Association of hepatic steatosis and fibrosis with 10-year estimated cardiovascular disease risk in hypertensive patients and the mediating role of triglyceride-glucose index

Association of hepatic steatosis and fibrosis with 10-year estimated cardiovascular disease risk in hypertensive patients and the mediating role of triglyceride-glucose index | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Association of hepatic steatosis and fibrosis with 10-year estimated cardiovascular disease risk in hypertensive patients and the mediating role of triglyceride-glucose index Yexin Yin, Lili Shi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6894190/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 Nonalcoholic fatty liver disease (NAFLD)/metabolic dysfunction-associated steatotic liver disease (MASLD) is the most prevalent chronic liver disease worldwide and has been significantly associated with both hypertension and cardiovascular disease (CVD). However, whether NAFLD/MASLD constitutes an independent risk factor for CVD remains inconclusive, and evidence from hypertensive populations is limited. Moreover, the underlying mechanisms of this complex association have not yet been fully elucidated. Methods A total of 1,083 participants from the NHANES database were included in this study. Eligible individuals were aged 30–79 years, had hypertension, and were free of cardiovascular disease (CVD) at baseline. Hepatic steatosis and significant liver fibrosis were assessed noninvasively using the United States Fatty Liver Index (USFLI) and the Fibrosis-4 (FIB-4) index, respectively. Hepatic steatosis was defined as a USFLI score ≥ 30, and significant fibrosis was defined as a FIB-4 index ≥ 1.3. Insulin resistance (IR) was estimated using the triglyceride-glucose (TyG) index. The 10-year risk of a first fatal or nonfatal CVD event was calculated using the PREVENT risk equation. Results Compared with individuals with simple steatosis (n = 483) or without hepatic steatosis (n = 313), those with both hepatic steatosis and significant fibrosis (n = 287) had a significantly higher estimated 10-year CVD risk (20.5% vs. 14.7% vs. 39.4%, p < 0.001). After adjusting for sex, education, race/ethnicity, physical activity, poverty-income ratio (PIR), and chronic kidney disease (CKD), individuals with both hepatic steatosis and significant fibrosis had a markedly increased risk of experiencing a first fatal or nonfatal CVD event over 10 years compared to those without steatosis (adjusted odds ratio: 15.2, 95% CI: 5.42–63.49). Sensitivity analyses confirmed the robustness of these findings. Furthermore, the TyG index significantly mediated 16.85% of the association between steatosis with significant fibrosis and the 10-year risk of CVD events. Conclusions Among individuals with hypertension but without a prior history of cardiovascular disease, those with both hepatic steatosis and significant fibrosis had a markedly higher estimated 10-year CVD risk compared to those with steatosis alone or without steatosis. Moreover, this association was significantly mediated by the TyG index. Health sciences/Cardiology Health sciences/Endocrinology Health sciences/Medical research Health sciences/Pathogenesis Metabolic dysfunction-Associated Steatotic Liver Disease Non-Alcoholic Fatty Liver Disease Insulin resistance Hypertension Prevention Cardiovascular risk Figures Figure 1 Figure 2 Figure 3 Figure 4 1. Introduction For many years, non-alcoholic fatty liver disease (NAFLD) has stood as the most prevalent liver condition globally. Recent investigations indicate a worldwide adult prevalence ranging from 25–30%, exhibiting geographical variations. In the United States, NAFLD prevalence is estimated at 26% and is projected to increase by over 100 million cases by 2040 [ 1 , 2 ]. NAFLD can progress to non-alcoholic steatohepatitis, which, with advancing fibrosis, may further lead to cirrhosis and subsequently a range of complications. Crucially, at any stage of NAFLD, cardiovascular disease remains the primary cause of mortality[ 3 ]. NAFLD is recognized as a systemic disease [ 4 ], In recent years, understanding of its intricate link with metabolic syndrome (MetS), encompassing hypertension, dyslipidemia, obesity, and type 2 diabetes, has deepened significantly within the academic community. To emphasize this association, a consensus has proposed replacing "non-alcoholic fatty liver disease (NAFLD)" with the new terminology "Metabolic dysfunction-Associated Steatotic Liver Disease (MASLD)" [ 5 , 6 ],This updated definition encompasses patients presenting with hepatic steatosis concurrently possessing at least one of five cardiometabolic risk factors. As implied by the description of this newly introduced terminology, clinical data also indicate its close association with CVD risk[ 7 ]. Hypertension, a core component of metabolic syndrome and an independent risk factor for CVD, has been shown to exhibit a comorbidity rate with NAFLD approximately 49.5% higher in hypertensive patients compared to the general population. Furthermore, bidirectional links between the two have been corroborated at the omics level [ 8 , 9 ]. These findings collectively support the notion that a complex and multifaceted interplay exists between NAFLD/MASLD and cardiovascular disease. However, to date, this association has been primarily observed at the clinical level, and the underlying pathophysiological mechanisms remain a matter of ongoing debate. The development of NAFLD is primarily driven by excessive nutritional intake, which promotes adipose tissue accumulation and imposes a metabolic burden on hepatic lipid handling. This imbalance disrupts lipid homeostasis in the liver, leading to mitochondrial dysfunction and oxidative stress. Subsequently, inflammatory cascades are activated, further aggravating hepatic injury and triggering the onset of IR[ 10 ]. The concept of MASLD further emphasizes the interplay between hepatic steatosis and metabolic syndrome, and how this interaction exacerbates systemic metabolic dysregulation in extrahepatic organs. Some researchers have proposed that traditional cardiovascular risk factors—such as dyslipidemia, insulin resistance, and chronic low-grade inflammation—may contribute to cardiovascular risk independently of NAFLD/MASLD itself. Meanwhile, other factors including coagulopathy, sympathetic nervous system dysregulation, and gut microbiota imbalance have been suggested as distinctive contributors to elevated CVD risk specifically among patients with NAFLD/MASLD. However, to date, no definitive evidence has been established to confirm either perspective[ 11 – 14 ]. Although NAFLD/MASLD imposes substantial economic and health burdens on hypertensive patients[ 15 ], particularly concerning CVD morbidity and mortality, current research investigating the impact of NAFLD/MASLD on CVD risk in this population remains limited[ 16 , 17 ] .Furthermore, studies exploring whether insulin resistance (IR) exerts a mediating effect in this association are notably scarce. Utilizing quantitative risk assessment tools to evaluate the risk of a first major adverse cardiovascular event in hypertensive individuals with NAFLD is a crucial starting point for clinicians in guiding primary CVD prevention decisions. Numerous CVD risk assessment tools have been proposed to predict the 10-year risk of first fatal and non-fatal CVD events. For instance, the Pooled Cohort Equation (PCE)[ 18 ], jointly developed and proposed by the American College of Cardiology (ACC) and the American Heart Association (AHA) in 2013, can be used to assess an individual's 10-year risk of atherosclerotic cardiovascular disease (ASCVD ) . As academic understanding of CVD and its comorbidities has deepened, the AHA introduced the concept of Cardiovascular-Kidney-Metabolic Syndrome (CKM), emphasizing the intricate interconnections and common progression among CVD, chronic kidney disease, type 2 diabetes, and obesity. Within this context, the PREVENT equations emerged as a comprehensive revision of the PCE model to better reflect contemporary cardiovascular risk profiles. It is thus evident that this tool is optimally suited for assessing CVD risk in NAFLD/MASLD patients[ 19 ]. Therefore, in this cross-sectional study, we aimed to investigate the association of hepatic steatosis (NAFLD/MASLD) and the severity of fibrosis (as determined by validated non-invasive biomarkers) with an increased 10-year CVD risk in adults with hypertension without pre-existing CVD. Concurrently, we conducted mediation analysis to explore and underscore the role of the TyG index in this process. 2. Methods 2.1 Data source and study population The data used in this study were derived from the National Health and Nutrition Examination Survey (NHANES) database. The survey protocol was approved by the Research Ethics Review Board of the National Center for Health Statistics (NCHS), and all participants provided written informed consent. Data collection was conducted in accordance with standardized procedures and was anonymized to ensure confidentiality. The datasets analyzed in this study are publicly available on the NHANES official website ( https://www.cdc.gov/nchs/nhanes/index.html ). We utilized data from the NHANES database spanning from 1999 to 2018, initially including 101,316 participants. The exclusion criteria were as follows: participants aged 79 years (n = 60,049); those without a diagnosis of hypertension (n = 25,432); individuals with missing or implausible data for calculating the triglyceride-glucose (TyG) index or the PREVENT risk equation (n = 14,239); those with missing data required for assessing NAFLD (n = 57); and participants with missing or positive status for hepatitis B surface antigen or hepatitis C antibody, excessive alcohol consumption, or missing sampling weight information (n = 456). After applying these criteria, a final sample of 1,083 participants (492 men and 591 women) was included in the analysis. The detailed selection process is illustrated in Fig. 1 and Supplementary Materials. 2.2 Clinical and laboratory data The extracted electronic health data included demographic characteristics (sex, age, race/ethnicity, education level), the poverty income ratio (PIR), physical activity levels, and clinical parameters such as body mass index (BMI), blood pressure, hepatitis B virus (HBV) and hepatitis C virus (HCV) serologies, and a range of biochemical markers. These included complete blood counts, lipid profiles, fasting glucose, urinary albumin and creatinine, glycated hemoglobin (HbA1c), serum creatinine, and liver enzymes [aspartate aminotransferase (AST) and alanine aminotransferase (ALT)]. The estimated glomerular filtration rate (eGFR) was calculated using the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equation [ 20 ]. Albuminuria was defined as an elevated urinary albumin-to-creatinine ratio (ACR ≥ 3.0 mg/mmol). Smoking status was categorized as current (yes) or non-smoking (no, including former smokers who had quit for more than one year). Physical activity was classified as none, moderate, vigorous, or both moderate and vigorous[ 21 ]. Significant alcohol intake was defined as ≥ 20 g/d for men and ≥ 10 g/d for women[ 22 ]. PIR was categorized into low income (< 1.3), middle income (≥ 1.3 and < 3.5), and high income (≥ 3.5), following established cutoffs in prior studies [ 23 , 24 ]. In addition, information was collected on the presence of chronic kidney disease (CKD), defined as eGFR < 60 mL/min/1.73 m² or urinary ACR ≥ 3.0 mg/mmol, as well as the use of antihypertensive, lipid-lowering, and glucose-lowering medications [ 25 , 26 ]. 2.3 Non-invasive biomarkers of hepatic steatosis and fibrosis The Fatty Liver Index (FLI), originally developed by an Italian working group, incorporates body mass index (BMI), waist circumference, triglyceride levels, and gamma-glutamyl transferase (GGT) activity to estimate hepatic steatosis [ 27 , 28 ]. Subsequently, American researchers evaluated this FLI within the National Health and Nutrition Examination Survey (NHANES), ultimately developing the United States Fatty Liver Index (USFLI), which proved more suitable for the diverse multi-ethnic population of the U.S. (AUC [95% CI]: 0.80 [0.77–0.83]). Its calculation method is as follows: A USFLI of ≥ 30 had a sensitivity of 62%, specificity of 88%, likelihood ratio positive of 5.2, and likelihood ratio negative of 0.43,and was selected to rule in fatty liver.[ 29 ]. USFLI has shown robust performance in studies targeting the general U.S. population and is considered an appropriate non-invasive diagnostic tool for large-scale epidemiological investigations [ 30 ]. We additionally calculated the Fibrosis-4 (FIB-4) index using the following formula: FIB-4 index = (Age × AST [IU/L]) / (Platelet count [10⁹/L] × √ALT [IU/L]). The FIB-4 index is one of the most widely used non-invasive scoring systems for the assessment of advanced liver fibrosis[ 31 , 32 ]. A cutoff value of FIB-4 ≥ 1.3 was considered indicative of significant fibrosis, whereas a threshold of FIB-4 ≥ 2.67 suggested the presence of advanced fibrosis. 2.4 The 10-year risk of CVD risk estimates The PREVENT equation was proposed in 2023 by the American College of Cardiology/American Heart Association (ACC/AHA) Practice Guidelines Writing Group, serving as a modification of the 2013 PCE. Specifically, this risk calculator estimates the 10-year incidence of a first CVD event (composite of ASCVD and heart failure). It incorporates 12 variables: age, sex, total cholesterol, high-density lipoprotein cholesterol, systolic blood pressure, BMI, eGFR, use of antihypertensive medication, use of lipid-lowering medication, smoking status, and diabetes status[ 18 ]. According to the equation, estimated 10-year CVD risk is categorized as follows: low risk (< 5%), borderline risk (5–7.4%), intermediate risk (7.5–19.9%), and high risk (≥ 20%) [ 18 ]. In the present study, borderline and intermediate risk categories were combined into a single group, referred to as the moderate-risk group. 2.5 Statistical analysis Continuous variables were presented as means ± standard deviations (SD) or medians with interquartile ranges (IQR), while categorical variables were expressed as proportions (%). Differences in major clinical and biochemical characteristics were assessed by hepatic steatosis status (with or without coexisting significant fibrosis) and by categories of 10-year estimated CVD risk. Specifically, one-way analysis of variance was used for normally distributed continuous variables, the Kruskal-Wallis test for non-normally distributed continuous variables, and the chi-squared test for categorical variables. Weighted univariable and multivariable logistic regression analyses were conducted to examine the association between hepatic steatosis (with or without significant fibrosis) and the estimated 10-year CVD risk in individuals with hypertension. In these weighted models, the binary outcome was defined as having moderate-to-high ASCVD risk vs low ASCVD risk. Two logistic regression models were fitted: Model 1 was unadjusted, while Model 2 was adjusted for sex, race/ethnicity, educational attainment, poverty-income ratio (PIR), physical activity level, and presence of chronic kidney disease (CKD), defined as either an eGFR < 60 mL/min/1.73 m² or albuminuria (ACR ≥ 3.0 mg/mmol). Notably, age is a component of both the FIB-4 index and the PREVENT risk equation. Similarly, smoking status, BMI, lipid-lowering medication use, and diabetes status are all incorporated in the PREVENT model. To mitigate potential multicollinearity, these variables were not included as covariates in the multivariable models. In the final dataset, to address missing covariate data, we employed a non-parametric imputation method based on random forest, implemented using the missForest R package[ 33 ]. This method is robust to different data types and can capture complex, non-linear relationships between variables, which is particularly advantageous for handling diverse datasets like NHANES. Mediation analysis was performed using the mediation package. CVD risk was treated as a continuous outcome, while other model specifications remained unchanged. All statistical tests were two-sided, and a p-value < 0.05 was considered statistically significant. Statistical analyses were performed using R software (version 4.5.0; https://www.r-project.org ), incorporating sampling weights, clustering, and stratification to account for the complex survey design of NHANES. In accordance with NHANES analysis guidelines, all participant data were weighted using the recommended Fasting Subsample 2-Year Mobile Examination Center Weight (WTSAF2YR), Details regarding the NHANES weighting calculation method are available at https://wwwn.cdc.gov/nchs/nhanes/tutorials/Weighting.aspx . 