-5","description":"","filename":"SUPPLE1.docx","url":"https://assets-eu.researchsquare.com/files/rs-8342646/v1/f487e49484d285a538355a83.docx"}],"financialInterests":"\u003cb\u003eYes\u003c/b\u003e there is potential Competing Interest.\nArun J. Sanyal: AJS has stock options in Tiziana, Rivus, Durect, and NorthSea. He has served as a paid consultant to Intercept, Genfit, Boehringer Ingelhiem, Eli Lilly, Novo Nordisk, Glaxo Smith Kline, Madrigal, Amgen, Genentech, Merck, Zydus, Astra Zeneca, Alnylam, Regeneron, Altimmune, Surrozen, Poxel, Hanmi, Akero Therapeutics, Boston Pharma, 89 Bio, Pliant, Chemomab, Salix, TARGET-MASH, Path AI, Histoindex. His institution receives funding from Avant Sante for consultation with him and has received grants from Novo Nordisk, Hanmi, 89 Bio, Madrigal, Gilead, Akero, Merck, Takeda, Salix, Intercept, and Genfit. He receives royalties from Elsevier and Wolter Kluwers.\r\n\r\nKavin Parmar:\t\tNo conflicts to declare\r\nAna Beatriz Oliveira:\tNo conflicts to declare\r\nVinay Jahagirdar:\t\tNo conflicts to declare\r\nEkaterina Smirnova:\tNo conflicts to declare","formattedTitle":"\u003cp\u003eGlucagon-like Peptide-1 Receptor Agonists Improve Liver and Metabolic Health Outcomes in Type 2 Diabetes and Obesity Regardless of Prior Liver Disease\u003c/p\u003e","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eNoncommunicable diseases (NCDs) account for about forty million deaths and seventy percent of premature deaths each year globally \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. Cardiovascular disease, type 2 diabetes mellitus (T2DM), hypertension, and cancers are well-recognized NCDs. It is, however, now recognized that NCDs also include additional conditions that are interlinked with these classical NCDs and impact the course of these conditions, such as metabolic dysfunction-associated steatotic liver disease (MASLD) \u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eMultiple NCDs like T2DM and MASLD are mechanistically linked by insulin resistance and are often concomitantly present in the same individual \u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. There is a collinear progression of these conditions; for example, those with more advanced MASLD with cirrhosis are more likely to both develop and have type 2 diabetes than those with early stages of MASLD \u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. These conditions also impact each other, and those with MASLD experience higher cardio-renal-metabolic outcomes and cancers, while those with a greater number of metabolic disorders are more likely to experience adverse liver-related outcomes \u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e,\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eGlucagon-like peptide-1 receptor agonists (GLP-1 RA) have transformed the care of T2DM and demonstrated improvement in mortality, glycemic control, and related cardio-renal-metabolic outcomes \u003csup\u003e\u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. There are currently limited data on major adverse liver outcomes (MALO)\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e, inclusive of ascites, overt encephalopathy, and variceal hemorrhage in broad populations with T2DM and the impact of GLP-1 RA on MALO in this population. In a study of US veterans, GLP-1 RA reduced mortality compared to those on DPP inhibitors, but no association with MALO was noted \u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. Further, the regulatory approval of GLP-1 RA was based on trials in populations enriched to have the outcome of interest and often excluded comorbidities that could impact outcomes \u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e,\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e, thus limiting the generalizability of the data.\u003c/p\u003e \u003cp\u003eIn order to fill these data gaps, we interrogated the TriN etX Global collaborative database, which includes individual patient-level data on over 200\u0026nbsp;million individuals longitudinally \u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. We evaluated the long-term outcomes in individuals with T2DM and obesity who received a GLP-1 RA and compared them to a propensity-matched control group that did not receive a GLP-1 RA. These provide a comprehensive assessment of the benefits versus risks of GLP-1 RA in a real-world clinical practice setting.\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cp\u003eA total of 3,307,931 adult individuals with T2DM and obesity were identified in the TriN\u0026nbsp; \u0026nbsp; \u0026nbsp;etX database \u003cstrong\u003e(Figure 1)\u003c/strong\u003e. \u0026nbsp;Of these, 795,273 individuals received one of several GLP-1 RAs, including Lixisenatide, Albiglutide, Exenatide, Liraglutide, Dulaglutide, Semaglutide, and Tirzepatide. \u0026nbsp; Those who remained on the same or a different GLP-1 RA for varying time intervals from 2-8 years were evaluated for the major outcomes of interest, including all-cause mortality, MACE, MALO, and cancers. \u0026nbsp;For the all-cause mortality analysis \u003cstrong\u003e(Table 1)\u003c/strong\u003e, the population included 66.2% Caucasians (n=198,634), 19.6% African Americans (n=59,059), 10.3% Hispanics (n=31,161), and 2.4% Asians (n=7,489). \u0026nbsp; \u0026nbsp;81.1% of individuals had hypertension, and 21.8% of individuals already had a diagnosis of a prior MACE event, while 17.5% had a prior history of CKD, and 2% had a prior history of a liver-related event. \u0026nbsp;The ICD-10 codes to identify individual events are provided in \u003cstrong\u003e\u003cem\u003eSupplemental Table 1\u003c/em\u003e\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eImpact on all-cause mortality\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn order to generate biologically plausible data and to assess the impact of varying durations of GLP-1 RA exposure on outcomes, we pre-specified various minimum durations of exposure (2-8 years) and measured outcomes over a 3-year period after that when continued GLP-1 use was not formally tracked. \u0026nbsp;The 3-year time frame was chosen because third-party payers often assess benefits of therapy within this time frame to make policy decisions related to access to care. In separate analyses, outcomes were measured during the time course of documented exposure only. \u0026nbsp;The data from both analyses were qualitatively similar and all data are presented using the former approach.\u003c/p\u003e\n\u003cp\u003eThe use of any GLP-1 RA, inclusive of all of the individual agents noted above, was associated with a decrease in all-cause mortality following a minimum period of exposure of 2 years \u003cstrong\u003e(Figure 1)\u003c/strong\u003e. \u0026nbsp; The absolute risk was 1.7 vs 5.4 % (Hazard Ratio (H.R.) with 95% CI: 0.38 (0.37-0.39), p\u0026lt; 0.0001). \u0026nbsp;The mortality rates remained relatively lower in those on a GLP-1 RA compared to controls, 2% vs 6.4% (H.R. 0.38, 0.34-0.42) even after 8 years of minimum exposure. The differences were statistically significant at all minimum durations of exposure studied. \u0026nbsp;These changes were further reflected in Kaplan-Meier analyses of time to death \u003cstrong\u003e\u003cem\u003e(Supplemental Figure 1)\u003c/em\u003e\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eImpact on major adverse cardiac events (MACE):\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFor those receiving any GLP-1 RA, 4.4% experienced MACE compared to 8.2% of controls after 2 years of minimum exposure (H.R. \u0026nbsp; 0.66 (0.65-0.68)) \u003cstrong\u003e(Figure 2, panel B)\u003c/strong\u003e. \u0026nbsp;GLP-1 RA reduced the risk of MACE at all durations of exposure studied. \u0026nbsp;The rates of MACE (GLP-1 vs control) were 4.9 vs 8.8%, 4.9 vs 9.2% and 4.9 vs 9.2% after 4, 6, and 8 years of minimum exposure, respectively, with a hazard ratio of 0.65-0.67 (P\u0026lt; 0.0001 at all time-points studied). Time-to-event analysis further demonstrated a prolongation of time to MACE with GLP-1 RA \u003cstrong\u003e(\u003cem\u003esupplemental Figure 2\u003c/em\u003e)\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure 3 panels A-F\u0026nbsp;\u003c/strong\u003edemonstrate the effects of GLP-1 RA on individual cardiovascular outcomes. \u0026nbsp;Those receiving any GLP-1 RA had a lower incidence of acute myocardial infarction, non-ST-elevated MI, cerebral infarction, unstable angina, heart failure, and coronary revascularization procedures. \u0026nbsp; As with all-cause mortality, the differences were statistically significant with 2 years of exposure and persisted at all durations of exposure studied. \u0026nbsp;Of note, those on GLP-1 RA had lower rates of both heart failure with preserved ejection fraction (2% vs 3.6%, (HR 0.7, 0.65-0.75) with 2 years of minimum exposure and 2.4% vs 4.8% (H.R. 0.61 0.54-0.69) with 8 years of minimum exposure) and with reduced ejection fraction (1.4% vs 2.5% (H.R. 0.7 (0.67-0.74) and 1.9% vs 3.3% (H.R. 0.68 (0.59-0.78) with at