AT1R gene polymorphism contributes to MACCEs in Hypertension patients

AT1R gene polymorphism contributes to MACCEs in Hypertension patients | 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 AT1R gene polymorphism contributes to MACCEs in Hypertension patients Jun-Yi Luo, Guo-Li Du, Yang-Min Hao, Fen Liu, Tong Zhang, Bin-Bin Fang, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-2062190/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 03 Jun, 2023 Read the published version in BMC Cardiovascular Disorders → Version 1 posted 10 You are reading this latest preprint version Abstract OBJECTIVE: To investigate the possible association between AT1R gene polymorphisms and major adverse cardiovascular and cerebrovascular events (MACCEs) in hypertension patients combined with or without coronary heart disease (CHD) in Xinjiang. METHODS: 374 CHD patients and 341 non-CHD individuals were enrolled as study participants and all of them have a hypertension diagnosis. AT1R gene polymorphisms were genotyped by SNPscan™ typing assays. During the follow-up in the clinic or by telephone interview, MACCEs were recorded. Kaplan–Meier curves and Cox survival analyses were used to explore the association between AT1R gene polymorphisms and the occurrence of MACCEs. RESULTS: AT1R gene rs389566 was associated with MACCEs. The TT genotype of the AT1R gene rs389566 had a significantly higher probability of MACCEs than the AA+AT genotype (75.2% vs 24.8%, P=0.033). Older age (OR=1.028, 95% CI: 1.009-1.0047, P=0.003) and TT genotype of rs389566 (OR=1.770, 95% CI: 1.148-2.729, P=0.01) were risk factors of MACCEs. AT1R gene rs389566 TT genotype may be a predisposing factor for the occurrence of MACCEs in hypertensive patients. CONDLUSION: AT1R SNP rs389566 may be a common genetic loci and optimal genetic susceptibility marker for MACCEs in hypertension patients. Angiotensin II type 1 receptor (AT1R) Coronary heart disease (CHD) Hypertension Major adverse cardiovascular events (MACCEs) Figures Figure 1 Introduction Coronary heart disease (CHD) and hypertensive are common diseases that endanger human health. As blood pressure regulatory system in the body, the renin-angiotensin system (RAS) is an important risk factors for CHD [ 1 – 3 ]. The angiotensin II (Ang II) type 1 receptor (AT1R) is involved in the classical physiological actions of Ang II, and plays a pivotal role in the pathogenesis of atherosclerosis in human [ 4 ]. Hypertension is a major risk factor for CHD and 25% of patients with CHD have hypertension [ 5 ]. CHD is the first cause of morbidity and mortality in hypertension [ 6 ]. As referred above, AT1R is very important for the CHD, but its roles in pathogenesis of hypertension patients combined with CHD remains to be understood, although the associations between the AT1R polymorphisms, CHD and hypertension had been proved in French and English Caucasians population respectively [ 7 , 8 ]. In this study, two single nucleotide polymorphisms (SNPs) of the AT1R gene were sequenced [ 9 ], and the differences in the distribution frequencies of these SNPs were compared between CHD patients and non-CHD patients combined with hypertension, and the association between AT1R gene polymorphisms and major adverse cardiovascular and cerebrovascular events (MACCEs) were analyzed. Materials And Methods Study Population In this case-control study, we recruited adult hypertension patients combined with CHD or non-CHD who were long-term residents of the Xinjiang region, China, and they were admitted to the Heart Center of the First Affiliated Hospital of the Xinjiang Medical University with symptoms of chest tightness or precordial discomfort during 2010–2018. Each subject signed an informed consent before participating in this study. Additionally, we excluded those patients with incomplete data and complicated with one or more than one disease, such as secondary hypertension, rheumatic heart disease, congenital heart disease, heart failure, systemic immune system diseases, and multiple organ failure. General data collection The medical record system of our hospital was consulted according to the name and hospitalization certificate number, and the required data were collected according to the inclusion criteria, and data entry was performed using an Excel sheet. General data were collected including gender, age, body mass index (BMI), hypertension, type 2 diabetes mellitus (T2DM), smoking, alcohol intake, family history of CHD, etc. Laboratory tests for blood glucose, lipids including cholesterol, triglycerides, high density lipoprotein cholesterol (HDL-c) and low density lipoprotein cholesterol (LDL-c) were also collected. Diagnostic of MACCEs, CHD and Hypertension MACCEs is defined as the occurrence of cardiovascular death, nonfatal myocardial infarction, nonfatal stroke, ischemia-driven revascularization, and stroke [ 10 ]. Typical symptom of CHD is exertional angina, with pressure pain in the precordial region during activity or emotional stress. It can radiate to the left shoulder or/and left upper arm for 5–10 minutes and can be relieved by rest or medications such as nitroglycerin. Diagnosis CHD is based on symptoms, signs and ancillary tests such as electrocardiography and coronary angiography (CAG). CAG is the gold standard for diagnosing CHD. Diagnosis of CHD should be at least one coronary arterial stenosis of 50% or its major branches in the CAG [ 11 ]. According to the Chinese Guidelines for the Prevention and Treatment of Hypertension 2010 [ 12 ], hypertension is diagnosed under the following conditions: systolic blood pressure (SBP) ≥ 140mmHg and / or diastolic blood pressure (DBP) ≥ 90mmHg on three different days in the absence of antihypertensive drugs; patients with a history of hypertension and currently taking antihypertensive drugs although their blood pressures were lower than 140 / 90mmHg. Genotyping assay A total of 5 mL of fasting peripheral venous blood was drawn from the subjects into ethylenediaminetetraacetic acid (EDTA)-containing blood collection tubes, and plasma and blood cells were separated through centrifugation and stored in a − 80°C refrigerator until further use. Plasma was were measured by biochemical indicator and blood cells were subjected to genomic DNA extraction using a whole blood genome extraction kit (Tiangen Biotech, China). AT1R gene polymorphism was detected by TaqMan® SNP genotyping qRT PCR. Genotyping accuracy was determined by genotypic concordance between replicate samples, and the accuracy of each SNP was 100%. The reaction system of qPCR amplification was composed of following reagents: 3 µL of TaqMan Universal Master Mix, 0.12 µL probes and 1.88 µL ddH 2 O in a 6 µL final reaction volume containing 50 ng DNA. Amplification cycling conditions were as follows: 95°C for 5 min; 35 cycles of 95°C for 15 s and 60°C for 1 min. Statistical methods SPSS 26.0 statistical software was used for statistical analysis. T-test was used for comparison between groups; χ chi-square test was used for comparison of count data. Cox regression was used for multi-factor analysis. The associations between patients’ survival rate and the AT1R gene polymorphism were evaluated using Kaplan–Meier analysis. A difference was considered statistically significant as P < 0.05 (two-sided). Ethic declaration This study was approved by the Ethics Committee of the First Affiliated Hospital of Xinjiang Medical University and conducted according to the standards of the Declaration of Helsinki and written informed consents were obtained from participants. Results General clinical characteristics In this study, we compared the general characteristics of patients between non-CHD and CHD patients combined with hypertension. We found that CHD patients tended to be older (55.7±9.7 vs 59.6 ±10.7 years, P<0.001), higher glucose levels of BMI (26.93±3.85 vs 26.07±3.10 kg/m 2 , P=0.030), SBP (132±17 vs 129±18 mmHg, P=0.023), DBP (80±11 vs 78±12 mmHg, P=0.043) compared with non-CHD patients. Patients with CHD also have higher levels of TC (4.17±1.01 vs 4.45±1.18 mmol/L, P=0.001), LDL-c (2.60±0.83 vs 2.76±0.96 mmol/L, P=0.010) and lower HDL-c (1.04±0.29 vs 0.99±0.28 mmol/L, P=0.020, Table 1). General characteristics and biochemical parameters between control (non-MACCEs) and MACCEs groups had been compared, as shown in Table 2. There was no significant difference regarding gender, smoking, alcohol intake, T2DM between these groups (P > 0.05). The prevalence of MACCEs in CHD patients was significantly higher than non-CHD patients (79.5% vs 20.5%), P<0.001. Patients with MACCEs showed higher blood glucose compared with those non-MACCEs patients (8.15±3.42 vs 6.93±3.22 mmol/L), P<0.001. Patients with MACCEs showed higher age (Table 2) compared with those non-MACCEs patients (57.1±10.3 vs 59.1±9.7 years), P<0.001. We then compared characteristics among different age groups in Table 3. In 51-60 years old and over 60 years old groups, MACCEs occurrence increased significantly (31.6% and 54.7%, respectively, P=0.001). The BMI, blood pressure, glucose, TG, HDL-c showed significantly difference among different age groups (P<0.05). There was no difference regarding rs16860760, rs389566 TT genotype among different age groups (P=0.932, P=0.446 respectively). Occurrence of MACCEs in patients with different genotypes of rs16860760 in AT1R gene There was no significant difference regarding the frequency of MACCEs in different AT1R rs16860760 SNPs (P>0.05), but the AT1R gene rs389566 polymorphism showed significant association with the probability of MACCEs in patients with hypertension (Table 4). And the patients carrying TT genotype at rs389566 locus had a higher risk of MACCEs than those carrying the AA+AT gene type (24.8% vs 75.2%, P=0.033). Risk factors of MACCEs In the present study, the mean follow-up duration was 65.6 (38.3, 91.8) months. The Kaplan–Meier analysis revealed that the MACCEs-free cumulative survival rate in the TT genotype group was obviously lower than that in the AA+AT genotype group (P=0.009, Fig. 1). Through univariate Cox survival analysis, we found that elderly, glucose, coronary heart disease, and rs389566 TT gene types may be risk factors for MACCEs in patients with hypertension. As shown in Table 5, age, AT1R gene rs389566 TT genotype, CHD and glucose variables were included to construct a multifactorial COX proportion-al risk model. The results showed old age may be a predisposing factor on the occurrence of MACCEs (OR=1.028, 95% CI: 1.009-1.047, P=0.003), and rs389566 TT genotype may be a predisposing factor on the occurrence of MACCEs (OR=1.770, 95%CI 1.148-2.729, P=0.010). Patients with CHD were prone to MACCEs (OR=4.118, 95%CI 2.542-6.672, P0.05). Discussion Many factors influence the occurrence of MACCEs, such as family history of CHD, smoking, obesity, hypertension, diabetes, abnormal lipid metabolism, insulin resistance, and homocysteine mia [13]. In the present study, AT1R gene rs389566 TT genotype was found to be associated with the occurrence of MACCEs in hypertension patients. Cardiovascular disease is the leading cause of death worldwide [14], and hypertension is the most common chronic disease and the most important risk factor for cardiovascular disease [15]. Although CHD mortality rates have gradually declined in Western countries over the past few decades, the condition still causes about one-third of deaths in people over 35 years of age [16]. MACCEs remain the major cause of mortality and morbidity in patients both in hypertension or CHD patients [17, 18]. However, it has been reported in the literature that the incidence of MACCEs is significantly higher in CHD combined with hypertension patients compared with non-CHD or non-hypertension patients [10, 19], but the reasons remain to be unknown. The traditional risk factors of MACCEs include fasting glucose, heart rate variability, blood pressure [20-23] and dyslipidemia [24]. As previous reported, AT1R gene polymorphism was found to be associated with the development of CHD in Chinese population [25, 26]. Here we found AT1R rs389566 TT genotype may be an independent risk factor for the development of MACCEs in patients with hypertension