3. Result 3.1 Baseline characteristics of study participants Table 1 presents the main clinical and biochemical characteristics of participants stratified by 10-year CVD risk as estimated by the PREVENT equation. The final analytic cohort included 1,083 participants, representing a weighted U.S. population of 8,807,596 individuals (weighted mean age: 60.25 years [SE: 0.24]; 54.46% were female). As CVD risk increased, there were significant upward trends in age, systolic blood pressure, waist circumference, fasting plasma glucose, HbA1c, and TyG index. In contrast, levels of HDL and Low-density lipoprotein -C decreased significantly across risk categories. Indicators of renal function, such as serum creatinine, increased with higher CVD risk, whereas eGFR) decreased, suggesting impaired kidney function in participants at higher risk. Sociodemographic and lifestyle differences were also observed: individuals in the high-risk group were more likely to have lower educational attainment (a lower proportion with education beyond high school) and a higher prevalence of physical inactivity. Prevalence of chronic conditions such as diabetes and chronic kidney disease (CKD), as well as the use of related medications (antihypertensive, lipid-lowering, and antidiabetic agents), increased significantly across the CVD risk strata. Moreover, there were significant differences in gender composition, racial/ethnic distribution, and socioeconomic status across groups. In contrast, no significant differences were found across CVD risk categories for marital status, body mass index (BMI), triglyceride levels, aspartate aminotransferase (AST), urinary creatinine, smoking status, or alcohol consumption. Table.1 Clinical and biochemical characteristics of adults with Hypertension, stratified by Prevent equations Overall Low Intermediate High P-Value N = 1083 N = 153 N = 672 N = 258 Age, y, mean (SD) 62.2 (10.3) 47.9 (6.99) 61.9 (8.07) 71.4 (6.49) <0.001 Gender : <0.001 Male, n (%) 492 (45.4%) 74 (48.4%) 272 (40.5%) 146 (56.6%) Female, n (%) 591 (54.6%) 79 (51.6%) 400 (59.5%) 112 (43.4%) Educational level, n (%) 0.001 High school 779 (72.0%) 125 (81.7%) 487 (72.5%) 167 (65.0%) Race, n (%) 0.022 Mexican American 176 (16.3%) 18 (11.8%) 114 (17.0%) 44 (17.1%) Non-Hispanic White 459 (42.4%) 69 (45.1%) 275 (40.9%) 115 (44.6%) Non-Hispanic Black 240 (22.2%) 41 (26.8%) 136 (20.2%) 63 (24.4%) Other Hispanic 104 (9.60%) 8 (5.23%) 73 (10.9%) 23 (8.91%) Other Race 104 (9.60%) 17 (11.1%) 74 (11.0%) 13 (5.04%) Marital status, n (%) 0.870 Married/Living with partner 707 (65.5%) 96 (63.6%) 441 (65.7%) 170 (65.9%) Widowed/Divorced/Separated/Never married 373 (34.5%) 55 (36.4%) 230 (34.3%) 88 (34.1%) Physical activity, n (%) 0.049 Inactive 496 (51.2%) 62 (42.8%) 309 (51.7%) 125 (55.6%) Moderate 288 (29.8%) 44 (30.3%) 177 (29.6%) 67 (29.8%) Vigorous 41 (4.24%) 12 (8.28%) 22 (3.68%) 7 (3.11%) Both Moderate and Vigorous 143 (14.8%) 27 (18.6%) 90 (15.1%) 26 (11.6%) BMI, kg/m 2 , mean (SD) 30.4 (4.63) 31.1 (4.77) 30.3 (4.63) 30.2 (4.53) 0.096 SBP, mmHg, mean (SD) 132 (17.3) 123 (13.5) 131 (16.3) 141 (18.0) <0.001 Waist circumference, cm, mean (SD) 104 (12.1) 103 (12.2) 104 (12.0) 107 (11.9) 0.003 Total cholesterol, mg/dL, mean (SD) 195 (39.2) 199 (36.5) 197 (40.2) 189 (37.2) 0.005 HDL-cholesterol, mg/dL, mean (SD) 51.2 (13.5) 52.7 (14.3) 51.9 (13.8) 48.6 (12.0) 0.001 LDL-cholesterol, mg/dL, mean (SD) 114 (34.8) 119 (32.6) 115 (35.3) 109 (34.3) 0.015 Triglyceride, mg/dL, mean (SD) 156 (109) 144 (102) 156 (102) 162 (128) 0.273 Albumin, urine, mg/L, mean (SD) 74.7 (463) 20.3 (62.4) 65.5 (537) 131 (378) 0.045 Creatinine(urine), µmol/L, mean (SD) 10481 (5983) 11553 (6035) 10325 (6004) 10249 (5853) 0.056 Glucose, mg/dL, mean (SD) 125 (46.8) 104 (24.3) 125 (46.4) 137 (53.3) <0.001 Glycohemoglobin, %, mean (SD) 6.34 (1.31) 5.70 (0.95) 6.35 (1.32) 6.68 (1.34) <0.001 AST, U/L, mean (SD) 25.1 (10.5) 24.0 (6.37) 25.1 (10.1) 25.9 (13.2) 0.211 ALT, U/L, mean (SD) 25.3 (14.0) 27.0 (12.8) 25.6 (14.8) 23.5 (12.4) 0.034 Creatinine, mg/dL, mean (SD) 0.91 (0.28) 0.86 (0.21) 0.86 (0.22) 1.06 (0.38) <0.001 TyG, mean (SD) 8.97 (0.67) 8.73 (0.59) 8.98 (0.68) 9.10 (0.65) <0.001 eGFR, mL/min/1.73m 2 , mean (SD) 84.1 (19.2) 96.4 (15.2) 86.2 (16.6) 71.4 (20.8) <0.001 Smoking status : 0.550 No 920 (84.9%) 127 (83.0%) 577 (85.9%) 216 (83.7%) Yes 163 (15.1%) 26 (17.0%) 95 (14.1%) 42 (16.3%) Drinking status : 0.885 No 1059 (98.3%) 147 (98.0%) 659 (98.4%) 253 (98.4%) Yes 18 (1.67%) 3 (2.00%) 11 (1.64%) 4 (1.56%) Antihypertensive : <0.001 No 93 (8.59%) 39 (25.5%) 49 (7.29%) 5 (1.94%) Yes 990 (91.4%) 114 (74.5%) 623 (92.7%) 253 (98.1%) Lipid-lowering : 0.003 No 211 (19.5%) 44 (28.8%) 128 (19.0%) 39 (15.1%) Yes 872 (80.5%) 109 (71.2%) 544 (81.0%) 219 (84.9%) Antidiabetic : <0.001 Yes 331 (65.2%) 7 (22.6%) 205 (64.9%) 119 (73.9%) No 177 (34.8%) 24 (77.4%) 111 (35.1%) 42 (26.1%) Diabetes status, n (%) : <0.001 No 614 (56.7%) 143 (93.5%) 391 (58.2%) 80 (31.0%) Yes 469 (43.3%) 10 (6.54%) 281 (41.8%) 178 (69.0%) PIR, n (%) : <0.001 Low incomes 317 (29.3%) 36 (23.5%) 198 (29.5%) 83 (32.2%) Middle incomes 415 (38.3%) 43 (28.1%) 247 (36.8%) 125 (48.4%) High incomes 351(32.4%) 74 (48.4%) 227 (33.8%) 50 (19.4%) CKD status, n (%) : <0.001 No 763 (70.6%) 139 (90.8%) 508 (75.8%) 116 (45.1%) Yes 317 (29.4%) 14 (9.15%) 162 (24.2%) 141 (54.9%) Cohort size : n = 1,083. Data are presented as means ± standard deviation (SD) for continuous variables and as percentages for categorical variables. Between-group differences were assessed using chi-squared tests for categorical variables, one-way analysis of variance (ANOVA) for normally distributed continuous variables, and Kruskal–Wallis tests for non-normally distributed variables. Note : Albuminuria was defined as a urinary albumin-to-creatinine ratio (ACR) ≥ 3.0 mg/mmol. Chronic kidney disease (CKD) was defined as an estimated glomerular filtration rate (eGFR CKD-EPI) < 60 mL/min/1.73 m² or the presence of albuminuria. Abbreviations : ALT, alanine aminotransferase; AST, aspartate aminotransferase; BMI, body mass index; SBP, systolic blood pressure; TyG, triglyceride-glucose index; CKD, chronic kidney disease; eGFR CKD-EPI, estimated glomerular filtration rate calculated using the CKD-Epidemiology Collaboration equation; GGT, gamma-glutamyl transferase; FIB-4, fibrosis-4 index; USFLI, United States Fatty Liver Index; PIR, Poverty Income Ratio. Table 2 presents the main clinical and biochemical characteristics of participants, stratified by the presence of hepatic steatosis with or without significant fibrosis. Compared to those without hepatic steatosis or with steatosis alone, participants with significant fibrosis were older, had greater waist circumference, and were less likely to be current smokers. They exhibited higher levels of blood pressure, fasting plasma glucose, AST and ALT, along with lower platelet counts, total cholesterol, low-density lipoprotein cholesterol, and e-GFR. Moreover, the prevalence of diabetes and CKD was higher in this group, accompanied by significantly increased use of antihypertensive and antidiabetic medications. No significant differences were observed across groups in marital status, physical activity, urinary albumin or creatinine levels, or the use of lipid-lowering medications. Table 2 Clinical and biochemical characteristics of adults with Hypertension, stratified by the presence of hepatic steatosis with or without coexisting significant liver fibrosis (non-invasively assessed by USFLI and FIB-4 scores) Overall USFLI = 30 & FIB_4 = 30 & FIB_4 > = 1.3 P-Value N = 1083 N = 483 N = 313 N = 287 Age, y, mean (SD) 62.2 (10.3) 62.3 (10.3) 57.0 (10.0) 67.6 (7.43) <0.001 Gender : <0.001 Male, n (%) 492 (45.4%) 184 (38.1%) 148 (47.3%) 160 (55.7%) Female, n (%) 591 (54.6%) 299 (61.9%) 165 (52.7%) 127 (44.3%) Educational level, n (%) 0.003 High school 779 (72.0%) 371 (76.8%) 220 (70.3%) 188 (65.7%) Race, n (%) <0.001 Mexican American 176 (16.3%) 46 (9.52%) 56 (17.9%) 74 (25.8%) Non-Hispanic White 459 (42.4%) 183 (37.9%) 140 (44.7%) 136 (47.4%) Non-Hispanic Black 240 (22.2%) 158 (32.7%) 50 (16.0%) 32 (11.1%) Other Hispanic 104 (9.60%) 46 (9.52%) 36 (11.5%) 22 (7.67%) Other Race 104 (9.60%) 50 (10.4%) 31 (9.90%) 23 (8.01%) Marital status, n (%) 0.078 Married/Living with partner 707 (65.5%) 298 (61.8%) 213 (68.5%) 196 (68.3%) Widowed/Divorced/Separated/Never married 373 (34.5%) 184 (38.2%) 98 (31.5%) 91 (31.7%) Physical activity, n (%) 0.805 Inactive 496 (51.2%) 225 (51.8%) 141 (50.5%) 130 (51.0%) Moderate 288 (29.8%) 120 (27.6%) 88 (31.5%) 80 (31.4%) Vigorous 41 (4.24%) 20 (4.61%) 13 (4.66%) 8 (3.14%) Both Moderate and Vigorous 143 (14.8%) 69 (15.9%) 37 (13.3%) 37 (14.5%) BMI, kg/m 2 , mean (SD) 30.4 (4.63) 28.3 (4.37) 32.5 (4.14) 31.5 (4.13) <0.001 SBP, mmHg, mean (SD) 132 (17.3) 132 (18.1) 131 (16.3) 135 (16.7) 0.013 Waist circumference, cm, mean (SD) 104 (12.1) 98.1 (10.7) 109 (10.5) 109 (11.1) <0.001 Total cholesterol, mg/dL, mean (SD) 195 (39.2) 198 (38.6) 200 (40.6) 186 (37.0) <0.001 HDL cholesterol, mg/dL, mean (SD) 51.2 (13.5) 56.4 (13.7) 45.3 (11.5) 48.9 (12.1) <0.001 LDL-cholesterol, mg/dL, mean (SD) 114 (34.8) 117 (35.2) 119 (35.3) 104 (31.4) <0.001 Triglyceride, mg/dL, mean (SD) 156 (109) 123 (68.6) 191 (122) 172 (130) <0.001 Albumin, urine, mg/L, mean (SD) 74.7 (463) 46.6 (171) 121 (800) 71.7 (254) 0.086 Creatinine(urine), µmol/L, mean (SD) 10481 (5983) 10279 (6011) 10723 (5738) 10557 (6200) 0.576 Glucose, mg/dL, mean (SD) 125 (46.8) 111 (38.6) 136 (54.2) 137 (44.3) <0.001 Glycohemoglobin, %, mean (SD) 6.34 (1.31) 6.01 (1.05) 6.63 (1.58) 6.57 (1.25) <0.001 AST, U/L, mean (SD) 25.1 (10.5) 22.9 (6.15) 23.5 (7.68) 30.7 (15.8) <0.001 ALT, U/L, mean (SD) 25.3 (14.0) 20.8 (8.61) 27.8 (13.3) 30.2 (19.0) <0.001 Creatinine, mg/dL, mean (SD) 0.91 (0.28) 0.92 (0.28) 0.86 (0.23) 0.94 (0.31) 0.001 TyG, mean (SD) 8.97 (0.67) 8.66 (0.57) 9.27 (0.64) 9.17 (0.63) <0.001 eGFR, mL/min/1.73m 2 , mean (SD) 84.1 (19.2) 81.7 (19.2) 90.7 (17.8) 81.0 (19.0) <0.001 Smoking status : 0.007 No 920 (84.9%) 407 (84.3%) 254 (81.2%) 259 (90.2%) Yes 163 (15.1%) 76 (15.7%) 59 (18.8%) 28 (9.76%) Drinking status : 1.000 No 1059 (98.3%) 472 (98.3%) 306 (98.4%) 281 (98.3%) Yes 18 (1.67%) 8 (1.67%) 5 (1.61%) 5 (1.75%) Antihypertensive : 0.032 No 93 (8.59%) 43 (8.90%) 35 (11.2%) 15 (5.23%) Yes 990 (91.4%) 440 (91.1%) 278 (88.8%) 272 (94.8%) Lipid lowering : 0.079 No 211 (19.5%) 103 (21.3%) 65 (20.8%) 43 (15.0%) Yes 872 (80.5%) 380 (78.7%) 248 (79.2%) 244 (85.0%) Antidiabetic : 0.003 Yes 331 (65.2%) 105 (57.1%) 110 (65.1%) 116 (74.8%) No 177 (34.8%) 79 (42.9%) 59 (34.9%) 39 (25.2%) Diabetes status, n (%) : <0.001 No 614 (56.7%) 337 (69.8%) 156 (49.8%) 121 (42.2%) Yes 469 (43.3%) 146 (30.2%) 157 (50.2%) 166 (57.8%) PIR, n (%) : 0.049 Low incomes 317 (29.3%) 126 (26.1%) 99 (31.6%) 92 (42.2%) Middle incomes 415 (38.3%) 196 (40.6%) 103 (32.9%) 116 (40.4%) High incomes 351 (32.4%) 161 (33.3%) 111 (35.5%) 79 (27.5%) CKD status, n (%) : 0.003 No 763 (70.6%) 356 (73.7%) 227 (73.2%) 180 (62.7%) Yes 317 (29.4%) 127 (26.3%) 83 (26.8%) 107 (37.3%) Cohort size : n = 1,083. Data are presented as means ± standard deviation (SD) for continuous variables and as percentages for categorical variables. Between-group differences were assessed using chi-squared tests for categorical variables, one-way analysis of variance (ANOVA) for normally distributed continuous variables, and Kruskal–Wallis tests for non-normally distributed variables. Note : Albuminuria was defined as a urinary albumin-to-creatinine ratio (ACR) ≥ 3.0 mg/mmol. Chronic kidney disease (CKD) was defined as an estimated glomerular filtration rate (eGFR CKD-EPI) < 60 mL/min/1.73 m² or the presence of albuminuria. Abbreviations : ALT, alanine aminotransferase; AST, aspartate aminotransferase; BMI, body mass index; SBP, systolic blood pressure; TyG, triglyceride-glucose index; CKD, chronic kidney disease; eGFR CKD-EPI, estimated glomerular filtration rate calculated using the CKD-Epidemiology Collaboration equation; GGT, gamma-glutamyl transferase; FIB-4, fibrosis-4 index; USFLI, United States Fatty Liver Index; PIR, Poverty Income Ratio. 3.2 The 10-year CVD risk assessment using the PREVENT Equation in NAFLD/MASLD patients with or without significant fibrosis We calculated the 10-year estimated prevalence of CVD risk categories using the PREVENT equation (Fig 2), stratified by the presence of hepatic steatosis with or without significant fibrosis. Compared to participants with either hepatic steatosis alone or without steatosis, those with both steatosis and significant fibrosis exhibited a markedly higher estimated 10-year risk of experiencing a first fatal or non-fatal CVD event (39.4% vs. 20.5% vs. 14.7%, p < 0.001 by chi-square test). The 10-year risk prevalence of first fatal or non-fatal CVD events in hypertensive adults, stratified by hepatic steatosis and its significant fibrosis status (as assessed by USFLI and FIB-4 scores). We conducted subgroup analyses based on median BMI values (< 30.29 vs. ≥ 30.29 kg/m 2 ) (Supplementary Fig.1), age (< 63 vs. ≥ 63 years) (Supplementary Fig.2) or sex (Supplementary Fig.3). These subgroup analyses confirmed that patients with hepatic steatosis accompanied by significant fibrosis exhibited a significantly higher 10-year estimated cardiovascular disease risk compared to those with isolated steatosis or no steatosis, irrespective of sex or any other patient subgroup considered. Table 3 presents the association between hepatic steatosis (with or without significant fibrosis) and 10-year estimated CVD risk. In the unadjusted weighted regression model, patients with hepatic steatosis and significant fibrosis exhibited an approximately 12-fold increased risk of high/intermediate 10-year estimated CVD risk compared to those without steatosis. This elevated risk remained significant even after adjusting for sex, marital status, education level, race/ethnicity, PIR, physical activity level, and the presence of CKD (adjusted weighted regression model). Furthermore, patients with isolated steatosis and those without steatosis had comparable 10-year estimated CVD risks (though not statistically significant). Among the covariates, higher education level, presence of CKD, and engagement in vigorous physical activity were also independently associated with elevated 10-year estimated CVD risk (p < 0.05 for all). Table 3 Association between hepatic steatosis with or without coexisting significant fibrosis and the 10-year estimated CVD risk Logistic Regression Analyses OR (95% CI) P-value Y = High or moderate risk vs. Low risk score Unadjusted model USFLI = 30 & FIB_4 = 30 & FIB_4 > = 1.3 11.9(3.02, 46.5) < 0.001 Adjusted model USFLI = 30 & FIB_4 = 30 & FIB_4 > = 1.3 11.6(2.90, 46.5) < 0.001 Cohort size : n = 1,083. Data are presented as odds ratio (OR) with 95% confidence interval (CI) and were evaluated using univariate and multivariate logistic regression analyses. The dependent variable in the logistic regression model was defined as membership in the combined moderate/high CVD risk group vs the low CVD risk group. The regression models were adjusted for sex, educational level, race/ethnicity, marital status, physical activity level, PIR, and the presence of CKD, defined as an eGFR < 60 mL/min/1.73 m² or abnormal albuminuria. To assess the robustness of our findings, we conducted several sensitivity analyses. First, we excluded data from the 2015–2016 NHANES cycle due to the unavailability of anti-HCV antibody results (replaced by HCV RNA data during this period). The results remained consistent (Supplementary Table.1). Second, we repeated the analysis after excluding all participants with any missing covariate data, and the results were unchanged (Supplementary Table 2).Furthermore, we calculated the E-value for the association between USFLI ≥ 30 combined with FIB-4 ≥ 1.3 and the elevated 10-year CVD risk, based on the fully adjusted model [34]. The E-value was 6.27 , indicating that an unmeasured confounder would need to be associated with both the exposure and outcome by a risk ratio of at least 6.27 to fully explain away the observed association. Thus, our findings suggest that the observed association is robust to potential unmeasured confounding. 3.3 Mediation Analysis of the Potential Role of the TyG Index in This Association We conducted a mediation analysis to examine whether, and to what extent, insulin resistance (IR), as assessed by the TyG index, mediates the association between NAFLD/MASLD and the estimated 10-year CVD risk. As shown in Fig.3, the total effect represents the overall impact of NAFLD/MASLD status on the 10-year estimated CVD risk in individuals with hypertension. The direct effect reflects the influence of NAFLD/MASLD (defined as USFLI ≥30 and FIB-4 ≥1.3) on CVD risk independent of the TyG index, while the indirect effect captures the portion of this association that is mediated through the TyG index. Overall, the direct effect was substantially greater than the indirect effect, although the mediation effect was still statistically significant. The proportion of the effect mediated by the TyG index was estimated at 16.85% (Fig.4), suggesting a modest but meaningful role of insulin resistance in linking NAFLD/MASLD with increased cardiovascular risk among hypertensive individuals. Discussion It is well established that NAFLD is closely associated with an increased risk of cardiovascular disease (CVD). Metabolic syndrome is defined by the coexistence of multiple metabolic risk factors for CVD, and some researchers have proposed that NAFLD/MASLD represents the hepatic manifestation of metabolic syndrome. Hypertension, as a central component of metabolic syndrome, frequently coexists with NAFLD—a relationship that is well-supported by substantial evidence. However, little is known about how NAFLD/MASLD and its severity of fibrosis are associated with CVD risk specifically among individuals with hypertension, the underlying mechanisms have yet to reach a consistent consensus. Our study investigated the association of NAFLD/MASLD and its fibrosis severity with CVD risk in hypertensive patients, further exploring the mediating role of the TyG index (a surrogate marker for IR) in this association. The main findings are as follows: (1) Compared with individuals without hepatic steatosis or those with steatosis alone, participants with both steatosis and advanced fibrosis exhibited a significantly higher 10-year estimated risk of experiencing a first fatal or non-fatal CVD event. Notably, the presence of steatosis alone was not associated with a statistically significant difference in CVD risk across groups. (2) This elevated CVD risk remained statistically significant even after adjusting for sex, education level, race/ethnicity, marital status, PIR, CKD, and physical activity. (3) Subgroup analyses confirmed that the elevated CVD risk associated with steatosis and fibrosis persisted across different strata of age, sex, and BMI. (4) Mediation analysis demonstrated that IR partially mediated the relationship between NAFLD/MASLD with advanced fibrosis and CVD risk, providing empirical support for IR as a potential mechanistic link underlying this complex association. A hallmark of early-stage NAFLD is the ectopic accumulation of triglycerides in the liver. This process requires a continuous supply of fatty acids, predominantly derived from adipose tissue lipolysis driven by unrestrained hormone-sensitive lipase (HSL) activity under conditions of IR. The resultant increase in circulating free fatty acids facilitates their hepatic uptake and storage. This ectopic lipid deposition further induces oxidative stress and heightens mitochondrial activity within hepatocytes, ultimately leading to hepatocellular injury, apoptosis, and progressive fibrosis. Moreover, insulin resistance—central to this pathological cascade—exacerbates atherogenic dyslipidemia and promotes the release of multiple pro-inflammatory and pro-atherosclerotic mediators. Together, these effects significantly elevate the risk of developing cardiovascular disease[3, 35]. IR is also closely associated with the development and progression of various comorbidities in individuals with NAFLD/MASLD, including hypertension, cardiovascular disease, hepatocellular carcinoma, type 2 diabetes, and chronic kidney disease [36]. This highlights the importance of risk prediction and management of CVD among hypertensive patients with NAFLD/MASLD, as well as the need to further investigate the mechanistic role of IR in this complex relationship. For instance, a prospective cohort study by Zhang et al. demonstrated that individuals with hypertension exhibit elevated cardiovascular risk, particularly when coexisting with moderate to severe NAFLD, suggesting that the severity of hepatic steatosis may aid in further risk stratification among those with prehypertension or hypertension[37]. Similarly, Hu et al. explored the temporal relationship between hepatic steatosis and blood pressure elevation and found that hepatic fat accumulation predisposes individuals to elevated blood pressure, which in turn positively mediates the association between hepatic steatosis and CVD risk[16]. Our study, building upon previous work, further substantiated that in hypertensive populations, hepatic steatosis accompanied by severe fibrosis is associated with a significantly higher estimated risk of a first fatal or non-fatal CVD event within 10 years, compared to those with isolated hepatic steatosis or no