Racial and ethnic disparities in early uptake of GLP-1 receptor agonists in patients with and without MASLD

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Al-Ajlouni, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6926160/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: Glucagon-like peptide-1 receptor agonists (GLP-1 RAs) are effective in managing Type 2 diabetes mellitus (T2DM), obesity, and metabolic dysfunction-associated steatotic liver disease (MASLD). While disparities in GLP-1 RA prescriptions exist, little is known about racial and ethnic differences in early initiation, particularly in MASLD patients. This study evaluates racial and ethnic disparities in early GLP-1 RA uptake among eligible patients with and without MASLD Methods: We conducted a retrospective cohort study using the All of Us Research Program (2016–2022), including adults (≥18 years) with T2DM and/or obesity eligible for GLP-1 RA therapy. Early uptake was defined as a prescription within one year of eligibility. Race/ethnicity was categorized as non-Hispanic (NH) White, Black, Hispanic/Latino, Asian, or Other. Multivariable Cox regression models assessed associations between race/ethnicity and early prescription, adjusting for sociodemographic and clinical factors. A sub-analysis examined disparities among MASLD patients. Results: Among 89,019 eligible patients, 1.17% (n=1,039) initiated GLP-1 RA therapy within one year. Black (HR: 0.79, 95%CI: 0.65–0.95, p=0.014) and Hispanic/Latino (HR: 0.66, 95%CI: 0.53–0.82, p<0.001) patients had significantly lower uptake than NH Whites. Among MASLD patients, Asians had significantly higher early uptake (HR: 5.41, 95%CI: 2.2–13.6, p<0.001). Higher BMI, T2DM, and hyperlipidemia predicted early initiation, while older age, male sex, Black, and Hispanic race were associated with lower uptake Conclusion(s) : Significant racial and ethnic disparities exist in early GLP-1 RA uptake. Efforts are needed to promote equitable access and utilization, particularly for high-risk populations GLP-1 receptor agonists racial disparities ethnic disparities early medication uptake MASLD Type 2 diabetes mellitus Figures Figure 1 Figure 2 Introduction Metabolic dysfunction-associated steatotic liver disease (MASLD) is defined as hepatic steatosis identified by imaging or biopsy in the presence of at least one out of five cardiometabolic risk factors (overweight/obesity, hyperglycemia, hypertension, dyslipidemia; elevated plasma triglycerides or low high-density lipoprotein cholesterol, or on lipid-lowering therapy) and absence of other etiologies of steatosis 1 . MASLD encompasses a spectrum of chronic liver disease from steatosis to metabolic dysfunction-associated steatohepatitis with or without fibrosis, to cirrhosis and/or hepatocellular carcinoma 2 . MASLD has emerged as the most common chronic liver disease globally, as the pandemic of cardiometabolic diseases and metabolic syndrome continues to rise 3 , 4 . The global prevalence of MASLD is projected to increase further by up to 30% and 43.2% in 2030 and 2040 respectively 5 , 6 . Major racial and ethnic disparities exist in the epidemiology, disease progression, receipt of evidence-based management, and clinical outcomes in patients with MASLD 7 – 11 . Compared to other races, Hispanics have a higher prevalence of MASLD, increased odds of developing MASH and progression to MASH-related cirrhosis and hepatocellular carcinoma, and high odds of liver-related mortality 8 , 9 , 11 . Despite a relatively lower prevalence of MASLD in Blacks, they are less likely to receive evidence-based hepatology evaluation, and conversely have higher odds of overall and non-liver-related mortality 7 , 9 , 12 . MASLD is associated with an increased risk of cardiovascular disease (CVD) and major adverse cardiovascular events (MACE), conversely, CVD remains the leading cause of mortality in patients with MASLD 2 . The pivotal role of glucagon-like peptide-1 receptor agonists (GLP-1RAs) in diabetes management is well established. GLP1-RAs promote glycemic control through multifaceted mechanisms including enhanced insulin secretion and sensitivity, preserved pancreatic beta-cell function, glucagon inhibition, and regulating appetite and gastrointestinal activity 13 . Beyond glycemic control, glucagon-like peptide-1 receptor agonists (GLP-1RAs) have demonstrated benefits in weight loss, lipid metabolism, MASLD, renoprotection, and cardiovascular event and mortality risk reduction regardless of diabetes mellitus status 13 , 14 . GLP-RAs have demonstrated benefits in MASLD including improvement in liver enzymes, reduction of hepatic steatosis, improvement/resolution of MASH, and reduction in fibrosis progression 2 , 15 , 16 . Based on these benefits, GLP-1RAs are now recommended as pharmacologic options for patients with MASH and Type 2 diabetes mellitus (T2DM) or obesity by the American Association for the Study of Liver Diseases 17 and the American Association of Clinical Endocrinology 18 . Despite these benefits, utilization of GLP- 1RAs has been suboptimal, with racial, ethnic, and socioeconomic disparities observed in patients with T2DM 19 – 21 . Upon those bases, this study sought to evaluate racial and ethnic disparities in early adoption of GLP-1 RAs in eligible patients with MASLD Methods Study Design and Data Source This study is a retrospective cohort analysis conducted using data from the "All of Us" Research Program, a nationwide initiative sponsored by the National Institutes of Health (NIH). The program is designed to advance precision medicine and health equity by collecting comprehensive data on diverse populations 22 . The All of Us program has been described in detail in foundational publications, which outline the study design, recruitment strategies, and data collection procedures 22 , 23 . Adults (≥ 18 years) were recruited online, starting in May 2018 via the All of Us portal (JoinAllofUs.org) or through affiliated healthcare provider organizations. Enrollment procedures included informed consent, completion of baseline surveys, and authorization for access to EHRs. Participants also had the option to contribute biospecimen and undergo physical measurements during in- person visits at enrollment sites. The program, which contained information from over 300,000 participants at the time of analysis, integrates electronic health records (EHRs), survey data, biospecimens, and physical measurements, providing a robust foundation for observational studies 24 . For this study, we utilized data from EHRs, recorded between January 1, 2016, and December 31, 2022, with other collected data, and was accessed through the All of Us Researcher Workbench 25 . All data were de-identified to ensure participant confidentiality, and the study was designated as non-human subjects research by the program’s Institutional Review Board. For this secondary analysis of de-identified data, the Yale Institutional Review Board determined the research to be IRB exempt. This study adheres to the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines. Study Population The study cohort consisted of adults (≥ 18 years) with Type 2 diabetes mellitus (T2DM) and/or obesity between January 1, 2016, and December 31, 2022. We identified participants with T2DM, or obesity based on EHR codes. Patients were included if they had T2DM or obesity coded diagnosis on at least two separate occasions, whether inpatient or outpatient to enhance diagnostic accuracy. Participants were also classified as obese if they had two or more in-person physical measurement visits with a Body Mass Index (BMI) ≥ 30 mg/kg 2 during the study period. Exposure The primary exposure was self-reported race/ethnicity, categorized as non-Hispanic (NH) White, Black, Hispanic/Latino, Asian, or Other. Covariates Potential confounders were included to adjust for demographic, clinical, and socioeconomic variables. Demographic factors encompassed age, sex, marital status, and geographic location (urban vs. rural). Clinical characteristics included BMI, HbA1c levels, blood pressure, hyperlipidemia, hypertension, and the presence of T2DM. Socioeconomic factors such as income level ( $ 50,000), insurance type (public, private, or uninsured), and education level (high school diploma, some college, or college degree) were also considered. Healthcare access metrics, including the frequency of primary care and specialty visits, as well as lifestyle factors such as smoking status, physical activity, and self- reported dietary habits, were incorporated into the analysis. Patients were excluded if they had a history of pancreatitis, chronic kidney disease (CKD) stages 3 to 5, or a personal or family history of multiple endocrine neoplasia due to contraindications for GLP-1 RA use. Those who did not report race or ethnicity were also excluded to ensure robust statistical power for subgroup analyses. Similarly, individuals reporting multiple races were excluded due to insufficient sample sizes to support meaningful inferences. Observations with missing values for key covariates, including age, income, insurance status, or education, were excluded to maintain internal validity and consistency across statistical models. Outcome Measures The primary outcome was early uptake of GLP-1 RA therapy, defined as having at least one documented prescription within one year of diagnosis of T2DM and/or obesity. Secondary outcomes included factors associated with early GLP-1 RA uptake and subgroup analyses for patients with two or more EHR diagnosis codes for MASLD. Statistical Analysis Descriptive statistics were used to summarize baseline characteristics, stratified by race/ethnicity and GLP-1 RA prescription status. Continuous variables were reported as means with standard deviations, and categorical variables as frequencies and percentages. Differences were assessed using t-tests or ANOVA for continuous variables and chi-square tests for categorical variables. Multivariable logistic regression models estimated odds ratios (ORs) for early GLP-1 RA uptake, adjusting for covariates. Cox proportional hazards models were used for time-to-event analyses, with results expressed as hazard ratios (HRs) and 95% confidence intervals (CIs). Subgroup analyses focused on MASLD patients to evaluate disparities specific to this population. Statistical significance was set at p < 0.05, and all analyses were conducted using R software (version 4.2.2). Results Baseline Characteristics The final study cohort included 89,019 patients with T2DM and/ or obesity. The majority were female (62.4%) and NH White (45.4%), with Black (26.4%), Hispanic/Latino (21.8%), Asian (3.4%), and Other (3.0%) racial/ethnic representation. The mean age was 48.7 years (SD 15.7), and the mean BMI was 35.6 kg/m² (SD 6.8). MASLD patients constituted 8,837 (9.9%) of the cohort. Socioeconomic disparities were evident across racial/ethnic groups. Black (65.8%) and Hispanic/Latino (51.1%) patients were significantly more likely to have annual incomes below $ 25,000 compared to NH White patients (23.5%, p < 0.001). Insurance coverage also varied, with NH White patients reporting the highest rates of private insurance (72.3%), whereas