Association of four Insulin Resistance Surrogate Indices and Hypertension in the MASHAD cohort study population: a cross-sectional study

Association of four Insulin Resistance Surrogate Indices and Hypertension in the MASHAD cohort study population: a cross-sectional study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Association of four Insulin Resistance Surrogate Indices and Hypertension in the MASHAD cohort study population: a cross-sectional study Ali Tajik, Majid Ghayour-Mobarhan, Susan Darroudi, Mohammad Kalate Rahmani, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7560704/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 09 Feb, 2026 Read the published version in European Journal of Medical Research → Version 1 posted 13 You are reading this latest preprint version Abstract Background: Insulin resistance (IR) is associated with a reduced response to insulin and an increased risk of cardiovascular disease and hypertension. The triglyceride glucose index (TyG), triglyceride to high- density lipoprotein cholesterol ratio (Tg/HDL-c), metabolic score for insulin resistance (METS-IR) and cholesterol, high-density lipoprotein, and glucose (CHG) index, are surrogate indices insulin resistance and offer a simpler approach to evaluating insulin resistance than the gold standard hyperinsulinemic-euglycemic glucose clamp technique. With the rising prevalence of metabolic syndrome and hypertension, we aimed to investigate the relationship between hypertension and IR in a large Iranian population. Method: The study cohort comprised 9438 individuals aged 35 to 65 years, who were recruited cross-sectionally from the Mashhad Stroke and Heart Atherosclerotic Disorder (MASHAD) cohort study. Hypertension was defined as a blood pressure reading exceeding 140/90 mmHg or the use of anti-hypertensive medications. Multivariate logistic regression analysis, receiver operating characteristic area under the curve (ROC-AUC) analysis, net reclassification index (NRI) and integrated discrimination improvement index (IDI) for incremental classification performance, restricted cubic splines (RCS), threshold effect analysis, subgroup analysis, and Poisson regression with robust variance to estimate the prevalence ratios, were employed in the statistical evaluation. Results: A total of 9438 adults were analyzed, with 2943 individuals identified as having hypertension. Spearman’s correlation coefficients indicated that all four IR indices exhibited a significantly positive correlation with both systolic and diastolic blood pressures. In multivariate analysis, after full adjustment, each 1 unit increase corresponded to 37.5% higher risk of hypertension for TyG (OR: 1.375, 95%CI: 1.256–1.505, p < 0.001), 2.8% for Tg/HDL-c (OR: 1.028, 95%CI: 1.010–1.046, p = 0.002), 3.2% for METS-IR (OR: 1.032, 95%CI: 1.022–1.042, p < 0.001), and 25.5% for CHG (OR: 1.255, 95%CI: 1.073–1.467, p = 0.004). Analysis of the ROC curve indicated that each of the four metabolic indices demonstrated a significant capacity to predict hypertension (all p < 0.001). The METS-IR index showed the highest discriminative performance among the indices, achieving an AUC of 0.642 (95%CI: 0.630–0.654). We investigated whether adding IR surrogate indices to Model 3 could improve hypertension detection. The TyG index had the highest incremental classification values (NRI: 13.58, 95%CI: [8.65–18.35]; IDI: 0.956, 95%CI: [0.834–1.079]). RCS analysis revealed a nonlinear relationship between TyG, Tg/HDL-c, and CHG with hypertension, while a linear relationship was observed between METS-IR and hypertension. Threshold effect analysis for the indices with non-linear relation with hypertension showed that these associations were strongest at lower index values and then flattened, and often lose statistical significance. The findings were consistent across various subgroups, although the effects appeared to be more pronounced in participants who did not have dyslipidemia across all indices. A sensitivity analysis employing Poisson models with robust variance validated the direction and ranking of effects, revealing smaller prevalence ratios compared to odds ratios. Conclusion: Surrogate IR indices showed a significant relationship with hypertension. They may serve as a viable option for efficiently stratifying risk in the primary prevention of hypertension. Insulin resistance Hypertension MASHAD TyG Tg/HDL-c METS-IR CHG Non-linear Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction High blood pressure ranks among the leading contributors to cardiovascular disease (CVD) ( 1 ). Roughly one in three adults between 30 and 79 years old worldwide is affected, with men showing a slightly higher rate (34%) than women (32%) ( 2 ). In Iran, estimates place the prevalence at 20–25%, again with a modest excess in men ( 3 – 6 ). Globally, the global hypertensive population rose from about 650 million in 1990 to nearly 1.3 billion by 2019. ( 2 ). This increasing prevalence has made hypertension a central contributor to the global health burden, particularly in countries with limited resources. Given the weight of cardiovascular disease, identifying groups at higher risk of hypertension and related conditions is critical. A variety of cardiovascular risk assessment tools have been introduced and tested in recent years ( 7 ). These tools often depend on charts or complex equations, which limits their use in everyday medical practice. Simpler and less costly indicators are needed, ideally ones that do not add financial strain to health systems. Insulin resistance describes a state in which body tissues respond less effectively to insulin ( 8 ). This may be associated with hyperinsulinemia and disrupt normal balance between glucose and lipid metabolism. It frequently occurs in patients with metabolic syndrome or diabetes ( 9 ). Insulin resistance is not only a hallmark of diabetes but also a risk factor for cardiovascular disease in individuals without diabetes ( 10 ). This implies that measures reflecting insulin resistance have value for predicting cardiovascular risk across a wide spectrum of patients. The hyperinsulinemic-euglycemic clamp (HIEC) is still regarded as the standard approach for measuring insulin resistance. Its accuracy is well established, but the procedure is lengthy, requires catheter placement in both arms, and is uncomfortable for many patients ( 11 ). The homeostasis model assessment for insulin resistance (HOMA-IR) is a practical alternative that relies on insulin levels. Still, the test can be expensive and sometimes unavailable in underserved settings ( 12 ). For these reasons, more accessible markers of insulin resistance are needed. Several surrogate indices have been proposed, including the Triglyceride–Glucose index (TyG), the Triglyceride to HDL cholesterol ratio (Tg/HDL-c), the Metabolic Score for Insulin Resistance (METS-IR), and the Cholesterol-HDL-Glucose index (CHG). These measures are relatively simple to calculate and have shown strong associations with cardiovascular disease ( 13 , 14 ), coronary artery disease severity ( 15 , 16 ), myocardial infarction ( 17 ), and cardiovascular mortality ( 18 ). Research exploring the link between IR markers and hypertension has mostly focused on cohorts from the United States and East Asia. Zhang et al. ( 19 ) found a correlation between elevated TyG index and Tg/HDL-c levels with prehypertension and hypertension in individuals with normal glucose levels. Guo et al. ( 20 ) found that an increase of one unit in the METS-IR score has been associated with roughly a three percent greater risk of hypertension. Zhu et al. ( 21 ) noticed a significant link between TyG and METS-IR in relation to hypertension. The relationship between the CHG index and hypertension has not been explored as thoroughly as the other surrogate insulin indices previously discussed. This study examines the relation between these indices and hypertension, while specifically evaluating their effectiveness in identifying hypertensive individuals within the north eastern Iranian population. Method Study sample Participants in this cross-sectional study were drawn from the Mashhad Stroke and Heart Atherosclerotic Disorder cohort ( 22 ). The MASHAD study commenced in 2010 and continued until 2020. The estimated total population of the city of Mashhad was derived from the national Iranian census conducted in 2006. Participants were selected from three regions in Mashhad, situated in north-eastern Iran, using a stratified cluster random sampling approach. Each region was segmented into nine sites, with a focus on the divisions of the Mashhad Healthcare Center. Households with members aged 35 to 65 years were included. Upon identifying eligible participants, they were contacted to schedule an appointment for the formal physical examination. Participants with incomplete profiles or absent data were removed from the study. In total, 9438 eligible subjects were enrolled in the study. Data collection and definitions A checklist was developed to record the demographic information including age, sex, education, and marital status, in accordance with a similar research protocol ( 22 ). The questionnaire also asked about past medical history, encompassing both personal and family history of cardiovascular disease. Anthropometric measurements were performed by trained nursing staff. participants were measured for weight and height while wearing light clothing and no shoes. Height was recorded with a stadiometer accurate to 0.1 cm, and weight was taken with an electronic scale precise to 0.1 kg. Subsequently, body mass index (BMI) was calculated by dividing weight (kg) by the square of height (m). Anxiety was evaluated with Beck’s Anxiety Inventory, and depression was measured using the Beck Depression Inventory II ( 23 ). Blood was drawn from the antecubital vein between 8 and 10 a.m. after an overnight fast of 14 hours. samples were collected in 20 ml vacuum tubes while participants were seated, using a standard protocol. All blood specimens underwent centrifugation at room temperature within 30 to 45 minutes of collection, serum and plasma were separated and divided into six 0.5 ml aliquots. Serum aliquots were preserved at -80°C for subsequent analysis. Low-density lipoprotein cholesterol (LDL-c) was calculated using the Friedewald formula ( 24 ), based on serum total cholesterol (TC), triglycerides (Tg), and high-density lipoprotein cholesterol (HDL-c) levels, all expressed in mg/dl. These calculations were carried out only if values were under 400 mg/dl. The surrogate IR indices were determined using the subsequent formulas: TyG = ln [Tg (mg/dl) * Fasting plasma glucose (FPG) (mg/dl) / 2] Tg/HDL-c = Tg (mg/dl) / HDL-c (mg/dl); METS-IR = (ln [2 * FPG (mg/dl) + Tg (mg/dl)] * BMI (kg/m2) / ln [HDL-c (mg/dl)]). CHG = Ln [TC (mg/dl) × FPG (mg/dl) / (2 × HDL-c (mg/dl))] Blood pressure was assessed with a mercury sphygmomanometer accurate to ± 1 mmHg. To ensure the precision of the measurement, participants were asked to rest and remain seated for a minimum of 15 minutes. The mean of two blood pressure readings was calculated for data analysis. A current smoker was defined as someone who smoked cigarettes every day, at least once per day. An individual who previously smoked daily has transitioned to a state of not engaging in smoking anymore. Information pertaining to non-cigarette smoking was also gathered. Diabetes mellitus was defined as FPG of 126 mg/dl or above, or the use of glucose-lowering medication or insulin therapy. Dyslipidemia was considered present if TC reached 200 mg/dl or more, LDL-c 130 mg/dl or more, Tg 150 mg/dl or more, or HDL-c cholesterol fell below 40 mg/dl in men and 50 mg/dl in women. Hypertension was identified in individuals exhibiting a systolic blood pressure of 140 mmHg or higher and/or a diastolic blood pressure of 90 mmHg or higher, as well as those who were receiving treatment with anti-hypertension medication. Renal function was estimated using the Modification of Diet in Renal Disease (MDRD) equation for calculation of estimated glomerular filtration rate (eGFR) ( 25 ). Ethical considerations Written informed consent was obtained from all participants. For participants lacking literacy skills, a literate individual read and signed the form on behalf of the participant. This study adhered to the ethical standards for medical research as outlined in the Declaration of Helsinki. The ethical considerations of this study have received approval from the Human Research Ethics Committee of Mashhad University of Medical Sciences. Statistical analysis All statistical analyses were performed using R version 4.5.1 (R Core Team (2025). _R: A Language and Environment for Statistical Computing_. R Foundation for Statistical Computing, Vienna, Austria. .), and IBM SPSS Statistics for Windows, Version 29.0 (IBM Corp., Armonk, NY). Baseline characteristics were first presented according to hypertensive condition and the quartiles of each insulin resistance index. Normality of the data was checked using Kolmogorov-Smirnov test and Quantile-Quantile plot. Numerical variables were reported as medians with interquartile ranges (IQR) and compared using Mann-Whitney U or the Kruskal-Wallis test. Categorical variables were expressed as counts and percentages, and compared using the chi-squared or fisher’s exact test. We analyzed the differences in systolic and diastolic blood pressure across quartiles of each surrogate index using Kruskal–Wallis test. When overall differences were statistically significant, Dunn’s post-hoc test with Holm-adjusted p-values was applied for pairwise comparisons ( 26 ). Violin plots were used to depict the distribution of systolic- and diastolic blood pressure values across quartiles. Furthermore, bivariate associations among four insulin resistance markers and systolic/diastolic blood pressure, along with other baseline variables, were evaluated using Spearman correlation analysis, and the strength of these correlations was summarized using a heatmap. Logistic regression was employed to investigate the relationship between IR surrogate indices and hypertension, with results expressed as odds ratios (OR) and 95% confidence intervals (95%CI). We conducted a univariate analysis along with three multivariate models to thoroughly investigate and account for any possible confounding factors. In univariate models, we exclusively utilized surrogate indices as predictors. In model 1, we made adjustments for gender and age. In model 2, we accounted for the covariates included in model 1, as well as educational level, job status, smoking status, diabetes mellitus, CVD history, dyslipidemia, CVD family history, and marital status. In model 3, we further adjusted for the covariates from model 2, including BMI, anxiety score, eGFR, hematocrit, and uric acid level. The indices underwent evaluation using three different methods: a 1-standard-deviation (SD) increase, a 1-unit increase, and an analysis by quartiles (Q1–Q4, with Q1 serving as the reference category). Tests for trend across quartiles were performed by modeling the median value of each quartile as a continuous variable. In order to check the multicollinearity, we employed Variance Inflation Factors (VIF) analysis among surrogate IR indices and covariates. We used generalized variance inflation factor (GVIF) for categorical variables with multiple levels ( 27 ). We used a VIF and GVIF threshold of 5 to indicate potential multicollinearity. We acquired model fit statistics such as Log-likelihood, Cox and Snell, and Nagelkerke R squared, along with predictive accuracy metrics including Brier score and calibration measures like Hosmer and Lemshow χ2 ( 28 ). In the subsequent phase, we conducted ROC curve analysis to assess the diagnostic efficacy of each surrogate index in differentiating hypertensive individuals from those with normal blood pressure. The area under the receiver operating characteristics (AUC-ROC) curve with 95%CI was computed for each index to assess overall discriminative ability. Additionally, the optimal cutoff value, sensitivity, and specificity were established using the Youden index ( 29 ). Pairwise comparisons of AUCs between predictors were conducted utilizing the DeLong test ( 30 ). Alongside the AUC values, we generated ROC curves for each surrogate index to visually illustrate their diagnostic performance at all potential cutoff points. In order to investigate possible non-linear relationships between the surrogate indices and the outcome variable, we employed restricted cubic spline (RCS) regression models. The optimal model for analysis, varying from 3 to 8 knots, was established using Bayesian information criteria (BIC) and Akaike information criterion (AIC). The optimal number of knots was identified by analyzing the minimum absolute values of BIC and AIC for each model. Subsequently, we performed threshold effect analysis in order to find and inconsistency across the entire range of the predictor with hypertension. We compared nested models using likelihood-ratio tests (LRTs) and AIC. Subgroup analyses were performed to evaluate possible variations in effect based on important demographic and clinical factors. Interaction terms were evaluated, and stratified models were applied for each subgroup. A sensitivity analysis was conducted utilizing Poisson regression with robust variance to estimate prevalence ratios, as odds ratios derived from logistic regression could potentially overstate the association in common outcomes ( 31 , 32 ). A two-sided P value of less than 0.05 was deemed statistically significant. Result Baseline Characteristics Table 1 shows the baseline characteristics of a total of 9438 participants, comprising 39.8% males, among whom 2943 were identified as hypertensive and 6495 as non-hypertensive. The median age was 45 [40–52] among participants with hypertension, while it was 52 [46–58] for normotensive participants. Female participants consisted 62.7% of hypertensive participants. Higher median values of weight, BMI, waist, and hip circumferences were observed in hypertensive participants (all p < 0.001). Laboratory data showed that hypertensive participants had significantly higher levels of TC, Tg, FPG, LDL-c, uric acid, whereas HDL-c levels were comparable between the groups. Participants with hypertension demonstrated reduced kidney function, as indicated by a significantly lower eGFR of 79.1 ml/min, compared to 85.22 ml/min in their counterparts. A notable increase in the prevalence of diabetes mellitus, dyslipidemia, positive family history of cardiovascular disease (CVD), and history of CVD was identified among individuals with hypertension (all p < 0.001). The psychological scores indicated a small yet significant increase in hypertensive individuals for both anxiety and depression ( p < 0.05). Table 2 presents the baseline characteristics of the participants. Figure 1 illustrates the quantity of normotensive and hypertensive participants across each quartile of the surrogate indices. Table 1 Baseline characteristics of hypertensive and non-hypertensive participants. Characteristic Overall (N = 9438) Hypertensive (N = 2943) Non-hypertensive (N = 6495) P-value Demographic Information Age (Years), median [IQR] 48.00 [41.00–54.00] 45.00 [40.00–52.00] 52.00 [46.00–58.00] < 0.001 Gender, n (%) < 0.001 Male 3755 (39.8) 1098 (37.3) 2657 (40.9) Female 5683 (60.2) 1845 (62.7) 3838 (59.1) Educational level, n (%) < 0.001 Illiterate 1265 (13.4) 541 (18.4) 724 (11.1) Elementary school 3880 (41.1) 1329 (45.2) 2551 (39.3) High school or equivalent 3250 (34.4) 782 (26.6) 2468 (38.0) Some college 365 (3.9) 121 (4.1) 244 (3.8) Bachelor’s degree 560 (5.9) 140 (4.8) 420 (6.5) Master’s degree 83 (0.9) 21 (0.7) 62 (1.0) Doctoral degree or above 22 (0.2) 6 (0.2) 16 (0.2) Seminary degree 13 (0.1) 3 (0.1) 10 (0.2) Job status, n (%) < 0.001 Employed 3489 (37.0) 874 (29.7) 2615 (40.3) Unemployed 5024 (53.2) 1664 (56.5) 3360 (51.7) Retired 925 (9.8) 405 (13.8) 520 (8.0) Smoking status, n (%) < 0.001 Non 6485 (68.7) 2029 (68.9) 4456 (68.6) Ex 927 (9.8) 346 (11.8) 581 (8.9) Current 2026 (21.5) 568 (19.3) 1458 (22.4) Marriage status, n (%) < 0.001 Single 59 (0.6) 9 (0.3) 50 (0.8) Married 8792 (93.2) 2684 (91.2) 6108 (94.0) Divorced 130 (1.4) 33 (1.1) 97 (1.5) Widowed 457 (4.8) 217 (7.4) 240 (3.7) Height (m), median [IQR] 1.59 [1.54–1.67] 1.59 [1.53–1.66] 1.60 [1.54–1.67] < 0.001 Weight (kg), median [IQR] 71.00 [63-79.6] 74.00 [65.9–82.7] 69.60 [61.70–78.10] < 0.001 BMI (kg/m 2 ), median [IQR] 27.62 [24.69–30.77] 29 [26.17–32.29] 27 [24.1-30.06] < 0.001 Waist circumference (cm), median [IQR] 95.00 [87.2–103] 99.00 [92.00-106.5] 93.00 [86.00-101.00] < 0.001 Hip circumference (cm), median [IQR] 103.00 [98.00-109.00] 105.00 [99.50-111.50] 102.00 [97.00-108.00] < 0.001 Blood pressure Systolic (mmHg), median [IQR] 120.00 [110.00-130.00] 139.30 [128.30–150.00] 113.30 [106.60-120.60] < 0.001 Diastolic (mmHg), median [IQR] 80.00 [70.00–86.00] 90.00 [80.60–95.30] 76.6 [70.00–80.00] < 0.001 Laboratory data Total cholesterol (mg/dl), median [IQR] 188.00 [165.00-214.00] 196.00 [171.00-221.00] 185.00 [162.00-211.00] < 0.001 Triglyceride (mg/dl), median [IQR] 120.00 [84.00-172.00] 137.00 [98.00-193.00] 113.00 [79.00-162.00] < 0.001 Fasting plasma glucose (mg/dl), median [IQR] 82.00 [74.00–93.00] 87.00 [78.00-104.00] 81.00 [73.00–90.00] < 0.001 Low Density Lipoprotein (mg/dl), median [IQR] 114.6 [93.7-137.4] 117.7 [95.9-140.8] 113.00 [92.8.-136.05] < 0.001 High Density Lipoprotein (mg/dl), median [IQR] 41.8 [36-48.4] 42 [36-48.3] 41.7 [36-48.5] 0.504 Uric acid (mg/dl), median [IQR] 4.5 [3.7–5.5] 4.8 [3.9–5.8] 4.4 [3.6–5.3] < 0.001 Glomerular Filtraion Rate (mL/min), median [IQR] 83.72 [70.02–97.01] 79.1 [67.11–92.76] 85.22 [72.06-100.36] < 0.001 Highly sensitive C-Reactive Protein (mg/dl), median [IQR] 10.00 [5.00–18.00] 11.00 [5.00–18.00] 10.00 [5.00–18.00] 0.015 White Blood Cell (*10 9 /L), median [IQR] 5.90 [5.00-6.90] 6.00 [5.10–7.10] 5.80 [5.00-6.80] < 0.001 Hemoglobin (g/dl), median [IQR] 13.7 [12.8–14.7] 13.8 [12.9–14.8] 13.7 [12.8–14.7] < 0.001 Hematocrit (%), median [IQR] 41.2 [38.7–43.8] 41.4 [39.1–43.9] 41.0 [38.5–43.8] < 0.001 Platelet (*109/L), median [IQR] 225.00 [190.00-265.00] 227.00 [190.00-267.00] 224.00 [186.00-264.00] 0.055 TyG, median [IQR] 9.22 [8.82–9.65] 9.42 [9.03–9.87] 9.13 [8.73–9.55] < 0.001 Tg/HDL, median [IQR] 2.87 [1.89–4.42] 3.23 [2.19-5] 2.7 [1.77–4.16] < 0.001 METS-IR, median [IQR] 37.03 [42.74–48.57] 40.03 [45.69–51.61] 41.31 [35.80–47.02] < 0.001 CHG, median [IQR] 5.02 [5.23–5.48] 5.32 [5.10–5.60] 5.20 [4.98–5.43] < 0.001 Past history Diabetes mellitus, n (%) Yes 1330 (14.1) 679 (23.1) 651 (10.0) < 0.001 No 8108 (85.9) 2264 (76.9) 5844 (90.0) Dyslipidemia, n (%) < 0.001 Yes 7870 (83.4) 2543 (86.4) 5327 (82) No 1568 (16.6) 400 (13.6) 1168 ( 18 ) Positive family history of CVD, n (%) < 0.001 Yes 3301 (35.0) 1101 (37.4) 2200 (33.9) No 6137 (65.0) 1842 (62.6) 4295 (66.1) CVD history, n (%) < 0.001 Yes 204 (2.2) 120 (4.1) 84 (1.3) No 9234 (97.8) 2823 (95.9) 6411 (98.7) Psychological measures Anxiety score, median [IQR] 8.00 [3.00–15.00] 9.00 [3.00–17.00] 8.00 [3.00–15.00] < 0.001 Depression score, median [IQR] 10.00 [5.00–18.00] 11.00 [5.00–18.00] 10.00 [5.00–18.00] 0.015 Abbreviation: BMI; body mass index, Tyg; triglyceride glucose index, Tg/HDL; triglyceride to high density lipoprotein ratio, METS-IR; Metabolic Score for Insulin Resistance, CHG; Cholesterol, High-Density Lipoprotein, and Glucose index. Table 2 Baseline characteristics based on surrogate insulin resistance indices quartiles Characteristic Overall TyG index levels Tg/HDL-C levels METS-IR levels CHG levels Q1 N = 2364(25.0) Q2 N = 2371(25.1) Q3 N = 2364(25.0) Q4 N = 2339(24.8) P-value Q1 N = 2365(25.) Q2 N = 2360(25.0) Q3 N = 2364(25.0) Q4 N = 2349(24.9) P-value Q1 N = 2363(25.0) Q2 N = 2360(25.0) Q3 N = 2361(25.0) Q4 N = 2354(24.9) P-value Q1 N = 2365(25.1) Q2 N = 2357(25.0) Q3 N = 2360(25.0) Q4 N = 2356(25.0) P-value Demographic Information Age (Years), median [IQR] 48.00 [41.00–54.00] 39.00[45.00–51.00] 47.00[41.00–53.00] 48.00[42.00–54.00] 51.00[44.00–56.00] < 0.001 46.00[40.00–53.00] 47.00[41.00–54.00] 48.00[42.00–54.00] 49.00[43.00–55.00] < 0.001 46.00[40.00–53.00] 47.00[41.00–54.00] 48.00[42.00–54.00] 49.00[42.00–55.00] < 0.001 45.00[39.00–51.00] 46.00[40.00–53.00] 48.00[42.00–55.00] 51.00[45.00–57.00] < 0.001 Gender, n (%) < 0.001 < 0.001 < 0.001 < 0.001 Male 3755 (39.8) 905 (38.3) 872 (36.8) 985 (41.7) 993 (42.5) 780 (33.0) 818 (34.7) 984 (41.6) 1173 (49.9) 1120 (47.4) 958 (40.6) 947 (40.1) 730 (31.0) 745 (31.5) 881 (37.4) 1076 (45.6) 1053 (44.7) Female 5683 (60.2) 1459 (61.7) 1499 (63.2) 1379 (58.3) 1346 (57.5) 1585 (67.0) 1542 (65.3) 1380 (58.4) 1176 (50.1) 1243 (52.6) 1402 (59.4) 1414 (59.9) 1624 (69.0) 1620 (68.5) 1476 (62.6) 1284 (54.4) 1303 (55.3) Educational level, n (%) 0.025 0.194 < 0.001 0.117 Illiterate 1265 (13.4) 305 (12.9) 283 (11.9) 320 (13.5) 357 (15.3) 328 (13.9) 327 (13.9) 298 (12.6) 312 (13.3) 336 (14.2) 262 (11.1) 321 (13.6) 346 (14.7) 295 (12.5) 312 (13.2) 299 (12.7) 359 (15.2) Elementary school 3880 (41.1) 926 (39.2) 989 (41.7) 972 (41.1) 993 (42.5) 958 (40.5) 1012 (42.9) 994 (42.0) 916 (39.0) 934 (39.5) 920 (39.0) 985 (41.7) 1041 (44.2) 991 (41.9) 952 (40.4) 936 (39.7) 1001 (42.5) High school or equivalent 3250 (34.4) 853 (36.1) 844 (35.6) 815 (34.5) 738 (31.6) 810 (34.2) 787 (33.3) 819 (34.6) 834 (355) 786 (33.3) 865 (36.7) 814 (34.5) 785 (33.3) 803 (34.0) 833 (35.3) 856 (36.3) 758 (32.2) Some college 365 (3.9) 93 (3.9) 88 (3.7) 91 (3.8) 93 (4.0) 87 (3.7) 78 (3.3) 100 (4.2) 100 (4.3) 93 (3.9) 113 (4.8) 85 (3.6) 74 (3.1) 90 (3.8) 96 (4.1) 89 (3.8) 90 (3.8) Bachelor’s degree 560 (5.9) 159 (6.7) 140 (5.9) 133 (5.6) 128 (5.5) 154 (6.5) 135 (5.7) 123 (5.2) 148 (6.3) 182 (7.7) 163 (6.9) 127 (5.4) 88 (3.7) 159 (6.7) 140 (5.9) 144 (6.1) 117 (5.0) Master’s degree 83 (0.9) 17 (0.7) 24 (1.0) 23 (1.0) 19 (0.8) 18 (0.8) 17 (0.7) 22 (0.9) 26 (1.1) 23 (1.0) 26 (1.1) 20 (0.8) 14 (0.6) 20 (0.8) 18 (0.8) 25 (1.1) 20 (0.8) Doctoral degree or above 22 (0.2) 8 (0.3) 2 (0.1) 7 (0.3) 5 (0.2) 8 (0.3) 2 (0.1) 5 (0.2) 7 (0.3) 7 (0.3) 6 (0.3) 6 (0.3) 3 (0.1) 5 (0.2) 4 (0.2) 6 (0.3) 7 (0.3) Seminary degree 13 (0.1) 3 (0.1) 1 (0.0) 3 (0.1) 6 (0.3) 2 (0.1) 2 (0.1) 3 (0.1) 6 (0.3) 2 (0.1) 5 (0.2) 3 (0.1) 3 (0.1) 2 (0.1) 2 (0.1) 5 (0.2) 4 (0.2) Job status, n (%) < 0.001 < 0.001 < 0.001 < 0.001 Employed 3489 (37.0) 158 (6.7) 187 (7.9) 278 (11.8) 302 (12.9) 160 (6.8) 211 (8.9) 249 (10.5) 305 (13.0) 209 (8.8) 251 (10.6) 245 (10.4) 220 (9.3) 151 (6.4) 178 (7.6) 277 (11.7) 319 (13.5) Unemployed 5024 (53.2) 943 (39.9) 867 (36.6) 859 (36.3) 820 (35.1) 867 (36.7) 817 (34.6) 870 (36.8) 935 (39.8) 1073 (45.4) 895 (37.9) 865 (36.6) 656 (27.9) 862 (36.4) 880 (37.3) 926 (39.2) 821 (34.8) Retired 925 (9.8) 1263 (53.4) 1317 (55.5) 1227 (51.9) 1217 (52.0) 1338 (56.6) 1332 (56.4) 1245 (52.7) 1109 (47.2) 1081 (45.7) 1214 (51.4) 1251 (53.0) 1478 (62.8) 1352 (57.2) 1299 (55.1) 1157 (49.0) 1216 (51.6) Smoking status, n (%) < 0.001 < 0.001 0.011 < 0.001 Non 6485 (68.7) 1681 (71.1) 1640 (69.2) 1600 (67.7) 1564 (66.9) 1752 (74.1) 1627 (68.9) 1593 (67.4) 1513 (64.4) 1581 (66.9) 1675 (71.0) 1641 (69.5) 1588 (67.5) 1708 (72.2) 1620 (68.7) 1594 (67.5) 1563 (66.3) Ex 927 (9.8) 188 (8.0) 213 (9.0) 266 (11.3) 260 (11.1) 190 (8.0) 216 (9.2) 258 (10.9) 263 (11.2) 225 (9.5) 219 (9.3) 245 (10.4) 238 (10.1) 191 (8.1) 217 (9.2) 248 (10.5) 271 (11.5) Current 2026 (21.5) 495 (20.9) 518 (21.8) 498 (21.1) 515 (22.0) 423 (17.9) 517 (21.9) 513 (21.7) 573 (24.4) 557 (23.6) 466 (19.7) 475 (20.1) 528 (22.4) 466 (19.7) 520 (22.1) 518 (21.9) 522 (22.2) Marriage status, n (%) < 0.001 0.114 < 0.001 < 0.001 Single 59 (0.6) 22 (0.9) 18 (0.8) 7 (0.3) 12 (0.5) 16 (0.7) 20 (0.8) 13 (0.5) 10 (0.4) 22 (0.9) 17 (0.7) 11 (0.5) 9 (0.4) 26 (1.1) 8 (0.3) 12 (0.5) 13 (0.6) Married 8792 (93.2) 2219 (93.9) 2223 (93.8) 2195 (92.9) 2155 (92.1) 2210 (93.4) 2192 (92.2) 2185 (92.4) 2205 (93.9) 2218 (93.9) 2205 (93.4) 2194 (92.9) 2175 (92.4) 2205 (93.2) 2207 (93.6) 2212 (93.7) 2168 (92.0) Divorced 130 (1.4) 43 (1.8) 36 (1.5) 28 (1.2) 23 (1.0) 39 (1.6) 30 (1.3) 40 (1.7) 21 (0.9) 43 (1.8) 28 (1.2) 27 (1.1) 32 (1.4) 40 (1.7) 35 (1.5) 32 (1.4) 23 (1.0) Widowed 457 (4.8) 80 (3.4) 94 (4.0) 134 (5.7) 149 (6.4) 100 (4.2) 118 (5.0) 126 (5.3) 113 (4.8) 80 (3.4) 110 (4.7) 129 (5.5) 138 (5.9) 94 (4.0) 107 (4.5) 104 (4.4) 152 (6.5) Height (m), median [IQR] 1.59 [1.54–1.67] 1.60 [1.54–1.66] 1.59 [1.53–1.67] 1.6 [1.54–1.67] 1.6 [1.54–1.67] 0.373 1.59 [1.53–1.65] 1.59 [1.53–1.66] 1.6 [1.54–1.68] 1.62 [1.54–1.69] < 0.001 1.61 [1.55–1.69] 1.6 [1.54–1.67] 1.59 [1.54–1.67] 1.57 [1.52–1.65] < 0.001 1.59 [1.53–1.65] 1.59 [1.54–1.67] 1.61 [1.54–1.68] 1.6 [1.54–1.68] < 0.001 Weight (kg), median [IQR] 71.00 [63.00-79.6] 65.5[57.75–73.3] 70.8[63.0-78.7] 73.5[65.3–82.0] 74.3[66.7–82.5] < 0.001 65.7[58.1–73.8] 69.9[62.4–78.8] 72.8[65.0-80.7] 75.3[67.8–83.2] < 0.001 59.2[53.8–64.6] 68.3[63.3–74.00] 74.3[69-80.1] 83[75.9–91.1] < 0.001 66.00[58.2–74.6] 70.00[62.3–78.6] 73.00[65.9-80.95] 74.3.00[66.5–82.5] < 0.001 BMI (kg/m 2 ), median [IQR] 27.62 [24.69–30.77] 25.49[22.51–28.73] 27.54[24.68–30.67] 28.34[25.55–31.49] 28.76[26.15–31.78] < 0.001 25.80 [22.80–29.00] 27.6 [24 .60-30.80] 28.1 [25.20–31.2] 28.5 [26.1–31.5] < 0.001 22.82[21.05–24.28] 26.57[25.22–27.91] 29.05[27.54–30.58] 32.87[30.82–35.35] < 0.001 26.07[22.91–29.42] 27.33[24.40-30.56] 28.11[25.45–31.20] 28.66[25.97–31.54] < 0.001 Waist circumference (cm), median [IQR] 95.00 [87.2–103] 90.00 [82.00–97.00] 94.50 [87.00-102.00] 97.00 [90.00-105.00] 99.00 [92.00-106.00] < 0.001 90.0 [83.0–98.0] 95.0 [87.0-103.0] 96.0 [89.0-104.0] 98.0 [98.0105.5] < 0.001 85.00 [78.50–91.00] 93.00 [88.00-98.50] 98.00 [92.00-103.50] 105.50 [99.00-112.00] < 0.001.00 91.00 [83.00–99.00] 