Influence of Social Determinants of Health on Cardiovascular Risk Profiles in Middle Eastern Patients with ASCVD: Insights from SMuRFS Stratification

Influence of Social Determinants of Health on Cardiovascular Risk Profiles in Middle Eastern Patients with ASCVD: Insights from SMuRFS Stratification | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Influence of Social Determinants of Health on Cardiovascular Risk Profiles in Middle Eastern Patients with ASCVD: Insights from SMuRFS Stratification Osama Alkouri, Zainab Albikawi, Ahmad M. Al-Bashaireh, Ahmad Rajeh Saifan, and 11 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6360446/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 Social determinants of health (SDOH) may significantly influence atherosclerotic cardiovascular disease (ASCVD) development and progression. However, no large-scale Middle Eastern studies have used the full SDOH framework to assess its impact on ASCVD patients. This study investigates how SDOH affect cardiovascular risk profiles in Middle Eastern ASCVD patients, with a focus on those without social, mental, and risk factors (SMuRFs). Methods Data from six established registries and the Jordan SMuRF-less study were analyzed, covering baseline demographics, cardiovascular risk factors, comorbidities, use of secondary prevention medications, and one-year outcomes for patients with 0, 1–2, or 3–4 SMuRFs. Results Significant associations were found between SMuRF categories and SDOH. Individuals with 3–4 SMuRFs had lower educational levels (71.5%) and were more likely to have health insurance (82.8%) compared to those with fewer SMuRFs. Higher education correlated with more males (72.4%) and higher smoking rates (46.8%), while lower education was linked to higher rates of hypertension, diabetes, chronic kidney disease, and heart failure. Health insurance was associated with greater medication use and higher prevalence of these conditions. Conclusion This study highlights the significant role of education and health insurance in cardiovascular risk in Middle Eastern ASCVD patients. Lower education levels are linked to higher health risks, while insured patients have better healthcare access but higher disease burdens. Targeted public health strategies are needed. Trial Registration The study is registered on ClinicalTrials.gov under the identifier (NCT06199869) as of January 9, 2024. Social Determinants of Health ASCVD Cardiovascular risk factors. Middle Eastern Patients SMuRFS Introduction Cardiovascular diseases (CVDs) remain the leading cause of mortality and morbidity globally as well as in the MENA region ( 1 ). Moreover, CVD prevalence in the Middle East has been significantly high and reported as being 10.1%. It has been observed that the high prevalence has been closely associated with the prevalence of traditional risk factors such as dyslipidemia, hypertension, and diabetes mellitus ( 2 ). Meanwhile, there has been growing awareness in recent years regarding the role that the social determinants of health (SDOH) play in influencing cardiovascular risk profiles. SDOH encompasses the conditions in which individuals are born, live, work, and age, and they significantly impact health outcomes. Specifically, socioeconomic status, education level, employment status, and access to health services have been identified as determinants of cardiovascular health( 3 ). In addition, in the Middle East, urbanization, economic inequalities, and cultural changes have altered lifestyle habits, dietary habits, and access to healthcare, further enhancing the effect of SDOH on cardiovascular risk ( 4 , 5 ). In particular, the GCC countries have reported higher rates of ASCVD among the youth than the worldwide averages, and this necessitates careful consideration of both the genetic and environmental factors( 6 ). for instance, there exists evidence reported of significantly younger patient populations with higher ASCVD risk in the Arabian Gulf that also have higher burden of potentially modifiable risk factors such as hypertension and diabetes( 6 ). Furthermore, traditional Modifiable Cardiovascular Risk Factors (SMuRFs) like hypertension, diabetes mellitus, hyperlipidemia, and smoking are well-established causes for atherosclerotic cardiovascular disease (ASCVD) ( 7 ). Controlling the ASCVD epidemic thus mostly depends on early identification and management of these risk factors( 8 – 12 ). At least one SMuRF is thought to be fundamental driver of coronary artery disease (CAD), acute coronary syndrome (ACS), carotid artery disease and stroke, and peripheral arterial disease (PAD). The fundamental components of the Framingham risk score and other validated algorithms are SMuRFs, which thus provide evidence-based recommendations for the prevention of cardiovascular disease in clinical practice and help to build focused treatments against SMuRFs( 13 ). However, a subset of patients presents with ASCVD in the absence of these traditional risk factors, referred to as SMuRF-less patients( 14 ). These patients account for about 1.5–26% of the ASCVD population( 7 , 15 ). Moreover, studies have proven that SMuRF-less patients have worse outcomes than patients who have detectable risk factors( 16 ). This paradox requires the identification of non-traditional risk factors like SDOH that may contribute towards the development and progression of ASCVD in these patients. It has been found that low socio-economic status is predictive of poorer health outcomes and higher mortality rates in patients who have ASCVD. Patients who are not SMuRFs and who would otherwise be considered low risk have unexpectedly high cardiovascular morbidity and mortality( 17 , 18 ). This indicates that one needs to have an understanding of the health systems locally, the cultural practices and the barriers that are socioeconomic in nature in order to effectively manage and assess cardiovascular risk( 19 ). The SMuRFs model offers one useful lens by which this study will examine the gap in cardiovascular outcomes among Middle Eastern patients. Additionally, the complexity of these interactions underscores the need for multifaceted approaches that integrate SDOH into the existing cardiovascular models of care, as observed in numerous studies exploring the convergence of these determinants among varied populations( 20 , 21 ). To our knowledge, no large-scale middle eastern studies have utilized the full SDOH framework in an effort to estimate the prevalence of social determinants among patients with ASCVD. Therefore, the current study aims to investigate the influence of social determinants of health on cardiovascular risk profiles among Middle Eastern patients with ASCVD, with a focus on the stratification of SMuRF-less individuals. Materials and methods Study Design This research utilized data from two key sources. The first source included a cohort of consecutive adult patients (aged 18 years or older) diagnosed with Atherosclerotic Cardiovascular Disease (ASCVD), who were prospectively enrolled in the Jordan SMuRF-less Study (ClinicalTrials.gov, identifier number NCT06199869). The enrolment period spanned from January 10, 2024, to August 20, 2024, at three community hospitals and six tertiary care centres across Jordan, which included three Ministry of Health hospitals, two university hospitals, and one private teaching hospital. The second data source involved a post hoc analysis of patients with ASCVD from six Middle Eastern registries ( 22 – 28 ). These registries include the First Jordan Percutaneous Coronary Intervention Registry (CliniclTrials.gov identifier NCT01841346) ( 22 ), the Jordan Covid-19 the Pandemic Acute Cardiovascular events Study (NCT04368637), the Jordan Atrial Fibrillation Study (NCT03917992) ( 25 ), Statin Eligibility Among Middle Eastern Patients Presenting with Acute Myocardial Infarction (NCT03485742) ( 26 ), the Atherosclerotic Cardiovascular Disease Novel and Classical Risk Factors in Young Middle Eastern Women Study (NCT04975503) ( 23 ), and Surviving a Decade or More after Coronary Revascularization in a Middle Eastern Population Study (NCT03491722) ( 24 ). Data collection was carried out by trained coordinators using standardized case report forms, documenting information on demographics, medical history, traditional and non-traditional risk factors, comorbidities, use of secondary cardiovascular prevention medications, and one-year survival after the first cardiovascular event. Inclusion Criteria and Exposure Definitions The study included patients with ASCVD, which encompassed stroke, coronary artery disease (CAD), peripheral arterial disease, and carotid artery disease. CAD patients included those with chronic coronary angina (CSA), acute coronary syndrome (ACS) (both ST-segment elevation myocardial infarction [STEMI] and non-ST-segment-elevation ACS), and CAD diagnosed through coronary computed tomography angiography (CCTA). Patients were categorized into three groups according to the number of traditional cardiovascular risk factors (SMuRFs) present: no SMuRFs, 1–2 SMuRFs, and 3–4 SMuRFs. Definitions of SMuRFs The SMuRFs were categorized as binary variables. The diagnostic criteria for high serum LDL-C levels, type 2 diabetes (T2D), hypertension (HTN), and smoking status adhered to established definitions from previous studies ( 28 – 33 ). Dyslipidemia was defined by a previous diagnosis, use of lipid-lowering medications, or elevated LDL-C levels exceeding target thresholds. T2D was diagnosed based on a previous diagnosis, use of glucose-lowering medications, or an A1c level ≥ 6.5%. HTN was diagnosed based on a prior diagnosis, use of antihypertensive medications, or a new diagnosis during hospitalization with repeated systolic blood pressure measurements ≥ 140 mm Hg and/or diastolic blood pressure ≥ 90 mm Hg. A participant was categorized as a current smoker if they had smoked regularly within the year prior to enrolment. Regarding other traditional risk factors, one traditional risk factor was evaluated: a family history of premature cardiovascular disease (CVD). A positive family history was defined as the occurrence of a cardiovascular event in a first-degree relative before the age of 55 for males or 65 for females. This non-interventional study was conducted in accordance with the ethical principles outlined in the Declaration of Helsinki and received approval from the Institutional Review Board/Independent Ethics Committee at Istishari Hospital, Amman, Jordan. Written informed consent was obtained from all participants. The study is registered with ClinicalTrials.gov (NCT06199869). Statistical analysis Data was analyzed using SPSS V 24. To investigate the associations between social, medical, and risk factor status (SMuRFS) groups and social determinants of health, participants were classified into three categories based on their SMuRFS count: Group 1 (SMuRFS-Less, no risk factors), Group 2 (one to two SMuRFS), and Group 3 (three to four SMuRFS). The social determinants examined included educational