Comparison of Hypertension Risk in Elderly Under Adiposity-Based Chronic Disease Model

Comparison of Hypertension Risk in Elderly Under Adiposity-Based Chronic Disease Model | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Comparison of Hypertension Risk in Elderly Under Adiposity-Based Chronic Disease Model Hui Wang, Yaqing Liu, Feifei Rao, Li Yang, Yankun Zhu, Feng Jiang, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8036118/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: The Adiposity-Based Chronic Disease (ABCD) model, introduced by the American College of Endocrinology, provides a novel, complication-centric framework for assessing obesity with enhanced pathophysiological relevance. However, its practical value for predicting specific health outcomes, such as hypertension in the elderly, remains largely unexamined in the Chinese population. Objective: This study is anchored in the theoretical framework of ABCD models related to excessive adipose tissue accumulation. Its primary objective is to systematically assess the relationship between various obesity indicators and the risk of hypertension among older adults. Additionally, it aims to compare the predictive efficacy of individual obesity indicators against combined indicators, with the goal of identifying the most effective and stage-specific predictors. Methods: This study focused on elderly individuals aged 65 years or older at a community health service centre in Wuhan City, with a total of 6,784 eligible elderly individuals included in the study. Basic information such as age, gender, family history, smoking, and alcohol consumption was collected for all study participants, along with biochemical indicators such as lipid levels and blood glucose. Obesity was classified into three stages—stage 0, stage 1, and stage 2—using the ABCD model. The performance of the model was evaluated using receiver operating characteristic (ROC) curves and area under the curve (AUC) values. the efficacy of obesity-related indicators such as the Chinese Visceral Adipose Index (CVAI), Cardiometabolic Index (CMI), Conicity Index (CI), and Body Shape Index (ABSI) in predicting hypertension risk was assessed; the predictive value of single obesity indicators and combined indicators was compared and analysed. Results: This study found that the optimal indicators for predicting hypertension in the elderly vary across different stages of obesity. During the normal stage, the Body Roundness Index (BRI) demonstrated the best predictive performance, with an AUC value of 0.6292. In stages 1 and 2, the Lipid Accumulation Product (LAP) and CMI showed more significant predictive effects, with AUC values of 0.6211 and 0.6243, respectively. Further multi-indicator combined predictive analysis showed that combining multiple obesity-related indicators for prediction can enhance the accuracy of predicting hypertension risk. the AUC value for the combined prediction of WC+AVI in the normal stage was 0.6311, higher than the predictive performance of any single obesity indicator; the AUC value for the combined prediction of WC+BRI in stage 1 reached 0.6354; while the AUC values for the combined predictions of WC+LAP, WC+CI, and WC+CMI in stage 2 were significantly higher than those of single obesity indicators, with the highest AUC value of 0.6478 for WC+LAP, at 0.6478. Conclusions: This study found that as obesity levels change, obesity indicators predicting hypertension in older adults also change, indicating that different prevention and intervention measures should be adopted for different stages of obesity in hypertension management. In addition, the combined use of multiple obesity indicators can improve the predictive ability of hypertension risk in older adults. Health sciences/Biomarkers Health sciences/Diseases Health sciences/Endocrinology Health sciences/Health care Health sciences/Medical research Health sciences/Risk factors Chronic disease models caused by excessive adipose tissue hypertension in the