94.00 [86.50–102.00] 96.00 [89.00-104.00] 98.50 [92.00-106.00] < 0.001 Hip circumference (cm), median [IQR] 103 [98–109] 100.00[95.00-106.00] 103.00[98.00-109.00] 104.00[99.00-110.00] 104.00[99.00-110.00] < 0.001 101.00[96.00-107.00] 103.00[98.00-109.40] 103.50[98.35- 110.00] 104.00[99.00-109.00] < 0.001 96.00[92.00-100.00] 102.00[98.00-105.10] 105.00[101.00-109.50] 111.00[106.00-118.00] < 0.001 101.00[96.00-107.00] 103.00[97.50–109.00] 104.00[99.00-109.50] 104.00[99.00-110.00] < 0.001 Blood pressure Systolic (mmHg), median [IQR] 120 [110–130] 114.00[105.00-123.30] 120.00110.00-129.30] 120.00110.00-131.30] 123.30113.30-138.60] < 0.001 116.60[106.60- 126.6] 1200[110.00-130.00] 120.00[110.00-131.00] 120.60[110.60–135.00] < 0.001 113.33[104.00-123.33] 120.00[110.00-130.00] 120.00[110.00-131.33] 124.00[113.33-139.33] < 0.001 116.66[106.66-126.66] 118.66[108.33-128.66] 120.00[110.00-130.66] 123.33[113.33-138.33] < 0.001 Diastolic (mmHg), median [IQR] 80.00 [70.00–86.00] 77.33[70.00–80.00] 80.00[70.00–85.00] 80.00[71.33–86.66] 80.00[75.00–90.00] < 0.001 79.3.00[70.00-81.3.00] 80.00[70.00–84.00] 80.00[70.60–86.6.00] 80.00[73.30–89.3.00] < 0.001 76.66[70.00–80.00] 80.00[70.00-83.33] 80.00[70.66–87.33] 80.00[76.66-90.00] < 0.001 79.33[70.00-81.33] 80.00[70.00-83.33] 80.00[70.66–86.66] 80.00[74.66-90.00] < 0.001 Laboratory data Total cholesterol (mg/dl), median [IQR] 188.00 [165.00-214.00] 168.00[150.00-189.00] 185.00[164.00-208.00] 196.00[173.00-220.00] 208.00[181.00-236.00] < 0.001 177.00[155.00-201.00] 186.00[164.00-212.00] 193.00[168.00-219.00] 198.00[173.00-227.00] < 0.001 179.00[157.00-205.00] 189.00[166.00-216.00] 192.00[169.00-219.00] 191.00[167.00 − 219.00] < 0.001 166.00[147.00-186.00] 183.00[164.00-204.00] 197.00[175.00-221.00] 212.00[186.00-240.00] < 0.001 Triglyceride (mg/dl), median [IQR] 120.00 [84.00-172.00] 68.00 [57.00–79.00] 105.00 [92.00-117.00] 147.00 [128.00-168.00] 224.00 [178.50–285.00] < 0.001 68.00 [57.00–80.00] 102.00 [90.00-117.00] 141.00 [122.00-160.50] 224.00 [183.00-285.00] < 0.001 83.00 [64.00-111.00] 111.00 [82.00-149.00] 136.00 [100.00-184.00] 168.00 [122.00-236.00] < 0.001 81.00[63.00-109.00] 111.00[83.00-144.00] 136.00[101.00-184.00] 172.00[126.00-242.50] < 0.001 Fasting plasma glucose (mg/dl), median [IQR] 82.00 [74.00–93.00] 76.00[69.00–82.00] 80.00[73.00–87.0.00] 84.00[77.00-92.5.00] 99.00[85.00-144.00] < 0.001 80.00[73.00–88.00] 82.00[74.00–92.00] 83.00[74.00–94.00] 86.00[76.00-102.00] < 0.001 78.00[71.00–85.00] 81.00[74.00–90.00] 84.00[76.00–96.00] 88.5.00[79.00-110.00] < 0.001 74.00[68.00–81.00] 79.00[73.00–85.00] 84.00[77.00–91.00] 106.00[90.00-151.00] < 0.001 Low Density Lipoprotein (mg/dl), median [IQR] 114.60 [93.70-137.40] 106.40[89.10-124.70] 117.00[97.40–138.00] 119 .76[97.8–143.0] 117.10[91.38–143.20] < 0.001 109.80[91.10-131.40] 118.00[97.40-140.25] 118.50[98.30-142.25] 111.60[87.90-136.40] < 0.001 109.81[91.23-133.35] 118.40[97.30-139.71] 117.40[95.80–141.00] 112.60[90.80-135.50] < 0.001 95.81[80.00-111.90] 112.70[95.90–131.00] 124.60[104.85–145.00] 131.78[106.80-157.05] < 0.001 High Density Lipoprotein (mg/dl), median [IQR] 41.80 [36.00-48.40] 44.90[38.70–52.00] 42.20[37.00–49.00] 40.60[35.10–47.00] 39.00[33.70–45.00] < 0.001 49.40[43.80–56.90] 43.20[38.60–49.10] 40.00[35.10–45.00] 35.00[31.00–40.00] < 0.001 46.90[41.00-54.40] 43.00[37.20–49.40] 40.10[35.0–46.00] 37.30[32.60–43.00] < 0.001 48.00[42.00–56.00] 42.80[37.20–49.00] 39.15[34.70–44.80] 37.40[32.60–43.00] < 0.001 Uric acid (mg/dl), median [IQR] 4.50 [3.70–5.50] 4.00[3.00-4.80] 4.30[3.60–5.30] 4.80[4.00-5.70] 5.00[4.10–6.05] < 0.001 4.00[3.30–4.80] 4.30[3.60–5.20] 4.70[3.80–5.60] 5.10[4.20–6.20] < 0.001 4.10[3.40-5.00] 4.40[3.60–5.40] 4.70[3.80–5.70] 4.90[4.00-5.90] < 0.001 4.00[3.30–4.80] 4.40[3.70–5.40] 4.80[4.00-5.80] 4.90[3.95–5.90] < 0.001 Glomerular filtration Rate (mL/min), median [IQR] 83.72 [70.02–97.01] 82.34[71.44–95.2] 82.75[69.7–95.4] 84.08[70.02–94.9] 85.07[69.00-101.5] 0.502 81.62[70.07–94.68] 82.75[70.02–95.48] 84.51[70.29–98.77] 84.95[69.75-101.29] 0.170 84.51[72.06–98.27] 82.75[69.75–94.95] 83.72[70.45–96.02] 82.75[68.66–99.99] 0.045 82.75[71.25–95.48] 82.75[71.25–95.22] 83.98[69.70-95.48] 84.24[68.82-100.73] 0.238 Highly sensitive C-Reactive Protein (mg/dl), median [IQR] 10.00 [5.00–18.00] 10.00[5.00–17.00] 10.00[5.00–18.00] 10.00[5.001.00–7.00] 11.00[6.00–18.00] 0.038 10.00[5.00–17.00] 11.00[5.00–18.00] 10.5.00[5.00–18.00] 11.00[6.00–18.00] 0.407 10.00[5.00–16.00] 10.00[5.00–17.00] 10.00[5.00–17.00] 12.00[6.00–19.00] < 0.001 10.00[5.00–17.00] 10.00[5.00–17.00] 10.00[5.00–18.00] 11.00[5.5.00–18.00] 0.081 White Blood Cell (*10 9 /L), median [IQR] 5.90 [5.00-6.90] 4.70[5.50–6.50] 5.00[5.80–6.90] 5.10[5.90-7.00] 6.20[5.40–7.30] < 0.001 5.50[4.70–6.40] 5.80[5.00-6.80] 6.00[5.10–7.10] 6.20[5.30–7.30] < 0.001 5.60[4.70–6.50] 5.70[4.90–6.80] 6.00[5.10-7.00] 6.20[5.40–7.30] < 0.001 5.70[4.80–6.60] 5.80[5.00-6.80] 5.90[5.10-7.00] 6.20[5.3–7.20] < 0.001 Hemoglobin (g/dl), median [IQR] 13.7 [12.8–14.7] 13.40[12.50–14.50] 13.70[12.80–14.70] 13.80[12.90–14.90] 14.00[13.10–15.00] < 0.001 13.40[12.50–14.40] 13.60[12.70–14.50] 13.80[12.90-14.85] 14.10[13.20–15.10] < 0.001 13.70[12.80–14.70] 13.80[12.90–14.80] 13.80[12.80–14.80] 13.70[12.80–14.70] 0.111 13.40[12.50–14.40] 13.60[12.70–14.60] 13.90[12.90–14.00.95] 14.00[13.10–15.00] < 0.001 Hematocrit (%), median [IQR] 41.20 [38.7–43.8] 40.30[37.9–43.00] 41.00[41.00-43.60] 41.50[38.90–44.10] 41.80[39.40–44.30] < 0.001 40.30[38.00-42.90] 40.80[38.40–43.40] 41.50[39.00-44.10] 42.20[39.50–44.60] < 0.001 41.00[38.50–43.80] 41.20[38.80–43.80] 41.30[38.80-43.09] 41.10[38.70–43.80] 0.158 40.30[38.00-42.90] 40.90[38.50–43.60] 41.60[39.00-44.10] 41.90[39.40–44.40] < 0.001 Platelet (*109/L), median [IQR] 225.00 [190.00-265.00] 219.00 [186.00-258.00] 226.00[189.00-265.00] 226.00[191.00265.00] 229.00[193.00-270.00] < 0.001 221.00[188.00-260.00] 228.00[190.00-267.00] 227.00[192.00-266.00] 224.00[190.00-266.00] 0.002 217.00[182.00-254.00] 226.00[189.00-263.00] 228.00[193.00-269.00] 230.00[195.00–272.00] < 0.001 223.00[188.00-264.00] 226.00[191.00-264.00] 226.00[190.00-265.00] 226.00[190.00-266.00] 0.268 TyG, median [IQR] 9.22 [8.82–9.65] 8.56 [8.36–8.70] 9.03 [8.93–9.13] 9.42[9.32–9.53] 10.03 [9.82–10.38] < 0.001 8.60 [8.37–8.81] 9.04 [8.84–9.25] 9.37 [9.18–9.58] 9.90 [9.63–10.29] < 0.001 8.77 [8.50–9.09] 9.12 [8.79–9.47] 9.36 [9.03–9.74] 9.65 [9.2710.13] < 0.001 8.70 [8.42–8.98] 9.06 [8.80–9.33] 9.36 [9.06–9.64] 9.90 [9.54–10.33] < 0.001 Tg/HDL, median [IQR] 2.87 [1.89–4.42] 1.49[1.18–1.89] 2.45[1.98-3.00] 3.58[2.87–4.43] 5.78[4.26–7.96] < 0.001 1.42[1.16–1.66] 2.36[2.12–2.60] 3.5[3.15–3.90] 6.05[5.10–8.06] < 0.001 1.77[1.29–2.5] 2.57[1.81–3.60] 3.34[2.37–4.90] 4.54[3.06–6.76] < 0.001 1.68[1.25–2.35] 2.57[1.87–3.47] 3.43[2.47–4.81] 4.68[3.14–6.91] < 0.001 METS-IR, median [IQR] 37.03 [42.74–48.57] 36.12 [31.72–41.21] 41.15 [36.60-46.03] 44.49 [39.53–49.36] 48.65 [43.89–54.16] < 0.001 36.03 [31.72–40.79] 36.64 [41.30-46.05] 44.09 [39.35–49.08] 48.86 [44.32–54.35] < 0.001 33.18 [30.18–35.25] 39.89 [38.54–41.32] 45.45 [44.08–46.94] 53.21 [50.63–57.05] < 0.001 36.66 [31.99–41.95] 41.00 [36.22–46.46] 44.38 [39.41–49.29] 48.12 [43.35–53.90] < 0.001 CHG, median [IQR] 5.02 [5.23–5.48] 4.94 [4.78–5.11] 5.01 [5.15–5.30] 5.31 [5.15–5.47] 5.62 [5.41–5.94] < 0.001 4.95 [4.78–5.13] 5.16 [5.00-5.35] 5.30 [5.14–5.49] 5.50 [5.32–5.73] < 0.001 5.00 [4.81–5.18] 5.18 [5.00-5.38] 5.32 [5.12–5.53] 5.45 [5.23–5.73] < 0.001 4.86 [4.74–4.95] 5.12 [5.07–5.18] 5.34 [5.28–5.40] 5.70 [5.57–5.97] < 0.001 Past history Diabetes mellitus, n (%) < 0.001 < 0.001 < 0.001 < 0.001 Yes 1330 (14.1) 59 (2.5) 134 (5.7) 226 (9.6) 911 (38.9) 181 (7.7) 298 (12.6) 345 (14.6) 506 (21.5) 114 (4.8) 257 (10.9) 369 (15.6) 590 (25.1) 65 (2.7) 97 (4.1) 166 (7.0) 1002 (42.5) No 8108 (85.9) 2305 (97.5) 2237 (94.3) 2138 (90.4) 1428 (61.1) 2184 (92.3) 2062 (87.4) 2019 (85.4) 1843 (78.5) 2249 (95.2) 2103 (89.1) 1992 (84.4) 1764 (74.9) 2300 (97.3) 2260 (95.9) 2194 (93.0) 1354 (57.5) Dyslipidemia, n (%) < 0.001 < 0.001 < 0.001 < 0.001 Yes 7870 (83.4) 1555 (65.8) 1879 (79.2) 2138 (90.4) 2298 (98.2) 1327 (56.1) 1914 (81.1) 2280 (96.4) 2349 (100.0) 1483 (37.2) 1969 (16.6) 2144 (9.2) 2274 (3.4) 1312 (55.5) 1993 (84.6) 2261 (95.8) 2304 (97.8) No 1568 (16.6) 809 (34.2) 492 (20.8) 226 (9.6) 41 (1.8) 1038 (43.9) 446 (18.9) 84 (3.6) 0 (0.0) 880 (62.8) 391 (83.4) 217 (90.8) 80 (96.6) 1053 (44.5) 364 (15.4) 99 (4.2) 52 (2.2) Positive family history of CVD, n (%) 0.045 0.041 < 0.001 0.082 Yes 3301 (35.0) 770 (32.6) 851 (35.9) 847 (35.8) 833 (35.6) 770 (32.6) 852 (36.1) 842 (35.6) 837 (35.6) 721 (30.5) 832 (35.3) 846 (35.8) 902 (38.3) 785 (33.2) 836 (35.3) 816 (34.6) 864 (36.7) No 6137 (65.0) 1594 (67.4) 1520 (64.1) 1517 (64.2) 1506 (64.4) 1595 (67.4) 1508 (63.9) 1522 (64.4) 1512 (64.4) 1642 (69.5) 1528 (64.7) 1515 (64.2) 1452 (61.7) 1580 (66.8) 1521 (64.5) 1544 (65.4) 1492 (63.3) CVD history, n (%) < 0.001 < 0.001 < 0.001 < 0.001 Yes 204 (2.2) 24 (1.0) 41 (1.7) 49 (2.1) 90 (3.8) 24 (1.0) 43 (1.8) 57 (2.4) 80 (3.4) 32 (1.4) 38 (1.6) 58 (2.5) 76 (3.2) 24 (1.0) 39 (1.7) 50 (2.1) 91 (3.9) No 9234 (97.8) 2340 (99.0) 2330 (98.3) 2315 (97.9) 2249 (96.2) 2341 (99.0) 2317 (98.2) 2307 (97.6) 2269 (96.6) 2331 (98.6) 2322 (98.4) 2303 (97.5) 2278 (96.8) 2341 (99.0) 2318 (98.3) 2310 (97.9) 2265 (96.1) Hypertension, n (%) < 0.001 < 0.001 < 0.001 < 0.001 Yes 2943 (31.2) 425 (18.0) 671 (28.3) 804 (34.0) 1043 (44.6) 525 (22.2) 706 (29.9) 800 (33.8) 912 (38.8) 415 (17.6) 630 (26.7) 814 (34.5) 1084 (46.0) 510 (21.6) 641 (27.2) 780 (33.1) 1012 (43.0) No 6495 (68.8) 1939 (82.0) 1700 (71.7) 1560 (66.0) 1296 (55.4) 1840 (77.8) 1654 (70.1) 1564 (66.2) 1437 (61.2) 1948 (82.4) 1730 (73.3) 1547 (65.5) 1270 (54.0) 1855 (78.4) 1716 (72.8) 1580 (66.9) 1344 (57.0) Psychological measures Anxiety score, median [IQR] 8.00 [3.00–15.00] 7.00[3.00–14.00] 8.00[3.00–15.00] 8.00[3.00–16.00] 8.00[3.00–16.00] 0.031 8.00[3.00–15.00] 8.00[3.00–16.00] 8.00[3.00–16.00] 8.00[3.00–15.00] 0.556 7.00[3.00–14.00] 8.00[3.00–14.00] 8.00[3.00–16.00] 9.00[4.00–18.00] < 0.001 8.00[3.00–15.00] 8.00[3.00–16.00] 8.00[3.00–15.00] 8.00[3.00–16.00] 0.506 Depression score, median [IQR] 10.00 [5.00–18.00] 10.00[5.00–17.00] 10.00[5.00–18.00] 10.00[5.00–17.00] 11.00[6.00–18.00] 0.038 10.00[5.00–17.00] 11.00[5.00–18.00] 10.5.00[5.00–18.00] 11.00[6.00–18.00] 0.407 10.00[5.00–16.00] 10.00[5.00–17.00] 10.00[5.00–17.00] 12.00[6.00–19.00] < 0.001 10.00[5.00–17.00] 10.00[5.00–17.00] 10.00[5.00–18.00] 10.00[5.5.00–18.00] 0.081 Abbreviation: BMI; body mass index, Tyg; triglyceride glucose index, Tg/HDL; triglyceride to high density lipoprotein ratio, METS-IR; Metabolic Score for Insulin Resistance, CHG; Cholesterol, High-Density Lipoprotein, and Glucose index. Systolic and Diastolic Blood Pressure Across Quartiles of the indices Violin plot visualizations (Fig. 2 ) and Kruskal-Wallis tests confirmed statistically significant differences in both systolic and diastolic blood pressures across all quartiles of the surrogate indices ( p < 0.001 for all comparisons). Dunn’s post-hoc test with Holm-adjusted p-values revealed consistent stepwise increases in blood pressure from Q1 to Q4 (Supplementary table 1 ). Correlation Analysis Spearman’s correlation coefficients showed that all four IR indices had significantly positive correlation with both systolic and diastolic blood pressures (Supplementary table 2). We illustrated the correlation coefficients of each variable with others using a heat map in a correlation matrix (Fig. 3 ). Association between surrogate IR indices and hypertension The results of the logistic regression analyses are presented in Table 3 . In unadjusted models, all four indices demonstrated a robust positive correlation with hypertension, evident both when assessed per 1 SD and per 1 unit increase, as well as across quartiles (all P for trend < 0.001). Following a systematic adjustment for potential confounding variables, the risk of hypertension was reduced yet continued to show significance in the majority of comparisons. Furthermore, the performance metrics of the models demonstrated a continuous enhancement from the unadjusted to the fully adjusted models across all four indices. Throughout the indices, there was a noticeable decrease in Brier scores following adjustments, which signifies an enhancement in overall prediction accuracy. The calibration showed notable improvement in fully adjusted models, as evidenced by non-significant calibration χ² P-values for several indices, indicating enhanced alignment between predicted and observed outcomes (Supplementary table 3). In the fully adjusted model (Model 3), each 1 SD increase in TyG, Tg/HDL-c, METS-IR, and CHG was linked to a 23.4%, 8.1%, 33.0%, and 9.9% increased risk of hypertension, respectively. The TyG index explained the greatest proportion of variance in hypertension risk in the fully adjusted model, as indicated by the highest Nagelkerke R² of 0.218, in comparison to other indices (Supplementary table 3). Furthermore, all VIF values were below 5 across models, indicating no evidence of problematic multicollinearity (Supplementary table 4). Table 3 Univariate and multivariate logistic regression analysis of the association between surrogate IR indices and hypertension Indexes Unadjusted model Adjusted model 1 Adjusted model 2 Adjusted model 3 OR [95%CI] P-value OR [95%CI] P-value OR [95%CI] P-value OR [95%CI] P-value Tyg Per 1 SD increase 1.595 [1.525–1.669] < 0.001 1.458 [1.391–1.529] < 0.001 1.386 [1.310–1.467] < 0.001 1.234 [1.163–1.310] < 0.001 Per 1 unit increase 2.027 [1.893–2.170] < 0.001 1.770 [1.648–1.901] < 0.001 1.639 [1.505–1.785] < 0.001 1.375 [1.256–1.505] < 0.001 Quartile 1 Reference Reference Reference Reference Quartile 2 1.801 [1.569–2.067] < 0.001 1.653 [1.432–1.908] < 0.001 1.646 [1.423–1.904] < 0.001 1.381 [1.189–1.603] < 0.001 Quartile 3 2.351 [2.054–2.692] < 0.001 2.055 [1.786–2.365] < 0.001 2.013 [1.739–2.330] < 0.001 1.517 [1.302–1.767] < 0.001 Quartile 4 3.672 [3.215–4.194] < 0.001 2.900 [2.525–3.332] < 0.001 2.514 [2.149–2.941] < 0.001 1.798 [1.523–2.122] < 0.001 P for trend < 0.001 < 0.001 < 0.001 < 0.001 Tg/HDL Per 1 SD increase 1.239 [1.187–1.294] < 0.001 1.221 [1.167–1.278] < 0.001 1.177 [1.121–1.236] < 0.001 1.081 [1.028–1.137] 0.002 Per 1 unit increase 1.078 [1.062–1.094] < 0.001 1.072 [1.055–1.089] < 0.001 1.058 [1.040–1.077] < 0.001 1.028 [1.010–1.046] 0.002 Quartile 1 Reference Reference Reference Reference Quartile 2 1.496 [1.312–1.705] < 0.001 1.418 [1.237–1.627] < 0.001 1.404 [1.216–1.621] < 0.001 1.198 [1.034–1.389] 0.016 Quartile 3 1.793 [1.576–2.040] < 0.001 1.671 [1.459–1.913] < 0.001 1.668 [1.435–1.939] < 0.001 1.312 [1.122–1.534] < 0.001 Quartile 4 2.224 [1.958–2.527] < 0.001 2.041 [1.783–2.335] < 0.001 1.962 [1.682–2.289] < 0.001 1.405 [1.195–1.653] < 0.001 P for trend < 0.001 < 0.001 < 0.001 < 0.001 METS-IR Per 1 SD increase 1.634 [1.561–1.711] < 0.001 1.617 [1.540–1.697] < 0.001 1.587 [1.506–1.673] < 0.001 1.330 [1.220–1.451] < 0.001 Per 1 unit increase 1.057 [1.051–1.062] < 0.001 1.056 [1.050–1.061] < 0.001 1.053 [1.047–1.060] < 0.001 1.032 [1.022–1.042] < 0.001 Quartile 1 Reference Reference Reference Reference Quartile 2 1.709 [1.486–1.966] < 0.001 1.645 [1.422–1.904] < 0.001 1.659 [1.428–1.928] < 0.001 1.301 [1.076–1.573] 0.003 Quartile 3 2.470 [2.156–2.829] < 0.001 2.315 [2.009–2.669] < 0.001 2.291 [1.973–2.660] < 0.001 1.584 [1.272–1.974] < 0.001 Quartile 4 4.007 [3.506–4.578] < 0.001 3.804 [3.305–4.379] < 0.001 3.653 [3.137–4.253] < 0.001 2.246 [1.742–2.901] < 0.001 P for trend < 0.001 < 0.001 < 0.001 < 0.001 CHG Per 1 SD increase 1.476 [1.412–1.542] < 0.001 1.323 [1.263–1.386] < 0.001 1.197 [1.124–1.274] < 0.001 1.099 [1.030–1.172] 0.004 Per 1 unit increase 2.558 [2.300-2.846] < 0.001 1.966 [1.758–2.198] < 0.001 1.543 [1.327–1.795] < 0.001 1.255 [1.073–1.467] 0.004 Quartile 1 Reference Reference Reference Reference Quartile 2 1.359 [1.189–1.553] < 0.001 1.288 [1.121–1.480] < 0.001 1.285 [1.109–1.488] < 0.001 1.131 [0.972–1.315] 0.111 Quartile 3 1.796 [1.576–2.045] < 0.001 1.572 [1.371–1.802] < 0.001 1.560 [1.340–1.816] < 0.001 1.241 [1.060–1.452] 0.007 Quartile 4 2.739 [2.411–3.111] < 0.001 2.088 [1.825–2.389] < 0.001 1.681 [1.426–1.982] < 0.001 1.260 [1.062–1.496] 0.008 P for trend < 0.001 < 0.001 < 0.001 0.031 Model 1: adjusted for gender and age Model 2: adjusted for covariates in model 1 and educational level, job status, smoking status, diabetes mellitus, CVD history, Dyslipidemia, CVD family history, and marriage status Model 3: adjusted for covariates in model 2 and BMI, anxiety score, GFR, Hematocrit, and uric acid level Abbreviation: SD; standard deviation, Tyg; triglyceride glucose index, Tg/HDL; triglyceride to high density lipoprotein ratio, METS-IR; Metabolic Score for Insulin Resistance, CHG; Cholesterol, High-Density Lipoprotein, and Glucose index. Diagnostic Accuracy via ROC Analysis Analysis of the ROC curve indicated that each of the four metabolic indices demonstrated a significant capacity to predict hypertension (all p < 0.001). Among the indices, METS-IR had the highest discriminative performance, with an AUC of 0.642 (95%CI: 0.630–0.654). TyG had an AUC of 0.633 (95% CI: 0.621–0.645), CHG at an AUC of 0.611 (95%CI: 0.599–0.623), and TG/HDL-c, had an AUC of 0.587 (95% CI: 0.575–0.599). DeLong’s test revealed that Tg/HDL-c and CHG exhibited significantly lower performance compared to TyG as reference ( p < 0.001), whereas METS-IR showed no significant difference from TyG ( p = 0.131). The optimal cutoff values identified were 8.30 for TyG, 1.23 for TG/HDL-c, 42.85 for METS-IR, and 4.61 for CHG. The sensitivities varied between 57.9% and 66.0%, while the specificities ranged from 47.0–61.2% (Table 4 ). Figure 4 presents the ROC-AUC curves. Table 4 Receiver operative characteristic curve analysis for differentiation of hypertensive and non-hypertensive participants by IR surrogate indices. Predictor Total Delong test Sensitivity (%) Specificity (%) Cutoff value AUC [95%CI] P-value Z-statistic P-value TyG 0.579 0.612 8.30 0.633 [0.621–0.645] < 0.001 Reference Tg/HDL-C 0.660 0.470 1.23 0.587 [0.575–0.599] < 0.001 12.804 < 0.001 METS-IR 0.649 0.564 42.85 0.642 [0.630–0.654] < 0.001 -1.5097 0.1311 CHG 0.579 0.583 4.61 0.611 [0.599–0.623] < 0.001 4.746 < 0.001 Abbreviation: AUC; area under curve, Tyg; triglyceride glucose index, Tg/HDL; triglyceride to high density lipoprotein ratio, METS-IR; Metabolic Score for Insulin Resistance, CHG; Cholesterol, High-Density Lipoprotein, and Glucose index. Incremental classification values of IR surrogate indices for hypertension We conducted additional analysis to determine if incorporating IR surrogate indices into the conventional model (Model 3) could improve the differentiation between participants with and without hypertension. The data presented in Table 5 indicates that the inclusion of IR surrogate indices led to a notable enhancement in the IDI and NRI for the detection of hypertension (all P values < 0.001). Among these indicators, the TyG index demonstrated the most significant incremental classification values (NRI: 13.58, 95%CI: [8.65–18.35]; IDI: 0.956, 95%CI: [0.834–1.079]). Table 5 Incremental classification value of IR surrogate indices for hypertension. Continuous NRI, % IDI, % Estimate 95%CI P-value Estimate 95%CI P-value Conventional model Conventional model + TyG 13.58 [8.65–18.35] < 0.001 0.439 [0.212–0.732] < 0.001 Conventional model + Tg/HDL-c 6.03 [1.37–11.66] 0.01 0.083 [0.007–0.242] 0.004 Conventional model + METS-IR 11.84 [6.66–16.91] < 0.001 0.398 [0.177–0.692] < 0.001 Conventional model + CHG 7.75 [2.40-12.08] 0.004 0.077 [0.007–0.227] < 0.001 Note: Conventional model was adjusted for gender, age, educational level, job status, smoking status, diabetes mellitus, CVD history, Dyslipidemia, CVD family history, marriage status, BMI, anxiety score, GFR, Hematocrit, and uric acid level. Abbreviation: SD; standard deviation, Tyg; triglyceride glucose index, Tg/HDL; triglyceride to high density lipoprotein ratio, METS-IR; Metabolic Score for Insulin Resistance, CHG; Cholesterol, High-Density Lipoprotein, and Glucose index Nonlinear relationship between surrogate IR indices and hypertension We conducted RCS analysis to effectively model and visualize the relationship between surrogate indices and hypertension. In addition to the significant overall relationship among all four surrogate indices and hypertension in our fully adjusted model, we identified a linear relationship between METS-IR and hypertension, as well as a non-linear relationship between TyG, Tg/HDL-c, and CHG. Furthermore, a threshold effect was identified in the non-linear relationship between TyG, Tg/HDL-c, and CHG with hypertension through threshold effect analysis. Across aforementioned indices, the association with hypertension is consistently stronger at lower values and then attenuates or plateaus beyond the identified cutpoints. In the lowest segments, the per-unit effects are clearly larger, while above these thresholds the slopes move toward 1.0 and often lose statistical significance (Supplementary table 5). Subgroup and Sensitivity Analyses Following the adjustment for potential covariables, we conducted a subgroup analysis to confirm the relationships between surrogate indices and hypertension within specific subgroups. The subgroups were classified based on age, gender, employment status, smoking habits, presence of diabetes mellitus, dyslipidemia, positive family history of CVDs, eGFR categories, and history of CVD. The associations between surrogate indices and hypertension were largely consistent across all subgroup analyses, although some variations were observed. The association between Tg/HDL-c and hypertension was notably stronger in participants under 40 years of age, as well as in those without dyslipidemia. Significant interactions were observed for age (p = 0.013), diabetes (p = 0.014), and dyslipidemia (p < 0.001) concerning TyG, indicating a higher risk for hypertension among participants under 40 years, non-diabetic individuals, and those without dyslipidemia (p < 0.001). Notable interactions were observed for gender, job status, dyslipidemia, and diabetes (p for interactions = 0.023, 0.008, 0.016, and 0.012, respectively) in CHG, indicating a stronger effect in males, retired individuals, non-diabetics, and those without dyslipidemia. For the sensitivity analysis, we used Poisson regression with robust variance to directly estimate prevalence ratios (PRs) and their 95%CI. The results from Poisson regression were consistent in direction and statistical significance with those from logistic regression for all four indices (Table 6 ). Although PRs were smaller in magnitude than the corresponding ORs, the trends across quartiles and the rank order of associations were preserved. Table 6 Univariate and multivariate Poisson regression with robust variance of the association between surrogate IR indices and hypertension prevalence. Indexes Unadjusted model Adjusted model 1 Adjusted model 2 Adjusted model 3 PR [95%CI] P-value PR [95%CI] P-value PR [95%CI] P-value PR [95%CI] P-value Tyg Per 1 SD increase 1.076 [1.070–1.083] < 0.001 1.056 [1.050–1.063] < 0.001 1.034 [1.026–1.042] < 0.001 1.029 [1.021–1.037] < 0.001 Per 1 unit increase 1.118 [1.108–1.128] < 0.001 1.086 [1.077–1.096] < 0.001 1.053 [1.040–1.064] < 0.001 1.044 [1.032–1.057] < 0.001 Quartile 1 Reference Reference Reference Reference Quartile 2 1.087 [1.068–1.106] < 0.001 1.067 [1.048–1.085] < 0.001 1.043 [1.024–1.062] < 0.001 1.038 [1.019–1.0576] < 0.001 Quartile 3 1.135 [1.116–1.155] < 0.001 1.102 [1.084–1.121] < 0.001 1.064 [1.044–1.084] < 0.001 1.053 [1.033–1.073] < 0.001 Quartile 4 1.225 [1.206–1.244] < 0.001 1.164 [1.145–1.184] < 0.001 1.096 [1.074–1.118] < 0.001 1.082 [1.059–1.105] < 0.001 P for trend < 0.001 < 0.001 < 0.001 < 0.001 Tg/HDL Per 1 SD increase 1.035 [1.027–1.042] < 0.001 1.029 [1.022–1.036] < 0.001 1.015 [1.008–1.023] < 0.001 1.010 [1.003–1.017] 0.005 Per 1 unit increase 1.012 [1.009–1.014] < 0.001 1.010 [1.007–1.012] < 0.001 1.005 [1.002–1.008] < 0.001 1.003 [1.001–1.006] 0.005 Quartile 1 Reference Reference Reference Reference Quartile 2 1.063 [1.043–1.082] < 0.001 1.048 [1.030–1.067] < 0.001 1.026 [1.007–1.045] 0.008 1.021 [1.001–1.040] 0.033 Quartile 3 1.095 [1.075–1.115] < 0.001 1.075 [1.056–1.094] < 0.001 1.044 [1.024–1.065] < 0.001 1.033 [1.012–1.054] 0.001 Quartile 4 1.136 [1.116–1.155] < 0.001 1.108 [1.089–1.127] < 0.001 1.060 [1.038–1.081] < 0.001 1.043 [1.021–1.065] < 0.001 P for trend < 0.001 < 0.001 < 0.001 < 0.001 METS-IR Per 1 SD increase 1.080 [1.073–1.086] < 0.001 1.070 [1.064–1.077] < 0.001 1.031 [1.013–1.048] < 0.001 1.040 [1.007–1.074] 0.015 Per 1 unit increase 1.008 [1.007–1.009] < 0.001 1.007 [1.006–1.008] < 0.001 1.003 [1.001–1.005] < 0.001 1.004 [1.000-1.008] 0.015 Quartile 1 Reference Reference Reference Reference Quartile 2 1.077 [1.058–1.096] < 0.001 1.063 [1.045–1.082] < 0.001 1.033 [1.011–1.055] 0.003 1.031 [0.964–1.103] 0.366 Quartile 3 1.143 [1.124–1.163] < 0.001 1.118 [1.100- 1.137] < 0.001 1.062 [1.036–1.088] < 0.001 1.062 [0.980–1.150] 0.139 Quartile 4 1.242 [1.223–1.261] < 0.001 1.208 [1.189–1.226] < 0.001 1.104 [1.069–1.139] < 0.001 1.119 [1.019–1.230] 0.018 P for trend < 0.001 < 0.001 < 0.001 0.013 CHG Per 1 SD increase 1.064 [1.057–1.071] < 0.001 1.042 [1.035–1.049] < 0.001 1.016 [1.007–1.025] < 0.001 1.012 [1.003–1.021] 0.006 Per 1 unit increase 1.163 [1.147–1.179] < 0.001 1.106 [1.090–1.122] < 0.001 1.040 [1.018–1.062] < 0.001 1.030 [1.008–1.052] 0.006 Quartile 1 Reference Reference Reference Reference Quartile 2 1.046 [1.026–1.065] < 0.001 1.034 [1.015–1.053] < 0.001 1.018 [0.998–1.038] 0.067 1.013 [0.993–1.033] 0.179 Quartile 3 1.094 [1.074–1.114] < 0.001 1.066 [1.046–1.085] < 0.001 1.036 [1.015–1.057] < 0.001 1.026 [1.004–1.047] 0.016 Quartile 4 1.175 [1.156–1.195] < 0.001 1.115 [1.095–1.134] < 0.001 1.043 [1.019–1.066] < 0.001 1.030 [1.006–1.054] 0.014 P for trend < 0.001 < 0.001 < 0.001 0.008 Model 1: adjusted for gender and age Model 2: adjusted for covariates in model 1 and educational level, job status, smoking status, diabetes mellitus, CVD history, Dyslipidemia, CVD family history, and marriage status Model 3: adjusted for covariates in model 2 and BMI, anxiety score, GFR, Hematocrit, and uric acid level Abbreviation: SD; standard deviation, Tyg; triglyceride glucose index, Tg/HDL; triglyceride to high density lipoprotein ratio, METS-IR; Metabolic Score for Insulin Resistance, CHG; Cholesterol, High-Density Lipoprotein, and Glucose index. Discussion This population-based cross-sectional study involved 9438 subjects aged 35 to 65 years and investigated the association between surrogate insulin indices and hypertension in an urban population in the Northeastern region of Iran. We found that surrogate indices of IR were associated with elevated systolic and diastolic blood pressure. Logistic regression showed a significant association between IR markers and hypertension in both unadjusted and adjusted models. Because BMI is embedded in the METS-IR formula, we evaluated potential collinearity between these predictors. By categorizing BMI into standard groups and assessing generalized VIF values, we confirmed that all adjusted GVIFs were below 5, indicating no problematic collinearity; thus, BMI was retained as an independent covariate to capture effects beyond insulin resistance. ROC curve analysis indicated that all four metabolic indices demonstrated significant predictive capability for hypertension. Additionally, these indices indicate small but statistically detectable improvements in the model’s ability to correctly assign higher probabilities to participants with hypertension and lower probabilities to those without. However, given the cross-sectional design, these gains indicate contemporaneous classification rather than the prediction of future risks. RCS analysis indicated a positive non-linear correlation between the indices and hypertension. The threshold effect analysis indicated that the risk gradient is primarily focused within the low-to-moderate range of each index, exhibiting diminishing incremental risk at elevated levels. Subgroup analysis showed a stronger association between the indices and hypertension in patients without dyslipidemia and diabetes. Sensitivity analysis was conducted employing Poisson regression with robust variance to estimate prevalence ratios, as odds ratios derived from logistic regression may overstate the association in common outcomes. Our results indicated that the findings were consistent across models. To our knowledge, this is the first investigation of the link between hypertension and IR indices in Iranian adults. This relationship has been examined extensively in certain populations ( 33 – 36 ). Zhang et al. ( 19 ) conducted a cross-sectional study involving 32124 normoglycemic adults, revealing that increased TyG index and TG/HDL-c levels correlate with prehypertension and hypertension. The TyG index demonstrated greater significance than TG/HDL-c in differentiating hypertension. A separate cross-sectional study indicated that METS-IR is correlated with an increased risk of hypertension (OR = 1.03; 95%CI: 1.03–1.04) ( 20 ). In longitudinal studies, TyG was correlated with an increased incidence of hypertension over extended follow-up periods. However, other surrogate indices have not been examined as thoroughly as TyG. Zheng et al. ( 37 ) observed that, in a cohort of 4686 participants over a 9-year follow-up period, the incidence of hypertension was more common in individuals with elevated TyG index. Xin et al. identified a correlation between elevated TyG index at baseline and long-term TyG index trajectories with the risk of hypertension ( 38 ). Insulin resistance and hypertension are regarded as