especially those combined with CHD. The main effects of Renin-Angiotensin-Aldosterone System (RAAS) on cardiovascular system are atherosclerosis and hypertension, leading to congestive heart failure and MACCEs [27]. And Ang II also promotes the development of atherosclerosis through AT1 receptors, stimulating the secretion of inflammatory mediators, and converting stable plaques into vulnerable plaques [28]. Overexpression of the AT1R gene leads to myocardial hypertrophy and ventricular remodeling [29]. The previously study demonstrates that the AT1R polymorphism is associated with abnormal coronary vasoconstriction which causes rupture of plaque and thrombus formation [30]. Our study found that AT1R gene mutation was associated with the occurrence of MACCEs in hypertension patients in the Xinjiang. The patients with hypertension carrying TT genotype of the AT1R gene rs389566 were prone to MACCEs. Previous studies have been conducted on AT1R gene polymorphisms in the Chinese population, but mainly on hypertension, atherosclerosis, cardiovascular disease risk factors, and intravascular restenosis. The association of AT1R gene polymorphisms with the occurrence of MACCEs events has not been reported before. Most previous studies have focused on the association of the AT1R rs5186 (A1166C) locus polymorphism and acute myocardial infarction in Caucasian, Asian, African, Brazilian, and Durban populations, and the C allele was proved to be a risk factor for occurrence of myocardial infarction [31]. In Asia, previous studies [31-33] reported that AT1R A1166C polymorphism may influence the occurrence of myocardial infarction susceptibility in Chinese. However, the sample size of these studies is relatively small, and fewer studies have focused on the relationship between AT1R rs16860760 and MACCEs. In the present study, we found the significant association between AT1R rs389566 polymorphism and MACCEs in Chinese hypertensive population which could help provide a clinical basis for future targeted interventions. Besides AT1R gene polymorphism, the age is also a factor affecting the occurrence of MACCEs. Our study found that the occurrence of MACCEs is higher in older age population, Patients with hypertension over 60 years are more likely to occur MACCEs and the prevalence is about 54.7% and it was consistent with previous study [34]. For aged population, MACCEs prevention should be emphasized in future. Our study confirmed that AT1R rs389566 TT genotype increased the occurrence of MACCEs in hypertension patients. Conclusion In summary, this study provides the current status of risk factors for the occurrence of MACCEs in hypertensive patients combined with CHD in Xinjiang, China. Especially those aged hypertensive patients carrying A1TR rs389566 TT genotype requires avoidance of unhealthy lifestyles, raising awareness about the prevention and better management of MACCEs. The present findings provide potential intervention targets for the prognosis of patients who are at high risk of MACCEs, and this will help clinician do genomics-based personalized therapy in future. Limitation This study also has some limitations. First, the sample size was not large enough. Second, participants in the current study were recruited only at the First Affiliated Hospital of Xinjiang Medical University, which may not necessarily reflect the true prevalence of hypertension combined with CHD and the occurrence of MACCEs at the provincial or national level. Finally, we focused our interest on the AT1R gene polymorphism: as discussed, many other factors are involved in MACCEs and may cause increase in occurrence. Broader analyses are therefore encouraged to better understand the complexity of the MACCEs occurrence process. Declarations Ethics approval and consent to participate The study conducted according to the standards of the Declaration of Helsinki and its experimental protocols was approved by the Ethics Committee of the First Affiliated Hospital of Xinjiang Medical University. Written informed consent was obtained from all subjects and/or their legal guardian(s). All participants consented for drawing their blood samples and collection of their relevant clinical data. Consent for publication Not applicable. Data availability The datasets used and analyzed during the current study available from the corresponding author on reasonable request. Competing Interests All of these authors declared that they had no competing interests. Funding The survey was funded by the National Natural Science Foundation of China (82160054, 81960078), State Key Laboratory of Pathogenesis, Prevention and Treatment of High Incidence Diseases in Central Asia, Xinjiang Medical University (SKL-HIDCA-2021-XXG4), and Xinjiang Young Scientific and Technical Talents Training Project (2019Q040). Author contributions X‑ML, X-MG and Y‑NY were involved in the study design of the experiments. J‑YL, G-LD, Y-MH, FL, TZ and B-BF performed the experiments, evaluated the data and wrote the manuscript. J-YL, G-LD and Y-MH were involved in data analysis and manuscript editing. All authors read and approved the final manuscript. Acknowledgements Not applicable. References Alderman M, Madhavan S, Ooi W, Cohen H, Sealey J, Laragh J. Association of the renin-sodium profile with the risk of myocardial infarction in patients with hypertension. N Engl J Med. 1991;324:1098–104. 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General characteristics in hypertension patients stratified with CHD Characteristics non-CHD （ n=341 ） CHD （ n=374 ） P Value Age (years) 55.7±9.67 59.6±10.73 <0.001 Gender, n (%) Male 173 (50.7%) 234 (62.6%) 0.001 Female 168 (49.3%) 140 (37.4%) Smoking, n (%) No 242 (71.0%) 229 (61.2%) 0.006 Yes 99 (29.0%) 145 (38.8%) Alcohol intake, n (%) No 251 (73.6%) 282 (79.5%) 0.582 Yes 90 (26.4%) 92 (20.5%) T2DM No 289 (84.8%) 261 (69.8%) <0.001 Yes 52 (15.2%) 113 (30.2%) SBP (mmHg) 132±17 129±19 0.023 DBP (mmHg) 80±12 79±13 0.043 BMI (kg/m 2 ) 26.93±3.85 26.07±3.10 0.030 Glucose (mmol/L) 5.67±1.96 8.44±3.66 <0.001 TG (mmol/L) 1.96±1.48 2.13±1.76 0.181 TC (mmol/L) 4.17±1.01 4.45±1.18 0.001 HDL–c (mmol/L) 1.04±0.29 0.99±0.28 0.020 LDL–c (mmol/L) 2.60±0.83 2.76±0.96 0.010 CHD: Coronary heart disease, BMI: Body mass index, SBP: Systolic blood pressure, DBP: Diastolic blood pressure, TG: Triglycerides, TC: Total Cholesterol, HDL-c: High Density lipoprotein cholesterol, LDL-c: Low density lipoprotein cholesterol, T2DM: Type 2 Diabetes Mellitus, P< 0.05 was considered significant difference. Table 2. Biochemical parameters in hypertension patients stratified with MACCEs Biochemical parameters non-MACCEs (n=598) MACCEs (n=117) P Value Age (years) 57.1±10.39 59.1±9.79 <0.001 Gender, n (%) Male 337 (56.4%) 70 (59.8%) 0.488 Female 261 (43.6%) 47 (40.2%) Smoking, n (%) No 391（65.4%） 80（68.4%） 0.533 Yes 207（34.6%） 37（31.6%） Alcohol intake, n (%) No 440 (73.6%) 93 (79.5%) 0.180 Yes 158 (26.4%) 24 (20.5%) T2DM No 461（77.1%） 89（76.1%） 0.810 Yes 137（22.9%） 28（23.9%） CHD No 317 (53.0%) 24 (20.5%) <0.001 Yes 281 (47.0%) 93 (79.5%) SBP (mmHg) 131±18 129±19 0.227 DBP (mmHg) 80±12 79±13 0.412 BMI (kg/m 2 ) 26.55±3.50 25.83±3.06 0.097 Glucose (mmol/L) 6.93±3.22 8.15±3.42 <0.001 TG (mmol/L) 2.04±1.57 2.09±1.94 0.810 TC (mmol/L) 4.30±1.10 4.40±1.16 0.389 HDL–c (mmol/L) 1.04±0.29 0.99±0.28 0.093 LDL–c (mmol/L) 2.68±0.89 2.73±0.96 0.542 MACCEs: Major adverse cardiovascular and cerebrovascular events, BMI: Body mass index, SBP: Systolic blood pressure, DBP: Diastolic blood pressure, TG: Triglycerides, TC: Total cholesterol, HDL-c: High density lipoprotein cholesterol, LDL-c: Low density lipoprotein cholesterol, T2DM: Type 2 Diabetes Mellitus, CHD: Coronary heart disease, P< 0.05 was considered significant difference. Table 3. Comparison of general characteristics in patients with hypertension according to different ages Characteristics 60 years old (n=293) P Value Gender, n (%) Male 18（90.0%） 139（77.7%） 123（56.2%） 127（42.8%） <0.001 Female 2（10.0%） 40（22.3%） 96（43.8%） 170（57.2%） Smoking, n (%) No 7（35.0%） 83（46.4%） 146（66.7%） 235（79.1%） <0.001 Yes 13（65.0%） 96（53.6%） 73（33.3%） 62（20.9%） Alcohol intake, n (%) No 9（1.7%） 104（19.5%） 164（30.8%） 256（48.0%） <0.001 Yes 11（55.0%） 75（41.9%） 55（25.1%） 41（13.8%） T2DM No 17（85.0%） 155（86.6%） 171（78.1%） 207（69.7%） <0.001 Yes 3（15.0%） 24（13.4%） 48（21.9%） 90（30.3%） CHD No 15（75.0%） 101（56.4%） 107（48.9%） 118（39.7%） <0.001 Yes 5（25.0%） 78（43.6%） 112（51.1%） 179（60.3%） SBP (mmHg) 137±16 129±17 129±16 132±20 0.039 DBP (mmHg) 87±14 82±12 80±11 77±13 <0.001 BMI (kg/m 2 ) 29.34±3.52 27.03±2.91 26.60±3.61 25.63±3.34 <0.001 Glucose(mmol/L) 6.16±2.06 7.06±3.27 6.30±2.63 7.76±3.58 <0.001 TG (mmol/L) 2.68±3.04 2.36±1.84 2.00±1.73 1.84±1.20 0.004 TC (mmol/L) 4.72±1.17 4.37±1.07 4.31±1.15 4.26±1.11 0.311 HDL–c (mmol/L) 1.02±0.25 0.96±0.23 1.04±0.31 1.08±0.30 0.001 LDL–c (mmol/L) 3.12±0.79 2.74±0.90 2.68±0.87 2.62±0.93 0.096 MACCEs, n (%) 1 (0.9%) 15 (12.8%) 37 (31.6%) 64 (54.7%) 0.001 RS389566 TT, n (%) 13 (2.8%) 116 (25.1%) 133 (28.7%) 201 (43.4%) 0.446 RS16860760, n (%) 20 (2.8%) 179 (25%) 219 (30.6%) 297 (41.5%) 0.932 BMI: Body mass index, SBP: Systolic blood pressure, DBP: Diastolic blood pressure, TG: Triglycerides, TC: Total Cholesterol, HDL-c: High density lipoprotein cholesterol, LDL-c: Low density lipoprotein cholesterol, MACCEs: major adverse cardiovascular events. P< 0.05 was considered significant difference. Table 4. AT1R gene polymorphisms in patients with MACCEs and control group. Polymorphisms non-MACCEs (n=598) MACCEs (n=117) P Value Rs16860760 AA+AG 62 (10.3%) 9 (7.7%) 0.599 GG 536 (89.6%) 108 (92.3%) Rs389566 AA+AT 223 (37.3%) 29 (24.8%) 0.033 TT 375 (62.7%) 88 (75.2%) MACCEs: Major adverse cardiovascular and cerebrovascular events, P < 0.05 was considered significant difference Table 5 Univariate and multivariate Cox analyses among the hypertension patients Risk factors Univariate cox regress Multivariate cox regress OR (95% CI) P Value OR (95% CI) P Value rs389566 AA+AT/TT 1.731 (1.138-2.635) 0.010 1.770(1.148-2.729) 0.010 CHD 4.912 (3.128-7.714) <0.001 4.118(2.542-6.672) <0.001 Age 1.041(1.023-1.060) <0.001 1.028(1.009-1.047) 0.003 Gender 0.789 (0.545-1.142) 0.208 - - BMI 0.951(0.890-1.016) 0.136 - - SBP 0.992 (0.982-1.002) 0.135 - - DBP 0.992(0.977-1.007) 0.278 - - Glucose 1.107(1.060-1.156) <0.001 1.036(0.985-1.089) 0.167 TG 1.008(0.897-1.132) 0.895 - - TC 1.101(0.930-1.303) 0.264 - - HDL-c 0.578(0.297-1.127) 0.108 - - LDL-c 1.108(0.900-1.363) 0.334 - - BMI: Body mass index, SBP: Systolic blood pressure, DBP: Diastolic blood pressure, TG: Triglycerides, TC: Total Cholesterol, HDL-c: High density lipoprotein cholesterol, LDL-c: Low density lipoprotein cholesterol, MACCEs: major adverse cardiovascular events. CHD: Coronary heart disease, OR: Odds ratio, CI: Confidence Interval, P< 0.05 was considered significant difference. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 03 Jun, 2023 Read the published version in BMC Cardiovascular Disorders → Version 1 posted Editorial decision: Major revision 25 Jan, 2023 Reviews received at journal 24 Jan, 2023 Reviewers agreed at journal 16 Jan, 2023 Reviews received at journal 15 Nov, 2022 Reviewers agreed at journal 11 Nov, 2022 Reviewers invited by journal 22 Sep, 2022 Editor assigned by journal 22 Sep, 2022 Editor invited by journal 15 Sep, 2022 Submission checks completed at journal 15 Sep, 2022 First submitted to journal 13 Sep, 2022 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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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-2062190","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":136918168,"identity":"c43dcc2f-66a4-42d2-9513-3dbdac896705","order_by":0,"name":"Jun-Yi Luo","email":"","orcid":"","institution":"First Affiliated Hospital of Xinjiang Medical University","correspondingAuthor":false,"prefix":"","firstName":"Jun-Yi","middleName":"","lastName":"Luo","suffix":""},{"id":136918169,"identity":"dae0640c-70ba-4403-8194-9c42954e5222","order_by":1,"name":"Guo-Li Du","email":"","orcid":"","institution":"First Affiliated Hospital of Xinjiang Medical University","correspondingAuthor":false,"prefix":"","firstName":"Guo-Li","middleName":"","lastName":"Du","suffix":""},{"id":136918170,"identity":"15fca55b-a00e-44ce-89f6-a359686a65d3","order_by":2,"name":"Yang-Min Hao","email":"","orcid":"","institution":"First Affiliated Hospital of Xinjiang Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yang-Min","middleName":"","lastName":"Hao","suffix":""},{"id":136918171,"identity":"1b41c936-f6b0-41d2-bc66-94611e342915","order_by":3,"name":"Fen Liu","email":"","orcid":"","institution":"First Affiliated Hospital of Xinjiang Medical University","correspondingAuthor":false,"prefix":"","firstName":"Fen","middleName":"","lastName":"Liu","suffix":""},{"id":136918172,"identity":"1b2e468c-3320-4810-8b16-1c3057c039b2","order_by":4,"name":"Tong Zhang","email":"","orcid":"","institution":"First Affiliated Hospital of Xinjiang Medical University","correspondingAuthor":false,"prefix":"","firstName":"Tong","middleName":"","lastName":"Zhang","suffix":""},{"id":136918173,"identity":"6dd868ad-5be3-430a-bcc2-7f2315eada79","order_by":5,"name":"Bin-Bin Fang","email":"","orcid":"","institution":"Clinical Medical Research Institute of First Affiliated Hospital of Xinjiang Medical University","correspondingAuthor":false,"prefix":"","firstName":"Bin-Bin","middleName":"","lastName":"Fang","suffix":""},{"id":136918174,"identity":"9d52aae8-7235-4fc9-a887-86428b131ac2","order_by":6,"name":"Xiao-Mei Li","email":"","orcid":"","institution":"First Affiliated Hospital of Xinjiang Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xiao-Mei","middleName":"","lastName":"Li","suffix":""},{"id":136918175,"identity":"2529f139-3985-4b3d-8860-fa325f889a7a","order_by":7,"name":"Xiao-Ming Gao","email":"","orcid":"","institution":"First Affiliated Hospital of Xinjiang Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xiao-Ming","middleName":"","lastName":"Gao","suffix":""},{"id":136918176,"identity":"84877458-c42a-4943-929c-02edf3c78dbd","order_by":8,"name":"Yi-Ning Yang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA3ElEQVRIiWNgGAWjYPCCfzz87M0HDnz4QbyWAzKSPccSD87sIUGLjcENH+PDHGxEqDU4fvbwa94dd3gYbvB8OMzAwyDPL3aAgJYzeWnWvGee8TDO7t1wuMCCwXDm7AQCWg7kmBnztjHzMMuc3XB4Bg9DgsFtQlrOv4FoYZPIeXCYh40YLTdyjB/zth3m4ZHIYSBOi+SNN2aMc9vSeCR4jhkAA1mCsF/4zucYf3jbZmNvf7z58YcPP2zk+aUJaFE4wMAmxYPgS+BXDgLyDQzMH0lIJqNgFIyCUTASAQBrUkqctOIIEQAAAABJRU5ErkJggg==","orcid":"","institution":"People’s Hospital of Xinjiang Uygur Autonomous Region","correspondingAuthor":true,"prefix":"","firstName":"Yi-Ning","middleName":"","lastName":"Yang","suffix":""}],"badges":[],"createdAt":"2022-09-13 18:29:08","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-2062190/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-2062190/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12872-023-03223-w","type":"published","date":"2023-06-03T21:00:56+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":26741010,"identity":"2cec50f4-f60e-4ddf-baee-220425e91be4","added_by":"auto","created_at":"2022-09-21 01:08:00","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":82629,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eKaplan-Meier curves of MACE survival analysis according to the rs389566 genotype.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-2062190/v1/3d504eddff9f420ccbbd941d.png"},{"id":44730139,"identity":"c206c281-7c4a-4bf1-ac1c-2748bbd1f3e1","added_by":"auto","created_at":"2023-10-16 21:26:46","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":569335,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-2062190/v1/c0d2ea10-9aac-4dd8-8488-9b32d7cb7e71.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"AT1R gene polymorphism contributes to MACCEs in Hypertension patients","fulltext":[{"header":"Introduction","content":"\u003cp\u003eCoronary heart disease (CHD) and hypertensive are common diseases that endanger human health. As blood pressure regulatory system in the body, the renin-angiotensin system (RAS) is an important risk factors for CHD [\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. The angiotensin II (Ang II) type 1 receptor (AT1R) is involved in the classical physiological actions of Ang II, and plays a pivotal role in the pathogenesis of atherosclerosis in human [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eHypertension is a major risk factor for CHD and 25% of patients with CHD have hypertension [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. CHD is the first cause of morbidity and mortality in hypertension [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. As referred above, AT1R is very important for the CHD, but its roles in pathogenesis of hypertension patients combined with CHD remains to be understood, although the associations between the AT1R polymorphisms, CHD and hypertension had been proved in French and English Caucasians population respectively [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn this study, two single nucleotide polymorphisms (SNPs) of the AT1R gene were sequenced [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], and the differences in the distribution frequencies of these SNPs were compared between CHD patients and non-CHD patients combined with hypertension, and the association between AT1R gene polymorphisms and major adverse cardiovascular and cerebrovascular events (MACCEs) were analyzed.\u003c/p\u003e"},{"header":"Materials And Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Population\u003c/h2\u003e \u003cp\u003eIn this case-control study, we recruited adult hypertension patients combined with CHD or non-CHD who were long-term residents of the Xinjiang region, China, and they were admitted to the Heart Center of the First Affiliated Hospital of the Xinjiang Medical University with symptoms of chest tightness or precordial discomfort during 2010\u0026ndash;2018. Each subject signed an informed consent before participating in this study. Additionally, we excluded those patients with incomplete data and complicated with one or more than one disease, such as secondary hypertension, rheumatic heart disease, congenital heart disease, heart failure, systemic immune system diseases, and multiple organ failure.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eGeneral data collection\u003c/h2\u003e \u003cp\u003eThe medical record system of our hospital was consulted according to the name and hospitalization certificate number, and the required data were collected according to the inclusion criteria, and data entry was performed using an Excel sheet. General data were collected including gender, age, body mass index (BMI), hypertension, type 2 diabetes mellitus (T2DM), smoking, alcohol intake, family history of CHD, etc. Laboratory tests for blood glucose, lipids including cholesterol, triglycerides, high density lipoprotein cholesterol (HDL-c) and low density lipoprotein cholesterol (LDL-c) were also collected.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eDiagnostic of MACCEs, CHD and Hypertension\u003c/h2\u003e \u003cp\u003eMACCEs is defined as the occurrence of cardiovascular death, nonfatal myocardial infarction, nonfatal stroke, ischemia-driven revascularization, and stroke [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Typical symptom of CHD is exertional angina, with pressure pain in the precordial region during activity or emotional stress. It can radiate to the left shoulder or/and left upper arm for 5\u0026ndash;10 minutes and can be relieved by rest or medications such as nitroglycerin. Diagnosis CHD is based on symptoms, signs and ancillary tests such as electrocardiography and coronary angiography (CAG). CAG is the gold standard for diagnosing CHD. Diagnosis of CHD should be at least one coronary arterial stenosis of 50% or its major branches in the CAG [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. According to the Chinese Guidelines for the Prevention and Treatment of Hypertension 2010 [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], hypertension is diagnosed under the following conditions: systolic blood pressure (SBP)\u0026thinsp;\u0026ge;\u0026thinsp;140mmHg and / or diastolic blood pressure (DBP)\u0026thinsp;\u0026ge;\u0026thinsp;90mmHg on three different days in the absence of antihypertensive drugs; patients with a history of hypertension and currently taking antihypertensive drugs although their blood pressures were lower than 140 / 90mmHg.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eGenotyping assay\u003c/h2\u003e \u003cp\u003eA total of 5 mL of fasting peripheral venous blood was drawn from the subjects into ethylenediaminetetraacetic acid (EDTA)-containing blood collection tubes, and plasma and blood cells were separated through centrifugation and stored in a \u0026minus;\u0026thinsp;80\u0026deg;C refrigerator until further use. Plasma was were measured by biochemical indicator and blood cells were subjected to genomic DNA extraction using a whole blood genome extraction kit (Tiangen Biotech, China). AT1R gene polymorphism was detected by TaqMan\u0026reg; SNP genotyping qRT PCR. Genotyping accuracy was determined by genotypic concordance between replicate samples, and the accuracy of each SNP was 100%. The reaction system of qPCR amplification was composed of following reagents: 3 \u0026micro;L of TaqMan Universal Master Mix, 0.12 \u0026micro;L probes and 1.88 \u0026micro;L ddH\u003csub\u003e2\u003c/sub\u003eO in a 6 \u0026micro;L final reaction volume containing 50 ng DNA. Amplification cycling conditions were as follows: 95\u0026deg;C for 5 min; 35 cycles of 95\u0026deg;C for 15 s and 60\u0026deg;C for 1 min.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStatistical methods\u003c/h2\u003e \u003cp\u003eSPSS 26.0 statistical software was used for statistical analysis. T-test was used for comparison between groups; χ chi-square test was used for comparison of count data. Cox regression was used for multi-factor analysis. The associations between patients\u0026rsquo; survival rate and the AT1R gene polymorphism were evaluated using Kaplan\u0026ndash;Meier analysis. A difference was considered statistically significant as P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 (two-sided).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eEthic declaration\u003c/h2\u003e \u003cp\u003e This study was approved by the Ethics Committee of the First Affiliated Hospital of Xinjiang Medical University and conducted according to the standards of the Declaration of Helsinki and written informed consents were obtained from participants.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003ch2\u003eGeneral clinical characteristics\u003c/h2\u003e\n\u003cp\u003eIn this study, we compared the general characteristics of patients between non-CHD and CHD patients combined with hypertension. We found that CHD patients tended to be older (55.7\u0026plusmn;9.7 vs 59.6 \u0026plusmn;10.7 years, P\u0026lt;0.001), higher glucose levels of BMI (26.93\u0026plusmn;3.85 vs 26.07\u0026plusmn;3.10 kg/m\u003csup\u003e2\u003c/sup\u003e, P=0.030), SBP (132\u0026plusmn;17 vs 129\u0026plusmn;18 mmHg, P=0.023), DBP (80\u0026plusmn;11 vs 78\u0026plusmn;12 mmHg, P=0.043) compared with non-CHD patients. Patients with CHD also have higher levels of TC (4.17\u0026plusmn;1.01 vs 4.45\u0026plusmn;1.18 mmol/L, P=0.001), LDL-c (2.60\u0026plusmn;0.83 vs 2.76\u0026plusmn;0.96 mmol/L, P=0.010) and lower HDL-c (1.04\u0026plusmn;0.29 vs 0.99\u0026plusmn;0.28 mmol/L, P=0.020, Table 1).\u003c/p\u003e\n\u003cp\u003eGeneral characteristics and biochemical parameters between control (non-MACCEs) and MACCEs groups had been compared, as shown in Table 2. There was no significant difference regarding gender, smoking, alcohol intake, T2DM between these groups (P \u0026gt; 0.05). The prevalence of MACCEs in CHD patients was significantly higher than non-CHD patients (79.5% vs 20.5%), P\u0026lt;0.001. Patients with MACCEs showed higher blood glucose compared with those non-MACCEs patients (8.15\u0026plusmn;3.42 vs 6.93\u0026plusmn;3.22 mmol/L), P\u0026lt;0.001.