Association Between Healthy and Non-Healthy Vascular Aging with Hypertensive Target Organ Damage

Association Between Healthy and Non-Healthy Vascular Aging with Hypertensive Target Organ Damage | 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 Between Healthy and Non-Healthy Vascular Aging with Hypertensive Target Organ Damage Huijuan Chao, Qian Wang, Yanqing Bao, Yaya Bai, Mark Butlin, Alberto Avolio, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6259968/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract Objective: To investigate the correlation between healthy vascular aging (HVA) and non-healthy vascular aging (NHVA) with hypertensive target organ damage (TOD). Methods: This study included individuals from the Geriatrics Department of Ruijin Hospital in Shanghai since January 2023. Participants were divided into HVA and NHVA groups based on blood pressure and carotid-femoral pulse wave velocity (cf-PWV). HVA was defined as no history of hypertension and cf-PWV < 7.6 m/s; NHVA was defined as a history of hypertension or cf-PWV ≥ 7.6 m/s. Hypertensive TOD indicators included carotid intima-media thickness (cIMT), chronic kidney disease, albumin-to-creatinine ratio (ACR), estimated glomerular filtration rate (eGFR), and left ventricular mass index (LVMI). Results: A total of 1,257 participants was included in the study. After grouping by cf-PWV and blood pressure, 31.8% met the HVA criteria. Compared to the HVA group, the NHVA group was older, had more smokers, and exhibited higher levels body mass index, blood glucose, lipids, and blood pressure. The NHVA group also showed lower creatinine clearance (88.72±17.27 mL/min/1.73 m² vs. 93.41 ± 15.79 mL/min/1.73 m², p <0.001), higher LVMI (108.00 ± 26.44 g/m² vs. 92.25 ± 22.29 g/m², p < 0.001), greater cIMT (0.75 ± 0.14 mm vs. 0.70 ± 0.12 mm, p < 0.001), and higher cf-PWV (9.06 ± 1.91 m/s vs. 6.5 ± 0.74 m/s, p < 0.001). Multivariate linear regression analysis revealed significant associations between vascular aging groups and LVMI ( p = 0.003) and lgACR ( p =0.022). Binary stepwise logistic regression results demonstrated a significant correlation between vascular aging and LVMI (OR = 2.201, 95% CI: 1.299–3.73, p = 0.003). Conclusion: Accelerated vascular aging is associated with cardiac and renal TOD, providing a potential target for intervention. Vascular aging shows a significant correlation with LVMI. Healthy vascular aging Carotid-femoral pulse wave velocity Hypertensive target organ damage Introduction With the exacerbation of population aging, the prevalence of cardiovascular disease has also exhibited an upward trend. Vascular aging, serving as the initial stage of cardiovascular disease, is significantly associated with adverse cardiovascular events, cardiovascular mortality, and disability[ 1 ]. As individuals age, vascular structure and function undergo degenerative alterations, culminating in vascular stiffening—a phenomenon referred to as vascular aging[ 2 ]. These modifications in vascular structure and function constitute significant independent risk factors for the onset and progression of cardiovascular events and serve as optimal indicators or surrogate endpoints for forecasting cardiovascular risk. The extent of vascular aging can be quantified through the analysis of arterial stiffness. Pulse wave velocity (PWV) is one of the most extensively recognized and utilized assessment methodologies. Notably, carotid-femoral artery pulse wave velocity (cf-PWV) is considered the gold standard for PWV measurement and is acknowledged as a predictive and prognostic marker of cardiovascular risk[ 3 ]. An increment of one standard deviation in cf-PWV corresponds to an elevated risk of mortality and cardiovascular events, equating to a risk augmentation greater than 1.5 times that associated with a 10-year increase in age or a 10 mm Hg rise in systolic blood pressure (SBP)[ 4 ]. Vascular health in the elderly is characterized by a cf-PWV of less than 7.6 m/s, which aligns with the mean ± standard deviation of the reference population aged 30 years [ 5 ]. Healthy vascular aging (HVA) denotes the absence of hypertension and a lack of substantial increases in arterial stiffness as indicated by PWV. Research has demonstrated that HVA is correlated with a diminished incidence of cardiovascular disease in Western populations[ 5 ]. In a chinese cohort study included 11,474 participants aged ≥ 50 years concludes that HVA significantly reduces the risk of first stroke in a community-based Chinese population, suggesting that assessing vascular aging could be useful for stroke risk assessment and prevention[ 6 ]. However, the majority of studies have utilized brachial-ankle pulse wave velocity (ba-PWV) as an indicator of arterial stiffness, rather than the gold standard cf-PWV. This study categorized participants based on peripheral blood pressure and cf-PWV into two groups: HVA and non-healthy vascular aging (NHVA), to examine the impact of these conditions on target organ damage (TOD) in hypertension. HVA was defined as no history of hypertension and cf-PWV < 7.6m/s; NHVA was defined as a history of hypertension or cf-PWV ≥ 7.6 m/s. Materials and Methods 2.1 Study population A total of 1,257 elderly patients who underwent physical examinations and were hospitalized at the northern branch of Shanghai Ruijin Hospital between December 2017 and September 2021 was included in this study. Exclusion criteria were: (1) severe cardiovascular conditions, including acute coronary syndrome, heart failure (both acute and chronic), severe arrhythmia, myocarditis, and Marfan syndrome; (2) severe neurological disorders, such as acute stroke, traumatic brain injury, and epilepsy; (3) severe renal disease, including acute glomerulonephritis, acute renal insufficiency, and renal tumors; (4) pulmonary conditions, such as severe pulmonary hypertension, acute and chronic pulmonary embolism, pulmonary heart disease, and acute asthma attacks; (5) patients with lower extremity arterial stenosis; (6) other significant medical conditions, including severe hepatic insufficiency, pregnancy, severe anemia, thyroid disorders, and malignant tumors. This study was approved by the Ethics Committee of Ruijin Hospital Affiliated to Shanghai Jiaotong University School of Medicine (Approval No. 2023 − 127). 2.2 General Data Collection The general data of all participants were systematically gathered, encompassing personal identifiers such as name, gender, and age, alongside physiological metrics such as weight, height, duration of hypertension, history of diabetes mellitus, smoking status, presence of specific diseases, medication history, and Body Mass Index (BMI). For the purpose of this study, current smokers were defined as individuals consuming more than one cigarette daily, while former smokers referred to those who had ceased smoking within the past six months. 2.3 Brachial Blood Pressure Measurement Following a five-minute rest period in a seated position, the brachial artery blood pressure of each participant was measured three times using an electronic sphygmomanometer (model HEM907, Omron, Japan). The measurements included brachiall systolic blood pressure (SBP), brachial diastolic blood pressure (DBP), and brachial pulse pressure (bPP). The average values from these readings were then calculated for further analysis. 2.4 Laboratory Tests Fasting blood samples were collected in the morning to measure total cholesterol (TC), triglycerides (TG), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), serum creatinine (Scr), and fasting blood glucose (FBG). The estimated glomerular filtration rate (eGFR) was calculated using the Chinese Simplified MDRD formula: eGFR (ml/min/1.73 m 2 ) = 175 × Cr (mg/dl) −1.234 × age (years old) −0.179 × 0.79 (if female patient).This formula has been validated for use in Chinese patients with chronic kidney disease (National EGFR Project Collaboration Group, 2006). Additionally, mid-morning urine was collected on the second day of hospitalization to determine the urinary albumin-to-creatinine ratio (ACR). 2.5 Measurement of pulse wave velocity Cf-PWV was measured using the SphygmoCor device (SphygmoCor-Px V8.0, AtCor Medical, Australia). The instrument's applanation tonometer probe was placed at the strongest pulsation of the right radial artery and femoral artery. The device automatically obtained 20 pulse waveforms for analysis, and cf-PWV value was calculated using to the distance between the sites of two pulse waves, after subtraction of the distance between the supra-sternal notch and the carotid site, and the pulse wave transit time. The measurements were repeated twice. 2.5 Measurement of left ventricular mass Cardiac two-dimensional, M-mode, color Doppler and tissue Doppler ultrasound examination were performed by a color ultrasound diagnostic instrument. Left atrial diameter (LAD), left ventricular end-diastolic diameter (LVEDD) and left ventricular end-systolic diameter (LVEDD) were measured in the parasternal long axis view, including left ventricular end-systolic diameter (LVESD), interventricular septal thickness (IVST), and left ventricular posterior wall thickness (LVPW). Simpson method was used to calculate left atrial volume (LAV) and left ventricular ejection fraction (LVEF). Left ventricular mass (LVM) was calculated according to the following formula from which was determined left ventricular mass index (LVMI) After 3 consecutive cardiac cycles, the average value was calculated, and the left ventricular mass (LVM) was corrected according to Devereux's formula: LVM (g) = 0.8×1.04×[(IVST + LVDD + LVPWT) 3 -LVDD 3 ] + 0.6, LVMI = LVM (g)/ body surface area, where body surface area = 0.0061× height (cm) + 0.0128× weight (kg) -0.1529. 2.6 Measurement of carotid intima-media thickness The subjects were placed in the supine position with the neck fully exposed. Color doppler ultrasound was used to assess the presence of plaque, plaque location and number, and echo in the intima of the bilateral common carotid artery and bifurcation, internal carotid artery, external carotid artery and each main trunk and branch. Carotid intima-media thickness (cIMT) was measured on both left and right common carotid artery starting ~ 1.5 cm proximal to the carotid artery bulb. Three recordings were taken, and the mean value was calculated for each side. “Plaque = 0” referred to the absence of plaque, while “plaque = 1” referred to the presence of plaque. 2.7 Relevant Diagnostic Criteria and Grouping 2.7.1 Hypertension . Hypertension was defined as SBP ≥ 140 mmHg, and/or a diastolic blood pressure ≥ 90 mmHg, or the current use of antihypertensive medication. Diabetes was diagnosed based on a fasting plasma glucose level exceeding 7.0 mmol/L, and/or a 2-hour plasma glucose level exceeding 11.1 mmol/L during an oral glucose tolerance test, or the current use of antidiabetic medications. 2.7.2 Target Organ Damage (TOD). Indicators of TOD were: (1) LVMI: ≥115 g/m² for males and ≥ 95 g/m² for females; (2) Chronic Kidney Disease: estimated Glomerular Filtration Rate (eGFR) 30mg/g;(3)cIMT ≥ 900µm(including intimal thickening and plaque). In this study, the presence of TOD was defined as the presence of any of the aforementioned indicators. 