Metabolically Healthy Versus Unhealthy Obese Phenotypes and Risk of Hypertension Incidence; A Case–Cohort Analysis

Metabolically Healthy Versus Unhealthy Obese Phenotypes and Risk of Hypertension Incidence; A Case–Cohort Analysis | 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 Metabolically Healthy Versus Unhealthy Obese Phenotypes and Risk of Hypertension Incidence; A Case–Cohort Analysis Behrooz Hamzeh, Yahya Pasdar, Shima Moradi, Mitra Darbandi, Ebrahim Shakiba, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-669988/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 13 You are reading this latest preprint version Abstract Background Although obesity contributes in increasing the risk of hypertension, it is not known the effect of obesity based on metabolic status on the incidence of hypertension. This study was aimed to determine association between obesity phenotypes including metabolically unhealthy obesity (MUO) and metabolically healthy obesity (MHO) and risk of hypertension incidence. Methods We conducted a case-cohort study on 6,747 adults 35–65 aged from Ravansar non- communicable diseases (RaNCD) study. Obesity was defined body mass index > 30 kg/m 2 and metabolically unhealthy was considered at least two metabolic disorders based on the International Diabetes Federation criteria. Obesity phenotypes were categorized four groups including MUO, MHO, metabolically unhealthy non obesity (MUNO), and metabolically healthy non obesity (MHNO). Cox proportional hazards regression models were applied to analyze associations with hypertension incidence. Results The incidence of hypertension was one case per 1000 person-months (393/391162). The MHO (HR: 1.37; 95% CI: 1.03–1.86) and MUO phenotype (HR: 2.44; 95% CI: 1.81–3.29) was linearly associated with higher hypertension risk compared to MHNO. In addition, MUNO phenotype was significantly associated with risk of hypertension incidence (HR: 1.65; 95% CI: 1.29–2.14). Conclusions Both metabolically healthy and unhealthy obesity was elevated risk of hypertension incidence, however, this increase in metabolically unhealthy phenotypes was higher. Cardiac & Cardiovascular Systems Cardiothoracic Surgery metabolically unhealthy obesity metabolically healthy obesity hypertension incidence PERSIAN Figures Figure 1 Background Hypertension is one of the strongest modifiable risk factors for cardiovascular disease (CVDs) which its prevalence is increasing especially in low- and middle-income countries [ 1 , 2 ]. In addition to CVDs, hypertension is involved in the pathogenesis of stroke, cerebral hemorrhage, subarachnoid hemorrhage, renal failure, and macrovascular disease [ 3 , 4 ]. Reports indicate that a quarter of men and a fifth of women have hypertension, and hypertension is responsible for approximately 45% of deaths from the CVDs [ 5 , 6 ]. Results of data from World Health Organization and United Nations Development Program for 182 countries showed that the prevalence of hypertension was 13–41% [ 5 ]. Many factors contribute to hypertension, including sedentary lifestyle, kidney disease, diabetes, obesity, high salt intake and processed foods [ 7 , 8 ]. Among these factors, obesity is contributed in the development of CVDs, type 2 diabetes, cancer, inflammatory diseases, and hypertension [ 9 – 11 ]. Evidence suggests that obesity, with its pro-inflammatory effects and oxidative stress, causes insulin resistance, dyslipidemia, and other metabolic disorders in which is considered metabolically unhealthy obesity (MUO) [ 12 , 13 ]. Nevertheless, some people with obesity have metabolically healthy status, in which are described metabolically healthy obesity (MHO) phenotype [ 12 ]. Additionally metabolically unhealthy non obesity (MUNO) phenotypes are at risk of type 2 diabetes, CVDs, fatty liver, and mortality [ 13 , 14 ]. Reports indicate that obesity is associated with a risk of developing hypertension. Since a study has not yet examined the types of obesity phenotypes based on the metabolic status of individuals, the present study was conducted with the aim of metabolically healthy versus unhealthy obese phenotypes and risk of hypertension incidence in the Ravansar non- communicable diseases (RaNCD) cohort study. Methods Study design and setting We conducted a case-cohort study nested in the RaNCD cohort The RaNCD study which is a first cohort study on Kurdish population, on aged 35–65 years living in Ravansar city, Kermanshah province, Western- Iran which started in October 2014. The RaNCD cohort study is a component of the PERSIAN (Prospective Epidemiological Research Studies in Iran) mega cohort study that was approved by the Ethics Committees in the Ministry of Health and Medical Education, the Digestive Diseases Research Institute, Tehran University of Medical Sciences, Iran. The details of this study were described in previous studies [ 15 , 16 ]. In this study, all recruitment phase participants included, which was surveyed from October 2014 to January 2017 and followed until January 2021 (n = 4764 men and 5258 women). The RaNCD cohort study was approved by the Ethics Committee of Kermanshah University of Medical Sciences (No: KUMS.REC.1394.318). Participants Among the RaNCD participants, 3300 of them were not included in the study for the following reasons: 1) participants with CVDs (n = 1709), type 2 diabetes (n = 870), hypertension (n = 1579), cancer (n = 83), and thyroid diseases (n = 763); 2) pregnant women (n = 138); 3) energy intake less than 800 Kcal/day or more than 4200 Kcal/day (n = 737). After excluding participants with missing data, overall, 6747 participants were included into this study. Measurements This current study was obtained demographic data including age, sex, smoking status, and physical activity, as well as, medical history, medication, anthropometric indices, blood pressure, and biochemical analysis. Anthropometry All participants’ height were measured by the automatic stadiometer BSM 370 (Biospace Co., Seoul, Korea) with a precision of 0.1 cm in standing position without shoes. InBody 770 device (Inbody Co, Seoul, Korea) was applied to measure the weight and body fat mass (BFM) of participants with the least clothing and without shoes. To determine obesity, body mass index (BMI) was calculated by dividing weight in kilogram to square height in meter 2 , after that BMI more than 30 kg/m 2 as obesity. Waist circumference (WC) was measured using non-stretched and flexible tape in standing position at the level of the iliac crest. Blood pressure In RaNCD cohort study, conventional sphygmomanometry and auscultation of the Korotkoff sounds was used to measure systolic and diastolic blood pressure (SBP and DBP) in sitting position after at least 4–5 minutes of rest. The blood pressure measuring was conducted two times with 10 minutes interval and the mean of them was calculated and reported as the final blood pressure [ 15 ]. Biochemical analysis 25 cc blood samples were collected from all RaNCD participants. The serum and whole blood samples were subdivided and were stored at -80 ◦ C the RaNCD cohort laboratory until analysis. Serum fasting blood sugar (FBS) was measured by glucose oxidase method. Total cholesterol (TC), high-density lipoproteins (HDL), triglyceride (TG) and low- density lipoproteins (LDL) concentration were measured by enzymatic kits (Pars Azmun, Iran) [ 15 ]. Obesity phenotypes We defined MUO presence of BMI > 30 kg/m 2 and at least two metabolic disorder according to the International Diabetes Federation (IDF) statement [ 17 ] as follow: HDL < 40 mg/dl in men and 150 mg/dl; SBP > 130 mmHg or DBP > 80 mmHg or antihypertensive medication; and FBS > 100 mg/dl or medication for diabetes. Also, MHO was defined BMI > 30 kg/m 2 and having at most one metabolic disorder mentioned in the previous sentences, as well as, MUNO phenotype was considered presence of BMI < 30 kg/m 2 and at least two metabolic disorder. In addition, MHNO participants were related to healthy participants without obesity and metabolic disorder. Outcome measurement hypertension incidence The hypertension was defined by codes I10 of the International classification of diseases Tenth Edition (ICD-10), which included SBP/DBP ≥ 140/90 mmHg and/or using anti-hypertensive medications in the time interval between baseline (first phase of Ravansar cohort which has been conducted from 2014) and hypertension diagnosis (from 2015 to 2021), which the overall duration of the follow-up was 391162 person-months. Statistical analysis Statistical analysis was performed using Stata, version 14 (Stata Corp, College Station, TX). Mean ± standard deviation (SD) and frequency percent was used to report baseline characteristics of studied participants. To compare results of baseline characteristics among different obesity phenotypes, one-way analysis of variance (ANOVA) was used for continuous variables, and a Chi-square test was used for categorical variables. Incidence rate (IR) calculated based on 1000 person/months Cox proportional hazards regression model were used to calculate hazard ratios (HRs) stratified by obesity phenotypes, with hypertension as the event and the time interval between baseline (first phase of RaNCD cohort) and hypertension diagnosis as the time covariate. The models of adjusted for confounding variables including age, sex, physical activity, smoking and energy intake, and reported as HR with 95% confidence interval (CI). Results A total of 6,747 participants were analyzed in this study as sub-cohort and 393 incidence cases were also in the sub-cohort. The incidence rate of hypertension was one cases per 1000 person-months (393/391162, male: 150/188718, female: 243/202443)) during a mean follow-up of 57.74 months (Minimum: 0.27, Maximum: 73.30). In addition, the new case of hypertension was significantly higher in female than male (6.84% vs. 4.63%, P < 0.001). ( Table 1 ) Table 1 Baseline characteristics of studied participants Variables Total (n = 6747) MHNO (n = 3965) MHO (n = 1036) MUNO (n = 1204) MUO (n = 542) Age (year) 45.77 ± 7.76* 45.67 ± 7.97 44.99 ± 7.04 46.76 ± 7.88 45.86 ± 7.02 < 0.001 Weight (kg) 71.87 ± 13.42 66.49 ± 11.02 84.54 ± 10.83 71.76 ± 10.11 