steatosis. Simultaneously, we delved into the potential underlying mechanisms of this clinical phenomenon, providing support for IR as a core driving factor in this process. Our findings may have the following clinical implications. They lend support to the notion that in hypertensive individuals, non-invasively identifying hepatic steatosis with concomitant severe fibrosis can help pinpoint those at higher risk of experiencing a first fatal or non-fatal CVD event within 10 years. Although modern medicine has developed numerous strategies to mitigate the threat of various CVDs to human health, the importance of early identification and prevention of CVD development cannot be overstated. Our findings suggest that the presence of hepatic steatosis with severe fibrosis may be a risk-enhancing factor for CVD in individuals with hypertension but no baseline CVD. This population is often in their middle to younger adult years, and as previously noted, CVD risk in younger individuals is more prone to being overlooked. Therefore, implementing prevention strategies within higher-risk subsets of younger adults represents a crucial area[38, 39]. These insights could aid in identifying at-risk individuals who may benefit from more rigorous control of modifiable cardiovascular risk factors. Furthermore, our findings regarding the mediating role of insulin resistance (IR) provide a potential therapeutic avenue: namely, interventions aimed at improving IR may hold promise for this subgroup. This aligns well with the growing emphasis on personalized treatment strategies in contemporary clinical practice. Lastly, we offer several additional exploratory findings of interest. According to 2019 ACC/AHA Guideline on the Primary Prevention of Cardiovascular Disease. In adults with confirmed hypertension and a 10-year ASCVD event risk of 10% or higher, a BP target of less than 130/80 mm Hg is recommended[21]. In our dataset, the blood pressure control rate was only 35.4%, and even lower (only 30.6 %) among individuals with both hepatic steatosis and significant fibrosis. This finding further highlights that blood pressure management in patients with NAFLD/MASLD is suboptimal in real-world settings, indicating that their actual cardiovascular risk may be considerably underestimated. Perspective on the Association Between NAFLD/MASLD and CVD Risk As previously discussed, there is currently no consensus in the academic community regarding the intrinsic mechanisms underlying the strong association between NAFLD and CVD observed in clinical data. Most scholars suggest that this may involve dyslipidemia, IR, increased systemic inflammatory tone, abnormalities in coagulation and the sympathetic nervous system, as well as gut microbiota dysbiosis[3, 11-14]. n our study, we analyzed indicators related to insulin resistance, systemic inflammation, dyslipidemia, and coagulation. However, only the insulin resistance surrogate (TyG index) demonstrated a mediating effect. Consequently, our findings support insulin resistance as a potential underlying mechanism, but do not provide support for inflammation, dyslipidemia, or coagulation abnormalities being potential intrinsic mechanisms in this specific context. We hypothesize that this discrepancy may be attributed to the stringent selection criteria of the PREVENT equation, resulting in a relatively small sample size for our analysis. In summary, the exploration of the intrinsic mechanisms underlying this phenomenon remains an ongoing process. Study limitations and strengths The primary strength of this study lies in its novel application, to our knowledge, of the PREVENT equation to estimate 10-year CVD risk in a hypertensive population, and its subsequent analysis of the relationship with NAFLD/MASLD. Cardiovascular disease absolute risk assessment is unequivocally the cornerstone of primary clinical prevention. Compared to previous assessment models, the PREVENT model is better adapted to the complex intrinsic connections associated with the Cardiovascular-Kidney-Metabolic (CKM) Syndrome state[19]. Similarly, as NAFLD/MASLD is considered a hepatic manifestation of a systemic disease, the PREVENT equation represents the optimal CVD risk prediction tool for this patient population. Our research supports the application and development of this tool in such individuals, potentially leading to greater benefits for this group in the future. Furthermore, our exploration of the potential underlying mechanisms contributes to a deeper understanding of the complex relationship between NAFLD/MASLD and CVD, even though definitive conclusions remain elusive. However, this study also has limitations. First, the cross-sectional design precludes establishing a reliable causal relationship between NAFLD/MASLD with severe fibrosis and the higher 10-year estimated CVD risk. Second, we relied on the USFLI and FIB-4 indices for diagnosing hepatic steatosis and fibrosis, respectively. In clinical practice, MRI and Vibration-controlled transient elastography (Fibro Scan) are considered first-line imaging methods for non-invasively detecting hepatic steatosis and fibrosis [3, 40]. Nevertheless, such data are challenging to acquire in large-scale epidemiological studies. Despite this, USFLI and FIB-4 remain among the most suitable non-invasive indices for the U.S. population and can serve as first-line diagnostic tools for this group. Previously, Aaron et al. suggested that combining the PREVENT risk calculator with coronary artery calcium scoring could help more accurately predict the risk of vascular disease onset and proposed adding coronary artery calcium scoring to the PREVENT risk calculator[41]. We believe that in future work, similar approaches could be explored to further enhance the accuracy of diagnostic tools and improve the identification capabilities for specific populations. However, it is unequivocally necessary that future research utilizes imaging methods to assess hepatic steatosis and fibrosis to validate our results. Concurrently, prospective cohort studies are needed to verify whether NAFLD (with varying degrees of fibrosis) increases the long-term risk of cardiovascular disease in hypertensive patients without baseline CVD, and whether targeted interventions for IR can mitigate this process. Conclusion In summary, our results support that NAFLD/MASLD is significantly associated with an increased 10-year risk of a first cardiovascular event in hypertensive patients without baseline CVD. Furthermore, among the complex interplay between NAFLD/MASLD and CVD, insulin resistance is more likely to be a definitively involved and crucial factor compared to other risk factors. However, the specific mechanisms remain unclear and warrant further clinical and basic research for elucidation. Abbreviations NAFLD: Non-Alcoholic Fatty Liver Disease MASLD: Metabolic Dysfunction-Associated Fatty Liver Disease CVD: Cardiovascular disease ASCVD: Atherosclerotic cardiovascular disease TyG: Triglyceride Glucose IR:Insulin resistance MetS: Metabolic syndrome AHA: American Heart Association ACC: American College of Cardiology NHANES:National Health and Nutrition Examination Survey USFLI: United States Fatty Liver Index FLI: Fatty liver index FIB-4: Fibrosis-4 HBV: Hepatitis B virus HCV: Hepatitis C virus AST: Aspartate aminotransferase ALT: Alanine aminotransferase GGT: Gamma-glutamyl transferase eGFR: Estimated glomerular filtration rate CKD: Chronic kidney disease CKD-EPI: Chronic kidney disease-Epidemiology collaboration equation ACR: Albumin-to-creatinine ratio PIR: Poverty income ratio BMI: Body mass index PCE: Pooled cohort equation CKM: Cardiovascular-Kidney-Metabolic syndrome Declarations Author information Authors and Affiliations First Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, CN Yexin Yin First Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, CN Lili Shi Contributions Conceptualization: Yexin Yin Data curation & Formal analysis: Yexin Yin Investigation & Methodology: Yexin Yin Project administration: Yexin Yin, Lili Shi. Supervision: Lili Shi Validation: Yexin Yin Writing– original draft: Yexin Yin Writing– review & editing: Yexin Yin. Lili Shi is the guarantor of integrity of the entire study, had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. All authors reviewed and approved the final version of the manuscript. Corresponding author Correspondence to Lili Shi Ethics declarations Ethics and consent to participate declarations Not applicable. Consent to publish Not applicable. Competing interests The authors declare no competing interests. Funding None. Acknowledgements None. Data Availability The experimental data and the simulation results that support the findings of this study are available at the following URL: https://wwwn.cdc.gov/nchs/nhanes/default.aspx References Le, M.H., et al., Forecasted 2040 global prevalence of nonalcoholic fatty liver disease using hierarchical bayesian approach. Clin Mol Hepatol, 2022. 28 (4): p. 841–850. Wong, V.W., et al., Changing epidemiology, global trends and implications for outcomes of NAFLD. J Hepatol, 2023. 79 (3): p. 842–852. Duell, P.B., et al., Nonalcoholic Fatty Liver Disease and Cardiovascular Risk: A Scientific Statement From the American Heart Association. Arterioscler Thromb Vasc Biol, 2022. 42 (6): p. e168–e185. Targher, G., H. Tilg, and C.D. Byrne, Non-alcoholic fatty liver disease: a multisystem disease requiring a multidisciplinary and holistic approach. Lancet Gastroenterol Hepatol, 2021. 6 (7): p. 578–588. Eslam, M., et al., A new definition for metabolic dysfunction-associated fatty liver disease: An international expert consensus statement. J Hepatol, 2020. 73 (1): p. 202–209. Mendez-Sanchez, N., et al., Global multi-stakeholder endorsement of the MAFLD definition. Lancet Gastroenterol Hepatol, 2022. 7 (5): p. 388–390. Alon, L., et al., Risk of cardiovascular events in patients with non-alcoholic fatty liver disease: a systematic review and meta-analysis. Eur J Prev Cardiol, 2022. 29 (6): p. 938–946. Ma, C., et al., The association between hypertension and nonalcoholic fatty liver disease (NAFLD): literature evidence and systems biology analysis. Bioengineered, 2021. 12 (1): p. 2187–2202. Yuan, M., et al., Hypertension and NAFLD risk: Insights from the NHANES 2017-2018 and Mendelian randomization analyses. Chin Med J (Engl), 2024. 137 (4): p. 457–464. Powell, E.E., V.W. Wong, and M. Rinella, Non-alcoholic fatty liver disease. Lancet, 2021. 397 (10290): p. 2212–2224. Li, M., et al., NAFLD: An Emerging Causal Factor for Cardiovascular Disease. Physiology (Bethesda), 2023. 38 (6): p. 0. Targher, G., C.D. Byrne, and H. Tilg, NAFLD and increased risk of cardiovascular disease: clinical associations, pathophysiological mechanisms and pharmacological implications. Gut, 2020. 69 (9): p. 1691–1705. Motamed, N., et al., Non-alcoholic fatty liver disease is not independent risk factor for cardiovascular disease event: A cohort study. World J Hepatol, 2020. 12 (6): p. 323–331. Mantovani, A., et al., Non-alcoholic fatty liver disease and risk of fatal and non-fatal cardiovascular events: an updated systematic review and meta-analysis. Lancet Gastroenterol Hepatol, 2021. 6 (11): p. 903–913. Muzurovic, E., et al., Nonalcoholic Fatty Liver Disease and Cardiovascular Disease: a Review of Shared Cardiometabolic Risk Factors. Hypertension, 2022. 79 (7): p. 1319–1326. Hu, Y., et al., Temporal relationship between hepatic steatosis and blood pressure elevation and the mediation effect in the development of cardiovascular disease. Hypertens Res, 2024. 47 (7): p. 1811–1821. Huang, Y., et al., Impact of NAFLD and Fibrosis on Adverse Cardiovascular Events in Patients With Hypertension. Hypertension, 2025. Goff, D.C., Jr., et al., 2013 ACC/AHA guideline on the assessment of cardiovascular risk: a report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines. Circulation, 2014. 129 (25 Suppl 2): p. S49–73. Khan, S.S., et al., Novel Prediction Equations for Absolute Risk Assessment of Total Cardiovascular Disease Incorporating Cardiovascular-Kidney-Metabolic Health: A Scientific Statement From the American Heart Association. Circulation, 2023. 148 (24): p. 1982–2004. Levey, A.S., et al., A new equation to estimate glomerular filtration rate. Ann Intern Med, 2009. 150 (9): p. 604–12. Arnett, D.K., 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. Circulation, 2019. 140 (11): p. e596–e646. van Kleef, L.A., R.J. de Knegt, and W.P. Brouwer, Metabolic dysfunction-associated fatty liver disease and excessive alcohol consumption are both independent risk factors for mortality. Hepatology, 2023. 77 (3): p. 942–948. Talha, K.M., et al., Relationship between race, income-level, and Cardiovascular Disease. J Natl Med Assoc, 2024. 116 (5): p. 539–552. Deng, Y., et al., Trends and key disparities of obesity among US adolescents: The NHANES from 2007 to 2020. PLoS One, 2024. 19 (10): p. e0290211. Global Cardiovascular Risk, C., et al., Global Effect of Modifiable Risk Factors on Cardiovascular Disease and Mortality. N Engl J Med, 2023. 389 (14): p. 1273–1285. Wang, M.C. and D.M. Lloyd-Jones, Cardiovascular Risk Assessment in Hypertensive Patients. Am J Hypertens, 2021. 34 (6): p. 569–577. Bedogni, G., et al., Prevalence of and risk factors for nonalcoholic fatty liver disease: the Dionysos nutrition and liver study. Hepatology, 2005. 42 (1): p. 44–52. Bedogni, G., et al., The Fatty Liver Index: a simple and accurate predictor of hepatic steatosis in the general population. BMC Gastroenterol, 2006. 6 : p. 33. Ruhl, C.E. and J.E. Everhart, Fatty liver indices in the multiethnic United States National Health and Nutrition Examination Survey. Aliment Pharmacol Ther, 2015. 41 (1): p. 65–76. Golabi, P., et al., Contribution of sarcopenia and physical inactivity to mortality in people with non-alcoholic fatty liver disease. JHEP Rep, 2020. 2 (6): p. 100171. Sterling, R.K., et al., Development of a simple noninvasive index to predict significant fibrosis in patients with HIV/HCV coinfection. Hepatology, 2006. 43 (6): p. 1317–25. Han, J.W., et al., Diagnostic accuracy of the Fibrosis-4 index for advanced liver fibrosis in nonalcoholic fatty liver disease with type 2 diabetes: A systematic review and meta-analysis. Clin Mol Hepatol, 2024. 30 (Suppl): p. S147–S158. Stekhoven, D.J. and P. Buhlmann, MissForest--non-parametric missing value imputation for mixed-type data. Bioinformatics, 2012. 28 (1): p. 112–8. VanderWeele, T.J. and P. Ding, Sensitivity Analysis in Observational Research: Introducing the E-Value. Ann Intern Med, 2017. 167 (4): p. 268–274. Bo, T., et al., Hepatic selective insulin resistance at the intersection of insulin signaling and metabolic dysfunction-associated steatotic liver disease. Cell Metab, 2024. 36 (5): p. 947–968. Badmus, O.O., T.D. Hinds, Jr., and D.E. Stec, Mechanisms Linking Metabolic-Associated Fatty Liver Disease (MAFLD) to Cardiovascular Disease. Curr Hypertens Rep, 2023. 25 (8): p. 151–162. Song, Q.R., et al., Severity of Nonalcoholic Fatty Liver Disease is Associated With Cardiovascular Outcomes in Patients With Prehypertension or Hypertension: A Community-Based Cohort Study. Front Endocrinol (Lausanne), 2022. 13 : p. 942647. Stone, N.J., et al., Managing Atherosclerotic Cardiovascular Risk in Young Adults: JACC State-of-the-Art Review. J Am Coll Cardiol, 2022. 79 (8): p. 819–836. Navar, A.M., et al., Earlier treatment in adults with high lifetime risk of cardiovascular diseases: What prevention trials are feasible and could change clinical practice? Report of a National Heart, Lung, and Blood Institute (NHLBI) Workshop. Am J Prev Cardiol, 2022. 12 : p. 100430. Byrne, C.D., et al., Tests for diagnosing and monitoring non-alcoholic fatty liver disease in adults. BMJ, 2018. 362 : p. k2734. Rhee, A.J., et al., Real-World Evidence Linking the Predicting Risk of Cardiovascular Disease Events Risk Score and Coronary Artery Calcium. J Am Heart Assoc, 2025. 14 (11): p. e038991. Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6894190","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":474412666,"identity":"05d79b81-1b78-463a-ab2a-b29cfe78fd17","order_by":0,"name":"Yexin Yin","email":"","orcid":"","institution":"First Affiliated Hospital of Harbin Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yexin","middleName":"","lastName":"Yin","suffix":""},{"id":474412667,"identity":"3822c8e6-3ef6-49da-9a9d-27bd2dc87b3e","order_by":1,"name":"Lili Shi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA30lEQVRIiWNgGAWjYJACxg8/JHj4JRjYINwDRGhhluyxkZGcQYoWBh62NBuDG8Rq4Rc7Y8AgwXOYx/h277NHN9sY5PhuJDB+LsCjRXJ2jgFDgcVhHrM7x82Nc9sYjCVvJDBLz8CjxeB2DsQWsxtpbNJALYkbbiSwMfMQ0sLDBnTYDIiWemK1pPEYSEC0JBgQ0iI5O60AFMg8EjfS2I1zzkkYzjzzsFkanxZ+6eQNoKi05wc67HFOmY083/Hkg5/xaWFg4DD/gcSTAGLGBrwaGBjYHxBQMApGwSgYBSMeAACPcEM42U39vwAAAABJRU5ErkJggg==","orcid":"","institution":"First Affiliated Hospital of Harbin Medical University","correspondingAuthor":true,"prefix":"","firstName":"Lili","middleName":"","lastName":"Shi","suffix":""}],"badges":[],"createdAt":"2025-06-14 13:23:17","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6894190/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6894190/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":85390798,"identity":"bc73b046-4328-4dfd-95f4-3fcadc29ebdb","added_by":"auto","created_at":"2025-06-25 10:25:36","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":63990,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart of the study\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6894190/v1/b30a3fe913adfeb471521a1f.png"},{"id":85390799,"identity":"121bb36e-e135-48d2-afef-d05261c3a4ee","added_by":"auto","created_at":"2025-06-25 10:25:36","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":52005,"visible":true,"origin":"","legend":"\u003cp\u003eLegend not included with this version\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6894190/v1/53a9c4c315ae1962aad00978.png"},{"id":85390803,"identity":"e5bbb434-6089-401e-9394-b44c337aded4","added_by":"auto","created_at":"2025-06-25 10:25:36","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":37037,"visible":true,"origin":"","legend":"\u003cp\u003eMediation analysis of the association between TyG index and 10-year estimated cardiovascular risk in hypertensive patients with NAFLD/MASLD.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6894190/v1/c30e806c88f45d4ec7a90838.png"},{"id":85390802,"identity":"8ef58ea3-8482-4aaa-a95e-f36acdd63250","added_by":"auto","created_at":"2025-06-25 10:25:36","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":52217,"visible":true,"origin":"","legend":"\u003cp\u003eMediation analysis of the association between TyG index and 10-year estimated cardiovascular risk in hypertensive patients with NAFLD/MASLD.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-6894190/v1/692e1884f3120ba881bd8df1.png"},{"id":89350352,"identity":"7440eaac-8739-4a1e-ad5d-4a4c61af5d1a","added_by":"auto","created_at":"2025-08-19 06:02:15","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2452608,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6894190/v1/c2691c2a-fd9d-416d-9a8d-27d80b7dbb57.pdf"},{"id":85391722,"identity":"35ce3a08-1eca-4463-8a03-6fc90fd37274","added_by":"auto","created_at":"2025-06-25 10:33:36","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":508555,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-6894190/v1/e69cb57d6abf3960ab0c1656.