elderly obesity indicators risk identification chronic disease management Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Introduction Obesity and overweight represent major global health challenges. Despite concerted efforts by countries and international organizations, their prevalence has continued to rise steadily over recent decades [1] .According to the World Health Organization (WHO) in 2021, over one billion individuals worldwide were obese, comprising approximately 650 million adults, 340 million adolescents, and 39 million children [2] .In China, similar trends have been observed over the past two decades, with rapid increases in the prevalence of overweight, obesity, and associated chronic diseases [3] .Obesity is not only an independent chronic disease but also a significant risk factor for numerous other chronic conditions, imposing a substantial burden on global health systems and economies. Against the backdrop of China's accelerating population aging, obesity among the elderly population has emerged as an increasingly critical public health challenge requiring urgent attention. Current diagnostic criteria for obesity, as defined by the World Health Organization rely primarily on body mass index (BMI), where a BMI ≥25 indicates overweight and ≥30 indicates obesity [4] .However, researchers have highlighted significant limitations in using BMI to assess obesity and its associated health risks. These limitations arise from variations in age, sex, and ethnicity; inaccurate stratification of obesity-related disease risk; incomplete characterization of obesity pathophysiology; and perpetuation of weight-related stigma [5] .To address these shortcomings, the American College of Endocrinology (ACE) and the American Association of Clinical Endocrinologists (AACE) proposed a novel diagnostic framework: the Adiposity-Based Chronic Disease (ABCD) model. This model aims to enable more comprehensive management and treatment of obesity and its complications by shifting focus from BMI and waist circumference (WC) alone to a complication-centric approach for guiding treatment decisions and outcomes [6] . The ABCD model comprises four key dimensions: A (Adiposity): Etiology of excess adiposity B (BMI): Anthropometric classification C (Complications): Obesity-related complications D (Disease Severity): Severity/staging of complications Unlike traditional obesity assessments, the ABCD model evaluates not only elevated total adiposity but also abnormal fat distribution and dysfunction. By staging obesity and its complications, it improves identification of high-risk populations for conditions such as hypertension. Furthermore, the model facilitates early identification and intervention in individuals with lower BMI but significant complications, enabling more precise personalized health management [7] .However, systematic research on the ABCD model remains limited globally. To address this gap, this study leverages data from 6,784 adults aged ≥65 years who underwent health examinations at 17 community in Hongshan District, Wuhan, China. We evaluate the predictive validity of individual and combined obesity assessment indicators for hypertension within the ABCD framework. The findings aim to inform evidence-based strategies for optimizing elderly health management and strengthening early identification/prevention of obesity-related chronic diseases. Material and methods 1.1Study design and participants A total of 6,784 elderly individuals aged ≥ 65 years who met the inclusion criteria were selected from health examinations conducted at a community health service centre in Wuhan City from January 2022 to December 2023, including 2,964 males and 3,820 females. Inclusion criteria: conscious, aged ≥ 65 years, and able to complete all examinations and questionnaires. Exclusion criteria: aged < 65 years, with severe mental disorders, unconscious, unable to communicate clearly, uncooperative, or unable to complete the survey or examination. This study was approved by the Ethics Review Committee of Tongji Medical College, Huazhong University of Science and Technology. Ethics approval number: 2023-S104. 