Black (42.8%) and Hispanic/Latino (35.6%) patients predominantly relied on public insurance (p < 0.001). Detailed description of the cohort are provided in Table 1 . Table 1 Baseline Characteristics of the Study Cohort Stratified by Race/Ethnicity Race/ EthnicityOverall White Asian Black Hispanic/Latino Other p-test n 89019 40312 1312 23519 22017 1859 Gender (%) Female 55579 ( 23534 ( 58.4) 710 ( 54.1) 15378 ( 65.4) 14803 ( 67.2) 1154 ( < 0.001 62.4) 62.1) Male 32104 ( 16188 ( 40.2) 581 ( 7748 ( 32.9) 6938 ( 31.5) 649 ( 36.1) 44.3) 34.9) Non Binary 183 ( 0.2) 119 ( 0.3) 6 ( 21 ( 0.1) 25 ( 0.1) 12 ( 0.5) 0.6) Transgender 117 ( 0.1) 47 ( 0.1) 1 ( 0.1) 44 ( 0.2) 17 ( 0.1) 8 ( 0.4) I prefer not to answer 103 ( 0.1) 29 ( 0.1) 2 ( 0.2) 28 ( 0.1) 40 ( 0.2) 4 ( 0.2) Age (mean (SD)) 48.68 52.91 (15.88) 44.00 47.16 (13.99) 43.30 (14.98) 42.91 < 0.001 (15.73) (15.99) (16.05) BMI (mean (SD)) 35.63 (6.78) 35.20 (6.36) 32.40 36.72 (7.52) 35.39 (6.52) 36.02 (7.17) < 0.001 (6.16) Income (%) Less than 25k 27945 ( 8286 ( 23.5) 168 ( 11661 ( 65.8) 7289 ( 51.1) 541 ( < 0.001 40.1) 16.5) 36.5) 25k to 50k 14364 ( 6889 ( 19.6) 180 ( 3386 ( 19.1) 3609 ( 25.3) 300 ( 20.6) 17.6) 20.3) 50k to 100k 15012 ( 10232 ( 29.1) 274 ( 1900 ( 10.7) 2241 ( 15.7) 365 ( 21.5) 26.9) 24.6) More than 100k 12363 ( 9791 ( 27.8) 398 ( 781 ( 4.4) 1118 ( 7.8) 275 ( 17.7) 39.0) 18.6) Education (%) Less than High School 4068 ( 5.1) 285 ( 0.7) 9 ( 0.7) 431 ( 2.2) 3327 ( 17.9) 16 ( < 0.001 0.9) High School Graduate/GED 21288 ( 6786 ( 17.5) 104 ( 8187 ( 41.9) 5867 ( 31.5) 344 ( 26.7) 8.1) 20.0) Some College 25544 ( 32.0 12421 ( 32.1) 227 ( 6714 ( 34.4) 5578 ( 29.9) 604 ( ) 17.8) 35.2) College or Higher 28921 ( 19179 ( 49.6) 938 ( 4188 ( 21.5) 3862 ( 20.7) 754 ( 36.2) 73.4) 43.9) Insurance Use (%) No 7738 ( 8.9) 1634 ( 4.1) 49 ( 3.8) 2914 ( 13.0) 3049 ( 14.4) 92 ( < 0.001 5.1) Yes 78833 ( 38157 ( 95.9) 1236 ( 19546 ( 87.0) 18198 ( 85.6) 1696 ( 91.1) 96.2) 94.9) Insurance group (%) No Coverage 78407 ( 35326 ( 88.8) 1119 ( 20860 ( 92.9) 19498 ( 91.8) 1604 < 0.001 90.6) 87.1) ( 89.7) Private Insurance 3709 ( 4.3) 2356 ( 5.9) 135 ( 450 ( 2.0) 674 ( 3.2) 94 ( 5.3) 10.5) Public Insurance 3820 ( 4.4) 1646 ( 4.1) 19 ( 1081 ( 4.8) 994 ( 4.7) 80 ( 1.5) 4.5) Multiple 598 ( 0.7) 451 ( 1.1) 12 ( 59 ( 0.3) 70 ( 0.3) 6 ( 0.3) 0.9) Smoking (%) No 50886 ( 21346 ( 54.3) 951 ( 12634 ( 56.2 ) 14905 ( 70.3) 1050 ( < 0.001 59.1) 75.5) 59.3) Yes 35144 ( 17964 ( 45.7) 308 ( 9847 ( 43.8) 6304 ( 29.7 721 ( 40.9) 24.5) 40.7) Ever use of 78713 ( 35403 ( 87.8) 1155 20947 ( 89.1) 19542 ( 88.8) 1666 ( 89.6) < 0.001 insulin (%) No 88.4) ( 88.0) Yes 10306 ( 4909 ( 12.2) 157 ( 2572 ( 10.9) 2475 ( 11.2) 193 ( 10.4) 11.6) 12.0) Non-insulin Diabetes 75323 ( 33013 ( 81.9) 1042 20316 ( 86.4) 19324 ( 87.8) 1628 ( 87.6) < 0.001 Medication use (%) No 84.6) ( 79.4) Yes 13696 ( 7299 ( 18.1) 270 ( 3203 ( 13.6) 2693 ( 12.2) 231 ( 12.4) 15.4) 20.6) Type 2 DM (%) No 71211 ( 32289 ( 80.1) 904 ( 19048 ( 81.0 17442 ( 79.2) 1528 ( 82.2) < 0.001 80.0) 68.9) Yes 17808 ( 8023 ( 19.9) 408 ( 4471 ( 19.0) 4575 ( 20.8) 331 ( 17.8) 20.0) 31.1) Hypertension (%) No 83902 ( 37550 ( 93.1) 1240 22117 ( 94.0) 21217 ( 96.4) 1778 ( 95.6) < 0.001 94.3) ( 94.5) Yes 5117 ( 5.7) 2762 ( 6.9) 72 ( 1402 ( 6.0) 800 ( 3.6) 81 ( 4.4) 5.5) Hyperlipidemia (%)No 63011 ( 24708 ( 61.3) 863 ( 18932 ( 80.5) 17118 ( 77.7) 1390 ( 74.8) < 0.001 70.8) 65.8) Yes 26008 ( 15604 ( 38.7) 449 ( 4587 ( 19.5) 4899 ( 22.3) 469 ( 25.2) 29.2) 34.2) Chronic Kidney 83903 ( 37445 ( 92.9) 1238 22268 ( 94.7) 21166 ( 96.1) 1786 ( 96.1) < 0.001 Disease (%)No 94.3) ( 94.4) Yes 5116 ( 5.7) 2867 ( 7.1) 74 ( 1251 ( 5.3) 851 ( 3.9) 73 ( 3.9) 5.6) Heart Failure (%) No 863 ( 65.8) 63011 ( 70.8) 24708 ( 18932 ( 80.5) 17118 ( 77.7) 1390 ( 74.8) < 0.001 61.3) Overall, 1.17% (n = 1,039) of the cohort initiated GLP-1 RA therapy within one year of diagnosis of T2DM or obesity. Adjusted Cox proportional hazards model analyses revealed significant racial/ethnic disparities ( Table 2 ) - Black patients had a significantly lower hazard of early GLP-1 RA initiation compared to NH White patients (HR: 0.705, 95% CI: 0.530– 0.939, p = 0.0168). Hispanic/Latino patients had a 38% lower hazard of early initiation compared to NH Whites (HR: 0.623, 95% CI: 0.455–0.852, p = 0.0031). Asian patients had comparable hazards to NH Whites (HR: 1.139, 95% CI: 0.636–2.042, p = 0.6615). Patients categorized as Others showed no statistically significant difference compared to NH Whites (HR: 0.884, 95% CI: 0.483–1.616, p = 0.688). Subgroup Analyses Diabetic, Non-Obese Patients In diabetic non-obese patients, Asian patients were more likely to initiate GLP-1 RA therapy compared to NH Whites (HR: 1.931, 95% CI: 1.092–3.416, p = 0.024) ( Table 2 ). No statistically significant differences were observed among Black or Hispanic/Latino patients compared to NH Whites in this subgroup. Non-Diabetic, Obese Patients Among non-diabetic obese patients, Black patients had a significantly lower hazard of early GLP-1 RA initiation compared to NH Whites (HR: 0.621, 95% CI: 0.405–0.954, p = 0.0298). However, Hispanic/Latino patients also had significantly lower hazards (HR: 0.257, 95% CI: 0.135–0.490, p < 0.001). Asian patients had no statistically significant differences compared to NH Whites (HR: 0.609, 95% CI: 0.150–2.468, p = 0.487) ( Table 2 ). Diabetic, Obese Patients Among patients with both diabetes and obesity, Black patients exhibited significantly reduced hazards for GLP-1 RA initiation compared to NH Whites (HR: 0.577, 95% CI: 0.458–0.726, p < 0.001) ( Table 2 ) . Hispanic/Latino patients similarly demonstrated lower hazards (HR: 0.570, 95% CI: 0.451–0.720, p < 0.001) ( Table 2 ) . Asian patients had a non-significant increased hazard of initiation (HR: 1.503, 95% CI: 0.840–2.689, p = 0.170). MASLD Patients In the MASLD subgroup, disparities were nuanced. Asian MASLD patients were significantly more likely to initiate GLP-1 RA therapy compared to NH Whites (HR: 5.406, 95% CI: 2.185– 13.612, p < 0.001) ( Table 2 ) . Black MASLD patients exhibited no statistically significant difference in hazards compared to NH Whites (HR: 1.076, 95% CI: 0.324–3.569, p = 0.905) ( Table 2 ) . Hispanic/Latino MASLD patients also showed no significant differences (HR: 0.986, 95% CI: 0.483–2.014, p = 0.969) ( Table 2 ) . The patterns of disparities are illustrated in the cumulative incidence curves ( Figs. 2 A–D ) , which depict one-year initiation rates stratified by race and ethnicity across various subgroups. Predictors of Early Uptake Independent predictors of early GLP-1 RA uptake included higher BMI (HR per unit increase: 1.04, 95% CI: 1.03–1.05, p < 0.001), presence of T2DM (HR: 9.43, 95% CI: 8.31–10.69, p < 0.001), and hyperlipidemia (HR: 2.24, 95% CI: 1.95–2.57, p < 0.001) ( Table 3 ) . Conversely, older age (HR per year: 0.98, 95% CI: 0.97–0.99, p < 0.001) and male sex (HR: 0.76, 95% CI: 0.68–0.85, p < 0.001) were associated with lower uptake hazards ( Table 3 ) . Discussion This study aimed to assess racial and ethnic disparities in the early uptake of GLP-1 RAs among patients with T2DM and/or obesity, with a specific focus on disparities in MASLD patients. Unlike previous research that primarily examined overall prescription trends, this study provides novel insights into disparities at the early initiation stage, a critical window for optimizing treatment outcomes. Our findings demonstrate that early GLP-1 RA utilization remains suboptimal, with Black and Hispanic patients significantly less likely to initiate therapy within one year of diagnosis compared to White patients. These disparities persisted despite adjustments for sociodemographic and clinical factors, highlighting systemic barriers to equitable access. Notably, among MASLD patients, Asian individuals were significantly more likely to receive early GLP-1 RA prescriptions, compared to NH Whites, Blacks and Asians, suggesting potential differences in provider prescribing patterns or patient engagement. These results underscore the urgent need for targeted interventions to address racial and ethnic disparities in early GLP-1 RA adoption. Despite the gradual increase in GLP-1 RA use among eligible patients amidst its incontrovertible cardiometabolic benefits and incorporation into societal guidelines, overall utilization remains suboptimal and relatively lower among historically marginalized ethnicities compared to other ethnicities 19 , 26 . A retrospective study conducted by Eberly et al 19 utilizing data from the Optum Clinformatics Data Mart found that among commercially insured patients with T2DM, being Black, Asian, and Hispanic was associated with low GLP1-RA use despite the 100% insurance coverage of this cohort and adjustments for visits to endocrinology and cardiology specialty providers. Historically marginalized ethnicities in the United States are disproportionately burdened with T2DM and its complications, including cardiovascular disease and mortality 19 . A recent study found Black patients experienced the highest obesity- related cardiovascular mortality rate in the United States between 1999 and 2020 27 . Our results highlight the public health injustice of the persistence of very low GLP-1RA initiation rates in these high-risk populations who are likely to derive the most benefits from its use. Previous studies have found socioeconomic status to be a major determinant of GLP-1 RA or SGLT2-I use in patients with T2DM 19 , 26 , 28 , 29 . Falkentoft et al 28 observed a higher probability of GLP- 1 RA or SGLT2-I initiation among high-income earners compared to patients with low income despite universal healthcare coverage and lower costs of medications in Denmark. The higher level of socioeconomic inequality observed in the United States compared to other developed countries like Denmark 30 may further exacerbate disparities in access to evidence-based medications. In this United States cohort, we found socioeconomic factors such as access to healthcare, annual income levels, and insurance coverage status as notable predictors of early prescription of GLP1-RA. Additionally, Blacks and Hispanics had lower insurance coverage and annual income levels compared to White patients. The prohibitive cost implication among non-insured patients, high co-pays, physician prescription fatigue from previous unapproved orders, and disparities in the availability of prescribed medications in poorer communities may lead to resorting to alternatives with less proven cardiovascular benefits 19 , 28 , 31 . Subgroup analysis of patients with T2DM and /or obesity with metabolic dysfunction- associated steatotic liver disease showed that patients of Asian ethnicity were four times more likely to be initiated on GLP-1 RA compared to patients of other ethnicities. In contrast to our findings, a recent cross-sectional study by Alexopoulos et al 7 did not identify any racial/ethnic disparities in prescription patterns of GLP-1 RA and pioglitazone among patients with T2DM and MASH but found overall low prescription rates. However, this study was a corss-sectional, single-center study with a relatively smaller sample size and a less diverse ethnic/racial composition. Our findings might be explained by the