least 2 and 8 years of exposure respectively. These benefits were also reflected in Kaplan-Meier time to event analyses \u003cstrong\u003e\u003cem\u003e(Supplemental Figure 2)\u003c/em\u003e\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eImpact of major adverse liver outcomes (MALO):\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe use of GLP-1 RA in this broad population with obesity and T2DM without well-characterized underlying liver disease but without pre-existing hepatitis C or B or alcohol use disorder diagnoses was associated with a decrease in MALO at all time points \u003cstrong\u003e(Figure 2, panel C)\u003c/strong\u003e. The absolute risk and related H.R. with minimum exposure for at least 2 and 8 years were 0.77% vs 1.82 % (H.R: 0.51 (0.49-0.55)) and 1.08% vs 2.17 % (H.R.: 0.61 (0.52-0.72)) (p\u0026lt; 0.0001 at all time-points). The time to MALO was also longer in those on GLP-1 RA \u003cstrong\u003e(\u003cem\u003esupplemental Figure 3\u003c/em\u003e)\u003c/strong\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure 3 panels G-L\u003c/strong\u003e demonstrate the impact of GLP-1 RA on individual liver-related events. \u0026nbsp;GLP-1 RA reduced the incidence of ascites by approximately 50% with 2 years of minimum exposure (0.64% vs 1.56 % H.R. 0.5 (0.47-0.53)). \u0026nbsp;These benefits were maintained up to 8 years of minimum drug exposure (0.85% vs 1.93 %, H.R. 0.54 (0.45-0.64)). \u0026nbsp;These benefits were further reflected in the reduced incidence of spontaneous bacterial peritonitis and hepatorenal syndrome. \u0026nbsp;The incidence of hepatic encephalopathy was similarly reduced by GLP-1 RA, with an absolute risk of 0.15% vs 0.29% with at least 2 years of exposure and 0.23% vs 0.4% with a minimum exposure of 8 years. \u0026nbsp;The risk of hepatocellular cancer was also decreased in those receiving GLP-1 RA, even with a minimum exposure for 2 years (absolute risk: 0.14% vs 0.22%, H.R.: 0.83 (0.71-0.96)). The risks of variceal hemorrhage were also reduced in those with 2-6 years of GLP-1 RA exposure; however, there were too few events in the group with 8 years or more of GLP-1 RA exposure to compute meaningfully.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eImpact on the development of cancers:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThere was a numerical decrease in overall obesity-\u0026nbsp; \u0026nbsp; \u0026nbsp;related cancers \u003cstrong\u003e(Figure 2, panel D)\u003c/strong\u003e. \u0026nbsp;After four years of minimum exposure, the decreases in rates of ovarian, pancreatic, and rectal cancer were most pronounced. \u0026nbsp;However, in those with 8 years of minimum exposure, many of these effects were less pronounced and non-significant. \u0026nbsp;GLP-1 RA also reduced the incidence of prostate cancer. \u0026nbsp; Importantly, the rates of thyroid cancer were not increased in those exposed to GLP-1 RA.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eImpact on other metabolic and renal outcomes:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGLP-1 RA reduced the incidence of hypertension \u003cstrong\u003e(\u003cem\u003eSupplemental Table 2\u003c/em\u003e)\u003c/strong\u003e. \u0026nbsp;Those receiving GLP-1 RA had a higher rate of receiving a diagnosis of a diabetic ophthalmic complication (ICD10 E11.3, E11.51, E11.39, E11.311); however, overall blindness (ICD10 H54) was decreased (0.7 vs 1.05% H.R. 0.68 (0.62-0.76) at year 4 and 0.76% vs 1.1% H.R. 0.8 (0.68-1.04) after 8 years of minimum exposure). \u0026nbsp; A new diagnosis of CKD also decreased with a cumulative incidence of 5.8% in those on GLP-1 RA versus 7.9% in controls after 8 years of minimum exposure. \u0026nbsp;Neuropathy was slightly reduced in patients using GLP-1 RA (5.39% vs 6.54%, H.R. 0.98 (0.94-1.03), and 4.91% vs 6.95%, H.R. 0.88 (0.8-0.97) after 4 and 8 years of minimum exposure, respectively).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDifferential effects of individual GLP-1 RA:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe biological activity of individual GLP-1 RAs is variable, with more recently approved drugs having more data to support their cardio-renal-metabolic benefits. \u0026nbsp;Since these agents have only been used for a shorter time frame, the effects of individual agents in use in routine practice on all-cause mortality, MACE, MALO, and cancer incidence with data available after 2 years of minimum exposure are shown in \u003cstrong\u003e\u003cem\u003eSupplemental Figure 4\u003c/em\u003e\u003c/strong\u003e. \u0026nbsp;Tirzepatide, a GLP-1/GIP co-agonist, had the greatest numerical reduction of each of these at the end of 2 years of exposure, followed by semaglutide, while the effects of liraglutide, dulaglutide, and exenatide were similar but numerically lower than that noted for semaglutide and tirzepatide. \u0026nbsp; Exenatide did not significantly impact MACE or cancer risk.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eImpact of GLP-1 in those with T2DM and MASLD:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe long-term benefits of GLP-1 RA on MASLD-related outcomes are not well-established. \u0026nbsp;We therefore evaluated the effects of GLP-1 RA in those who also had a diagnosis of MASLD in addition to T2DM and obesity at the time of initiation of therapy and compared them to controls who did not receive GLP-1 RA \u003cstrong\u003e(\u003cem\u003eSupplemental Figure 5\u003c/em\u003e)\u003c/strong\u003e. GLP-1 RA had a substantial benefit on all-cause mortality (2.2% vs 6.24% with 8 years of minimum exposure, H.R.: 0.39, (0.31-0.5)). \u0026nbsp;It also improved MACE (5.32% vs 7.14%, H.R. 0.91 (0.74-1.11), MALO (1.5% vs 2.84%, H.R. 0.63 (0.46-0.85), and cancer (1.84% vs 3%, H.R. 0.71 (0.53-0.96)) with 8 years of minimum exposure. \u0026nbsp;The effects on individual MACE and MALO components are provided in \u003cstrong\u003e\u003cem\u003esupplemental Tables 3 and 4, respectively\u003c/em\u003e\u003c/strong\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eImpact on those with T2DM and cirrhosis:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA phase 2 trial of semaglutide in those with cirrhosis due to MASLD demonstrated its safety but was unable to demonstrate histological benefit \u003csup\u003e16\u003c/sup\u003e. \u0026nbsp;This study had a small sample size and was of short duration. \u0026nbsp;The potential effects of GLP-1 RA in those who already have cirrhosis remain to be fully established. \u0026nbsp;To address this knowledge gap, we evaluated the effects of GLP-1 RA in those with T2DM and Obesity who also had a diagnosis of cirrhosis but did not have hepatitis C, hepatitis B, Alcohol related liver disease, or Alcohol Use Disorder \u003cstrong\u003e(Figure 4)\u003c/strong\u003e. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe mortality in those with cirrhosis was higher than for the overall population. \u0026nbsp;GLP-1 RA had a major impact on all-cause mortality (6% vs 14% H.R. 0.45 (0.4-0.5) after 2 years of exposure. \u0026nbsp;It also had a major impact on MALO (6.33% vs 10.37%, H.R.:0.66 (0.58-0.76) with a minimum exposure of 2 years to the drug). \u0026nbsp;Benefits were also seen for MACE and all-cause cancer. \u0026nbsp;The impact on individual MALO components is provided in \u003cstrong\u003e\u003cem\u003esupplemental Table 5\u003c/em\u003e\u003c/strong\u003e. \u0026nbsp;Importantly, in this high-risk population, the rate of hepatocellular cancer was also lower in those receiving GLP-1 RA after 2 years of exposure. \u0026nbsp;There were too few individuals with cirrhosis and past 2 years of exposure to allow statistical comparisons.\u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eNCDs are the principal causes of mortality worldwide \u003csup\u003e1\u003c/sup\u003e. \u0026nbsp;GLP-1 RAs are a major class of drugs that have established benefits for mortality, MACE, glycemic control, and renal dysfunction in highly selected trial populations \u003csup\u003e8-10,17\u003c/sup\u003e. \u0026nbsp;The current study focused on a broad population of individuals receiving GLP-1 RA for T2DM and obesity. It demonstrated clinically meaningful benefit in real-world clinical practice, arguably a highly relevant setting for assessing the real value of drugs. The benefits range from mortality, cardiovascular disease, CKD, MALO, and cancers in this population. They further provide data on the safety of these agents with data on ophthalmic complications of T2DM and blindness, thyroid cancer, and pancreatitis. \u0026nbsp;These data have several important implications for the field.\u003c/p\u003e\n\u003cp\u003eFor clinicians, the broad benefits across multiple end-organ related outcomes in a large diabetic population not specifically selected to be enriched with one particular end-organ disease, such as heart failure or MASH, are both reassuring and clinically valuable. \u0026nbsp; These benefits across multiple disease states that are likely to be concomitantly present further strengthen the utility of GLP-1 RA as anchor-therapy for multiple key NCDs and have the potential to “bend the curve” of NCD-associated premature morbidity and mortality.