1.2 Data collection The target population arrived at the designated screening hospital and completed the screening registration and informed consent form, followed by physical measurements by two experienced epidemiological investigators. A one-to-one epidemiological survey based on a standardised questionnaire administered by trained doctors, nurses or epidemiological investigators, with no cues regarding the answers to the questions. Then, venous blood was collected and tested for laboratory biochemical indices. Finally, ultrasound examinations were performed. Figure 1 illustrates the flowchart of the participant selection process. Questionnaires, physical examinations, and laboratory tests are all conducted by staff members of community health service centres who have undergone standardised training. The questionnaire covers personal basic information and lifestyle factors, while the physical examination includes measurements of height, weight, waist circumference, blood pressure, and other parameters. All study participants fasted overnight (for at least 8 hours) and had venous blood drawn by medical staff at the community health service centre for biochemical testing, primarily including fasting blood glucose (FBG),, triglycerides (TG), total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C), and low-density lipoprotein cholesterol (LDL-C). 2. Variable description 2.1The Adiposity-Based Chronic Disease (ABCD) model The ABCD model classifies obesity into three stages based on the presence and severity of obesity-related complications [ 8 ] . The specific staging criteria are detailed in Table 1 . Stage 0: Individuals who do not meet the criteria for obesity (BMI < 28 kg/m² and WC < 90 cm for men or < 80 cm for women) and have no associated metabolic risk factors; OR individuals who meet the criteria for obesity (BMI ≥ 28 kg/m² or WC ≥ 90 cm for men or WC ≥ 80 cm for women) but have no associated metabolic risk factors. Stage 1: Individuals meeting the criteria for obesity (BMI ≥ 28 kg/m² or WC ≥ 90 cm for men or WC ≥ 80 cm for women) and presenting with 1–2 metabolic risk factors (e.g., elevated blood glucose, dyslipidemia). Stage 2: Individuals meeting the criteria for obesity (BMI ≥ 28 kg/m² or WC ≥ 90 cm for men or WC ≥ 80 cm for women) and presenting with 3 or more metabolic risk factors. Table 1 ABCD Staging Criteria [ 9 , 10 ] Stage BMI and WC Criteria Number of metabolic risk factors Metabolic Risk Factors 0 Not obese: BMI < 28 kg/m² and WC < 90 cm (M) / < 80 cm (F) None present ① Increased WC (M ≥ 112 cm, F ≥ 88 cm) ② Elevated BP (SBP ≥ 130 mmHg and/or DBP ≥ 85 mmHg) or on antihypertensive medication ③ Reduced HDL-C (M < 1.0 mmol/L, F < 1.3 mmol/L) or on medication ④ Elevated fasting serum triglycerides (≥ 1.7 mmol/L) or on medication ⑤ Metabolic syndrome ⑥ Impaired fasting blood glucose (FBG ≥ 5.6 mmol/L) ⑦ Impaired glucose tolerance (2h glucose ≥ 7.8 mmol/L) ⑧ Type 2 diabetes (FBG ≥ 7.0 mmol/L or 2h glucose ≥ 11.1 mmol/L or on diabetes medication) ⑨ Cardiovascular disease (angina or post-event status, e.g., acute coronary syndrome, stent placement, coronary artery bypass grafting, thrombotic stroke, non-traumatic amputation due to peripheral vascular disease) ⑩ Non-alcoholic fatty liver disease (NAFLD) and non-alcoholic steatohepatitis (NASH) 1 Obese: BMI ≥ 28 kg/m² or WC ≥ 90 cm (M) or WC ≥ 80 cm (F) 1–2 2 Obese: BMI ≥ 28 kg/m² or WC ≥ 90 cm (M) or WC ≥ 80 cm (F) ≥ 3 Note:1 mmHg = 0.133 kPa;HDL-C = high-density lipoprotein cholesterol,LDL-C = low-density lipoprotein cholesterol,TG = triglycerides,FBG = fasting blood glucose. 