increasingly recognized risks of metabolic syndrome including MASLD at a relatively lower body mass index (BMI) in Asians compared to other ethnicities. Physicians might therefore have a lower BMI threshold for evaluating for MASLD and subsequently prescribing metabolic disease-modifying medications in this population 32 . GLP-1 RAs have been shown in clinical trials to result in the improvement/resolution of MASH and reduction in the progression of fibrosis 15 , 16 . Findings from a retrospective study utilizing data from the Veterans Health Administration Corporate Data Warehouse and Central Cancer Registry showed that early initiation of GLP-1 RA may have a role in preventing progression to cirrhosis and lowering the risk of cirrhosis decompensating events in patients with MASLD (without cirrhosis) and diabetes compared to placebo. However, no statistically significant difference in terms of cirrhosis decompensation, hepatocellular carcinoma, and all-cause mortality was observed with initiation of GLP-1 RAs in patients already diagnosed with cirrhosis emphasizing the importance of early initiation of GLP-1 RAs 33 . Considering the increased morbidity and mortality risks in historically marginalized groups (Blacks, Hispanics, and Native Americans/Alaskans) with MASLD 8 , 9 , 11 , 34 , increased efforts to curb this disparity and ensure early initiation of GLP-1 RAs have the potential to significantly improve clinical outcomes. Strengths and Limitations This study has several notable strengths. The use of the "All of Us" Research Program provided a large and diverse dataset, allowing for robust analyses of racial and ethnic disparities across various subgroups, including MASLD. The study’s longitudinal design, which enabled dynamic observation of time-to-therapy initiation, adds a unique dimension to the analysis by capturing disparities in timing as well as uptake rates. Additionally, the incorporation of comprehensive clinical, demographic, lifestyle and socioeconomic data ensured a nuanced understanding of factors influencing GLP-1 RA uptake. By focusing on MASLD, the study addresses an emerging area of research, offering novel insights into disparities within this population. Furthermore, the generalizability of findings is enhanced by the diversity of the All of Us cohort, which reflects the broader U.S. population, particularly underrepresented groups. Despite these strengths, the study has limitations. The exclusion of participants with missing data for key variables or underrepresented racial/ethnic groups may introduce selection bias and limit the generalizability of findings to smaller populations, such as Native American or Pacific Islander groups. The retrospective design relies on existing records, which may lack granularity for certain variables, such as provider-level factors or patient preferences, potentially introducing information bias. While robust adjustments were made for socioeconomic and clinical confounders, residual confounding cannot be completely ruled out. Additionally, the observational nature of the study precludes causal inferences regarding the relationship between race/ethnicity and GLP-1 RA uptake. Finally, the reliance on electronic health records introduces variability in coding practices and may not fully capture eligibility or adherence to therapy, further complicating interpretations. Future Research Future research should explore the underlying mechanisms driving racial and ethnic disparities in early GLP-1 RA uptake, particularly the roles of healthcare provider prescribing behaviors, patient preferences, and structural barriers such as medication cost and insurance coverage. Longitudinal studies incorporating real-world adherence data and patient-reported outcomes would provide valuable insights into how these disparities evolve over time and impact long- term health outcomes. Additionally, further investigation is warranted into the high early uptake of GLP-1 RAs among Asian patients with MASLD, particularly whether this reflects provider awareness, patient demand, or biological differences influencing treatment decisions. Comparative effectiveness research evaluating alternative treatment pathways in marginalized populations could inform tailored interventions to improve equitable access. Finally, policy-focused studies assessing the impact of insurance coverage expansions, cost-reduction strategies, and provider education initiatives on medication uptake could help identify scalable solutions to mitigate disparities in GLP-1 RA utilization. Conclusion In conclusion, this study of US adults with T2DM and/or obesity found significant racial and ethnic disparities in the early adoption of GLP-1 receptor agonists (GLP-1 RAs) among adults of Hispanic and Black ethnicity. Subgroup analysis of eligible patients with MASLD identified a higher early uptake of GLP-RA among Asians compared to other ethnicities. A higher BMI, T2DM, and hyperlipidemia were independently associated with higher GLP-1 RA initiation. Our findings emphasize the need for targeted efforts to promote equitable and early GLP-1 RA utilization for all eligible patients. Declarations Conflicts of Interest: All authors declare no potential conflicts of interest relevant to this manuscript for the past three years. Funding Disclosure: No funding was received for this study from the National Institutes of Health (NIH), Wellcome Trust, Howard Hughes Medical Institute (HHMI), or any other organization. Author Contribution Sarpong Boateng, MD, MPH: Conceptualization; Methodology; Data curation; Formal analysis.Prince Ameyaw, MD: Conceptualization; Methodology; Investigation; Yussif Issaka, MD: Investigation; Resources; Writing – Review & Editing; Supervision; Corresponding author.Yazan A. Al-Ajlouni, MD, MPhil: Formal analysis; Visualization; Writing – Review & Editing.Basile Njei, MD, MPH, PhD: Methodology; Supervision; Funding acquisition; Writing – Review & Editing.All authors have read and approved the final version of the manuscript. References Rinella ME, Lazarus JV, Ratziu V, et al. 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Oct 2023;29(4):1002–1012. doi: 10.3350/cmh.2023.0205 Rich NE, Oji S, Mufti AR, et al. Racial and Ethnic Disparities in Nonalcoholic Fatty Liver Disease Prevalence, Severity, and Outcomes in the United States: A Systematic Review and Meta-analysis. Clin Gastroenterol Hepatol . Feb 2018;16(2):198–210 e2. doi: 10.1016/j.cgh.2017.09.041 Samji NS, Snell PD, Singal AK, Satapathy SK. Racial Disparities in Diagnosis and Prognosis of Nonalcoholic Fatty Liver Disease. Clin Liver Dis (Hoboken) . Aug 2020;16(2):66–72. doi: 10.1002/cld.948 Younossi ZM, Otgonsuren M, Venkatesan C, Mishra A. In patients with non-alcoholic fatty liver disease, metabolically abnormal individuals are at a higher risk for mortality while metabolically normal individuals are not. Metabolism . Mar 2013;62(3):352–60. doi: 10.1016/j.metabol.2012.08.005 Zheng Z, Zong Y, Ma Y, et al. Glucagon-like peptide-1 receptor: mechanisms and advances in therapy. Signal Transduct Target Ther . Sep 18 2024;9(1):234. doi: 10.1038/s41392-024-01931-z Rivera FB, Cruz LLA, Magalong JV, et al. Cardiovascular and renal outcomes of glucagon-like peptide 1 receptor agonists among patients with and without type 2 diabetes mellitus: A meta-analysis of randomized placebo-controlled trials. Am J Prev Cardiol . Jun 2024;18:100679. doi: 10.1016/j.ajpc.2024.100679 Loomba R, Hartman ML, Lawitz EJ, et al. Tirzepatide for Metabolic Dysfunction- Associated Steatohepatitis with Liver Fibrosis. N Engl J Med . Jul 25 2024;391(4):299–310. doi: 10.1056/NEJMoa2401943 Newsome PN, Buchholtz K, Cusi K, et al. A Placebo-Controlled Trial of Subcutaneous Semaglutide in Nonalcoholic Steatohepatitis. N Engl J Med . Mar 25 2021;384(12):1113–1124. doi: 10.1056/NEJMoa2028395 Rinella ME, Neuschwander-Tetri BA, Siddiqui MS, et al. AASLD Practice Guidance on the clinical assessment and management of nonalcoholic fatty liver disease. Hepatology . May 1 2023;77(5):1797–1835. doi: 10.1097/HEP.0000000000000323 Cusi K, Isaacs S, Barb D, et al. American Association of Clinical Endocrinology Clinical Practice Guideline for the Diagnosis and Management of Nonalcoholic Fatty Liver Disease in Primary Care and Endocrinology Clinical Settings: Co-Sponsored by the American Association for the Study of Liver Diseases (AASLD). Endocr Pract . May 2022;28(5):528–562. doi: 10.1016/j.eprac.2022.03.010 Eberly LA, Yang L, Essien UR, et al. Racial, Ethnic, and Socioeconomic Inequities in Glucagon-Like Peptide-1 Receptor Agonist Use Among Patients With Diabetes in the US. JAMA Health Forum . Dec 2021;2(12):e214182. doi: 10.1001/jamahealthforum.2021.4182 Limonte CP, Hall YN, Trikudanathan S, et al. Prevalence of SGLT2i and GLP1RA use among US adults with type 2 diabetes. J Diabetes Complications . Jun 2022;36(6):108204. doi: 10.1016/j.jdiacomp.2022.108204 Mahtta D, Ramsey DJ, Lee MT, et al. Utilization Rates of SGLT2 Inhibitors and GLP- 1 Receptor Agonists and Their Facility-Level Variation Among Patients With Atherosclerotic Cardiovascular Disease and Type 2 Diabetes: Insights From the Department of Veterans Affairs. Diabetes Care . Feb 1 2022;45(2):372–380. doi: 10.2337/dc21-1815 All of Us Research Program I, Denny JC, Rutter JL, et al. The "All of Us" Research Program. N Engl J Med . Aug 15 2019;381(7):668–676. doi: 10.1056/NEJMsr1809937 NIH. All of Us Research Program. Research Roundup: Meet the Resource Access Board. Accessed 12/30/2024, 2024. https://allofus.nih.gov/news- events/announcements/research-roundup-meet-resource-access-board Ramirez AH, Sulieman L, Schlueter DJ, et al. The All of Us Research Program: Data quality, utility, and diversity. Patterns (N Y) . Aug 12 2022;3(8):100570. doi: 10.1016/j.patter.2022.100570 Smith LH, Cavanaugh R. allofus: an R package to facilitate use of the All of Us Researcher Workbench. J Am Med Inform Assoc . Dec 1 2024;31(12):3013–3021. doi: 10.1093/jamia/ocae198 Cromer SJ, Lauffenburger JC, Levin R, Patorno E. Deficits and Disparities in Early Uptake of Glucagon-Like Peptide 1 Receptor Agonists and SGLT2i Among Medicare-Insured Adults Following a New Diagnosis of Cardiovascular Disease or Heart Failure. Diabetes Care . Jan 1 2023;46(1):65–74. doi: 10.2337/dc22-0383 Raisi-Estabragh Z, Kobo O, Mieres JH, et al. Racial Disparities in Obesity-Related Cardiovascular Mortality in the United States: Temporal Trends From 1999 to 2020. J Am Heart Assoc. Sep 19 2023;12(18):e028409. doi: 10.1161/JAHA.122.028409 Falkentoft AC, Andersen J, Malik ME, et al. Impact of socioeconomic position on initiation of SGLT-2 inhibitors or GLP-1 receptor agonists in patients with type 2 diabetes - a Danish nationwide observational study. Lancet Reg Health Eur . Mar 2022;14:100308. doi: 10.1016/j.lanepe.2022.100308 McCoy RG, Van Houten HK, Deng Y, et al. Comparison of Diabetes Medications Used by Adults With Commercial Insurance vs Medicare Advantage, 2016 to 2019. JAMA Netw Open . Feb 1 2021;4(2):e2035792. doi: 10.1001/jamanetworkopen.2020.35792 Smeeding TM. Public Policy, Economic Inequality, and Poverty: The United States in Comparative Perspective. Social Science Quarterly . 