\u003c/p\u003e\n\u003cp\u003ePatients with MACCEs showed higher age (Table 2) compared with those non-MACCEs patients (57.1\u0026plusmn;10.3 vs 59.1\u0026plusmn;9.7 years), P\u0026lt;0.001. We then compared characteristics among different age groups in Table 3. In 51-60 years old and over 60 years old groups, MACCEs occurrence increased significantly (31.6% and 54.7%, respectively, P=0.001). The BMI, blood pressure, glucose, TG, HDL-c showed significantly difference among different age groups (P\u0026lt;0.05). There was no difference regarding rs16860760, rs389566 TT genotype among different age groups (P=0.932, P=0.446 respectively).\u003c/p\u003e\n\u003ch2\u003eOccurrence of MACCEs in patients with different genotypes of rs16860760 in AT1R gene\u003c/h2\u003e\n\u003cp\u003eThere was no significant difference regarding the frequency of MACCEs in different AT1R rs16860760 SNPs (P\u0026gt;0.05), but the AT1R gene rs389566 polymorphism showed significant association with the probability of MACCEs in patients with hypertension (Table 4). And the patients carrying TT genotype at rs389566 locus had a higher risk of MACCEs than those carrying the AA+AT gene type (24.8% vs 75.2%, P=0.033).\u003c/p\u003e\n\u003ch2\u003eRisk factors of MACCEs\u003c/h2\u003e\n\u003cp\u003eIn the present study, the mean follow-up duration was 65.6 (38.3, 91.8) months. The Kaplan\u0026ndash;Meier analysis revealed that the MACCEs-free cumulative survival rate in the TT genotype group was obviously lower than that in the\u0026nbsp;AA+AT\u0026nbsp;genotype group (P=0.009, Fig. 1).\u003c/p\u003e\n\u003cp\u003eThrough univariate Cox survival analysis, we found that elderly, glucose, coronary heart disease, and rs389566 TT gene types may be risk factors for MACCEs in patients with hypertension. As shown in Table 5, age, AT1R gene rs389566 TT genotype, CHD and glucose variables were included to construct a multifactorial COX proportion-al risk model. The results showed old age may be a predisposing factor on the occurrence of MACCEs (OR=1.028, 95% CI: 1.009-1.047, P=0.003), and rs389566 TT genotype may be a predisposing factor on the occurrence of MACCEs (OR=1.770, 95%CI 1.148-2.729, P=0.010). Patients with CHD were prone to MACCEs (OR=4.118, 95%CI 2.542-6.672, P\u0026lt;0.001). However, the glucose showed no significant different effect on occurrence of MACCEs in the final model (P\u0026gt;0.05).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eMany factors influence the occurrence of MACCEs, such as family history of CHD, smoking, obesity, hypertension, diabetes, abnormal lipid metabolism, insulin resistance, and homocysteine mia\u0026nbsp;[13]. In the present study, AT1R gene rs389566 TT genotype was found to be associated with the occurrence of MACCEs in hypertension patients.\u003c/p\u003e\n\u003cp\u003eCardiovascular disease is the leading cause of death worldwide\u0026nbsp;[14], and hypertension is the most common chronic disease and the most important risk factor for cardiovascular disease\u0026nbsp;[15]. Although CHD mortality rates have gradually declined in Western countries over the past few decades, the condition still causes about one-third of deaths in people over 35 years of age\u0026nbsp;[16].\u0026nbsp;MACCEs remain the major cause of mortality and morbidity in patients both in hypertension or CHD patients\u0026nbsp;[17, 18].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHowever, it has been reported in the literature that the incidence of MACCEs is significantly higher in CHD combined with hypertension patients compared with non-CHD or non-hypertension patients\u0026nbsp;[10, 19], but the reasons remain to be unknown. The traditional risk factors of MACCEs include fasting glucose, heart rate variability, blood pressure\u0026nbsp;[20-23]\u0026nbsp;and dyslipidemia\u0026nbsp;[24]. As previous reported, AT1R gene polymorphism was found to be associated with the development of CHD in Chinese population\u0026nbsp;[25, 26]. Here we found AT1R rs389566 TT genotype may be an independent risk factor for the development of MACCEs in patients with hypertension especially those combined with CHD.\u0026nbsp;The main effects of Renin-Angiotensin-Aldosterone System (RAAS) on cardiovascular system are atherosclerosis and hypertension, leading to congestive heart failure and MACCEs\u0026nbsp;[27].\u0026nbsp;And\u0026nbsp;Ang II also promotes the development of atherosclerosis through AT1 receptors, stimulating the secretion of inflammatory mediators, and converting stable plaques into vulnerable plaques\u0026nbsp;[28]. Overexpression of the AT1R gene leads to myocardial hypertrophy and ventricular remodeling\u0026nbsp;[29]. The previously study demonstrates that the AT1R\u0026nbsp;polymorphism\u0026nbsp;is associated with abnormal coronary vasoconstriction which causes rupture of plaque and thrombus formation\u0026nbsp;[30].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOur study found that AT1R gene mutation was associated with the occurrence of MACCEs in hypertension patients in the Xinjiang. The patients with hypertension carrying TT genotype of the AT1R gene rs389566 were prone to MACCEs. Previous studies have been conducted on AT1R gene polymorphisms in the Chinese population, but mainly on hypertension, atherosclerosis, cardiovascular disease risk factors, and intravascular restenosis. The association of AT1R gene polymorphisms with the occurrence of MACCEs events has not been reported before. Most previous studies have focused on the association of the AT1R rs5186 (A1166C) locus polymorphism and acute myocardial infarction in Caucasian, Asian, African, Brazilian, and Durban populations, and the C allele was proved to be a risk factor for occurrence of myocardial infarction\u0026nbsp;[31]. In Asia, previous studies\u0026nbsp;[31-33]\u0026nbsp;reported that AT1R A1166C polymorphism may influence the occurrence of myocardial infarction susceptibility in Chinese. However, the sample size of these studies is relatively small, and fewer studies have focused on the relationship between AT1R rs16860760 and MACCEs.\u0026nbsp;In the present study, we found the significant association between AT1R rs389566 polymorphism and MACCEs in Chinese hypertensive population which could help provide a clinical basis for future targeted interventions.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eBesides AT1R gene polymorphism, the age is also a factor affecting the occurrence of MACCEs. Our study found that the occurrence of MACCEs is higher in older age population, Patients with hypertension over 60 years are more likely to occur MACCEs and the prevalence is about 54.7% and it was consistent with previous study\u0026nbsp;[34]. For aged population, MACCEs prevention should be emphasized in future.\u003c/p\u003e\n\u003cp\u003eOur study confirmed that AT1R rs389566 TT genotype increased the occurrence of MACCEs in hypertension patients.\u0026nbsp;\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn summary, this study provides the current status of risk factors for the occurrence of MACCEs in hypertensive patients combined with CHD in Xinjiang, China. Especially those aged hypertensive patients carrying A1TR rs389566 TT genotype \u0026nbsp;requires avoidance of unhealthy lifestyles, raising awareness about the prevention and better management of MACCEs. The present findings provide potential intervention targets for the prognosis of patients who are at high risk of MACCEs, and this will help clinician do genomics-based personalized therapy in future.\u003c/p\u003e"},{"header":"Limitation","content":"\u003cp\u003eThis study also has some limitations. First, the sample size was not large enough. Second, participants in the current study were recruited only at the First Affiliated Hospital of Xinjiang Medical University, which may not necessarily reflect the true prevalence of hypertension combined with CHD and the occurrence of MACCEs at the provincial or national level. Finally, we focused our interest on the AT1R gene polymorphism: as discussed, many other factors are involved in MACCEs and may cause increase in occurrence. Broader analyses are therefore encouraged to better understand the complexity of the MACCEs occurrence process.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study conducted according to the standards of the Declaration of Helsinki and its experimental protocols was approved by the Ethics Committee of the First Affiliated Hospital of Xinjiang Medical University. Written informed consent was obtained from all subjects and/or their legal guardian(s). All participants consented for drawing their blood samples and collection of their relevant clinical data.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and analyzed during the current study available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll of these authors declared that they had no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe survey was funded by the National Natural Science Foundation of China (82160054, 81960078), State Key Laboratory of Pathogenesis, Prevention and Treatment of High Incidence Diseases in Central Asia, Xinjiang Medical University (SKL-HIDCA-2021-XXG4), and Xinjiang Young Scientific and Technical Talents Training Project (2019Q040).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eX‑ML, X-MG and Y‑NY were involved in the study design of the experiments. J‑YL, G-LD, Y-MH, FL, TZ and B-BF performed the experiments, evaluated the data and wrote the manuscript. J-YL, G-LD and Y-MH were involved in data analysis and manuscript editing. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003e\u003cspan\u003eAlderman M, Madhavan S, Ooi W, Cohen H, Sealey J, Laragh J. Association of the renin-sodium profile with the risk of myocardial infarction in patients with hypertension. N Engl J Med. 1991;324:1098\u0026ndash;104.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eYusuf S, Pepine C, Garces C, Pouleur H, Rousseau M, Salem D, Kostis J, Benedict C, Bourassa M, Pitt B. Effect of enalapril on myocardial infarction and unstable angina in patients with low ejection fractions. 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Chin Gen Pract. 2018;21:3562\u0026ndash;70.\u003c/span\u003e\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1.\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;General characteristics in hypertension patients stratified with CHD\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellpadding=\"0\" cellspacing=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.6530612244898%\"\u003e\n \u003cp\u003e\u003cstrong\u003eCharacteristics\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.591836734693878%\"\u003e\n \u003cp\u003e\u003cstrong\u003enon-CHD\u003c/strong\u003e\u003cstrong\u003e（\u003c/strong\u003e\u003cstrong\u003en=341\u003c/strong\u003e\u003cstrong\u003e）\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.428571428571427%\"\u003e\n \u003cp\u003e\u003cstrong\u003eCHD\u003c/strong\u003e\u003cstrong\u003e（\u003c/strong\u003e\u003cstrong\u003en=374\u003c/strong\u003e\u003cstrong\u003e）\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.3265306122449%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eP\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;Value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.6530612244898%\"\u003e\n \u003cp\u003eAge (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.591836734693878%\"\u003e\n \u003cp\u003e55.7\u0026plusmn;9.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.428571428571427%\"\u003e\n \u003cp\u003e59.6\u0026plusmn;10.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.3265306122449%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"32.6530612244898%\"\u003e\n \u003cp\u003eGender, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"29.591836734693878%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.428571428571427%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.3265306122449%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"32.6530612244898%\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"29.591836734693878%\"\u003e\n \u003cp\u003e173 (50.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.428571428571427%\"\u003e\n \u003cp\u003e234\u0026nbsp; \u0026nbsp; \u0026nbsp; (62.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.3265306122449%\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"32.6530612244898%\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"29.591836734693878%\"\u003e\n \u003cp\u003e168 (49.