\u003c/p\u003e\n\u003cp\u003eA key and novel finding is the robust reduction of MALO in the current study. \u0026nbsp;The ongoing ESSENCE trial of patients with histologically-defined MASH with stage 2 or 3 fibrosis will capture the effects of Semaglutide on liver-related events in this population \u003csup\u003e14\u003c/sup\u003e. \u0026nbsp;However, this is likely to take several years to complete, and prior experience from the REGENERATE trial indicates that even after five years of follow-up, reduced progression to cirrhosis is the only outcome that occurs in enough numbers to allow assessment of drug benefits. \u0026nbsp;Also, phase 3 trials of tirzepatide, survodutide, and retatrutide are all early in their life-cycle, and outcomes data will not be available for several years. \u0026nbsp;In this setting, the current data are particularly relevant and support the use of GLP-1 RA for MASH, especially given the recent approval of Semaglutide. \u0026nbsp; It is however important to note that these data do not permit response or benefit assessment at an individual patient level. \u0026nbsp;Therefore, it should not be construed to support the use of GLP-1 RA without baseline assessment of severity of liver disease and monitoring its course in those on treatment.\u003c/p\u003e\n\u003cp\u003eThe observed benefits of GLP-1 RA on MALO in those with cirrhosis are also important in the context of failure to demonstrate histological regression of cirrhosis or MASH resolution in a small phase 2 trial of Semaglutide, although it was generally well tolerated \u003csup\u003e16\u003c/sup\u003e. \u0026nbsp;This likely reflects the much larger population evaluated in the current study. \u0026nbsp; Many patients with MASH-associated cirrhosis have T2DM \u003csup\u003e5,18\u003c/sup\u003e. \u0026nbsp;The all-cause mortality, MACE, MALO, and HCC benefits in those with cirrhosis support their use for the treatment of T2DM in this population, which is also expected to reduce the use of insulin for glycemic control, which carries the potential for hypoglycemia and also weight gain \u003csup\u003e19,20\u003c/sup\u003e. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe current study also confirms the observed improvement in the risk of coronary events and stroke noted in prior trials \u003csup\u003e21,22\u003c/sup\u003e. \u0026nbsp;The current study further demonstrated a statistically significant decrease in heart failure with either reduced ejection fraction or preserved ejection fraction. \u0026nbsp;This could reflect the size and varying populations for this analysis versus prior clinical trials. Hypertension is a major contributor to cardiovascular and renal outcomes and disability-adjusted life-years lost \u003csup\u003e23\u003c/sup\u003e. \u0026nbsp;Another important finding of this study is the decrease in new-onset hypertension, which is likely to have contributed to the observed decrease in cardiovascular events and development of CKD.\u003c/p\u003e\n\u003cp\u003eThe current study demonstrated a decrease in the number of obesity-associated cancers, particularly in the gastrointestinal tract and liver especially up to four years of minimum exposure. \u0026nbsp;Recently, colorectal cancers have been diagnosed at earlier ages, leading guidelines for screening to be modified to start at age 45 years, even in normal risk individuals \u003csup\u003e24,25\u003c/sup\u003e. \u0026nbsp; The reduction of such cancers as well as esophageal, stomach, and gallbladder cancers is of significant public health relevance. \u0026nbsp;The dilution of the benefits on cancer incidence at time points past the 4 year mark may reflect the lower potency of older approved agents for whom long-term data were available only.\u003c/p\u003e\n\u003cp\u003eFor those designing clinical trials, particularly for MASH, these data provide a general estimate of the rates of outcomes that can be expected within 2-4 years in populations with the characteristics of the current study population and help in the design of such trials. Also, the reporting of outcomes related to many of the competing threats to life allows consideration of benefits and risks, which will facilitate the development of novel approaches for drug development targeting multiple NCDs, such as heart disease, CKD, and MASH, simultaneously, as is being considered by the MOSAIC group and reported previously \u003csup\u003e26\u003c/sup\u003e. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAnother key implication of the current data is support for greater access to GLP-1 RA in those with T2DM. \u0026nbsp;Third-party payers often expect a return on investment for payment for drugs within 3 years. The current study demonstrates that benefits accrue within 2 years of exposure, and the data on benefits across multiple end-organ outcomes should allow estimation of the actuarial benefits per patient treated and generate the evidence needed to support broader and easier access to GLP-1 RA. \u0026nbsp;It is also hoped that the current data will further fuel conversations between governmental and health policy agencies about the designation of GLP-1 RA as essential drugs to contain the current global pandemic of NCD-related morbidity and mortality.\u003c/p\u003e\n\u003cp\u003eThe current study has several strengths and some weaknesses. \u0026nbsp;The principal strengths are the large sample size, data from real-world practice, a broad population of individuals with T2DM, and reporting of outcomes across multiple organ systems, as well as safety reporting. \u0026nbsp;The principal weakness is the nature of real-world data which does not allow granular interrogation of outcomes at an individual level. \u0026nbsp;Further outcomes are assessed from coding data, which can be unreliable; however, as with other real-world data, this weakness is compensated for by the large amount of data and the tight confidence limits around the observed risk differences.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn summary, the current study robustly demonstrates the multifaceted benefits and safety of GLP-1 RA in patients with T2DM and obesity. \u0026nbsp; It provides novel data on the benefits of GLP-1 RA on liver-related outcomes and extends the benefit profile from those already known from clinical trials to include both macrovascular and microvascular complications of T2DM. \u0026nbsp;These data are expected to assist in clinical decision-making, future trial design and innovation, and to support greater access to such agents for those with T2DM and obesity.\u003c/p\u003e"},{"header":"MATERIALS AND METHODS","content":"\u003cp\u003eA retrospective analysis of the TriNetX Global collaborative database was performed to evaluate the long-term outcomes of individuals with T2DM and obesity who received GLP-1 RA therapy. \u0026nbsp; The analysis covered the period from 2010 to 2025. \u0026nbsp;The study was considered exempt from IRB review because it involved existing de-identified data for retrospective analysis. \u0026nbsp;The analyses were conducted from April to October 2025 by the investigators who are fully responsible for the veracity and integrity of the data and the contents of this manuscript. \u0026nbsp;While the work was supported by intramural resources of the Stravitz-Sanyal Institute at Virginia Commonwealth University School of Medicine, neither the institute nor the School of Medicine had any involvement in the design, conduct, \u0026nbsp; \u0026nbsp; \u0026nbsp;data analysis, and interpretation of this study. The study followed the\u0026nbsp;\u0026nbsp; \u0026nbsp; \u0026nbsp;Strengthening\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;the\u0026nbsp;\u0026nbsp; \u0026nbsp; \u0026nbsp;Reporting\u0026nbsp;of\u0026nbsp;\u0026nbsp; \u0026nbsp; \u0026nbsp;Observational \u0026nbsp; \u0026nbsp; \u0026nbsp;Studies\u0026nbsp;in\u0026nbsp;\u0026nbsp; \u0026nbsp; \u0026nbsp;Epidemiology\u0026nbsp;(STROBE) \u0026nbsp; \u0026nbsp; \u0026nbsp;guidelines (provided in supplemental materials).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThe TriNetX Data Resource:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe TriNetX platform is a data resource that includes anonymized individual patient-level data from multiple institutions, including over 212 million individuals across 120 major health care organizations,\u0026nbsp;\u0026nbsp; \u0026nbsp; \u0026nbsp;of which 117 million are from the US \u003csup\u003e15\u003c/sup\u003e. \u0026nbsp;For participating institutions, it provides access via secure portals and has built-in analytic capabilities that can be used to interrogate the data from these individuals. \u0026nbsp;These data analysis capabilities allow individual patient-level data analysis to inform population-level data reports.