2005;86(s1):955–983. doi: https://doi.org/10.1111/j.0038-4941.2005.00331.x Amstislavski P, Matthews A, Sheffield S, Maroko AR, Weedon J. Medication deserts: survey of neighborhood disparities in availability of prescription medications. Int J Health Geogr . Nov 9 2012;11:48. doi: 10.1186/1476-072X-11-48 Szanto KB, Li J, Cordero P, Oben JA. Ethnic differences and heterogeneity in genetic and metabolic makeup contributing to nonalcoholic fatty liver disease. Diabetes Metab Syndr Obes . 2019;12:357–367. doi: 10.2147/DMSO.S182331 Kanwal F, Kramer JR, Li L, et al. GLP-1 Receptor Agonists and Risk for Cirrhosis and Related Complications in Patients With Metabolic Dysfunction-Associated Steatotic Liver Disease. JAMA Intern Med . Nov 1 2024;184(11):1314–1323. doi: 10.1001/jamainternmed.2024.4661 Aboona MB, Faulkner C, Rangan P, et al. Disparities among ethnic groups in mortality and outcomes among adults with MASLD: A multicenter study. Liver Int . Jun 2024;44(6):1316–1328. doi: 10.1111/liv.15880 Tables Tables 1 to 3 are available in the Supplementary Files section. Additional Declarations No competing interests reported. Supplementary Files Tables.docx Cite Share Download PDF Status: Posted 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. 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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-6926160","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":473666027,"identity":"ada85f0e-8c58-4c4a-9087-729974c4d87c","order_by":0,"name":"Sarpong Boateng","email":"","orcid":"","institution":"Yale Affiliated Hospitals Program Bridgeport Hospital","correspondingAuthor":false,"prefix":"","firstName":"Sarpong","middleName":"","lastName":"Boateng","suffix":""},{"id":473666028,"identity":"3fc9aba8-8b25-4d9e-8f09-11658b479cb8","order_by":1,"name":"Prince Ameyaw","email":"","orcid":"","institution":"Yale Affiliated Hospitals Program Bridgeport Hospital","correspondingAuthor":false,"prefix":"","firstName":"Prince","middleName":"","lastName":"Ameyaw","suffix":""},{"id":473666029,"identity":"1aa4e90c-5183-48ca-8122-c87252e389ce","order_by":2,"name":"Yussif Issaka","email":"data:image/png;base64,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","orcid":"","institution":"Yale Affiliated Hospitals Program Bridgeport Hospital","correspondingAuthor":true,"prefix":"","firstName":"Yussif","middleName":"","lastName":"Issaka","suffix":""},{"id":473666030,"identity":"21105701-8f88-4326-8abb-4093ee683e34","order_by":3,"name":"Yazan A. Al-Ajlouni","email":"","orcid":"","institution":"New York Medical College","correspondingAuthor":false,"prefix":"","firstName":"Yazan","middleName":"A.","lastName":"Al-Ajlouni","suffix":""},{"id":473666031,"identity":"7556adfb-9597-4001-a1ad-e638edffde78","order_by":4,"name":"Basile Njei","email":"","orcid":"","institution":"Yale University","correspondingAuthor":false,"prefix":"","firstName":"Basile","middleName":"","lastName":"Njei","suffix":""}],"badges":[],"createdAt":"2025-06-18 23:08:05","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6926160/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6926160/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":85185489,"identity":"08a30a5c-34cf-41a3-9d21-b64b4e5bec21","added_by":"auto","created_at":"2025-06-23 08:08:24","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":117646,"visible":true,"origin":"","legend":"\u003cp\u003eCumulative Incidence of GLP-1 Receptor Agonist Uptake Within 1 Year of MASLD Diagnosis by Race/Ethnicity\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6926160/v1/700b9595e54887bf11397444.png"},{"id":85185490,"identity":"b56f70c6-4160-4909-910b-92c43de2de6d","added_by":"auto","created_at":"2025-06-23 08:08:24","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":186982,"visible":true,"origin":"","legend":"\u003cp\u003eCumulative Incidence of GLP-1 Receptor Agonist Uptake Within 1 Year of MASLD Diagnosis by Race/Ethnicity accorss subgroups of A- Diabetes, non-obese; B- Obese, no diabetes; C-Diabetes and obese; D- Patients with MASLD\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6926160/v1/9ced2cf2c34421e27072ef42.png"},{"id":86587631,"identity":"0c1f0ad7-b787-4f2e-ae6b-07774a8ae263","added_by":"auto","created_at":"2025-07-13 04:46:30","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1543675,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6926160/v1/c5d64b37-ced8-485e-90b1-4c1af7917ef9.pdf"},{"id":85184805,"identity":"f5a71a1c-0728-426d-b53f-a4b47932959f","added_by":"auto","created_at":"2025-06-23 08:00:24","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":57281,"visible":true,"origin":"","legend":"","description":"","filename":"Tables.docx","url":"https://assets-eu.researchsquare.com/files/rs-6926160/v1/0983b15ca42c107247526f7b.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Racial and ethnic disparities in early uptake of GLP-1 receptor agonists in patients with and without MASLD","fulltext":[{"header":"Introduction","content":"\u003cp\u003eMetabolic dysfunction-associated steatotic liver disease (MASLD) is defined as hepatic steatosis identified by imaging or biopsy in the presence of at least one out of five cardiometabolic risk factors (overweight/obesity, hyperglycemia, hypertension, dyslipidemia; elevated plasma triglycerides or low high-density lipoprotein cholesterol, or on lipid-lowering therapy) and absence of other etiologies of steatosis \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. MASLD encompasses a spectrum of chronic liver disease from steatosis to metabolic dysfunction-associated steatohepatitis with or without fibrosis, to cirrhosis and/or hepatocellular carcinoma \u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. MASLD has emerged as the most common chronic liver disease globally, as the pandemic of cardiometabolic diseases and metabolic syndrome continues to rise \u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. The global prevalence of MASLD is projected to increase further by up to 30% and 43.2% in 2030 and 2040 respectively \u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e .\u003c/p\u003e \u003cp\u003eMajor racial and ethnic disparities exist in the epidemiology, disease progression, receipt of evidence-based management, and clinical outcomes in patients with MASLD \u003csup\u003e\u003cspan additionalcitationids=\"CR8 CR9 CR10\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. Compared to other races, Hispanics have a higher prevalence of MASLD, increased odds of developing MASH and progression to MASH-related cirrhosis and hepatocellular carcinoma, and high odds of liver-related mortality \u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. Despite a relatively lower prevalence of MASLD in Blacks,\u003c/p\u003e \u003cp\u003ethey are less likely to receive evidence-based hepatology evaluation, and conversely have higher odds of overall and non-liver-related mortality \u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e,\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eMASLD is associated with an increased risk of cardiovascular disease (CVD) and major adverse cardiovascular events (MACE), conversely, CVD remains the leading cause of mortality in patients with MASLD \u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. The pivotal role of glucagon-like peptide-1 receptor agonists (GLP-1RAs) in diabetes management is well established. GLP1-RAs promote glycemic control through multifaceted mechanisms including enhanced insulin secretion and sensitivity, preserved pancreatic beta-cell function, glucagon inhibition, and regulating appetite and gastrointestinal activity \u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. Beyond glycemic control, glucagon-like peptide-1 receptor agonists (GLP-1RAs) have demonstrated benefits in weight loss, lipid metabolism, MASLD, renoprotection, and cardiovascular event and mortality risk reduction regardless of diabetes mellitus status \u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e,\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. GLP-RAs have demonstrated benefits in MASLD including improvement in liver enzymes, reduction of hepatic steatosis, improvement/resolution of MASH, and reduction in fibrosis progression \u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e,\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e,\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. Based on these benefits, GLP-1RAs are now recommended as pharmacologic options for patients with MASH and Type 2 diabetes mellitus (T2DM) or obesity by the American Association for the Study of Liver Diseases \u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e and the American Association of Clinical Endocrinology \u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. Despite these benefits, utilization of GLP- 1RAs has been suboptimal, with racial, ethnic, and socioeconomic disparities observed in patients with T2DM \u003csup\u003e\u003cspan additionalcitationids=\"CR20\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. Upon those bases, this study sought to evaluate racial and ethnic disparities in early adoption of GLP-1 RAs in eligible patients with MASLD\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Design and Data Source\u003c/h2\u003e \u003cp\u003eThis study is a retrospective cohort analysis conducted using data from the \"All of Us\" Research Program, a nationwide initiative sponsored by the National Institutes of Health (NIH). The program is designed to advance precision medicine and health equity by collecting comprehensive data on diverse populations \u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe All of Us program has been described in detail in foundational publications, which outline the study design, recruitment strategies, and data collection procedures \u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e,\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. Adults (\u0026ge;\u0026thinsp;18 years) were recruited online, starting in May 2018 via the All of Us portal \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e(JoinAllofUs.org)\u003c/span\u003e or through affiliated healthcare provider organizations. Enrollment procedures included informed consent, completion of baseline surveys, and authorization for access to EHRs. Participants also had the option to contribute biospecimen and undergo physical measurements during in- person visits at enrollment sites. The program, which contained information from over 300,000 participants at the time of analysis, integrates electronic health records (EHRs), survey data, biospecimens, and physical measurements, providing a robust foundation for observational studies \u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. For this study, we utilized data from EHRs, recorded between January 1, 2016, and December 31, 2022, with other collected data, and was accessed through the All of Us Researcher Workbench \u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eAll data were de-identified to ensure participant confidentiality, and the study was designated as non-human subjects research by the program\u0026rsquo;s Institutional Review Board. For this secondary analysis of de-identified data, the Yale Institutional Review Board determined the research to be IRB exempt. This study adheres to the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eStudy Population\u003c/h3\u003e\n\u003cp\u003eThe study cohort consisted of adults (\u0026ge;\u0026thinsp;18 years) with Type 2 diabetes mellitus (T2DM) and/or obesity between January 1, 2016, and December 31, 2022. We identified participants with\u003c/p\u003e \u003cp\u003eT2DM, or obesity based on EHR codes. Patients were included if they had T2DM or obesity coded diagnosis on at least two separate occasions, whether inpatient or outpatient to enhance diagnostic accuracy. Participants were also classified as obese if they had two or more in-person physical measurement visits with a Body Mass Index (BMI)\u0026thinsp;\u0026ge;\u0026thinsp;30 mg/kg\u003csup\u003e2\u003c/sup\u003e during the study period.