2.7.3 Population stratification. The population was stratified into two groups based on blood pressure cf-PWV: HVA and NHVA. HVA was defined as individuals with no history of hypertension and a cf-PWV < 7.6 m/s; NHVA was defined as individuals with a history of hypertension and/or a cfPWV ≥ 7.6 m/s. 3. Statistical analysis SPSS 26.0 software package (SPSS, Chicago, IL) and Excel were used for statistical analysis. Continuous variables are expressed as mean ± SD, and frequencies (percentage) are reported for categorical variables after testing for normality Continuous and categorical variables were compared using t-test and Chi-square test respectively for males and females. To compare continuous and categorical variables between HVA and NHVA, t-test and Chi-square test respectively were used. Multivariate linear regression was conducted to compare the TOD between the HVA and NVHA groups. Multivariate binary logistic regression analysis was used on TOD of HVA and NHVA groups. A two-sided p < 0.05 was considered statistically significant. Results 4.1 Population characteristics A total of 1257 patients was included in this study, with an average age of 53.13 ± 12.65 years, 807(64.2%) males, 382(30.4%) hypertensive patients taking antihypertensive treatment, 176(13.7%) diabetic patients. Table 1 shows the general basic data between male and female groups, including cardiovascular risk factors, combined clinical diseases, and indicators of TOD in hypertension. Compared with the female group, the male group was younger (52.09 ± 12.55 VS 54.99 ± 12.63 years, p < 0.001), more patients were older than 65 years (148 (18.3%), p = 0.003), and more smokers (24.7% VS 3.6%, p < 0.001). BMI (25.93 ± 3.75kg/m 2 VS 24.23 ± 3.93 kg/m 2 , p < 0.001) and fasting blood glucose (5.90 ± 1.78 mmol/l VS 5.68 ± 1.87mmol/l, p = 0.064) were higher. Triglyceride was higher (2.07 ± 1.76mmol/l VS 1.62 ± 1.05mmol/l, p < 0.001), cholesterol was lower (4.69 ± 1.09mmol/l VS 4.97 ± 1.02 mmol/l, p < 0.001). Lower high density lipoprotein (1.08 ± 0.35 mmol/l VS 1.26 ± 0.29mmol/l, p < 0.001), higher low density lipoprotein (3.06 ± 0.81 mmol/l VS 3.21 ± 0.79 mmol/l, p = 0.005); Brachial diastolic blood pressure was higher (78.12 ± 11.50 mmHg VS 74.13 ± 12.35mmHg, p < 0.001), and the brachial pulse pressure was lower (53.31 ± 12.52 mmHg VS 55.12 ± 14.61mmHg, p < 0.001). Compared with asymptomatic TOD, the creatinine clearance rate of male and female patients with hypertension was higher (92.17 ± 17.55 mL/min 1.73 m 2 VS 86.27 ± 15.19 mL/min 1.73 m 2 , p < 0.001). LVMI was higher (107.67 ± 26.52 g/m 2 VS 96.08 ± 24.60 g/m 2 , p < 0.001). cf-PWV was higher in males (8.35 ± 1.96 m/s VS 8.06 ± 2.11 m/s, p = 0.018), and cf-PWV ≥ 7.6m/s was more common in males (492, 61.0%, p = 0.016). Table 1 Clinical Characteristics of participants by Sex Overall N = 1257 Men N = 807 Women N = 450 P -Value Age, years 53.13±12.65 52.09 ± 12.55 54.99 ± 12.63 <0.001 Smoking history,n(%) 215(17.1) 199(24.7) 16(3.6) <0.001 Body mass index, kg/m 2 25.32±3.90 25.93 ± 3.75 24.23 ± 3.93 <0.001 Hypertension, n (%) 398(31.7) 293(36.3) 105(23.3) <0.001 Antihypertensive treatment, n (%) 382(30.4) 282(34.9) 100(22.2) <0.001 Diabetes treatment, n (%) 176(13.7) 120(14.6) 56(12.1) 0.218 Total triglycerides, mmol/L 1.91±1.56 2.07 ± 1.76 1.62 ± 1.05 <0.001 Total cholesterol, mmol/l 4.79±1.07 4.69 ± 1.09 4.97 ± 1.02 <0.001 High-density lipoprotein, mmol/l 1.15±0.34 1.08 ± 0.35 1.26 ± 0.29 <0.001 Low density lipoprotein, mmol/l 3.12±0.81 3.06 ± 0.81 3.21 ± 0.79 0.005 Fasting plasmaglucose, mmol/l 5.82±1.81 5.90 ± 1.78 5.68 ± 1.87 0.064 Brachial SBP, mmHg 130.65±18.56 131.43 ± 17.38 129.25 ± 20.45 0.056 Brachial DBP, mmHg 76.69±11.96 78.12 ± 11.50 74.13 ± 12.35 <0.001 Heart rate ,bpm 69.25±10.39 69.20 ± 10.23 69.34 ± 10.67 0.819 Brachial pulse pressure,mmHg 53.96±13.33 53.31 ± 12.52 55.12 ± 14.61 0.027 eGFR, mL/min 1.73 m 2 90.07±16.98 92.17 ± 17.55 86.27 ± 15.19 <0.001 Urinary ACR, mg/mmol 6.43±28.72 7.78 ± 34.29 3.38 ± 4.54 0.007 LVMI, g/m 2 104.05±26.35 107.67 ± 26.52 96.08 ± 24.60 65year 262(20.8) 148(18.3) 114(25.3) 0.003 Data are means standard deviation or numbers with percentages in parentheses. Student t test and chi-squared test were conducted to compare the differences between men and women for quantitative and qualitative variables, respectively; cf-PWV, carotid femoral pulse wave velocity; eGFR, estimated glomerular filtration rate; cIMT, carotid intima-media thickness; LVMI, left ventricular mass index; ACR, urine albumin-creatinine ratio. 4.12 Healthy and Non-Healthy Vascular Aging Table 2 shows that the total population was further divided into HVA and NHVA groups. There were 857 patients (68.2%) in NHVA group, and the average Age was older than that in HVA group (55.62 ± 12.40 years). There were 234 patients (27.3%) older than 65 years, and 583 patients (68.0%) were male. Compared with the HVA group, there were more smokers (8.8% VS 21.0%, p < 0.001) and higher BMI (25.87 ± 3.80kg/m 2 VS 24.13 ± 3.82 kg/m 2 , p < 0.001). Fasting blood glucose (6.00 ± 1.94mmol/l VS 5.40 ± 1.38mmol/l, p = 0.064) and triglyceride (2.05 ± 1.75mmol/l VS1.55 ± 0.87mmol/l, p < 0.001) were higher. Cholesterol (4.76 ± 1.12mmol/l VS 4.85 ± 0.95mmol/l, p < 0.001) and high density lipoprotein (1.11 ± 0.32 mmol/l vs 1.23 ± 0.37mmol/l, p < 0.001) were lower. Lower density lipoprotein (3.10 ± 0.83mmol/l VS 3.17 ± 0.76 mmol/l, p = 0.005) and higher brachial SBP systolic (136.12 ± 17.34mmHg VS 118.93 ± 15.40mmHg, p < 0.001). Brachial DBP (79.39 ± 11.61 mmHg VS 70.92 ± 10.61mmHg, p < 0.001) and barchial pulse pressure (56.74 ± 13.56 mmHg VS 48.01 ± 10.61mmHg, p < 0.001) were higher. Compared with asymptomatic TOD, the creatinine clearance rate was lower in the two groups (88.72 ± 17.27 mL/min 1.73 m 2 VS 93.41 ± 15.79 mL/min 1.73 m 2 , p < 0.001). Left ventricular hypertrophy index (108.00 ± 26.44 g/m 2 VS92.25 ± 22.29g/m 2 , p < 0.001) and cIMT(0.75 ± 0.14 mmVS0.70 ± 0.12mm, p < 0.001) were higher. cf-PWV was higher (9.06 ± 1.91 m/s VS 6.5 ± 0.74 m/s, p < 0.001). Table 2 Clinical Characteristics of participants by HVA and NHV Overall N = 1257 HVA N = 400 NHVA N = 857 p -Value Age, years 53.13±12.65 47.8 ± 11.51 55.62 ± 12.40 65year 262(20.8) 28(7.0) 234(27.3) <0.001 Sex 807(64.2) 224(56.0) 583(68.0) <0.001 Smoking history,n(%) 215(17.1) 35(8.8) 180(21.0) <0.001 Body mass index, kg/m 2 25.32±3.90 24.13 ± 3.82 25.87 ± 3.80 <0.001 Total triglycerides, mmol/L 1.91±1.56 1.55 ± 0.87 2.05 ± 1.75 <0.001 Total cholesterol, mmol/l 4.79±1.07 4.85 ± 0.95 4.76 ± 1.12 <0.001 High-density lipoprotein, mmol/l 1.15±0.34 1.23 ± 0.37 1.11 ± 0.32 <0.001 Low density lipoprotein, mmol/l 3.12±0.81 3.17 ± 0.76 3.10 ± 0.83 0.207 Fasting plasmaglucose, mmol/l 5.82±1.81 5.40 ± 1.38 6.00 ± 1.94 <0.001 Peripheral systolic blood pressure, mmHg 130.65±18.56 118.93 ± 15.40 136.12 ± 17.34 <0.001 Peripheral diastolic pressure, mmHg 76.69±11.96 70.92 ± 10.61 79.39 ± 11.61 <0.001 Heart rate bpm 69.25±10.39 68.33 ± 10.43 69.68 ± 10.35 0.031 Peripheral pulse pressure, mmHg 53.96±13.33 48.01 ± 10.61 56.74 ± 13.56 <0.001 Creatinine clearance rate, mL/min 1.73 m 2 90.07±16.98 93.41 ± 15.79 88.72 ± 17.27 <0.001 Urinary creatinine ratio rate, mg/mmol 6.43±28.72 2.83 ± 2.25 7.67 ± 33.17 <0.001 Left ventricular mass index, g/m 2 104.05±26.35 92.25 ± 22.29 108.00 ± 26.44 <0.001 Carotid intima-media thickness, mm 0.74±0.14 0.70 ± 0.12 0.75 ± 0.14 <0.001 cf-PWV m/s 8.24±2.02 6.5 ± 0.74 9.06 ± 1.91 <0.001 Data are means standard deviation or numbers with percentages in parentheses. Student t test and chi-squared test were conducted to compare the differences between men and women for quantitative and qualitative variables, respectively; cf-PWV, carotid femoral pulse wave velocity. HVA, healthy vascular aging. NHVA, non-healthy vascular aging. cf-PWV, carotid femoral pulse wave velocity. 4.1.3 Target Organ Damage Taking TOD indicators as dependent variables, both general cardiovascular risk factors and the groups of HVA versus NHVA were incorporated into a multiple linear regression model. The analysis revealed a statistically significant correlation between the two vascular aging groups and both LMVI ( p = 0.003) and eGFR ( p = 0.022). (Table 3 ) Table 3 Multiple linear regression was used to compare the target organ damage between the healthy vascular aging group and the non-healthy vascular aging group TOD Factors B Std. Error Beta P -value R 2 LVMI 0.124 Hypertension 0.662 2.315 0.013 0.775 Smoking history 2.715 2.209 0.046 0.219 Body mass index 0.827 0.261 0.125 0.002 Age 0.481 0.081 0.237 <0.001 Total triglycerides 0.412 0.724 0.022 0.569 Low density lipoprotein -1.984 1.158 -0.064 0.087 Fasting plasmaglucose 0.002 0.57 0 0.997 HVA/NHVA 8.421 2.803 0.141 0.003 cIMT 0.166 Hypertension 0.003 0.012 0.012 0.786 Smoking history 0.028 0.011 0.093 0.015 Body mass index 0.003 0.001 0.081 0.041 Age 0.004 0 0.4 <0.001 Total triglycerides -0.005 0.004 -0.052 0.183 Low density lipoprotein -0.001 0.006 -0.004 0.914 Fasting plasmaglucose 0.004 0.003 0.053 0.166 HVA/NHVA 0.001 0.015 0.004 0.928 lgACR 0.05 Hypertension -0.006 0.028 -0.011 0.835 Smoking history 0.031 0.025 0.051 0.204 Body mass index -0.002 0.003 -0.026 0.535 Age -0.001 0.001 -0.045 0.295 Total triglycerides 0.015 0.007 0.088 0.034 Low density lipoprotein 0.027 0.013 0.08 0.043 Fasting plasmaglucose 0.013 0.006 0.091 0.029 HVA/NHVA 0.079 0.034 0.124 0.022 eGFR 0.145 Hypertension 0.227 1.189 0.007 0.849 Smoking history 0.466 1.276 0.011 0.715 Body mass index -0.067 0.132 -0.016 0.611 Age -0.493 0.042 -0.377 <0.001 Total triglycerides -0.211 0.324 -0.02 0.515 Low density lipoprotein -2.377 0.609 -0.117 <0.001 Fasting plasmaglucose 0.716 0.282 0.078 0.011 HVA/NHVA -0.532 1.31 -0.015 0.685 HVA, healthy vascular aging. NHVA, non-healthy vascular aging. eGFR, estimated glomerular filtration rate; CIMT, carotid intima-media thickness; LVMI, left ventricular mass index; ACR, urine albumin-creatinine ratio. In this study, binary stepwise logistic regression was used to evaluate the influence of age, gender, BMI, hypertension, fasting blood glucose, triglyceride, low-density lipoprotein, and vascular aging on TOD of patients. Logistic regression analysis showed that vascular aging was significantly associated with LVMI (OR = 2.201 [1.299–3.73], p = 0.003). (Table 4 ) Table 4 Multivariate binary logistic regression analysis of healthy vascular aging group and non-healthy vascular aging group target organ damage Factors p -value OR 95% CI eGFR Smoking history 0.098 2.396 0.852–6.739 Body mass index 0.537 0.965 0.863–1.08 Age <0.001 1.072 1.033–1.113 Sex 0.325 1.543 0.650–3.663 Hypertension 0.909 0.95 0.392–2.299 Total cholesterol 0.845 0.897 0.301–2.674 Low density lipoprotein 0.489 1.637 0.406–6.602 Fasting plasmaglucose 0.857 1.017 0.844–1.226 HVA/NHVA 0.53 1.475 0.438–4.964 ACR Smoking history 0.684 1.103 0.688–1.77 Body mass index 0.859 1.005 0.950–1.064 Age 0.889 0.999 0.980–1.018 Sex 0.546 0.852 0.506–1.434 Hypertension 0.065 1.684 0.967–2.93 Total cholesterol 0.512 1.124 0.793–1.595 Low density lipoprotein 0.978 0.993 0.623–1.585 Fasting plasmaglucose 0.008 1.14 1.035–1.256 HVA/NHVA 0.092 1.978 0.895–4.373 LVMI Smoking history 0.668 0.914 0.604–1.382 Body mass index 0.075 1.045 0.995–1.097 Age <0.001 1.046 1.031–1.062 Sex <0.001 2.158 1.460–3.191 Hypertension 0.883 1.03 0.691–1.536 Total cholesterol 0.524 0.847 0.509–1.411 Low density lipoprotein 0.66 1.158 0.602–2.227 Fasting plasmaglucose 0.874 0.992 0.901–1.093 HVA/NHVA 0.003 2.201 1.299–3.73 HVA, healthy vascular aging. NHVA, non-healthy vascular aging. eGFR, estimated glomerular filtration rate; CIMT, carotid intima-media thickness; LVMI, left ventricular mass index; ACR, urine albumin-creatinine ratio; OR, Odds Ratio. DISCUSSION After grouping by cf-PWV and blood pressure, we found that 31.8% of participants met the HVA criteria. Compared with the HVA group, the NHVA group was older, with more smokers, and higher levels of BMI, blood glucose, lipids, and blood pressure. It also had elevated TOD indicators such as creatinine clearance rate, left ventricular hypertrophy, and cIMT. Multiple linear regression showed the NHVA was more correlated with the increase of LMVI and the decrease of eGFR. Binary logistic regression indicated NHVA had a higher risk of LMVI elevation (OR = 2.201 [1.299–3.73], p = 0.003). Age, as a major risk factor for cardiovascular disease, leads to the dilation of elastic arteries, thickening and stiffening of artery walls, and decline of endothelial function even in seemingly healthy people, with differences between individuals[ 7 ]. Blood pressure is another important factor in vascular aging and a key indicator for HVA assessment. Hypertension is closely related to the increase in arterial stiffness, and the two often interact[ 8 ].Metabolic syndrome, including components such as obesity, dyslipidemia, and hyperglycemia, is closely related to vascular aging[ 3 , 7 ]. The strong association between smoking and early vascular aging is well known[ 9 , 10 ].Consistent with previous studies, we found that the NHVA group was higher than the HVA group in terms of age, percentage of smokers, BMI, blood glucose, lipids, and blood pressure. Management of these conventional cardiovascular risk factors may contribute to achieving HVA. It has been shown that reducing body mass, maintaining a healthy dietary pattern, and using medications to lower blood pressure and lipids can significantly reduce the increase in arterial stiffness and thus maintain HVA[ 11 ]. Although the relationship between TOD and vascular aging is not fully understood, previous studies have suggested that arterial stiffness or early vascular aging increases cardiac load, aggravates cardiac ischemia and hypoxia, increases blood pulsation transmission, and causes kidney and brain microvascular damage[ 12 – 14 ]. In studies based on Chinese community - dwelling populations, HVA is found to be associated with a reduced risk of first stroke[ 6 ], and on the contrary, accelerated vascular aging was associated with left ventricular diastolic dysfunction (LVDD), left ventricular hypertrophy (LVH), and micro - albuminuria (MAU) [ 15 ]. In our study, NHVA seemed to have higher risks of LMVI elevation and eGFR decline. Therefore, enhanced TOD screening and early intervention in