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStudy population:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe population of interest (test-group) included adults with T2DM and obesity who received a GLP-1 RA (ICD-10/RxNorm codes- E11 + TNX 9083 + ATC A10BJ or \u0026nbsp;RxNorm 2601723 or 60548 or 1991302 or 475968 or 1551291). Patients with a history of bariatric surgery (CPT- 1007385) were excluded. \u003cstrong\u003e(Figure 1)\u003c/strong\u003e A detailed list of ICD-10 codes used for this study to define the populations and outcomes is provided in \u003cem\u003eSupplemental Table 1\u003c/em\u003e. \u0026nbsp;For those who received a GLP-1 RA, ongoing GLP-1 RA use was identified by continued prescriptions for a GLP-1 RA with varying pre-specified durations of drug exposure from 2 to 8 years. \u0026nbsp;Outcomes were further analyzed over an additional 3 years from the last date of the prescription period. \u0026nbsp;The rationale for a minimum duration of exposure for 2 years was to have plausibility that the GLP-1 RA had time to affect underlying disease biology.\u003c/p\u003e\n\u003cp\u003eIndependent analyses were performed separately for those with T2DM and obesity without consideration of the presence of MASLD (K76.0 or K75.81) or cirrhosis (K74) and in those with T2DM who also carried a diagnosis of MASLD/MASH or cirrhosis before initiation of GLP-1 RA. \u0026nbsp;For analysis of liver outcomes and individuals with concomitant MASLD/MASH or cirrhosis, those with hepatitis C, Hepatitis B, Alcohol Use Disorder, or a diagnosis of Alcohol-associated hepatitis or liver disease were excluded.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eControl populations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData from the test group were compared with a propensity-matched control group using 1:1 propensity scores generated by greedy nearest-neighbor algorithms with a caliper width of 0.1 in the TriN\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;etX analytics systems. A standard mean difference of \u0026lt; 0.1 was considered a negligible difference\u003csup\u003e27\u003c/sup\u003e. \u0026nbsp;The covariates included demographics (age, race, gender), known comorbidity profile (hypertension, known heart disease, CKD, stroke, cirrhosis), body mass index (BMI), laboratory data (hemoglobin, bilirubin, AST, platelets, INR, creatinine, LDL-cholesterol), smoking status, and use of statins, SGLT2 inhibitors, and angiotensin receptor blockers. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEndpoints:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe first endpoint measured was all-cause mortality. Cardiovascular outcomes included a 5-point\u003c/p\u003e\n\u003cp\u003eMACE (acute myocardial infarction, cerebral infarction, \u0026nbsp; unstable angina, heart failure, coronary revascularisation procedures) (I21, I21.0, I21.1, I21.2, I21.3, I21.29, I21.11, I21.21, I21.01,\u0026nbsp;\u0026nbsp;I21.4, I21.A9, I63, I50, I20.0). Additional outcomes included new diagnosis of heart failure with reduced ejection fraction as well as heart failure with preserved ejection fraction (I50.2 or I50.3).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMajor adverse liver outcomes (MALO) were defined as a composite of acute variceal hemorrhage (ICD-10 185.01), overt ascites (R18), and hepatic encephalopathy (K76.82). \u0026nbsp;Hepatocellular cancer (C22) was measured separately. \u0026nbsp;Additional outcomes included spontaneous bacterial peritonitis and hepatorenal syndrome (K65.2 and K76.7). \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCancer-related outcomes included breast cancer, GI cancers (esophageal cancer, gastric cancer, gallbladder cancer, pancreatic cancer, colorectal cancer), ovarian and endometrial cancer, renal cell cancer, prostate cancer, and thyroid cancer (C50, C15, C16, C23, C25, C18, C20, C56, C54.1, C64, C65, C61, and C73). \u0026nbsp;Microangiopathic outcomes, including diabetes-related ophthalmic complications (E11.3 or E11.51 or E11.39 or E11.311) and blindness (H54), and neuropathy (G62 or E11.40) were also included. \u0026nbsp;Renal outcomes included CKD and renal replacement therapy (N18 and CPT- 1012740). \u0026nbsp;Safety-related outcomes of interest included thyroid medullary cancer, acute pancreatitis, acute cholecystitis, or cholecystectomy (C73, K85, K81.0, CPT- 1014153 or 47562 or SNOMED- 45595009). \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical plan:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThose who received any GLP-1 RA were compared to those who did not. \u0026nbsp;Also, a sensitivity analysis of data for the individual approved GLP-1 RA was performed. \u0026nbsp; Outcomes were measured up to a fixed time point (3 years) after the minimum duration of exposure\u0026nbsp;\u0026nbsp; \u0026nbsp; \u0026nbsp;requirement was satisfied (2, 4, 6, and 8 years) and hazard ratios with 95% confidence limits were computed and reported. The absolute outcome rates were also reported alongside, since hazard ratios do not provide an estimate of absolute risk. \u0026nbsp;For time-to-event analyses, proportional hazards models were used with censoring and accounting of confounding factors, and hazard ratios with 95% CI were provided. \u0026nbsp; Groups were compared using the log-rank test. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFor all-cause mortality, the entire population, regardless of prior events, was included. \u0026nbsp;For analysis of other outcomes, those who had experienced the outcome prior to initiation of the GLP-1 RA were excluded, and only new-\u0026nbsp; \u0026nbsp; \u0026nbsp;onset of events were computed to provide data on the impact of GLP-1 RA on the incidence of these outcomes.\u003c/p\u003e\n\u003cp\u003eSignificance was set at p\u0026lt; 0.001 for all-cause mortality, MACE, MALO, any cancer, and CKD. \u0026nbsp;This statistical bar was deliberately set at a high level to increase certainty, i.e., confidence in the interpretation of the data due to potential “noise” in EMR data. \u0026nbsp;Also, we agreed internally at the initiation of data analysis that we would proceed with assessment of statistical significance for all-cause mortality, then sequentially to MACE, followed by MALO, and then any cancer with this fixed significance level if it met significance for mortality, followed by each sequential outcome. \u0026nbsp;This was done to minimize over-interpretation due to multiple comparisons. \u0026nbsp;If any outcome in this sequence did not meet our agreed-upon threshold, the remaining downstream data would only report significance nominally and provide OR with confidence limits.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eReferences unique to the methods section:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e27. Austin P.C. \u0026nbsp; \u0026nbsp; \u0026nbsp;Optimal caliper widths for propensity-score matching when estimating differences in means and differences in proportions in observational studies. \u0026nbsp;Statistics in Medicine, 2011, 30.1:150-161.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLDL\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003elow density lipoprotein\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eBMI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eBody mass index\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMASLD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMetabolic Dysfunction-Associated Steatotic Liver Disease\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCKD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eChronic Kidney Disease INR=international normalized ratio\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAST\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003easpartate aminotransferase\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eALT\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ealanine aminotransferase\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eALP- alkaline phosphatase\u003c/div\u003e \u003cdiv class=\"Description\"\u003e\u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eACKNOWLEDGEMENTS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eN/A\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAUTHOR CONTRIBUTIONS\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"623\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eConcept\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDesign\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eImplementation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAnalysis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eInterpretation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCritical Review\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eK.P.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e√\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e√\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e√\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e√\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e√\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e√\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eA.B.