typical "diseases of civilization," emerging in contemporary settings characterized by abundant food and a sedentary lifestyle. Insights from evolutionary biology suggest that the thrifty genotype, characterized by high cytokine response (facilitating injury eradication), mild insulin resistance (providing protection against starvation), and sodium preservation (ensuring body fluid maintenance), was advantageous for our ancestors in overcoming critical challenges such as famine, infection, trauma, and physical stressors. However, these traits may now be maladaptive in the context of modern lifestyles, potentially leading to insulin resistance, hypertension, type II diabetes, and cardiovascular diseases ( 39 ). Insulin resistance and hypertension may be independent outcomes of a shared cellular disorder, specifically an increase in intracellular free calcium, leading to both vasoconstriction and impaired insulin action ( 40 , 41 ). Hyperinsulinemia may be considered a significant factor in the development of hypertension through various mechanisms. Factors include enhanced sodium reabsorption in kidney tubules ( 42 ), formation of advanced glycated end products (AGEs) ( 43 ), activation of the sympathetic nervous system ( 44 ), changes in vascular resistance due to elevated calcium levels in smooth muscle cells ( 45 ), and hyperaldosteronism alongside hyperactivation of the renin-angiotensin-aldosterone system (RAAS) ( 46 ). In our study, we observed stronger association between surrogate IR indices and hypertension in participants who did not have diabetes or dyslipidemia. Likewise, several studies revealed a strong association between IR and hypertension in metabolically normal patients ( 47 – 50 ). These findings indicates that surrogate IR markers are early warning signs of hypertension in people without overt metabolic disease. Once diabetes or dyslipidemia is established, the incremental predictive value of these indices diminishes. This pattern may be explained by the fact that, in metabolically healthier populations, these indices more accurately capture early pathophysiological processes - such as compensatory hyperinsulinemia, sympathetic overactivity, endothelial dysfunction, and activation of the RAAS system - that directly contribute to elevated blood pressure ( 39 , 51 ). By contrast, in established diabetes hyperglycemia itself and long-term vascular changes contribute to hypertension independently of IR. Similarly, in dyslipidemic individuals (high Tg/low HDL), chronic lipid abnormalities drive atherosclerosis and stiffness. Lipid‐based IR markers (TyG, Tg/HDL) are elevated by high triglycerides, but if dyslipidemia is present or treated, these markers may be less discriminating of IR. Nonetheless, additional longitudinal and mechanistic studies are required to confirm these associations and clarify the underlying pathways. Research indicates that an increase in insulin resistance surrogate indices may also correlate with worse outcomes in hypertensive patients. Liu et al. ( 52 ) found that each SD increase in the TyG index, is associated with higher risk of all-cause mortality or CVD events by 28% (Hazard ratio (HR) = 1.28, 95% CI: 1.04–1.59). Another study by Tao et al. ( 53 ) also demonstrated that a higher TyG index is associated with an increased 1-year risk of major adverse cardiovascular events (MACEs) in hypertensive patients with coronary heart disease (CHD). Specifically, individuals in the highest quartile of the TyG index exhibited a 47.0% greater risk of MACEs during the 1-year follow-up period (HR = 1.470, 95%CI: 1.071–2.018). Our investigation may have certain limitations. The cross-sectional design introduces temporal ambiguity, preventing us from establishing causality because exposure and outcome were measured simultaneously. Selection bias, recall bias, and survivorship bias can also be introduced. Conclusion We found a strong positive relationship between hypertension and IR surrogate indices. Findings underscore the necessity of overseeing and evaluating components these indices, such as BMI, FPG, HDL-c, and Tg to improve hypertension risk assessments and refine interventions. This approach aims to facilitate early lifestyle changes, including dietary adjustments and increased physical activity, while also formulating prevention strategies for populations affected by metabolic disorders and insulin resistance to avert the advancement of hypertension. Declarations Data availability The datasets produced and/or examined in this study are not available to the public, owing to the data ownership policies and copyright regulations established by Mashhad University of Medical Sciences. Access may be provided in response to a thoughtful request from the corresponding authors. Acknowledgements We recognize the introduction of AI-driven tools aimed at enhancing the quality of language and style in our manuscript draft. The authors assume full responsibility for all content and editorial choices made. Author contributions Study concept and design: A.T, M.G.M, S.S, M.M, H.A; data collection: S.S, M.K.R, B.S, H.A, M.G.M, M.M; Analysis and interpretation of data: A.T, H.E; Drafting of the manuscript: A.T; Critical revision of the manuscript for important intellectual content: S.D, M.G.M, G.F. All authors have reviewed and given their consent to the manuscript. Clinical trial number Not applicable. Funding This research received financial support from the Vice Chancellor for Research and Technology of Mashhad University of Medical Sciences [Grant Number: 4040685]. Competing interests The authors declare no competing interests. References Kjeldsen SE. Hypertension and cardiovascular risk: General aspects. Pharmacol Res. 2018;129:95–9. Organization WH. Global report on hypertension: the race against a silent killer: World Health Organization; 2023. Oori MJ, Mohammadi F, Norozi K, Fallahi-Khoshknab M, Ebadi A, Gheshlagh RG. 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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-7560704","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":540216061,"identity":"704759d9-8885-463d-bdfc-dbea82fd771c","order_by":0,"name":"Ali Tajik","email":"","orcid":"","institution":"Mashhad University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Ali","middleName":"","lastName":"Tajik","suffix":""},{"id":540216063,"identity":"f1bb3a7b-4a61-4aed-8575-5383bee043f7","order_by":1,"name":"Majid Ghayour-Mobarhan","email":"","orcid":"","institution":"Mashhad University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Majid","middleName":"","lastName":"Ghayour-Mobarhan","suffix":""},{"id":540216065,"identity":"02a3bfd6-0584-45c3-aa4b-5867e5bec0b9","order_by":2,"name":"Susan Darroudi","email":"","orcid":"","institution":"Mashhad University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Susan","middleName":"","lastName":"Darroudi","suffix":""},{"id":540216067,"identity":"c8338587-e913-4e0b-aea5-1febc5234c92","order_by":3,"name":"Mohammad Kalate Rahmani","email":"","orcid":"","institution":"Mashhad University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Mohammad","middleName":"Kalate","lastName":"Rahmani","suffix":""},{"id":540216069,"identity":"f96a3c5f-af58-49a8-ae5b-e1065bd42823","order_by":4,"name":"Bahram Shahri","email":"","orcid":"","institution":"Mashhad University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Bahram","middleName":"","lastName":"Shahri","suffix":""},{"id":540216071,"identity":"dd8cd6ac-3c8a-4113-819d-2372d243f8c5","order_by":5,"name":"Habibollah Esmaily","email":"","orcid":"","institution":"Mashhad University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Habibollah","middleName":"","lastName":"Esmaily","suffix":""},{"id":540216074,"identity":"b79f060c-08f5-4fcc-92ef-7e23812d479c","order_by":6,"name":"Sara Saffar Soflaei","email":"","orcid":"","institution":"Mashhad University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Sara","middleName":"Saffar","lastName":"Soflaei","suffix":""},{"id":540216076,"identity":"d8f70b20-d061-4d0f-b21e-2097c94726ef","order_by":7,"name":"Gordon A. 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15:51:52","extension":"html","order_by":39,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":341926,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7560704/v1/42eee0564f2b47f9425e0861.html"},{"id":95276737,"identity":"ced9aa78-b157-4be5-964e-a3abdc476606","added_by":"auto","created_at":"2025-11-06 08:31:53","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":103002,"visible":true,"origin":"","legend":"\u003cp\u003eBar graphs comparing hypertensive and non-hypertensive participants categorized by quartiles of insulin resistance surrogate indices.\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7560704/v1/c19d10a4e68aa71c6228602d.jpg"},{"id":95314101,"identity":"cff6b65a-7733-4e0e-b13b-6fed2763c36f","added_by":"auto","created_at":"2025-11-06 15:52:27","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":149008,"visible":true,"origin":"","legend":"\u003cp\u003eViolin plots for IR surrogate indices quartiles and systolic and diastolic blood pressure in mmHg. A, violin plot for TyG and systolic blood pressure; B, violin plot for TyG and diastolic blood pressure; C, violin plot for Tg/HDL-c and systolic blood pressure, D, violin plot for Tg/HDL-c and diastolic blood pressure; E, violin plot for METS-IR and systolic blood pressure; F, violin plot for METS-IR and diastolic blood pressure; G, violin plot for CHG and systolic blood pressure; H, violin plot for CHG and diastolic blood pressure.\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7560704/v1/7364044ea607e8680016906d.jpg"},{"id":95276746,"identity":"e8a4f60d-67af-4d2c-8c82-c5a91121476b","added_by":"auto","created_at":"2025-11-06 08:31:53","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":283204,"visible":true,"origin":"","legend":"\u003cp\u003eHeatmap of spearman correlation matrix among baseline variables. Abbreviation: BMI; body mass index, FPG; fasting plasma glucose, HDL-c; high density lipoprotein concentration, GFR MDRD; glomerular filtration rate Modification of Diet in Renal Disease, Tyg; triglyceride glucose index, Tg/HDL; triglyceride to high density lipoprotein ratio, METS-IR; Metabolic Score for Insulin Resistance, CHG; Cholesterol, High-Density Lipoprotein, and Glucose index, WBC; white blood cell, PLT; platelet, LDL-c; low density lipoprotein concentration, SBP; systolic blood pressure, DBP; diastolic blood pressure.\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7560704/v1/59970109e4cc1007cc395683.jpg"},{"id":95276739,"identity":"c44acb44-317a-4fcf-aa5c-c4354de7db5a","added_by":"auto","created_at":"2025-11-06 08:31:53","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":67045,"visible":true,"origin":"","legend":"\u003cp\u003eReceiver operating characteristic curves for the identification of hypertension using surrogate insulin resistance indices. Tyg; triglyceride glucose index, Tg/HDL; triglyceride to high density lipoprotein ratio, METS-IR; Metabolic Score for Insulin Resistance, CHG; Cholesterol, High-Density Lipoprotein, and Glucose index\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7560704/v1/29d7d73ea6c9eea02a0eff08.jpg"},{"id":95276738,"identity":"27ad41c7-14db-4d5f-ac0d-db9d1f65bd19","added_by":"auto","created_at":"2025-11-06 08:31:53","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":104208,"visible":true,"origin":"","legend":"\u003cp\u003eMultivariable-adjusted OR for hypertension based on restricted cubic spines for the surrogate IR indices. A, TyG; B; Tg/HDL-c; C, METS-IR; D, CHG. Red lines represented references for OR, and red areas represent 95% CI. OR was adjusted for gender and age, educational level, job status, smoking status, diabetes mellitus, CVD history, Dyslipidemia, CVD family history, and marriage status, BMI, anxiety score, GFR, Hematocrit, and uric acid level.\u003c/p\u003e","description":"","filename":"5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7560704/v1/b14329d5bc119a5c457682d1.jpg"},{"id":95276744,"identity":"93608403-10ce-43e3-89b7-c6175504e17a","added_by":"auto","created_at":"2025-11-06 08:31:53","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":163815,"visible":true,"origin":"","legend":"\u003cp\u003eMultivariate logistic regression analysis evaluating association of surrogate IR indices and hypertension in various subgroups. A, TyG; B; Tg/HDL-c; C, METS-IR; D, CHG. OR was evaluated by 1-unit increase of each index. OR was adjusted for gender and age, educational level, job status, smoking status, diabetes mellitus, CVD history, Dyslipidemia, CVD family history, and marriage status, BMI, anxiety score, GFR, Hematocrit, and uric acid level.\u003c/p\u003e","description":"","filename":"6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7560704/v1/75dcf60fc2a433d40855fc49.jpg"},{"id":102785642,"identity":"c9279103-50af-479b-a11f-678f1d06b495","added_by":"auto","created_at":"2026-02-16 16:08:57","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3055400,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7560704/v1/7e99a4b2-c131-44fe-9f1e-106b72eff629.pdf"},{"id":95313428,"identity":"b62285d4-0740-40c9-81e2-130e914511d0","added_by":"auto","created_at":"2025-11-06 15:51:23","extension":"rar","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":133465,"visible":true,"origin":"","legend":"","description":"","filename":"Additionalfile1.rar","url":"https://assets-eu.researchsquare.com/files/rs-7560704/v1/6867e69a4b8553c58b49716d.rar"}],"financialInterests":"No competing interests reported.","formattedTitle":"Association of four Insulin Resistance Surrogate Indices and Hypertension in the MASHAD cohort study population: a cross-sectional study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eHigh blood pressure ranks among the leading contributors to cardiovascular disease (CVD) (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). Roughly one in three adults between 30 and 79 years old worldwide is affected, with men showing a slightly higher rate (34%) than women (32%) (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). In Iran, estimates place the prevalence at 20–25%, again with a modest excess in men (\u003cspan additionalcitationids=\"CR4 CR5\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e–\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). Globally, the global hypertensive population rose from about 650\u0026nbsp;million in 1990 to nearly 1.3\u0026nbsp;billion by 2019. (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). This increasing prevalence has made hypertension a central contributor to the global health burden, particularly in countries with limited resources.\u003c/p\u003e\u003cp\u003eGiven the weight of cardiovascular disease, identifying groups at higher risk of hypertension and related conditions is critical. A variety of cardiovascular risk assessment tools have been introduced and tested in recent years (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). These tools often depend on charts or complex equations, which limits their use in everyday medical practice. Simpler and less costly indicators are needed, ideally ones that do not add financial strain to health systems.\u003c/p\u003e\u003cp\u003eInsulin resistance describes a state in which body tissues respond less effectively to insulin (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). This may be associated with hyperinsulinemia and disrupt normal balance between glucose and lipid metabolism. It frequently occurs in patients with metabolic syndrome or diabetes (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eInsulin resistance is not only a hallmark of diabetes but also a risk factor for cardiovascular disease in individuals without diabetes (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). This implies that measures reflecting insulin resistance have value for predicting cardiovascular risk across a wide spectrum of patients.\u003c/p\u003e\u003cp\u003eThe hyperinsulinemic-euglycemic clamp (HIEC) is still regarded as the standard approach for measuring insulin resistance. Its accuracy is well established, but the procedure is lengthy, requires catheter placement in both arms, and is uncomfortable for many patients (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). The homeostasis model assessment for insulin resistance (HOMA-IR) is a practical alternative that relies on insulin levels. Still, the test can be expensive and sometimes unavailable in underserved settings (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). For these reasons, more accessible markers of insulin resistance are needed.\u003c/p\u003e\u003cp\u003eSeveral surrogate indices have been proposed, including the Triglyceride–Glucose index (TyG), the Triglyceride to HDL cholesterol ratio (Tg/HDL-c), the Metabolic Score for Insulin Resistance (METS-IR), and the Cholesterol-HDL-Glucose index (CHG). These measures are relatively simple to calculate and have shown strong associations with cardiovascular disease (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e), coronary artery disease severity (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e), myocardial infarction (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e), and cardiovascular mortality (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eResearch exploring the link between IR markers and hypertension has mostly focused on cohorts from the United States and East Asia. Zhang et al. (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e) found a correlation between elevated TyG index and Tg/HDL-c levels with prehypertension and hypertension in individuals with normal glucose levels. Guo et al. (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e) found that an increase of one unit in the METS-IR score has been associated with roughly a three percent greater risk of hypertension. Zhu et al. (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e) noticed a significant link between TyG and METS-IR in relation to hypertension. The relationship between the CHG index and hypertension has not been explored as thoroughly as the other surrogate insulin indices previously discussed. This study examines the relation between these indices and hypertension, while specifically evaluating their effectiveness in identifying hypertensive individuals within the north eastern Iranian population.\u003c/p\u003e"},{"header":"Method","content":"\u003cp\u003eStudy sample\u003c/p\u003e\u003cp\u003eParticipants in this cross-sectional study were drawn from the Mashhad Stroke and Heart Atherosclerotic Disorder cohort (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). The MASHAD study commenced in 2010 and continued until 2020. The estimated total population of the city of Mashhad was derived from the national Iranian census conducted in 2006. Participants were selected from three regions in Mashhad, situated in north-eastern Iran, using a stratified cluster random sampling approach. Each region was segmented into nine sites, with a focus on the divisions of the Mashhad Healthcare Center. Households with members aged 35 to 65 years were included. Upon identifying eligible participants, they were contacted to schedule an appointment for the formal physical examination. Participants with incomplete profiles or absent data were removed from the study. In total, 9438 eligible subjects were enrolled in the study.\u003c/p\u003e\u003cp\u003eData collection and definitions\u003c/p\u003e\u003cp\u003eA checklist was developed to record the demographic information including age, sex, education, and marital status, in accordance with a similar research protocol (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). The questionnaire also asked about past medical history, encompassing both personal and family history of cardiovascular disease.\u003c/p\u003e\u003cp\u003eAnthropometric measurements were performed by trained nursing staff. participants were measured for weight and height while wearing light clothing and no shoes. Height was recorded with a stadiometer accurate to 0.1 cm, and weight was taken with an electronic scale precise to 0.1 kg. Subsequently, body mass index (BMI) was calculated by dividing weight (kg) by the square of height (m). Anxiety was evaluated with Beck’s Anxiety Inventory, and depression was measured using the Beck Depression Inventory II (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eBlood was drawn from the antecubital vein between 8 and 10 a.m. after an overnight fast of 14 hours. samples were collected in 20 ml vacuum tubes while participants were seated, using a standard protocol. All blood specimens underwent centrifugation at room temperature within 30 to 45 minutes of collection, serum and plasma were separated and divided into six 0.5 ml aliquots. Serum aliquots were preserved at -80°C for subsequent analysis. Low-density lipoprotein cholesterol (LDL-c) was calculated using the Friedewald formula (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e), based on serum total cholesterol (TC), triglycerides (Tg), and high-density lipoprotein cholesterol (HDL-c) levels, all expressed in mg/dl. These calculations were carried out only if values were under 400 mg/dl.\u003c/p\u003e\u003cp\u003eThe surrogate IR indices were determined using the subsequent formulas:\u003c/p\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eTyG = ln [Tg (mg/dl) * Fasting plasma glucose (FPG) (mg/dl) / 2]\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eTg/HDL-c = Tg (mg/dl) / HDL-c (mg/dl);\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eMETS-IR = (ln [2 * FPG (mg/dl) + Tg (mg/dl)] * BMI (kg/m2) / ln [HDL-c (mg/dl)]).\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eCHG = Ln [TC (mg/dl) × FPG (mg/dl) / (2 × HDL-c (mg/dl))]\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003cp\u003eBlood pressure was assessed with a mercury sphygmomanometer accurate to ± 1 mmHg. To ensure the precision of the measurement, participants were asked to rest and remain seated for a minimum of 15 minutes. The mean of two blood pressure readings was calculated for data analysis. A current smoker was defined as someone who smoked cigarettes every day, at least once per day. An individual who previously smoked daily has transitioned to a state of not engaging in smoking anymore. Information pertaining to non-cigarette smoking was also gathered. Diabetes mellitus was defined as FPG of 126 mg/dl or above, or the use of glucose-lowering medication or insulin therapy. Dyslipidemia was considered present if TC reached 200 mg/dl or more, LDL-c 130 mg/dl or more, Tg 150 mg/dl or more, or HDL-c cholesterol fell below 40 mg/dl in men and 50 mg/dl in women. Hypertension was identified in individuals exhibiting a systolic blood pressure of 140 mmHg or higher and/or a diastolic blood pressure of 90 mmHg or higher, as well as those who were receiving treatment with anti-hypertension medication. Renal function was estimated using the Modification of Diet in Renal Disease (MDRD) equation for calculation of estimated glomerular filtration rate (eGFR) (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eEthical considerations\u003c/p\u003e\u003cp\u003e Written informed consent was obtained from all participants. For participants lacking literacy skills, a literate individual read and signed the form on behalf of the participant. This study adhered to the ethical standards for medical research as outlined in the Declaration of Helsinki. The ethical considerations of this study have received approval from the Human Research Ethics Committee of Mashhad University of Medical Sciences.\u003c/p\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003eAll statistical analyses were performed using R version 4.5.1 (R Core Team (2025). _R: A Language and Environment for Statistical Computing_. R Foundation for Statistical Computing, Vienna, Austria. \u0026lt;\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.R-project.org/\u0026gt;\u003c/span\u003e\u003cspan address=\"https://www.R-project.org/%3E\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.), and IBM SPSS Statistics for Windows, Version 29.0 (IBM Corp., Armonk, NY). Baseline characteristics were first presented according to hypertensive condition and the quartiles of each insulin resistance index. Normality of the data was checked using Kolmogorov-Smirnov test and Quantile-Quantile plot. Numerical variables were reported as medians with interquartile ranges (IQR) and compared using Mann-Whitney U or the Kruskal-Wallis test. Categorical variables were expressed as counts and percentages, and compared using the chi-squared or fisher’s exact test. We analyzed the differences in systolic and diastolic blood pressure across quartiles of each surrogate index using Kruskal–Wallis test. When overall differences were statistically significant, Dunn’s post-hoc test with Holm-adjusted p-values was applied for pairwise comparisons (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). Violin plots were used to depict the distribution of systolic- and diastolic blood pressure values across quartiles. Furthermore, bivariate associations among four insulin resistance markers and systolic/diastolic blood pressure, along with other baseline variables, were evaluated using Spearman correlation analysis, and the strength of these correlations was summarized using a heatmap. Logistic regression was employed to investigate the relationship between IR surrogate indices and hypertension, with results expressed as odds ratios (OR) and 95% confidence intervals (95%CI). We conducted a univariate analysis along with three multivariate models to thoroughly investigate and account for any possible confounding factors. In univariate models, we exclusively utilized surrogate indices as predictors. In model 1, we made adjustments for gender and age. In model 2, we accounted for the covariates included in model 1, as well as educational level, job status, smoking status, diabetes mellitus, CVD history, dyslipidemia, CVD family history, and marital status. In model 3, we further adjusted for the covariates from model 2, including BMI, anxiety score, eGFR, hematocrit, and uric acid level. The indices underwent evaluation using three different methods: a 1-standard-deviation (SD) increase, a 1-unit increase, and an analysis by quartiles (Q1–Q4, with Q1 serving as the reference category). Tests for trend across quartiles were performed by modeling the median value of each quartile as a continuous variable. In order to check the multicollinearity, we employed Variance Inflation Factors (VIF) analysis among surrogate IR indices and covariates. We used generalized variance inflation factor (GVIF) for categorical variables with multiple levels (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). We used a VIF and GVIF threshold of 5 to indicate potential multicollinearity. We acquired model fit statistics such as Log-likelihood, Cox and Snell, and Nagelkerke R squared, along with predictive accuracy metrics including Brier score and calibration measures like Hosmer and Lemshow χ2 (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). In the subsequent phase, we conducted ROC curve analysis to assess the diagnostic efficacy of each surrogate index in differentiating hypertensive individuals from those with normal blood pressure. The area under the receiver operating characteristics (AUC-ROC) curve with 95%CI was computed for each index to assess overall discriminative ability. Additionally, the optimal cutoff value, sensitivity, and specificity were established using the Youden index (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). Pairwise comparisons of AUCs between predictors were conducted utilizing the DeLong test (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e). Alongside the AUC values, we generated ROC curves for each surrogate index to visually illustrate their diagnostic performance at all potential cutoff points. In order to investigate possible non-linear relationships between the surrogate indices and the outcome variable, we employed restricted cubic spline (RCS) regression models. The optimal model for analysis, varying from 3 to 8 knots, was established using Bayesian information criteria (BIC) and Akaike information criterion (AIC). The optimal number of knots was identified by analyzing the minimum absolute values of BIC and AIC for each model. Subsequently, we performed threshold effect analysis in order to find and inconsistency across the entire range of the predictor with hypertension. We compared nested models using likelihood-ratio tests (LRTs) and AIC. Subgroup analyses were performed to evaluate possible variations in effect based on important demographic and clinical factors. Interaction terms were evaluated, and stratified models were applied for each subgroup. A sensitivity analysis was conducted utilizing Poisson regression with robust variance to estimate prevalence ratios, as odds ratios derived from logistic regression could potentially overstate the association in common outcomes (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e). A two-sided P value of less than 0.05 was deemed statistically significant.