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Association of hepatic steatosis and fibrosis with 10-year estimated cardiovascular disease risk in hypertensive patients and the mediating role of triglyceride-glucose index","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eFor many years, non-alcoholic fatty liver disease (NAFLD) has stood as the most prevalent liver condition globally. Recent investigations indicate a worldwide adult prevalence ranging from 25\u0026ndash;30%, exhibiting geographical variations. In the United States, NAFLD prevalence is estimated at 26% and is projected to increase by over 100\u0026nbsp;million cases by 2040 [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eNAFLD can progress to non-alcoholic steatohepatitis, which, with advancing fibrosis, may further lead to cirrhosis and subsequently a range of complications. Crucially, at any stage of NAFLD, cardiovascular disease remains the primary cause of mortality[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eNAFLD is recognized as a systemic disease [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e], In recent years, understanding of its intricate link with metabolic syndrome (MetS), encompassing hypertension, dyslipidemia, obesity, and type 2 diabetes, has deepened significantly within the academic community. To emphasize this association, a consensus has proposed replacing \"non-alcoholic fatty liver disease (NAFLD)\" with the new terminology \"Metabolic dysfunction-Associated Steatotic Liver Disease (MASLD)\" [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e],This updated definition encompasses patients presenting with hepatic steatosis concurrently possessing at least one of five cardiometabolic risk factors.\u003c/p\u003e \u003cp\u003eAs implied by the description of this newly introduced terminology, clinical data also indicate its close association with CVD risk[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Hypertension, a core component of metabolic syndrome and an independent risk factor for CVD, has been shown to exhibit a comorbidity rate with NAFLD approximately 49.5% higher in hypertensive patients compared to the general population. Furthermore, bidirectional links between the two have been corroborated at the omics level [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. These findings collectively support the notion that a complex and multifaceted interplay exists between NAFLD/MASLD and cardiovascular disease. However, to date, this association has been primarily observed at the clinical level, and the underlying pathophysiological mechanisms remain a matter of ongoing debate. The development of NAFLD is primarily driven by excessive nutritional intake, which promotes adipose tissue accumulation and imposes a metabolic burden on hepatic lipid handling. This imbalance disrupts lipid homeostasis in the liver, leading to mitochondrial dysfunction and oxidative stress. Subsequently, inflammatory cascades are activated, further aggravating hepatic injury and triggering the onset of IR[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. The concept of MASLD further emphasizes the interplay between hepatic steatosis and metabolic syndrome, and how this interaction exacerbates systemic metabolic dysregulation in extrahepatic organs. Some researchers have proposed that traditional cardiovascular risk factors\u0026mdash;such as dyslipidemia, insulin resistance, and chronic low-grade inflammation\u0026mdash;may contribute to cardiovascular risk independently of NAFLD/MASLD itself. Meanwhile, other factors including coagulopathy, sympathetic nervous system dysregulation, and gut microbiota imbalance have been suggested as distinctive contributors to elevated CVD risk specifically among patients with NAFLD/MASLD. However, to date, no definitive evidence has been established to confirm either perspective[\u003cspan additionalcitationids=\"CR12 CR13\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Although NAFLD/MASLD imposes substantial economic and health burdens on hypertensive patients[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], particularly concerning CVD morbidity and mortality, current research investigating the impact of NAFLD/MASLD on CVD risk in this population remains limited[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] .Furthermore, studies exploring whether insulin resistance (IR) exerts a mediating effect in this association are notably scarce.\u003c/p\u003e \u003cp\u003eUtilizing quantitative risk assessment tools to evaluate the risk of a first major adverse cardiovascular event in hypertensive individuals with NAFLD is a crucial starting point for clinicians in guiding primary CVD prevention decisions. Numerous CVD risk assessment tools have been proposed to predict the 10-year risk of first fatal and non-fatal CVD events. For instance, the Pooled Cohort Equation (PCE)[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], jointly developed and proposed by the American College of Cardiology (ACC) and the American Heart Association (AHA) in 2013, can be used to assess an individual's 10-year risk of atherosclerotic cardiovascular disease (ASCVD\u003cb\u003e)\u003c/b\u003e. As academic understanding of CVD and its comorbidities has deepened, the AHA introduced the concept of Cardiovascular-Kidney-Metabolic Syndrome (CKM), emphasizing the intricate interconnections and common progression among CVD, chronic kidney disease, type 2 diabetes, and obesity. Within this context, the PREVENT equations emerged as a comprehensive revision of the PCE model to better reflect contemporary cardiovascular risk profiles. It is thus evident that this tool is optimally suited for assessing CVD risk in NAFLD/MASLD patients[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTherefore, in this cross-sectional study, we aimed to investigate the association of hepatic steatosis (NAFLD/MASLD) and the severity of fibrosis (as determined by validated non-invasive biomarkers) with an increased 10-year CVD risk in adults with hypertension without pre-existing CVD. Concurrently, we conducted mediation analysis to explore and underscore the role of the TyG index in this process.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Data source and study population\u003c/h2\u003e \u003cp\u003eThe data used in this study were derived from the National Health and Nutrition Examination Survey (NHANES) database. The survey protocol was approved by the Research Ethics Review Board of the National Center for Health Statistics (NCHS), and all participants provided written informed consent. Data collection was conducted in accordance with standardized procedures and was anonymized to ensure confidentiality. The datasets analyzed in this study are publicly available on the NHANES official website (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.cdc.gov/nchs/nhanes/index.html\u003c/span\u003e\u003cspan address=\"https://www.cdc.gov/nchs/nhanes/index.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e We utilized data from the NHANES database spanning from 1999 to 2018, initially including 101,316 participants. The exclusion criteria were as follows: participants aged\u0026thinsp;\u0026lt;\u0026thinsp;30 or \u0026gt;\u0026thinsp;79 years (n\u0026thinsp;=\u0026thinsp;60,049); those without a diagnosis of hypertension (n\u0026thinsp;=\u0026thinsp;25,432); individuals with missing or implausible data for calculating the triglyceride-glucose (TyG) index or the PREVENT risk equation (n\u0026thinsp;=\u0026thinsp;14,239); those with missing data required for assessing NAFLD (n\u0026thinsp;=\u0026thinsp;57); and participants with missing or positive status for hepatitis B surface antigen or hepatitis C antibody, excessive alcohol consumption, or missing sampling weight information (n\u0026thinsp;=\u0026thinsp;456). After applying these criteria, a final sample of 1,083 participants (492 men and 591 women) was included in the analysis. The detailed selection process is illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and Supplementary Materials.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Clinical and laboratory data\u003c/h2\u003e \u003cp\u003eThe extracted electronic health data included demographic characteristics (sex, age, race/ethnicity, education level), the poverty income ratio (PIR), physical activity levels, and clinical parameters such as body mass index (BMI), blood pressure, hepatitis B virus (HBV) and hepatitis C virus (HCV) serologies, and a range of biochemical markers. These included complete blood counts, lipid profiles, fasting glucose, urinary albumin and creatinine, glycated hemoglobin (HbA1c), serum creatinine, and liver enzymes [aspartate aminotransferase (AST) and alanine aminotransferase (ALT)].\u003c/p\u003e \u003cp\u003eThe estimated glomerular filtration rate (eGFR) was calculated using the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equation [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAlbuminuria was defined as an elevated urinary albumin-to-creatinine ratio (ACR\u0026thinsp;\u0026ge;\u0026thinsp;3.0 mg/mmol). Smoking status was categorized as current (yes) or non-smoking (no, including former smokers who had quit for more than one year). Physical activity was classified as none, moderate, vigorous, or both moderate and vigorous[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSignificant alcohol intake was defined as \u0026ge;\u0026thinsp;20 g/d for men and \u0026ge;\u0026thinsp;10 g/d for women[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. PIR was categorized into low income (\u0026lt;\u0026thinsp;1.3), middle income (\u0026ge;\u0026thinsp;1.3 and \u0026lt;\u0026thinsp;3.5), and high income (\u0026ge;\u0026thinsp;3.5), following established cutoffs in prior studies [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn addition, information was collected on the presence of chronic kidney disease (CKD), defined as eGFR\u0026thinsp;\u0026lt;\u0026thinsp;60 mL/min/1.73 m\u0026sup2; or urinary ACR\u0026thinsp;\u0026ge;\u0026thinsp;3.0 mg/mmol, as well as the use of antihypertensive, lipid-lowering, and glucose-lowering medications [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Non-invasive biomarkers of hepatic steatosis and fibrosis\u003c/h2\u003e \u003cp\u003eThe Fatty Liver Index (FLI), originally developed by an Italian working group, incorporates body mass index (BMI), waist circumference, triglyceride levels, and gamma-glutamyl transferase (GGT) activity to estimate hepatic steatosis [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Subsequently, American researchers evaluated this FLI within the National Health and Nutrition Examination Survey (NHANES), ultimately developing the United States Fatty Liver Index (USFLI), which proved more suitable for the diverse multi-ethnic population of the U.S. (AUC [95% CI]: 0.80 [0.77\u0026ndash;0.83]). Its calculation method is as follows:\u003c/p\u003e \u003cp\u003e\u003cimg 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\" width=\"511\" height=\"214\"\u003e\u003c/p\u003e \u003cp\u003eA USFLI of \u0026ge;\u0026thinsp;30 had a sensitivity of 62%, specificity of 88%, likelihood ratio positive of 5.2, and likelihood ratio negative of 0.43,and was selected to rule in fatty liver.[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. USFLI has shown robust performance in studies targeting the general U.S. population and is considered an appropriate non-invasive diagnostic tool for large-scale epidemiological investigations [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eWe additionally calculated the Fibrosis-4 (FIB-4) index using the following formula:\u003c/p\u003e \u003cp\u003eFIB-4 index = (Age \u0026times; AST [IU/L]) / (Platelet count [10⁹/L] \u0026times; \u0026radic;ALT [IU/L]).\u003c/p\u003e \u003cp\u003eThe FIB-4 index is one of the most widely used non-invasive scoring systems for the assessment of advanced liver fibrosis[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. A cutoff value of FIB-4\u0026thinsp;\u0026ge;\u0026thinsp;1.3 was considered indicative of significant fibrosis, whereas a threshold of FIB-4\u0026thinsp;\u0026ge;\u0026thinsp;2.67 suggested the presence of advanced fibrosis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 The 10-year risk of CVD risk estimates\u003c/h2\u003e \u003cp\u003e The PREVENT equation was proposed in 2023 by the American College of Cardiology/American Heart Association (ACC/AHA) Practice Guidelines Writing Group, serving as a modification of the 2013 PCE. Specifically, this risk calculator estimates the 10-year incidence of a first CVD event (composite of ASCVD and heart failure). It incorporates 12 variables: age, sex, total cholesterol, high-density lipoprotein cholesterol, systolic blood pressure, BMI, eGFR, use of antihypertensive medication, use of lipid-lowering medication, smoking status, and diabetes status[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. According to the equation, estimated 10-year CVD risk is categorized as follows: low risk (\u0026lt;\u0026thinsp;5%), borderline risk (5\u0026ndash;7.4%), intermediate risk (7.5\u0026ndash;19.9%), and high risk (\u0026ge;\u0026thinsp;20%) [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. In the present study, borderline and intermediate risk categories were combined into a single group, referred to as the moderate-risk group.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Statistical analysis\u003c/h2\u003e \u003cp\u003eContinuous variables were presented as means\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviations (SD) or medians with interquartile ranges (IQR), while categorical variables were expressed as proportions (%). Differences in major clinical and biochemical characteristics were assessed by hepatic steatosis status (with or without coexisting significant fibrosis) and by categories of 10-year estimated CVD risk. Specifically, one-way analysis of variance was used for normally distributed continuous variables, the Kruskal-Wallis test for non-normally distributed continuous variables, and the chi-squared test for categorical variables. Weighted univariable and multivariable logistic regression analyses were conducted to examine the association between hepatic steatosis (with or without significant fibrosis) and the estimated 10-year CVD risk in individuals with hypertension. In these weighted models, the binary outcome was defined as having moderate-to-high ASCVD risk vs low ASCVD risk. Two logistic regression models were fitted: Model 1 was unadjusted, while Model 2 was adjusted for sex, race/ethnicity, educational attainment, poverty-income ratio (PIR), physical activity level, and presence of chronic kidney disease (CKD), defined as either an eGFR\u0026thinsp;\u0026lt;\u0026thinsp;60 mL/min/1.73 m\u0026sup2; or albuminuria (ACR\u0026thinsp;\u0026ge;\u0026thinsp;3.0 mg/mmol). Notably, age is a component of both the FIB-4 index and the PREVENT risk equation. Similarly, smoking status, BMI, lipid-lowering medication use, and diabetes status are all incorporated in the PREVENT model. To mitigate potential multicollinearity, these variables were not included as covariates in the multivariable models. In the final dataset, to address missing covariate data, we employed a non-parametric imputation method based on random forest, implemented using the missForest R package[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. This method is robust to different data types and can capture complex, non-linear relationships between variables, which is particularly advantageous for handling diverse datasets like NHANES. Mediation analysis was performed using the mediation package. CVD risk was treated as a continuous outcome, while other model specifications remained unchanged. All statistical tests were two-sided, and a p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant. Statistical analyses were performed using R software (version 4.5.0; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.r-project.org\u003c/span\u003e\u003cspan address=\"https://www.r-project.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), incorporating sampling weights, clustering, and stratification to account for the complex survey design of NHANES. In accordance with NHANES analysis guidelines, all participant data were weighted using the recommended Fasting Subsample 2-Year Mobile Examination Center Weight (WTSAF2YR), Details regarding the NHANES weighting calculation method are available at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://wwwn.cdc.gov/nchs/nhanes/tutorials/Weighting.aspx\u003c/span\u003e\u003cspan address=\"https://wwwn.cdc.gov/nchs/nhanes/tutorials/Weighting.aspx\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Result","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Baseline characteristics of study participants\u003c/h2\u003e \u003cp\u003eTable 1 presents the main clinical and biochemical characteristics of participants stratified by 10-year CVD risk as estimated by the PREVENT equation. The final analytic cohort included 1,083 participants, representing a weighted U.S. population of 8,807,596 individuals (weighted mean age: 60.25 years [SE: 0.24]; 54.46% were female). As CVD risk increased, there were significant upward trends in age, systolic blood pressure, waist circumference, fasting plasma glucose, HbA1c, and TyG index. In contrast, levels of HDL and Low-density lipoprotein -C decreased significantly across risk categories. Indicators of renal function, such as serum creatinine, increased with higher CVD risk, whereas eGFR) decreased, suggesting impaired kidney function in participants at higher risk. Sociodemographic and lifestyle differences were also observed: individuals in the high-risk group were more likely to have lower educational attainment (a lower proportion with education beyond high school) and a higher prevalence of physical inactivity. Prevalence of chronic conditions such as diabetes and chronic kidney disease (CKD), as well as the use of related medications (antihypertensive, lipid-lowering, and antidiabetic agents), increased significantly across the CVD risk strata. Moreover, there were significant differences in gender composition, racial/ethnic distribution, and socioeconomic status across groups. In contrast, no significant differences were found across CVD risk categories for marital status, body mass index (BMI), triglyceride levels, aspartate aminotransferase (AST), urinary creatinine, smoking status, or alcohol consumption.