\u003c/p\u003e\n\u003ch3\u003eExposure\u003c/h3\u003e\n\u003cp\u003eThe primary exposure was self-reported race/ethnicity, categorized as non-Hispanic (NH) White, Black, Hispanic/Latino, Asian, or Other.\u003c/p\u003e\n\u003ch3\u003eCovariates\u003c/h3\u003e\n\u003cp\u003ePotential confounders were included to adjust for demographic, clinical, and socioeconomic variables. Demographic factors encompassed age, sex, marital status, and geographic location (urban vs. rural). Clinical characteristics included BMI, HbA1c levels, blood pressure, hyperlipidemia, hypertension, and the presence of T2DM. Socioeconomic factors such as income level (\u0026lt;\u003cspan\u003e$\u003c/span\u003e25,000, \u003cspan\u003e$\u003c/span\u003e25,000\u0026ndash;\u003cspan\u003e$\u003c/span\u003e50,000, \u0026gt;\u003cspan\u003e$\u003c/span\u003e50,000), insurance type (public, private, or uninsured), and education level (high school diploma, some college, or college degree) were also considered. Healthcare access metrics, including the frequency of primary care and specialty visits, as well as lifestyle factors such as smoking status, physical activity, and self- reported dietary habits, were incorporated into the analysis.\u003c/p\u003e \u003cp\u003ePatients were excluded if they had a history of pancreatitis, chronic kidney disease (CKD) stages 3 to 5, or a personal or family history of multiple endocrine neoplasia due to contraindications for GLP-1 RA use. Those who did not report race or ethnicity were also excluded to ensure robust statistical power for subgroup analyses. Similarly, individuals reporting multiple races were excluded due to insufficient sample sizes to support meaningful inferences. Observations with missing values for key covariates, including age, income, insurance status, or education, were excluded to maintain internal validity and consistency across statistical models.\u003c/p\u003e\n\u003ch3\u003eOutcome Measures\u003c/h3\u003e\n \u003cp\u003eThe primary outcome was early uptake of GLP-1 RA therapy, defined as having at least one documented prescription within one year of diagnosis of T2DM and/or obesity. Secondary outcomes included factors associated with early GLP-1 RA uptake and subgroup analyses for patients with two or more EHR diagnosis codes for MASLD.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eDescriptive statistics were used to summarize baseline characteristics, stratified by race/ethnicity and GLP-1 RA prescription status. Continuous variables were reported as means with standard deviations, and categorical variables as frequencies and percentages. Differences were assessed using t-tests or ANOVA for continuous variables and chi-square tests for categorical variables.\u003c/p\u003e \u003cp\u003eMultivariable logistic regression models estimated odds ratios (ORs) for early GLP-1 RA uptake, adjusting for covariates. Cox proportional hazards models were used for time-to-event analyses, with results expressed as hazard ratios (HRs) and 95% confidence intervals (CIs). Subgroup analyses focused on MASLD patients to evaluate disparities specific to this population. Statistical significance was set at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, and all analyses were conducted using R software (version 4.2.2).\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eBaseline Characteristics\u003c/h2\u003e \u003cp\u003eThe final study cohort included 89,019 patients with T2DM and/ or obesity. The majority were female (62.4%) and NH White (45.4%), with Black (26.4%), Hispanic/Latino (21.8%), Asian (3.4%), and Other (3.0%) racial/ethnic representation. The mean age was 48.7 years (SD 15.7), and the mean BMI was 35.6 kg/m\u0026sup2; (SD 6.8). MASLD patients constituted 8,837 (9.9%) of the cohort.\u003c/p\u003e \u003cp\u003eSocioeconomic disparities were evident across racial/ethnic groups. Black (65.8%) and Hispanic/Latino (51.1%) patients were significantly more likely to have annual incomes below \u003cspan\u003e$\u003c/span\u003e25,000 compared to NH White patients (23.5%, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Insurance coverage also varied, with NH White patients reporting the highest rates of private insurance (72.3%), whereas Black (42.8%) and Hispanic/Latino (35.6%) patients predominantly relied on public insurance (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Detailed description of the cohort are provided in Table\u0026nbsp; \u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e . \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline Characteristics of the Study Cohort Stratified by Race/Ethnicity\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRace/ EthnicityOverall\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWhite\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAsian\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBlack\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHispanic/Latino\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003ep-test\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003en\u003c/b\u003e89019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e40312\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1312\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23519\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e22017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1859\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGender (%) Female\u003c/b\u003e55579 (\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23534 ( 58.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e710 ( 54.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15378 ( 65.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14803 ( 67.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1154 (\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e62.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e62.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMale\u003c/b\u003e32104 (\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16188 ( 40.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e581 (\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7748 ( 32.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6938 ( 31.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e649 (\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e36.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e44.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e34.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNon Binary\u003c/b\u003e183 ( 0.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e119 ( 0.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6 (\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21 ( 0.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e25 ( 0.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e12 (\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTransgender\u003c/b\u003e117 ( 0.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e47 ( 0.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 ( 0.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e44 ( 0.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e17 ( 0.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8 (\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eI prefer not to answer\u003c/b\u003e103 ( 0.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29 ( 0.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 ( 0.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28 ( 0.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e40 ( 0.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4 ( 0.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge (mean (SD))\u003c/b\u003e48.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e52.91 (15.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e44.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e47.16 (13.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e43.30 (14.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e42.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(15.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(15.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(16.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBMI (mean (SD))\u003c/b\u003e35.63 (6.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e35.20 (6.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e36.72 (7.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e35.39 (6.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e36.02 (7.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(6.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eIncome (%) Less than 25k\u003c/b\u003e27945 (\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8286 ( 23.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e168 (\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11661 ( 65.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7289 ( 51.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e541 (\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e40.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e36.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e25k to 50k\u003c/b\u003e14364 (\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6889 ( 19.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e180 (\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3386 ( 19.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3609 ( 25.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e300 (\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e20.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e20.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e50k to 100k\u003c/b\u003e15012 (\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10232 ( 29.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e274 (\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1900 ( 10.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2241 ( 15.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e365 (\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e21.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e24.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMore than 100k\u003c/b\u003e12363 (\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9791 ( 27.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e398 (\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e781 ( 4.