\u003c/p\u003e"},{"header":"Result","content":"\u003ch2\u003eBaseline Characteristics\u003c/h2\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows the baseline characteristics of a total of 9438 participants, comprising 39.8% males, among whom 2943 were identified as hypertensive and 6495 as non-hypertensive. The median age was 45 [40–52] among participants with hypertension, while it was 52 [46–58] for normotensive participants. Female participants consisted 62.7% of hypertensive participants. Higher median values of weight, BMI, waist, and hip circumferences were observed in hypertensive participants (all \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001). Laboratory data showed that hypertensive participants had significantly higher levels of TC, Tg, FPG, LDL-c, uric acid, whereas HDL-c levels were comparable between the groups. Participants with hypertension demonstrated reduced kidney function, as indicated by a significantly lower eGFR of 79.1 ml/min, compared to 85.22 ml/min in their counterparts. A notable increase in the prevalence of diabetes mellitus, dyslipidemia, positive family history of cardiovascular disease (CVD), and history of CVD was identified among individuals with hypertension (all \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001). The psychological scores indicated a small yet significant increase in hypertensive individuals for both anxiety and depression (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05). Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents the baseline characteristics of the participants. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e illustrates the quantity of normotensive and hypertensive participants across each quartile of the surrogate indices.\u003c/p\u003e\u003cdiv class=\"gridtable\"\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\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eBaseline characteristics of hypertensive and non-hypertensive participants.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCharacteristic\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOverall (N = 9438)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHypertensive (N = 2943)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNon-hypertensive (N = 6495)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eP-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e\u003cp\u003eDemographic Information\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge (Years), median [IQR]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e48.00 [41.00–54.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e45.00 [40.00–52.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e52.00 [46.00–58.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGender, n (%)\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\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3755 (39.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1098 (37.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2657 (40.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5683 (60.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1845 (62.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3838 (59.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEducational level, n (%)\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\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIlliterate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1265 (13.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e541 (18.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e724 (11.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eElementary school\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3880 (41.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1329 (45.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2551 (39.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh school or equivalent\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3250 (34.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e782 (26.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2468 (38.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSome college\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e365 (3.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e121 (4.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e244 (3.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBachelor’s degree\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e560 (5.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e140 (4.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e420 (6.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMaster’s degree\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e83 (0.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e21 (0.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e62 (1.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDoctoral degree or above\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e22 (0.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6 (0.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e16 (0.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSeminary degree\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e13 (0.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3 (0.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e10 (0.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eJob status, n (%)\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\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEmployed\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3489 (37.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e874 (29.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2615 (40.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUnemployed\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5024 (53.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1664 (56.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3360 (51.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRetired\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e925 (9.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e405 (13.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e520 (8.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSmoking status, n (%)\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\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNon\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6485 (68.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2029 (68.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4456 (68.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEx\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e927 (9.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e346 (11.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e581 (8.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCurrent\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2026 (21.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e568 (19.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1458 (22.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMarriage status, n (%)\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\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSingle\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e59 (0.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e9 (0.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e50 (0.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMarried\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8792 (93.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2684 (91.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6108 (94.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDivorced\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e130 (1.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e33 (1.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e97 (1.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWidowed\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e457 (4.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e217 (7.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e240 (3.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHeight (m), median [IQR]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.59 [1.54–1.67]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.59 [1.53–1.66]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.60 [1.54–1.67]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWeight (kg), median [IQR]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e71.00 [63-79.6]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e74.00 [65.9–82.7]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e69.60 [61.70–78.10]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBMI (kg/m\u003csup\u003e2\u003c/sup\u003e), median [IQR]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e27.62 [24.69–30.77]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e29 [26.17–32.29]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e27 [24.1-30.06]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWaist circumference (cm), median [IQR]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e95.00 [87.2–103]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e99.00 [92.00-106.5]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e93.00 [86.00-101.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHip circumference (cm), median [IQR]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e103.00 [98.00-109.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e105.00 [99.50-111.50]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e102.00 [97.00-108.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e\u003cp\u003eBlood pressure\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSystolic (mmHg), median [IQR]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e120.00 [110.00-130.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e139.30 [128.30–150.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e113.30 [106.60-120.60]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDiastolic (mmHg), median [IQR]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e80.00 [70.00–86.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e90.00 [80.60–95.30]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e76.6 [70.00–80.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e\u003cp\u003eLaboratory data\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal cholesterol (mg/dl), median [IQR]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e188.00 [165.00-214.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e196.00 [171.00-221.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e185.00 [162.00-211.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTriglyceride (mg/dl), median [IQR]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e120.00 [84.00-172.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e137.00 [98.00-193.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e113.00 [79.00-162.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFasting plasma glucose (mg/dl), median [IQR]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e82.00 [74.00–93.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e87.00 [78.00-104.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e81.00 [73.00–90.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\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 Density Lipoprotein (mg/dl), median [IQR]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e114.6 [93.7-137.4]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e117.7 [95.9-140.8]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e113.00 [92.8.-136.05]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh Density Lipoprotein (mg/dl), median [IQR]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e41.8 [36-48.4]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e42 [36-48.3]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e41.7 [36-48.5]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.504\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUric acid (mg/dl), median [IQR]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.5 [3.7–5.5]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.8 [3.9–5.8]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.4 [3.6–5.3]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGlomerular Filtraion Rate (mL/min), median [IQR]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e83.72 [70.02–97.01]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e79.1 [67.11–92.76]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e85.22 [72.06-100.36]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHighly sensitive C-Reactive Protein (mg/dl), median [IQR]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10.00 [5.00–18.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e11.00 [5.00–18.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e10.00 [5.00–18.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.015\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWhite Blood Cell (*10\u003csup\u003e9\u003c/sup\u003e/L), median [IQR]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5.90 [5.00-6.90]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6.00 [5.10–7.10]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5.80 [5.00-6.80]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHemoglobin (g/dl), median [IQR]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e13.7 [12.8–14.7]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e13.8 [12.9–14.8]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e13.7 [12.8–14.7]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHematocrit (%), median [IQR]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e41.2 [38.7–43.8]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e41.4 [39.1–43.9]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e41.0 [38.5–43.8]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePlatelet (*109/L), median [IQR]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e225.00 [190.00-265.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e227.00 [190.00-267.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e224.00 [186.00-264.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.055\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTyG, median [IQR]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e9.22 [8.82–9.65]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e9.42 [9.03–9.87]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9.13 [8.73–9.55]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTg/HDL, median [IQR]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.87 [1.89–4.42]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.23 [2.19-5]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.7 [1.77–4.16]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMETS-IR, median [IQR]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e37.03 [42.74–48.57]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e40.03 [45.69–51.61]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e41.31 [35.80–47.02]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCHG, median [IQR]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5.02 [5.23–5.48]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5.32 [5.10–5.60]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5.20 [4.98–5.43]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e\u003cp\u003ePast history\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDiabetes mellitus, n (%)\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\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\u003e1330 (14.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e679 (23.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e651 (10.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\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\u003e8108 (85.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2264 (76.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5844 (90.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDyslipidemia, n (%)\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\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\u003e7870 (83.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2543 (86.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5327 (82)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\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\u003e1568 (16.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e400 (13.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1168 (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePositive family history of CVD, n (%)\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\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\u003e3301 (35.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1101 (37.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2200 (33.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\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\u003e6137 (65.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1842 (62.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4295 (66.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCVD history, n (%)\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\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\u003e204 (2.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e120 (4.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e84 (1.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\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\u003e9234 (97.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2823 (95.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6411 (98.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e\u003cp\u003ePsychological measures\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAnxiety score, median [IQR]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8.00 [3.00–15.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e9.00 [3.00–17.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8.00 [3.00–15.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDepression score, median [IQR]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10.00 [5.00–18.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e11.00 [5.00–18.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e10.00 [5.00–18.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.015\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003eAbbreviation: BMI; body mass index, Tyg; triglyceride glucose index, Tg/HDL; triglyceride to high density lipoprotein ratio, METS-IR; Metabolic Score for Insulin Resistance, CHG; Cholesterol, High-Density Lipoprotein, and Glucose index.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c14\" colnum=\"14\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c15\" colnum=\"15\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c16\" colnum=\"16\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c17\" colnum=\"17\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c18\" colnum=\"18\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c19\" colnum=\"19\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c20\" colnum=\"20\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c21\" colnum=\"21\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c22\" colnum=\"22\"\u003e\u003c/div\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\u003eBaseline characteristics based on surrogate insulin resistance indices quartiles\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"22\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eCharacteristic\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eOverall\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"5\" nameend=\"c7\" namest=\"c3\"\u003e\u003cp\u003eTyG index levels\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"5\" nameend=\"c12\" namest=\"c8\"\u003e\u003cp\u003eTg/HDL-C levels\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"5\" nameend=\"c17\" namest=\"c13\"\u003e\u003cp\u003eMETS-IR levels\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"5\" nameend=\"c22\" namest=\"c18\"\u003e\u003cp\u003eCHG levels\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eQ1 N = 2364(25.0)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eQ2 N = 2371(25.1)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eQ3 N = 2364(25.0)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eQ4 N = 2339(24.8)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eP-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eQ1 N = 2365(25.)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eQ2 N = 2360(25.0)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u003cp\u003eQ3 N = 2364(25.0)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c11\"\u003e\u003cp\u003eQ4 N = 2349(24.9)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c12\"\u003e\u003cp\u003eP-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c13\"\u003e\u003cp\u003eQ1 N = 2363(25.0)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c14\"\u003e\u003cp\u003eQ2 N = 2360(25.0)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c15\"\u003e\u003cp\u003eQ3 N = 2361(25.0)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c16\"\u003e\u003cp\u003eQ4 N = 2354(24.9)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c17\"\u003e\u003cp\u003eP-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c18\"\u003e\u003cp\u003eQ1 N = 2365(25.1)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c19\"\u003e\u003cp\u003eQ2 N = 2357(25.0)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c20\"\u003e\u003cp\u003eQ3 N = 2360(25.0)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c21\"\u003e\u003cp\u003eQ4 N = 2356(25.0)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c22\"\u003e\u003cp\u003eP-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"22\" nameend=\"c22\" namest=\"c1\"\u003e\u003cp\u003eDemographic Information\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge (Years), median [IQR]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e48.00 [41.00–54.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e39.00[45.00–51.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e47.00[41.00–53.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e48.00[42.00–54.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e51.00[44.00–56.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e46.00[40.00–53.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e47.00[41.00–54.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e48.00[42.00–54.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e49.00[43.00–55.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e46.00[40.00–53.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e47.00[41.00–54.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e48.00[42.00–54.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003e49.00[42.00–55.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c17\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c18\"\u003e\u003cp\u003e45.00[39.00–51.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c19\"\u003e\u003cp\u003e46.00[40.00–53.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c20\"\u003e\u003cp\u003e48.00[42.00–55.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c21\"\u003e\u003cp\u003e51.00[45.00–57.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c22\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGender, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c17\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c18\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c19\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c20\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c21\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c22\"\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\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3755 (39.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e905 (38.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e872 (36.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e985 (41.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e993 (42.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e780 (33.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e818 (34.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e984 (41.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e1173 (49.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e1120 (47.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e958 (40.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e947 (40.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003e730 (31.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c18\"\u003e\u003cp\u003e745 (31.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c19\"\u003e\u003cp\u003e881 (37.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c20\"\u003e\u003cp\u003e1076 (45.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c21\"\u003e\u003cp\u003e1053 (44.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c22\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5683 (60.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1459 (61.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1499 (63.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1379 (58.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1346 (57.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1585 (67.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1542 (65.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e1380 (58.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e1176 (50.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e1243 (52.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e1402 (59.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e1414 (59.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003e1624 (69.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c18\"\u003e\u003cp\u003e1620 (68.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c19\"\u003e\u003cp\u003e1476 (62.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c20\"\u003e\u003cp\u003e1284 (54.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c21\"\u003e\u003cp\u003e1303 (55.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c22\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEducational level, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.025\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.194\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c17\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c18\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c19\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c20\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c21\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c22\"\u003e\u003cp\u003e0.117\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIlliterate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1265 (13.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e305 (12.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e283 (11.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e320 (13.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e357 (15.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e328 (13.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e327 (13.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e298 (12.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e312 (13.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e336 (14.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e262 (11.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e321 (13.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003e346 (14.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c18\"\u003e\u003cp\u003e295 (12.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c19\"\u003e\u003cp\u003e312 (13.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c20\"\u003e\u003cp\u003e299 (12.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c21\"\u003e\u003cp\u003e359 (15.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c22\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eElementary school\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3880 (41.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e926 (39.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e989 (41.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e972 (41.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e993 (42.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e958 (40.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1012 (42.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e994 (42.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e916 (39.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e934 (39.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e920 (39.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e985 (41.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003e1041 (44.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c18\"\u003e\u003cp\u003e991 (41.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c19\"\u003e\u003cp\u003e952 (40.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c20\"\u003e\u003cp\u003e936 (39.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c21\"\u003e\u003cp\u003e1001 (42.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c22\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh school or equivalent\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3250 (34.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e853 (36.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e844 (35.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e815 (34.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e738 (31.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e810 (34.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e787 (33.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e819 (34.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e834 (355)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e786 (33.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e865 (36.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e814 (34.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003e785 (33.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c18\"\u003e\u003cp\u003e803 (34.