attainment, health insurance status, and residential area. Chi-square tests were employed to assess differences among these categorical variables, with statistical significance defined as p < 0.05. Additionally, comparisons of sample characteristics and medication use were made based on educational level (low vs. high) and health insurance status (insured vs. uninsured), due to significant differences observed in these variables. For continuous variables, independent t-tests were conducted, while categorical variables were analyzed using chi-square tests. Results Social Determinants of Health by SMuRFS Groups The findings revealed a significant relationship between educational attainment and SMuRFS categories (p < .05). Notably, a larger percentage of individuals with three to four SMuRFS had lower educational levels (71.5%) compared to those with one to two SMuRFS (64.2%) and those without SMuRFS (63.6%) (see Table 1). Furthermore, individuals with three to four SMuRFS were more likely to have health insurance (82.8%) than those with one to two SMuRFS (75.7%) or those without SMuRFS (50.0%). On the other hand, residency (urban vs. rural) did not show a significant correlation with SMuRFS categories (p > .05) (see Table 1). Table 1: Social Determinants of Health of the sample based on SMuRFS groups (N=1059) Variable Total sample (N=1059) (G1): SMuRFS-Less (n=44) (G2) one to two SMuRFS (n=528) (G3) three to four SMuRFS (487) p value Level of Education No education and low education till Diploma High Education (BSc, MSc, and PhD) 715 (67.5) 344 (32.5) 28 (63.6) 16 (36.4) 339 (64.2) 189 (35.8) 348 (71.5) 139 (28.5) <0.05 Health Insurance Yes No 824 (77.9) 235 (22.1) 22 (50.0) 22 (50.0) 399 (75.7) 129 (24.3) 403 (82.8) 84 (17.2) <0.001 Residency City Urban 822 (77.7) 237 (22.3) 33 (75.0) 12 (27.3) 406 (76.9) 122 (23.1) 383 (78.8) 103 (21.2) NS Depression Yes No NS NS: Not significant. Sample Characteristics by Educational Level The analysis revealed a significant relationship between education level and gender (p < .001), with a greater proportion of males in the high-education group (72.4%) compared to the low-education group (49.5%). Furthermore, individuals with lower education levels exhibited significantly higher rates of hypertension (68.4%, p < .001), diabetes mellitus (53.4%, p < .001), chronic kidney disease (12.7%, p < .05), and heart failure (29.2%, p < .001). In contrast, the prevalence of smoking was notably higher among those with higher education (46.8%) than those with lower education (39.4%). Additionally, patients with lower education had significantly higher body mass index (BMI) (29.8 ± 5.6) compared to those with higher education (28.4 ± 4.8), as well as elevated triglyceride levels (144.3 ± 129.9 vs. 126.1 ± 101.1) (see Table 2). Table 2: Sample characteristics based on the educational level (N=1059) Variable Total sample (N=1059) (G1): Low Education (n=715) (G2) High Education (n=344) t test or X 2 , p value Age 57.4±12.3 57.8±12.3 56.1±12.3 NS Gender Male Female 603 (56.9) 456 (43.1) 354 (49.5) 361(50.5) 249 (72.4) 95 (27.6) 49.6, p <0.001 History of hypertension 680 (64.2) 489 (68.4) 191 (55.5) 16.7, p <0.001 History of diabetes Meletus 513 (48.4) 382 (53.4) 131 (38.1) 21.9, p <0.001 History of dyslipidemia 827 (78.1) 558 (78.0) 269 (78.2) NS Smoking 443 (41.8) 282(39.4) 161(46.8) 5.2, p <0.05 Family history of premature CVD 439 (41.5) 289 (40.4) 150 (43.6) NS History of CKD 118 (11.1) 91 (12.7) 27 (7.8) 5.6, p <0.05 History of Heart Failure 277 (26.2) 209 (29.2) 68 (19.8) 10.8, p <0.001 BMI (kg/m 2 ) 29.4±5.4 29.8±5.6 28.4±4.8 4.0, p <0.001 LDL 102.6±45.3 102.0±44.8 103.9±46.4 NS Total cholesterol 172.5±53.4 173.1±53.3 171.5±53.6 NS Triglycerides 138.4±121.6 144.3±129.9 126.1±101.1 2.3, p<0.05 HDL 42.6±20.3 41.7±12.0 43.0±23.2 NS NS: Not significant. CKD: Chronic kidney disease, CVD: Cardiovascular diseases, HDL: High density lipoproteins, LDL: Low density lipoproteins. Medication Use by Educational Level Significant differences were observed in the use of aspirin (p < .05), with a higher proportion of individuals in the low-education group using aspirin (82.4%) compared to those in the high-education group (76.5%). Furthermore, beta-blocker usage was notably more prevalent in the low-education group (76.5%). The use of oral hypoglycemic agents was also higher among individuals in the low-education group (37.1%) (see Table 3). Table 3: Comparisons of medication use based on the educational level (N= 1059) Medication used Total sample (N=1059) (G1): Low Education (n=715) (G2) High Education (n=344) X 2 , p value Statins 936 (88.4) 638 (89.2) 298 (86.6) NS Aspirin 852 (80.5) 589 (82.4) 263 (76.5) 5.2, p <0.05 Plavix 526 (49.7) 355 (49.7) 171 (49.7) NS P2Y12 inhibitors 72 (6.8) 42 (5.9) 30 (8.7) NS Dual antiplatelet therapy 495 (46.7) 340 (47.6) 155 (45.1) NS Beta blockers 789 (74.5) 547 (76.5) 242 (70.3) 4.6, p <0.05 Oral hypoglycemic agents 368 (34.7) 265 (37.1) 103 (29.9) 5.2, p <0.05 NS: Not significant. Values are presented as number (%) Sample Characteristics by Health Insurance Status Individuals with health insurance were significantly older (58.0 ± 12.3 years) compared to those without insurance (55.0 ± 12.2 years). Additionally, patients with insurance had notably higher rates of hypertension (67.6%), diabetes mellitus (51.3%), dyslipidemia (81.1%), chronic kidney disease (12.6%), and heart failure (29.1%). Moreover, patients with health insurance had significantly higher triglyceride levels (148.4 ± 122.1) than those without insurance (103.7 ± 112.7) (see Table 4). Table 4: Sample characteristics based on the insurance status (N=1058*) Variable Total sample (N=1058*) (G1): Have Insurance (n=824) (G2) No Insurance (n=234) t test or X 2 , p value Age 57.4±12.3 58.0±12.3 55.0±12.2 3.4, p <0.001 Gender Male Female 602 (56.9) 456 (43.1) 354 (49.5) 361(50.5) 126 (53.8) 108 (46.2) NS History of hypertension 679 (64.2) 557 (67.6) 122 (52.1) 19.0, p <0.001 History of diabetes Meletus 513 (48.4) 423 (51.3) 90 (38.5) 12.1, p <0.001 History of dyslipidemia 826 (78.1) 668 (81.1) 158 (67.5) 19.5, p <0.001 Smoking 443 (41.8) 339 (41.1) 104 (44.4) NS Family history of premature CVD 439 (41.5) 343 (41.6) 96 (41.0) NS History of CKD 118 (11.1) 104 (12.6) 14 (6.0) 8.1, p <0.005 History of Heart Failure 277 (26.2) 240 (29.1) 37 (15.8) 16.7, p <0.001 BMI (kg/m 2 ) 29.4±5.4 29.4±5.5 29.4±5.3 NS LDL 102.6±45.3 101.5±44.9 107.6±50.0 NS Total cholesterol 172.5±53.4 171.1±53.1 179.1±54.5 NS Triglycerides 138.4±121.6 148.4±122.1 103.7±112.7 5.0, p<0.001 HDL 42.6±20.3 42.4±21.5 43.1±12.9 NS *There was one missing data. NS: Not significant. Values are presented as a number (%) or M±SD. CKD: Chronic kidney disease, CVD: Cardiovascular diseases, HDL: High density lipoproteins, LDL: Low density lipoproteins Medication Use by Health Insurance Status Significant differences in medication use were observed between insured and uninsured patients. A greater percentage of insured patients used statins (89.9%), aspirin (83.1%), and dual antiplatelet therapy (48.8%). Additionally, the use of beta-blockers was notably higher among insured patients (76.5%), and oral hypoglycemic agents were more commonly used by those with insurance (36.2%) (see Table 5). Table 5: Comparisons of medication use based on the insurance status (N=1058*) Medication used Total sample (N=1058*) (G1): Have Insurance (n=824) (G2) No Insurance (n=234) X 2 , p value Statins 935 (88.4) 741 (89.9) 194 (82.9) 8.7 p <0.005 Aspirin 851 (80.5) 685 (83.1) 166 (70.9) 17.2, p <0.001 Plavix 526 (49.7) 413 (50.1) 113 (48.3) NS P2Y12 inhibitors 72 (6.8) 57 (6.9) 15 (6.4) NS Dual antiplatelet therapy 495 (46.7) 402 (48.8) 93(39.7) 6.0, p <0.01 Beta blockers 789 (74.5) 630 (76.5) 159 (67.9) 7.0, p <0.05 Oral hypoglycemic agents 368 (34.7) 298 (36.2) 70 (29.9) 3.1, p <0.05 *There was one missing data. NS: Not significant. Discussion The current study aims to investigate the influence of social determinants of health on cardiovascular risk profiles among Middle Eastern patients with ASCVD, focusing on the stratification of SMuRF-less individuals. The findings illustrate the high relationship of education with categorization into Standard Modifiable Risk Factors (SMuRFS) types. Specifically, the prevalence of low educational level was greater in the three to four SMuRFS group (71.5%) compared to the one to two SMuRFS group (64.2%) and the no SMuRFS group (63.6%). This trend is reinforced by the literature showing that educational status determines health literacy, subsequently determining the management and knowledge about cardiovascular risk factors( 34 ). This result aligns with regional evidence that indicates that lower educational levels are associated with higher rates of cardiovascular risk factors and worse outcomes. To illustrate, one study in the Arabian Gulf demonstrated that lower education was associated with increased rates of major adverse cardiac events in patients who present with acute coronary syndromes and emphasized the need for educational intervention towards healthy lifestyles and optimal risk factor management( 35 ). Furthermore, the fact that higher SMuRFS categories have higher health insurance rates is essential. Outcomes suggest that 82.8% of the three to four SMuRFS group were insured, while 75.7% of the one to two SMuRFS group and only 50.0% of the no SMuRFS group were. This result warrants critical examination into the correlation between the availability of health resources such as insurance and the frequency of preventive health behavior among SMuRFS groups. Interestingly, the analyses found no statistically significant correlation between residency status (rural or urban) and SMuRFS categories (p > 0.05). This contrasts with existing research that has highlighted urban environments as positively and negatively impacting health outcomes depending on the varied access to health facilities and resources( 36 ). Lower levels of education were observed among the individuals who demonstrated higher rates of prevalence of hypertension (68.4%, p < .001), diabetes mellitus (53.4%, p < .001), chronic kidney disease (CKD) (12.7%, p < .05), and heart failure (29.2%, p < .001). Previous studies have proven that there is a direct correlation between low education and higher cardiovascular morbidity and that education is a protective factor for the development of chronic diseases( 37 , 38 ). These findings are consistent with studies from the Eastern Mediterranean region showing a strong inverse association between education and cardiometabolic diseases( 39 ). Smoking prevalence also demonstrated a contradictory pattern in that higher education levels correlate with increased smoking rates (46.8% among high-education respondents and 39.4% among low-education respondents). This could suggest that there exists a complex relationship where more educated people have increased contact with smoking norms or perceive smoking as being a social activity even when they are well aware of the health risks involved in the use