2.2 Other variables ①Diagnostic criteria for hypertension in the elderly [ 11 ] : According to the 2023 Chinese Guidelines for the Management of Hypertension in the Elderly, individuals with systolic blood pressure (SBP) ≥ 140 mmHg and/or diastolic blood pressure (DBP) ≥ 90 mmHg when not taking antihypertensive medications are diagnosed with hypertension;Older adults who have been diagnosed with hypertension and are currently receiving antihypertensive medication should be classified as older adults with hypertension, even if their SBP is less than 140 mmHg or their DBP is less than 90 mmHg. Similarly, older adults who self-report having hypertension should also be included. ②Waist to Height Ratio(WHtR) the calculation formula is as follows [ 12 ] : $$\:\text{WHtR＝WC(cm)/height(cm)}$$ ; ③Conicity Index(CI) the calculation formula is as follows [ 13 , 14 ] : $$\:\text{CI＝}\frac{\text{WC}\text{(cm)}}{\text{0.109×}\sqrt{\text{weight}\text{(kg)/}\text{height}\text{(m}\text{)}}}$$ ④Visceral Adipose Index(VAI) the calculation formula is as follows [ 15 ] : $$\:\text{Male:}\text{VAI＝}\frac{\text{WC}\text{(cm)}}{\text{39.68}\text{+}\text{1.88×BMI}}\text{×}\frac{\text{TG(}\text{mmol/L}\text{)}}{\text{1.03}}\text{×}\frac{\text{1.31}}{\text{HDL-C(}\text{mmol/L}\text{)}}$$ $$\:\text{F}\text{emale:}\text{VAI＝}\frac{\text{WC}\text{(cm)}}{\text{39.68}\text{+}\text{1.88×BMI}}\text{×}\frac{\text{TG(}\text{mmol/L}\text{)}}{\text{0.:81}}\text{×}\frac{\text{1.52}}{\text{HDL-C(}\text{mmol/L}\text{)}}\:$$ ⑤Chinese Visceral Adipose Index(CVAI) the calculation formula is as follows [ 16 ] : $$\:\begin{array}{c}\text{M}\text{a}\text{l}\text{e}\text{}\text{C}\text{V}\text{A}\text{I}\text{＝}\text{-}\text{267.93}\text{+}\text{0.68}\text{×}\text{a}\text{g}\text{e}\text{+}\text{0.03}\text{×}\text{B}\text{M}\text{I}\text{+}\text{4.00}\text{×}\text{W}\text{C}\text{+}\\\:\text{22.00}\text{×}\text{l}\text{g}\text{T}\text{G}\text{-}\text{16.32}\text{×}\text{H}\text{D}\text{L}\text{-}\text{C}\end{array}$$ $$\:\begin{array}{c}\text{F}\text{e}\text{m}\text{a}\text{l}\text{e}\text{}\text{C}\text{V}\text{A}\text{I}\text{＝}\text{-}\text{187.32}\text{+}\text{1.71}\text{×}\text{a}\text{g}\text{e}\text{+}\text{4.32}\text{×}\text{B}\text{M}\text{I}\text{+}\text{1.12}\text{×}\text{W}\text{C}\text{+}\\\:\text{39.76}\text{×}\text{l}\text{g}\text{T}\text{G}\text{-}\text{11.66}\text{×}\text{H}\text{D}\text{L}\text{-}\text{C}\end{array}$$ ⑥Waist Circumference Triglyceride Index(WTI) the calculation formula is as follows [ 17 ] : $$\:\text{WTI＝}\text{WC}\text{(cm)}\text{×TG}\text{(mmol/L)；}$$ ⑦Lipid Accumulation Product(LAP) the calculation formula is as follows [ 18 , 19 ] : $$\:\text{Male:}\text{LAP＝[WC(cm)-65]}\text{×}\text{TG(mmol/L)}$$ $$\:\text{F}\text{emale:}\text{LAP＝[WC(cm)-58]}\text{×}\text{TG(mmol/L)}$$ ⑧Body Shape Index (ABSI) the calculation formula is as follows [ 16 ] : \(\:\text{ABSI＝}\text{WC(cm)/(}{\text{BMI}}^{\frac{\text{2}}{\text{3}}}\text{×}{\text{height}}^{\frac{\text{1}}{2}}{\text{(cm)}}^{\frac{\text{1}}{2}}\text{)}\) ⑨Body Roundness Index(BRI) the calculation formula is as follows [ 16 ] : $$\:\text{BRI}\text{＝364.2-365.5}\text{×}\sqrt{\text{1-}{\text{(WC}\text{(cm)}\text{/2π)}}^{\text{2}}\text{/}{\text{(0.5}\text{×}\text{height}\text{(cm)}\text{)}}^{\text{2}}}$$ ⑩Cardiometabolic Index(CMI) the calculation formula is as follows [ 20 ] $$\:\text{CMI＝}\frac{\text{TG(}\text{mmol/L}\text{)}}{\text{HDL-C(}\text{mmol/L}\text{)}}\text{×WHtR}$$ 3.Statistical analysis This study used SPSS 27.0 and MedCalc 19.0 for data organisation and analysis. For continuous variables that followed a normal distribution, the mean ± standard deviation (SD) was used; for those that did not follow a normal distribution, the median (interquartile range) [M (Q1, Q3)] was used; and for categorical variables, the number of cases (percentage) [n (%)] was used. For intergroup comparisons, continuous variables were analysed using analysis of variance (ANOVA) or the Wilcoxon rank-sum test, while categorical variables were analysed using the chi-square test or Fisher's exact probability test. To explore the relationship between various indicators and hypertension, this study used a binary logistic regression model for analysis. ROC curves were plotted to assess the predictive performance of different