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c19\"\u003e\u003cp\u003e833 (35.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c20\"\u003e\u003cp\u003e856 (36.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c21\"\u003e\u003cp\u003e758 (32.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c22\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSome college\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e365 (3.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e93 (3.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e88 (3.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e91 (3.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e93 (4.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e87 (3.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e78 (3.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e100 (4.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e100 (4.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e93 (3.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e113 (4.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e85 (3.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003e74 (3.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c18\"\u003e\u003cp\u003e90 (3.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c19\"\u003e\u003cp\u003e96 (4.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c20\"\u003e\u003cp\u003e89 (3.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c21\"\u003e\u003cp\u003e90 (3.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c22\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBachelor’s degree\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e560 (5.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e159 (6.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e140 (5.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e133 (5.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e128 (5.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e154 (6.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e135 (5.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e123 (5.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e148 (6.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e182 (7.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e163 (6.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e127 (5.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003e88 (3.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c18\"\u003e\u003cp\u003e159 (6.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c19\"\u003e\u003cp\u003e140 (5.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c20\"\u003e\u003cp\u003e144 (6.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c21\"\u003e\u003cp\u003e117 (5.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c22\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMaster’s degree\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e83 (0.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e17 (0.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e24 (1.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e23 (1.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e19 (0.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e18 (0.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e17 (0.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e22 (0.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e26 (1.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e23 (1.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e26 (1.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e20 (0.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003e14 (0.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c18\"\u003e\u003cp\u003e20 (0.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c19\"\u003e\u003cp\u003e18 (0.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c20\"\u003e\u003cp\u003e25 (1.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c21\"\u003e\u003cp\u003e20 (0.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c22\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDoctoral degree or above\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e22 (0.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8 (0.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2 (0.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e7 (0.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e5 (0.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e8 (0.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e2 (0.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e5 (0.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e7 (0.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e7 (0.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e6 (0.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e6 (0.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003e3 (0.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c18\"\u003e\u003cp\u003e5 (0.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c19\"\u003e\u003cp\u003e4 (0.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c20\"\u003e\u003cp\u003e6 (0.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c21\"\u003e\u003cp\u003e7 (0.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c22\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSeminary degree\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e13 (0.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3 (0.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1 (0.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3 (0.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e6 (0.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e2 (0.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e2 (0.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e3 (0.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e6 (0.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e2 (0.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e5 (0.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e3 (0.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003e3 (0.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c18\"\u003e\u003cp\u003e2 (0.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c19\"\u003e\u003cp\u003e2 (0.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c20\"\u003e\u003cp\u003e5 (0.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c21\"\u003e\u003cp\u003e4 (0.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c22\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eJob status, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c17\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c18\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c19\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c20\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c21\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c22\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEmployed\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3489 (37.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e158 (6.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e187 (7.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e278 (11.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e302 (12.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e160 (6.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e211 (8.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e249 (10.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e305 (13.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e209 (8.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e251 (10.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e245 (10.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003e220 (9.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c18\"\u003e\u003cp\u003e151 (6.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c19\"\u003e\u003cp\u003e178 (7.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c20\"\u003e\u003cp\u003e277 (11.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c21\"\u003e\u003cp\u003e319 (13.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c22\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUnemployed\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5024 (53.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e943 (39.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e867 (36.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e859 (36.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e820 (35.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e867 (36.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e817 (34.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e870 (36.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e935 (39.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e1073 (45.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e895 (37.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e865 (36.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003e656 (27.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c18\"\u003e\u003cp\u003e862 (36.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c19\"\u003e\u003cp\u003e880 (37.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c20\"\u003e\u003cp\u003e926 (39.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c21\"\u003e\u003cp\u003e821 (34.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c22\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRetired\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e925 (9.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1263 (53.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1317 (55.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1227 (51.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1217 (52.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1338 (56.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1332 (56.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e1245 (52.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e1109 (47.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e1081 (45.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e1214 (51.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e1251 (53.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003e1478 (62.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c18\"\u003e\u003cp\u003e1352 (57.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c19\"\u003e\u003cp\u003e1299 (55.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c20\"\u003e\u003cp\u003e1157 (49.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c21\"\u003e\u003cp\u003e1216 (51.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c22\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSmoking status, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c17\"\u003e\u003cp\u003e0.011\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c18\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c19\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c20\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c21\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c22\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNon\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6485 (68.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1681 (71.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1640 (69.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1600 (67.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1564 (66.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1752 (74.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1627 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colname=\"c20\"\u003e\u003cp\u003e1594 (67.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c21\"\u003e\u003cp\u003e1563 (66.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c22\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEx\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e927 (9.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e188 (8.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e213 (9.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e266 (11.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e260 (11.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e190 (8.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e216 (9.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e258 (10.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e263 (11.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e225 (9.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e219 (9.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e245 (10.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003e238 (10.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c18\"\u003e\u003cp\u003e191 (8.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" 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0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.114\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c17\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c18\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c19\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c20\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c21\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c22\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSingle\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e59 (0.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e22 (0.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e18 (0.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e7 (0.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e12 (0.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e16 (0.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e20 (0.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e13 (0.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e10 (0.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e22 (0.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e17 (0.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e11 (0.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003e9 (0.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c18\"\u003e\u003cp\u003e26 (1.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c19\"\u003e\u003cp\u003e8 (0.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c20\"\u003e\u003cp\u003e12 (0.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c21\"\u003e\u003cp\u003e13 (0.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c22\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMarried\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8792 (93.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2219 (93.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2223 (93.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2195 (92.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2155 (92.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e2210 (93.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e2192 (92.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e2185 (92.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e2205 (93.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e2218 (93.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e2205 (93.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e2194 (92.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003e2175 (92.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c18\"\u003e\u003cp\u003e2205 (93.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c19\"\u003e\u003cp\u003e2207 (93.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c20\"\u003e\u003cp\u003e2212 (93.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c21\"\u003e\u003cp\u003e2168 (92.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c22\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDivorced\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e130 (1.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e43 (1.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e36 (1.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e28 (1.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e23 (1.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e39 (1.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e30 (1.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e40 (1.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e21 (0.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e43 (1.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e28 (1.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e27 (1.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003e32 (1.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c18\"\u003e\u003cp\u003e40 (1.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c19\"\u003e\u003cp\u003e35 (1.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c20\"\u003e\u003cp\u003e32 (1.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c21\"\u003e\u003cp\u003e23 (1.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c22\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWidowed\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e457 (4.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e80 (3.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e94 (4.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e134 (5.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e149 (6.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e100 (4.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e118 (5.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e126 (5.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e113 (4.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e80 (3.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e110 (4.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e129 (5.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003e138 (5.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c18\"\u003e\u003cp\u003e94 (4.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c19\"\u003e\u003cp\u003e107 (4.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c20\"\u003e\u003cp\u003e104 (4.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c21\"\u003e\u003cp\u003e152 (6.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c22\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHeight (m), median [IQR]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.59 [1.54–1.67]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.60 [1.54–1.66]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.59 [1.53–1.67]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.6 [1.54–1.67]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.6 [1.54–1.67]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.373\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.59 [1.53–1.65]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.59 [1.53–1.66]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e1.6 [1.54–1.68]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e1.62 [1.54–1.69]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e1.61 [1.55–1.69]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e1.6 [1.54–1.67]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e1.59 [1.54–1.67]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003e1.57 [1.52–1.65]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c17\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c18\"\u003e\u003cp\u003e1.59 [1.53–1.65]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c19\"\u003e\u003cp\u003e1.59 [1.54–1.67]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c20\"\u003e\u003cp\u003e1.61 [1.54–1.68]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c21\"\u003e\u003cp\u003e1.6 [1.54–1.68]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c22\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWeight (kg), median [IQR]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e71.00 [63.00-79.6]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e65.5[57.75–73.3]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e70.8[63.0-78.7]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e73.5[65.3–82.0]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e74.3[66.7–82.5]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e65.7[58.1–73.8]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e69.9[62.4–78.8]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e72.8[65.0-80.7]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e75.3[67.8–83.2]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e59.2[53.8–64.6]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e68.3[63.3–74.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e74.3[69-80.1]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003e83[75.9–91.1]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c17\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c18\"\u003e\u003cp\u003e66.00[58.2–74.6]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c19\"\u003e\u003cp\u003e70.00[62.3–78.6]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c20\"\u003e\u003cp\u003e73.00[65.9-80.95]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c21\"\u003e\u003cp\u003e74.3.00[66.5–82.5]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c22\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBMI (kg/m\u003csup\u003e2\u003c/sup\u003e), median [IQR]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e27.62 [24.69–30.77]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e25.49[22.51–28.73]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e27.54[24.68–30.67]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e28.34[25.55–31.49]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e28.76[26.15–31.78]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e25.80 [22.80–29.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e27.6 [24 .60-30.80]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e28.1 [25.20–31.2]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e28.5 [26.1–31.5]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e22.82[21.05–24.28]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e26.57[25.22–27.91]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e29.05[27.54–30.58]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003e32.87[30.82–35.35]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c17\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c18\"\u003e\u003cp\u003e26.07[22.91–29.42]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c19\"\u003e\u003cp\u003e27.33[24.40-30.56]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c20\"\u003e\u003cp\u003e28.11[25.45–31.20]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c21\"\u003e\u003cp\u003e28.66[25.97–31.54]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c22\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWaist circumference (cm), median [IQR]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e95.00 [87.2–103]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e90.00 [82.00–97.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e94.50 [87.00-102.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e97.00 [90.00-105.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e99.00 [92.00-106.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e90.0 [83.0–98.0]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e95.0\u003c/p\u003e\u003cp\u003e[87.0-103.0]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e96.0 [89.0-104.0]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e98.0\u003c/p\u003e\u003cp\u003e[98.0105.5]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e85.00 [78.50–91.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e93.00 [88.00-98.50]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e98.00 [92.00-103.50]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003e105.50 [99.00-112.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c17\"\u003e\u003cp\u003e\u0026lt; 0.001.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c18\"\u003e\u003cp\u003e91.00 [83.00–99.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c19\"\u003e\u003cp\u003e94.00 [86.50–102.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c20\"\u003e\u003cp\u003e96.00 [89.00-104.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c21\"\u003e\u003cp\u003e98.50 [92.00-106.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c22\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHip circumference (cm), median [IQR]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e103 [98–109]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e100.00[95.00-106.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e103.00[98.00-109.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e104.00[99.00-110.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e104.00[99.00-110.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e101.00[96.00-107.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e103.00[98.00-109.40]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e103.50[98.35-\u003c/p\u003e\u003cp\u003e110.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e104.00[99.00-109.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e96.00[92.00-100.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e102.00[98.00-105.10]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e105.00[101.00-109.50]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003e111.00[106.00-118.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c17\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c18\"\u003e\u003cp\u003e101.00[96.00-107.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c19\"\u003e\u003cp\u003e103.00[97.50–109.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c20\"\u003e\u003cp\u003e104.00[99.00-109.50]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c21\"\u003e\u003cp\u003e104.00[99.00-110.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c22\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"22\" nameend=\"c22\" namest=\"c1\"\u003e\u003cp\u003eBlood pressure\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSystolic (mmHg), median [IQR]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e120 [110–130]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e114.00[105.00-123.30]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e120.00110.00-129.30]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e120.00110.00-131.30]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e123.30113.30-138.60]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e116.60[106.60-\u003c/p\u003e\u003cp\u003e126.6]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1200[110.00-130.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e120.00[110.00-131.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e120.60[110.60–135.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e113.33[104.00-123.33]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e120.00[110.00-130.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e120.00[110.00-131.33]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003e124.00[113.33-139.33]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c17\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c18\"\u003e\u003cp\u003e116.66[106.66-126.66]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c19\"\u003e\u003cp\u003e118.66[108.33-128.66]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c20\"\u003e\u003cp\u003e120.00[110.00-130.66]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c21\"\u003e\u003cp\u003e123.33[113.33-138.33]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c22\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDiastolic (mmHg), median [IQR]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e80.00 [70.00–86.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e77.33[70.00–80.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e80.00[70.00–85.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e80.00[71.33–86.66]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e80.00[75.00–90.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e79.3.00[70.00-81.3.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e80.00[70.00–84.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e80.00[70.60–86.6.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e80.00[73.30–89.3.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e76.66[70.00–80.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e80.00[70.00-83.33]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e80.00[70.66–87.33]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003e80.00[76.66-90.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c17\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c18\"\u003e\u003cp\u003e79.33[70.00-81.33]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c19\"\u003e\u003cp\u003e80.00[70.00-83.33]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c20\"\u003e\u003cp\u003e80.00[70.66–86.66]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c21\"\u003e\u003cp\u003e80.00[74.66-90.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c22\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"22\" nameend=\"c22\" namest=\"c1\"\u003e\u003cp\u003eLaboratory data\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal cholesterol (mg/dl), median [IQR]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e188.00 [165.00-214.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e168.00[150.00-189.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e185.00[164.00-208.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e196.00[173.00-220.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e208.00[181.00-236.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e177.00[155.00-201.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e186.00[164.00-212.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e193.00[168.00-219.