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1118 ( 7.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e275 (\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e17.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e39.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e18.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEducation (%) Less than High School\u003c/b\u003e4068 ( 5.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e285 ( 0.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9 ( 0.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e431 ( 2.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3327 ( 17.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e16 (\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHigh School Graduate/GED\u003c/b\u003e21288 (\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6786 ( 17.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e104 (\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8187 ( 41.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5867 ( 31.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e344 (\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e26.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e20.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSome College\u003c/b\u003e25544 ( 32.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12421 ( 32.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e227 (\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6714 ( 34.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5578 ( 29.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e604 (\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e35.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCollege or Higher\u003c/b\u003e28921 (\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19179 ( 49.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e938 (\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4188 ( 21.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3862 ( 20.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e754 (\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e36.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e73.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e43.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eInsurance Use (%) No\u003c/b\u003e7738 ( 8.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1634 ( 4.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e49 ( 3.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2914 ( 13.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3049 ( 14.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e92 (\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eYes\u003c/b\u003e78833 (\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e38157 ( 95.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1236 (\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19546 ( 87.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e18198 ( 85.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1696 (\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e91.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e96.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e94.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eInsurance group (%) No Coverage\u003c/b\u003e78407 (\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e35326 ( 88.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1119 (\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20860 ( 92.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e19498 ( 91.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1604\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e90.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e87.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e89.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePrivate Insurance\u003c/b\u003e3709 ( 4.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2356 ( 5.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e135 (\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e450 ( 2.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e674 ( 3.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e94 ( 5.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePublic Insurance\u003c/b\u003e3820 ( 4.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1646 ( 4.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19 (\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1081 ( 4.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e994 ( 4.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e80 (\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMultiple\u003c/b\u003e598 ( 0.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e451 ( 1.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12 (\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e59 ( 0.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e70 ( 0.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6 ( 0.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSmoking (%) No\u003c/b\u003e50886 (\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21346 ( 54.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e951 (\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12634 ( 56.2 )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14905 ( 70.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1050 (\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e59.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e75.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e59.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eYes\u003c/b\u003e35144 (\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17964 ( 45.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e308 (\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9847 ( 43.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6304 ( 29.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e721 (\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e40.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e40.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEver use of\u003c/b\u003e78713 (\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e35403 ( 87.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1155\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20947 ( 89.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e19542 ( 88.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1666 ( 89.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003einsulin (%) No\u003c/b\u003e88.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e88.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eYes\u003c/b\u003e10306 (\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4909 ( 12.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e157 (\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2572 ( 10.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2475 ( 11.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e193 ( 10.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e11.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNon-insulin Diabetes\u003c/b\u003e75323 (\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e33013 ( 81.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1042\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20316 ( 86.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e19324 ( 87.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1628 ( 87.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMedication use (%) No\u003c/b\u003e84.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e79.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eYes\u003c/b\u003e13696 (\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7299 ( 18.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e270 (\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3203 ( 13.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2693 ( 12.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e231 ( 12.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e15.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eType 2 DM (%) No\u003c/b\u003e71211 (\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e32289 ( 80.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e904 (\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19048 ( 81.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e17442 ( 79.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1528 ( 82.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e80.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e68.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eYes\u003c/b\u003e17808 (\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8023 ( 19.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e408 (\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4471 ( 19.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4575 ( 20.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e331 ( 17.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e20.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHypertension (%) No\u003c/b\u003e83902 (\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e37550 ( 93.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1240\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22117 ( 94.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e21217 ( 96.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1778 ( 95.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e94.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e94.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eYes\u003c/b\u003e5117 ( 5.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2762 ( 6.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e72 (\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1402 ( 6.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e800 ( 3.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e81 ( 4.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHyperlipidemia (%)No\u003c/b\u003e63011 (\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24708 ( 61.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e863 (\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18932 ( 80.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e17118 ( 77.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1390 ( 74.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e70.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e65.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eYes\u003c/b\u003e26008 (\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15604 ( 38.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e449 (\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4587 ( 19.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4899 ( 22.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e469 ( 25.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e29.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e34.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eChronic Kidney\u003c/b\u003e83903 (\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e37445 ( 92.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1238\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22268 ( 94.