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e√\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e√\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e√\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e√\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eV.J.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e√\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e√\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eE.S.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e√\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e√\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e√\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eA.J.S.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e√\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e√\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e√\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e√\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e√\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of Interest:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eArun J. Sanyal:\u003c/strong\u003e\u0026nbsp; \u0026nbsp; AJS has stock options in Tiziana, Rivus, Durect, and NorthSea. \u0026nbsp;He has served as a paid consultant to Intercept, Genfit, Boehringer Ingelhiem, Eli Lilly, Novo Nordisk, Glaxo Smith Kline, Madrigal, Amgen, Genentech, Merck, Zydus, Astra Zeneca, Alnylam, Regeneron, Altimmune, Surrozen, Poxel, Hanmi, Akero Therapeutics, Boston Pharma, 89 Bio, Pliant, Chemomab, Salix, TARGET-MASH, Path AI, Histoindex. His institution receives funding from Avant Sante for consultation with him and has received grants from Novo Nordisk, Hanmi, 89 Bio, Madrigal, Gilead, Akero, Merck, Takeda, Salix, Intercept, and Genfit. He receives royalties from Elsevier and Wolter Kluwers.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eKavin Parmar:\u003c/strong\u003e No conflicts to declare\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAna Beatriz Oliveira:\u003c/strong\u003e No conflicts to declare\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eVinay Jahagirdar:\u003c/strong\u003e No conflicts to declare\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEkaterina Smirnova:\u003c/strong\u003e No conflicts to declare\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eData Availability Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe analysis was performed on the TriN\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;etX data analytics platform, and the results were downloaded from the platform. \u0026nbsp;Upon acceptance of the manuscript, all of the relevant reports will be uploaded to GitHub.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCode Availability Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eStatistical analytic codes used for the reported study are proprietary to Tri \u003cspan lang=\"EN-US\"\u003eN\u003c/span\u003eetX and are publicly available to users of the platform.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eCollaborators, N.C.D.C. NCD Countdown 2030: worldwide trends in non-communicable disease mortality and progress towards Sustainable Development Goal target 3.4. \u003cem\u003eLancet\u003c/em\u003e 392, 1072\u0026ndash;1088 (2018).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChan, K.E., \u003cem\u003eet al.\u003c/em\u003e The Spectrum and Impact of Metabolic Dysfunction in MAFLD: A Longitudinal Cohort Analysis of 32,683 Overweight and Obese Individuals. \u003cem\u003eClin Gastroenterol Hepatol\u003c/em\u003e 21, 2560\u0026ndash;2569 e2515 (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSanyal, A.J., \u003cem\u003eet al.\u003c/em\u003e Nonalcoholic steatohepatitis: association of insulin resistance and mitochondrial abnormalities. \u003cem\u003eGastroenterology\u003c/em\u003e 120, 1183\u0026ndash;1192 (2001).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSheka, A.C., \u003cem\u003eet al.\u003c/em\u003e Nonalcoholic Steatohepatitis: A Review. \u003cem\u003eJama\u003c/em\u003e 323, 1175\u0026ndash;1183 (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCusi, K., \u003cem\u003eet al.\u003c/em\u003e Metabolic Dysfunction-Associated Steatotic Liver Disease (MASLD) in People With Diabetes: The Need for Screening and Early Intervention. A Consensus Report of the American Diabetes Association. \u003cem\u003eDiabetes care\u003c/em\u003e (2025).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSanyal, A.J., \u003cem\u003eet al.\u003c/em\u003e Prospective Study of Outcomes in Adults with Nonalcoholic Fatty Liver Disease. \u003cem\u003eThe New England journal of medicine\u003c/em\u003e 385, 1559\u0026ndash;1569 (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShang, Y., \u003cem\u003eet al.\u003c/em\u003e Metabolic Syndrome Traits Increase the Risk of Major Adverse Liver Outcomes in Type 2 Diabetes. \u003cem\u003eDiabetes care\u003c/em\u003e 47, 978\u0026ndash;985 (2024).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePerkovic, V., \u003cem\u003eet al.\u003c/em\u003e Effects of Semaglutide on Chronic Kidney Disease in Patients with Type 2 Diabetes. \u003cem\u003eThe New England journal of medicine\u003c/em\u003e 391, 109\u0026ndash;121 (2024).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLincoff, A.M., \u003cem\u003eet al.\u003c/em\u003e Semaglutide and Cardiovascular Outcomes in Obesity without Diabetes. \u003cem\u003eThe New England journal of medicine\u003c/em\u003e 389, 2221\u0026ndash;2232 (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJastreboff, A.M., \u003cem\u003eet al.\u003c/em\u003e Tirzepatide for Obesity Treatment and Diabetes Prevention. \u003cem\u003eThe New England journal of medicine\u003c/em\u003e 392, 958\u0026ndash;971 (2025).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAbbott, B.P., \u003cem\u003eet al.\u003c/em\u003e Prospects for observing and localizing gravitational-wave transients with Advanced LIGO, Advanced Virgo and KAGRA. \u003cem\u003eLiving reviews in relativity\u003c/em\u003e 21, 3 (2018).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKanwal, F., \u003cem\u003eet al.\u003c/em\u003e GLP-1 Receptor Agonists and Risk for Cirrhosis and Related Complications in Patients With Metabolic Dysfunction-Associated Steatotic Liver Disease. \u003cem\u003eJAMA Intern Med\u003c/em\u003e 184, 1314\u0026ndash;1323 (2024).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSanyal, A.J., \u003cem\u003eet al.\u003c/em\u003e A Phase 2 Randomized Trial of Survodutide in MASH and Fibrosis. \u003cem\u003eThe New England journal of medicine\u003c/em\u003e 391, 311\u0026ndash;319 (2024).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSanyal, A.J., \u003cem\u003eet al.\u003c/em\u003e Phase 3 Trial of Semaglutide in Metabolic Dysfunction-Associated Steatohepatitis. \u003cem\u003eThe New England journal of medicine\u003c/em\u003e 392, 2089\u0026ndash;2099 (2025).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePalchuk, M.B., \u003cem\u003eet al.\u003c/em\u003e A global federated real-world data and analytics platform for research. \u003cem\u003eJAMIA Open\u003c/em\u003e 6, ooad035 (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLoomba, R., \u003cem\u003eet al.\u003c/em\u003e Semaglutide 2.4 mg once weekly in patients with non-alcoholic steatohepatitis-related cirrhosis: a randomised, placebo-controlled phase 2 trial. \u003cem\u003eThe lancet. Gastroenterology \u0026amp; hepatology\u003c/em\u003e 8, 511\u0026ndash;522 (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMarso, S.P., \u003cem\u003eet al.\u003c/em\u003e Liraglutide and Cardiovascular Outcomes in Type 2 Diabetes. \u003cem\u003eThe New England journal of medicine\u003c/em\u003e 375, 311\u0026ndash;322 (2016).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eArab, J.P., \u003cem\u003eet al.\u003c/em\u003e High prevalence of undiagnosed liver cirrhosis and advanced fibrosis in type 2 diabetic patients. \u003cem\u003eAnn Hepatol\u003c/em\u003e 15, 721\u0026ndash;728 (2016).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYen, F.S., \u003cem\u003eet al.\u003c/em\u003e The Risk of Severe Hypoglycemia and Mortality in Patients With Type 2 Diabetes and Discharged With Acute Liver Injury. \u003cem\u003eDiabetes care\u003c/em\u003e 46, 20\u0026ndash;27 (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHodish, I. Insulin therapy, weight gain and prognosis. \u003cem\u003eDiabetes, obesity \u0026amp; metabolism\u003c/em\u003e 20, 2085\u0026ndash;2092 (2018).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMarso, S.P., Holst, A.G. \u0026amp; Vilsboll, T. Semaglutide and Cardiovascular Outcomes in Patients with Type 2 Diabetes. \u003cem\u003eThe New England journal of medicine\u003c/em\u003e 376, 891\u0026ndash;892 (2017).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStrain, W.D., \u003cem\u003eet al.\u003c/em\u003e Effects of Semaglutide on Stroke Subtypes in Type 2 Diabetes: Post Hoc Analysis of the Randomized SUSTAIN 6 and PIONEER 6. \u003cem\u003eStroke\u003c/em\u003e 53, 2749\u0026ndash;2757 (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChew, N.W.S., \u003cem\u003eet al.