of tobacco( 40 , 41 ). This analysis also revealed that there was significant educational group variation in the body mass index (BMI) in that the less educated participants' BMI was significantly higher (29.8 ± 5.6) than that of the more educated participants' (28.4 ± 4.8, p < .001). Prior research has often reported a strong correlation between education level, lifestyle factors, and obesity. Less education accompanies worse dietary habits and less access to physical activity( 38 , 42 ). Also, increased levels of triglycerides were observed among less educated individuals (144.3 ± 129.9 versus 126.1 ± 101.1), again pointing towards poor metabolic status in these individuals consistent with existing evidence that educational inequalities are associated with cardiovascular disease risk factors( 43 ). The less educated group reported higher rates of aspirin use (82.4%) than the highly educated group (76.5%), and this result was statistically significant at p < .05. This result would indicate that less educated patients are more likely to take aspirin as a preventive treatment for cardiovascular events. Aspirin is one of the cornerstones in pharmacotherapy for patients at risk for cardiovascular disease that includes atherosclerosis, so this trend among less educated patients would suggest greater use of established treatment regimens. Higher use of aspirin (82.4%) and beta-blockers (76.5%) in the low-education group may also reflect the increased prevalence of comorbid conditions such as hypertension and diabetes mellitus in this group. This trend also mirrors the global trend where poorer socio-economic status is linked with increased CVD risk and resultant drug use( 44 ). Higher use of oral hypoglycemic medications among the less educated (37.1%) accords with the higher prevalence of diabetes mellitus among them. This result concurs with regional data that indicate that less education has been associated with a higher prevalence of DM, potentially because less educated individuals have poorer health literacy and less access to preventive health services( 44 ). Those who have health insurance are also much older and show greater incidences of dyslipidemia, chronic renal disease, heart failure, diabetes mellitus, and hypertension. This trend implies that those with current medical issues are more likely to have health insurance, maybe because of higher healthcare demand. Moreover, insured individuals showed far higher triglyceride levels, suggesting a more significant load of metabolic diseases. These results are consistent with studies showing that health insurance coverage is linked to better detection and management of chronic diseases, influencing reported prevalence rates( 45 ). Compared to their uninsured colleagues, insured patients showed considerably more use of statins (89.9% vs. 82.9%), aspirin (83.1% vs. 70.9%), and dual antiplatelet treatment (48.8% vs. 39.7%). This trend mirrors a larger trend shown in several Middle Eastern research whereby access to evidence-based therapies and insurance status are linked to higher usage of cardiovascular drugs( 35 ). Studies have found that having insurance usually corresponds with improved adherence to recommended medication schedules since the financial obstacles are progressively eliminated, allowing patients to get required medications more easily( 46 ). Further underlining the differences in healthcare experiences between the insured and uninsured populations, a systematic review on medication adherence found that insurance status dramatically affects the capacity to get drugs( 47 ). This financial aspect plays a vital role in medication utilization, especially in cases of chronic conditions, where the continuous need for medications is evident( 48 ). Additionally, most common among insured patients were oral hypoglycemic medicines (36.2% vs. 29.9%) and beta-blockers (76.5% vs. 67.9%). This most certainly reflects the higher frequency of diabetes, hypertension, and other comorbidities this group exhibits, as found in previous analyses. The noted correlations highlight how insurance coverage provides access to drugs and could affect clinical decision-making and long-term illness control in patients with complicated medical profiles. These disparities highlight a key issue within healthcare systems across the Middle East: inequitable access to essential medications due to insurance status. In several countries, including those in the Gulf Cooperation Council (GCC), access to health insurance can vary considerably based on employment, nationality, and socioeconomic position( 49 – 51 ). This also suggests that financial constraints associated with lacking insurance can detrimentally affect diabetes management and, by extension, long-term health outcomes( 52 ). Previous research has shown that insured people are more likely to follow recommended drug schedules, partly because of less financial strain, mirroring a more significant trend whereby people without insurance find it difficult to sustain treatment regimens( 53 ). The findings of this study have key implications for clinical practice and public health. Enhancing health literacy and education could reduce cardiovascular risk, particularly in less educated populations. Community-based educational programs promoting healthy eating, physical activity, and smoking cessation may help alleviate the impact of SMuRFs ( 54 , 55 ). While health insurance improves care access, the higher prevalence of risk factors among insured individuals calls for more effective preventive strategies. Policymakers should integrate preventive care into insurance coverage, such as offering incentives for wellness programs or covering preventive services like nutritional counseling ( 56 ). The higher use of cardiovascular medications among the insured and less educated individuals underscores the need for equitable access to essential treatments. Reducing out-of-pocket costs and supporting medication adherence through programs like medication therapy management could improve treatment outcomes ( 56 , 57 ). Conclusion This study highlights the significant impact of social determinants, particularly education and health insurance, on the cardiovascular risk profiles of Middle Eastern patients with atherosclerotic cardiovascular disease (ASCVD). Lower education levels are associated with a higher burden of traditional risk factors, worse metabolic health, and increased prevalence of hypertension, diabetes, and heart failure. While medication adherence is higher among less-educated individuals, their elevated risk factor burden points to ongoing gaps in preventive care. Additionally, insured patients showed higher rates of chronic conditions and greater use of guideline-recommended therapies, reflecting both better healthcare access and a higher disease burden. The lack of a link between residency status and risk factors highlights regional healthcare disparities. The findings of this study underscore the importance of comprehensive public health strategies that tackle socio-economic inequalities, improve health education, and ensure equal access to healthcare. Addressing these social factors is crucial for reducing cardiovascular risk and enhancing long-term health outcomes for Middle Eastern patients with ASCVD. Future research should focus on developing targeted interventions incorporating culturally appropriate health promotion and chronic disease management methods in this diverse population. Declarations Author Contributions Conceptualization, O. A, A.H, M.J; Methodology, A.H, A.R.S, Z.A; A. M.A; Validation, A.R. S, N.A.R.; Formal Analysis, M.E.A, M.A, O.Q; Investigation, A.H.A. N.A.O, M. J. and M.A.; Data Curation, A.H.; Writing – Original Draft Preparation, O. A, A.R.S, N.A.R, O.Q, O.K., N.A.O, N. A and N.A. R; Writing – Review & Editing, O.A, A.H.A, N.A.N, Z.A., N. A; Visualization; O.A,O.K, N.A.N; Supervision, N.A.O, M.J, A.M.A, and M. E. A. Data Availability Statement The data from the current study is available upon request from the corresponding author. Ethics approval and consent to participate This study was conducted according to the Declaration of Helsinki. It received ethical approval and Institutional Review Board authorization from the participating institutions, including the Institutional Review Board/Independent Ethics Committee at Istishari Hospital in Amman, Jordan. All patients provided written informed consent. The study is registered on ClinicalTrials.gov under the identifier (NCT06199869). Acknowledgments The authors would like to thank all participants in the current study. Funding The study was not funded Conflicts of Interest All authors declare that they have no conflicts of interest in this work. Consent for publication Not applicable References Alhuneafat L, Al Ta'ani O, Jabri A, Tarawneh T, ElHamdan A, Naser A, et al. 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Health Literacy and Social Determinants of Health. The Southwest Respiratory and Critical Care Chronicles. 2023;11(47):26-32. !!! INVALID CITATION !!! {}. Ghobain MA, Ahmed A, Abdrabalnabi Z, Mutairi W, Khathaami AA. Prevalence of and Attitudes to Waterpipe Smoking Among Saudi Arabian Physicians. Eastern Mediterranean Health Journal. 2018;24(03):277-82. Bosdriesz JR, Mehmedovic S, Witvliet MG, Kunst AE. Socioeconomic Inequalities in Smoking in Low and Mid Income Countries: Positive Gradients Among Women? International Journal for Equity in Health. 2014;13(1):14. Bradley EH, Elkins BR, Herrin J, Elbel B. Health and Social Services Expenditures: Associations With Health Outcomes. BMJ Quality & Safety. 2011;20(10):826-31. Ingles J, Johnson R, Sarina T, Yeates L, Burns C, Gray B, et al. Social Determinants of Health in the Setting of Hypertrophic Cardiomyopathy. International Journal of Cardiology. 2015;184:743-9. Marzà-Florensa A, Vaartjes I, Graham I, Klipstein-Grobusch K, Grobbee DE, Joseph M, et al. A global perspective on cardiovascular risk factors by educational level in CHD patients: SURF CHD II. 2024;19(1):60. Wilder B, Pinedo A, Abusin S, Ansell D, Bacong AM, Calvin J, et al. A global perspective on socioeconomic determinants of cardiovascular health. 2024. Sarpong E, Bernard D, Miller GE. Changes in Pharmaceutical Treatment of Diabetes and Family Financial Burdens. Medical Care Research and Review. 2012;69(4):474-91. Majeed A, Rehman MA, Hussain I, Imran I, Saleem M, Saeed H, et al. The Impact of Treatment Adherence on Quality of Life Among Type 2 Diabetes Mellitus Patients – Findings From a Cross-Sectional Study. Patient Preference and Adherence. 2021;Volume 15:475-81. Agarwal A, Sandip S, Joshi A, Ashok A. The Patterns of Non-Adherence to Medication in Managing Cardiovascular Disease: A Descriptive Study. Cureus. 2024. Turk-Adawi K, Sarrafzadegan N, Fadhil I, Taubert K, Sadeghi M, Wenger NK, et al. Cardiovascular disease in the Eastern Mediterranean region: epidemiology and risk factor burden. 