obesity indicators, and AUC was calculated to determine predictive accuracy and optimal cutoff values. AUC comparisons were performed using MedCalc 19.0 software, with P < 0.05 considered statistically significant. Results 4.1 Participants characteristics at baseline A total of 6784 subjects were included in the analysis, including 3077 in the hypertensive group and 3707 in the non - hypertensive group (Table 2 ). Compared with the non-hypertensive group, the hypertensive group had lower proportions of people in the 65–69 age group, higher proportions in older age groups (70–74, 75–79, ≥ 80), lower proportions of married individuals, higher proportions of allergic history, family history of hypertension, and family history of diabetes (all P < 0.05). The hypertensive group had higher SBP, DBP, TC, TG, LDL-C, FBG, weight, WC, BMI, WHR,VAI, CMI,LAP, Body Fat Percentage(BF%), ABSI,BRI, CI, and WTI (all P 0.05). The baseline characteristics of all participants, stratified by hypertension status, are presented in Table 2 . Table 2 Comparison of general characteristics of all study subjects [n (%), M(Q1, Q3)] Project Indicators Hypertensive Group(n = 3077) Non - Hypertensive Group (n = 3707) X 2 /Z- Value P-value Age (years) 48.108 < 0.001 65–69 1475(47.9) 2048(55.2) 70–74 1025(33.3) 1149(31) 75–79 403(13.1) 375(10.1) ≥ 80 174(5.7) 135(3.6) Gender 1.981 0.159 Male 1373(44.6) 1591(42.9) Female 1704(55.4) 2116(57.1) Marital Status 8.625 0.035 Married 2672(86.8) 3296(88.9) Unmarried 1(0) 4(0.1) Divorced 13(0.4) 13(0.4) Widowed 391(12.7) 394(10.6) Educational Attainment 0.411 0.938 Primary school and below 581(18.9) 722(19.5) Junior high school 1045(34) 1253(33.8) Secondary vocational or high school 1028(33.4) 1223(33) College degree or above 423(13.7) 509(13.7) Allergic History 7.950 0.005 Yes 336(10.9) 329(8.9) No 2741(89.1) 3378(91.1) Family History of Hypertension 699.576 < 0.001 Yes 1370(44.5) 570(15.4) No 1707(55.5) 3137(84.6) Family History of Diabetes 10.439 0.001 Yes 240(7.8) 216(5.8) No 2837(92.2) 3491(94.2) Satisfaction with Current Health Status 173.814 < 0.001 Satisfied 639(20.8) 1188(32) Basically Satisfied 1931(62.8) 2196(59.2) Unclear 327(10.6) 180(4.9) Not Very Satisfied 115(3.7) 102(2.8) Unsatisfied 65(2.1) 41(1.1) Eating Habits 1.599 0.450 Balanced 2846(92.5) 3437(92.7) Meat - based 52(1.7) 49(1.3) Vegetarian - based 179(5.8) 221(6) Whether to Exercise 0.175 0.676 Yes 2625(85.3) 3149(84.9) No 452(14.7) 558(15.1) Smoking Status 0.758 0.384 Yes 621(20.2) 780(21) No 2456(79.8) 2927(79) Drinking Status 0.000 0.989 Yes 691(22.5) 833(22.5) No 2386(77.5) 2874(77.5) SBP (mmHg) 141.11 (130.00 ,151.00 ) 134.54(123.00,14.00) -16.003 < 0.001 DBP (mmHg) 77.99 (71.00 ,85.00 ) 76.61(69.00,84.00) -5.056 < 0.001 TC(mmol/L) 4.80 (4.10 ,5.49 ) 5.04(4.38,5.68) -9.124 < 0.001 TG(mmol/L) 1.69 (1.06 ,1.99 ) 1.53(0.94,1.77) -10.590 < 0.001 HDL-C(mmol/L) 1.24 (1.03 ,1.41 ) 1.33(1.11,1.52) -12.087 < 0.001 LDL-C(mmol/L) 2.87 (2.28 ,3.41 ) 3.04(2.5,3.53) -8.253 < 0.001 FBG(mmol/L) 5.75 (4.92 ,6.03 ) 5.44(4.77,5.56) -13.818 < 0.001 Height (cm) 160.84 (155.00 ,167.00 ) 160.47(154.50,166.50) -1.754 0.079 Weight (kg) 65.01 (57.50 ,71.50 ) 60.7087(54,67) -16.601 < 0.001 WC(cm) 87.34 (81.00 ,93.00 ) 83.2504(77,90) -17.903 < 0.001 BMI (kg/m²) 25.07 (22.86 ,27.02 ) 23.5213(21.46,25.44) -19.320 < 0.001 WHtR 0.54 (0.51 ,0.58 ) 0.5193(0.48,0.56) -17.480 < 0.001 VAI 0.63 (0.27 ,0.76 ) 0.48081(0.19,0.56) -16.671 < 0.001 CVAI 885.10 (713.95 ,1019.29 ) 961.54(784.19,1097.82) -13.246 < 0.001 LAP 51.01 (27.77 ,64.18 ) 40.38196(19.68,50.66) -16.745 < 0.001 BF% 256.40 (207.30 ,294.60 ) 279.77(229.01,319.7) -14.093 < 0.001 ABSI 3.21 (2.60 ,3.70 ) 2.88(2.34,3.34) -16.326 < 0.001 BRI 4.27 (3.53 ,4.93 ) 3.79(3.02,4.46) -17.519 < 0.001 CI 0.72 (0.70 ,0.75 ) 0.71(0.69,0.74) -10.811 < 0.001 WTI 149.26 (90.93 ,178.89 ) 129.03(75.44,152.64) -13.496 < 0.001 CMI 0.85 (0.43 ,1.01 ) 0.70(0.33,0.82) -14.500 < 0.001 4.2 Construction of prediction model The results of the binary logistic regression analysis for