\u003c/em\u003e The global burden of metabolic disease: Data from 2000 to 2019. \u003cem\u003eCell metabolism\u003c/em\u003e 35, 414\u0026ndash;428 e413 (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSung, H., \u003cem\u003eet al.\u003c/em\u003e Colorectal cancer incidence trends in younger versus older adults: an analysis of population-based cancer registry data. \u003cem\u003eThe Lancet. Oncology\u003c/em\u003e 26, 51\u0026ndash;63 (2025).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShaukat, A., \u003cem\u003eet al.\u003c/em\u003e ACG Clinical Guidelines: Colorectal Cancer Screening 2021. \u003cem\u003eThe American journal of gastroenterology\u003c/em\u003e 116, 458\u0026ndash;479 (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZannad, F., Sanyal, A.J., Butler, J., Miller, V. \u0026amp; Harrison, S.A. Integrating liver endpoints in clinical trials of cardiovascular and kidney disease. \u003cem\u003eNature medicine\u003c/em\u003e 30, 2423\u0026ndash;2431 (2024).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1:\u003c/strong\u003e \u003cstrong\u003eBaseline characteristics for the study population for assessment of all-cause mortality\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"604\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 262px;\"\u003e\n \u003cp\u003eBefore propensity matching\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 229px;\"\u003e\n \u003cp\u003eAfter propensity matching\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003eGLP-1 RA\u003c/p\u003e\n \u003cp\u003eN=\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e305,328\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003eControl\u003c/p\u003e\n \u003cp\u003eN=\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e2,392,239\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003eStandardized mean difference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003eGLP-1 RA\u003c/p\u003e\n \u003cp\u003eN=\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e215,108\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003eControl\u003c/p\u003e\n \u003cp\u003eN=\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e215,108\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003eStandardized mean difference\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003eAge mean (SD) yrs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e56 \u0026plusmn; 10.8\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e53.6 \u0026plusmn; 12\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e0.2086\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e55.5 \u0026plusmn; 11.1\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e55.7 \u0026plusmn; 11.1\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e0.0132\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003eSex (females)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e174,739\u003c/p\u003e\n \u003cp\u003e(58.2%)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e1,155,210\u003c/p\u003e\n \u003cp\u003e(50.6%)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e0.1524\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e124,808\u003c/p\u003e\n \u003cp\u003e(58%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e123,727\u003c/p\u003e\n \u003cp\u003e(57.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e0.0102\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003eSex (males)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e124,887\u003c/p\u003e\n \u003cp\u003e(41.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e1,122,428\u003c/p\u003e\n \u003cp\u003e(49.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e0.1534\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e90,104\u003c/p\u003e\n \u003cp\u003e(41.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e91,167\u003c/p\u003e\n \u003cp\u003e(42.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e0.0100\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003eWhite\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e198,634\u003c/p\u003e\n \u003cp\u003e(66.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e1,381,692\u003c/p\u003e\n \u003cp\u003e(60.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e0.1163\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e140,297\u003c/p\u003e\n \u003cp\u003e(65.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e140,179\u003c/p\u003e\n \u003cp\u003e(65.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e0.0012\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003eAfrican-American\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e59,059\u003c/p\u003e\n \u003cp\u003e(19.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e491,015\u003c/p\u003e\n \u003cp\u003e(21.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e0.0459\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e43,667\u003c/p\u003e\n \u003cp\u003e(20.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e43,585\u003c/p\u003e\n \u003cp\u003e(20.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e0.0009\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003eHispanic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e31,161\u003c/p\u003e\n \u003cp\u003e(10.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e279,743\u003c/p\u003e\n \u003cp\u003e(12.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e0.0595\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e21,733\u003c/p\u003e\n \u003cp\u003e(10.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e20,566\u003c/p\u003e\n \u003cp\u003e(9.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e0.0182\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003eAsian\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e7,489\u003c/p\u003e\n \u003cp\u003e(2.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e67,256\u003c/p\u003e\n \u003cp\u003e(2.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e0.0279\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e5,337\u003c/p\u003e\n \u003cp\u003e(2.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e5,565\u003c/p\u003e\n \u003cp\u003e(2.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e0.0067\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003eBMI, mean (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e37 \u0026plusmn; 7.72\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e35.4 \u0026plusmn; 7.35\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e0.2150\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e37.2 \u0026plusmn; 7.81\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e34.7 \u0026plusmn; 7.4\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e0.3364\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003eIschemic Heart Disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e70,473\u003c/p\u003e\n \u003cp\u003e(23.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e260,986\u003c/p\u003e\n \u003cp\u003e(11.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e0.3212\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e44,858\u003c/p\u003e\n \u003cp\u003e(20.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e54,334\u003c/p\u003e\n \u003cp\u003e(25.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e0.1047\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003eCKD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e52,556\u003c/p\u003e\n \u003cp\u003e(17.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e169,737\u003c/p\u003e\n \u003cp\u003e(7.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e0.3084\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e33,384\u003c/p\u003e\n \u003cp\u003e(15.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e33,637\u003c/p\u003e\n \u003cp\u003e(15.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e0.0032\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003eHypertension\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e243,524\u003c/p\u003e\n \u003cp\u003e(81.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e895,163\u003c/p\u003e\n \u003cp\u003e(39.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e0.9477\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e165,876\u003c/p\u003e\n \u003cp\u003e(77.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e165,603\u003c/p\u003e\n \u003cp\u003e(76.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e0.0030\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003eFatty liver Disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e57,733\u003c/p\u003e\n \u003cp\u003e(19.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e100,780\u003c/p\u003e\n \u003cp\u003e(4.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e0.4716\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e33,274\u003c/p\u003e\n \u003cp\u003e(15.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e31,101\u003c/p\u003e\n \u003cp\u003e(14.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e0.0283\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003eNASH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e10,472\u003c/p\u003e\n \u003cp\u003e(3.