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e198.00[173.00-227.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e179.00[157.00-205.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e189.00[166.00-216.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e192.00[169.00-219.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003e191.00[167.00\u003c/p\u003e\u003cp\u003e− 219.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c17\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c18\"\u003e\u003cp\u003e166.00[147.00-186.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c19\"\u003e\u003cp\u003e183.00[164.00-204.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c20\"\u003e\u003cp\u003e197.00[175.00-221.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c21\"\u003e\u003cp\u003e212.00[186.00-240.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c22\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTriglyceride (mg/dl), median [IQR]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e120.00 [84.00-172.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e68.00 [57.00–79.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e105.00\u003c/p\u003e\u003cp\u003e [92.00-117.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e147.00 [128.00-168.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e224.00 [178.50–285.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e68.00 [57.00–80.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e102.00 [90.00-117.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e141.00 [122.00-160.50]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e224.00 [183.00-285.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e83.00 [64.00-111.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e111.00 [82.00-149.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e136.00 [100.00-184.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003e168.00 [122.00-236.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c17\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c18\"\u003e\u003cp\u003e81.00[63.00-109.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c19\"\u003e\u003cp\u003e111.00[83.00-144.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c20\"\u003e\u003cp\u003e136.00[101.00-184.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c21\"\u003e\u003cp\u003e172.00[126.00-242.50]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c22\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFasting plasma glucose (mg/dl), median [IQR]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e82.00 [74.00–93.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e76.00[69.00–82.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e80.00[73.00–87.0.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e84.00[77.00-92.5.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e99.00[85.00-144.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e80.00[73.00–88.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e82.00[74.00–92.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e83.00[74.00–94.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e86.00[76.00-102.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e78.00[71.00–85.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e81.00[74.00–90.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e84.00[76.00–96.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003e88.5.00[79.00-110.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c17\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c18\"\u003e\u003cp\u003e74.00[68.00–81.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c19\"\u003e\u003cp\u003e79.00[73.00–85.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c20\"\u003e\u003cp\u003e84.00[77.00–91.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c21\"\u003e\u003cp\u003e106.00[90.00-151.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c22\"\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 Density Lipoprotein (mg/dl), median [IQR]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e114.60 [93.70-137.40]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e106.40[89.10-124.70]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e117.00[97.40–138.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e119 .76[97.8–143.0]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e117.10[91.38–143.20]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e109.80[91.10-131.40]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e118.00[97.40-140.25]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e118.50[98.30-142.25]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e111.60[87.90-136.40]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e109.81[91.23-133.35]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e118.40[97.30-139.71]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e117.40[95.80–141.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003e112.60[90.80-135.50]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c17\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c18\"\u003e\u003cp\u003e95.81[80.00-111.90]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c19\"\u003e\u003cp\u003e112.70[95.90–131.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c20\"\u003e\u003cp\u003e124.60[104.85–145.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c21\"\u003e\u003cp\u003e131.78[106.80-157.05]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c22\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh Density Lipoprotein (mg/dl), median [IQR]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e41.80 [36.00-48.40]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e44.90[38.70–52.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e42.20[37.00–49.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e40.60[35.10–47.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e39.00[33.70–45.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e49.40[43.80–56.90]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e43.20[38.60–49.10]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e40.00[35.10–45.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e35.00[31.00–40.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e46.90[41.00-54.40]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e43.00[37.20–49.40]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e40.10[35.0–46.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003e37.30[32.60–43.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c17\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c18\"\u003e\u003cp\u003e48.00[42.00–56.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c19\"\u003e\u003cp\u003e42.80[37.20–49.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c20\"\u003e\u003cp\u003e39.15[34.70–44.80]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c21\"\u003e\u003cp\u003e37.40[32.60–43.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c22\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUric acid (mg/dl), median [IQR]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.50 [3.70–5.50]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.00[3.00-4.80]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.30[3.60–5.30]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.80[4.00-5.70]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e5.00[4.10–6.05]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e4.00[3.30–4.80]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e4.30[3.60–5.20]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e4.70[3.80–5.60]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e5.10[4.20–6.20]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e4.10[3.40-5.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e4.40[3.60–5.40]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e4.70[3.80–5.70]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003e4.90[4.00-5.90]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c17\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c18\"\u003e\u003cp\u003e4.00[3.30–4.80]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c19\"\u003e\u003cp\u003e4.40[3.70–5.40]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c20\"\u003e\u003cp\u003e4.80[4.00-5.80]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c21\"\u003e\u003cp\u003e4.90[3.95–5.90]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c22\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGlomerular filtration Rate (mL/min), median [IQR]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e83.72 [70.02–97.01]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e82.34[71.44–95.2]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e82.75[69.7–95.4]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e84.08[70.02–94.9]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e85.07[69.00-101.5]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.502\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e81.62[70.07–94.68]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e82.75[70.02–95.48]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e84.51[70.29–98.77]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e84.95[69.75-101.29]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.170\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e84.51[72.06–98.27]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e82.75[69.75–94.95]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e83.72[70.45–96.02]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003e82.75[68.66–99.99]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c17\"\u003e\u003cp\u003e0.045\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c18\"\u003e\u003cp\u003e82.75[71.25–95.48]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c19\"\u003e\u003cp\u003e82.75[71.25–95.22]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c20\"\u003e\u003cp\u003e83.98[69.70-95.48]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c21\"\u003e\u003cp\u003e84.24[68.82-100.73]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c22\"\u003e\u003cp\u003e0.238\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHighly sensitive C-Reactive Protein (mg/dl), median [IQR]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10.00 [5.00–18.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10.00[5.00–17.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e10.00[5.00–18.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e10.00[5.001.00–7.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e11.00[6.00–18.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.038\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e10.00[5.00–17.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e11.00[5.00–18.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e10.5.00[5.00–18.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e11.00[6.00–18.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.407\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e10.00[5.00–16.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e10.00[5.00–17.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e10.00[5.00–17.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003e12.00[6.00–19.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c17\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c18\"\u003e\u003cp\u003e10.00[5.00–17.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c19\"\u003e\u003cp\u003e10.00[5.00–17.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c20\"\u003e\u003cp\u003e10.00[5.00–18.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c21\"\u003e\u003cp\u003e11.00[5.5.00–18.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c22\"\u003e\u003cp\u003e0.081\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWhite Blood Cell (*10\u003csup\u003e9\u003c/sup\u003e/L), median [IQR]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5.90 [5.00-6.90]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.70[5.50–6.50]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5.00[5.80–6.90]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5.10[5.90-7.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e6.20[5.40–7.30]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e5.50[4.70–6.40]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e5.80[5.00-6.80]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e6.00[5.10–7.10]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e6.20[5.30–7.30]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e5.60[4.70–6.50]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e5.70[4.90–6.80]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e6.00[5.10-7.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003e6.20[5.40–7.30]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c17\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c18\"\u003e\u003cp\u003e5.70[4.80–6.60]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c19\"\u003e\u003cp\u003e5.80[5.00-6.80]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c20\"\u003e\u003cp\u003e5.90[5.10-7.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c21\"\u003e\u003cp\u003e6.20[5.3–7.20]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c22\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHemoglobin (g/dl), median [IQR]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e13.7 [12.8–14.7]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e13.40[12.50–14.50]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e13.70[12.80–14.70]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e13.80[12.90–14.90]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e14.00[13.10–15.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e13.40[12.50–14.40]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e13.60[12.70–14.50]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e13.80[12.90-14.85]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e14.10[13.20–15.10]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e13.70[12.80–14.70]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e13.80[12.90–14.80]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e13.80[12.80–14.80]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003e13.70[12.80–14.70]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c17\"\u003e\u003cp\u003e0.111\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c18\"\u003e\u003cp\u003e13.40[12.50–14.40]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c19\"\u003e\u003cp\u003e13.60[12.70–14.60]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c20\"\u003e\u003cp\u003e13.90[12.90–14.00.95]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c21\"\u003e\u003cp\u003e14.00[13.10–15.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c22\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHematocrit (%), median [IQR]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e41.20 [38.7–43.8]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e40.30[37.9–43.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e41.00[41.00-43.60]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e41.50[38.90–44.10]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e41.80[39.40–44.30]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e40.30[38.00-42.90]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e40.80[38.40–43.40]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e41.50[39.00-44.10]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e42.20[39.50–44.60]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e41.00[38.50–43.80]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e41.20[38.80–43.80]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e41.30[38.80-43.09]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003e41.10[38.70–43.80]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c17\"\u003e\u003cp\u003e0.158\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c18\"\u003e\u003cp\u003e40.30[38.00-42.90]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c19\"\u003e\u003cp\u003e40.90[38.50–43.60]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c20\"\u003e\u003cp\u003e41.60[39.00-44.10]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c21\"\u003e\u003cp\u003e41.90[39.40–44.40]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c22\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePlatelet (*109/L), median [IQR]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e225.00 [190.00-265.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e219.00\u003c/p\u003e\u003cp\u003e[186.00-258.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e226.00[189.00-265.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e226.00[191.00265.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e229.00[193.00-270.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e221.00[188.00-260.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e228.00[190.00-267.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e227.00[192.00-266.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e224.00[190.00-266.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e217.00[182.00-254.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e226.00[189.00-263.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e228.00[193.00-269.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003e230.00[195.00–272.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c17\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c18\"\u003e\u003cp\u003e223.00[188.00-264.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c19\"\u003e\u003cp\u003e226.00[191.00-264.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c20\"\u003e\u003cp\u003e226.00[190.00-265.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c21\"\u003e\u003cp\u003e226.00[190.00-266.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c22\"\u003e\u003cp\u003e0.268\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTyG, median [IQR]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e9.22 [8.82–9.65]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8.56 [8.36–8.70]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9.03 [8.93–9.13]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e9.42[9.32–9.53]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e10.03 [9.82–10.38]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e8.60 [8.37–8.81]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e9.04 [8.84–9.25]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e9.37 [9.18–9.58]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e9.90 [9.63–10.29]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e8.77 [8.50–9.09]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e9.12 [8.79–9.47]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e9.36 [9.03–9.74]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003e9.65 [9.2710.13]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c17\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c18\"\u003e\u003cp\u003e8.70 [8.42–8.98]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c19\"\u003e\u003cp\u003e9.06 [8.80–9.33]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c20\"\u003e\u003cp\u003e9.36 [9.06–9.64]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c21\"\u003e\u003cp\u003e9.90 [9.54–10.33]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c22\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTg/HDL, median [IQR]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.87 [1.89–4.42]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.49[1.18–1.89]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.45[1.98-3.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.58[2.87–4.43]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e5.78[4.26–7.96]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.42[1.16–1.66]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e2.36[2.12–2.60]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e3.5[3.15–3.90]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e6.05[5.10–8.06]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e1.77[1.29–2.5]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e2.57[1.81–3.60]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e3.34[2.37–4.90]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003e4.54[3.06–6.76]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c17\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c18\"\u003e\u003cp\u003e1.68[1.25–2.35]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c19\"\u003e\u003cp\u003e2.57[1.87–3.47]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c20\"\u003e\u003cp\u003e3.43[2.47–4.81]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c21\"\u003e\u003cp\u003e4.68[3.14–6.91]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c22\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMETS-IR, median [IQR]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e37.03 [42.74–48.57]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e36.12 [31.72–41.21]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e41.15 [36.60-46.03]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e44.49 [39.53–49.36]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e48.65 [43.89–54.16]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e36.03 [31.72–40.79]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e36.64 [41.30-46.05]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e44.09 [39.35–49.08]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e48.86 [44.32–54.35]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e33.18 [30.18–35.25]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e39.89 [38.54–41.32]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e45.45 [44.08–46.94]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003e53.21 [50.63–57.05]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c17\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c18\"\u003e\u003cp\u003e36.66 [31.99–41.95]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c19\"\u003e\u003cp\u003e41.00 [36.22–46.46]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c20\"\u003e\u003cp\u003e44.38 [39.41–49.29]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c21\"\u003e\u003cp\u003e48.12 [43.35–53.90]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c22\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCHG, median [IQR]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5.02 [5.23–5.48]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.94 [4.78–5.11]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5.01\u003c/p\u003e\u003cp\u003e[5.15–5.30]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5.31 [5.15–5.47]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e5.62 [5.41–5.94]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e4.95 [4.78–5.13]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e5.16 [5.00-5.35]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e5.30 [5.14–5.49]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e5.50 [5.32–5.73]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e5.00 [4.81–5.18]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e5.18 [5.00-5.38]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e5.32 [5.12–5.53]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003e5.45 [5.23–5.73]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c17\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c18\"\u003e\u003cp\u003e4.86 [4.74–4.95]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c19\"\u003e\u003cp\u003e5.12 [5.07–5.18]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c20\"\u003e\u003cp\u003e5.34 [5.28–5.40]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c21\"\u003e\u003cp\u003e5.70 [5.57–5.97]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c22\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"22\" nameend=\"c22\" namest=\"c1\"\u003e\u003cp\u003ePast history\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDiabetes mellitus, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c17\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c18\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c19\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c20\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c21\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c22\"\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\u003e1330 (14.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e59\u003c/p\u003e\u003cp\u003e(2.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e134 (5.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e226 (9.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e911 (38.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e181 (7.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e298 (12.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e345 (14.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e506 (21.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e114 (4.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e257 (10.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e369 (15.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003e590 (25.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c18\"\u003e\u003cp\u003e65\u003c/p\u003e\u003cp\u003e(2.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c19\"\u003e\u003cp\u003e97\u003c/p\u003e\u003cp\u003e(4.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c20\"\u003e\u003cp\u003e166 (7.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c21\"\u003e\u003cp\u003e1002 (42.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c22\"\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\u003e8108 (85.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2305 (97.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2237 (94.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2138 (90.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1428 (61.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e2184 (92.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e2062 (87.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e2019 (85.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e1843 (78.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e2249 (95.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e2103 (89.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e1992 (84.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003e1764 (74.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c18\"\u003e\u003cp\u003e2300 (97.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c19\"\u003e\u003cp\u003e2260 (95.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c20\"\u003e\u003cp\u003e2194 (93.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c21\"\u003e\u003cp\u003e1354 (57.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c22\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDyslipidemia, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c17\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c18\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c19\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c20\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c21\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c22\"\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\u003e7870 (83.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1555 (65.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1879 (79.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2138 (90.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2298 (98.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1327 (56.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1914 (81.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e2280 (96.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e2349 (100.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e1483 (37.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e1969 (16.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e2144 (9.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003e2274 (3.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c18\"\u003e\u003cp\u003e1312 (55.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c19\"\u003e\u003cp\u003e1993 (84.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c20\"\u003e\u003cp\u003e2261 (95.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c21\"\u003e\u003cp\u003e2304 (97.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c22\"\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\u003e1568 (16.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e809 (34.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e492 (20.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e226 (9.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e41\u003c/p\u003e\u003cp\u003e(1.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1038 (43.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e446 (18.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e84\u003c/p\u003e\u003cp\u003e(3.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0\u003c/p\u003e\u003cp\u003e(0.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e880 (62.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e391 (83.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e217 (90.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003e80 (96.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c18\"\u003e\u003cp\u003e1053 (44.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c19\"\u003e\u003cp\u003e364 (15.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c20\"\u003e\u003cp\u003e99\u003c/p\u003e\u003cp\u003e(4.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c21\"\u003e\u003cp\u003e52\u003c/p\u003e\u003cp\u003e(2.