87.12 ± 12.25 < 0.001 WC (cm) 96.26 ± 10.36 92.03 ± 8.64 106.89 ± 8.23 95.94 ± 7.01 107.54 ± 8.81 < 0.001 BMI (kg/m 2 ) 27.01 ± 4.67 24.73 ± 3.34 33.11 ± 2.93 26.45 ± 2.45 33.33 ± 3.28 < 0.001 BFM (kg) 24.27 ± 9.41 19.76 ± 6.61 36.38 ± 6.78 22.41 ± 5.25 36.24 ± 7.61 < 0.001 SBP (mmHg) 103.55 ± 12.42 101.62 ± 11.83 104.10 ± 11.31 107.16 ± 13.07 108.59 ± 13.83 < 0.001 DBP (mmHg) 67.44 ± 7.82 66.37 ± 7.36 67.76 ± 7.51 69.44 ± 8.28 70.18 ± 8.95 < 0.001 FBS (mg/dl) 89.92 ± 9.49 87.91 ± 8.03 88.96 ± 8.08 94.36 ± 11.29 96.58 ± 11.04 < 0.001 TC (mg/dl) 184.01 ± 36.79 180.03 ± 37.31 186.74 ± 33.72 190.40 ± 36.80 193.68 ± 34.36 < 0.001 TG (mg/dl) 130.01 ± 73.75 101.75 ± 46.35 114.06 ± 47.34 205.82 ± 87.54 198.37 ± 83.06 < 0.001 HDL(mg/dl) 46.83 ± 11.41 49.67 ± 11.21 49.82 ± 10.55 38.04 ± 7.45 39.85 ± 8.31 < 0.001 LDL (mg/dl) 101.26 ± 24.90 98.66 ± 25.16 102.64 ± 22.76 105.89 ± 25.49 107.33 ± 22.79 < 0.001 PA (MET hour/ day( 41.08 ± 8.15 41.90 ± 8.78 39.71 ± 6.19 40.37 ± 7.78 39.32 ± 6.55 < 0.001 Current smoking (%) 11.9 20.7 9.4 23.1 14.1 < 0.001 Hypertension incidence 5.79 4.4 6.2 7.7 10.9 < 0.001 *Mean ± SD **P-values were obtained one-way ANOVA and Chi square. MHNO: metabolically healthy non-obese; MHO: metabolically healthy obese; MUNO: metabolically unhealthy non-obese; MUO: metabolically unhealthy obese; WC: waist circumference; BMI: body mass index; BFM: body fat mass; SBP: systolic blood pressure; DBP: diastolic blood pressure; FBS: fasting blood sugar; TC: total cholesterol; TG: triglyceride; HDL: high density lipoprotein; LDL: low density lipoprotein; PA: physical activity The prevalence of MHO, MUNO, and MUO were 15.3, 17.4, and 8.03 %; respectively. The mean of physical activity in MHNO was significantly higher than other three obesity phenotypes (MHO, MUNO, and MUO) in both men and women. Table 2 are presented baseline characteristics of studied participants based on the different types of obesity phenotypes. Table 2 Baseline characteristics of studied participants based on the different types of obesity phenotypes Variables Men (n = 3217) P** Women (n = 3530) P** MHNO (n = 2094) MHO (n = 246) MUNO (n = 692) MUO (n = 185) MHNO (n = 1871) MHO (n = 790) MUNO (n = 512) MUO (n = 357) Age (year) 45.95 ± 7.88* 45.13 ± 7.09 45.96 ± 7.40 44.98 ± 7.01 0.174 45.35 ± 8.06 44.94 ± 7.03 47.84 ± 8.37 46.31 ± 6.99 < 0.001 Weight (kg) 71.01 ± 10.74 94.50 ± 9.53 76.69 ± 8.93 95.74 ± 10.33 < 0.001 61.44 ± 8.94 81.44 ± 9.22 65.11 ± 7.47 82.65 ± 10.70 < 0.001 WC (cm) 92.49 ± 8.30 107.53 ± 7.70 96.14 ± 6.87 106.97 ± 7.71 < 0.001 91.51 ± 8.97 106.68 ± 8.39 95.66 ± 7.20 107.83 ± 9.32 < 0.001 BMI (kg/m 2 ) 24.36 ± 3.40 32.42 ± 2.30 26.26 ± 2.52 32.37 ± 2.29 < 0.001 25.15 ± 3.22 33.32 ± 3.07 26.70 ± 2.34 33.82 ± 3.60 < 0.001 BFM (kg) 17.74 ± 6.24 33.77 ± 6.69 20.91 ± 5.01 33.54 ± 7.17 < 0.001 22.31 ± 6.18 37.32 ± 6.58 24.71 ± 4.77 37.90 ± 7.41 < 0.001 SBP (mmHg) 103.59 ± 11.53 108.20 ± 11.10 107.77 ± 12.22 110.03 ± 12.87 < 0.001 99.41 ± 11.77 102.83 ± 11.08 106.33 ± 14.11 107.84 ± 14.26 < 0.001 DBP (mmHg) 67.31 ± 7.50 70.30 ± 7.65 69.98 ± 8.00 71.25 ± 8.77 < 0.001 65.32 ± 7.05 66.97 ± 7.29 68.72 ± 8.60 69.62 ± 9.00 < 0.001 FBS (mg/dl) 88.32 ± 8.14 89.74 ± 8.57 93.71 ± 10.90 95.53 ± 9.94 < 0.001 87.45 ± 7.88 88.72 ± 7.91 95.25 ± 11.74 97.12 ± 11.55 < 0.001 TC (mg/dl) 178.53 ± 36.02 187.56 ± 35.36 185.02 ± 33.58 188.54 ± 30.04 < 0.001 181.72 ± 38.64 186.49 ± 33.22 197.68 ± 39.64 196.34 ± 36.15 < 0.001 TG (mg/dl) 108.41 ± 51.12 134.62 ± 67.99 216.65 ± 89.80 220.14 ± 86.05 < 0.001 94.28 ± 39.00 107.65 ± 36.49 191.19 ± 82.24 187.09 ± 79.27 < 0.001 HDL(mg/dl) 46.37 ± 9.98 44.71 ± 8.98 35.24 ± 5.92 35.29 ± 5.74 < 0.001 53.39 ± 11.35 51.41 ± 10.50 41.82 ± 7.64 42.22 ± 8.46 < 0.001 LDL (mg/dl) 99.23 ± 24.50 106.41 ± 23.50 103.03 ± 22.89 105.91 ± 20.73 < 0.001 98.03 ± 25.87 101.46 ± 22.42 109.77 ± 28.19 108.08 ± 23.78 < 0.001 PA (MET hour/ day( 43.70 ± 10.87 41.59 ± 9.87 41.31 ± 9.59 40.09 ± 9.78 < 0.001 39.89 ± 4.84 39.12 ± 4.30 39.10 ± 3.92 38.93 ± 3.91 < 0.001 Current smoking (%) 35.9 30.7 34.8 35.3 0.462 3.7 2.8 7.1 3.1 0.001 Hypertension incidence 4 6.5 5.1 8.1 0.029 4.9 6.1 11.3 12.3 < 0.001 *Mean ± SD **P-values were obtained one-way ANOVA and Chi square. MHNO: metabolically healthy non-obese; MHO: metabolically healthy obese; MUNO: metabolically unhealthy non-obese; MUO: metabolically unhealthy obese; WC: waist circumference; BMI: body mass index; BFM: body fat mass; SBP: systolic blood pressure; DBP: diastolic blood pressure; FBS: fasting blood sugar; TC: total cholesterol; TG: triglyceride; HDL: high density lipoprotein; LDL: low density lipoprotein; PA: physical activity The risk increased in MHO phenotype compared to MHNO (HR: 1.41; 95% CI: 1.05–1.88) in model І, which remained significant after adjustment for age, sex, physical activity and smoking (HR: 1.37; 95% CI: 1.03–1.86). The risk increased in MUO phenotype compared to MHNO (HR: 2.44; 95% CI: 1.81–3.29) after adjust sex and age, which remained significant after adjustment for age, sex, physical activity and smoking (HR: 2.40; 95% CI: 1.77, 3.26). ( Table 3 ) Table 3 Hazard ratio of incident hypertension according to obesity phenotypes Obesity phenotypes N % (N) of cases Hazard ratio (95% CI) Model І Model II Model III MHNO 3965 4.4 (175) Ref. Ref. Ref. MHO 1036 6.2 (64) 1.41 ( 1.05, 1.88) 1.37 (1.02, 1.83) 1.37 ( 1.03,1.86) MUNO 1204 7.7 (93) 1.68 (1.31, 2.16) 1.64 (1.27, 2.11) 1.65 (1.29, 2.14) MUO 542 10.9 (59) 2.44 ( 1.81, 3.29) 2.36 ( 1.75, 3.20) 2.40 (1.77, 3.26) Model 1 : Adjusted for age and sex; Model 2 : Adjusted for age, sex and physical activity; Model 3 : Adjusted for age, sex, physical activity and smoking MHNO, metabolically healthy non-obese; MHO, metabolically healthy obese; MUNO, metabolically unhealthy non-obese; MUO, metabolically unhealthy obese In addition, risk of hypertension significantly increased in MUNO phenotype compared to MHNO in all adjusted models (HR: 1.65; 95% CI: 1.29–2.14). The cumulative hazard curves show the incidence of hypertension has increased by approximately 7% in MUO phenotype over 70 months; and this increase was more than other phenotypes in over time ( Fig. 1 ) . Discussion Our results shows both phenotypes obesity MHO and MUO increase to develop risk of the hypertension compared to MHNO phenotype. Furthermore, MUNO phenotype was associated with higher risk of hypertension incidence compared to MHNO phenotype. Overall, the MUO phenotype increased risk of hypertension incidence more than other phenotypes in the follow-up study time. The obesity epidemic is growing and increases the risk of chronic non-communicable diseases leading to increased health system costs [ 18 ]. Epidemiological studies highlight the persistent link between obesity and hypertension, and the presence of obesity increases the risk of developing hypertension [ 8 , 19 ]. Since there are different phenotypes of obesity based on the metabolic status, to best our knowledge, we examined the association between obesity phenotypes and risk of hypertension incidence. The results of Whitehall II cohort study by Hinnouho et al. [ 20 ] on 5269 participants indicated that both obesity phenotypes, MHO and MUO lead to increased risk of mortality after seventeen years following. Another prospective study by Fingeret et al. [ 21 ] after 10.9 years follow up was not seen any difference between MHO and MUO in hypertension incidence (odds ratio (OR): 1.3, CI 95%: 0.8–2.09). Yuan et al. [ 22 ] showed that MHO had no association with arterial stiffness developing (OR: 0.99; CI 95%: 0.61–1.6), while MUO and MUNO phenotypes lead to significantly progressed arterial stiffness (OR: 4.56; CI 95%: 2.60–8) and (OR: 5.05; CI 95%: 3.12–8.19), respectively. In current study we observed that MUO and MUNO increase the risk of hypertension incidence more than MHO. Also, BFM and WC of the participants were higher in all three groups than MHNO phenotype. Obesity, especially the high excess visceral fat distribution is increased inflammatory cytokines and endothelial disorders in which stimulate several mechanisms contribute to hypertension [ 18 , 23 ]. High excess adipose tissue increases the production of pro- inflammatory factors such as leptin, tumor necrosis factor- α, interleukin-6, and resistin in which develops various metabolic diseases [ 24 ]. High calorie intake and increase in adipocytes stimulate α and β adrenergic receptors, thereby increasing the activity of the sympathetic nervous system [ 25 ]. Obesity activates the renin-angiotensin nervous system and the sympathetic nervous system, which leads to increased sodium reabsorption and arterial blood pressure [ 26 , 27 ]. On the other hand, increasing adipose tissue leads to decreased adiponectin production and increased insulin resistance [ 28 , 29 ]. Therefore, chronic hyperinsulinemia in obese people causes vascular vasoconstrictor and also increases urinary sodium reabsorption and is involved in the pathogenesis of hypertension [ 30 ]. Also, increased circulating leptin levels in response to increased adipose tissue lead to impaired nitric oxide synthesis and ultimately vascular endothelial dysfunction [ 18 ]. Therefore, obesity increases the production of adipose tissue, causing the production of pro-inflammatory cytokines, which play an important role in the pathogenesis of hypertension by disrupting the metabolic status. Strength And Limits The present prospective study follows for the first time the Kurdish population and examines the types of obesity based on metabolic status and risk of hypertension incidence. In this study, we also applied appropriate exclusion criteria, such as people who did not have normal calorie intake. However, this study had its limitations. First, the follow-up period seems to have been short. Second, the hypertension incidence was small for the study groups, and we could not assess the relationship between hypertension incidence and obesity phenotypes based on the sex, although it was adjusted for sex. Conclusion In conclusion, present study stated that both of MHO and MUO phenotypes lead to rise hypertension incidence compared to MHNO phenotype, as well as, MUNO phenotype can increase hypertension incidence. However, MUO