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.428571428571427%\"\u003e\n \u003ctable align=\"left\" border=\"0\" cellpadding=\"0\" cellspacing=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"100%\"\u003e\n \u003cp\u003e140\u0026nbsp; \u0026nbsp; \u0026nbsp; (37.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.3265306122449%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"32.6530612244898%\"\u003e\n \u003cp\u003eSmoking, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"29.591836734693878%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.428571428571427%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.3265306122449%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"32.6530612244898%\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"29.591836734693878%\"\u003e\n \u003cp\u003e242 (71.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.428571428571427%\"\u003e\n \u003cp\u003e229 (61.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.3265306122449%\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"32.6530612244898%\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"29.591836734693878%\"\u003e\n \u003cp\u003e99 (29.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.428571428571427%\"\u003e\n \u003cp\u003e145 (38.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.3265306122449%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"32.6530612244898%\"\u003e\n \u003cp\u003eAlcohol intake, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"29.591836734693878%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.428571428571427%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.3265306122449%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"32.6530612244898%\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"29.591836734693878%\"\u003e\n \u003cp\u003e251 (73.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.428571428571427%\"\u003e\n \u003cp\u003e282 (79.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.3265306122449%\"\u003e\n \u003cp\u003e0.582\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"32.6530612244898%\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"29.591836734693878%\"\u003e\n \u003cp\u003e90 (26.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.428571428571427%\"\u003e\n \u003cp\u003e92 (20.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.3265306122449%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"32.6530612244898%\"\u003e\n \u003cp\u003eT2DM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"29.591836734693878%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.428571428571427%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.3265306122449%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"32.6530612244898%\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"29.591836734693878%\"\u003e\n \u003cp\u003e289 (84.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.428571428571427%\"\u003e\n \u003cp\u003e261 (69.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.3265306122449%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"32.6530612244898%\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"29.591836734693878%\"\u003e\n \u003cp\u003e52 (15.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.428571428571427%\"\u003e\n \u003cp\u003e113 (30.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.3265306122449%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.6530612244898%\"\u003e\n \u003cp\u003eSBP (mmHg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.591836734693878%\"\u003e\n \u003cp\u003e132\u0026plusmn;17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.428571428571427%\"\u003e\n \u003cp\u003e129\u0026plusmn;19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.3265306122449%\"\u003e\n \u003cp\u003e0.023\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.6530612244898%\"\u003e\n \u003cp\u003eDBP (mmHg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.591836734693878%\"\u003e\n \u003cp\u003e80\u0026plusmn;12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.428571428571427%\"\u003e\n \u003cp\u003e79\u0026plusmn;13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.3265306122449%\"\u003e\n \u003cp\u003e0.043\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.6530612244898%\"\u003e\n \u003cp\u003eBMI (kg/m\u003csup\u003e2\u003c/sup\u003e)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.591836734693878%\"\u003e\n \u003cp\u003e26.93\u0026plusmn;3.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.428571428571427%\"\u003e\n \u003cp\u003e26.07\u0026plusmn;3.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.3265306122449%\"\u003e\n \u003cp\u003e0.030\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.6530612244898%\"\u003e\n \u003cp\u003eGlucose (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.591836734693878%\"\u003e\n \u003cp\u003e5.67\u0026plusmn;1.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.428571428571427%\"\u003e\n \u003cp\u003e8.44\u0026plusmn;3.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.3265306122449%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.6530612244898%\"\u003e\n \u003cp\u003eTG (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.591836734693878%\"\u003e\n \u003cp\u003e1.96\u0026plusmn;1.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.428571428571427%\"\u003e\n \u003cp\u003e2.13\u0026plusmn;1.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.3265306122449%\"\u003e\n \u003cp\u003e0.181\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.6530612244898%\"\u003e\n \u003cp\u003eTC (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.591836734693878%\"\u003e\n \u003cp\u003e4.17\u0026plusmn;1.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.428571428571427%\"\u003e\n \u003cp\u003e4.45\u0026plusmn;1.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.3265306122449%\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.6530612244898%\"\u003e\n \u003cp\u003eHDL\u0026ndash;c (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.591836734693878%\"\u003e\n \u003cp\u003e1.04\u0026plusmn;0.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.428571428571427%\"\u003e\n \u003cp\u003e0.99\u0026plusmn;0.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.3265306122449%\"\u003e\n \u003cp\u003e0.020\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.6530612244898%\"\u003e\n \u003cp\u003eLDL\u0026ndash;c (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.591836734693878%\"\u003e\n \u003cp\u003e2.60\u0026plusmn;0.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.428571428571427%\"\u003e\n \u003cp\u003e2.76\u0026plusmn;0.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.3265306122449%\"\u003e\n \u003cp\u003e0.010\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eCHD: Coronary heart disease, BMI: Body mass index, SBP: Systolic blood pressure, DBP: Diastolic blood pressure, TG: Triglycerides, TC: Total Cholesterol, HDL-c: High Density lipoprotein cholesterol, LDL-c: Low density lipoprotein cholesterol,\u0026nbsp;T2DM: Type 2 Diabetes Mellitus, P\u0026lt; 0.05 was considered significant difference.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eTable 2.\u003c/strong\u003e \u003cstrong\u003eBiochemical parameters\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;in hypertension patients stratified with MACCEs\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellpadding=\"0\" cellspacing=\"0\" width=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"36.19744058500914%\"\u003e\n \u003cp\u003e\u003cstrong\u003eBiochemical parameters\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"22.486288848263253%\"\u003e\n \u003cp\u003e\u003cstrong\u003enon-MACCEs (n=598) \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"27.60511882998172%\"\u003e\n \u003cp\u003e\u003cstrong\u003eMACCEs\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(n=117)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.711151736745887%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eP\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;Value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"36.19744058500914%\"\u003e\n \u003cp\u003eAge (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"22.486288848263253%\"\u003e\n \u003cp\u003e57.1\u0026plusmn;10.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"25.045703839122485%\"\u003e\n \u003cp\u003e59.1\u0026plusmn;9.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"16.270566727605118%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"36.19744058500914%\"\u003e\n \u003cp\u003eGender, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"22.486288848263253%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"25.045703839122485%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"16.270566727605118%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"36.19744058500914%\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"22.486288848263253%\"\u003e\n \u003cp\u003e337 (56.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"25.045703839122485%\"\u003e\n \u003cp\u003e70 (59.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"16.270566727605118%\"\u003e\n \u003cp\u003e0.488\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"36.19744058500914%\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"22.486288848263253%\"\u003e\n \u003cp\u003e261 (43.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"25.045703839122485%\"\u003e\n \u003cp\u003e47 (40.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"16.270566727605118%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"36.19744058500914%\"\u003e\n \u003cp\u003eSmoking, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"22.486288848263253%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"25.045703839122485%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"16.270566727605118%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"36.19744058500914%\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"22.486288848263253%\"\u003e\n \u003cp\u003e391（65.4%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"25.045703839122485%\"\u003e\n \u003cp\u003e80（68.4%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"16.270566727605118%\"\u003e\n \u003cp\u003e0.533\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"36.19744058500914%\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"22.486288848263253%\"\u003e\n \u003cp\u003e207（34.6%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"25.045703839122485%\"\u003e\n \u003cp\u003e37（31.6%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"16.270566727605118%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"36.19744058500914%\"\u003e\n \u003cp\u003eAlcohol intake, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"22.486288848263253%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"25.045703839122485%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"16.270566727605118%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"36.19744058500914%\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"22.486288848263253%\"\u003e\n \u003cp\u003e440 (73.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"25.045703839122485%\"\u003e\n \u003cp\u003e93 (79.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"16.270566727605118%\"\u003e\n \u003cp\u003e0.180\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"36.19744058500914%\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"22.486288848263253%\"\u003e\n \u003cp\u003e158 (26.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"25.045703839122485%\"\u003e\n \u003cp\u003e24 (20.