the NHVA population may help reverse or terminate the occurrence of cardiac and renal endpoint events. Our study attempted to use the gold standard cf-PWV as a grouping criterion to verify the association between HVA and cardiovascular disease risk. However, results from cIMT and lgACR were of little statistical significance. This may be due to the fact that there was no strict age restriction or age stratification in this study, the overall age of participants was younger than in previous studies, and the participants were mostly outpatient or inpatient patients who received more drug interventions, resulting in fewer abnormal TOD-related indicators and thus fewer positive results. There are other limitations. First, this study is a cross-sectional correlation study, which can only confirm the correlation between HVA and cardiovascular disease risk, but cannot confirm the causal relationship between the two, and cannot avoid reverse causality. Therefore, prospective follow-up studies are needed to further verify the findings of this study. Second, results of this study referred only to Chinese population and perhaps could not be applied to other populations. Third, exercise has long been thought to be a protective factor against vascular aging. Exercise may reduce blood flow resistance in the central and peripheral vascular beds, thereby significantly reducing hypertension and reducing arterial stiffness in the clinic [ 16 – 18 ]. Family history of early cardiovascular disease has been significantly associated with the risk of early cardiovascular disease[ 19 ]. The above two points were not considered in this study. In conclusion, accelerated vascular aging is related to cardiac and renal TOD, providing a potential target for intervention. A healthy lifestyle with better control of BP, body weight and metabolic profile may help to alleviate vascular aging. Declarations Funding sources Shanghai Municipal Health Commission 20234Y2139, Shanghai Municipal Commission of Science and Technology 23015820100. Conflict of interest disclosure The authors declare that they have no competing interests. Ethics of approval statement The study protocol was reviewed and approved by the Ethics Committee of Ruijin Hospital, Shanghai Jiaotong University School of Medicine (2023 − 127). Author Contribution Huijuan Chao, Qian Wang, and Yanqing Bao contributed equally to this work. Huijuan Chao and Qian Wang were involved in the conception and design of the study, data collection, and drafting of the manuscript. Yanqing Bao contributed to the data analysis and interpretation and critically revised the manuscript for important intellectual content. Yaya Bai assisted with data collection and management. Mark Butlin and Alberto Avolio provided expertise in the measurement of pulse wave velocity and critically reviewed the manuscript. Junli Zuo supervised the overall study, provided guidance on study design and interpretation of results, and approved the final version of the manuscript for submission. All authors read and approved the final manuscript. Data availability statement The data that support the findings of this study are available from the corresponding author, [JL Z], upon reasonable request. References D. Tsavachidou-Fenner et al. , "Gene and protein expression markers of response to combined antiangiogenic and epidermal growth factor targeted therapy in renal cell carcinoma," Ann Oncol, vol. 21, no. 8, pp. 1599-1606, Aug 2010. Q. Cao, J. Wu, X. Wang, and C. Song, "Noncoding RNAs in Vascular Aging," Oxid Med Cell Longev, vol. 2020, p. 7914957, 2020. C. Vlachopoulos, K. Aznaouridis, and C. Stefanadis, "Prediction of cardiovascular events and all-cause mortality with arterial stiffness: a systematic review and meta-analysis," J Am Coll Cardiol, vol. 55, no. 13, pp. 1318-27, Mar 30 2010. A. R. Khoshdel, S. L. Carney, B. R. Nair, and A. Gillies, "Better management of cardiovascular diseases by pulse wave velocity: combining clinical practice with clinical research using evidence-based medicine," Clin Med Res, vol. 5, no. 1, pp. 45-52, Mar 2007. T. J. Niiranen et al. , "Prevalence, Correlates, and Prognosis of Healthy Vascular Aging in a Western Community-Dwelling Cohort: The Framingham Heart Study," Hypertension, vol. 70, no. 2, pp. 267-274, Aug 2017. Y. Yang et al. , "Association between healthy vascular aging and the risk of the first stroke in a community-based Chinese cohort," Aging (Albany NY), vol. 11, no. 15, pp. 5807-5816, Aug 15 2019. S. S. Najjar, A. Scuteri, and E. G. Lakatta, "Arterial aging: is it an immutable cardiovascular risk factor?," Hypertension, vol. 46, no. 3, pp. 454-62, Sep 2005. G. F. Mitchell, "Arterial stiffness and hypertension: chicken or egg?," Hypertension, vol. 64, no. 2, pp. 210-4, Aug 2014. M. Gomez-Sanchez et al. , "Relationship of healthy vascular aging with lifestyle and metabolic syndrome in the general Spanish population. The EVA study," Rev Esp Cardiol (Engl Ed), vol. 74, no. 10, pp. 854-861, Oct 2021. A. Staudt et al. , "Impact of lifestyle and cardiovascular risk factors on early atherosclerosis in a large cohort of healthy adolescents: The Early Vascular Ageing (EVA)-Tyrol Study," Atherosclerosis, vol. 305, pp. 26-33, Jul 2020. K. L. Nowak, M. J. Rossman, M. Chonchol, and D. R. Seals, "Strategies for Achieving Healthy Vascular Aging," Hypertension, vol. 71, no. 3, pp. 389-402, Mar 2018. P. M. Nilsson, E. Lurbe, and S. Laurent, "The early life origins of vascular ageing and cardiovascular risk: the EVA syndrome," J Hypertens, vol. 26, no. 6, pp. 1049-57, Jun 2008. A. Heimdahl and C. E. Nord, "Antimicrobial prophylaxis in oral surgery," Scand J Infect Dis Suppl, vol. 70, pp. 91-101, 1990. J. C. Verhave, P. Fesler, G. du Cailar, J. Ribstein, M. E. Safar, and A. Mimran, "Elevated pulse pressure is associated with low renal function in elderly patients with isolated systolic hypertension," Hypertension, vol. 45, no. 4, pp. 586-91, Apr 2005. H. Ji et al. , "Vascular aging and preclinical target organ damage in community-dwelling elderly: the Northern Shanghai Study," J Hypertens, vol. 36, no. 6, pp. 1391-1398, Jun 2018. H. Tanaka, C. A. DeSouza, and D. R. Seals, "Absence of age-related increase in central arterial stiffness in physically active women," Arterioscler Thromb Vasc Biol, vol. 18, no. 1, pp. 127-32, Jan 1998. H. Tanaka, F. A. Dinenno, K. D. Monahan, C. M. Clevenger, C. A. DeSouza, and D. R. Seals, "Aging, habitual exercise, and dynamic arterial compliance," Circulation, vol. 102, no. 11, pp. 1270-5, Sep 12 2000. S. Ahmadi-Abhari et al. , "Physical Activity, Sedentary Behavior, and Long-Term Changes in Aortic Stiffness: The Whitehall II Study," J Am Heart Assoc, vol. 6, no. 8, Aug 7 2017. Y. Wexler et al. , "Familial tendency for hypertension is associated with increased vascular stiffness," J Hypertens, vol. 39, no. 4, pp. 627-632, Apr 1 2021. Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6259968","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":436508738,"identity":"9b5f2858-cb40-4d99-960c-f93a2fcbb663","order_by":0,"name":"Huijuan Chao","email":"","orcid":"","institution":"Ruijin Hospital, Shanghai Jiao Tong University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Huijuan","middleName":"","lastName":"Chao","suffix":""},{"id":436508739,"identity":"c3bc97a2-a942-4e9d-84c9-19730eae3a53","order_by":1,"name":"Qian Wang","email":"","orcid":"","institution":"Ruijin Hospital, Shanghai Jiao Tong University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Qian","middleName":"","lastName":"Wang","suffix":""},{"id":436508740,"identity":"de25412c-a1bd-47bf-bef0-e32009271a10","order_by":2,"name":"Yanqing Bao","email":"","orcid":"","institution":"Zhenxin Community Health Service Center","correspondingAuthor":false,"prefix":"","firstName":"Yanqing","middleName":"","lastName":"Bao","suffix":""},{"id":436508741,"identity":"200dca89-297c-49a0-94f6-e9e70f1512f4","order_by":3,"name":"Yaya Bai","email":"","orcid":"","institution":"Ruijin Hospital, Shanghai Jiao Tong University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Yaya","middleName":"","lastName":"Bai","suffix":""},{"id":436508742,"identity":"1fb368d5-0490-4735-95e2-9305abf7184c","order_by":4,"name":"Mark Butlin","email":"","orcid":"","institution":"Macquarie University","correspondingAuthor":false,"prefix":"","firstName":"Mark","middleName":"","lastName":"Butlin","suffix":""},{"id":436508743,"identity":"1826c07f-c0dc-4fb7-a062-c729d4750291","order_by":5,"name":"Alberto Avolio","email":"","orcid":"","institution":"Macquarie University","correspondingAuthor":false,"prefix":"","firstName":"Alberto","middleName":"","lastName":"Avolio","suffix":""},{"id":436508744,"identity":"f3aaa44c-ef4b-47fb-aeeb-97a597a8c63e","order_by":6,"name":"Junli Zuo","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAwklEQVRIiWNgGAWjYLACCQYGGTb2xsaHH0jRwsPGc7jZWIIUi3gYJNLbBHiIUSo/I/kAg2XbHR4+yYdtQOvs5HQbCGhhnJGWwCDZ9oyHTTqx7UEBQ7Kx2QECWpglcsx/SLYdBmlpN5BgOJC4jZAWNokcAwawFsmDbRI8xGjhgWuRYCRSiwTPswQGiXNALTyJwEA2IMIv8u3JB5glyg7Lybcff/jwQ4WdHEEtIMCMiEEDIpSDACPx6WQUjIJRMApGJAAAkww2bH3zfuoAAAAASUVORK5CYII=","orcid":"","institution":"Ruijin Hospital, Shanghai Jiao Tong University School of Medicine","correspondingAuthor":true,"prefix":"","firstName":"Junli","middleName":"","lastName":"Zuo","suffix":""}],"badges":[],"createdAt":"2025-03-19 09:23:23","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6259968/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6259968/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":79771776,"identity":"015a9a9f-f9ba-4111-9545-cfb968825a7b","added_by":"auto","created_at":"2025-04-02 13:25:11","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":978394,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6259968/v1/5d7dfd24-b151-4d3b-8b15-f15a6598cc75.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Association Between Healthy and Non-Healthy Vascular Aging with Hypertensive Target Organ Damage","fulltext":[{"header":"Introduction","content":"\u003cp\u003eWith the exacerbation of population aging, the prevalence of cardiovascular disease has also exhibited an upward trend. Vascular aging, serving as the initial stage of cardiovascular disease, is significantly associated with adverse cardiovascular events, cardiovascular mortality, and disability[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. As individuals age, vascular structure and function undergo degenerative alterations, culminating in vascular stiffening\u0026mdash;a phenomenon referred to as vascular aging[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. These modifications in vascular structure and function constitute significant independent risk factors for the onset and progression of cardiovascular events and serve as optimal indicators or surrogate endpoints for forecasting cardiovascular risk. The extent of vascular aging can be quantified through the analysis of arterial stiffness.\u003c/p\u003e \u003cp\u003ePulse wave velocity (PWV) is one of the most extensively recognized and utilized assessment methodologies. Notably, carotid-femoral artery pulse wave velocity (cf-PWV) is considered the gold standard for PWV measurement and is acknowledged as a predictive and prognostic marker of cardiovascular risk[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. An increment of one standard deviation in cf-PWV corresponds to an elevated risk of mortality and cardiovascular events, equating to a risk augmentation greater than 1.5 times that associated with a 10-year increase in age or a 10 mm Hg rise in systolic blood pressure (SBP)[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Vascular health in the elderly is characterized by a cf-PWV of less than 7.6 m/s, which aligns with the mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation of the reference population aged 30 years [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eHealthy vascular aging (HVA) denotes the absence of hypertension and a lack of substantial increases in arterial stiffness as indicated by PWV. Research has demonstrated that HVA is correlated with a diminished incidence of cardiovascular disease in Western populations[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. In a chinese cohort study included 11,474 participants aged\u0026thinsp;\u0026ge;\u0026thinsp;50 years concludes that HVA significantly reduces the risk of first stroke in a community-based Chinese population, suggesting that assessing vascular aging could be useful for stroke risk assessment and prevention[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. However, the majority of studies have utilized brachial-ankle pulse wave velocity (ba-PWV) as an indicator of arterial stiffness, rather than the gold standard cf-PWV. This study categorized participants based on peripheral blood pressure and cf-PWV into two groups: HVA and non-healthy vascular aging (NHVA), to examine the impact of these conditions on target organ damage (TOD) in hypertension. HVA was defined as no history of hypertension and cf-PWV\u0026thinsp;\u0026lt;\u0026thinsp;7.6m/s; NHVA was defined as a history of hypertension or cf-PWV\u0026thinsp;\u0026ge;\u0026thinsp;7.6 m/s.