and MUNO phenotypes increase the risk of hypertension incidence more than MHO compared to MHNO phenotype. For protecting from hypertension maintaining normal weight and controlling central obesity as well as visceral fat is highly recommended. Declarations Acknowledgments RaNCD is part of PERSIAN national cohort and we would like to thank Professor Reza Malekzadeh, Ex-Deputy of Research and Technology at the Ministry of Health and Medical Education of Iran and Director of the PERSIAN cohort, and also Dr. Hossein Poustchi Executive Director of PERSIAN cohort for all their supports during design and running of RaNCD. We gratefully acknowledge our RaNCD field workers and site staffs and participants of the RaNCD cohort for their important contributions. Funding: This study was supported by Ministry of Health and Medical Education of Iran and Kermanshah University of Medical Science (Grant No: 92472). Compliance with ethical standards Ethics approval and consent to participate: All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. This study was approved by the Ethics Committee of Kermanshah University of Medical Sciences (ethics approval number: KUMS.REC.1394.318). Informed consent: Written informed consent was obtained from each studied subject after explaining the purpose of the study. The right of subjects to withdraw from the study at any time and subject’s information is reserved and will not be published. Consent for publication: Not applicable Availability of data and materials: Data will be available upon request from the corresponding author. Competing interests: All authors have no conflict of interest. Authors' contributions: F, Njafi, B, Hamzeh , S, Moradi and Y, Pasdar equally contributed to the conception and design of the research; F, Njafi, B, Hamzeh, E, Shakiba and Y, Pasdar contributed to data collection; S, Moradi, Y, Pasdar and M, Darbandi contributed to the acquisition and analysis of the data; S, Moradi, Y, Pasdar and M, Darbandi contributed to the interpretation of the data; and S, Moradi, Y, Pasdar and M, Darbandi contributed to draft the manuscript. All authors are in agreement with the manuscript and declare that the content has not been published elsewhere. References Mills KT, Stefanescu A, He J. The global epidemiology of hypertension. Nature Rev Nephrol. 2020;16(4):223–37. Fisher ND, Curfman G. Hypertension—a public health challenge of global proportions. Jama. 2018;320(17):1757–9. Umemura S, Arima H, Arima S, Asayama K, Dohi Y, Hirooka Y, et al. The Japanese Society of Hypertension guidelines for the management of hypertension (JSH 2019). 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De Boer MP, Meijer RI, Wijnstok NJ, Jonk AM, Houben AJ, Stehouwer CD, et al. Microvascular dysfunction: a potential mechanism in the pathogenesis of obesity-associated insulin resistance and hypertension. Microcirculation. 2012;19(1):5–18. Soleimani M. Insulin resistance and hypertension: new insights. Kidney Inter 2015;87(3):497–9. Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-669988","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":44355909,"identity":"ca538361-9d0d-438f-aea2-462c09183586","order_by":0,"name":"Behrooz Hamzeh","email":"","orcid":"","institution":"Kermanshah University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Behrooz","middleName":"","lastName":"Hamzeh","suffix":""},{"id":44355910,"identity":"5a3cbf30-8345-4603-b943-c943a82e9a27","order_by":1,"name":"Yahya Pasdar","email":"","orcid":"","institution":"Kermanshah University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Yahya","middleName":"","lastName":"Pasdar","suffix":""},{"id":44355911,"identity":"0c05544e-aa15-4396-b3fe-244cc8b63fa7","order_by":2,"name":"Shima Moradi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+0lEQVRIiWNgGAWjYBAC+RmMDUDqMIhtcODDAZi4AW4tBjfAWp6D2QdnEKVFAkz9B7OZeQ7gVonQIt3cuuEHw2058/bDGw/bnDkcLd/A/PADQ8E9nFrk5xxsu9nDcNtY5kxaweGcG4dzNxxgM5ZgMCjGbc2NxLYbPAy3E2cw5BgczvkA1MLAYAa0PQGvlpt/GA4nzuB/Y3DYAqhlfgP7N4JabvOAtEgAbWEAOqzhAA9+WwxAWmQMDhtLSDwrONhzJj13w2GeYokEPFrkZ6Q/u/mm4rCcBH/y5g8/jlnnzm9v3/jhwx88DoPYhcxhBmJCGkbBKBgFo2AU4AcAfT9gAV/DktMAAAAASUVORK5CYII=","orcid":"","institution":"Kermanshah University of Medical Sciences","correspondingAuthor":true,"prefix":"","firstName":"Shima","middleName":"","lastName":"Moradi","suffix":""},{"id":44355912,"identity":"7eaf498f-d8a1-4865-95d0-6929efa517fd","order_by":3,"name":"Mitra Darbandi","email":"","orcid":"","institution":"Kermanshah University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Mitra","middleName":"","lastName":"Darbandi","suffix":""},{"id":44355913,"identity":"5fe86dd2-bfdf-4fd7-9bf4-063aa4aa1c32","order_by":4,"name":"Ebrahim Shakiba","email":"","orcid":"","institution":"Kermanshah University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Ebrahim","middleName":"","lastName":"Shakiba","suffix":""},{"id":44355914,"identity":"fbfd92da-f4d0-45d5-b47b-0319e02c18f3","order_by":5,"name":"Farid Najafi","email":"","orcid":"","institution":"Kermanshah University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Farid","middleName":"","lastName":"Najafi","suffix":""}],"badges":[],"createdAt":"2021-06-30 08:59:06","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-669988/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-669988/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":12258750,"identity":"323f012c-efdc-4c59-90ec-f4c6a3bbd8c0","added_by":"auto","created_at":"2021-08-09 18:11:52","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":26806,"visible":true,"origin":"","legend":"Cumulative hazard curves for the incidence of hypertension in over time according to obesity phenotypes ","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-669988/v1/a9ab321411a161aaad73f3fe.png"},{"id":13708437,"identity":"67e717d6-327c-4162-b864-e4e99da7c212","added_by":"auto","created_at":"2021-09-17 14:07:46","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":387225,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-669988/v1/49a29275-7505-46d5-a5cf-c5132d5ed34b.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eMetabolically Healthy Versus Unhealthy Obese Phenotypes and Risk of Hypertension Incidence; A Case–Cohort Analysis\u003c/p\u003e","fulltext":[{"header":"Background","content":"\u003cp\u003eHypertension is one of the strongest modifiable risk factors for cardiovascular disease (CVDs) which its prevalence is increasing especially in low- and middle-income countries [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. In addition to CVDs, hypertension is involved in the pathogenesis of stroke, cerebral hemorrhage, subarachnoid hemorrhage, renal failure, and macrovascular disease [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Reports indicate that a quarter of men and a fifth of women have hypertension, and hypertension is responsible for approximately 45% of deaths from the CVDs [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Results of data from World Health Organization and United Nations Development Program for 182 countries showed that the prevalence of hypertension was 13\u0026ndash;41% [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eMany factors contribute to hypertension, including sedentary lifestyle, kidney disease, diabetes, obesity, high salt intake and processed foods [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Among these factors, obesity is contributed in the development of CVDs, type 2 diabetes, cancer, inflammatory diseases, and hypertension [\u003cspan additionalcitationids=\"CR10\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Evidence suggests that obesity, with its pro-inflammatory effects and oxidative stress, causes insulin resistance, dyslipidemia, and other metabolic disorders in which is considered metabolically unhealthy obesity (MUO) [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Nevertheless, some people with obesity have metabolically healthy status, in which are described metabolically healthy obesity (MHO) phenotype [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Additionally metabolically unhealthy non obesity (MUNO) phenotypes are at risk of type 2 diabetes, CVDs, fatty liver, and mortality [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eReports indicate that obesity is associated with a risk of developing hypertension. Since a study has not yet examined the types of obesity phenotypes based on the metabolic status of individuals, the present study was conducted with the aim of metabolically healthy versus unhealthy obese phenotypes and risk of hypertension incidence in the Ravansar non- communicable diseases (RaNCD) cohort study.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eStudy design and setting\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe conducted a case-cohort study nested in the RaNCD cohort The RaNCD study which is a first cohort study on Kurdish population, on aged 35\u0026ndash;65 years living in Ravansar city, Kermanshah province, Western- Iran which started in October 2014. The RaNCD cohort study is a component of the PERSIAN (Prospective Epidemiological Research Studies in Iran) mega cohort study that was approved by the Ethics Committees in the Ministry of Health and Medical Education, the Digestive Diseases Research Institute, Tehran University of Medical Sciences, Iran. The details of this study were described in previous studies [\u003cspan class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e16\u003c/span\u003e]. In this study, all recruitment phase participants included, which was surveyed from October 2014 to January 2017 and followed until January 2021 (n\u0026thinsp;=\u0026thinsp;4764 men and 5258 women). The RaNCD cohort study was approved by the Ethics Committee of Kermanshah University of Medical Sciences (No: KUMS.REC.1394.318).