\u003c/p\u003e\u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003e\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e Table.1 Clinical and biochemical characteristics of adults with Hypertension, stratified by Prevent equations\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOverall\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIntermediate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP-Value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eN\u0026thinsp;=\u0026thinsp;1083\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eN\u0026thinsp;=\u0026thinsp;153\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eN\u0026thinsp;=\u0026thinsp;672\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eN\u0026thinsp;=\u0026thinsp;258\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge, y, mean (SD)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e62.2 (10.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e47.9 (6.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e61.9 (8.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e71.4 (6.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGender\u003c/b\u003e:\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e492 (45.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e74 (48.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e272 (40.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e146 (56.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e591 (54.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e79 (51.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e400 (59.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e112 (43.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEducational level, n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;High school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e147 (13.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7 (4.58%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e90 (13.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e50 (19.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCompleted high school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e156 (14.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21 (13.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e95 (14.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e40 (15.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;High school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e779 (72.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e125 (81.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e487 (72.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e167 (65.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRace, n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.022\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMexican American\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e176 (16.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18 (11.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e114 (17.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e44 (17.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-Hispanic White\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e459 (42.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e69 (45.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e275 (40.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e115 (44.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-Hispanic Black\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e240 (22.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e41 (26.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e136 (20.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e63 (24.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther Hispanic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e104 (9.60%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8 (5.23%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e73 (10.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e23 (8.91%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther Race\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e104 (9.60%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17 (11.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e74 (11.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13 (5.04%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMarital status, n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.870\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarried/Living with partner\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e707 (65.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e96 (63.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e441 (65.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e170 (65.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWidowed/Divorced/Separated/Never married\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e373 (34.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e55 (36.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e230 (34.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e88 (34.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePhysical activity, n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.049\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInactive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e496 (51.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e62 (42.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e309 (51.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e125 (55.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e288 (29.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e44 (30.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e177 (29.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e67 (29.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVigorous\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e41 (4.24%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12 (8.28%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22 (3.68%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7 (3.11%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBoth Moderate and Vigorous\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e143 (14.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27 (18.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e90 (15.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e26 (11.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBMI, kg/m\u003c/b\u003e\u003csup\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sup\u003e, \u003cb\u003emean (SD)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30.4 (4.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31.1 (4.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30.3 (4.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e30.2 (4.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.096\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSBP, mmHg, mean (SD)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e132 (17.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e123 (13.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e131 (16.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e141 (18.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWaist circumference, cm, mean (SD)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e104 (12.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e103 (12.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e104 (12.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e107 (11.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTotal cholesterol, mg/dL, mean (SD)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e195 (39.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e199 (36.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e197 (40.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e189 (37.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHDL-cholesterol, mg/dL, mean (SD)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e51.2 (13.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e52.7 (14.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e51.9 (13.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e48.6 (12.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLDL-cholesterol, mg/dL, mean (SD)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e114 (34.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e119 (32.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e115 (35.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e109 (34.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTriglyceride, mg/dL, mean (SD)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e156 (109)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e144 (102)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e156 (102)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e162 (128)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.273\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAlbumin, urine, mg/L, mean (SD)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e74.7 (463)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20.3 (62.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e65.5 (537)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e131 (378)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.045\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCreatinine(urine), \u0026micro;mol/L, mean (SD)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10481 (5983)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11553 (6035)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10325 (6004)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10249 (5853)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.056\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGlucose, mg/dL, mean (SD)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e125 (46.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e104 (24.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e125 (46.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e137 (53.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGlycohemoglobin, %, mean (SD)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.34 (1.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.70 (0.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.35 (1.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.68 (1.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAST, U/L, mean (SD)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25.1 (10.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24.0 (6.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25.1 (10.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e25.9 (13.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.211\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eALT, U/L, mean (SD)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25.3 (14.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27.0 (12.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25.6 (14.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e23.5 (12.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.034\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCreatinine, mg/dL, mean (SD)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.91 (0.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.86 (0.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.86 (0.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.06 (0.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTyG, mean (SD)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.97 (0.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.73 (0.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.98 (0.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.10 (0.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eeGFR, mL/min/1.73m\u003c/b\u003e\u003csup\u003e\u003cb\u003e2\u003c/b\u003e,\u003c/sup\u003e \u003cb\u003emean (SD)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e84.1 (19.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e96.4 (15.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e86.2 (16.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e71.4 (20.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSmoking status\u003c/b\u003e:\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.550\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e920 (84.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e127 (83.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e577 (85.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e216 (83.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e163 (15.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26 (17.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e95 (14.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e42 (16.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDrinking status\u003c/b\u003e:\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.885\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1059 (98.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e147 (98.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e659 (98.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e253 (98.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18 (1.67%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 (2.00%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11 (1.64%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4 (1.56%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAntihypertensive\u003c/b\u003e:\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e93 (8.59%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e39 (25.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e49 (7.29%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5 (1.94%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e990 (91.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e114 (74.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e623 (92.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e253 (98.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLipid-lowering\u003c/b\u003e:\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e211 (19.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e44 (28.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e128 (19.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e39 (15.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e872 (80.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e109 (71.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e544 (81.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e219 (84.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAntidiabetic\u003c/b\u003e:\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e331 (65.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7 (22.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e205 (64.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e119 (73.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e177 (34.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24 (77.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e111 (35.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e42 (26.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDiabetes status, n (%)\u003c/b\u003e:\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e614 (56.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e143 (93.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e391 (58.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e80 (31.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e469 (43.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10 (6.54%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e281 (41.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e178 (69.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePIR, n (%)\u003c/b\u003e:\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow incomes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e317 (29.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36 (23.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e198 (29.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e83 (32.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMiddle incomes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e415 (38.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e43 (28.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e247 (36.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e125 (48.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh incomes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e351(32.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e74 (48.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e227 (33.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e50 (19.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCKD status, n (%)\u003c/b\u003e:\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e763 (70.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e139 (90.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e508 (75.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e116 (45.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e317 (29.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14 (9.15%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e162 (24.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e141 (54.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e\u003cstrong\u003eCohort size\u003c/strong\u003e: n = 1,083. Data are presented as means \u0026plusmn; standard deviation (SD) for continuous variables and as percentages for categorical variables. Between-group differences were assessed using chi-squared tests for categorical variables, one-way analysis of variance (ANOVA) for normally distributed continuous variables, and Kruskal\u0026ndash;Wallis tests for non-normally distributed variables.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNote\u003c/strong\u003e: Albuminuria was defined as a urinary albumin-to-creatinine ratio (ACR) \u0026ge; 3.0 mg/mmol. Chronic kidney disease (CKD) was defined as an estimated glomerular filtration rate (eGFR CKD-EPI) \u0026lt; 60 mL/min/1.73 m\u0026sup2; or the presence of albuminuria.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAbbreviations\u003c/strong\u003e: ALT, alanine aminotransferase; AST, aspartate aminotransferase; BMI, body mass index; SBP, systolic blood pressure; TyG, triglyceride-glucose index; CKD, chronic kidney disease; eGFR CKD-EPI, estimated glomerular filtration rate calculated using the CKD-Epidemiology Collaboration equation; GGT, gamma-glutamyl transferase; FIB-4, fibrosis-4 index; USFLI, United States Fatty Liver Index; PIR, Poverty Income Ratio.