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Study population\u003c/h2\u003e \u003cp\u003eA total of 1,257 elderly patients who underwent physical examinations and were hospitalized at the northern branch of Shanghai Ruijin Hospital between December 2017 and September 2021 was included in this study. Exclusion criteria were: (1) severe cardiovascular conditions, including acute coronary syndrome, heart failure (both acute and chronic), severe arrhythmia, myocarditis, and Marfan syndrome; (2) severe neurological disorders, such as acute stroke, traumatic brain injury, and epilepsy; (3) severe renal disease, including acute glomerulonephritis, acute renal insufficiency, and renal tumors; (4) pulmonary conditions, such as severe pulmonary hypertension, acute and chronic pulmonary embolism, pulmonary heart disease, and acute asthma attacks; (5) patients with lower extremity arterial stenosis; (6) other significant medical conditions, including severe hepatic insufficiency, pregnancy, severe anemia, thyroid disorders, and malignant tumors. This study was approved by the Ethics Committee of Ruijin Hospital Affiliated to Shanghai Jiaotong University School of Medicine (Approval No. 2023\u0026thinsp;\u0026minus;\u0026thinsp;127).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 General Data Collection\u003c/h2\u003e \u003cp\u003eThe general data of all participants were systematically gathered, encompassing personal identifiers such as name, gender, and age, alongside physiological metrics such as weight, height, duration of hypertension, history of diabetes mellitus, smoking status, presence of specific diseases, medication history, and Body Mass Index (BMI). For the purpose of this study, current smokers were defined as individuals consuming more than one cigarette daily, while former smokers referred to those who had ceased smoking within the past six months.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Brachial Blood Pressure Measurement\u003c/h2\u003e \u003cp\u003eFollowing a five-minute rest period in a seated position, the brachial artery blood pressure of each participant was measured three times using an electronic sphygmomanometer (model HEM907, Omron, Japan). The measurements included brachiall systolic blood pressure (SBP), brachial diastolic blood pressure (DBP), and brachial pulse pressure (bPP). The average values from these readings were then calculated for further analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Laboratory Tests\u003c/h2\u003e \u003cp\u003eFasting blood samples were collected in the morning to measure total cholesterol (TC), triglycerides (TG), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), serum creatinine (Scr), and fasting blood glucose (FBG). The estimated glomerular filtration rate (eGFR) was calculated using the Chinese Simplified MDRD formula: eGFR (ml/min/1.73 m\u003csup\u003e2\u003c/sup\u003e)\u0026thinsp;=\u0026thinsp;175 \u0026times; Cr (mg/dl)\u003csup\u003e\u0026minus;1.234\u003c/sup\u003e \u0026times; age (years old)\u003csup\u003e\u0026minus;0.179\u003c/sup\u003e \u0026times; 0.79 (if female patient).This formula has been validated for use in Chinese patients with chronic kidney disease (National EGFR Project Collaboration Group, 2006). Additionally, mid-morning urine was collected on the second day of hospitalization to determine the urinary albumin-to-creatinine ratio (ACR).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Measurement of pulse wave velocity\u003c/h2\u003e \u003cp\u003eCf-PWV was measured using the SphygmoCor device (SphygmoCor-Px V8.0, AtCor Medical, Australia). The instrument's applanation tonometer probe was placed at the strongest pulsation of the right radial artery and femoral artery. The device automatically obtained 20 pulse waveforms for analysis, and cf-PWV value was calculated using to the distance between the sites of two pulse waves, after subtraction of the distance between the supra-sternal notch and the carotid site, and the pulse wave transit time. The measurements were repeated twice.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Measurement of left ventricular mass\u003c/h2\u003e \u003cp\u003eCardiac two-dimensional, M-mode, color Doppler and tissue Doppler ultrasound examination were performed by a color ultrasound diagnostic instrument. Left atrial diameter (LAD), left ventricular end-diastolic diameter (LVEDD) and left ventricular end-systolic diameter (LVEDD) were measured in the parasternal long axis view, including left ventricular end-systolic diameter (LVESD), interventricular septal thickness (IVST), and left ventricular posterior wall thickness (LVPW). Simpson method was used to calculate left atrial volume (LAV) and left ventricular ejection fraction (LVEF). Left ventricular mass (LVM) was calculated according to the following formula from which was determined left ventricular mass index (LVMI) After 3 consecutive cardiac cycles, the average value was calculated, and the left ventricular mass (LVM) was corrected according to Devereux's formula: LVM (g)\u0026thinsp;=\u0026thinsp;0.8\u0026times;1.04\u0026times;[(IVST\u0026thinsp;+\u0026thinsp;LVDD\u0026thinsp;+\u0026thinsp;LVPWT) \u003csup\u003e3\u003c/sup\u003e-LVDD \u003csup\u003e3\u003c/sup\u003e]\u0026thinsp;+\u0026thinsp;0.6, LVMI\u0026thinsp;=\u0026thinsp;LVM (g)/ body surface area, where body surface area\u0026thinsp;=\u0026thinsp;0.0061\u0026times; height (cm)\u0026thinsp;+\u0026thinsp;0.0128\u0026times; weight (kg) -0.1529.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Measurement of carotid intima-media thickness\u003c/h2\u003e \u003cp\u003eThe subjects were placed in the supine position with the neck fully exposed. Color doppler ultrasound was used to assess the presence of plaque, plaque location and number, and echo in the intima of the bilateral common carotid artery and bifurcation, internal carotid artery, external carotid artery and each main trunk and branch. Carotid intima-media thickness (cIMT) was measured on both left and right common carotid artery starting\u0026thinsp;~\u0026thinsp;1.5 cm proximal to the carotid artery bulb. Three recordings were taken, and the mean value was calculated for each side. \u0026ldquo;Plaque\u0026thinsp;=\u0026thinsp;0\u0026rdquo; referred to the absence of plaque, while \u0026ldquo;plaque\u0026thinsp;=\u0026thinsp;1\u0026rdquo; referred to the presence of plaque.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.7 Relevant Diagnostic Criteria and Grouping\u003c/h2\u003e \u003cp\u003e \u003cem\u003e2.7.1 Hypertension\u003c/em\u003e. Hypertension was defined as SBP\u0026thinsp;\u0026ge;\u0026thinsp;140 mmHg, and/or a diastolic blood pressure\u0026thinsp;\u0026ge;\u0026thinsp;90 mmHg, or the current use of antihypertensive medication. Diabetes was diagnosed based on a fasting plasma glucose level exceeding 7.0 mmol/L, and/or a 2-hour plasma glucose level exceeding 11.1 mmol/L during an oral glucose tolerance test, or the current use of antidiabetic medications.\u003c/p\u003e \u003cp\u003e \u003cem\u003e2.7.2 Target Organ Damage (TOD).\u003c/em\u003e Indicators of TOD were: (1) LVMI: \u0026ge;115 g/m\u0026sup2; for males and \u0026ge;\u0026thinsp;95 g/m\u0026sup2; for females; (2) Chronic Kidney Disease: estimated Glomerular Filtration Rate (eGFR)\u0026thinsp;\u0026lt;\u0026thinsp;60 mL/min/1.73m\u0026sup2; and/or Urinary Albumin-to-Creatinine Ratio(ACR)\u0026thinsp;\u0026gt;\u0026thinsp;30mg/g;(3)cIMT\u0026thinsp;\u0026ge;\u0026thinsp;900\u0026micro;m(including intimal thickening and plaque). In this study, the presence of TOD was defined as the presence of any of the aforementioned indicators.\u003c/p\u003e \u003cp\u003e \u003cem\u003e2.7.3 Population stratification.\u003c/em\u003e The population was stratified into two groups based on blood pressure cf-PWV: HVA and NHVA. HVA was defined as individuals with no history of hypertension and a cf-PWV\u0026thinsp;\u0026lt;\u0026thinsp;7.6 m/s; NHVA was defined as individuals with a history of hypertension and/or a cfPWV\u0026thinsp;\u0026ge;\u0026thinsp;7.6 m/s.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003e3. Statistical analysis\u003c/h3\u003e\n\u003cp\u003eSPSS 26.0 software package (SPSS, Chicago, IL) and Excel were used for statistical analysis. Continuous variables are expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD, and frequencies (percentage) are reported for categorical variables after testing for normality Continuous and categorical variables were compared using t-test and Chi-square test respectively for males and females. To compare continuous and categorical variables between HVA and NHVA, t-test and Chi-square test respectively were used. Multivariate linear regression was conducted to compare the TOD between the HVA and NVHA groups. Multivariate binary logistic regression analysis was used on TOD of HVA and NHVA groups. A two-sided \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Population characteristics\u003c/h2\u003e \u003cp\u003eA total of 1257 patients was included in this study, with an average age of 53.13 ± 12.65 years, 807(64.2%) males, 382(30.4%) hypertensive patients taking antihypertensive treatment, 176(13.7%) diabetic patients. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows the general basic data between male and female groups, including cardiovascular risk factors, combined clinical diseases, and indicators of TOD in hypertension. Compared with the female group, the male group was younger (52.09 ± 12.55 VS 54.99 ± 12.63 years, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001), more patients were older than 65 years (148 (18.3%), \u003cem\u003ep\u003c/em\u003e = 0.003), and more smokers (24.7% VS 3.6%, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001). BMI (25.93 ± 3.75kg/m\u003csup\u003e2\u003c/sup\u003e VS 24.23 ± 3.93 kg/m\u003csup\u003e2\u003c/sup\u003e, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001) and fasting blood glucose (5.90 ± 1.78 mmol/l VS 5.68 ± 1.87mmol/l, \u003cem\u003ep\u003c/em\u003e = 0.064) were higher. Triglyceride was higher (2.07 ± 1.76mmol/l VS 1.62 ± 1.05mmol/l, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001), cholesterol was lower (4.69 ± 1.09mmol/l VS 4.97 ± 1.02 mmol/l, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001). Lower high density lipoprotein (1.08 ± 0.35 mmol/l VS 1.26 ± 0.29mmol/l, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001), higher low density lipoprotein (3.06 ± 0.81 mmol/l VS 3.21 ± 0.79 mmol/l, \u003cem\u003ep\u003c/em\u003e = 0.005); Brachial diastolic blood pressure was higher (78.12 ± 11.50 mmHg VS 74.13 ± 12.35mmHg, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001), and the brachial pulse pressure was lower (53.31 ± 12.52 mmHg VS 55.12 ± 14.61mmHg, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001). Compared with asymptomatic TOD, the creatinine clearance rate of male and female patients with hypertension was higher (92.17 ± 17.55 mL/min 1.73 m\u003csup\u003e2\u003c/sup\u003e VS 86.27 ± 15.19 mL/min 1.73 m\u003csup\u003e2\u003c/sup\u003e, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001). LVMI was higher (107.67 ± 26.52 g/m\u003csup\u003e2\u003c/sup\u003e VS 96.08 ± 24.60 g/m\u003csup\u003e2\u003c/sup\u003e, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001). cf-PWV was higher in males (8.35 ± 1.96 m/s VS 8.06 ± 2.11 m/s, \u003cem\u003ep\u003c/em\u003e = 0.018), and cf-PWV ≥ 7.6m/s was more common in males (492, 61.0%, \u003cem\u003ep =\u003c/em\u003e 0.016).\u003c/p\u003e \u003cp\u003e \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=\"char\" char=\".