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eParticipants\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAmong the RaNCD participants, 3300 of them were not included in the study for the following reasons: 1) participants with CVDs (n\u0026thinsp;=\u0026thinsp;1709), type 2 diabetes (n\u0026thinsp;=\u0026thinsp;870), hypertension (n\u0026thinsp;=\u0026thinsp;1579), cancer (n\u0026thinsp;=\u0026thinsp;83), and thyroid diseases (n\u0026thinsp;=\u0026thinsp;763); 2) pregnant women (n\u0026thinsp;=\u0026thinsp;138); 3) energy intake less than 800 Kcal/day or more than 4200 Kcal/day (n\u0026thinsp;=\u0026thinsp;737). After excluding participants with missing data, overall, 6747 participants were included into this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMeasurements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis current study was obtained demographic data including age, sex, smoking status, and physical activity, as well as, medical history, medication, anthropometric indices, blood pressure, and biochemical analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAnthropometry\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll participants\u0026rsquo; height were measured by the automatic stadiometer BSM 370 (Biospace Co., Seoul, Korea) with a precision of 0.1 cm in standing position without shoes. InBody 770 device (Inbody Co, Seoul, Korea) was applied to measure the weight and body fat mass (BFM) of participants with the least clothing and without shoes. To determine obesity, body mass index (BMI) was calculated by dividing weight in kilogram to square height in meter\u003csup\u003e2\u003c/sup\u003e, after that BMI more than 30 kg/m\u003csup\u003e2\u003c/sup\u003e as obesity. Waist circumference (WC) was measured using non-stretched and flexible tape in standing position at the level of the iliac crest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBlood pressure\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn RaNCD cohort study, conventional sphygmomanometry and auscultation of the Korotkoff sounds was used to measure systolic and diastolic blood pressure (SBP and DBP) in sitting position after at least 4\u0026ndash;5 minutes of rest. The blood pressure measuring was conducted two times with 10 minutes interval and the mean of them was calculated and reported as the final blood pressure [\u003cspan class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBiochemical analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e25 cc blood samples were collected from all RaNCD participants. The serum and whole blood samples were subdivided and were stored at -80\u003csup\u003e◦\u003c/sup\u003eC the RaNCD cohort laboratory until analysis. Serum fasting blood sugar (FBS) was measured by glucose oxidase method. Total cholesterol (TC), high-density lipoproteins (HDL), triglyceride (TG) and low- density lipoproteins (LDL) concentration were measured by enzymatic kits (Pars Azmun, Iran) [\u003cspan class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eObesity phenotypes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe defined MUO presence of BMI\u0026thinsp;\u0026gt;\u0026thinsp;30 kg/m\u003csup\u003e2\u003c/sup\u003e and at least two metabolic disorder according to the International Diabetes Federation (IDF) statement [\u003cspan class=\"CitationRef\"\u003e17\u003c/span\u003e] as follow: HDL\u0026thinsp;\u0026lt;\u0026thinsp;40 mg/dl in men and \u0026lt;\u0026thinsp;50 mg/dl in women; increased TG\u0026thinsp;\u0026gt;\u0026thinsp;150 mg/dl; SBP\u0026thinsp;\u0026gt;\u0026thinsp;130 mmHg or DBP\u0026thinsp;\u0026gt;\u0026thinsp;80 mmHg or antihypertensive medication; and FBS\u0026thinsp;\u0026gt;\u0026thinsp;100 mg/dl or medication for diabetes. Also, MHO was defined BMI\u0026thinsp;\u0026gt;\u0026thinsp;30 kg/m\u003csup\u003e2\u003c/sup\u003e and having at most one metabolic disorder mentioned in the previous sentences, as well as, MUNO phenotype was considered presence of BMI\u0026thinsp;\u0026lt;\u0026thinsp;30 kg/m\u003csup\u003e2\u003c/sup\u003e and at least two metabolic disorder. In addition, MHNO participants were related to healthy participants without obesity and metabolic disorder.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eOutcome measurement hypertension incidence\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe hypertension was defined by codes I10 of the International classification of diseases Tenth Edition (ICD-10), which included SBP/DBP\u0026thinsp;\u0026ge;\u0026thinsp;140/90 mmHg and/or using anti-hypertensive medications in the time interval between baseline (first phase of Ravansar cohort which has been conducted from 2014) and hypertension diagnosis (from 2015 to 2021), which the overall duration of the follow-up was 391162 person-months.\u003c/p\u003e\n\u003cdiv class=\"Section2\" id=\"Sec3\"\u003e\n \u003cp\u003e\u003cstrong\u003eStatistical analysis\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eStatistical analysis was performed using Stata, version 14 (Stata Corp, College Station, TX). Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD) and frequency percent was used to report baseline characteristics of studied participants. To compare results of baseline characteristics among different obesity phenotypes, one-way analysis of variance (ANOVA) was used for continuous variables, and a Chi-square test was used for categorical variables.\u003c/p\u003e\n \u003cp\u003eIncidence rate (IR) calculated based on 1000 person/months Cox proportional hazards regression model were used to calculate hazard ratios (HRs) stratified by obesity phenotypes, with hypertension as the event and the time interval between baseline (first phase of RaNCD cohort) and hypertension diagnosis as the time covariate. The models of adjusted for confounding variables including age, sex, physical activity, smoking and energy intake, and reported as HR with 95% confidence interval (CI).\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eA total of 6,747 participants were analyzed in this study as sub-cohort and 393 incidence cases were also in the sub-cohort. The incidence rate of hypertension was one cases per 1000 person-months (393/391162, male: 150/188718, female: 243/202443)) during a mean follow-up of 57.74 months (Minimum: 0.27, Maximum: 73.30). In addition, the new case of hypertension was significantly higher in female than male (6.84% vs. 4.63%, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). \u003cb\u003e(\u003c/b\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline characteristics of studied participants\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;6747)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMHNO\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;3965)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMHO\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;1036)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMUNO\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;1204)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMUO\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;542)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (year)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e45.77\u0026thinsp;\u0026plusmn;\u0026thinsp;7.76*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e45.67\u0026thinsp;\u0026plusmn;\u0026thinsp;7.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e44.99\u0026thinsp;\u0026plusmn;\u0026thinsp;7.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e46.76\u0026thinsp;\u0026plusmn;\u0026thinsp;7.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e45.86\u0026thinsp;\u0026plusmn;\u0026thinsp;7.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeight (kg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e71.87\u0026thinsp;\u0026plusmn;\u0026thinsp;13.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e66.49\u0026thinsp;\u0026plusmn;\u0026thinsp;11.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e84.54\u0026thinsp;\u0026plusmn;\u0026thinsp;10.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e71.76\u0026thinsp;\u0026plusmn;\u0026thinsp;10.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e87.12\u0026thinsp;\u0026plusmn;\u0026thinsp;12.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWC (cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e96.26\u0026thinsp;\u0026plusmn;\u0026thinsp;10.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e92.03\u0026thinsp;\u0026plusmn;\u0026thinsp;8.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e106.89\u0026thinsp;\u0026plusmn;\u0026thinsp;8.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e95.94\u0026thinsp;\u0026plusmn;\u0026thinsp;7.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e107.54\u0026thinsp;\u0026plusmn;\u0026thinsp;8.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27.01\u0026thinsp;\u0026plusmn;\u0026thinsp;4.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24.73\u0026thinsp;\u0026plusmn;\u0026thinsp;3.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e33.11\u0026thinsp;\u0026plusmn;\u0026thinsp;2.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e26.45\u0026thinsp;\u0026plusmn;\u0026thinsp;2.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e33.33\u0026thinsp;\u0026plusmn;\u0026thinsp;3.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBFM (kg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24.27\u0026thinsp;\u0026plusmn;\u0026thinsp;9.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19.76\u0026thinsp;\u0026plusmn;\u0026thinsp;6.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e36.38\u0026thinsp;\u0026plusmn;\u0026thinsp;6.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e22.41\u0026thinsp;\u0026plusmn;\u0026thinsp;5.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e36.24\u0026thinsp;\u0026plusmn;\u0026thinsp;7.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSBP (mmHg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e103.55\u0026thinsp;\u0026plusmn;\u0026thinsp;12.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e101.62\u0026thinsp;\u0026plusmn;\u0026thinsp;11.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e104.10\u0026thinsp;\u0026plusmn;\u0026thinsp;11.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e107.16\u0026thinsp;\u0026plusmn;\u0026thinsp;13.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e108.59\u0026thinsp;\u0026plusmn;\u0026thinsp;13.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDBP (mmHg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e67.44\u0026thinsp;\u0026plusmn;\u0026thinsp;7.