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;\u003c/strong\u003e2 presents the main clinical and biochemical characteristics of participants, stratified by the presence of hepatic steatosis with or without significant fibrosis. Compared to those without hepatic steatosis or with steatosis alone, participants with significant fibrosis were older, had greater waist circumference, and were less likely to be current smokers. They exhibited higher levels of blood pressure, fasting plasma glucose, AST and ALT, along with lower platelet counts, total cholesterol, low-density lipoprotein cholesterol, and e-GFR. Moreover, the prevalence of diabetes and CKD was higher in this group, accompanied by significantly increased use of antihypertensive and antidiabetic medications. No significant differences were observed across groups in marital status, physical activity, urinary albumin or creatinine levels, or the use of lipid-lowering medications. \u0026nbsp; \u0026nbsp;\u003c/p\u003e\u003cp\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eClinical and biochemical characteristics of adults with Hypertension, stratified by the presence of hepatic steatosis with or without coexisting significant liver fibrosis (non-invasively assessed by USFLI and FIB-4 scores)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOverall\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUSFLI\u0026thinsp;\u0026lt;\u0026thinsp;30\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUSFLI\u0026thinsp;\u0026gt;\u0026thinsp;=\u0026thinsp;30 \u0026amp;\u003c/p\u003e \u003cp\u003eFIB_4\u0026thinsp;\u0026lt;\u0026thinsp;1.3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eUSFLI\u0026thinsp;\u0026gt;\u0026thinsp;=\u0026thinsp;30 \u0026amp;\u003c/p\u003e \u003cp\u003eFIB_4\u0026thinsp;\u0026gt;\u0026thinsp;=\u0026thinsp;1.3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP-Value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eN\u0026thinsp;=\u0026thinsp;1083\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eN\u0026thinsp;=\u0026thinsp;483\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eN\u0026thinsp;=\u0026thinsp;313\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eN\u0026thinsp;=\u0026thinsp;287\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge, y, mean (SD)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e62.2 (10.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e62.3 (10.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e57.0 (10.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e67.6 (7.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGender\u003c/b\u003e:\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e492 (45.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e184 (38.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e148 (47.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e160 (55.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e591 (54.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e299 (61.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e165 (52.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e127 (44.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEducational level, n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;High school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e147 (13.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e52 (10.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e40 (12.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e55 (19.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCompleted high school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e156 (14.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e60 (12.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e53 (16.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e43 (15.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;High school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e779 (72.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e371 (76.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e220 (70.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e188 (65.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRace, n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMexican American\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e176 (16.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e46 (9.52%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e56 (17.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e74 (25.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-Hispanic White\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e459 (42.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e183 (37.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e140 (44.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e136 (47.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-Hispanic Black\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e240 (22.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e158 (32.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e50 (16.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e32 (11.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther Hispanic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e104 (9.60%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e46 (9.52%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e36 (11.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e22 (7.67%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther Race\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e104 (9.60%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e50 (10.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e31 (9.90%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e23 (8.01%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMarital status, n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.078\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarried/Living with partner\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e707 (65.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e298 (61.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e213 (68.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e196 (68.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWidowed/Divorced/Separated/Never married\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e373 (34.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e184 (38.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e98 (31.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e91 (31.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePhysical activity, n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.805\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInactive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e496 (51.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e225 (51.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e141 (50.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e130 (51.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e288 (29.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e120 (27.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e88 (31.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e80 (31.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVigorous\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e41 (4.24%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20 (4.61%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13 (4.66%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8 (3.14%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBoth Moderate and Vigorous\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e143 (14.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e69 (15.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e37 (13.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e37 (14.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBMI, kg/m\u003c/b\u003e\u003csup\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sup\u003e, \u003cb\u003emean (SD)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30.4 (4.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28.3 (4.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e32.5 (4.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e31.5 (4.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSBP, mmHg, mean (SD)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e132 (17.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e132 (18.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e131 (16.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e135 (16.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWaist circumference, cm, mean (SD)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e104 (12.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e98.1 (10.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e109 (10.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e109 (11.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTotal cholesterol, mg/dL, mean (SD)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e195 (39.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e198 (38.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e200 (40.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e186 (37.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHDL cholesterol, mg/dL, mean (SD)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e51.2 (13.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e56.4 (13.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e45.3 (11.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e48.9 (12.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLDL-cholesterol, mg/dL, mean (SD)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e114 (34.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e117 (35.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e119 (35.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e104 (31.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTriglyceride, mg/dL, mean (SD)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e156 (109)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e123 (68.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e191 (122)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e172 (130)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAlbumin, urine, mg/L, mean (SD)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e74.7 (463)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e46.6 (171)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e121 (800)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e71.7 (254)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.086\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCreatinine(urine), \u0026micro;mol/L, mean (SD)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10481 (5983)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10279 (6011)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10723 (5738)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10557 (6200)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.576\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGlucose, mg/dL, mean (SD)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e125 (46.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e111 (38.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e136 (54.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e137 (44.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGlycohemoglobin, %, mean (SD)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.34 (1.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.01 (1.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.63 (1.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.57 (1.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAST, U/L, mean (SD)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25.1 (10.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22.9 (6.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23.5 (7.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e30.7 (15.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eALT, U/L, mean (SD)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25.3 (14.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20.8 (8.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27.8 (13.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e30.2 (19.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCreatinine, mg/dL, mean (SD)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.91 (0.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.92 (0.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.86 (0.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.94 (0.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTyG, mean (SD)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.97 (0.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.66 (0.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.27 (0.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.17 (0.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eeGFR, mL/min/1.73m\u003c/b\u003e\u003csup\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sup\u003e, \u003cb\u003emean (SD)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e84.1 (19.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e81.7 (19.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e90.7 (17.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e81.0 (19.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSmoking status\u003c/b\u003e:\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e920 (84.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e407 (84.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e254 (81.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e259 (90.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e163 (15.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e76 (15.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e59 (18.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e28 (9.76%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDrinking status\u003c/b\u003e:\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1059 (98.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e472 (98.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e306 (98.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e281 (98.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18 (1.67%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8 (1.67%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5 (1.61%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5 (1.75%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAntihypertensive\u003c/b\u003e:\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.032\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e93 (8.59%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e43 (8.90%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e35 (11.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e15 (5.23%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e990 (91.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e440 (91.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e278 (88.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e272 (94.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLipid lowering\u003c/b\u003e:\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.079\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e211 (19.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e103 (21.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e65 (20.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e43 (15.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e872 (80.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e380 (78.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e248 (79.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e244 (85.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAntidiabetic\u003c/b\u003e:\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e331 (65.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e105 (57.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e110 (65.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e116 (74.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e177 (34.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e79 (42.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e59 (34.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e39 (25.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDiabetes status, n (%)\u003c/b\u003e:\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e614 (56.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e337 (69.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e156 (49.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e121 (42.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e469 (43.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e146 (30.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e157 (50.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e166 (57.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePIR, n (%)\u003c/b\u003e:\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.049\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow incomes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e317 (29.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e126 (26.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e99 (31.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e92 (42.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMiddle incomes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e415 (38.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e196 (40.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e103 (32.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e116 (40.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh incomes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e351 (32.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e161 (33.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e111 (35.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e79 (27.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCKD status, n (%)\u003c/b\u003e:\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e763 (70.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e356 (73.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e227 (73.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e180 (62.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e317 (29.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e127 (26.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e83 (26.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e107 (37.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\u003cp\u003e\u003cstrong\u003eCohort size\u003c/strong\u003e: n = 1,083. Data are presented as means \u0026plusmn; standard deviation (SD) for continuous variables and as percentages for categorical variables. Between-group differences were assessed using chi-squared tests for categorical variables, one-way analysis of variance (ANOVA) for normally distributed continuous variables, and Kruskal\u0026ndash;Wallis tests for non-normally distributed variables.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNote\u003c/strong\u003e: Albuminuria was defined as a urinary albumin-to-creatinine ratio (ACR) \u0026ge; 3.0 mg/mmol. Chronic kidney disease (CKD) was defined as an estimated glomerular filtration rate (eGFR CKD-EPI) \u0026lt; 60 mL/min/1.73 m\u0026sup2; or the presence of albuminuria.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAbbreviations\u003c/strong\u003e: ALT, alanine aminotransferase; AST, aspartate aminotransferase; BMI, body mass index; SBP, systolic blood pressure; TyG, triglyceride-glucose index; CKD, chronic kidney disease; eGFR CKD-EPI, estimated glomerular filtration rate calculated using the CKD-Epidemiology Collaboration equation; GGT, gamma-glutamyl transferase; FIB-4, fibrosis-4 index; USFLI, United States Fatty Liver Index; PIR, Poverty Income Ratio.