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c22\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePositive family history of CVD, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.045\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.041\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c17\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c18\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c19\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c20\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c21\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c22\"\u003e\u003cp\u003e0.082\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\u003e3301 (35.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e770 (32.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e851 (35.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e847 (35.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e833 (35.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e770 (32.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e852 (36.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e842 (35.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e837 (35.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e721 (30.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e832 (35.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e846 (35.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" 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(64.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1517 (64.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1506 (64.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1595 (67.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1508 (63.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e1522 (64.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e1512 (64.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e1642 (69.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e1528 (64.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" 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colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e525 (22.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e706 (29.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e800 (33.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e912 (38.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e415 (17.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e630 (26.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e814 (34.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003e1084 (46.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c18\"\u003e\u003cp\u003e510 (21.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c19\"\u003e\u003cp\u003e641 (27.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c20\"\u003e\u003cp\u003e780 (33.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c21\"\u003e\u003cp\u003e1012 (43.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c22\"\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\u003e6495 (68.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1939 (82.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1700 (71.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1560 (66.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1296 (55.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1840 (77.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1654 (70.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e1564 (66.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e1437 (61.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e1948 (82.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e1730 (73.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e1547 (65.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003e1270 (54.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c18\"\u003e\u003cp\u003e1855 (78.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c19\"\u003e\u003cp\u003e1716 (72.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c20\"\u003e\u003cp\u003e1580 (66.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c21\"\u003e\u003cp\u003e1344 (57.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c22\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"22\" nameend=\"c22\" namest=\"c1\"\u003e\u003cp\u003ePsychological measures\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAnxiety score, median [IQR]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8.00 [3.00–15.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7.00[3.00–14.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8.00[3.00–15.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e8.00[3.00–16.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e8.00[3.00–16.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.031\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e8.00[3.00–15.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e8.00[3.00–16.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e8.00[3.00–16.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e8.00[3.00–15.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.556\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e7.00[3.00–14.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e8.00[3.00–14.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e8.00[3.00–16.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003e9.00[4.00–18.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c17\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c18\"\u003e\u003cp\u003e8.00[3.00–15.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c19\"\u003e\u003cp\u003e8.00[3.00–16.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c20\"\u003e\u003cp\u003e8.00[3.00–15.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c21\"\u003e\u003cp\u003e8.00[3.00–16.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c22\"\u003e\u003cp\u003e0.506\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDepression score, median [IQR]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10.00 [5.00–18.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10.00[5.00–17.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e10.00[5.00–18.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e10.00[5.00–17.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e11.00[6.00–18.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.038\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e10.00[5.00–17.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e11.00[5.00–18.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e10.5.00[5.00–18.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e11.00[6.00–18.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.407\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e10.00[5.00–16.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e10.00[5.00–17.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e10.00[5.00–17.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003e12.00[6.00–19.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c17\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c18\"\u003e\u003cp\u003e10.00[5.00–17.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c19\"\u003e\u003cp\u003e10.00[5.00–17.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c20\"\u003e\u003cp\u003e10.00[5.00–18.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c21\"\u003e\u003cp\u003e10.00[5.5.00–18.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c22\"\u003e\u003cp\u003e0.081\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"22\"\u003eAbbreviation: BMI; body mass index, Tyg; triglyceride glucose index, Tg/HDL; triglyceride to high density lipoprotein ratio, METS-IR; Metabolic Score for Insulin Resistance, CHG; Cholesterol, High-Density Lipoprotein, and Glucose index.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003ch3\u003eSystolic and Diastolic Blood Pressure Across Quartiles of the indices\u003c/h3\u003e\u003cp\u003eViolin plot visualizations (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) and Kruskal-Wallis tests confirmed statistically significant differences in both systolic and diastolic blood pressures across all quartiles of the surrogate indices (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001 for all comparisons). Dunn’s post-hoc test with Holm-adjusted p-values revealed consistent stepwise increases in blood pressure from Q1 to Q4 (Supplementary table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003ch3\u003eCorrelation Analysis\u003c/h3\u003e\u003cp\u003eSpearman’s correlation coefficients showed that all four IR indices had significantly positive correlation with both systolic and diastolic blood pressures (Supplementary table 2). We illustrated the correlation coefficients of each variable with others using a heat map in a correlation matrix (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003ch3\u003eAssociation between surrogate IR indices and hypertension\u003c/h3\u003e\u003cp\u003eThe results of the logistic regression analyses are presented in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. In unadjusted models, all four indices demonstrated a robust positive correlation with hypertension, evident both when assessed per 1 SD and per 1 unit increase, as well as across quartiles (all P for trend \u0026lt; 0.001). Following a systematic adjustment for potential confounding variables, the risk of hypertension was reduced yet continued to show significance in the majority of comparisons. Furthermore, the performance metrics of the models demonstrated a continuous enhancement from the unadjusted to the fully adjusted models across all four indices. Throughout the indices, there was a noticeable decrease in Brier scores following adjustments, which signifies an enhancement in overall prediction accuracy. The calibration showed notable improvement in fully adjusted models, as evidenced by non-significant calibration χ² P-values for several indices, indicating enhanced alignment between predicted and observed outcomes (Supplementary table 3). In the fully adjusted model (Model 3), each 1 SD increase in TyG, Tg/HDL-c, METS-IR, and CHG was linked to a 23.4%, 8.1%, 33.0%, and 9.9% increased risk of hypertension, respectively. The TyG index explained the greatest proportion of variance in hypertension risk in the fully adjusted model, as indicated by the highest Nagelkerke R² of 0.218, in comparison to other indices (Supplementary table 3). Furthermore, all VIF values were below 5 across models, indicating no evidence of problematic multicollinearity (Supplementary table 4).\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\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\u003eUnivariate and multivariate logistic regression analysis of the association between surrogate IR indices and hypertension\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"10\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"2\" morerows=\"1\" nameend=\"c2\" namest=\"c1\" rowspan=\"2\"\u003e\u003cp\u003eIndexes\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003eUnadjusted model\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003eAdjusted model 1\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003eAdjusted model 2\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u003cp\u003eAdjusted model 3\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eOR [95%CI]\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eP-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eOR [95%CI]\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eP-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eOR [95%CI]\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eP-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eOR [95%CI]\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u003cp\u003eP-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"6\" rowspan=\"7\"\u003e\u003cp\u003eTyg\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePer 1 SD increase\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.595 [1.525–1.669]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.458 [1.391–1.529]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.386 [1.310–1.467]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.234 [1.163–1.310]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePer 1 unit increase\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.027 [1.893–2.170]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.770 [1.648–1.901]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.639 [1.505–1.785]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.375 [1.256–1.505]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eQuartile 1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eQuartile 2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.801 [1.569–2.067]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.653 [1.432–1.908]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.646 [1.423–1.904]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.381 [1.189–1.603]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eQuartile 3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.351 [2.054–2.692]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.055 [1.786–2.365]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2.013 [1.739–2.330]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.517 [1.302–1.767]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eQuartile 4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.672 [3.215–4.194]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.900 [2.525–3.332]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2.514 [2.149–2.941]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.798 [1.523–2.122]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eP for trend\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"6\" rowspan=\"7\"\u003e\u003cp\u003eTg/HDL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePer 1 SD increase\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.239 [1.187–1.294]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.221 [1.167–1.278]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.177 [1.121–1.236]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.081 [1.028–1.137]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePer 1 unit increase\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.078 [1.062–1.094]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.072 [1.055–1.089]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.058 [1.040–1.077]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.028 [1.010–1.046]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eQuartile 1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eQuartile 2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.496 [1.312–1.705]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.418 [1.237–1.627]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.404 [1.216–1.621]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.198 [1.034–1.389]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.016\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eQuartile 3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.793 [1.576–2.040]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.671 [1.459–1.913]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.668 [1.435–1.939]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.312 [1.122–1.534]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eQuartile 4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.224 [1.958–2.527]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.041 [1.783–2.335]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.962 [1.682–2.289]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.405 [1.195–1.653]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eP for trend\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"6\" rowspan=\"7\"\u003e\u003cp\u003eMETS-IR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePer 1 SD increase\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.634 [1.561–1.711]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.617 [1.540–1.697]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.587 [1.506–1.673]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.330 [1.220–1.451]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePer 1 unit increase\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.057 [1.051–1.062]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.056 [1.050–1.061]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.053 [1.047–1.060]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.032 [1.022–1.042]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eQuartile 1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eQuartile 2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.709 [1.486–1.966]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.645 [1.422–1.904]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.659 [1.428–1.928]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.301 [1.076–1.573]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.003\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eQuartile 3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.470 [2.156–2.829]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.315 [2.009–2.669]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2.291 [1.973–2.660]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.584 [1.272–1.974]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eQuartile 4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.007 [3.506–4.578]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.804 [3.305–4.379]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e3.653 [3.137–4.253]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e2.246 [1.742–2.901]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eP for trend\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"6\" rowspan=\"7\"\u003e\u003cp\u003eCHG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePer 1 SD increase\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.476 [1.412–1.542]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.323 [1.263–1.386]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.197 [1.124–1.274]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.099 [1.030–1.172]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.004\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePer 1 unit increase\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.558 [2.300-2.846]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.966 [1.758–2.198]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.543 [1.327–1.795]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.255 [1.073–1.467]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.004\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eQuartile 1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eQuartile 2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.359 [1.189–1.553]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.288 [1.121–1.480]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.285 [1.109–1.488]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.131 [0.972–1.315]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.111\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eQuartile 3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.796 [1.576–2.045]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.572 [1.371–1.802]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.560 [1.340–1.816]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.241 [1.060–1.452]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.007\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eQuartile 4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.739 [2.411–3.111]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.088 [1.825–2.389]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.681 [1.426–1.982]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.260 [1.062–1.496]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.008\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eP for trend\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.031\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"10\" nameend=\"c10\" namest=\"c1\"\u003e\u003cp\u003eModel 1: adjusted for gender and age\u003c/p\u003e\u003cp\u003eModel 2: adjusted for covariates in model 1 and educational level, job status, smoking status, diabetes mellitus, CVD history, Dyslipidemia, CVD family history, and marriage status\u003c/p\u003e\u003cp\u003eModel 3: adjusted for covariates in model 2 and BMI, anxiety score, GFR, Hematocrit, and uric acid level\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"10\"\u003eAbbreviation: SD; standard deviation, Tyg; triglyceride glucose index, Tg/HDL; triglyceride to high density lipoprotein ratio, METS-IR; Metabolic Score for Insulin Resistance, CHG; Cholesterol, High-Density Lipoprotein, and Glucose index.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003ch3\u003eDiagnostic Accuracy via ROC Analysis\u003c/h3\u003e\u003cp\u003eAnalysis of the ROC curve indicated that each of the four metabolic indices demonstrated a significant capacity to predict hypertension (all \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001). Among the indices, METS-IR had the highest discriminative performance, with an AUC of 0.642 (95%CI: 0.630–0.654). TyG had an AUC of 0.633 (95% CI: 0.621–0.645), CHG at an AUC of 0.611 (95%CI: 0.599–0.623), and TG/HDL-c, had an AUC of 0.587 (95% CI: 0.575–0.599). DeLong’s test revealed that Tg/HDL-c and CHG exhibited significantly lower performance compared to TyG as reference (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001), whereas METS-IR showed no significant difference from TyG (\u003cem\u003ep\u003c/em\u003e = 0.131). The optimal cutoff values identified were 8.30 for TyG, 1.23 for TG/HDL-c, 42.85 for METS-IR, and 4.61 for CHG. The sensitivities varied between 57.9% and 66.0%, while the specificities ranged from 47.0–61.2% (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Figure\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e presents the ROC-AUC curves.\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eReceiver operative characteristic curve analysis for differentiation of hypertensive and non-hypertensive participants by IR surrogate indices.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"8\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003ePredictor\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"5\" nameend=\"c6\" namest=\"c2\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003eDelong test\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSensitivity (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSpecificity (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCutoff value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eAUC [95%CI]\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eP-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eZ-statistic\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eP-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTyG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.579\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.612\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.633 [0.621–0.645]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTg/HDL-C\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.660\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.470\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.587 [0.575–0.599]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e12.804\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMETS-IR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.649\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.564\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e42.85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.642 [0.630–0.654]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-1.5097\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.1311\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCHG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.579\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.583\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.611 [0.599–0.623]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e4.746\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"8\"\u003eAbbreviation: AUC; area under curve, Tyg; triglyceride glucose index, Tg/HDL; triglyceride to high density lipoprotein ratio, METS-IR; Metabolic Score for Insulin Resistance, CHG; Cholesterol, High-Density Lipoprotein, and Glucose index.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003ch2\u003eIncremental classification values of IR surrogate indices for hypertension\u003c/h2\u003e\u003cp\u003eWe conducted additional analysis to determine if incorporating IR surrogate indices into the conventional model (Model 3) could improve the differentiation between participants with and without hypertension. The data presented in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e indicates that the inclusion of IR surrogate indices led to a notable enhancement in the IDI and NRI for the detection of hypertension (all P values \u0026lt; 0.001). Among these indicators, the TyG index demonstrated the most significant incremental classification values (NRI: 13.58, 95%CI: [8.65–18.35]; IDI: 0.956, 95%CI: [0.834–1.079]).\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eIncremental classification value of IR surrogate indices for hypertension.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003eContinuous NRI, %\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u003cp\u003eIDI, %\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEstimate\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e95%CI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eP-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eEstimate\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e95%CI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eP-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eConventional model\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eConventional model + TyG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e13.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e[8.65–18.35]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.439\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e[0.212–0.732]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eConventional model + Tg/HDL-c\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e[1.37–11.66]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.083\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e[0.007–0.242]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.004\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eConventional model + METS-IR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e11.84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e[6.66–16.91]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.398\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e[0.177–0.692]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eConventional model + CHG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e[2.40-12.08]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.004\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.077\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e[0.007–0.227]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003eNote: Conventional model was adjusted for gender, age, educational level, job status, smoking status, diabetes mellitus, CVD history, Dyslipidemia, CVD family history, marriage status, BMI, anxiety score, GFR, Hematocrit, and uric acid level.\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003eAbbreviation: SD; standard deviation, Tyg; triglyceride glucose index, Tg/HDL; triglyceride to high density lipoprotein ratio, METS-IR; Metabolic Score for Insulin Resistance, CHG; Cholesterol, High-Density Lipoprotein, and Glucose index\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003ch3\u003eNonlinear relationship between surrogate IR indices and hypertension\u003c/h3\u003e\u003cp\u003eWe conducted RCS analysis to effectively model and visualize the relationship between surrogate indices and hypertension. In addition to the significant overall relationship among all four surrogate indices and hypertension in our fully adjusted model, we identified a linear relationship between METS-IR and hypertension, as well as a non-linear relationship between TyG, Tg/HDL-c, and CHG. Furthermore, a threshold effect was identified in the non-linear relationship between TyG, Tg/HDL-c, and CHG with hypertension through threshold effect analysis. Across aforementioned indices, the association with hypertension is consistently stronger at lower values and then attenuates or plateaus beyond the identified cutpoints. In the lowest segments, the per-unit effects are clearly larger, while above these thresholds the slopes move toward 1.0 and often lose statistical significance (Supplementary table 5).\u003c/p\u003e\u003ch3\u003eSubgroup and Sensitivity Analyses\u003c/h3\u003e\u003cp\u003eFollowing the adjustment for potential covariables, we conducted a subgroup analysis to confirm the relationships between surrogate indices and hypertension within specific subgroups. The subgroups were classified based on age, gender, employment status, smoking habits, presence of diabetes mellitus, dyslipidemia, positive family history of CVDs, eGFR categories, and history of CVD. The associations between surrogate indices and hypertension were largely consistent across all subgroup analyses, although some variations were observed. The association between Tg/HDL-c and hypertension was notably stronger in participants under 40 years of age, as well as in those without dyslipidemia. Significant interactions were observed for age (p = 0.013), diabetes (p = 0.014), and dyslipidemia (p \u0026lt; 0.001) concerning TyG, indicating a higher risk for hypertension among participants under 40 years, non-diabetic individuals, and those without dyslipidemia (p \u0026lt; 0.001). Notable interactions were observed for gender, job status, dyslipidemia, and diabetes (p for interactions = 0.023, 0.008, 0.016, and 0.012, respectively) in CHG, indicating a stronger effect in males, retired individuals, non-diabetics, and those without dyslipidemia.