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e66.37\u0026thinsp;\u0026plusmn;\u0026thinsp;7.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e67.76\u0026thinsp;\u0026plusmn;\u0026thinsp;7.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e69.44\u0026thinsp;\u0026plusmn;\u0026thinsp;8.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e70.18\u0026thinsp;\u0026plusmn;\u0026thinsp;8.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFBS (mg/dl)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e89.92\u0026thinsp;\u0026plusmn;\u0026thinsp;9.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e87.91\u0026thinsp;\u0026plusmn;\u0026thinsp;8.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e88.96\u0026thinsp;\u0026plusmn;\u0026thinsp;8.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e94.36\u0026thinsp;\u0026plusmn;\u0026thinsp;11.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e96.58\u0026thinsp;\u0026plusmn;\u0026thinsp;11.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTC (mg/dl)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e184.01\u0026thinsp;\u0026plusmn;\u0026thinsp;36.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e180.03\u0026thinsp;\u0026plusmn;\u0026thinsp;37.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e186.74\u0026thinsp;\u0026plusmn;\u0026thinsp;33.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e190.40\u0026thinsp;\u0026plusmn;\u0026thinsp;36.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e193.68\u0026thinsp;\u0026plusmn;\u0026thinsp;34.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTG (mg/dl)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e130.01\u0026thinsp;\u0026plusmn;\u0026thinsp;73.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e101.75\u0026thinsp;\u0026plusmn;\u0026thinsp;46.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e114.06\u0026thinsp;\u0026plusmn;\u0026thinsp;47.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e205.82\u0026thinsp;\u0026plusmn;\u0026thinsp;87.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e198.37\u0026thinsp;\u0026plusmn;\u0026thinsp;83.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHDL(mg/dl)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e46.83\u0026thinsp;\u0026plusmn;\u0026thinsp;11.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e49.67\u0026thinsp;\u0026plusmn;\u0026thinsp;11.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e49.82\u0026thinsp;\u0026plusmn;\u0026thinsp;10.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e38.04\u0026thinsp;\u0026plusmn;\u0026thinsp;7.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e39.85\u0026thinsp;\u0026plusmn;\u0026thinsp;8.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDL (mg/dl)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e101.26\u0026thinsp;\u0026plusmn;\u0026thinsp;24.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e98.66\u0026thinsp;\u0026plusmn;\u0026thinsp;25.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e102.64\u0026thinsp;\u0026plusmn;\u0026thinsp;22.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e105.89\u0026thinsp;\u0026plusmn;\u0026thinsp;25.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e107.33\u0026thinsp;\u0026plusmn;\u0026thinsp;22.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePA (MET hour/ day(\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e41.08\u0026thinsp;\u0026plusmn;\u0026thinsp;8.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e41.90\u0026thinsp;\u0026plusmn;\u0026thinsp;8.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e39.71\u0026thinsp;\u0026plusmn;\u0026thinsp;6.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e40.37\u0026thinsp;\u0026plusmn;\u0026thinsp;7.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e39.32\u0026thinsp;\u0026plusmn;\u0026thinsp;6.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurrent smoking (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e23.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e14.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension incidence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e*Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e**P-values were obtained one-way ANOVA and Chi square.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eMHNO: metabolically healthy non-obese; MHO: metabolically healthy obese; MUNO: metabolically unhealthy non-obese; MUO: metabolically unhealthy obese; WC: waist circumference; BMI: body mass index; BFM: body fat mass; SBP: systolic blood pressure; DBP: diastolic blood pressure; FBS: fasting blood sugar; TC: total cholesterol; TG: triglyceride; HDL: high density lipoprotein; LDL: low density lipoprotein; PA: physical activity\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe prevalence of MHO, MUNO, and MUO were 15.3, 17.4, and 8.03 %; respectively. The mean of physical activity in MHNO was significantly higher than other three obesity phenotypes (MHO, MUNO, and MUO) in both men and women. Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e are presented baseline characteristics of studied participants based on the different types of obesity phenotypes.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline characteristics of studied participants based on the different types of obesity phenotypes\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"11\"\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003eMen (n\u0026thinsp;=\u0026thinsp;3217)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eP**\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c10\" namest=\"c7\"\u003e \u003cp\u003eWomen (n\u0026thinsp;=\u0026thinsp;3530)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eP**\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMHNO\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;2094)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMHO\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;246)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMUNO\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;692)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMUO\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;185)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMHNO\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;1871)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMHO\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;790)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eMUNO\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;512)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eMUO\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;357)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (year)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e45.95\u0026thinsp;\u0026plusmn;\u0026thinsp;7.88*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e45.13\u0026thinsp;\u0026plusmn;\u0026thinsp;7.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e45.96\u0026thinsp;\u0026plusmn;\u0026thinsp;7.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e44.98\u0026thinsp;\u0026plusmn;\u0026thinsp;7.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.174\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e45.35\u0026thinsp;\u0026plusmn;\u0026thinsp;8.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e44.94\u0026thinsp;\u0026plusmn;\u0026thinsp;7.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e47.84\u0026thinsp;\u0026plusmn;\u0026thinsp;8.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e46.31\u0026thinsp;\u0026plusmn;\u0026thinsp;6.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeight (kg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e71.01\u0026thinsp;\u0026plusmn;\u0026thinsp;10.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e94.50\u0026thinsp;\u0026plusmn;\u0026thinsp;9.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e76.69\u0026thinsp;\u0026plusmn;\u0026thinsp;8.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e95.74\u0026thinsp;\u0026plusmn;\u0026thinsp;10.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e61.44\u0026thinsp;\u0026plusmn;\u0026thinsp;8.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e81.44\u0026thinsp;\u0026plusmn;\u0026thinsp;9.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e65.11\u0026thinsp;\u0026plusmn;\u0026thinsp;7.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e82.65\u0026thinsp;\u0026plusmn;\u0026thinsp;10.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWC (cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e92.49\u0026thinsp;\u0026plusmn;\u0026thinsp;8.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e107.53\u0026thinsp;\u0026plusmn;\u0026thinsp;7.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e96.14\u0026thinsp;\u0026plusmn;\u0026thinsp;6.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e106.97\u0026thinsp;\u0026plusmn;\u0026thinsp;7.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e91.51\u0026thinsp;\u0026plusmn;\u0026thinsp;8.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e106.68\u0026thinsp;\u0026plusmn;\u0026thinsp;8.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e95.66\u0026thinsp;\u0026plusmn;\u0026thinsp;7.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e107.83\u0026thinsp;\u0026plusmn;\u0026thinsp;9.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24.36\u0026thinsp;\u0026plusmn;\u0026thinsp;3.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32.42\u0026thinsp;\u0026plusmn;\u0026thinsp;2.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26.26\u0026thinsp;\u0026plusmn;\u0026thinsp;2.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e32.37\u0026thinsp;\u0026plusmn;\u0026thinsp;2.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e25.15\u0026thinsp;\u0026plusmn;\u0026thinsp;3.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e33.32\u0026thinsp;\u0026plusmn;\u0026thinsp;3.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e26.70\u0026thinsp;\u0026plusmn;\u0026thinsp;2.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e33.82\u0026thinsp;\u0026plusmn;\u0026thinsp;3.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBFM (kg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17.74\u0026thinsp;\u0026plusmn;\u0026thinsp;6.