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e14,351\u003c/p\u003e\n \u003cp\u003e(0.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e0.2025\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e5,248\u003c/p\u003e\n \u003cp\u003e(2.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e4,699\u003c/p\u003e\n \u003cp\u003e(2.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e0.0170\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003eCirrhosis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e10,368\u003c/p\u003e\n \u003cp\u003e(3.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e42,845\u003c/p\u003e\n \u003cp\u003e(1.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e0.0979\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e6,417\u003c/p\u003e\n \u003cp\u003e(2.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e5,868\u003c/p\u003e\n \u003cp\u003e(2.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e0.0153\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003eNicotine Dependence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e33,008\u003c/p\u003e\n \u003cp\u003e(11%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e128,994\u003c/p\u003e\n \u003cp\u003e(5.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e0.1943\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e22,271\u003c/p\u003e\n \u003cp\u003e(10.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e22,420\u003c/p\u003e\n \u003cp\u003e(10.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e0.0023\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003eHeart Failure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e35,975 (11.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e140,262 (6.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e0.2043\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e23,943 (11.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e24,935 (11.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e0.0145\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003eStroke\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e14,724\u003c/p\u003e\n \u003cp\u003e(4.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e62,481\u003c/p\u003e\n \u003cp\u003e(2.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e0.1132\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e10,246\u003c/p\u003e\n \u003cp\u003e(4.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e10,134\u003c/p\u003e\n \u003cp\u003e(4.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e0.0025\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003eUnstable Angina\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e7,850 (2.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e22,156 (0.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e0.1241\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e4,994 (2.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e4,993 (2.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e0.0019\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003eHemoglobin (gm/dl)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;Mean S.D.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e13.6 \u0026plusmn; 1.83\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e13.2 \u0026plusmn; 2.19\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e0.2074\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e13.6 \u0026plusmn; 1.82\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e13.2 \u0026plusmn; 2.12\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e0.1635\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003ePlatelets (/mm3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e262 \u0026plusmn; 77.8\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e252 \u0026plusmn; 86.9\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e0.1177\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e263 \u0026plusmn; 77.9\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e254 \u0026plusmn; 83.9\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e0.1136\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003eLDL-cholesterol (mg/dl)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e87.4 \u0026plusmn; 36.3\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e102 \u0026plusmn; 38.7\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e0.3940\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e88.8 \u0026plusmn; 35.8\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e101 \u0026plusmn; 40.3\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e0.3082\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003eHb A1C (%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e7.52 \u0026plusmn; 1.88\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e7.12 \u0026plusmn; 1.91\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e0.2064\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e7.39 \u0026plusmn; 1.89\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e6.96 \u0026plusmn; 1.75\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e0.2330\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003eAST (IU/l)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e24.8 \u0026plusmn; 18.5\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e31.4 \u0026plusmn; 96.5\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e0.0951\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e24.8 \u0026plusmn; 18\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e29.6 \u0026plusmn; 74.9\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e0.0894\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003eBilirubin (mg/dl)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e0.53 \u0026plusmn; 0.38\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e0.65 \u0026plusmn; 1.7\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e0.0964\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e0.53 \u0026plusmn; 0.39\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e0.61 \u0026plusmn; 1.41\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e0.0753\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003eINR\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e1.11 \u0026plusmn; 0.5\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e1.15 \u0026plusmn; 0.472\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e0.0880\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e1.11 \u0026plusmn; 0.474\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e1.15 \u0026plusmn; 0.489\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e0.0707\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003eCreatinine (mg/dl)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e1.16 \u0026plusmn; 5.37\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e1.28 \u0026plusmn; 5.11\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e0.0222\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e1.16 \u0026plusmn; 5.24\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e1.26 \u0026plusmn; 4.73\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e0.0213\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003eALT (IU/l)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e29.1 \u0026plusmn; 25.3\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e35 \u0026plusmn; 68.6\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e0.1135\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e29 \u0026plusmn; 26.2\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e33.1 \u0026plusmn; 53.6\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e0.0957\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003eALP (IU/l)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e85.8 \u0026plusmn; 38.8\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e91.8 \u0026plusmn; 58\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e0.1198\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e85.4 \u0026plusmn; 40.1\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e90.5 \u0026plusmn; 52.2\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e0.1092\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAbbreviations: \u0026nbsp;LDL= low density lipoprotein , BMI= Body mass index, MASLD= Metabolic Dysfunction-Associated Steatotic Liver Disease, CKD= Chronic Kidney Disease INR= international normalized ratio, AST= aspartate aminotransferase \u0026nbsp; \u0026nbsp; , ALT= \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003cspan lang=\"EN-US\"\u003ea\u003c/span\u003elanine \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003cspan lang=\"EN-US\"\u003eaminotransferase\u003c/span\u003e, ALP- \u003cspan lang=\"EN-US\"\u003ea\u003c/span\u003elkaline \u003cspan lang=\"EN-US\"\u003ephosphatase\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eSI conversion factors: \u0026nbsp; To convert alanine aminotransferase to \u0026mu;kat/L, multiply by 0.0167; aspartate aminotransferase to \u0026mu;kat/L, multiply by 0.0167; bilirubin, total to \u0026mu;mol/L, multiply by 17.104; creatinine to \u0026mu;mol/L, multiply by 88.4;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eData are presented as number and percent (%) unless otherwise indicated. \u0026nbsp;Percentages may not sum up to 100% due to rounding. \u0026nbsp;Also note that an individual may have more than one comorbidity.