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"16.270566727605118%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"36.19744058500914%\"\u003e\n \u003cp\u003eT2DM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"22.486288848263253%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"25.045703839122485%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"16.270566727605118%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"36.19744058500914%\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"22.486288848263253%\"\u003e\n \u003cp\u003e461（77.1%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"25.045703839122485%\"\u003e\n \u003cp\u003e89（76.1%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"16.270566727605118%\"\u003e\n \u003cp\u003e0.810\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"36.19744058500914%\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"22.486288848263253%\"\u003e\n \u003cp\u003e137（22.9%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"25.045703839122485%\"\u003e\n \u003cp\u003e28（23.9%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"16.270566727605118%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"36.19744058500914%\"\u003e\n \u003cp\u003eCHD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"22.486288848263253%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"25.045703839122485%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"16.270566727605118%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"36.19744058500914%\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"22.486288848263253%\"\u003e\n \u003cp\u003e317 (53.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"25.045703839122485%\"\u003e\n \u003cp\u003e24 (20.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"16.270566727605118%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"36.19744058500914%\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"22.486288848263253%\"\u003e\n \u003cp\u003e281 (47.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"25.045703839122485%\"\u003e\n \u003cp\u003e93 (79.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"16.270566727605118%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"36.19744058500914%\"\u003e\n \u003cp\u003eSBP (mmHg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"22.486288848263253%\"\u003e\n \u003cp\u003e131\u0026plusmn;18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"25.045703839122485%\"\u003e\n \u003cp\u003e129\u0026plusmn;19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"16.270566727605118%\"\u003e\n \u003cp\u003e0.227\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"36.19744058500914%\"\u003e\n \u003cp\u003eDBP (mmHg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"22.486288848263253%\"\u003e\n \u003cp\u003e80\u0026plusmn;12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"25.045703839122485%\"\u003e\n \u003cp\u003e79\u0026plusmn;13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"16.270566727605118%\"\u003e\n \u003cp\u003e0.412\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"36.19744058500914%\"\u003e\n \u003cp\u003eBMI (kg/m\u003csup\u003e2\u003c/sup\u003e)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"22.486288848263253%\"\u003e\n \u003cp\u003e26.55\u0026plusmn;3.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"25.045703839122485%\"\u003e\n \u003cp\u003e25.83\u0026plusmn;3.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"16.270566727605118%\"\u003e\n \u003cp\u003e0.097\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"36.19744058500914%\"\u003e\n \u003cp\u003eGlucose (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"22.486288848263253%\"\u003e\n \u003cp\u003e6.93\u0026plusmn;3.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"25.045703839122485%\"\u003e\n \u003cp\u003e8.15\u0026plusmn;3.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"16.270566727605118%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"36.19744058500914%\"\u003e\n \u003cp\u003eTG (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"22.486288848263253%\"\u003e\n \u003cp\u003e2.04\u0026plusmn;1.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"25.045703839122485%\"\u003e\n \u003cp\u003e2.09\u0026plusmn;1.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"16.270566727605118%\"\u003e\n \u003cp\u003e0.810\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"36.19744058500914%\"\u003e\n \u003cp\u003eTC (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"22.486288848263253%\"\u003e\n \u003cp\u003e4.30\u0026plusmn;1.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"25.045703839122485%\"\u003e\n \u003cp\u003e4.40\u0026plusmn;1.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"16.270566727605118%\"\u003e\n \u003cp\u003e0.389\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"36.19744058500914%\"\u003e\n \u003cp\u003eHDL\u0026ndash;c (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"22.486288848263253%\"\u003e\n \u003cp\u003e1.04\u0026plusmn;0.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"25.045703839122485%\"\u003e\n \u003cp\u003e0.99\u0026plusmn;0.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"16.270566727605118%\"\u003e\n \u003cp\u003e0.093\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"36.19744058500914%\"\u003e\n \u003cp\u003eLDL\u0026ndash;c (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"22.486288848263253%\"\u003e\n \u003cp\u003e2.68\u0026plusmn;0.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"25.045703839122485%\"\u003e\n \u003cp\u003e2.73\u0026plusmn;0.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"16.270566727605118%\"\u003e\n \u003cp\u003e0.542\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eMACCEs: Major adverse cardiovascular and cerebrovascular events, BMI: Body mass index, SBP: Systolic blood pressure, DBP: Diastolic blood pressure, TG: Triglycerides, TC: Total cholesterol, HDL-c: High density lipoprotein cholesterol, LDL-c: Low density lipoprotein cholesterol, T2DM: Type 2 Diabetes Mellitus, CHD: Coronary heart disease, P\u0026lt; 0.05 was considered significant difference.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3. Comparison of general characteristics in patients with hypertension according to different ages\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellpadding=\"0\" cellspacing=\"0\" width=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.70149253731343%\"\u003e\n \u003cp\u003e\u003cstrong\u003eCharacteristics \u0026nbsp; \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.865671641791046%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;40 years old\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(n=20)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.35820895522388%\"\u003e\n \u003cp\u003e\u003cstrong\u003e41-50 years old\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(n=178)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.35820895522388%\"\u003e\n \u003cp\u003e\u003cstrong\u003e51-60 years old\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(n=218)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.865671641791046%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026gt;60 years old\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(n=293)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.850746268656716%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eP\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;Value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"19.70149253731343%\"\u003e\n \u003cp\u003eGender, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.865671641791046%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"18.35820895522388%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"18.35820895522388%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.865671641791046%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.850746268656716%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"19.70149253731343%\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.865671641791046%\"\u003e\n \u003cp\u003e18（90.0%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"18.35820895522388%\"\u003e\n \u003cp\u003e139（77.7%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"18.35820895522388%\"\u003e\n \u003cp\u003e123（56.2%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.865671641791046%\"\u003e\n \u003cp\u003e127（42.8%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" width=\"9.850746268656716%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"21.85430463576159%\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"18.70860927152318%\"\u003e\n \u003cp\u003e2（10.0%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.364238410596027%\"\u003e\n \u003cp\u003e40（22.3%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.364238410596027%\"\u003e\n \u003cp\u003e96（43.8%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"18.70860927152318%\"\u003e\n \u003cp\u003e170（57.2%）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"19.70149253731343%\"\u003e\n \u003cp\u003eSmoking, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.865671641791046%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"18.35820895522388%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"18.35820895522388%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.865671641791046%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.850746268656716%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"19.70149253731343%\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.865671641791046%\"\u003e\n \u003cp\u003e7（35.0%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"18.35820895522388%\"\u003e\n \u003cp\u003e83（46.4%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"18.35820895522388%\"\u003e\n \u003cp\u003e146（66.7%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.865671641791046%\"\u003e\n \u003cp\u003e235（79.1%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" width=\"9.850746268656716%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"21.85430463576159%\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"18.70860927152318%\"\u003e\n \u003cp\u003e13（65.0%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.364238410596027%\"\u003e\n \u003cp\u003e96（53.6%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.364238410596027%\"\u003e\n \u003cp\u003e73（33.3%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"18.70860927152318%\"\u003e\n \u003cp\u003e62（20.9%）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"36.56716417910448%\"\u003e\n \u003cp\u003eAlcohol intake, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"18.35820895522388%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"18.35820895522388%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.865671641791046%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.850746268656716%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"19.70149253731343%\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.865671641791046%\"\u003e\n \u003cp\u003e9（1.7%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"18.35820895522388%\"\u003e\n \u003cp\u003e104（19.5%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"18.35820895522388%\"\u003e\n \u003cp\u003e164（30.8%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.865671641791046%\"\u003e\n \u003cp\u003e256（48.0%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" width=\"9.850746268656716%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"21.85430463576159%\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"18.70860927152318%\"\u003e\n \u003cp\u003e11（55.0%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.364238410596027%\"\u003e\n \u003cp\u003e75（41.9%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.364238410596027%\"\u003e\n \u003cp\u003e55（25.1%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"18.70860927152318%\"\u003e\n \u003cp\u003e41（13.8%）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"19.70149253731343%\"\u003e\n \u003cp\u003eT2DM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.865671641791046%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"18.35820895522388%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"18.35820895522388%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.865671641791046%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.850746268656716%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"19.70149253731343%\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.865671641791046%\"\u003e\n \u003cp\u003e17（85.0%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"18.35820895522388%\"\u003e\n \u003cp\u003e155（86.6%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"18.35820895522388%\"\u003e\n \u003cp\u003e171（78.1%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.865671641791046%\"\u003e\n \u003cp\u003e207（69.7%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" width=\"9.850746268656716%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"21.85430463576159%\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"18.70860927152318%\"\u003e\n \u003cp\u003e3（15.0%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.364238410596027%\"\u003e\n \u003cp\u003e24（13.4%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.364238410596027%\"\u003e\n \u003cp\u003e48（21.9%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"18.70860927152318%\"\u003e\n \u003cp\u003e90（30.3%）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"19.70149253731343%\"\u003e\n \u003cp\u003eCHD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.865671641791046%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"18.35820895522388%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"18.35820895522388%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.865671641791046%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.850746268656716%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"19.70149253731343%\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.865671641791046%\"\u003e\n \u003cp\u003e15（75.0%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"18.35820895522388%\"\u003e\n \u003cp\u003e101（56.4%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"18.35820895522388%\"\u003e\n \u003cp\u003e107（48.9%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.865671641791046%\"\u003e\n \u003cp\u003e118（39.7%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" width=\"9.850746268656716%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"21.85430463576159%\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"18.70860927152318%\"\u003e\n \u003cp\u003e5（25.0%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.364238410596027%\"\u003e\n \u003cp\u003e78（43.6%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.364238410596027%\"\u003e\n \u003cp\u003e112（51.1%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"18.70860927152318%\"\u003e\n \u003cp\u003e179（60.3%）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.70149253731343%\"\u003e\n \u003cp\u003eSBP (mmHg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.865671641791046%\"\u003e\n \u003cp\u003e137\u0026plusmn;16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.35820895522388%\"\u003e\n \u003cp\u003e129\u0026plusmn;17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.35820895522388%\"\u003e\n \u003cp\u003e129\u0026plusmn;16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.865671641791046%\"\u003e\n \u003cp\u003e132\u0026plusmn;20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.850746268656716%\"\u003e\n \u003cp\u003e0.039\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.70149253731343%\"\u003e\n \u003cp\u003eDBP (mmHg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.865671641791046%\"\u003e\n \u003cp\u003e87\u0026plusmn;14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.35820895522388%\"\u003e\n \u003cp\u003e82\u0026plusmn;12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.35820895522388%\"\u003e\n \u003cp\u003e80\u0026plusmn;11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.865671641791046%\"\u003e\n \u003cp\u003e77\u0026plusmn;13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.850746268656716%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.70149253731343%\"\u003e\n \u003cp\u003eBMI (kg/m\u003csup\u003e2\u003c/sup\u003e)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.865671641791046%\"\u003e\n \u003cp\u003e29.34\u0026plusmn;3.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.35820895522388%\"\u003e\n \u003cp\u003e27.03\u0026plusmn;2.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.35820895522388%\"\u003e\n \u003cp\u003e26.60\u0026plusmn;3.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.865671641791046%\"\u003e\n \u003cp\u003e25.63\u0026plusmn;3.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.850746268656716%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.70149253731343%\"\u003e\n \u003cp\u003eGlucose(mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.865671641791046%\"\u003e\n \u003cp\u003e6.16\u0026plusmn;2.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.35820895522388%\"\u003e\n \u003cp\u003e7.06\u0026plusmn;3.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.35820895522388%\"\u003e\n \u003cp\u003e6.30\u0026plusmn;2.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.865671641791046%\"\u003e\n \u003cp\u003e7.76\u0026plusmn;3.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.850746268656716%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.70149253731343%\"\u003e\n \u003cp\u003eTG (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.865671641791046%\"\u003e\n \u003cp\u003e2.68\u0026plusmn;3.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.35820895522388%\"\u003e\n \u003cp\u003e2.36\u0026plusmn;1.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.35820895522388%\"\u003e\n \u003cp\u003e2.00\u0026plusmn;1.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.865671641791046%\"\u003e\n \u003cp\u003e1.84\u0026plusmn;1.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.850746268656716%\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.70149253731343%\"\u003e\n \u003cp\u003eTC (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.865671641791046%\"\u003e\n \u003cp\u003e4.72\u0026plusmn;1.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.35820895522388%\"\u003e\n \u003cp\u003e4.37\u0026plusmn;1.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.35820895522388%\"\u003e\n \u003cp\u003e4.31\u0026plusmn;1.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.865671641791046%\"\u003e\n \u003cp\u003e4.26\u0026plusmn;1.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.850746268656716%\"\u003e\n \u003cp\u003e0.311\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.70149253731343%\"\u003e\n \u003cp\u003eHDL\u0026ndash;c (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.865671641791046%\"\u003e\n \u003cp\u003e1.02\u0026plusmn;0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.35820895522388%\"\u003e\n \u003cp\u003e0.96\u0026plusmn;0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.35820895522388%\"\u003e\n \u003cp\u003e1.04\u0026plusmn;0.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.865671641791046%\"\u003e\n \u003cp\u003e1.08\u0026plusmn;0.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.850746268656716%\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.70149253731343%\"\u003e\n \u003cp\u003eLDL\u0026ndash;c (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.865671641791046%\"\u003e\n \u003cp\u003e3.12\u0026plusmn;0.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.35820895522388%\"\u003e\n \u003cp\u003e2.74\u0026plusmn;0.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.35820895522388%\"\u003e\n \u003cp\u003e2.68\u0026plusmn;0.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.865671641791046%\"\u003e\n \u003cp\u003e2.62\u0026plusmn;0.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.850746268656716%\"\u003e\n \u003cp\u003e0.096\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.70149253731343%\"\u003e\n \u003cp\u003eMACCEs, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.865671641791046%\"\u003e\n \u003cp\u003e1 (0.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"18.35820895522388%\"\u003e\n \u003cp\u003e15 (12.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"18.35820895522388%\"\u003e\n \u003cp\u003e37 (31.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.865671641791046%\"\u003e\n \u003cp\u003e64 (54.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.850746268656716%\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.70149253731343%\"\u003e\n \u003cp\u003eRS389566 TT,\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.865671641791046%\"\u003e\n \u003cp\u003e13 (2.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"18.35820895522388%\"\u003e\n \u003cp\u003e116 (25.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"18.35820895522388%\"\u003e\n \u003cp\u003e133 (28.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.865671641791046%\"\u003e\n \u003cp\u003e201 (43.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.850746268656716%\"\u003e\n \u003cp\u003e0.446\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.70149253731343%\"\u003e\n \u003cp\u003eRS16860760,\u0026nbsp;\u003c/p\u003e\n \u003cp\u003en (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.865671641791046%\"\u003e\n \u003cp\u003e20 (2.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"18.35820895522388%\"\u003e\n \u003cp\u003e179 (25%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"18.35820895522388%\"\u003e\n \u003cp\u003e219 (30.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.865671641791046%\"\u003e\n \u003cp\u003e297 (41.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.850746268656716%\"\u003e\n \u003cp\u003e0.932\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eBMI: Body mass index, SBP: Systolic blood pressure, DBP: Diastolic blood pressure, TG: Triglycerides, TC: Total Cholesterol, HDL-c: High density lipoprotein cholesterol, LDL-c: Low density lipoprotein cholesterol,\u0026nbsp;MACCEs: major adverse cardiovascular events. P\u0026lt; 0.05 was considered significant difference.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4.\u003c/strong\u003e \u003cstrong\u003eAT1R gene polymorphisms in patients with MACCEs and control group.\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellpadding=\"0\" cellspacing=\"0\" width=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.52593917710197%\"\u003e\n \u003cp\u003e\u003cstrong\u003ePolymorphisms\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.792486583184257%\"\u003e\n \u003cp\u003e\u003cstrong\u003enon-MACCEs (n=598)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.508050089445437%\"\u003e\n \u003cp\u003e\u003cstrong\u003eMACCEs (n=117)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.173524150268335%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eP\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;Value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" width=\"100%\"\u003e\n \u003cp\u003e\u003cem\u003eRs16860760\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.52593917710197%\"\u003e\n \u003cp\u003eAA+AG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.792486583184257%\"\u003e\n \u003cp\u003e62 (10.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.508050089445437%\"\u003e\n \u003cp\u003e9 (7.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" width=\"17.173524150268335%\"\u003e\n \u003cp\u003e0.599\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"41.68466522678186%\"\u003e\n \u003cp\u003eGG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.72570194384449%\"\u003e\n \u003cp\u003e536 (89.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.58963282937365%\"\u003e\n \u003cp\u003e108 (92.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" width=\"100%\"\u003e\n \u003cp\u003e\u003cem\u003eRs389566\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.52593917710197%\"\u003e\n \u003cp\u003eAA+AT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.792486583184257%\"\u003e\n \u003cp\u003e223 (37.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.508050089445437%\"\u003e\n \u003cp\u003e29 (24.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" width=\"17.173524150268335%\"\u003e\n \u003cp\u003e0.033\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"41.68466522678186%\"\u003e\n \u003cp\u003eTT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.72570194384449%\"\u003e\n \u003cp\u003e375 (62.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.58963282937365%\"\u003e\n \u003cp\u003e88 (75.