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e33.77\u0026thinsp;\u0026plusmn;\u0026thinsp;6.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20.91\u0026thinsp;\u0026plusmn;\u0026thinsp;5.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e33.54\u0026thinsp;\u0026plusmn;\u0026thinsp;7.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e22.31\u0026thinsp;\u0026plusmn;\u0026thinsp;6.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e37.32\u0026thinsp;\u0026plusmn;\u0026thinsp;6.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e24.71\u0026thinsp;\u0026plusmn;\u0026thinsp;4.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e37.90\u0026thinsp;\u0026plusmn;\u0026thinsp;7.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSBP (mmHg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e103.59\u0026thinsp;\u0026plusmn;\u0026thinsp;11.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e108.20\u0026thinsp;\u0026plusmn;\u0026thinsp;11.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e107.77\u0026thinsp;\u0026plusmn;\u0026thinsp;12.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e110.03\u0026thinsp;\u0026plusmn;\u0026thinsp;12.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e99.41\u0026thinsp;\u0026plusmn;\u0026thinsp;11.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e102.83\u0026thinsp;\u0026plusmn;\u0026thinsp;11.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e106.33\u0026thinsp;\u0026plusmn;\u0026thinsp;14.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e107.84\u0026thinsp;\u0026plusmn;\u0026thinsp;14.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDBP (mmHg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e67.31\u0026thinsp;\u0026plusmn;\u0026thinsp;7.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e70.30\u0026thinsp;\u0026plusmn;\u0026thinsp;7.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e69.98\u0026thinsp;\u0026plusmn;\u0026thinsp;8.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e71.25\u0026thinsp;\u0026plusmn;\u0026thinsp;8.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e65.32\u0026thinsp;\u0026plusmn;\u0026thinsp;7.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e66.97\u0026thinsp;\u0026plusmn;\u0026thinsp;7.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e68.72\u0026thinsp;\u0026plusmn;\u0026thinsp;8.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e69.62\u0026thinsp;\u0026plusmn;\u0026thinsp;9.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFBS (mg/dl)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e88.32\u0026thinsp;\u0026plusmn;\u0026thinsp;8.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e89.74\u0026thinsp;\u0026plusmn;\u0026thinsp;8.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e93.71\u0026thinsp;\u0026plusmn;\u0026thinsp;10.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e95.53\u0026thinsp;\u0026plusmn;\u0026thinsp;9.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e87.45\u0026thinsp;\u0026plusmn;\u0026thinsp;7.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e88.72\u0026thinsp;\u0026plusmn;\u0026thinsp;7.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e95.25\u0026thinsp;\u0026plusmn;\u0026thinsp;11.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e97.12\u0026thinsp;\u0026plusmn;\u0026thinsp;11.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTC (mg/dl)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e178.53\u0026thinsp;\u0026plusmn;\u0026thinsp;36.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e187.56\u0026thinsp;\u0026plusmn;\u0026thinsp;35.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e185.02\u0026thinsp;\u0026plusmn;\u0026thinsp;33.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e188.54\u0026thinsp;\u0026plusmn;\u0026thinsp;30.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e181.72\u0026thinsp;\u0026plusmn;\u0026thinsp;38.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e186.49\u0026thinsp;\u0026plusmn;\u0026thinsp;33.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e197.68\u0026thinsp;\u0026plusmn;\u0026thinsp;39.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e196.34\u0026thinsp;\u0026plusmn;\u0026thinsp;36.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTG (mg/dl)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e108.41\u0026thinsp;\u0026plusmn;\u0026thinsp;51.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e134.62\u0026thinsp;\u0026plusmn;\u0026thinsp;67.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e216.65\u0026thinsp;\u0026plusmn;\u0026thinsp;89.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e220.14\u0026thinsp;\u0026plusmn;\u0026thinsp;86.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e94.28\u0026thinsp;\u0026plusmn;\u0026thinsp;39.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e107.65\u0026thinsp;\u0026plusmn;\u0026thinsp;36.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e191.19\u0026thinsp;\u0026plusmn;\u0026thinsp;82.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e187.09\u0026thinsp;\u0026plusmn;\u0026thinsp;79.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHDL(mg/dl)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e46.37\u0026thinsp;\u0026plusmn;\u0026thinsp;9.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e44.71\u0026thinsp;\u0026plusmn;\u0026thinsp;8.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e35.24\u0026thinsp;\u0026plusmn;\u0026thinsp;5.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e35.29\u0026thinsp;\u0026plusmn;\u0026thinsp;5.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e53.39\u0026thinsp;\u0026plusmn;\u0026thinsp;11.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e51.41\u0026thinsp;\u0026plusmn;\u0026thinsp;10.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e41.82\u0026thinsp;\u0026plusmn;\u0026thinsp;7.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e42.22\u0026thinsp;\u0026plusmn;\u0026thinsp;8.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDL (mg/dl)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e99.23\u0026thinsp;\u0026plusmn;\u0026thinsp;24.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e106.41\u0026thinsp;\u0026plusmn;\u0026thinsp;23.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e103.03\u0026thinsp;\u0026plusmn;\u0026thinsp;22.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e105.91\u0026thinsp;\u0026plusmn;\u0026thinsp;20.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e98.03\u0026thinsp;\u0026plusmn;\u0026thinsp;25.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e101.46\u0026thinsp;\u0026plusmn;\u0026thinsp;22.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e109.77\u0026thinsp;\u0026plusmn;\u0026thinsp;28.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e108.08\u0026thinsp;\u0026plusmn;\u0026thinsp;23.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePA (MET hour/ day(\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e43.70\u0026thinsp;\u0026plusmn;\u0026thinsp;10.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e41.59\u0026thinsp;\u0026plusmn;\u0026thinsp;9.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e41.31\u0026thinsp;\u0026plusmn;\u0026thinsp;9.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e40.09\u0026thinsp;\u0026plusmn;\u0026thinsp;9.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e39.89\u0026thinsp;\u0026plusmn;\u0026thinsp;4.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e39.12\u0026thinsp;\u0026plusmn;\u0026thinsp;4.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e39.10\u0026thinsp;\u0026plusmn;\u0026thinsp;3.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e38.93\u0026thinsp;\u0026plusmn;\u0026thinsp;3.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurrent smoking (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e35.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e34.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e35.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.462\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e7.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e3.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension incidence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.029\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e6.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e11.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e12.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"11\"\u003e*Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"11\"\u003e**P-values were obtained one-way ANOVA and Chi square.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"11\"\u003eMHNO: metabolically healthy non-obese; MHO: metabolically healthy obese; MUNO: metabolically unhealthy non-obese; MUO: metabolically unhealthy obese; WC: waist circumference; BMI: body mass index; BFM: body fat mass; SBP: systolic blood pressure; DBP: diastolic blood pressure; FBS: fasting blood sugar; TC: total cholesterol; TG: triglyceride; HDL: high density lipoprotein; LDL: low density lipoprotein; PA: physical activity\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe risk increased in MHO phenotype compared to MHNO (HR: 1.41; 95% CI: 1.05\u0026ndash;1.88) in model І, which remained significant after adjustment for age, sex, physical activity and smoking (HR: 1.37; 95% CI: 1.03\u0026ndash;1.86). The risk increased in MUO phenotype compared to MHNO (HR: 2.44; 95% CI: 1.81\u0026ndash;3.29) after adjust sex and age, which remained significant after adjustment for age, sex, physical activity and smoking (HR: 2.40; 95% CI: 1.77, 3.26). \u003cb\u003e(\u003c/b\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eHazard ratio of incident hypertension according to obesity phenotypes\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eObesity phenotypes\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e% (N) of cases\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e \u003cp\u003eHazard ratio (95% CI)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eModel І\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eModel II\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003eModel III\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMHNO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3965\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.4 (175)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMHO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1036\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.2 (64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.41 ( 1.05, 1.