\" 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\u003eClinical Characteristics of participants by Sex\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\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOverall\u003c/p\u003e \u003cp\u003eN = 1257\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMen\u003c/p\u003e \u003cp\u003eN = 807\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWomen\u003c/p\u003e \u003cp\u003eN = 450\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-Value\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, years\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e53.13±12.65\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e52.09 ± 12.55\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e54.99 ± 12.63\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" 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\u003eSmoking history,n(%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e215(17.1)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e199(24.7)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16(3.6)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" 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\u003eBody mass index, kg/m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25.32±3.90\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25.93 ± 3.75\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24.23 ± 3.93\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" 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\u003eHypertension, n (%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e398(31.7)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e293(36.3)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e105(23.3)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" 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\u003eAntihypertensive treatment, n (%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e382(30.4)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e282(34.9)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e100(22.2)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" 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\u003eDiabetes treatment, n (%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e176(13.7)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e120(14.6)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e56(12.1)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.218\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal triglycerides, mmol/L\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.91±1.56\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.07 ± 1.76\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.62 ± 1.05\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" 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\u003eTotal cholesterol, mmol/l\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.79±1.07\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.69 ± 1.09\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.97 ± 1.02\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" 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, mmol/l\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.15±0.34\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.08 ± 0.35\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.26 ± 0.29\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" 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, mmol/l\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.12±0.81\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.06 ± 0.81\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.21 ± 0.79\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFasting plasmaglucose, mmol/l\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.82±1.81\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.90 ± 1.78\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.68 ± 1.87\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.064\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBrachial SBP, mmHg\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e130.65±18.56\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e131.43 ± 17.38\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e129.25 ± 20.45\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.056\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBrachial DBP, mmHg\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e76.69±11.96\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e78.12 ± 11.50\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e74.13 ± 12.35\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" 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\u003eHeart rate ,bpm\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e69.25±10.39\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e69.20 ± 10.23\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e69.34 ± 10.67\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.819\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBrachial pulse pressure,mmHg\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e53.96±13.33\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e53.31 ± 12.52\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e55.12 ± 14.61\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.027\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eeGFR, mL/min 1.73 m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e90.07±16.98\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e92.17 ± 17.55\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e86.27 ± 15.19\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" 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\u003eUrinary ACR, mg/mmol\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.43±28.72\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.78 ± 34.29\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.38 ± 4.54\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLVMI, g/m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e104.05±26.35\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e107.67 ± 26.52\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e96.08 ± 24.60\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" 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\u003ecIMT, mm\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.74±0.14\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.74 ± 0.14\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.73 ± 0.12\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.095\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecf-PWV m/s\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.24±2.02\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.35 ± 1.96\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.06 ± 2.11\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.018\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecf-PWV ≥ 7.6 m/s\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e735(58.5)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e492(61.0)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e243(54.0)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.016\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u0026gt;65year\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e262(20.8)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e148(18.3)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e114(25.3)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003eData are means standard deviation or numbers with percentages in parentheses. Student t test and chi-squared test were conducted to compare the differences between men and women for quantitative and qualitative variables, respectively; cf-PWV, carotid femoral pulse wave velocity; eGFR, estimated glomerular filtration rate; cIMT, carotid intima-media thickness; LVMI, left ventricular mass index; ACR, urine albumin-creatinine ratio.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e \u003cp\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e4.12 Healthy and Non-Healthy Vascular Aging\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows that the total population was further divided into HVA and NHVA groups. There were 857 patients (68.2%) in NHVA group, and the average Age was older than that in HVA group (55.62 ± 12.40 years). There were 234 patients (27.3%) older than 65 years, and 583 patients (68.0%) were male. Compared with the HVA group, there were more smokers (8.8% VS 21.0%, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001) and higher BMI (25.87 ± 3.80kg/m\u003csup\u003e2\u003c/sup\u003e VS 24.13 ± 3.82 kg/m\u003csup\u003e2\u003c/sup\u003e, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001). Fasting blood glucose (6.00 ± 1.94mmol/l VS 5.40 ± 1.38mmol/l, \u003cem\u003ep\u003c/em\u003e = 0.064) and triglyceride (2.05 ± 1.75mmol/l VS1.55 ± 0.87mmol/l, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001) were higher. Cholesterol (4.76 ± 1.12mmol/l VS 4.85 ± 0.95mmol/l, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001) and high density lipoprotein (1.11 ± 0.32 mmol/l vs 1.23 ± 0.37mmol/l, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001) were lower. Lower density lipoprotein (3.10 ± 0.83mmol/l VS 3.17 ± 0.76 mmol/l, \u003cem\u003ep\u003c/em\u003e = 0.005) and higher brachial SBP systolic (136.12 ± 17.34mmHg VS 118.93 ± 15.40mmHg, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001). Brachial DBP (79.39 ± 11.61 mmHg VS 70.92 ± 10.61mmHg, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001) and barchial pulse pressure (56.74 ± 13.56 mmHg VS 48.01 ± 10.61mmHg, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001) were higher. Compared with asymptomatic TOD, the creatinine clearance rate was lower in the two groups (88.72 ± 17.27 mL/min 1.73 m\u003csup\u003e2\u003c/sup\u003e VS 93.41 ± 15.79 mL/min 1.73 m\u003csup\u003e2\u003c/sup\u003e, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001). Left ventricular hypertrophy index (108.00 ± 26.44 g/m\u003csup\u003e2\u003c/sup\u003eVS92.25 ± 22.29g/m\u003csup\u003e2\u003c/sup\u003e, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001) and cIMT(0.75 ± 0.14 mmVS0.70 ± 0.12mm, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001) were higher. cf-PWV was higher (9.06 ± 1.91 m/s VS 6.5 ± 0.74 m/s, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001).\u003c/p\u003e \u003cp\u003e \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=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\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\u003eClinical Characteristics of participants by HVA and NHV\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\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOverall\u003c/p\u003e \u003cp\u003eN = 1257\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHVA\u003c/p\u003e \u003cp\u003eN = 400\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNHVA\u003c/p\u003e \u003cp\u003eN = 857\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-Value\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, years\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e53.13±12.65\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e47.8 ± 11.51\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e55.62 ± 12.40\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" 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\u003eAge\u0026gt;65year\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e262(20.8)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28(7.0)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e234(27.3)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" 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\u003eSex\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e807(64.2)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e224(56.0)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e583(68.0)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" 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\u003eSmoking history,n(%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e215(17.1)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35(8.8)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e180(21.0)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" 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\u003eBody mass index, kg/m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25.32±3.90\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24.13 ± 3.82\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25.87 ± 3.80\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" 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\u003eTotal triglycerides, mmol/L\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.91±1.56\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.55 ± 0.87\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.05 ± 1.75\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" 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\u003eTotal cholesterol, mmol/l\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.79±1.07\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.85 ± 0.95\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.76 ± 1.12\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" 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, mmol/l\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.15±0.34\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.23 ± 0.37\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.11 ± 0.32\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" 