2018;15(2):106-19. Mokdad AH, Forouzanfar MH, Daoud F, El Bcheraoui C, Moradi-Lakeh M, Khalil I, et al. Health in times of uncertainty in the eastern Mediterranean region, 1990–2013: a systematic analysis for the Global Burden of Disease Study 2013. 2016;4(10):e704-e13. Grace S, Tan N, Wenger N, Sadeghi M, Taubert K, Fadhil I, et al. Cardiovascular disease in the Eastern Mediterranean region: Epidemiology and risk factor burden. 2017. Campbell P, Axon DR, Taylor AM, Smith KL, Pickering M, Black H, et al. Hypertension, Cholesterol and Diabetes Medication Adherence, Health Care Utilization and Expenditure in a Medicare Supplemental Sample. Medicine. 2021;100(35):e27143. Gellad WF, Grenard JL, Marcum ZA. A Systematic Review of Barriers to Medication Adherence in the Elderly: Looking Beyond Cost and Regimen Complexity. The American Journal of Geriatric Pharmacotherapy. 2011;9(1):11-23. Magnani JW, Mujahid MS, Aronow HD, Cené CW, Dickson VV, Havranek E, et al. Health literacy and cardiovascular disease: fundamental relevance to primary and secondary prevention: a scientific statement from the American Heart Association. Circulation. 2018;138(2):e48-e74. Sørensen K, Pelikan JM, Röthlin F, Ganahl K, Slonska Z, Doyle G, et al. Health literacy in Europe: comparative results of the European health literacy survey (HLS-EU). The European journal of public health. 2015;25(6):1053-8. Visseren FL, Mach F, Smulders YM, Carballo D, Koskinas KC, Bäck M, et al. 2021 ESC Guidelines on cardiovascular disease prevention in clinical practice: Developed by the Task Force for cardiovascular disease prevention in clinical practice with representatives of the European Society of Cardiology and 12 medical societies With the special contribution of the European Association of Preventive Cardiology (EAPC). European heart journal. 2021;42(34):3227-337. Arnett DK, Blumenthal RS, Albert MA, Buroker AB, Goldberger ZD, Hahn EJ, et al. 2019 ACC/AHA guideline on the primary prevention of cardiovascular disease: a report of the American College of Cardiology/American Heart Association Task Force on Clinical Practice Guidelines. Journal of the American College of cardiology. 2019;74(10):e177-e232. Additional Declarations No competing interests reported. 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10:53:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6360446/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6360446/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":101474261,"identity":"21449448-77b5-4eda-967f-1863da8dc139","added_by":"auto","created_at":"2026-01-30 06:26:27","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1095721,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6360446/v1/46c9feb2-9d06-4c7b-975c-882bdc9af66c.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Influence of Social Determinants of Health on Cardiovascular Risk Profiles in Middle Eastern Patients with ASCVD: Insights from SMuRFS Stratification","fulltext":[{"header":"Introduction","content":"\u003cp\u003eCardiovascular diseases (CVDs) remain the leading cause of mortality and morbidity globally as well as in the MENA region (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). Moreover, CVD prevalence in the Middle East has been significantly high and reported as being 10.1%. It has been observed that the high prevalence has been closely associated with the prevalence of traditional risk factors such as dyslipidemia, hypertension, and diabetes mellitus (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). Meanwhile, there has been growing awareness in recent years regarding the role that the social determinants of health (SDOH) play in influencing cardiovascular risk profiles. SDOH encompasses the conditions in which individuals are born, live, work, and age, and they significantly impact health outcomes. Specifically, socioeconomic status, education level, employment status, and access to health services have been identified as determinants of cardiovascular health(\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn addition, in the Middle East, urbanization, economic inequalities, and cultural changes have altered lifestyle habits, dietary habits, and access to healthcare, further enhancing the effect of SDOH on cardiovascular risk (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). In particular, the GCC countries have reported higher rates of ASCVD among the youth than the worldwide averages, and this necessitates careful consideration of both the genetic and environmental factors(\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). for instance, there exists evidence reported of significantly younger patient populations with higher ASCVD risk in the Arabian Gulf that also have higher burden of potentially modifiable risk factors such as hypertension and diabetes(\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFurthermore, traditional Modifiable Cardiovascular Risk Factors (SMuRFs) like hypertension, diabetes mellitus, hyperlipidemia, and smoking are well-established causes for atherosclerotic cardiovascular disease (ASCVD) (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). Controlling the ASCVD epidemic thus mostly depends on early identification and management of these risk factors(\u003cspan additionalcitationids=\"CR9 CR10 CR11\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). At least one SMuRF is thought to be fundamental driver of coronary artery disease (CAD), acute coronary syndrome (ACS), carotid artery disease and stroke, and peripheral arterial disease (PAD). The fundamental components of the Framingham risk score and other validated algorithms are SMuRFs, which thus provide evidence-based recommendations for the prevention of cardiovascular disease in clinical practice and help to build focused treatments against SMuRFs(\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eHowever, a subset of patients presents with ASCVD in the absence of these traditional risk factors, referred to as SMuRF-less patients(\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). These patients account for about 1.5\u0026ndash;26% of the ASCVD population(\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). Moreover, studies have proven that SMuRF-less patients have worse outcomes than patients who have detectable risk factors(\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). This paradox requires the identification of non-traditional risk factors like SDOH that may contribute towards the development and progression of ASCVD in these patients. It has been found that low socio-economic status is predictive of poorer health outcomes and higher mortality rates in patients who have ASCVD.\u003c/p\u003e \u003cp\u003ePatients who are not SMuRFs and who would otherwise be considered low risk have unexpectedly high cardiovascular morbidity and mortality(\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). This indicates that one needs to have an understanding of the health systems locally, the cultural practices and the barriers that are socioeconomic in nature in order to effectively manage and assess cardiovascular risk(\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). The SMuRFs model offers one useful lens by which this study will examine the gap in cardiovascular outcomes among Middle Eastern patients. Additionally, the complexity of these interactions underscores the need for multifaceted approaches that integrate SDOH into the existing cardiovascular models of care, as observed in numerous studies exploring the convergence of these determinants among varied populations(\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). To our knowledge, no large-scale middle eastern studies have utilized the full SDOH framework in an effort to estimate the prevalence of social determinants among patients with ASCVD. Therefore, the current study aims to investigate the influence of social determinants of health on cardiovascular risk profiles among Middle Eastern patients with ASCVD, with a focus on the stratification of SMuRF-less individuals.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Design\u003c/h2\u003e \u003cp\u003eThis research utilized data from two key sources. The first source included a cohort of consecutive adult patients (aged 18 years or older) diagnosed with Atherosclerotic Cardiovascular Disease (ASCVD), who were prospectively enrolled in the Jordan SMuRF-less Study (ClinicalTrials.gov, identifier number NCT06199869). The enrolment period spanned from January 10, 2024, to August 20, 2024, at three community hospitals and six tertiary care centres across Jordan, which included three Ministry of Health hospitals, two university hospitals, and one private teaching hospital. The second data source involved a post hoc analysis of patients with ASCVD from six Middle Eastern registries (\u003cspan additionalcitationids=\"CR23 CR24 CR25 CR26 CR27\" citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThese registries include the First Jordan Percutaneous Coronary Intervention Registry (CliniclTrials.gov identifier NCT01841346) (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e), the Jordan Covid-19 the Pandemic Acute Cardiovascular events Study (NCT04368637), the Jordan Atrial Fibrillation Study (NCT03917992) (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e), Statin Eligibility Among Middle Eastern Patients Presenting with Acute Myocardial Infarction (NCT03485742) (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e), the Atherosclerotic Cardiovascular Disease Novel and Classical Risk Factors in Young Middle Eastern Women Study (NCT04975503) (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e), and Surviving a Decade or More after Coronary Revascularization in a Middle Eastern Population Study (NCT03491722) (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eData collection was carried out by trained coordinators using standardized case report forms, documenting information on demographics, medical history, traditional and non-traditional risk factors, comorbidities, use of secondary cardiovascular prevention medications, and one-year survival after the first cardiovascular event.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eInclusion Criteria and Exposure Definitions\u003c/h3\u003e\n\u003cp\u003eThe study included patients with ASCVD, which encompassed stroke, coronary artery disease (CAD), peripheral arterial disease, and carotid artery disease. CAD patients included those with chronic coronary angina (CSA), acute coronary syndrome (ACS) (both ST-segment elevation myocardial infarction [STEMI] and non-ST-segment-elevation ACS), and CAD diagnosed through coronary computed tomography angiography (CCTA). Patients were categorized into three groups according to the number of traditional cardiovascular risk factors (SMuRFs) present: no SMuRFs, 1\u0026ndash;2 SMuRFs, and 3\u0026ndash;4 SMuRFs.