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e3.2 The 10-year CVD risk assessment using the PREVENT Equation in NAFLD/MASLD patients with or without significant fibrosis\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe calculated the 10-year estimated prevalence of CVD risk categories using the PREVENT equation (Fig 2), stratified by the presence of hepatic steatosis with or without significant fibrosis. Compared to participants with either hepatic steatosis alone or without steatosis, those with both steatosis and significant fibrosis exhibited a markedly higher estimated 10-year risk of experiencing a first fatal or non-fatal CVD event (39.4% vs. 20.5% vs. 14.7%, \u003cem\u003ep\u003c/em\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u0026lt; 0.001 by chi-square test).\u003c/p\u003e\u003cp\u003eThe 10-year risk prevalence of first fatal or non-fatal CVD events in hypertensive adults, stratified by hepatic steatosis and its significant fibrosis status (as assessed by USFLI and FIB-4 scores).\u003c/p\u003e\n\u003cp\u003eWe conducted subgroup analyses based on median BMI values (\u0026lt; 30.29 vs. \u0026ge; 30.29 kg/m\u003csup\u003e2\u003c/sup\u003e) (Supplementary Fig.1), age (\u0026lt; 63 vs. \u0026ge; 63 years) (Supplementary Fig.2) or sex (Supplementary Fig.3). These subgroup analyses confirmed that patients with hepatic steatosis accompanied by significant fibrosis exhibited a significantly higher 10-year estimated cardiovascular disease risk compared to those with isolated steatosis or no steatosis, irrespective of sex or any other patient subgroup considered.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 3 presents the association between hepatic steatosis (with or without significant fibrosis) and 10-year estimated CVD risk. In the unadjusted weighted regression model, patients with hepatic steatosis and significant fibrosis exhibited an approximately 12-fold increased risk of high/intermediate 10-year estimated CVD risk compared to those without steatosis. This elevated risk remained significant even after adjusting for sex, marital status, education level, race/ethnicity, PIR, physical activity level, and the presence of CKD (adjusted weighted regression model). Furthermore, patients with isolated steatosis and those without steatosis had comparable 10-year estimated CVD risks (though not statistically significant). Among the covariates, higher education level, presence of CKD, and engagement in vigorous physical activity were also independently associated with elevated 10-year estimated CVD risk (p \u0026lt; 0.05 for all).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAssociation between hepatic steatosis with or without coexisting significant fibrosis and the 10-year estimated CVD risk\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLogistic Regression Analyses\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eY\u0026thinsp;=\u0026thinsp;High or moderate risk vs.\u003c/p\u003e \u003cp\u003eLow risk score\u003c/p\u003e \u003cp\u003e\u003cem\u003eUnadjusted model\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUSFLI\u0026thinsp;\u0026lt;\u0026thinsp;30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUSFLI\u0026thinsp;\u0026gt;\u0026thinsp;=\u0026thinsp;30 \u0026amp; FIB_4\u0026thinsp;\u0026lt;\u0026thinsp;1.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.85(0.47, 1.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.597\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUSFLI\u0026thinsp;\u0026gt;\u0026thinsp;=\u0026thinsp;30 \u0026amp; FIB_4\u0026thinsp;\u0026gt;\u0026thinsp;=\u0026thinsp;1.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11.9(3.02, 46.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eAdjusted model\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUSFLI\u0026thinsp;\u0026lt;\u0026thinsp;30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUSFLI\u0026thinsp;\u0026gt;\u0026thinsp;=\u0026thinsp;30 \u0026amp; FIB_4\u0026thinsp;\u0026lt;\u0026thinsp;1.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.93(0.48, 1.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.818\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUSFLI\u0026thinsp;\u0026gt;\u0026thinsp;=\u0026thinsp;30 \u0026amp; FIB_4\u0026thinsp;\u0026gt;\u0026thinsp;=\u0026thinsp;1.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11.6(2.90, 46.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003cp\u003e\u003cstrong\u003eCohort size\u003c/strong\u003e: n = 1,083. Data are presented as odds ratio (OR) with 95% confidence interval (CI) and were evaluated using univariate and multivariate logistic regression analyses. The dependent variable in the logistic regression model was defined as membership in the combined moderate/high CVD risk group vs the low CVD risk group. The regression models were adjusted for sex, educational level, race/ethnicity, marital status, physical activity level, PIR, and the presence of CKD, defined as an eGFR \u0026lt; 60 mL/min/1.73 m\u0026sup2; or abnormal albuminuria.\u003c/p\u003e\n\u003cp\u003eTo assess the robustness of our findings, we conducted several sensitivity analyses. First, we excluded data from the 2015\u0026ndash;2016 NHANES cycle due to the unavailability of anti-HCV antibody results (replaced by HCV RNA data during this period). The results remained consistent (Supplementary Table.1). Second, we repeated the analysis after excluding all participants with any missing covariate data, and the results were unchanged (Supplementary Table 2).Furthermore, we calculated the \u003cstrong\u003eE-value\u003c/strong\u003e for the association between USFLI \u0026ge; 30 combined with FIB-4 \u0026ge; 1.3 and the elevated 10-year CVD risk, based on the fully adjusted model [34]. The E-value was \u003cstrong\u003e6.27\u003c/strong\u003e, indicating that an unmeasured confounder would need to be associated with both the exposure and outcome by a risk ratio of at least 6.27 to fully explain away the observed association. Thus, our findings suggest that the observed association is robust to potential unmeasured confounding.\u003c/p\u003e\n\u003cp\u003e3.3 Mediation Analysis of the Potential Role of the TyG Index in This Association\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eWe conducted a mediation analysis to examine whether, and to what extent, insulin resistance (IR), as assessed by the TyG index, mediates the association between NAFLD/MASLD and the estimated 10-year CVD risk.\u003c/strong\u003e As shown in Fig.3, the total effect represents the overall impact of NAFLD/MASLD status on the 10-year estimated CVD risk in individuals with hypertension. The direct effect reflects the influence of NAFLD/MASLD (defined as USFLI \u0026ge;30 and FIB-4 \u0026ge;1.3) on CVD risk independent of the TyG index, while the indirect effect captures the portion of this association that is mediated through the TyG index. Overall, the direct effect was substantially greater than the indirect effect, although the mediation effect was still statistically significant. The proportion of the effect mediated by the TyG index was estimated at \u003cstrong\u003e16.85%\u003c/strong\u003e (Fig.4), suggesting a modest but meaningful role of insulin resistance in linking NAFLD/MASLD with increased cardiovascular risk among hypertensive individuals.\u003c/p\u003e"},{"header":"Discussion ","content":"\u003cp\u003eIt is well established that NAFLD is closely associated with an increased risk of cardiovascular disease (CVD). Metabolic syndrome is defined by the coexistence of multiple metabolic risk factors for CVD, and some researchers have proposed that NAFLD/MASLD represents the hepatic manifestation of metabolic syndrome. Hypertension, as a central component of metabolic syndrome, frequently coexists with NAFLD—a relationship that is well-supported by substantial evidence. However, little is known about how NAFLD/MASLD and its severity of fibrosis are associated with CVD risk specifically among individuals with hypertension, the underlying mechanisms have yet to reach a consistent consensus.\u003c/p\u003e\n\u003cp\u003eOur study investigated the association of NAFLD/MASLD and its fibrosis severity with CVD risk in hypertensive patients, further exploring the mediating role of the TyG index (a surrogate marker for IR) in this association. The main findings are as follows: (1) Compared with individuals without hepatic steatosis or those with steatosis alone, participants with both steatosis and advanced fibrosis exhibited a significantly higher 10-year estimated risk of experiencing a first fatal or non-fatal CVD event. Notably, the presence of steatosis alone was not associated with a statistically significant difference in CVD risk across groups. \u0026nbsp;(2) This elevated CVD risk remained statistically significant even after adjusting for sex, education level, race/ethnicity, marital status, PIR, CKD, and physical activity. \u0026nbsp;(3) Subgroup analyses confirmed that the elevated CVD risk associated with steatosis and fibrosis persisted across different strata of age, sex, and BMI. \u0026nbsp;(4) Mediation analysis demonstrated that IR partially mediated the relationship between NAFLD/MASLD with advanced fibrosis and CVD risk, providing empirical support for IR as a potential mechanistic link underlying this complex association.\u003c/p\u003e\n\u003cp\u003eA hallmark of early-stage NAFLD is the ectopic accumulation of triglycerides in the liver. This process requires a continuous supply of fatty acids, predominantly derived from adipose tissue lipolysis driven by unrestrained hormone-sensitive lipase (HSL) activity under conditions of IR. The resultant increase in circulating free fatty acids facilitates their hepatic uptake and storage. This ectopic lipid deposition further induces oxidative stress and heightens mitochondrial activity within hepatocytes, ultimately leading to hepatocellular injury, apoptosis, and progressive fibrosis. Moreover, insulin resistance—central to this pathological cascade—exacerbates atherogenic dyslipidemia and promotes the release of multiple pro-inflammatory and pro-atherosclerotic mediators. Together, these effects significantly elevate the risk of developing cardiovascular disease[3, 35].\u0026nbsp;IR is also closely associated with the development and progression of various comorbidities in individuals with NAFLD/MASLD, including hypertension, cardiovascular disease, hepatocellular carcinoma, type 2 diabetes, and chronic kidney disease\u0026nbsp;[36].\u0026nbsp;This highlights the importance of risk prediction and management of CVD among hypertensive patients with NAFLD/MASLD, as well as the need to further investigate the mechanistic role of IR in this complex relationship.\u003c/p\u003e\n\u003cp\u003eFor instance, a prospective cohort study by Zhang et al. demonstrated that individuals with hypertension exhibit elevated cardiovascular risk, particularly when coexisting with moderate to severe NAFLD, suggesting that the severity of hepatic steatosis may aid in further risk stratification among those with prehypertension or hypertension[37].\u0026nbsp;Similarly, Hu et al. explored the temporal relationship between hepatic steatosis and blood pressure elevation and found that hepatic fat accumulation predisposes individuals to elevated blood pressure, which in turn positively mediates the association between hepatic steatosis and CVD risk[16].\u0026nbsp;Our study, building upon previous work, further substantiated that in hypertensive populations, hepatic steatosis accompanied by severe fibrosis is associated with a significantly higher estimated risk of a first fatal or non-fatal CVD event within 10 years, compared to those with isolated hepatic steatosis or no steatosis. Simultaneously, we delved into the potential underlying mechanisms of this clinical phenomenon, providing support for IR as a core driving factor in this process.\u003c/p\u003e\n\u003cp\u003eOur findings may have the following clinical implications. They lend support to the notion that in hypertensive individuals, non-invasively identifying hepatic steatosis with concomitant severe fibrosis can help pinpoint those at higher risk of experiencing a first fatal or non-fatal CVD event within 10 years. Although modern medicine has developed numerous strategies to mitigate the threat of various CVDs to human health, the importance of early identification and prevention of CVD development cannot be overstated. Our findings suggest that the presence of hepatic steatosis with severe fibrosis may be a risk-enhancing factor for CVD in individuals with hypertension but no baseline CVD. This population is often in their middle to younger adult years, and as previously noted, CVD risk in younger individuals is more prone to being overlooked. Therefore, implementing prevention strategies within higher-risk subsets of younger adults represents a crucial area[38, 39].\u0026nbsp;These insights could aid in identifying at-risk individuals who may benefit from more rigorous control of modifiable cardiovascular risk factors. Furthermore, our findings regarding the mediating role of insulin resistance (IR) provide a potential therapeutic avenue: namely, interventions aimed at improving IR may hold promise for this subgroup. This aligns well with the growing emphasis on personalized treatment strategies in contemporary clinical practice. Lastly, we offer several additional exploratory findings of interest. According to\u0026nbsp;2019 ACC/AHA Guideline on the Primary Prevention of Cardiovascular Disease. In adults with confirmed hypertension and a 10-year ASCVD event risk of 10% or higher, a BP target of less than 130/80 mm Hg is recommended[21].\u0026nbsp;In our dataset, the blood pressure control rate was only 35.4%, and even lower (only 30.6 %) among individuals with both hepatic steatosis and significant fibrosis. This finding further highlights that blood pressure management in patients with NAFLD/MASLD is suboptimal in real-world settings, indicating that their actual cardiovascular risk may be considerably underestimated.\u003c/p\u003e\n\u003cp\u003ePerspective on the Association Between NAFLD/MASLD and CVD Risk\u003c/p\u003e\n\u003cp\u003eAs previously discussed, there is currently no consensus in the academic community regarding the intrinsic mechanisms underlying the strong association between NAFLD and CVD observed in clinical data. Most scholars suggest that this may involve dyslipidemia, IR, increased systemic inflammatory tone, abnormalities in coagulation and the sympathetic nervous system, as well as gut microbiota dysbiosis[3, 11-14].\u0026nbsp;n our study, we analyzed indicators related to insulin resistance, systemic inflammation, dyslipidemia, and coagulation. However, only the insulin resistance surrogate (TyG index) demonstrated a mediating effect. Consequently, our findings support insulin resistance as a potential underlying mechanism, but do not provide support for inflammation, dyslipidemia, or coagulation abnormalities being potential intrinsic mechanisms in this specific context. We hypothesize that this discrepancy may be attributed to the stringent selection criteria of the PREVENT equation, resulting in a relatively small sample size for our analysis. In summary, the exploration of the intrinsic mechanisms underlying this phenomenon remains an ongoing process.\u003c/p\u003e\n\u003cp\u003eStudy limitations and strengths\u003c/p\u003e\n\u003cp\u003eThe primary strength of this study lies in its novel application, to our knowledge, of the PREVENT equation to estimate 10-year CVD risk in a hypertensive population, and its subsequent analysis of the relationship with NAFLD/MASLD. Cardiovascular disease absolute risk assessment is unequivocally the cornerstone of primary clinical prevention. Compared to previous assessment models, the PREVENT model is better adapted to the complex intrinsic connections associated with the \u003cstrong\u003eCardiovascular-Kidney-Metabolic (CKM) Syndrome\u003c/strong\u003estate[19].\u0026nbsp;Similarly, as NAFLD/MASLD is considered a hepatic manifestation of a systemic disease, the PREVENT equation represents the optimal CVD risk prediction tool for this patient population. Our research supports the application and development of this tool in such individuals, potentially leading to greater benefits for this group in the future. Furthermore, our exploration of the potential underlying mechanisms contributes to a deeper understanding of the complex relationship between NAFLD/MASLD and CVD, even though definitive conclusions remain elusive.\u003c/p\u003e\n\u003cp\u003eHowever, this study also has limitations. First, the cross-sectional design precludes establishing a reliable causal relationship between NAFLD/MASLD with severe fibrosis and the higher 10-year estimated CVD risk. Second, we relied on the USFLI and FIB-4 indices for diagnosing hepatic steatosis and fibrosis, respectively. In clinical practice, MRI and Vibration-controlled transient elastography (Fibro Scan) are considered first-line imaging methods for non-invasively detecting hepatic steatosis and fibrosis [3, 40]. Nevertheless, such data are challenging to acquire in large-scale epidemiological studies. Despite this, USFLI and FIB-4 remain among the most suitable non-invasive indices for the U.S. population and can serve as first-line diagnostic tools for this group. Previously, Aaron et al. suggested that combining the PREVENT risk calculator with coronary artery calcium scoring could help more accurately predict the risk of vascular disease onset and proposed adding coronary artery calcium scoring to the PREVENT risk calculator[41]. We believe that in future work, similar approaches could be explored to further enhance the accuracy of diagnostic tools and improve the identification capabilities for specific populations. However, it is unequivocally necessary that future research utilizes imaging methods to assess hepatic steatosis and fibrosis to validate our results. Concurrently, prospective cohort studies are needed to verify whether NAFLD (with varying degrees of fibrosis) increases the long-term risk of cardiovascular disease in hypertensive patients without baseline CVD, and whether targeted interventions for IR can mitigate this process.\u0026nbsp;\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn summary, our results support that NAFLD/MASLD is significantly associated with an increased 10-year risk of a first cardiovascular event in hypertensive patients without baseline CVD. Furthermore, among the complex interplay between NAFLD/MASLD and CVD, insulin resistance is more likely to be a definitively involved and crucial factor compared to other risk factors. However, the specific mechanisms remain unclear and warrant further clinical and basic research for elucidation.