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.37 (1.02, 1.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.37 ( 1.03,1.86)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMUNO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1204\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.7 (93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.68 (1.31, 2.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.64 (1.27, 2.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.65 (1.29, 2.14)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMUO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e542\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10.9 (59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.44 ( 1.81, 3.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.36 ( 1.75, 3.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.40 (1.77, 3.26)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003cb\u003eModel 1\u003c/b\u003e: Adjusted for age and sex; \u003cb\u003eModel 2\u003c/b\u003e: Adjusted for age, sex and physical activity; \u003cb\u003eModel 3\u003c/b\u003e: Adjusted for age, sex, physical activity and smoking\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eMHNO, metabolically healthy non-obese; MHO, metabolically healthy obese; MUNO, metabolically unhealthy non-obese; MUO, metabolically unhealthy obese\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eIn addition, risk of hypertension significantly increased in MUNO phenotype compared to MHNO in all adjusted models (HR: 1.65; 95% CI: 1.29\u0026ndash;2.14). The cumulative hazard curves show the incidence of hypertension has increased by approximately 7% in MUO phenotype over 70 months; and this increase was more than other phenotypes in over time \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eOur results shows both phenotypes obesity MHO and MUO increase to develop risk of the hypertension compared to MHNO phenotype. Furthermore, MUNO phenotype was associated with higher risk of hypertension incidence compared to MHNO phenotype. Overall, the MUO phenotype increased risk of hypertension incidence more than other phenotypes in the follow-up study time. The obesity epidemic is growing and increases the risk of chronic non-communicable diseases leading to increased health system costs [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Epidemiological studies highlight the persistent link between obesity and hypertension, and the presence of obesity increases the risk of developing hypertension [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Since there are different phenotypes of obesity based on the metabolic status, to best our knowledge, we examined the association between obesity phenotypes and risk of hypertension incidence.\u003c/p\u003e \u003cp\u003eThe results of Whitehall II cohort study by Hinnouho et al. [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] on 5269 participants indicated that both obesity phenotypes, MHO and MUO lead to increased risk of mortality after seventeen years following. Another prospective study by Fingeret et al. [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] after 10.9 years follow up was not seen any difference between MHO and MUO in hypertension incidence (odds ratio (OR): 1.3, CI 95%: 0.8\u0026ndash;2.09). Yuan et al. [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] showed that MHO had no association with arterial stiffness developing (OR: 0.99; CI 95%: 0.61\u0026ndash;1.6), while MUO and MUNO phenotypes lead to significantly progressed arterial stiffness (OR: 4.56; CI 95%: 2.60\u0026ndash;8) and (OR: 5.05; CI 95%: 3.12\u0026ndash;8.19), respectively.\u003c/p\u003e \u003cp\u003eIn current study we observed that MUO and MUNO increase the risk of hypertension incidence more than MHO. Also, BFM and WC of the participants were higher in all three groups than MHNO phenotype. Obesity, especially the high excess visceral fat distribution is increased inflammatory cytokines and endothelial disorders in which stimulate several mechanisms contribute to hypertension [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. High excess adipose tissue increases the production of pro- inflammatory factors such as leptin, tumor necrosis factor- α, interleukin-6, and resistin in which develops various metabolic diseases [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. High calorie intake and increase in adipocytes stimulate α and β adrenergic receptors, thereby increasing the activity of the sympathetic nervous system [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Obesity activates the renin-angiotensin nervous system and the sympathetic nervous system, which leads to increased sodium reabsorption and arterial blood pressure [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. On the other hand, increasing adipose tissue leads to decreased adiponectin production and increased insulin resistance [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Therefore, chronic hyperinsulinemia in obese people causes vascular vasoconstrictor and also increases urinary sodium reabsorption and is involved in the pathogenesis of hypertension [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Also, increased circulating leptin levels in response to increased adipose tissue lead to impaired nitric oxide synthesis and ultimately vascular endothelial dysfunction [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Therefore, obesity increases the production of adipose tissue, causing the production of pro-inflammatory cytokines, which play an important role in the pathogenesis of hypertension by disrupting the metabolic status.\u003c/p\u003e"},{"header":"Strength And Limits","content":"\u003cp\u003eThe present prospective study follows for the first time the Kurdish population and examines the types of obesity based on metabolic status and risk of hypertension incidence. In this study, we also applied appropriate exclusion criteria, such as people who did not have normal calorie intake. However, this study had its limitations. First, the follow-up period seems to have been short. Second, the hypertension incidence was small for the study groups, and we could not assess the relationship between hypertension incidence and obesity phenotypes based on the sex, although it was adjusted for sex.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn conclusion, present study stated that both of MHO and MUO phenotypes lead to rise hypertension incidence compared to MHNO phenotype, as well as, MUNO phenotype can increase hypertension incidence. However, MUO and MUNO phenotypes increase the risk of hypertension incidence more than MHO compared to MHNO phenotype. For protecting from hypertension maintaining normal weight and controlling central obesity as well as visceral fat is highly recommended.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRaNCD is part of PERSIAN national cohort and we would like to thank Professor Reza Malekzadeh, Ex-Deputy of Research and Technology at the Ministry of Health and Medical Education of Iran and Director of the PERSIAN cohort, and also Dr. Hossein Poustchi Executive Director of PERSIAN cohort for all their supports during design and running of RaNCD.\u0026nbsp;We gratefully \u003cem\u003eacknowledge\u003c/em\u003e our RaNCD field workers and site staffs and \u003cem\u003eparticipants\u003c/em\u003e of the RaNCD cohort for their important contributions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e This study was supported by Ministry of Health and Medical Education of Iran and Kermanshah University of Medical Science (Grant No: 92472).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompliance with ethical standards\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate:\u0026nbsp;\u003c/strong\u003eAll procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. This study was approved by the Ethics Committee of Kermanshah University of Medical Sciences (ethics approval number: KUMS.REC.1394.318).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInformed consent:\u003c/strong\u003e Written informed consent was obtained from each studied subject after explaining the purpose of the study. The right of subjects to withdraw from the study at any time and subject\u0026rsquo;s information is reserved and will not be published.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication:\u0026nbsp;\u003c/strong\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials:\u003c/strong\u003e Data will be available upon request from the corresponding author.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests:\u003c/strong\u003e All authors\u0026nbsp;have no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions:\u0026nbsp;\u003c/strong\u003eF, Njafi, B, Hamzeh\u003cstrong\u003e,\u0026nbsp;\u003c/strong\u003eS, Moradi and Y, Pasdar equally contributed to the conception and design of the research; F, Njafi, B, Hamzeh, E, Shakiba and Y, Pasdar contributed to data collection; S, Moradi, Y, Pasdar and M, Darbandi contributed to the acquisition and analysis of the data; S, Moradi, Y, Pasdar and M, Darbandi contributed to the interpretation of the data; and S, Moradi, Y, Pasdar and M, Darbandi contributed to draft the manuscript. All authors are in agreement with the manuscript and declare that the content has not been published elsewhere.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eMills KT, Stefanescu A, He J. The global epidemiology of hypertension. Nature Rev Nephrol. 2020;16(4):223\u0026ndash;37.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFisher ND, Curfman G. Hypertension\u0026mdash;a public health challenge of global proportions. Jama. 2018;320(17):1757\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eUmemura S, Arima H, Arima S, Asayama K, Dohi Y, Hirooka Y, et al. The Japanese Society of Hypertension guidelines for the management of hypertension (JSH 2019). Hypertension Res. 2019;42(9):1235\u0026ndash;481.