\u003c/p\u003e\u003cp\u003eFor the sensitivity analysis, we used Poisson regression with robust variance to directly estimate prevalence ratios (PRs) and their 95%CI. The results from Poisson regression were consistent in direction and statistical significance with those from logistic regression for all four indices (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). Although PRs were smaller in magnitude than the corresponding ORs, the trends across quartiles and the rank order of associations were preserved.\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eUnivariate and multivariate Poisson regression with robust variance of the association between surrogate IR indices and hypertension prevalence.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"10\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"2\" morerows=\"1\" nameend=\"c2\" namest=\"c1\" rowspan=\"2\"\u003e\u003cp\u003eIndexes\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003eUnadjusted model\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003eAdjusted model 1\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003eAdjusted model 2\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u003cp\u003eAdjusted model 3\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePR [95%CI]\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eP-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ePR [95%CI]\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eP-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003ePR [95%CI]\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eP-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003ePR [95%CI]\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u003cp\u003eP-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"6\" rowspan=\"7\"\u003e\u003cp\u003eTyg\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePer 1 SD increase\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.076 [1.070–1.083]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.056 [1.050–1.063]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.034 [1.026–1.042]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.029 [1.021–1.037]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePer 1 unit increase\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.118 [1.108–1.128]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.086 [1.077–1.096]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.053 [1.040–1.064]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.044 [1.032–1.057]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eQuartile 1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eQuartile 2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.087 [1.068–1.106]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.067 [1.048–1.085]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.043 [1.024–1.062]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.038 [1.019–1.0576]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eQuartile 3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.135 [1.116–1.155]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.102 [1.084–1.121]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.064 [1.044–1.084]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.053 [1.033–1.073]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eQuartile 4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.225 [1.206–1.244]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.164 [1.145–1.184]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.096 [1.074–1.118]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.082 [1.059–1.105]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eP for trend\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"6\" rowspan=\"7\"\u003e\u003cp\u003eTg/HDL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePer 1 SD increase\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.035 [1.027–1.042]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.029 [1.022–1.036]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.015 [1.008–1.023]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.010 [1.003–1.017]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.005\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePer 1 unit increase\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.012 [1.009–1.014]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.010 [1.007–1.012]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.005 [1.002–1.008]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.003 [1.001–1.006]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.005\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eQuartile 1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eQuartile 2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.063 [1.043–1.082]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.048 [1.030–1.067]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.026 [1.007–1.045]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.008\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.021 [1.001–1.040]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.033\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eQuartile 3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.095 [1.075–1.115]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.075 [1.056–1.094]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.044 [1.024–1.065]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.033 [1.012–1.054]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eQuartile 4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.136 [1.116–1.155]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.108 [1.089–1.127]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.060 [1.038–1.081]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.043 [1.021–1.065]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eP for trend\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"6\" rowspan=\"7\"\u003e\u003cp\u003eMETS-IR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePer 1 SD increase\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.080 [1.073–1.086]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.070 [1.064–1.077]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.031 [1.013–1.048]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.040 [1.007–1.074]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.015\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePer 1 unit increase\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.008 [1.007–1.009]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.007 [1.006–1.008]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.003 [1.001–1.005]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.004 [1.000-1.008]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.015\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eQuartile 1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eQuartile 2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.077 [1.058–1.096]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.063 [1.045–1.082]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.033 [1.011–1.055]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.031 [0.964–1.103]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.366\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eQuartile 3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.143 [1.124–1.163]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.118 [1.100- 1.137]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.062 [1.036–1.088]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.062 [0.980–1.150]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.139\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eQuartile 4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.242 [1.223–1.261]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.208 [1.189–1.226]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.104 [1.069–1.139]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.119 [1.019–1.230]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.018\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eP for trend\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.013\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"6\" rowspan=\"7\"\u003e\u003cp\u003eCHG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePer 1 SD increase\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.064 [1.057–1.071]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.042 [1.035–1.049]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.016 [1.007–1.025]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.012 [1.003–1.021]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.006\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePer 1 unit increase\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.163 [1.147–1.179]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.106 [1.090–1.122]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.040 [1.018–1.062]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.030 [1.008–1.052]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.006\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eQuartile 1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eQuartile 2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.046 [1.026–1.065]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.034 [1.015–1.053]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.018 [0.998–1.038]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.067\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.013 [0.993–1.033]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.179\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eQuartile 3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.094 [1.074–1.114]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.066 [1.046–1.085]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.036 [1.015–1.057]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.026 [1.004–1.047]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.016\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eQuartile 4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.175 [1.156–1.195]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.115 [1.095–1.134]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.043 [1.019–1.066]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.030 [1.006–1.054]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.014\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eP for trend\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.008\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"10\" nameend=\"c10\" namest=\"c1\"\u003e\u003cp\u003eModel 1: adjusted for gender and age\u003c/p\u003e\u003cp\u003eModel 2: adjusted for covariates in model 1 and educational level, job status, smoking status, diabetes mellitus, CVD history, Dyslipidemia, CVD family history, and marriage status\u003c/p\u003e\u003cp\u003eModel 3: adjusted for covariates in model 2 and BMI, anxiety score, GFR, Hematocrit, and uric acid level\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"10\"\u003eAbbreviation: SD; standard deviation, Tyg; triglyceride glucose index, Tg/HDL; triglyceride to high density lipoprotein ratio, METS-IR; Metabolic Score for Insulin Resistance, CHG; Cholesterol, High-Density Lipoprotein, and Glucose index.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis population-based cross-sectional study involved 9438 subjects aged 35 to 65 years and investigated the association between surrogate insulin indices and hypertension in an urban population in the Northeastern region of Iran. We found that surrogate indices of IR were associated with elevated systolic and diastolic blood pressure. Logistic regression showed a significant association between IR markers and hypertension in both unadjusted and adjusted models. Because BMI is embedded in the METS-IR formula, we evaluated potential collinearity between these predictors. By categorizing BMI into standard groups and assessing generalized VIF values, we confirmed that all adjusted GVIFs were below 5, indicating no problematic collinearity; thus, BMI was retained as an independent covariate to capture effects beyond insulin resistance. ROC curve analysis indicated that all four metabolic indices demonstrated significant predictive capability for hypertension. Additionally, these indices indicate small but statistically detectable improvements in the model\u0026rsquo;s ability to correctly assign higher probabilities to participants with hypertension and lower probabilities to those without. However, given the cross-sectional design, these gains indicate contemporaneous classification rather than the prediction of future risks. RCS analysis indicated a positive non-linear correlation between the indices and hypertension. The threshold effect analysis indicated that the risk gradient is primarily focused within the low-to-moderate range of each index, exhibiting diminishing incremental risk at elevated levels. Subgroup analysis showed a stronger association between the indices and hypertension in patients without dyslipidemia and diabetes. Sensitivity analysis was conducted employing Poisson regression with robust variance to estimate prevalence ratios, as odds ratios derived from logistic regression may overstate the association in common outcomes. Our results indicated that the findings were consistent across models.\u003c/p\u003e\u003cp\u003eTo our knowledge, this is the first investigation of the link between hypertension and IR indices in Iranian adults. This relationship has been examined extensively in certain populations (\u003cspan additionalcitationids=\"CR34 CR35\" citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e). Zhang et al. (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e) conducted a cross-sectional study involving 32124 normoglycemic adults, revealing that increased TyG index and TG/HDL-c levels correlate with prehypertension and hypertension. The TyG index demonstrated greater significance than TG/HDL-c in differentiating hypertension. A separate cross-sectional study indicated that METS-IR is correlated with an increased risk of hypertension (OR\u0026thinsp;=\u0026thinsp;1.03; 95%CI: 1.03\u0026ndash;1.04) (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). In longitudinal studies, TyG was correlated with an increased incidence of hypertension over extended follow-up periods. However, other surrogate indices have not been examined as thoroughly as TyG. Zheng et al. (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e) observed that, in a cohort of 4686 participants over a 9-year follow-up period, the incidence of hypertension was more common in individuals with elevated TyG index. Xin et al. identified a correlation between elevated TyG index at baseline and long-term TyG index trajectories with the risk of hypertension (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eInsulin resistance and hypertension are regarded as typical \"diseases of civilization,\" emerging in contemporary settings characterized by abundant food and a sedentary lifestyle. Insights from evolutionary biology suggest that the thrifty genotype, characterized by high cytokine response (facilitating injury eradication), mild insulin resistance (providing protection against starvation), and sodium preservation (ensuring body fluid maintenance), was advantageous for our ancestors in overcoming critical challenges such as famine, infection, trauma, and physical stressors. However, these traits may now be maladaptive in the context of modern lifestyles, potentially leading to insulin resistance, hypertension, type II diabetes, and cardiovascular diseases (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e). Insulin resistance and hypertension may be independent outcomes of a shared cellular disorder, specifically an increase in intracellular free calcium, leading to both vasoconstriction and impaired insulin action (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e). Hyperinsulinemia may be considered a significant factor in the development of hypertension through various mechanisms. Factors include enhanced sodium reabsorption in kidney tubules (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e), formation of advanced glycated end products (AGEs) (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e), activation of the sympathetic nervous system (\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e), changes in vascular resistance due to elevated calcium levels in smooth muscle cells (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e), and hyperaldosteronism alongside hyperactivation of the renin-angiotensin-aldosterone system (RAAS) (\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn our study, we observed stronger association between surrogate IR indices and hypertension in participants who did not have diabetes or dyslipidemia. Likewise, several studies revealed a strong association between IR and hypertension in metabolically normal patients (\u003cspan additionalcitationids=\"CR48 CR49\" citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e). These findings indicates that surrogate IR markers are early warning signs of hypertension in people without overt metabolic disease. Once diabetes or dyslipidemia is established, the incremental predictive value of these indices diminishes. This pattern may be explained by the fact that, in metabolically healthier populations, these indices more accurately capture early pathophysiological processes - such as compensatory hyperinsulinemia, sympathetic overactivity, endothelial dysfunction, and activation of the RAAS system - that directly contribute to elevated blood pressure (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e). By contrast, in established diabetes hyperglycemia itself and long-term vascular changes contribute to hypertension independently of IR. Similarly, in dyslipidemic individuals (high Tg/low HDL), chronic lipid abnormalities drive atherosclerosis and stiffness. Lipid‐based IR markers (TyG, Tg/HDL) are elevated by high triglycerides, but if dyslipidemia is present or treated, these markers may be less discriminating of IR. Nonetheless, additional longitudinal and mechanistic studies are required to confirm these associations and clarify the underlying pathways.\u003c/p\u003e\u003cp\u003eResearch indicates that an increase in insulin resistance surrogate indices may also correlate with worse outcomes in hypertensive patients. Liu et al. (\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e) found that each SD increase in the TyG index, is associated with higher risk of all-cause mortality or CVD events by 28% (Hazard ratio (HR)\u0026thinsp;=\u0026thinsp;1.28, 95% CI: 1.04\u0026ndash;1.59). Another study by Tao et al. (\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e) also demonstrated that a higher TyG index is associated with an increased 1-year risk of major adverse cardiovascular events (MACEs) in hypertensive patients with coronary heart disease (CHD). Specifically, individuals in the highest quartile of the TyG index exhibited a 47.0% greater risk of MACEs during the 1-year follow-up period (HR\u0026thinsp;=\u0026thinsp;1.470, 95%CI: 1.071\u0026ndash;2.018).\u003c/p\u003e\u003cp\u003eOur investigation may have certain limitations. The cross-sectional design introduces temporal ambiguity, preventing us from establishing causality because exposure and outcome were measured simultaneously. Selection bias, recall bias, and survivorship bias can also be introduced.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eWe found a strong positive relationship between hypertension and IR surrogate indices. Findings underscore the necessity of overseeing and evaluating components these indices, such as BMI, FPG, HDL-c, and Tg to improve hypertension risk assessments and refine interventions. This approach aims to facilitate early lifestyle changes, including dietary adjustments and increased physical activity, while also formulating prevention strategies for populations affected by metabolic disorders and insulin resistance to avert the advancement of hypertension.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eData availability\u003c/p\u003e\n\u003cp\u003eThe datasets produced and/or examined in this study are not available to the public, owing to the data ownership policies and copyright regulations established by Mashhad University of Medical Sciences. Access may be provided in response to a thoughtful request from the corresponding authors.\u003c/p\u003e\n\u003cp\u003eAcknowledgements\u003c/p\u003e\n\u003cp\u003eWe recognize the introduction of AI-driven tools aimed at enhancing the quality of language and style in our manuscript draft. The authors assume full responsibility for all content and editorial choices made.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAuthor contributions\u003c/p\u003e\n\u003cp\u003eStudy concept and design: A.T, M.G.M, S.S, M.M, H.A; data collection: S.S, M.K.R, B.S, H.A, M.G.M, M.M; Analysis and interpretation of data: A.T, H.E; Drafting of the manuscript: A.T; Critical revision of the manuscript for important intellectual content: S.D, M.G.M, G.F. All authors have reviewed and given their consent to the manuscript.\u003c/p\u003e\n\u003cp\u003eClinical trial number\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003eFunding\u003c/p\u003e\n\u003cp\u003eThis research received financial support from the Vice Chancellor for Research and Technology of Mashhad University of Medical Sciences [Grant Number: 4040685].\u003c/p\u003e\n\u003cp\u003eCompeting interests\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eKjeldsen SE. Hypertension and cardiovascular risk: General aspects. Pharmacol Res. 2018;129:95\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eOrganization WH. 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Cardiovascular Diabetology. 2023;22(1):305.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"european-journal-of-medical-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ejmr","sideBox":"Learn more about [European Journal of Medical Research](http://eurjmedres.biomedcentral.com)","snPcode":"40001","submissionUrl":"https://submission.nature.com/new-submission/40001/3","title":"European Journal of Medical Research","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Insulin resistance, Hypertension, MASHAD, TyG, Tg/HDL-c, METS-IR, CHG, Non-linear","lastPublishedDoi":"10.21203/rs.3.rs-7560704/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7560704/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground:\u003c/h2\u003e\u003cp\u003eInsulin resistance (IR) is associated with a reduced response to insulin and an increased risk of cardiovascular disease and hypertension. The triglyceride glucose index (TyG), triglyceride to high- density lipoprotein cholesterol ratio (Tg/HDL-c), metabolic score for insulin resistance (METS-IR) and cholesterol, high-density lipoprotein, and glucose (CHG) index, are surrogate indices insulin resistance and offer a simpler approach to evaluating insulin resistance than the gold standard hyperinsulinemic-euglycemic glucose clamp technique. With the rising prevalence of metabolic syndrome and hypertension, we aimed to investigate the relationship between hypertension and IR in a large Iranian population.\u003c/p\u003e\u003ch2\u003eMethod:\u003c/h2\u003e\u003cp\u003eThe study cohort comprised 9438 individuals aged 35 to 65 years, who were recruited cross-sectionally from the Mashhad Stroke and Heart Atherosclerotic Disorder (MASHAD) cohort study. Hypertension was defined as a blood pressure reading exceeding 140/90 mmHg or the use of anti-hypertensive medications. Multivariate logistic regression analysis, receiver operating characteristic area under the curve (ROC-AUC) analysis, net reclassification index (NRI) and integrated discrimination improvement index (IDI) for incremental classification performance, restricted cubic splines (RCS), threshold effect analysis, subgroup analysis, and Poisson regression with robust variance to estimate the prevalence ratios, were employed in the statistical evaluation.\u003c/p\u003e\u003ch2\u003eResults:\u003c/h2\u003e\u003cp\u003eA total of 9438 adults were analyzed, with 2943 individuals identified as having hypertension. Spearman\u0026rsquo;s correlation coefficients indicated that all four IR indices exhibited a significantly positive correlation with both systolic and diastolic blood pressures. In multivariate analysis, after full adjustment, each 1 unit increase corresponded to 37.5% higher risk of hypertension for TyG (OR: 1.375, 95%CI: 1.256\u0026ndash;1.505, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), 2.8% for Tg/HDL-c (OR: 1.028, 95%CI: 1.010\u0026ndash;1.046, p\u0026thinsp;=\u0026thinsp;0.002), 3.2% for METS-IR (OR: 1.032, 95%CI: 1.022\u0026ndash;1.042, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and 25.5% for CHG (OR: 1.255, 95%CI: 1.073\u0026ndash;1.467, p\u0026thinsp;=\u0026thinsp;0.004). Analysis of the ROC curve indicated that each of the four metabolic indices demonstrated a significant capacity to predict hypertension (all p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The METS-IR index showed the highest discriminative performance among the indices, achieving an AUC of 0.642 (95%CI: 0.630\u0026ndash;0.654). We investigated whether adding IR surrogate indices to Model 3 could improve hypertension detection. The TyG index had the highest incremental classification values (NRI: 13.58, 95%CI: [8.65\u0026ndash;18.35]; IDI: 0.956, 95%CI: [0.834\u0026ndash;1.079]). RCS analysis revealed a nonlinear relationship between TyG, Tg/HDL-c, and CHG with hypertension, while a linear relationship was observed between METS-IR and hypertension. Threshold effect analysis for the indices with non-linear relation with hypertension showed that these associations were strongest at lower index values and then flattened, and often lose statistical significance. The findings were consistent across various subgroups, although the effects appeared to be more pronounced in participants who did not have dyslipidemia across all indices. A sensitivity analysis employing Poisson models with robust variance validated the direction and ranking of effects, revealing smaller prevalence ratios compared to odds ratios.\u003c/p\u003e\u003ch2\u003eConclusion:\u003c/h2\u003e\u003cp\u003eSurrogate IR indices showed a significant relationship with hypertension. They may serve as a viable option for efficiently stratifying risk in the primary prevention of hypertension.\u003c/p\u003e","manuscriptTitle":"Association of four Insulin Resistance Surrogate Indices and Hypertension in the MASHAD cohort study population: a cross-sectional study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-06 08:31:48","doi":"10.21203/rs.3.rs-7560704/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-11-20T11:43:08+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-20T03:13:20+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-10T02:44:53+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"260508552436369955586833926395439388250","date":"2025-11-05T07:08:53+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-02T03:01:32+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"314416781621402941622342653574823072614","date":"2025-11-02T02:07:50+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-31T05:47:43+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"129637442398179497647454359618838212625","date":"2025-10-30T08:36:42+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"170525340769886993162871264215531606665","date":"2025-10-29T15:05:41+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-10-27T12:01:12+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-13T13:29:27+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-09-13T12:21:20+00:00","index":"","fulltext":""},{"type":"submitted","content":"European Journal of Medical Research","date":"2025-09-08T06:31:12+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"european-journal-of-medical-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ejmr","sideBox":"Learn more about [European Journal of Medical Research](http://eurjmedres.biomedcentral.com)","snPcode":"40001","submissionUrl":"https://submission.nature.com/new-submission/40001/3","title":"European Journal of Medical Research","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"4f81856d-a96d-4aab-a614-cea724b136f9","owner":[],"postedDate":"November 6th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-02-16T16:06:46+00:00","versionOfRecord":{"articleIdentity":"rs-7560704","link":"https://doi.org/10.1186/s40001-026-03843-w","journal":{"identity":"european-journal-of-medical-research","isVorOnly":false,"title":"European Journal of Medical Research"},"publishedOn":"2026-02-09 15:59:10","publishedOnDateReadable":"February 9th, 2026"},"versionCreatedAt":"2025-11-06 08:31:48","video":"","vorDoi":"10.1186/s40001-026-03843-w","vorDoiUrl":"https://doi.org/10.1186/s40001-026-03843-w","workflowStages":[]},"version":"v1","identity":"rs-7560704","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7560704","identity":"rs-7560704","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}