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eNAFLD:\u003cstrong\u003e\u0026nbsp;Non-Alcoholic Fatty Liver Disease\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMASLD:\u003cstrong\u003e\u0026nbsp;Metabolic Dysfunction-Associated Fatty Liver Disease\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCVD:\u0026nbsp;Cardiovascular disease\u003c/p\u003e\n\u003cp\u003eASCVD: \u003cstrong\u003eAtherosclerotic cardiovascular disease\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTyG:\u003cstrong\u003e\u0026nbsp;Triglyceride Glucose\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIR:Insulin resistance\u003c/p\u003e\n\u003cp\u003eMetS:\u003cstrong\u003e\u0026nbsp;Metabolic syndrome\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAHA:\u003c/strong\u003e American Heart Association\u003c/p\u003e\n\u003cp\u003eACC: American College of Cardiology\u003c/p\u003e\n\u003cp\u003eNHANES:National Health and Nutrition Examination Survey\u003c/p\u003e\n\u003cp\u003eUSFLI:\u0026nbsp;United States Fatty Liver Index\u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFLI: Fatty liver index\u003c/p\u003e\n\u003cp\u003eFIB-4: Fibrosis-4\u003c/p\u003e\n\u003cp\u003eHBV: Hepatitis B virus\u003c/p\u003e\n\u003cp\u003eHCV: Hepatitis C virus\u003c/p\u003e\n\u003cp\u003eAST: Aspartate aminotransferase\u003c/p\u003e\n\u003cp\u003eALT: Alanine aminotransferase\u003c/p\u003e\n\u003cp\u003eGGT: Gamma-glutamyl transferase\u003c/p\u003e\n\u003cp\u003eeGFR: Estimated glomerular filtration rate\u003c/p\u003e\n\u003cp\u003eCKD: Chronic kidney disease\u003c/p\u003e\n\u003cp\u003eCKD-EPI: Chronic kidney disease-Epidemiology collaboration equation\u003c/p\u003e\n\u003cp\u003eACR: Albumin-to-creatinine ratio\u003c/p\u003e\n\u003cp\u003ePIR:\u0026nbsp;Poverty income ratio\u003c/p\u003e\n\u003cp\u003eBMI: Body mass index\u003c/p\u003e\n\u003cp\u003ePCE:\u003cstrong\u003e\u0026nbsp;Pooled cohort equation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCKM: \u003cstrong\u003eCardiovascular-Kidney-Metabolic syndrome\u003c/strong\u003e\u003c/p\u003e"},{"header":"Declarations","content":" \u003ch2\u003eAuthor information\u003c/h2\u003e \u003cp\u003eAuthors and Affiliations\u003c/p\u003e \u003cp\u003eFirst Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, CN\u003c/p\u003e \u003cp\u003eYexin Yin\u003c/p\u003e \u003cp\u003eFirst Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, CN\u003c/p\u003e \u003cp\u003eLili Shi\u003c/p\u003e \u003cp\u003eContributions\u003c/p\u003e \u003cp\u003eConceptualization: Yexin Yin Data curation \u0026amp; Formal analysis: Yexin Yin Investigation \u0026amp; Methodology: Yexin Yin Project administration: Yexin Yin, Lili Shi. Supervision: Lili Shi Validation: Yexin Yin Writing\u0026ndash; original draft: Yexin Yin Writing\u0026ndash; review \u0026amp; editing: Yexin Yin. Lili Shi is the guarantor of integrity of the entire study, had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. All authors reviewed and approved the final version of the manuscript.\u003c/p\u003e \u003cp\u003eCorresponding author\u003c/p\u003e \u003cp\u003eCorrespondence to Lili Shi\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eEthics declarations\u003c/strong\u003e \u003cp\u003eEthics and consent to participate declarations\u003c/p\u003e \u003cp\u003eNot applicable.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eConsent to publish\u003c/h2\u003e \u003cp\u003eNot applicable.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eCompeting interests\u003c/strong\u003e \u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eNone.\u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e \u003cp\u003eNone.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe experimental data and the simulation results that support the findings of this study are available at the following URL: https://wwwn.cdc.gov/nchs/nhanes/default.aspx\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eLe, M.H., et al., \u003cem\u003eForecasted 2040 global prevalence of nonalcoholic fatty liver disease using hierarchical bayesian approach.\u003c/em\u003e Clin Mol Hepatol, 2022. \u003cstrong\u003e28\u003c/strong\u003e(4): p. 841\u0026ndash;850.\u003c/li\u003e\n\u003cli\u003eWong, V.W., et al., \u003cem\u003eChanging epidemiology, global trends and implications for outcomes of NAFLD.\u003c/em\u003e J Hepatol, 2023. \u003cstrong\u003e79\u003c/strong\u003e(3): p. 842\u0026ndash;852.\u003c/li\u003e\n\u003cli\u003eDuell, P.B., et al., \u003cem\u003eNonalcoholic Fatty Liver Disease and Cardiovascular Risk: A Scientific Statement From the American Heart Association.\u003c/em\u003e Arterioscler Thromb Vasc Biol, 2022. \u003cstrong\u003e42\u003c/strong\u003e(6): p. e168\u0026ndash;e185.\u003c/li\u003e\n\u003cli\u003eTargher, G., H. Tilg, and C.D. Byrne, \u003cem\u003eNon-alcoholic fatty liver disease: a multisystem disease requiring a multidisciplinary and holistic approach.\u003c/em\u003e Lancet Gastroenterol Hepatol, 2021. \u003cstrong\u003e6\u003c/strong\u003e(7): p. 578\u0026ndash;588.\u003c/li\u003e\n\u003cli\u003eEslam, M., et al., \u003cem\u003eA new definition for metabolic dysfunction-associated fatty liver disease: An international expert consensus statement.\u003c/em\u003e J Hepatol, 2020. \u003cstrong\u003e73\u003c/strong\u003e(1): p. 202\u0026ndash;209.\u003c/li\u003e\n\u003cli\u003eMendez-Sanchez, N., et al., \u003cem\u003eGlobal multi-stakeholder endorsement of the MAFLD definition.\u003c/em\u003e Lancet Gastroenterol Hepatol, 2022. \u003cstrong\u003e7\u003c/strong\u003e(5): p. 388\u0026ndash;390.\u003c/li\u003e\n\u003cli\u003eAlon, L., et al., \u003cem\u003eRisk of cardiovascular events in patients with non-alcoholic fatty liver disease: a systematic review and meta-analysis.\u003c/em\u003e Eur J Prev Cardiol, 2022. \u003cstrong\u003e29\u003c/strong\u003e(6): p. 938\u0026ndash;946.\u003c/li\u003e\n\u003cli\u003eMa, C., et al., \u003cem\u003eThe association between hypertension and nonalcoholic fatty liver disease (NAFLD): literature evidence and systems biology analysis.\u003c/em\u003e Bioengineered, 2021. \u003cstrong\u003e12\u003c/strong\u003e(1): p. 2187\u0026ndash;2202.\u003c/li\u003e\n\u003cli\u003eYuan, M., et al., \u003cem\u003eHypertension and NAFLD risk: Insights from the NHANES 2017-2018 and Mendelian randomization analyses.\u003c/em\u003e Chin Med J (Engl), 2024. \u003cstrong\u003e137\u003c/strong\u003e(4): p. 457\u0026ndash;464.\u003c/li\u003e\n\u003cli\u003ePowell, E.E., V.W. Wong, and M. Rinella, \u003cem\u003eNon-alcoholic fatty liver disease.\u003c/em\u003e Lancet, 2021. \u003cstrong\u003e397\u003c/strong\u003e(10290): p. 2212\u0026ndash;2224.\u003c/li\u003e\n\u003cli\u003eLi, M., et al., \u003cem\u003eNAFLD: An Emerging Causal Factor for Cardiovascular Disease.\u003c/em\u003e Physiology (Bethesda), 2023. \u003cstrong\u003e38\u003c/strong\u003e(6): p. 0.\u003c/li\u003e\n\u003cli\u003eTargher, G., C.D. Byrne, and H. Tilg, \u003cem\u003eNAFLD and increased risk of cardiovascular disease: clinical associations, pathophysiological mechanisms and pharmacological implications.\u003c/em\u003e Gut, 2020. \u003cstrong\u003e69\u003c/strong\u003e(9): p. 1691\u0026ndash;1705.\u003c/li\u003e\n\u003cli\u003eMotamed, N., et al., \u003cem\u003eNon-alcoholic fatty liver disease is not independent risk factor for cardiovascular disease event: A cohort study.\u003c/em\u003e World J Hepatol, 2020. \u003cstrong\u003e12\u003c/strong\u003e(6): p. 323\u0026ndash;331.\u003c/li\u003e\n\u003cli\u003eMantovani, A., et al., \u003cem\u003eNon-alcoholic fatty liver disease and risk of fatal and non-fatal cardiovascular events: an updated systematic review and meta-analysis.\u003c/em\u003e Lancet Gastroenterol Hepatol, 2021. \u003cstrong\u003e6\u003c/strong\u003e(11): p. 903\u0026ndash;913.\u003c/li\u003e\n\u003cli\u003eMuzurovic, E., et al., \u003cem\u003eNonalcoholic Fatty Liver Disease and Cardiovascular Disease: a Review of Shared Cardiometabolic Risk Factors.\u003c/em\u003e Hypertension, 2022. \u003cstrong\u003e79\u003c/strong\u003e(7): p. 1319\u0026ndash;1326.\u003c/li\u003e\n\u003cli\u003eHu, Y., et al., \u003cem\u003eTemporal relationship between hepatic steatosis and blood pressure elevation and the mediation effect in the development of cardiovascular disease.\u003c/em\u003e Hypertens Res, 2024. \u003cstrong\u003e47\u003c/strong\u003e(7): p. 1811\u0026ndash;1821.\u003c/li\u003e\n\u003cli\u003eHuang, Y., et al., \u003cem\u003eImpact of NAFLD and Fibrosis on Adverse Cardiovascular Events in Patients With Hypertension.\u003c/em\u003e Hypertension, 2025.\u003c/li\u003e\n\u003cli\u003eGoff, D.C., Jr., et al., \u003cem\u003e2013 ACC/AHA guideline on the assessment of cardiovascular risk: a report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines.\u003c/em\u003e Circulation, 2014. \u003cstrong\u003e129\u003c/strong\u003e(25 Suppl 2): p. S49\u0026ndash;73.\u003c/li\u003e\n\u003cli\u003eKhan, S.S., et al., \u003cem\u003eNovel Prediction Equations for Absolute Risk Assessment of Total Cardiovascular Disease Incorporating Cardiovascular-Kidney-Metabolic Health: A Scientific Statement From the American Heart Association.\u003c/em\u003e Circulation, 2023. \u003cstrong\u003e148\u003c/strong\u003e(24): p. 1982\u0026ndash;2004.\u003c/li\u003e\n\u003cli\u003eLevey, A.S., et al., \u003cem\u003eA new equation to estimate glomerular filtration rate.\u003c/em\u003e Ann Intern Med, 2009. \u003cstrong\u003e150\u003c/strong\u003e(9): p. 604\u0026ndash;12.\u003c/li\u003e\n\u003cli\u003eArnett, D.K., et al., \u003cem\u003e2019 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.\u003c/em\u003e Circulation, 2019. \u003cstrong\u003e140\u003c/strong\u003e(11): p. e596\u0026ndash;e646.\u003c/li\u003e\n\u003cli\u003evan Kleef, L.A., R.J. de Knegt, and W.P. Brouwer, \u003cem\u003eMetabolic dysfunction-associated fatty liver disease and excessive alcohol consumption are both independent risk factors for mortality.\u003c/em\u003e Hepatology, 2023. \u003cstrong\u003e77\u003c/strong\u003e(3): p. 942\u0026ndash;948.\u003c/li\u003e\n\u003cli\u003eTalha, K.M., et al., \u003cem\u003eRelationship between race, income-level, and Cardiovascular Disease.\u003c/em\u003e J Natl Med Assoc, 2024. \u003cstrong\u003e116\u003c/strong\u003e(5): p. 539\u0026ndash;552.\u003c/li\u003e\n\u003cli\u003eDeng, Y., et al., \u003cem\u003eTrends and key disparities of obesity among US adolescents: The NHANES from 2007 to 2020.\u003c/em\u003e PLoS One, 2024. \u003cstrong\u003e19\u003c/strong\u003e(10): p. e0290211.\u003c/li\u003e\n\u003cli\u003eGlobal Cardiovascular Risk, C., et al., \u003cem\u003eGlobal Effect of Modifiable Risk Factors on Cardiovascular Disease and Mortality.\u003c/em\u003e N Engl J Med, 2023. \u003cstrong\u003e389\u003c/strong\u003e(14): p. 1273\u0026ndash;1285.\u003c/li\u003e\n\u003cli\u003eWang, M.C. and D.M. Lloyd-Jones, \u003cem\u003eCardiovascular Risk Assessment in Hypertensive Patients.\u003c/em\u003e Am J Hypertens, 2021. \u003cstrong\u003e34\u003c/strong\u003e(6): p. 569\u0026ndash;577.\u003c/li\u003e\n\u003cli\u003eBedogni, G., et al., \u003cem\u003ePrevalence of and risk factors for nonalcoholic fatty liver disease: the Dionysos nutrition and liver study.\u003c/em\u003e Hepatology, 2005. \u003cstrong\u003e42\u003c/strong\u003e(1): p. 44\u0026ndash;52.\u003c/li\u003e\n\u003cli\u003eBedogni, G., et al., \u003cem\u003eThe Fatty Liver Index: a simple and accurate predictor of hepatic steatosis in the general population.\u003c/em\u003e BMC Gastroenterol, 2006. \u003cstrong\u003e6\u003c/strong\u003e: p. 33.\u003c/li\u003e\n\u003cli\u003eRuhl, C.E. and J.E. Everhart, \u003cem\u003eFatty liver indices in the multiethnic United States National Health and Nutrition Examination Survey.\u003c/em\u003e Aliment Pharmacol Ther, 2015. \u003cstrong\u003e41\u003c/strong\u003e(1): p. 65\u0026ndash;76.\u003c/li\u003e\n\u003cli\u003eGolabi, P., et al., \u003cem\u003eContribution of sarcopenia and physical inactivity to mortality in people with non-alcoholic fatty liver disease.\u003c/em\u003e JHEP Rep, 2020. \u003cstrong\u003e2\u003c/strong\u003e(6): p. 100171.\u003c/li\u003e\n\u003cli\u003eSterling, R.K., et al., \u003cem\u003eDevelopment of a simple noninvasive index to predict significant fibrosis in patients with HIV/HCV coinfection.\u003c/em\u003e Hepatology, 2006. \u003cstrong\u003e43\u003c/strong\u003e(6): p. 1317\u0026ndash;25.\u003c/li\u003e\n\u003cli\u003eHan, J.W., et al., \u003cem\u003eDiagnostic accuracy of the Fibrosis-4 index for advanced liver fibrosis in nonalcoholic fatty liver disease with type 2 diabetes: A systematic review and meta-analysis.\u003c/em\u003e Clin Mol Hepatol, 2024. \u003cstrong\u003e30\u003c/strong\u003e(Suppl): p. S147\u0026ndash;S158.\u003c/li\u003e\n\u003cli\u003eStekhoven, D.J. and P. Buhlmann, \u003cem\u003eMissForest--non-parametric missing value imputation for mixed-type data.\u003c/em\u003e Bioinformatics, 2012. \u003cstrong\u003e28\u003c/strong\u003e(1): p. 112\u0026ndash;8.\u003c/li\u003e\n\u003cli\u003eVanderWeele, T.J. and P. Ding, \u003cem\u003eSensitivity Analysis in Observational Research: Introducing the E-Value.\u003c/em\u003e Ann Intern Med, 2017. \u003cstrong\u003e167\u003c/strong\u003e(4): p. 268\u0026ndash;274.\u003c/li\u003e\n\u003cli\u003eBo, T., et al., \u003cem\u003eHepatic selective insulin resistance at the intersection of insulin signaling and metabolic dysfunction-associated steatotic liver disease.\u003c/em\u003e Cell Metab, 2024. \u003cstrong\u003e36\u003c/strong\u003e(5): p. 947\u0026ndash;968.\u003c/li\u003e\n\u003cli\u003eBadmus, O.O., T.D. Hinds, Jr., and D.E. Stec, \u003cem\u003eMechanisms Linking Metabolic-Associated Fatty Liver Disease (MAFLD) to Cardiovascular Disease.\u003c/em\u003e Curr Hypertens Rep, 2023. \u003cstrong\u003e25\u003c/strong\u003e(8): p. 151\u0026ndash;162.\u003c/li\u003e\n\u003cli\u003eSong, Q.R., et al., \u003cem\u003eSeverity of Nonalcoholic Fatty Liver Disease is Associated With Cardiovascular Outcomes in Patients With Prehypertension or Hypertension: A Community-Based Cohort Study.\u003c/em\u003e Front Endocrinol (Lausanne), 2022. \u003cstrong\u003e13\u003c/strong\u003e: p. 942647.\u003c/li\u003e\n\u003cli\u003eStone, N.J., et al., \u003cem\u003eManaging Atherosclerotic Cardiovascular Risk in Young Adults: JACC State-of-the-Art Review.\u003c/em\u003e J Am Coll Cardiol, 2022. \u003cstrong\u003e79\u003c/strong\u003e(8): p. 819\u0026ndash;836.\u003c/li\u003e\n\u003cli\u003eNavar, A.M., et al., \u003cem\u003eEarlier treatment in adults with high lifetime risk of cardiovascular diseases: What prevention trials are feasible and could change clinical practice? Report of a National Heart, Lung, and Blood Institute (NHLBI) Workshop.\u003c/em\u003e Am J Prev Cardiol, 2022. \u003cstrong\u003e12\u003c/strong\u003e: p. 100430.\u003c/li\u003e\n\u003cli\u003eByrne, C.D., et al., \u003cem\u003eTests for diagnosing and monitoring non-alcoholic fatty liver disease in adults.\u003c/em\u003e BMJ, 2018. \u003cstrong\u003e362\u003c/strong\u003e: p. k2734.\u003c/li\u003e\n\u003cli\u003eRhee, A.J., et al., \u003cem\u003eReal-World Evidence Linking the Predicting Risk of Cardiovascular Disease Events Risk Score and Coronary Artery Calcium.\u003c/em\u003e J Am Heart Assoc, 2025. \u003cstrong\u003e14\u003c/strong\u003e(11): p. e038991.\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":"Metabolic dysfunction-Associated Steatotic Liver Disease, Non-Alcoholic Fatty Liver Disease, Insulin resistance, Hypertension, Prevention, Cardiovascular risk","lastPublishedDoi":"10.21203/rs.3.rs-6894190/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6894190/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cb\u003eBackground\u003c/b\u003e\u003c/p\u003e \u003cp\u003eNonalcoholic fatty liver disease (NAFLD)/metabolic dysfunction-associated steatotic liver disease (MASLD) is the most prevalent chronic liver disease worldwide and has been significantly associated with both hypertension and cardiovascular disease (CVD). However, whether NAFLD/MASLD constitutes an independent risk factor for CVD remains inconclusive, and evidence from hypertensive populations is limited. Moreover, the underlying mechanisms of this complex association have not yet been fully elucidated.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMethods\u003c/b\u003e\u003c/p\u003e \u003cp\u003eA total of 1,083 participants from the NHANES database were included in this study. Eligible individuals were aged 30\u0026ndash;79 years, had hypertension, and were free of cardiovascular disease (CVD) at baseline. Hepatic steatosis and significant liver fibrosis were assessed noninvasively using the United States Fatty Liver Index (USFLI) and the Fibrosis-4 (FIB-4) index, respectively. Hepatic steatosis was defined as a USFLI score\u0026thinsp;\u0026ge;\u0026thinsp;30, and significant fibrosis was defined as a FIB-4 index\u0026thinsp;\u0026ge;\u0026thinsp;1.3. Insulin resistance (IR) was estimated using the triglyceride-glucose (TyG) index. The 10-year risk of a first fatal or nonfatal CVD event was calculated using the PREVENT risk equation.\u003c/p\u003e\u003cp\u003e\u003cb\u003eResults\u003c/b\u003e\u003c/p\u003e \u003cp\u003eCompared with individuals with simple steatosis (n\u0026thinsp;=\u0026thinsp;483) or without hepatic steatosis (n\u0026thinsp;=\u0026thinsp;313), those with both hepatic steatosis and significant fibrosis (n\u0026thinsp;=\u0026thinsp;287) had a significantly higher estimated 10-year CVD risk (20.5% vs. 14.7% vs. 39.4%, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). After adjusting for sex, education, race/ethnicity, physical activity, poverty-income ratio (PIR), and chronic kidney disease (CKD), individuals with both hepatic steatosis and significant fibrosis had a markedly increased risk of experiencing a first fatal or nonfatal CVD event over 10 years compared to those without steatosis (adjusted odds ratio: 15.2, 95% CI: 5.42\u0026ndash;63.49). Sensitivity analyses confirmed the robustness of these findings. Furthermore, the TyG index significantly mediated 16.85% of the association between steatosis with significant fibrosis and the 10-year risk of CVD events.\u003c/p\u003e\u003cp\u003e\u003cb\u003eConclusions\u003c/b\u003e\u003c/p\u003e \u003cp\u003eAmong individuals with hypertension but without a prior history of cardiovascular disease, those with both hepatic steatosis and significant fibrosis had a markedly higher estimated 10-year CVD risk compared to those with steatosis alone or without steatosis. Moreover, this association was significantly mediated by the TyG index.\u003c/p\u003e","manuscriptTitle":"Association of hepatic steatosis and fibrosis with 10-year estimated cardiovascular disease risk in hypertensive patients and the mediating role of triglyceride-glucose index","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-25 10:25:31","doi":"10.21203/rs.3.rs-6894190/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":"ff693ad5-7b8a-43f6-87ba-ae1ee4bc99a7","owner":[],"postedDate":"June 25th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":50383118,"name":"Health sciences/Cardiology"},{"id":50383119,"name":"Health sciences/Endocrinology"},{"id":50383120,"name":"Health sciences/Medical research"},{"id":50383121,"name":"Health sciences/Pathogenesis"}],"tags":[],"updatedAt":"2025-08-19T05:54:06+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-25 10:25:31","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6894190","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6894190","identity":"rs-6894190","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}