\u003c/p\u003e\n\u003ch3\u003eDefinitions of SMuRFs\u003c/h3\u003e\n\u003cp\u003eThe SMuRFs were categorized as binary variables. The diagnostic criteria for high serum LDL-C levels, type 2 diabetes (T2D), hypertension (HTN), and smoking status adhered to established definitions from previous studies (\u003cspan additionalcitationids=\"CR29 CR30 CR31 CR32\" citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e). Dyslipidemia was defined by a previous diagnosis, use of lipid-lowering medications, or elevated LDL-C levels exceeding target thresholds. T2D was diagnosed based on a previous diagnosis, use of glucose-lowering medications, or an A1c level\u0026thinsp;\u0026ge;\u0026thinsp;6.5%. HTN was diagnosed based on a prior diagnosis, use of antihypertensive medications, or a new diagnosis during hospitalization with repeated systolic blood pressure measurements\u0026thinsp;\u0026ge;\u0026thinsp;140 mm Hg and/or diastolic blood pressure\u0026thinsp;\u0026ge;\u0026thinsp;90 mm Hg. A participant was categorized as a current smoker if they had smoked regularly within the year prior to enrolment.\u003c/p\u003e \u003cp\u003eRegarding other traditional risk factors, one traditional risk factor was evaluated: a family history of premature cardiovascular disease (CVD). A positive family history was defined as the occurrence of a cardiovascular event in a first-degree relative before the age of 55 for males or 65 for females. This non-interventional study was conducted in accordance with the ethical principles outlined in the Declaration of Helsinki and received approval from the Institutional Review Board/Independent Ethics Committee at Istishari Hospital, Amman, Jordan. Written informed consent was obtained from all participants. The study is registered with ClinicalTrials.gov (NCT06199869).\u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eData was analyzed using SPSS V 24. To investigate the associations between social, medical, and risk factor status (SMuRFS) groups and social determinants of health, participants were classified into three categories based on their SMuRFS count: Group 1 (SMuRFS-Less, no risk factors), Group 2 (one to two SMuRFS), and Group 3 (three to four SMuRFS). The social determinants examined included educational attainment, health insurance status, and residential area. Chi-square tests were employed to assess differences among these categorical variables, with statistical significance defined as p\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003cp\u003eAdditionally, comparisons of sample characteristics and medication use were made based on educational level (low vs. high) and health insurance status (insured vs. uninsured), due to significant differences observed in these variables. For continuous variables, independent t-tests were conducted, while categorical variables were analyzed using chi-square tests.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eSocial Determinants of Health by SMuRFS Groups\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe findings revealed a significant relationship between educational attainment and SMuRFS categories (p \u0026lt; .05). Notably, a larger percentage of individuals with three to four SMuRFS had lower educational levels (71.5%) compared to those with one to two SMuRFS (64.2%) and those without SMuRFS (63.6%) (see Table 1). Furthermore, individuals with three to four SMuRFS were more likely to have health insurance (82.8%) than those with one to two SMuRFS (75.7%) or those without SMuRFS (50.0%). On the other hand, residency (urban vs. rural) did not show a significant correlation with SMuRFS categories (p \u0026gt; .05) (see Table 1).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 1: Social Determinants of Health\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eof the sample based on SMuRFS groups (N=1059)\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"101%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal sample\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(N=1059)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e(G1): SMuRFS-Less\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(n=44)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e(G2) one to two SMuRFS (n=528)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e(G3) three to four SMuRFS (487)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003ep value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLevel of Education\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eNo education and low\u003cspan style=\"text-align: inherit;\"\u003eeducation till Diploma\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;High Education (BSc, MSc,\u0026nbsp;\u003cspan style=\"text-align: inherit;\"\u003eand PhD)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e715 (67.5)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e344 (32.5)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e28 (63.6)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e16 (36.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e339 (64.2)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e189 (35.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e348 (71.5)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e139 (28.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026lt;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHealth Insurance\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Yes\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;No\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e824 (77.9)\u003c/p\u003e\n \u003cp\u003e235 (22.1)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e22 (50.0)\u003c/p\u003e\n \u003cp\u003e22 (50.0)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e399 (75.7)\u003c/p\u003e\n \u003cp\u003e129 (24.3)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e403 (82.8)\u003c/p\u003e\n \u003cp\u003e84 (17.2)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eResidency\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eCity\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Urban\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e822 (77.7)\u003c/p\u003e\n \u003cp\u003e237 (22.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e33 (75.0)\u003c/p\u003e\n \u003cp\u003e12 (27.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e406 (76.9)\u003c/p\u003e\n \u003cp\u003e122 (23.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e383 (78.8)\u003c/p\u003e\n \u003cp\u003e103 (21.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eNS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDepression\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eYes\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;No\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003eNS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cul\u003e\n \u003cli\u003eNS: Not significant.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eSample Characteristics by Educational Level\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe analysis revealed a significant relationship between education level and gender (p \u0026lt; .001), with a greater proportion of males in the high-education group (72.4%) compared to the low-education group (49.5%). Furthermore, individuals with lower education levels exhibited significantly higher rates of hypertension (68.4%, p \u0026lt; .001), diabetes mellitus (53.4%, p \u0026lt; .001), chronic kidney disease (12.7%, p \u0026lt; .05), and heart failure (29.2%, p \u0026lt; .001).\u003c/p\u003e\n\u003cp\u003eIn contrast, the prevalence of smoking was notably higher among those with higher education (46.8%) than those with lower education (39.4%). Additionally, patients with lower education had significantly higher body mass index (BMI) (29.8 \u0026plusmn; 5.6) compared to those with higher education (28.4 \u0026plusmn; 4.8), as well as elevated triglyceride levels (144.3 \u0026plusmn; 129.9 vs. 126.1 \u0026plusmn; 101.1) (see Table 2).\u003c/p\u003e\n\u003cp\u003eTable 2: Sample characteristics based on the educational level (N=1059)\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal sample\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(N=1059)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e(G1): Low Education\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(n=715)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e(G2) High Education (n=344)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u003cstrong\u003et test or \u003cem\u003eX\u003csup\u003e2\u003c/sup\u003e,\u0026nbsp;\u003c/em\u003ep value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e57.4\u0026plusmn;12.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e57.8\u0026plusmn;12.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003e56.1\u0026plusmn;12.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003eNS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGender\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Male\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Female\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e603 (56.9)\u003c/p\u003e\n \u003cp\u003e456 (43.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e354 (49.5)\u003c/p\u003e\n \u003cp\u003e361(50.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e249 (72.4)\u003c/p\u003e\n \u003cp\u003e95 (27.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e49.6, p \u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003eHistory of hypertension\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e680 (64.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e489 (68.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003e191 (55.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e16.7, p \u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003eHistory of diabetes Meletus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e513 (48.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e382 (53.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003e131 (38.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e21.9, p \u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003eHistory of dyslipidemia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e827 (78.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e558 (78.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003e269 (78.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003eNS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003eSmoking\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e443 (41.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e282(39.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003e161(46.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e5.2, p \u0026lt;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003eFamily history of premature CVD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e439 (41.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e289 (40.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003e150 (43.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003eNS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003eHistory of CKD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e118 (11.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e91 (12.