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, mmol/l\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.12±0.81\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.17 ± 0.76\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.10 ± 0.83\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.207\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFasting plasmaglucose, mmol/l\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.82±1.81\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.40 ± 1.38\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.00 ± 1.94\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" 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\u003ePeripheral systolic blood pressure, mmHg\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e130.65±18.56\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e118.93 ± 15.40\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e136.12 ± 17.34\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" 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\u003ePeripheral diastolic pressure, mmHg\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e76.69±11.96\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e70.92 ± 10.61\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e79.39 ± 11.61\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" 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\u003eHeart rate bpm\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e69.25±10.39\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e68.33 ± 10.43\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e69.68 ± 10.35\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.031\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePeripheral pulse pressure, mmHg\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e53.96±13.33\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e48.01 ± 10.61\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e56.74 ± 13.56\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" 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\u003eCreatinine clearance rate, mL/min 1.73 m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e90.07±16.98\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e93.41 ± 15.79\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e88.72 ± 17.27\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" 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\u003eUrinary creatinine ratio rate, mg/mmol\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.43±28.72\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.83 ± 2.25\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.67 ± 33.17\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" 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\u003eLeft ventricular mass index, g/m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e104.05±26.35\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e92.25 ± 22.29\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e108.00 ± 26.44\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" 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\u003eCarotid intima-media thickness, mm\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.74±0.14\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.70 ± 0.12\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.75 ± 0.14\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" 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\u003ecf-PWV m/s\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.24±2.02\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.5 ± 0.74\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.06 ± 1.91\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003eData are means standard deviation or numbers with percentages in parentheses. Student t test and chi-squared test were conducted to compare the differences between men and women for quantitative and qualitative variables, respectively; cf-PWV, carotid femoral pulse wave velocity. HVA, healthy vascular aging. NHVA, non-healthy vascular aging. cf-PWV, carotid femoral pulse wave velocity.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e \u003cp\u003e\u003c/p\u003e \u003cdiv id=\"Sec15\" class=\"Section3\"\u003e \u003ch2\u003e4.1.3 Target Organ Damage\u003c/h2\u003e \u003cp\u003eTaking TOD indicators as dependent variables, both general cardiovascular risk factors and the groups of HVA versus NHVA were incorporated into a multiple linear regression model. The analysis revealed a statistically significant correlation between the two vascular aging groups and both LMVI (\u003cem\u003ep\u003c/em\u003e = 0.003) and eGFR (\u003cem\u003ep\u003c/em\u003e = 0.022). (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e \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=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\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\u003eMultiple linear regression was used to compare the target organ damage between the healthy vascular aging group and the non-healthy vascular aging group\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\"\u003e \u003cp\u003eTOD\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFactors\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStd. Error\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eBeta\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLVMI\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=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.124\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"7\" rowspan=\"8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHypertension\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.662\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.315\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.775\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSmoking history\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.715\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.209\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.046\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.219\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBody mass index\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.827\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.261\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.125\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.481\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.081\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.237\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal triglycerides\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.412\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.724\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.022\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.569\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLow density lipoprotein\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-1.984\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.158\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.064\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.087\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFasting plasmaglucose\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.57\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.997\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHVA/NHVA\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8.421\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.803\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.141\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecIMT\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=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.166\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"7\" rowspan=\"8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHypertension\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.786\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSmoking history\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.028\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.093\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBody mass index\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.081\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.041\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.4\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal triglycerides\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.005\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\u003e-0.052\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.183\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLow density lipoprotein\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.001\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.004\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.914\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFasting plasmaglucose\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.053\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.166\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHVA/NHVA\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.928\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elgACR\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=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"7\" rowspan=\"8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHypertension\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.006\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.028\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.011\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.835\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSmoking history\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.031\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.025\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.051\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.204\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBody mass index\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.002\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.026\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.535\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.001\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.045\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.295\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal triglycerides\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.088\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.034\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLow density lipoprotein\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.027\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.043\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFasting plasmaglucose\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.091\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.029\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHVA/NHVA\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.079\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.034\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.124\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.022\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eeGFR\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=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.145\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"7\" rowspan=\"8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHypertension\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.227\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.189\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.849\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSmoking history\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.466\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.276\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.715\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBody mass index\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.067\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.132\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.016\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.611\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.493\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.042\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.377\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal triglycerides\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.211\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.324\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.02\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.515\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLow density lipoprotein\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-2.377\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.609\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.117\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFasting plasmaglucose\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.716\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.282\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.078\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHVA/NHVA\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.532\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.31\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.015\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.685\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003eHVA, healthy vascular aging. NHVA, non-healthy vascular aging. eGFR, estimated glomerular filtration rate; CIMT, carotid intima-media thickness; LVMI, left ventricular mass index; ACR, urine albumin-creatinine ratio.