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKjeldsen SE. Hypertension and cardiovascular risk: general aspects. 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Factors associated with hypertension control in US adults using 2017 ACC/AHA guidelines: National Health and Nutrition Examination Survey 1999\u0026ndash;2016. Am J Hypertension. 2018;31(8):886\u0026ndash;94.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAvgerinos KI, Spyrou N, Mantzoros CS, Dalamaga M. Obesity and cancer risk: Emerging biological mechanisms and perspectives. Metabolism. 2019;92:121\u0026ndash;35.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKoliaki C, Liatis S, Kokkinos A. Obesity and cardiovascular disease: revisiting an old relationship. Metabolism. 2019;92:98\u0026ndash;107.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTrends in obesity and diabetes across Africa from 1980 to 2014: an analysis of pooled population-based studies. Inter J Epidemiol. 2017;46(5):1421\u0026ndash;32.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eC\u0026Oacute;\u0026lsquo;toi AF, P\u0026acirc;rvu AE, Andreicuț AD, Mironiuc A, Cr\u0026Oacute;\u0026lsquo;ciun A, C\u0026Oacute;\u0026lsquo;toi C, et al. Metabolically healthy versus unhealthy morbidly obese: chronic inflammation, nitro-oxidative stress, and insulin resistance. Nutrients. 2018;10(9):1199.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKim Y, Chang Y, Cho YK, Ahn J, Shin H, Ryu S. Metabolically healthy versus unhealthy obesity and risk of fibrosis progression in non-alcoholic fatty liver disease. Liver Inter. 2019;39(10):1884\u0026ndash;94.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHsu ARC, Ames SL, Xie B, Peterson DV, Garcia L, Going SB, et al. Incidence of diabetes according to metabolically healthy or unhealthy normal weight or overweight/obesity in postmenopausal women: the Women's Health Initiative. Menopause. 2020;27(6):640\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePasdar Y, Najafi F, Moradinazar M, Shakiba E, Karim H, Hamzeh B, et al. Cohort profile: Ravansar Non-Communicable Disease cohort study: the first cohort study in a Kurdish population. International journal of epidemiology. 2019;\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/ije/dyy296\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePoustchi H, Eghtesad S, Kamangar F, Etemadi A, Keshtkar A-A, Hekmatdoost A, et al. Prospective epidemiological research studies in Iran (the PERSIAN Cohort Study): rationale, objectives, and design. American journal of epidemiology. 2017;187(4):647\u0026ndash;55.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlberti KGM, Zimmet P, Shaw J. The metabolic syndrome\u0026mdash;a new worldwide definition. The Lancet. 2005;366(9491):1059\u0026ndash;62.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLeggio M, Lombardi M, Caldarone E, Severi P, D'emidio S, Armeni M, et al. The relationship between obesity and hypertension: an updated comprehensive overview on vicious twins. Hypertension Res. 2017;40(12):947\u0026ndash;63.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRuilope LM, Nunes Filho A, Nadruz Jr W, Rosales FR, Verdejo-Paris J. Obesity and hypertension in Latin America: Current perspectives. Hipertension y riesgo vascular. 2018;35(2):70\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHinnouho G-M, Czernichow S, Dugravot A, Batty GD, Kivimaki M, Singh-Manoux A. Metabolically healthy obesity and risk of mortality: does the definition of metabolic health matter? Diabetes care. 2013;36(8):2294\u0026ndash;300.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFingeret M, Marques-Vidal P, Vollenweider P. Incidence of type 2 diabetes, hypertension, and dyslipidemia in metabolically healthy obese and non-obese. Nutrition, Metabolism Cardiovasc Dis. 2018;28(10):1036\u0026ndash;44.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYuan Y, Mu J-J, Chu C, Zheng W-L, Wang Y, Hu J-W, et al. Effect of metabolically healthy obesity on the development of arterial stiffness: a prospective cohort study. Nutrition Metabolism. 2020;17(1):1\u0026ndash;10.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSeravalle G, Grassi G. Obesity and hypertension. Pharmacol Res. 2017;122:1\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJiang P, Ma D, Wang X, Wang Y, Bi Y, Yang J, et al. Astragaloside IV prevents obesity-associated hypertension by improving pro-inflammatory reaction and leptin resistance. Molecule Cell. 2018;41(3):244.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLambert EA, Straznicky NE, Dixon JB, Lambert GW. Should the sympathetic nervous system be a target to improve cardiometabolic risk in obesity? Am J Physiol Heart Circ Physiol. 2015;309(2):H244-H58.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAronow WS. Association of obesity with hypertension. Ann Translational Med. 2017;5(17).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCwynar M, Gąsowski J, Gryglewska B, Głuszewska A, Kwater A, Kr\u0026oacute;lczyk J, et al. Insulin resistance and renal sodium handling influence arterial stiffness in hypertensive patients with prevailing sodium intake. Am J Hypertension. 2019;32(9):848\u0026ndash;57.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOhashi K, Kihara S, Ouchi N, Kumada M, Fujita K, Hiuge A, et al. Adiponectin replenishment ameliorates obesity-related hypertension. Hypertension. 2006;47(6):1108\u0026ndash;16.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDe Boer MP, Meijer RI, Wijnstok NJ, Jonk AM, Houben AJ, Stehouwer CD, et al. Microvascular dysfunction: a potential mechanism in the pathogenesis of obesity-associated insulin resistance and hypertension. Microcirculation. 2012;19(1):5\u0026ndash;18.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSoleimani M. Insulin resistance and hypertension: new insights. Kidney Inter 2015;87(3):497\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"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":"metabolically unhealthy obesity, metabolically healthy obesity, hypertension, incidence, PERSIAN","lastPublishedDoi":"10.21203/rs.3.rs-669988/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-669988/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eAlthough obesity contributes in increasing the risk of hypertension, it is not known the effect of obesity based on metabolic status on the incidence of hypertension. This study was aimed to determine association between obesity phenotypes including metabolically unhealthy obesity (MUO) and metabolically healthy obesity (MHO) and risk of hypertension incidence.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe conducted a case-cohort study on 6,747 adults 35\u0026ndash;65 aged from Ravansar non- communicable diseases (RaNCD) study. Obesity was defined body mass index\u0026thinsp;\u0026gt;\u0026thinsp;30 kg/m\u003csup\u003e2\u003c/sup\u003e and metabolically unhealthy was considered at least two metabolic disorders based on the International Diabetes Federation criteria. Obesity phenotypes were categorized four groups including MUO, MHO, metabolically unhealthy non obesity (MUNO), and metabolically healthy non obesity (MHNO). Cox proportional hazards regression models were applied to analyze associations with hypertension incidence.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe incidence of hypertension was one case per 1000 person-months (393/391162). The MHO (HR: 1.37; 95% CI: 1.03\u0026ndash;1.86) and MUO phenotype (HR: 2.44; 95% CI: 1.81\u0026ndash;3.29) was linearly associated with higher hypertension risk compared to MHNO. In addition, MUNO phenotype was significantly associated with risk of hypertension incidence (HR: 1.65; 95% CI: 1.29\u0026ndash;2.14).\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eBoth metabolically healthy and unhealthy obesity was elevated risk of hypertension incidence, however, this increase in metabolically unhealthy phenotypes was higher.\u003c/p\u003e","manuscriptTitle":"Metabolically Healthy Versus Unhealthy Obese Phenotypes and Risk of Hypertension Incidence; A Case–Cohort Analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2021-08-09 18:11:50","doi":"10.21203/rs.3.rs-669988/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Major revision","date":"2021-11-09T08:06:30+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2021-10-09T15:37:56+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"c4bcdf82-d079-4856-8ac2-8c11fbaa39ba","date":"2021-10-05T23:56:11+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2021-09-16T10:27:16+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"b94855e5-9e77-4b9c-bff4-1a6d797e3d27","date":"2021-09-12T20:22:30+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"509834be-a31c-473c-a2f5-e14c581b3815","date":"2021-09-08T18:17:28+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2021-08-24T20:07:09+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"446a9158-94d6-488e-a1c4-9da650d57be3","date":"2021-08-10T23:17:33+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2021-08-07T21:15:54+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2021-08-07T21:14:07+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2021-08-07T19:36:26+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2021-08-07T19:28:29+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Cardiovascular Disorders","date":"2021-06-30T08:46:48+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":"d90ea37d-f532-472d-8b63-ffa5d23bcd62","owner":[],"postedDate":"August 9th, 2021","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":6303398,"name":"Cardiac \u0026 Cardiovascular Systems"},{"id":6303399,"name":"Cardiothoracic Surgery"}],"tags":[],"updatedAt":"2022-03-09T05:29:10+00:00","versionOfRecord":[],"versionCreatedAt":"2021-08-09 18:11:50","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-669988","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-669988","identity":"rs-669988","version":["v1"]},"buildId":"J0_U0BvcaRcwD8yVFaRlm","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}