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003e27 (7.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e5.6, p \u0026lt;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003eHistory of Heart Failure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e277 (26.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e209 (29.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003e68 (19.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e10.8, p \u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003eBMI (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e29.4\u0026plusmn;5.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e29.8\u0026plusmn;5.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003e28.4\u0026plusmn;4.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e4.0, p \u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003eLDL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e102.6\u0026plusmn;45.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e102.0\u0026plusmn;44.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003e103.9\u0026plusmn;46.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003eNS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003eTotal cholesterol\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e172.5\u0026plusmn;53.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e173.1\u0026plusmn;53.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003e171.5\u0026plusmn;53.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003eNS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003eTriglycerides\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e138.4\u0026plusmn;121.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e144.3\u0026plusmn;129.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003e126.1\u0026plusmn;101.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e2.3, p\u0026lt;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003eHDL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e42.6\u0026plusmn;20.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e41.7\u0026plusmn;12.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003e43.0\u0026plusmn;23.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003eNS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNS: Not significant. CKD: Chronic kidney disease, CVD: Cardiovascular diseases, HDL: High density lipoproteins, LDL: Low density lipoproteins.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMedication Use by Educational Level\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSignificant differences were observed in the use of aspirin (p \u0026lt; .05), with a higher proportion of individuals in the low-education group using aspirin (82.4%) compared to those in the high-education group (76.5%). Furthermore, beta-blocker usage was notably more prevalent in the low-education group (76.5%). The use of oral hypoglycemic agents was also higher among individuals in the low-education group (37.1%) (see Table 3).\u003c/p\u003e\n\u003cp\u003eTable 3: Comparisons of medication use based on the educational level (N= 1059)\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMedication used\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal sample\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(N=1059)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e(G1): Low Education\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(n=715)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e(G2) High Education (n=344)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eX\u003csup\u003e2\u003c/sup\u003e,\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003ep value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003eStatins\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e936 (88.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e638 (89.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003e298 (86.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003eNS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003eAspirin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e852 (80.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e589 (82.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003e263 (76.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e5.2, p \u0026lt;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003ePlavix\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e526 (49.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e355 (49.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003e171 (49.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003eNS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003eP2Y12 inhibitors\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e72 (6.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e42 (5.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003e30 (8.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003eNS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003eDual antiplatelet therapy\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e495 (46.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e340 (47.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003e155 (45.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003eNS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003eBeta blockers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e789 (74.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e547 (76.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003e242 (70.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e4.6, p \u0026lt;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003eOral hypoglycemic agents\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e368 (34.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e265 (37.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003e103 (29.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e5.2, p \u0026lt;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cul\u003e\n \u003cli\u003eNS: Not significant.\u003c/li\u003e\n \u003cli\u003eValues are presented as number (%)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eSample Characteristics by Health Insurance Status\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIndividuals with health insurance were significantly older (58.0 \u0026plusmn; 12.3 years) compared to those without insurance (55.0 \u0026plusmn; 12.2 years). Additionally, patients with insurance had notably higher rates of hypertension (67.6%), diabetes mellitus (51.3%), dyslipidemia (81.1%), chronic kidney disease (12.6%), and heart failure (29.1%). Moreover, patients with health insurance had significantly higher triglyceride levels (148.4 \u0026plusmn; 122.1) than those without insurance (103.7 \u0026plusmn; 112.7) (see Table 4).\u003c/p\u003e\n\u003cp\u003eTable 4: Sample characteristics based on the insurance status (N=1058*)\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal sample\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(N=1058*)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e(G1): Have Insurance\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(n=824)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e(G2) No Insurance \u0026nbsp;(n=234)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u003cstrong\u003et test or \u003cem\u003eX\u003csup\u003e2\u003c/sup\u003e,\u0026nbsp;\u003c/em\u003ep value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e57.4\u0026plusmn;12.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e58.0\u0026plusmn;12.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003e55.0\u0026plusmn;12.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e3.4, p \u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGender\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Male\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Female\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e602 (56.9)\u003c/p\u003e\n \u003cp\u003e456 (43.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e354 (49.5)\u003c/p\u003e\n \u003cp\u003e361(50.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e126 (53.8)\u003c/p\u003e\n \u003cp\u003e108 (46.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eNS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003eHistory of hypertension\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e679 (64.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e557 (67.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003e122 (52.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e19.0, p \u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003eHistory of diabetes Meletus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e513 (48.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e423 (51.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003e90 (38.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e12.1, p \u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003eHistory of dyslipidemia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e826 (78.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e668 (81.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003e158 (67.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e19.5, p \u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003eSmoking\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e443 (41.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e339 (41.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003e104 (44.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003eNS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003eFamily history of premature CVD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e439 (41.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e343 (41.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003e96 (41.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003eNS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003eHistory of CKD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e118 (11.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e104 (12.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003e14 (6.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e8.1, p \u0026lt;0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003eHistory of Heart Failure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e277 (26.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e240 (29.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003e37 (15.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e16.7, p \u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003eBMI (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e29.4\u0026plusmn;5.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e29.4\u0026plusmn;5.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003e29.4\u0026plusmn;5.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003eNS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003eLDL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e102.6\u0026plusmn;45.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e101.5\u0026plusmn;44.