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e21166 ( 96.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1786 ( 96.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDisease (%)No\u003c/b\u003e94.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e94.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eYes\u003c/b\u003e5116 ( 5.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2867 ( 7.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e74 (\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1251 ( 5.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e851 ( 3.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e73 ( 3.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHeart Failure (%) No\u003c/b\u003e863 ( 65.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e63011 ( 70.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24708 (\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18932 ( 80.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e17118 ( 77.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1390 ( 74.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e61.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eOverall, 1.17% (n\u0026thinsp;=\u0026thinsp;1,039) of the cohort initiated GLP-1 RA therapy within one year of diagnosis of T2DM or obesity. Adjusted Cox proportional hazards model analyses revealed significant racial/ethnic disparities \u003cb\u003e(\u003c/b\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e - Black patients had a significantly lower hazard of early GLP-1 RA initiation compared to NH White patients (HR: 0.705, 95% CI: 0.530\u0026ndash; 0.939, p\u0026thinsp;=\u0026thinsp;0.0168). Hispanic/Latino patients had a 38% lower hazard of early initiation compared to NH Whites (HR: 0.623, 95% CI: 0.455\u0026ndash;0.852, p\u0026thinsp;=\u0026thinsp;0.0031). Asian patients had comparable hazards to NH Whites (HR: 1.139, 95% CI: 0.636\u0026ndash;2.042, p\u0026thinsp;=\u0026thinsp;0.6615). Patients categorized as Others showed no statistically significant difference compared to NH Whites (HR: 0.884, 95% CI: 0.483\u0026ndash;1.616, p\u0026thinsp;=\u0026thinsp;0.688).\u003c/p\u003e \n\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eSubgroup Analyses\u003c/h2\u003e \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e \u003ch2\u003eDiabetic, Non-Obese Patients\u003c/h2\u003e \u003cp\u003eIn diabetic non-obese patients, Asian patients were more likely to initiate GLP-1 RA therapy compared to NH Whites (HR: 1.931, 95% CI: 1.092\u0026ndash;3.416, p\u0026thinsp;=\u0026thinsp;0.024) \u003cb\u003e(\u003c/b\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e\u003cb\u003e).\u003c/b\u003e No statistically significant differences were observed among Black or Hispanic/Latino patients compared to NH Whites in this subgroup.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eNon-Diabetic, Obese Patients\u003c/h2\u003e \u003cp\u003eAmong non-diabetic obese patients, Black patients had a significantly lower hazard of early GLP-1 RA initiation compared to NH Whites (HR: 0.621, 95% CI: 0.405\u0026ndash;0.954, p\u0026thinsp;=\u0026thinsp;0.0298). However, Hispanic/Latino patients also had significantly lower hazards (HR: 0.257, 95% CI: 0.135\u0026ndash;0.490, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Asian patients had no statistically significant differences compared to NH Whites (HR: 0.609, 95% CI: 0.150\u0026ndash;2.468, p\u0026thinsp;=\u0026thinsp;0.487) \u003cb\u003e(\u003c/b\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e\u003cb\u003e).\u003c/b\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eDiabetic, Obese Patients\u003c/h2\u003e \u003cp\u003eAmong patients with both diabetes and obesity, Black patients exhibited significantly reduced hazards for GLP-1 RA initiation compared to NH Whites (HR: 0.577, 95% CI: 0.458\u0026ndash;0.726, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) \u003cb\u003e(\u003c/b\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e. Hispanic/Latino patients similarly demonstrated lower hazards (HR: 0.570, 95% CI: 0.451\u0026ndash;0.720, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) \u003cb\u003e(\u003c/b\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e. Asian patients had a non-significant increased hazard of initiation (HR: 1.503, 95% CI: 0.840\u0026ndash;2.689, p\u0026thinsp;=\u0026thinsp;0.170).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eMASLD Patients\u003c/h2\u003e \u003cp\u003eIn the MASLD subgroup, disparities were nuanced. Asian MASLD patients were significantly more likely to initiate GLP-1 RA therapy compared to NH Whites (HR: 5.406, 95% CI: 2.185\u0026ndash; 13.612, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) \u003cb\u003e(\u003c/b\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e. Black MASLD patients exhibited no statistically significant difference in hazards compared to NH Whites (HR: 1.076, 95% CI: 0.324\u0026ndash;3.569, p\u0026thinsp;=\u0026thinsp;0.905) \u003cb\u003e(\u003c/b\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e. Hispanic/Latino MASLD patients also showed no significant differences (HR: 0.986, 95% CI: 0.483\u0026ndash;2.014, p\u0026thinsp;=\u0026thinsp;0.969) \u003cb\u003e(\u003c/b\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e.\u003c/p\u003e \u003cp\u003eThe patterns of disparities are illustrated in the cumulative incidence curves \u003cb\u003e(\u003c/b\u003eFigs.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e2\u003c/span\u003eA\u0026ndash;D\u003cb\u003e)\u003c/b\u003e, which depict one-year initiation rates stratified by race and ethnicity across various subgroups.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003ePredictors of Early Uptake\u003c/h2\u003e \u003cp\u003eIndependent predictors of early GLP-1 RA uptake included higher BMI (HR per unit increase: 1.04, 95% CI: 1.03\u0026ndash;1.05, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), presence of T2DM (HR: 9.43, 95% CI: 8.31\u0026ndash;10.69, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and hyperlipidemia (HR: 2.24, 95% CI: 1.95\u0026ndash;2.57, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) \u003cb\u003e(\u003c/b\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e. Conversely, older age (HR per year: 0.98, 95% CI: 0.97\u0026ndash;0.99, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and male sex (HR: 0.76, 95% CI: 0.68\u0026ndash;0.85, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) were associated with lower uptake hazards \u003cb\u003e(\u003c/b\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study aimed to assess racial and ethnic disparities in the early uptake of GLP-1 RAs among patients with T2DM and/or obesity, with a specific focus on disparities in MASLD patients. Unlike previous research that primarily examined overall prescription trends, this study provides novel insights into disparities at the early initiation stage, a critical window for optimizing treatment outcomes. Our findings demonstrate that early GLP-1 RA utilization remains suboptimal, with Black and Hispanic patients significantly less likely to initiate therapy within one year of diagnosis compared to White patients. These disparities persisted despite adjustments for sociodemographic and clinical factors, highlighting systemic barriers to equitable access. Notably, among MASLD patients, Asian individuals were significantly more likely to receive early GLP-1 RA prescriptions, compared to NH Whites, Blacks and Asians, suggesting potential differences in provider prescribing patterns or patient engagement. These results underscore the urgent need for targeted interventions to address racial and ethnic disparities in early GLP-1 RA adoption.\u003c/p\u003e \u003cp\u003eDespite the gradual increase in GLP-1 RA use among eligible patients amidst its incontrovertible cardiometabolic benefits and incorporation into societal guidelines, overall utilization remains suboptimal and relatively lower among historically marginalized ethnicities compared to other ethnicities \u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e,\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. A retrospective study conducted by Eberly et al \u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e utilizing data from the Optum Clinformatics Data Mart found that among commercially insured patients\u003c/p\u003e \u003cp\u003ewith T2DM, being Black, Asian, and Hispanic was associated with low GLP1-RA use despite the 100% insurance coverage of this cohort and adjustments for visits to endocrinology and cardiology specialty providers. Historically marginalized ethnicities in the United States are disproportionately burdened with T2DM and its complications, including cardiovascular disease and mortality \u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. A recent study found Black patients experienced the highest obesity- related cardiovascular mortality rate in the United States between 1999 and 2020 \u003csup\u003e27\u003c/sup\u003e. Our results highlight the public health injustice of the persistence of very low GLP-1RA initiation rates in these high-risk populations who are likely to derive the most benefits from its use. Previous studies have found socioeconomic status to be a major determinant of GLP-1 RA or SGLT2-I use in patients with T2DM \u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e,\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e,\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e,\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. Falkentoft et al \u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e observed a higher probability of GLP- 1 RA or SGLT2-I initiation among high-income earners compared to patients with low income despite universal healthcare coverage and lower costs of medications in Denmark. The higher level of socioeconomic inequality observed in the United States compared to other developed countries like Denmark \u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e may further exacerbate disparities in access to evidence-based medications. In this United States cohort, we found socioeconomic factors such as access to healthcare, annual income levels, and insurance coverage status as notable predictors of early prescription of GLP1-RA. Additionally, Blacks and Hispanics had lower insurance coverage and annual income levels compared to White patients. The prohibitive cost implication among non-insured patients, high co-pays, physician prescription fatigue from previous unapproved orders, and disparities in the availability of prescribed medications in poorer communities may lead to resorting to alternatives with less proven cardiovascular benefits \u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e,\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e,\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eSubgroup analysis of patients with T2DM and /or obesity with metabolic dysfunction- associated steatotic liver disease showed that patients of Asian ethnicity were four times more likely to be initiated on GLP-1 RA compared to patients of other ethnicities. In contrast to our findings, a recent