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2: \u0026nbsp;Cancer rates in those who received a GLP-1 RA vs controls\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"622\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 271px;\"\u003e\n \u003cp\u003eAfter 4 years of exposure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 252px;\"\u003e\n \u003cp\u003eAfter 8 years of exposure\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGLP-1 RA (%)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eN=93,413\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eControl (%)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eN=93,413\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHR (95% CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGLP-1 RA (%)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eN=20,661\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eControl (%)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eN=20,661\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 97px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHR (95% CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003eBreast cancer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e0.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e0.8(0.7-0.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e0.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e0.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 97px;\"\u003e\n \u003cp\u003e0.95(0.73-1.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003eOvarian cancer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e0.56(0.39-0.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 97px;\"\u003e\n \u003cp\u003e0.53(0.27-1.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003eEndometrial cancer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e0.8(0.63-1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 97px;\"\u003e\n \u003cp\u003e0.89(0.52-1.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" style=\"width: 622px;\"\u003e\n \u003cp\u003eGI-Liver Cancers\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003eEsophageal cancer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e0.64(0.4-1.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 97px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003eGastric cancer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e0.71(0.48-1.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 97px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003ePancreatic cancer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e0.49(0.38-0.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 97px;\"\u003e\n \u003cp\u003e0.69(0.43-1.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003eColon cancer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e0.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e0.82(0.68-1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e0.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 97px;\"\u003e\n \u003cp\u003e0.92(0.6-1.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003eRectal cancer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e0.64(0.43-0.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 97px;\"\u003e\n \u003cp\u003e0.74(0.34-1.64)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" style=\"width: 622px;\"\u003e\n \u003cp\u003eOther Cancers\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003eRenal cell cancer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e0.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e0.98(0.81-1.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 97px;\"\u003e\n \u003cp\u003e0.81(0.54-1.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003eThyroid papillary cancer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e0.98(0.75-1.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 97px;\"\u003e\n \u003cp\u003e1.28(0.67-2.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003eProstate cancer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e0.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e0.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e0.77(0.67-0.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e0.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e0.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 97px;\"\u003e\n \u003cp\u003e0.56(0.41-0.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAbsolute rates of cumulative incidence are reported within\u0026nbsp;\u003cspan lang=\"EN-US\"\u003etwo\u0026nbsp;\u003c/span\u003edifferent time-frames (4 years and 8 years) of exposure. For\u0026nbsp;\u003cspan lang=\"EN-US\"\u003eg\u003c/span\u003eall \u0026nbsp; \u0026nbsp; \u0026nbsp; bladder cancer at 4 and 8 years, the patient count was too small to report results, whereas for Esophageal and Gastric cancer at 8 years, the patient count was too small to report results.\u0026nbsp;\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"nature-portfolio","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"","title":"Nature Portfolio","twitterHandle":"","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"ejp","reportingPortfolio":"","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"nonalcoholic fatty liver disease, metabolic dysfunction associated steatotic liver disease, metabolic dysfunction associated steatohepatitis, cirrhosis, type 2 diabetes mellitus, obesity, GLP-1 receptor agonists, Semaglutide, Tirzepatide, macrovascular, microangiopathic, renal, major adverse liver outcomes, mortality, cardio-renal-metabolic outcomes, real-world data","lastPublishedDoi":"10.21203/rs.3.rs-8342646/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8342646/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eMetabolic dysfunction- associated steatotic liver disease (MASLD) is a common comorbidity in obesity and type 2 diabetes mellitus (T2DM), which are treated with glucagon-like peptide-1 receptor agonists (GLP-1 RA). The benefits of GLP-1 RA on major adverse liver outcomes (MALO) in this population are not well established. We retrospectively evaluated the impact of GLP-1 RA, compared to propensity-matched controls, on mortality, MALO, major adverse cardiac events (MACE), and obesity- related cancers in obese adults with T2DM using the Tri NetX global collaborative database. GLP-1 RA decreased all-cause mortality (1.7 vs 5.4% H.R. (95% CI) 0.38, 0.37\u0026ndash;0.39 at 2 years), MACE (4.4 vs 8.2% H.R. 0.66, 0.65\u0026ndash;0.68), MALO (0.7 vs 1.8% H.R. 0.5, 0.49\u0026ndash;0.55) and obesity-related cancers (1.5 vs 2.2%, H.R. 0.85, 0.81\u0026ndash;0.89) and at all durations of exposure studied. GLP-1 RA improved the rates of each of the MACE components (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001 for each). GLP-1 RA reduced ascites, hepatic encephalopathy, variceal hemorrhage, and hepatocellular cancer (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01 for all outcomes). These benefits were confirmed in those who also had a diagnosis of either MASLD or cirrhosis prior to the initiation of GLP-1 RA. These data demonstrate the liver and metabolic health benefits of GLP-RAs and support access to these agents.\u003c/p\u003e \u003cp\u003eWord count 199\u003c/p\u003e","manuscriptTitle":"Glucagon-like Peptide-1 Receptor Agonists Improve Liver and Metabolic Health Outcomes in Type 2 Diabetes and Obesity Regardless of Prior Liver Disease","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-02 10:37:14","doi":"10.21203/rs.3.rs-8342646/v1","editorialEvents":[],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"communications-medicine","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"commsmed","sideBox":"Learn more about [Communications Medicine](http://www.nature.com/commsmed)","snPcode":"43856","submissionUrl":"https://mts-commsmed.nature.com/cgi-bin/main.plex","title":"Communications Medicine","twitterHandle":"@commsmedicine","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Communications Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"1cc73f17-2f05-4572-a00d-ce60c9ae141b","owner":[],"postedDate":"March 2nd, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":63761393,"name":"Health sciences/Gastroenterology/Hepatology/Liver diseases/Non-alcoholic fatty liver disease"},{"id":63761394,"name":"Health sciences/Gastroenterology/Hepatology/Liver diseases/Non-alcoholic steatohepatitis"}],"tags":[],"updatedAt":"2026-03-02T10:37:15+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-02 10:37:14","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8342646","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8342646","identity":"rs-8342646","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}