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003e107.6\u0026plusmn;50.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003eNS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003eTotal cholesterol\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e172.5\u0026plusmn;53.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e171.1\u0026plusmn;53.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003e179.1\u0026plusmn;54.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003eNS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003eTriglycerides\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e138.4\u0026plusmn;121.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e148.4\u0026plusmn;122.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003e103.7\u0026plusmn;112.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e5.0, p\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003eHDL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e42.6\u0026plusmn;20.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e42.4\u0026plusmn;21.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003e43.1\u0026plusmn;12.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003eNS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e*There was one missing data. NS: Not significant. Values are presented as a number (%) or M\u0026plusmn;SD. CKD: Chronic kidney disease, CVD: Cardiovascular diseases, HDL: High density lipoproteins, LDL: Low density lipoproteins\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMedication Use by Health Insurance Status\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSignificant differences in medication use were observed between insured and uninsured patients. A greater percentage of insured patients used statins (89.9%), aspirin (83.1%), and dual antiplatelet therapy (48.8%). Additionally, the use of beta-blockers was notably higher among insured patients (76.5%), and oral hypoglycemic agents were more commonly used by those with insurance (36.2%) (see Table 5).\u003c/p\u003e\n\u003cp\u003eTable 5: Comparisons of medication use based on the insurance status (N=1058*)\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMedication used\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal sample\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(N=1058*)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e(G1): Have Insurance\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(n=824)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e(G2) No Insurance \u0026nbsp;(n=234)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eX\u003csup\u003e2\u003c/sup\u003e,\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003ep value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003eStatins\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e935 (88.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e741 (89.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003e194 (82.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e8.7 p \u0026lt;0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003eAspirin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e851 (80.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e685 (83.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003e166 (70.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e17.2, p \u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003ePlavix\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e526 (49.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e413 (50.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003e113 (48.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003eNS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003eP2Y12 inhibitors\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e72 (6.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e57 (6.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003e15 (6.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003eNS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003eDual antiplatelet therapy\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e495 (46.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e402 (48.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003e93(39.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e6.0, p \u0026lt;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003eBeta blockers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e789 (74.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e630 (76.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003e159 (67.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e7.0, p \u0026lt;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003eOral hypoglycemic agents\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e368 (34.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e298 (36.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003e70 (29.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e3.1, p \u0026lt;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e*There was one missing data. NS: Not significant. \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe current study aims to investigate the influence of social determinants of health on cardiovascular risk profiles among Middle Eastern patients with ASCVD, focusing on the stratification of SMuRF-less individuals. The findings illustrate the high relationship of education with categorization into Standard Modifiable Risk Factors (SMuRFS) types. Specifically, the prevalence of low educational level was greater in the three to four SMuRFS group (71.5%) compared to the one to two SMuRFS group (64.2%) and the no SMuRFS group (63.6%). This trend is reinforced by the literature showing that educational status determines health literacy, subsequently determining the management and knowledge about cardiovascular risk factors(\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e). This result aligns with regional evidence that indicates that lower educational levels are associated with higher rates of cardiovascular risk factors and worse outcomes. To illustrate, one study in the Arabian Gulf demonstrated that lower education was associated with increased rates of major adverse cardiac events in patients who present with acute coronary syndromes and emphasized the need for educational intervention towards healthy lifestyles and optimal risk factor management(\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e). Furthermore, the fact that higher SMuRFS categories have higher health insurance rates is essential. Outcomes suggest that 82.8% of the three to four SMuRFS group were insured, while 75.7% of the one to two SMuRFS group and only 50.0% of the no SMuRFS group were. This result warrants critical examination into the correlation between the availability of health resources such as insurance and the frequency of preventive health behavior among SMuRFS groups.\u003c/p\u003e \u003cp\u003eInterestingly, the analyses found no statistically significant correlation between residency status (rural or urban) and SMuRFS categories (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05). This contrasts with existing research that has highlighted urban environments as positively and negatively impacting health outcomes depending on the varied access to health facilities and resources(\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e). Lower levels of education were observed among the individuals who demonstrated higher rates of prevalence of hypertension (68.4%, p\u0026thinsp;\u0026lt;\u0026thinsp;.001), diabetes mellitus (53.4%, p\u0026thinsp;\u0026lt;\u0026thinsp;.001), chronic kidney disease (CKD) (12.7%, p\u0026thinsp;\u0026lt;\u0026thinsp;.05), and heart failure (29.2%, p\u0026thinsp;\u0026lt;\u0026thinsp;.001). Previous studies have proven that there is a direct correlation between low education and higher cardiovascular morbidity and that education is a protective factor for the development of chronic diseases(\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e). These findings are consistent with studies from the Eastern Mediterranean region showing a strong inverse association between education and cardiometabolic diseases(\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e). Smoking prevalence also demonstrated a contradictory pattern in that higher education levels correlate with increased smoking rates (46.8% among high-education respondents and 39.4% among low-education respondents). This could suggest that there exists a complex relationship where more educated people have increased contact with smoking norms or perceive smoking as being a social activity even when they are well aware of the health risks involved in the use of tobacco(\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e). This analysis also revealed that there was significant educational group variation in the body mass index (BMI) in that the less educated participants' BMI was significantly higher (29.8\u0026thinsp;\u0026plusmn;\u0026thinsp;5.6) than that of the more educated participants' (28.4\u0026thinsp;\u0026plusmn;\u0026thinsp;4.8, p\u0026thinsp;\u0026lt;\u0026thinsp;.001). Prior research has often reported a strong correlation between education level, lifestyle factors, and obesity. Less education accompanies worse dietary habits and less access to physical activity(\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e). Also, increased levels of triglycerides were observed among less educated individuals (144.3\u0026thinsp;\u0026plusmn;\u0026thinsp;129.9 versus 126.1\u0026thinsp;\u0026plusmn;\u0026thinsp;101.1), again pointing towards poor metabolic status in these individuals consistent with existing evidence that educational inequalities are associated with cardiovascular disease risk factors(\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e). The less educated group reported higher rates of aspirin use (82.4%) than the highly educated group (76.5%), and this result was statistically significant at p\u0026thinsp;\u0026lt;\u0026thinsp;.05. This result would indicate that less educated patients are more likely to take aspirin as a preventive treatment for cardiovascular events. Aspirin is one of the cornerstones in pharmacotherapy for patients at risk for cardiovascular disease that includes atherosclerosis, so this trend among less educated patients would suggest greater use of established treatment regimens.