cross-sectional study by Alexopoulos et al \u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e did not identify any racial/ethnic\u003c/p\u003e \u003cp\u003edisparities in prescription patterns of GLP-1 RA and pioglitazone among patients with T2DM and MASH but found overall low prescription rates. However, this study was a corss-sectional, single-center study with a relatively smaller sample size and a less diverse ethnic/racial composition. Our findings might be explained by the increasingly recognized risks of metabolic syndrome including MASLD at a relatively lower body mass index (BMI) in Asians compared to other ethnicities. Physicians might therefore have a lower BMI threshold for evaluating for MASLD and subsequently prescribing metabolic disease-modifying medications in this population \u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. GLP-1 RAs have been shown in clinical trials to result in the improvement/resolution of MASH and reduction in the progression of fibrosis \u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e,\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. Findings from a retrospective study utilizing data from the Veterans Health Administration Corporate Data Warehouse and Central Cancer Registry showed that early initiation of GLP-1 RA may have a role in preventing progression to cirrhosis and lowering the risk of cirrhosis decompensating events in patients with MASLD (without cirrhosis) and diabetes compared to placebo. However, no statistically significant difference in terms of cirrhosis decompensation, hepatocellular carcinoma, and all-cause mortality was observed with initiation of GLP-1 RAs in patients already diagnosed with cirrhosis emphasizing the importance of early initiation of GLP-1 RAs\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. Considering the increased morbidity and mortality risks in historically marginalized groups (Blacks, Hispanics, and Native Americans/Alaskans) with MASLD \u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e, increased efforts to curb this disparity and ensure early initiation of GLP-1 RAs have the potential to significantly improve clinical outcomes.\u003c/p\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eStrengths and Limitations\u003c/h2\u003e \u003cp\u003eThis study has several notable strengths. The use of the \"All of Us\" Research Program provided a large and diverse dataset, allowing for robust analyses of racial and ethnic disparities across various subgroups, including MASLD. The study\u0026rsquo;s longitudinal design, which enabled dynamic observation of time-to-therapy initiation, adds a unique dimension to the analysis by\u003c/p\u003e \u003cp\u003ecapturing disparities in timing as well as uptake rates. Additionally, the incorporation of comprehensive clinical, demographic, lifestyle and socioeconomic data ensured a nuanced understanding of factors influencing GLP-1 RA uptake. By focusing on MASLD, the study addresses an emerging area of research, offering novel insights into disparities within this population. Furthermore, the generalizability of findings is enhanced by the diversity of the All of Us cohort, which reflects the broader U.S. population, particularly underrepresented groups.\u003c/p\u003e \u003cp\u003eDespite these strengths, the study has limitations. The exclusion of participants with missing data for key variables or underrepresented racial/ethnic groups may introduce selection bias and limit the generalizability of findings to smaller populations, such as Native American or Pacific Islander groups. The retrospective design relies on existing records, which may lack granularity for certain variables, such as provider-level factors or patient preferences, potentially introducing information bias. While robust adjustments were made for socioeconomic and clinical confounders, residual confounding cannot be completely ruled out. Additionally, the observational nature of the study precludes causal inferences regarding the relationship between race/ethnicity and GLP-1 RA uptake. Finally, the reliance on electronic health records introduces variability in coding practices and may not fully capture eligibility or adherence to therapy, further complicating interpretations.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eFuture Research\u003c/h2\u003e \u003cp\u003eFuture research should explore the underlying mechanisms driving racial and ethnic disparities in early GLP-1 RA uptake, particularly the roles of healthcare provider prescribing behaviors, patient preferences, and structural barriers such as medication cost and insurance coverage. Longitudinal studies incorporating real-world adherence data and patient-reported outcomes would provide valuable insights into how these disparities evolve over time and impact long- term health outcomes.\u003c/p\u003e \u003cp\u003eAdditionally, further investigation is warranted into the high early uptake of GLP-1 RAs among Asian patients with MASLD, particularly whether this reflects provider awareness, patient demand, or biological differences influencing treatment decisions. Comparative effectiveness research evaluating alternative treatment pathways in marginalized populations could inform tailored interventions to improve equitable access. Finally, policy-focused studies assessing the impact of insurance coverage expansions, cost-reduction strategies, and provider education initiatives on medication uptake could help identify scalable solutions to mitigate disparities in GLP-1 RA utilization.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn conclusion, this study of US adults with T2DM and/or obesity found significant racial and ethnic disparities in the early adoption of GLP-1 receptor agonists (GLP-1 RAs) among adults of Hispanic and Black ethnicity. Subgroup analysis of eligible patients with MASLD identified a higher early uptake of GLP-RA among Asians compared to other ethnicities. A higher BMI, T2DM, and hyperlipidemia were independently associated with higher GLP-1 RA initiation. Our findings emphasize the need for targeted efforts to promote equitable and early GLP-1 RA utilization for all eligible patients.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eConflicts of Interest:\u003c/h2\u003e\n\u003cp\u003eAll authors declare no potential conflicts of interest relevant to this manuscript for the past three years.\u003c/p\u003e\n\u003ch2\u003eFunding\u0026nbsp;Disclosure:\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eNo funding was received for this study from the National Institutes of Health (NIH), Wellcome Trust, Howard Hughes Medical Institute (HHMI), or any other organization.\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\n\u003cp\u003eSarpong Boateng, MD, MPH: Conceptualization; Methodology; Data curation; Formal analysis.Prince Ameyaw, MD: Conceptualization; Methodology; Investigation; Yussif Issaka, MD: Investigation; Resources; Writing \u0026ndash; Review \u0026amp; Editing; Supervision; Corresponding author.Yazan A. Al-Ajlouni, MD, MPhil: Formal analysis; Visualization; Writing \u0026ndash; Review \u0026amp; Editing.Basile Njei, MD, MPH, PhD: Methodology; Supervision; Funding acquisition; Writing \u0026ndash; Review \u0026amp; Editing.All authors have read and approved the final version of the manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eRinella ME, Lazarus JV, Ratziu V, et al. A multisociety Delphi consensus statement on new fatty liver disease nomenclature. \u003cem\u003eHepatology\u003c/em\u003e. Dec 1 2023;78(6):1966\u0026ndash;1986. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1097/HEP.0000000000000520\u003c/span\u003e\u003cspan address=\"10.1097/HEP.0000000000000520\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAbushamat LA, Shah PA, Eckel RH, Harrison SA, Barb D. 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Jun 2024;44(6):1316\u0026ndash;1328. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1111/liv.15880\u003c/span\u003e\u003cspan address=\"10.1111/liv.15880\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTables 1 to 3 are available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"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":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"GLP-1 receptor agonists, racial disparities, ethnic disparities, early medication uptake, MASLD, Type 2 diabetes mellitus","lastPublishedDoi":"10.21203/rs.3.rs-6926160/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6926160/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003eGlucagon-like peptide-1 receptor agonists (GLP-1 RAs) are effective in managing Type 2 diabetes mellitus (T2DM), obesity, and metabolic dysfunction-associated steatotic liver disease (MASLD). While disparities in GLP-1 RA prescriptions exist, little is known about racial and ethnic differences in early initiation, particularly in MASLD patients. This study evaluates racial and ethnic disparities in early GLP-1 RA uptake among eligible patients with and without MASLD\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eWe conducted a retrospective cohort study using the All of Us Research Program (2016–2022), including adults (≥18 years) with T2DM and/or obesity eligible for GLP-1 RA therapy. Early uptake was defined as a prescription within one year of eligibility.\u003c/p\u003e\n\u003cp\u003eRace/ethnicity was categorized as non-Hispanic (NH) White, Black, Hispanic/Latino, Asian, or Other. Multivariable Cox regression models assessed associations between race/ethnicity and early prescription, adjusting for sociodemographic and clinical factors. A sub-analysis examined disparities among MASLD patients.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eAmong 89,019 eligible patients, 1.17% (n=1,039) initiated GLP-1 RA therapy within one year. Black (HR: 0.79, 95%CI: 0.65–0.95, p=0.014) and Hispanic/Latino (HR: 0.66, 95%CI: 0.53–0.82, p\u0026lt;0.001) patients had significantly lower uptake than NH Whites. Among MASLD patients, Asians had significantly higher early uptake (HR: 5.41, 95%CI: 2.2–13.6, p\u0026lt;0.001). Higher BMI, T2DM, and hyperlipidemia predicted early initiation, while older age, male sex, Black, and Hispanic race were associated with lower uptake\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion(s)\u003c/strong\u003e: Significant racial and ethnic disparities exist in early GLP-1 RA uptake. Efforts are needed to promote equitable access and utilization, particularly for high-risk populations\u003c/p\u003e","manuscriptTitle":"Racial and ethnic disparities in early uptake of GLP-1 receptor agonists in patients with and without MASLD","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-23 08:00:20","doi":"10.21203/rs.3.rs-6926160/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"9222d45d-974b-4b0d-9847-20d45e5b8d2d","owner":[],"postedDate":"June 23rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-07-13T04:38:23+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-23 08:00:20","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6926160","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6926160","identity":"rs-6926160","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}