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e \u003cp\u003e\u003c/p\u003e \u003cp\u003eIn this study, binary stepwise logistic regression was used to evaluate the influence of age, gender, BMI, hypertension, fasting blood glucose, triglyceride, low-density lipoprotein, and vascular aging on TOD of patients. Logistic regression analysis showed that vascular aging was significantly associated with LVMI (OR = 2.201 [1.299–3.73], \u003cem\u003ep\u003c/em\u003e = 0.003). (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e \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=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\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\u003eMultivariate binary logistic regression analysis of healthy vascular aging group and non-healthy vascular aging group target organ damage\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\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFactors\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eeGFR\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\" morerows=\"7\" rowspan=\"8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSmoking history\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.098\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.396\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.852–6.739\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBody mass index\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.537\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.965\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.863–1.08\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.072\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.033–1.113\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.325\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.543\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.650–3.663\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHypertension\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.909\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.95\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.392–2.299\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal cholesterol\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.845\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.897\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.301–2.674\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLow density lipoprotein\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.489\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.637\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.406–6.602\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFasting plasmaglucose\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.857\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.017\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.844–1.226\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHVA/NHVA\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.53\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.475\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.438–4.964\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eACR\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\" morerows=\"7\" rowspan=\"8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSmoking history\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.684\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.103\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.688–1.77\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBody mass index\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.859\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.005\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.950–1.064\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.889\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.999\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.980–1.018\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.546\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.852\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.506–1.434\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHypertension\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.065\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.684\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.967–2.93\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal cholesterol\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.512\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.124\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.793–1.595\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLow density lipoprotein\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.978\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.993\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.623–1.585\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFasting plasmaglucose\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.14\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.035–1.256\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHVA/NHVA\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.092\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.978\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.895–4.373\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLVMI\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\" morerows=\"7\" rowspan=\"8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSmoking history\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.668\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.914\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.604–1.382\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBody mass index\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.075\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.045\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.995–1.097\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.046\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.031–1.062\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.158\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.460–3.191\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHypertension\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.883\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.03\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.691–1.536\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal cholesterol\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.524\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.847\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.509–1.411\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLow density lipoprotein\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.66\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.158\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.602–2.227\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFasting plasmaglucose\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.874\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.992\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.901–1.093\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHVA/NHVA\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.201\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.299–3.73\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003eHVA, healthy vascular aging. NHVA, non-healthy vascular aging. eGFR, estimated glomerular filtration rate; CIMT, carotid intima-media thickness; LVMI, left ventricular mass index; ACR, urine albumin-creatinine ratio; OR, Odds Ratio.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e \u003cp\u003e\u003c/p\u003e \u003c/div\u003e\u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eAfter grouping by cf-PWV and blood pressure, we found that 31.8% of participants met the HVA criteria. Compared with the HVA group, the NHVA group was older, with more smokers, and higher levels of BMI, blood glucose, lipids, and blood pressure. It also had elevated TOD indicators such as creatinine clearance rate, left ventricular hypertrophy, and cIMT. Multiple linear regression showed the NHVA was more correlated with the increase of LMVI and the decrease of eGFR. Binary logistic regression indicated NHVA had a higher risk of LMVI elevation (OR = 2.201 [1.299–3.73], \u003cem\u003ep\u003c/em\u003e = 0.003).\u003c/p\u003e\u003cp\u003eAge, as a major risk factor for cardiovascular disease, leads to the dilation of elastic arteries, thickening and stiffening of artery walls, and decline of endothelial function even in seemingly healthy people, with differences between individuals[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Blood pressure is another important factor in vascular aging and a key indicator for HVA assessment. Hypertension is closely related to the increase in arterial stiffness, and the two often interact[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].Metabolic syndrome, including components such as obesity, dyslipidemia, and hyperglycemia, is closely related to vascular aging[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. The strong association between smoking and early vascular aging is well known[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].Consistent with previous studies, we found that the NHVA group was higher than the HVA group in terms of age, percentage of smokers, BMI, blood glucose, lipids, and blood pressure. Management of these conventional cardiovascular risk factors may contribute to achieving HVA. It has been shown that reducing body mass, maintaining a healthy dietary pattern, and using medications to lower blood pressure and lipids can significantly reduce the increase in arterial stiffness and thus maintain HVA[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eAlthough the relationship between TOD and vascular aging is not fully understood, previous studies have suggested that arterial stiffness or early vascular aging increases cardiac load, aggravates cardiac ischemia and hypoxia, increases blood pulsation transmission, and causes kidney and brain microvascular damage[\u003cspan additionalcitationids=\"CR13\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e–\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. In studies based on Chinese community - dwelling populations, HVA is found to be associated with a reduced risk of first stroke[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], and on the contrary, accelerated vascular aging was associated with left ventricular diastolic dysfunction (LVDD), left ventricular hypertrophy (LVH), and micro - albuminuria (MAU) [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. In our study, NHVA seemed to have higher risks of LMVI elevation and eGFR decline. Therefore, enhanced TOD screening and early intervention in the NHVA population may help reverse or terminate the occurrence of cardiac and renal endpoint events.\u003c/p\u003e\u003cp\u003eOur study attempted to use the gold standard cf-PWV as a grouping criterion to verify the association between HVA and cardiovascular disease risk. However, results from cIMT and lgACR were of little statistical significance. This may be due to the fact that there was no strict age restriction or age stratification in this study, the overall age of participants was younger than in previous studies, and the participants were mostly outpatient or inpatient patients who received more drug interventions, resulting in fewer abnormal TOD-related indicators and thus fewer positive results.