\u003c/p\u003e \u003cp\u003eHigher use of aspirin (82.4%) and beta-blockers (76.5%) in the low-education group may also reflect the increased prevalence of comorbid conditions such as hypertension and diabetes mellitus in this group. This trend also mirrors the global trend where poorer socio-economic status is linked with increased CVD risk and resultant drug use(\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e). Higher use of oral hypoglycemic medications among the less educated (37.1%) accords with the higher prevalence of diabetes mellitus among them. This result concurs with regional data that indicate that less education has been associated with a higher prevalence of DM, potentially because less educated individuals have poorer health literacy and less access to preventive health services(\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e). Those who have health insurance are also much older and show greater incidences of dyslipidemia, chronic renal disease, heart failure, diabetes mellitus, and hypertension. This trend implies that those with current medical issues are more likely to have health insurance, maybe because of higher healthcare demand. Moreover, insured individuals showed far higher triglyceride levels, suggesting a more significant load of metabolic diseases. These results are consistent with studies showing that health insurance coverage is linked to better detection and management of chronic diseases, influencing reported prevalence rates(\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eCompared to their uninsured colleagues, insured patients showed considerably more use of statins (89.9% vs. 82.9%), aspirin (83.1% vs. 70.9%), and dual antiplatelet treatment (48.8% vs. 39.7%). This trend mirrors a larger trend shown in several Middle Eastern research whereby access to evidence-based therapies and insurance status are linked to higher usage of cardiovascular drugs(\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e). Studies have found that having insurance usually corresponds with improved adherence to recommended medication schedules since the financial obstacles are progressively eliminated, allowing patients to get required medications more easily(\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e). Further underlining the differences in healthcare experiences between the insured and uninsured populations, a systematic review on medication adherence found that insurance status dramatically affects the capacity to get drugs(\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e). This financial aspect plays a vital role in medication utilization, especially in cases of chronic conditions, where the continuous need for medications is evident(\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAdditionally, most common among insured patients were oral hypoglycemic medicines (36.2% vs. 29.9%) and beta-blockers (76.5% vs. 67.9%). This most certainly reflects the higher frequency of diabetes, hypertension, and other comorbidities this group exhibits, as found in previous analyses. The noted correlations highlight how insurance coverage provides access to drugs and could affect clinical decision-making and long-term illness control in patients with complicated medical profiles. These disparities highlight a key issue within healthcare systems across the Middle East: inequitable access to essential medications due to insurance status. In several countries, including those in the Gulf Cooperation Council (GCC), access to health insurance can vary considerably based on employment, nationality, and socioeconomic position(\u003cspan additionalcitationids=\"CR50\" citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e). This also suggests that financial constraints associated with lacking insurance can detrimentally affect diabetes management and, by extension, long-term health outcomes(\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e). Previous research has shown that insured people are more likely to follow recommended drug schedules, partly because of less financial strain, mirroring a more significant trend whereby people without insurance find it difficult to sustain treatment regimens(\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe findings of this study have key implications for clinical practice and public health. Enhancing health literacy and education could reduce cardiovascular risk, particularly in less educated populations. Community-based educational programs promoting healthy eating, physical activity, and smoking cessation may help alleviate the impact of SMuRFs (\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e). While health insurance improves care access, the higher prevalence of risk factors among insured individuals calls for more effective preventive strategies. Policymakers should integrate preventive care into insurance coverage, such as offering incentives for wellness programs or covering preventive services like nutritional counseling (\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e). The higher use of cardiovascular medications among the insured and less educated individuals underscores the need for equitable access to essential treatments. Reducing out-of-pocket costs and supporting medication adherence through programs like medication therapy management could improve treatment outcomes (\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e).\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study highlights the significant impact of social determinants, particularly education and health insurance, on the cardiovascular risk profiles of Middle Eastern patients with atherosclerotic cardiovascular disease (ASCVD). Lower education levels are associated with a higher burden of traditional risk factors, worse metabolic health, and increased prevalence of hypertension, diabetes, and heart failure. While medication adherence is higher among less-educated individuals, their elevated risk factor burden points to ongoing gaps in preventive care. Additionally, insured patients showed higher rates of chronic conditions and greater use of guideline-recommended therapies, reflecting both better healthcare access and a higher disease burden. The lack of a link between residency status and risk factors highlights regional healthcare disparities. The findings of this study underscore the importance of comprehensive public health strategies that tackle socio-economic inequalities, improve health education, and ensure equal access to healthcare. Addressing these social factors is crucial for reducing cardiovascular risk and enhancing long-term health outcomes for Middle Eastern patients with ASCVD. Future research should focus on developing targeted interventions incorporating culturally appropriate health promotion and chronic disease management methods in this diverse population.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization, O. A, A.H, M.J; Methodology, A.H, A.R.S, Z.A; A. M.A; Validation, A.R. S, N.A.R.; Formal Analysis, M.E.A, M.A, O.Q; Investigation, A.H.A. N.A.O, M. J. and M.A.; Data Curation, A.H.; Writing \u0026ndash; Original Draft Preparation, O. A, A.R.S, N.A.R, O.Q, O.K., N.A.O, N. A and N.A. R; \u0026nbsp;Writing \u0026ndash; Review \u0026amp; Editing, O.A, A.H.A, N.A.N, Z.A., N. A; Visualization; O.A,O.K, N.A.N; Supervision, N.A.O, M.J, A.M.A, \u0026nbsp;and M. E. A.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data from the current study is available upon request from the corresponding author.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was conducted according to the Declaration of Helsinki. It received ethical approval and Institutional Review Board authorization from the participating institutions, including the Institutional Review Board/Independent Ethics Committee at Istishari Hospital in Amman, Jordan. \u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eAll patients provided written informed consent. The study is registered on ClinicalTrials.gov under the identifier (NCT06199869).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would like to thank all participants in the current study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was not funded\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors declare that they have no conflicts of interest in this work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAlhuneafat L, Al Ta\u0026apos;ani O, Jabri A, Tarawneh T, ElHamdan A, Naser A, et al. 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Journal of the American College of cardiology. 2019;74(10):e177-e232.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"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":"Social Determinants of Health, ASCVD, Cardiovascular risk factors. Middle Eastern Patients, SMuRFS","lastPublishedDoi":"10.21203/rs.3.rs-6360446/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6360446/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eSocial determinants of health (SDOH) may significantly influence atherosclerotic cardiovascular disease (ASCVD) development and progression. However, no large-scale Middle Eastern studies have used the full SDOH framework to assess its impact on ASCVD patients. This study investigates how SDOH affect cardiovascular risk profiles in Middle Eastern ASCVD patients, with a focus on those without social, mental, and risk factors (SMuRFs).\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eData from six established registries and the Jordan SMuRF-less study were analyzed, covering baseline demographics, cardiovascular risk factors, comorbidities, use of secondary prevention medications, and one-year outcomes for patients with 0, 1\u0026ndash;2, or 3\u0026ndash;4 SMuRFs.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eSignificant associations were found between SMuRF categories and SDOH. Individuals with 3\u0026ndash;4 SMuRFs had lower educational levels (71.5%) and were more likely to have health insurance (82.8%) compared to those with fewer SMuRFs. Higher education correlated with more males (72.4%) and higher smoking rates (46.8%), while lower education was linked to higher rates of hypertension, diabetes, chronic kidney disease, and heart failure. Health insurance was associated with greater medication use and higher prevalence of these conditions.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThis study highlights the significant role of education and health insurance in cardiovascular risk in Middle Eastern ASCVD patients. Lower education levels are linked to higher health risks, while insured patients have better healthcare access but higher disease burdens. Targeted public health strategies are needed.\u003c/p\u003e\u003ch2\u003eTrial Registration\u003c/h2\u003e \u003cp\u003eThe study is registered on ClinicalTrials.gov under the identifier (NCT06199869) as of January 9, 2024.\u003c/p\u003e","manuscriptTitle":"Influence of Social Determinants of Health on Cardiovascular Risk Profiles in Middle Eastern Patients with ASCVD: Insights from SMuRFS Stratification","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-08 11:31:45","doi":"10.21203/rs.3.rs-6360446/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":"a2dcb086-7dfa-49df-8142-87d9a6723732","owner":[],"postedDate":"May 8th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-01-30T06:25:51+00:00","versionOfRecord":[],"versionCreatedAt":"2025-05-08 11:31:45","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6360446","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6360446","identity":"rs-6360446","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}