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eMACCEs: Major adverse cardiovascular and cerebrovascular events, P \u0026lt; 0.05 was considered significant difference\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 5 Univariate and multivariate Cox analyses among the hypertension patients\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellpadding=\"0\" cellspacing=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" width=\"24.63768115942029%\"\u003e\n \u003cp\u003e\u003cstrong\u003eRisk factors\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"36.3768115942029%\"\u003e\n \u003cp\u003e\u003cstrong\u003eUnivariate cox regress\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" width=\"3.4782608695652173%\"\u003e\u0026nbsp;\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"35.507246376811594%\"\u003e\n \u003cp\u003e\u003cstrong\u003eMultivariate cox regress\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"50.604838709677416%\"\u003e\n \u003cp\u003e\u003cstrong\u003eOR (95% CI) \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003eP\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;Value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"49.395161290322584%\"\u003e\n \u003cp\u003e\u003cstrong\u003eOR (95% CI) \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003eP\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;Value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"24.63768115942029%\"\u003e\n \u003cp\u003ers389566 AA+AT/TT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"23.768115942028984%\"\u003e\n \u003cp\u003e1.731 (1.138-2.635)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.608695652173912%\"\u003e\n \u003cp\u003e0.010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"3.4782608695652173%\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"24.492753623188406%\"\u003e\n \u003cp\u003e1.770(1.148-2.729)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.014492753623188%\"\u003e\n \u003cp\u003e0.010\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"24.63768115942029%\"\u003e\n \u003cp\u003eCHD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"23.768115942028984%\"\u003e\n \u003cp\u003e4.912 (3.128-7.714)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.608695652173912%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"3.4782608695652173%\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"24.492753623188406%\"\u003e\n \u003cp\u003e4.118(2.542-6.672) \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.014492753623188%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"24.63768115942029%\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"23.768115942028984%\"\u003e\n \u003cp\u003e1.041(1.023-1.060) \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.608695652173912%\"\u003e\n \u003cp\u003e\u0026lt;0.001 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"3.4782608695652173%\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"24.492753623188406%\"\u003e\n \u003cp\u003e1.028(1.009-1.047)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.014492753623188%\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"24.63768115942029%\"\u003e\n \u003cp\u003eGender\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"23.768115942028984%\"\u003e\n \u003cp\u003e0.789 (0.545-1.142)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.608695652173912%\"\u003e\n \u003cp\u003e0.208\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"3.4782608695652173%\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"24.492753623188406%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.014492753623188%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"24.63768115942029%\"\u003e\n \u003cp\u003eBMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"23.768115942028984%\"\u003e\n \u003cp\u003e0.951(0.890-1.016)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.608695652173912%\"\u003e\n \u003cp\u003e0.136 \u0026nbsp;\u003c/p\u003e\n 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\u003ctd valign=\"top\" width=\"11.014492753623188%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"24.63768115942029%\"\u003e\n \u003cp\u003eDBP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"23.768115942028984%\"\u003e\n \u003cp\u003e0.992(0.977-1.007)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.608695652173912%\"\u003e\n \u003cp\u003e0.278\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"3.4782608695652173%\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"24.492753623188406%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.014492753623188%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"24.63768115942029%\"\u003e\n \u003cp\u003eGlucose\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"23.768115942028984%\"\u003e\n \u003cp\u003e1.107(1.060-1.156)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.608695652173912%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"3.4782608695652173%\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"24.492753623188406%\"\u003e\n \u003cp\u003e1.036(0.985-1.089) \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.014492753623188%\"\u003e\n \u003cp\u003e0.167\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"24.63768115942029%\"\u003e\n \u003cp\u003eTG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"23.768115942028984%\"\u003e\n \u003cp\u003e1.008(0.897-1.132)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.608695652173912%\"\u003e\n \u003cp\u003e0.895\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"3.4782608695652173%\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"24.492753623188406%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.014492753623188%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"24.63768115942029%\"\u003e\n \u003cp\u003eTC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"23.768115942028984%\"\u003e\n \u003cp\u003e1.101(0.930-1.303)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.608695652173912%\"\u003e\n \u003cp\u003e0.264\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"3.4782608695652173%\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"24.492753623188406%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.014492753623188%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"24.63768115942029%\"\u003e\n \u003cp\u003eHDL-c\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"23.768115942028984%\"\u003e\n \u003cp\u003e0.578(0.297-1.127)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.608695652173912%\"\u003e\n \u003cp\u003e0.108\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"3.4782608695652173%\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"24.492753623188406%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.014492753623188%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"24.63768115942029%\"\u003e\n \u003cp\u003eLDL-c\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"23.768115942028984%\"\u003e\n \u003cp\u003e1.108(0.900-1.363)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.608695652173912%\"\u003e\n \u003cp\u003e0.334\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"3.4782608695652173%\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"24.492753623188406%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.014492753623188%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eBMI: Body mass index, SBP: Systolic blood pressure, DBP: Diastolic blood pressure, TG: Triglycerides, TC: Total Cholesterol,\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHDL-c: High density lipoprotein cholesterol, LDL-c: Low density lipoprotein cholesterol, MACCEs: major adverse cardiovascular\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eevents. CHD: Coronary heart disease, OR: Odds ratio, CI: Confidence Interval, P\u0026lt; 0.05 was considered significant difference.\u0026nbsp;\u003c/p\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":"bmc-cardiovascular-disorders","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcar","sideBox":"Learn more about [BMC Cardiovascular Disorders](http://bmccardiovascdisord.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcar/default.aspx","title":"BMC Cardiovascular Disorders","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Angiotensin II type 1 receptor (AT1R), Coronary heart disease (CHD), Hypertension, Major adverse cardiovascular events (MACCEs)","lastPublishedDoi":"10.21203/rs.3.rs-2062190/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-2062190/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eOBJECTIVE:\u003c/p\u003e\n\u003cp\u003eTo investigate the possible association between AT1R gene polymorphisms and major adverse cardiovascular and cerebrovascular events (MACCEs) in hypertension patients combined with or without coronary heart disease (CHD) in Xinjiang.\u003c/p\u003e\n\u003cp\u003eMETHODS:\u003c/p\u003e\n\u003cp\u003e374 CHD patients and 341 non-CHD individuals were enrolled as study participants and all of them have a hypertension diagnosis. AT1R gene polymorphisms were genotyped by SNPscan™ typing assays. During the follow-up in the clinic or by telephone interview, MACCEs were recorded. Kaplan–Meier curves and Cox survival analyses were used to explore the association between AT1R gene polymorphisms and the occurrence of MACCEs.\u003c/p\u003e\n\u003cp\u003eRESULTS:\u003c/p\u003e\n\u003cp\u003eAT1R gene rs389566 was associated with MACCEs. The TT genotype of the AT1R gene rs389566 had a significantly higher probability of MACCEs than the AA+AT genotype (75.2% vs 24.8%, P=0.033). Older age (OR=1.028, 95% CI: 1.009-1.0047, P=0.003) and TT genotype of rs389566 (OR=1.770, 95% CI: 1.148-2.729, P=0.01) were risk factors of MACCEs. AT1R gene rs389566 TT genotype may be a predisposing factor for the occurrence of MACCEs in hypertensive patients.\u003c/p\u003e\n\u003cp\u003eCONDLUSION:\u003c/p\u003e\n\u003cp\u003eAT1R SNP rs389566 may be a common genetic loci and optimal genetic susceptibility marker for MACCEs in hypertension patients.\u003c/p\u003e","manuscriptTitle":"AT1R gene polymorphism contributes to MACCEs in Hypertension patients","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2022-09-21 01:07:58","doi":"10.21203/rs.3.rs-2062190/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Major revision","date":"2023-01-25T08:39:44+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2023-01-24T18:38:28+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"fab405b2-3d46-4914-9fc4-acff212c11d3","date":"2023-01-16T16:49:49+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2022-11-15T16:57:16+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"ebb17e4e-7ac5-4be4-91e3-b387ddd9fb01","date":"2022-11-11T08:24:17+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2022-09-22T16:53:20+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2022-09-22T16:44:55+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2022-09-15T11:07:32+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2022-09-15T11:03:59+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Cardiovascular Disorders","date":"2022-09-13T18:19:23+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-cardiovascular-disorders","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcar","sideBox":"Learn more about [BMC Cardiovascular Disorders](http://bmccardiovascdisord.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcar/default.aspx","title":"BMC Cardiovascular Disorders","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"3b02a950-bb17-46e5-9556-ebd9feb4c8cf","owner":[],"postedDate":"September 21st, 2022","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2023-10-16T21:10:40+00:00","versionOfRecord":{"articleIdentity":"rs-2062190","link":"https://doi.org/10.1186/s12872-023-03223-w","journal":{"identity":"bmc-cardiovascular-disorders","isVorOnly":false,"title":"BMC Cardiovascular Disorders"},"publishedOn":"2023-06-03 21:00:56","publishedOnDateReadable":"June 3rd, 2023"},"versionCreatedAt":"2022-09-21 01:07:58","video":"","vorDoi":"10.1186/s12872-023-03223-w","vorDoiUrl":"https://doi.org/10.1186/s12872-023-03223-w","workflowStages":[]},"version":"v1","identity":"rs-2062190","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-2062190","identity":"rs-2062190","version":["v1"]},"buildId":"J0_U0BvcaRcwD8yVFaRlm","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}