\u003c/p\u003e\u003cp\u003eThere are other limitations. First, this study is a cross-sectional correlation study, which can only confirm the correlation between HVA and cardiovascular disease risk, but cannot confirm the causal relationship between the two, and cannot avoid reverse causality. Therefore, prospective follow-up studies are needed to further verify the findings of this study. Second, results of this study referred only to Chinese population and perhaps could not be applied to other populations. Third, exercise has long been thought to be a protective factor against vascular aging. Exercise may reduce blood flow resistance in the central and peripheral vascular beds, thereby significantly reducing hypertension and reducing arterial stiffness in the clinic [\u003cspan additionalcitationids=\"CR17\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e–\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Family history of early cardiovascular disease has been significantly associated with the risk of early cardiovascular disease[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. The above two points were not considered in this study.\u003c/p\u003e\u003cp\u003eIn conclusion, accelerated vascular aging is related to cardiac and renal TOD, providing a potential target for intervention. A healthy lifestyle with better control of BP, body weight and metabolic profile may help to alleviate vascular aging.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003cb\u003eFunding sources\u003c/b\u003e \u003c/p\u003e \u003cp\u003eShanghai Municipal Health Commission 20234Y2139, Shanghai Municipal Commission of Science and Technology 23015820100.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e\u003cp\u003e \u003ch2\u003eConflict of interest disclosure\u003c/h2\u003e \u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eEthics of approval statement\u003c/h2\u003e \u003cp\u003e The study protocol was reviewed and approved by the Ethics Committee of Ruijin Hospital, Shanghai Jiaotong University School of Medicine (2023\u0026thinsp;\u0026minus;\u0026thinsp;127).\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eHuijuan Chao, Qian Wang, and Yanqing Bao contributed equally to this work. Huijuan Chao and Qian Wang were involved in the conception and design of the study, data collection, and drafting of the manuscript. Yanqing Bao contributed to the data analysis and interpretation and critically revised the manuscript for important intellectual content. Yaya Bai assisted with data collection and management. Mark Butlin and Alberto Avolio provided expertise in the measurement of pulse wave velocity and critically reviewed the manuscript. Junli Zuo supervised the overall study, provided guidance on study design and interpretation of results, and approved the final version of the manuscript for submission. All authors read and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eData availability statement\u003c/h2\u003e \u003cp\u003eThe data that support the findings of this study are available from the corresponding author, [JL Z], upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eD. Tsavachidou-Fenner\u003cem\u003e et al.\u003c/em\u003e, \u0026quot;Gene and protein expression markers of response to combined antiangiogenic and epidermal growth factor targeted therapy in renal cell carcinoma,\u0026quot; \u003cem\u003eAnn Oncol, \u003c/em\u003evol. 21, no. 8, pp. 1599-1606, Aug 2010.\u003c/li\u003e\n\u003cli\u003eQ. Cao, J. Wu, X. Wang, and C. Song, \u0026quot;Noncoding RNAs in Vascular Aging,\u0026quot; \u003cem\u003eOxid Med Cell Longev, \u003c/em\u003evol. 2020, p. 7914957, 2020.\u003c/li\u003e\n\u003cli\u003eC. Vlachopoulos, K. Aznaouridis, and C. Stefanadis, \u0026quot;Prediction of cardiovascular events and all-cause mortality with arterial stiffness: a systematic review and meta-analysis,\u0026quot; \u003cem\u003eJ Am Coll Cardiol, \u003c/em\u003evol. 55, no. 13, pp. 1318-27, Mar 30 2010.\u003c/li\u003e\n\u003cli\u003eA. R. Khoshdel, S. L. Carney, B. R. Nair, and A. Gillies, \u0026quot;Better management of cardiovascular diseases by pulse wave velocity: combining clinical practice with clinical research using evidence-based medicine,\u0026quot; \u003cem\u003eClin Med Res, \u003c/em\u003evol. 5, no. 1, pp. 45-52, Mar 2007.\u003c/li\u003e\n\u003cli\u003eT. J. Niiranen\u003cem\u003e et al.\u003c/em\u003e, \u0026quot;Prevalence, Correlates, and Prognosis of Healthy Vascular Aging in a Western Community-Dwelling Cohort: The Framingham Heart Study,\u0026quot; \u003cem\u003eHypertension, \u003c/em\u003evol. 70, no. 2, pp. 267-274, Aug 2017.\u003c/li\u003e\n\u003cli\u003eY. Yang\u003cem\u003e et al.\u003c/em\u003e, \u0026quot;Association between healthy vascular aging and the risk of the first stroke in a community-based Chinese cohort,\u0026quot; \u003cem\u003eAging (Albany NY), \u003c/em\u003evol. 11, no. 15, pp. 5807-5816, Aug 15 2019.\u003c/li\u003e\n\u003cli\u003eS. S. Najjar, A. Scuteri, and E. G. Lakatta, \u0026quot;Arterial aging: is it an immutable cardiovascular risk factor?,\u0026quot; \u003cem\u003eHypertension, \u003c/em\u003evol. 46, no. 3, pp. 454-62, Sep 2005.\u003c/li\u003e\n\u003cli\u003eG. F. Mitchell, \u0026quot;Arterial stiffness and hypertension: chicken or egg?,\u0026quot; \u003cem\u003eHypertension, \u003c/em\u003evol. 64, no. 2, pp. 210-4, Aug 2014.\u003c/li\u003e\n\u003cli\u003eM. Gomez-Sanchez\u003cem\u003e et al.\u003c/em\u003e, \u0026quot;Relationship of healthy vascular aging with lifestyle and metabolic syndrome in the general Spanish population. The EVA study,\u0026quot; \u003cem\u003eRev Esp Cardiol (Engl Ed), \u003c/em\u003evol. 74, no. 10, pp. 854-861, Oct 2021.\u003c/li\u003e\n\u003cli\u003eA. Staudt\u003cem\u003e et al.\u003c/em\u003e, \u0026quot;Impact of lifestyle and cardiovascular risk factors on early atherosclerosis in a large cohort of healthy adolescents: The Early Vascular Ageing (EVA)-Tyrol Study,\u0026quot; \u003cem\u003eAtherosclerosis, \u003c/em\u003evol. 305, pp. 26-33, Jul 2020.\u003c/li\u003e\n\u003cli\u003eK. L. Nowak, M. J. Rossman, M. Chonchol, and D. R. Seals, \u0026quot;Strategies for Achieving Healthy Vascular Aging,\u0026quot; \u003cem\u003eHypertension, \u003c/em\u003evol. 71, no. 3, pp. 389-402, Mar 2018.\u003c/li\u003e\n\u003cli\u003eP. M. Nilsson, E. Lurbe, and S. Laurent, \u0026quot;The early life origins of vascular ageing and cardiovascular risk: the EVA syndrome,\u0026quot; \u003cem\u003eJ Hypertens, \u003c/em\u003evol. 26, no. 6, pp. 1049-57, Jun 2008.\u003c/li\u003e\n\u003cli\u003eA. Heimdahl and C. E. Nord, \u0026quot;Antimicrobial prophylaxis in oral surgery,\u0026quot; \u003cem\u003eScand J Infect Dis Suppl, \u003c/em\u003evol. 70, pp. 91-101, 1990.\u003c/li\u003e\n\u003cli\u003eJ. C. Verhave, P. Fesler, G. du Cailar, J. Ribstein, M. E. Safar, and A. Mimran, \u0026quot;Elevated pulse pressure is associated with low renal function in elderly patients with isolated systolic hypertension,\u0026quot; \u003cem\u003eHypertension, \u003c/em\u003evol. 45, no. 4, pp. 586-91, Apr 2005.\u003c/li\u003e\n\u003cli\u003eH. Ji\u003cem\u003e et al.\u003c/em\u003e, \u0026quot;Vascular aging and preclinical target organ damage in community-dwelling elderly: the Northern Shanghai Study,\u0026quot; \u003cem\u003eJ Hypertens, \u003c/em\u003evol. 36, no. 6, pp. 1391-1398, Jun 2018.\u003c/li\u003e\n\u003cli\u003eH. Tanaka, C. A. DeSouza, and D. R. Seals, \u0026quot;Absence of age-related increase in central arterial stiffness in physically active women,\u0026quot; \u003cem\u003eArterioscler Thromb Vasc Biol, \u003c/em\u003evol. 18, no. 1, pp. 127-32, Jan 1998.\u003c/li\u003e\n\u003cli\u003eH. Tanaka, F. A. Dinenno, K. D. Monahan, C. M. Clevenger, C. A. DeSouza, and D. R. Seals, \u0026quot;Aging, habitual exercise, and dynamic arterial compliance,\u0026quot; \u003cem\u003eCirculation, \u003c/em\u003evol. 102, no. 11, pp. 1270-5, Sep 12 2000.\u003c/li\u003e\n\u003cli\u003eS. Ahmadi-Abhari\u003cem\u003e et al.\u003c/em\u003e, \u0026quot;Physical Activity, Sedentary Behavior, and Long-Term Changes in Aortic Stiffness: The Whitehall II Study,\u0026quot; \u003cem\u003eJ Am Heart Assoc, \u003c/em\u003evol. 6, no. 8, Aug 7 2017.\u003c/li\u003e\n\u003cli\u003eY. Wexler\u003cem\u003e et al.\u003c/em\u003e, \u0026quot;Familial tendency for hypertension is associated with increased vascular stiffness,\u0026quot; \u003cem\u003eJ Hypertens, \u003c/em\u003evol. 39, no. 4, pp. 627-632, Apr 1 2021.\u003c/li\u003e\n\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":"artery-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Artery Research](https://arteryresearch.biomedcentral.com/)","snPcode":"44200","submissionUrl":"https://submission.springernature.com/new-submission/44200/3","title":"Artery Research","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Healthy vascular aging, Carotid-femoral pulse wave velocity, Hypertensive target organ damage","lastPublishedDoi":"10.21203/rs.3.rs-6259968/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6259968/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eObjective: To investigate the correlation between healthy vascular aging (HVA) and non-healthy vascular aging (NHVA) with hypertensive target organ damage (TOD).\u003c/p\u003e\n\u003cp\u003eMethods: This study included individuals from the Geriatrics Department of Ruijin Hospital in Shanghai since January 2023. Participants were divided into HVA and NHVA groups based on blood pressure and carotid-femoral pulse wave velocity (cf-PWV). HVA was defined as no history of hypertension and cf-PWV \u0026lt; 7.6 m/s; NHVA was defined as a history of hypertension or cf-PWV ≥ 7.6 m/s. Hypertensive TOD indicators included carotid intima-media thickness (cIMT), chronic kidney disease, albumin-to-creatinine ratio (ACR), estimated glomerular filtration rate (eGFR), and left ventricular mass index (LVMI).\u003c/p\u003e\n\u003cp\u003eResults: A total of 1,257 participants was included in the study. After grouping by cf-PWV and blood pressure, 31.8% met the HVA criteria. Compared to the HVA group, the NHVA group was older, had more smokers, and exhibited higher levels body mass index, blood glucose, lipids, and blood pressure. The NHVA group also showed lower creatinine clearance (88.72±17.27 mL/min/1.73 m² vs. 93.41 ± 15.79 mL/min/1.73 m², \u003cem\u003ep\u003c/em\u003e\u0026lt;0.001), higher LVMI (108.00 ± 26.44 g/m² vs. 92.25 ± 22.29 g/m², \u003cem\u003ep \u003c/em\u003e\u0026lt; 0.001), greater cIMT (0.75 ± 0.14 mm vs. 0.70 ± 0.12 mm, \u003cem\u003ep \u003c/em\u003e\u0026lt; 0.001), and higher cf-PWV (9.06 ± 1.91 m/s vs. 6.5 ± 0.74 m/s, p \u0026lt; 0.001). Multivariate linear regression analysis revealed significant associations between vascular aging groups and LVMI (\u003cem\u003ep \u003c/em\u003e= 0.003) and lgACR (\u003cem\u003ep\u003c/em\u003e=0.022). Binary stepwise logistic regression results demonstrated a significant correlation between vascular aging and LVMI (OR = 2.201, 95% CI: 1.299–3.73, \u003cem\u003ep \u003c/em\u003e= 0.003).\u003c/p\u003e\n\u003cp\u003eConclusion: Accelerated vascular aging is associated with cardiac and renal TOD, providing a potential target for intervention. Vascular aging shows a significant correlation with LVMI.\u003c/p\u003e","manuscriptTitle":"Association Between Healthy and Non-Healthy Vascular Aging with Hypertensive Target Organ Damage","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-02 13:01:06","doi":"10.21203/rs.3.rs-6259968/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-04-06T23:39:45+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-06T09:42:32+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-03-31T19:04:25+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"254290152347503070314260222703166214404","date":"2025-03-26T15:51:04+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"161971614194876867502853515288413103321","date":"2025-03-25T09:35:20+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-03-24T14:58:17+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-03-19T11:42:33+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-03-19T11:41:38+00:00","index":"","fulltext":""},{"type":"submitted","content":"Artery Research","date":"2025-03-19T09:18:34+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"artery-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Artery Research](https://arteryresearch.biomedcentral.com/)","snPcode":"44200","submissionUrl":"https://submission.springernature.com/new-submission/44200/3","title":"Artery Research","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"b3205d1f-f72c-4e67-b534-750ef0717483","owner":[],"postedDate":"April 2nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-05-11T14:53:14+00:00","versionOfRecord":[],"versionCreatedAt":"2025-04-02 13:01:06","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6259968","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6259968","identity":"rs-6259968","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}