each obesity indicator across different ABCD stages are shown in Table 3 . According to the ABCD model, all study subjects were grouped. Hypertension status was used as the dependent variable (no = 0, yes = 1), and WC, WHtR, VAI, CVAI, LAP, BF%, ABSI, BRI, CI, WTI, and CMI were grouped into quartiles as independent variables for binary logistic regression analysis. Model 1 and Model 2 both used the Q1 group as the reference group. Model 1 did not adjust for confounding factors, while Model 2 adjusted for age, marital status, allergy history, family history of hypertension, family history of diabetes, satisfaction with current health, systolic blood pressure, diastolic blood pressure, total cholesterol (TC), triglycerides (TG), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), fasting blood glucose, weight, and body mass index (BMI). The results are shown in Table 3 . Compared with the Q1 group, during the normal period, WC, WHtR, VAI, CVAI, LAP, ABSI, BRI, CI, WTI, and CMI were all significantly associated with an increased risk of hypertension. After adjusting for confounding factors, the risk of hypertension in each group was 1.389, 1.403, and 1.590 times that of the Q1 group, respectively. After adjusting for confounding factors, the relative risks of hypertension for each group were 1.316, 1.406, and 1.563 times that of the Q1 group, respectively. In Phase 1, the differences in WC, LAP, WTI, and CMI were all statistically significant (P < 0.05). LAP was significantly associated with the risk of hypertension in the Q2, Q3, and Q4 groups in Model 2, with odds ratios of 1.626, 1.772, and 2.02 times that of the Q1 group, respectively. This indicates that as LAP increases, the risk of hypertension in Phase 1 significantly rises. Compared to the Q1 group, after adjusting for confounding factors, each unit increase in WTI was associated with a corresponding increase in the risk of hypertension of 1.299, 2.061, and 2.687 times in the Q2, Q3, and Q4 groups, respectively.The increase in CMI in phase 2 was significantly associated with the risk of hypertension. The OR values in model 2 from Q2 to Q4 were 1.377, 1.775, and 2.109, respectively, all indicating that an increase in CMI can increase the risk of hypertension ( P trend < 0.05). The results of the binary logistic regression analysis for each obesity indicator across different ABCD stages are shown in Table 3 . Table 3 Logistic regression analysis of various indicators in different obesity cycles and hypertension [ OR (95% CI )] Indicators Stage 0( n = 3843) Stage 1(n = 1514) Stage 2(n = 1427) Model 1 Model 2 Model 1 Model 2 Model 1 Model 2 WC Q2 1.672(1.389,2.014) 1.413(1.132,1.763) 1.752(1.304,2.352) 1.346(0.946,1.914) 1.382(1.018,1.874) 1.071(0.739,1.552) Q3 2.155(1.788,2.598) 1.553(1.211,1.993) 1.944(1.448,2.609) 1.001(0.669,1.498) 2.043(1.523,2.742) 1.127(0.737,1.723) Q4 3.179(2.637,3.833) 1.726(1.263,2.36) 2.708(2.006,3.656) 0.995(0.593,1.67) 3.377(2.47,4.617) 1.328(0.749,2.357) P trend < 0.001 0.002 < 0.001 0.043 < 0.001 0.791 WHtR Q2 1.543(1.286,1.852) 1.514(1.222,1.874) 1.574(1.137,2.011) 1.133(0.812,1.581) 1.859(1.39,2.486) 1.231(0.874,1.733) Q3 1.972(1.624,2.393) 1.643(1.315,2.053) 1.832(1.296,2.367) 1.112(0.772,1.602) 2.112(1.567,2.846) 1.244(0.857,1.803) Q4 2.32(1.924,2.797) 2.092(1.625,2.693) 2.731(1.948,3.514) 1.441(0.978,2.122) 3.338(2.42,4.606) 1.680(1.093,2.581) P trend < 0.001 < 0.001 < 0.001 0.281 < 0.001 0.127 VAI Q2 1.326(1.092,1.61) 1.595(1.251,2.034) 1.571(1.305,1.891) 1.351(0.917,1.992) 1.562(1.152,2.118) 1.318(0.886,1.961) Q3 1.758(1.457,2.122) 1.766(1.309,2.383) 2.073(1.722,2.496) 1.840(1.147,2.951) 2.51(1.851,3.404) 1.691(1.033,2.77) Q4 2.509(2.081,3.025) 2.008(1.337,3.015) 2.693(2.234,3.247) 2.450(1.307,4.593) 3.641(2.674,4.959) 1.944(0.991,3.812) P trend < 0.001 0.001 < 0.001 0.077 < 0.001 0.202 CVAI Q2 0.753(0.63,0.901) 1.351(1.041,1.754) 0.708(0.532,0.943) 1.097(0.725,1.659) 0.674(0.501,0.905) 1.631(1.064,2.499) Q3 0.669(0.559,0.801) 1.795(1.279,2.518) 0.516(0.387,0.689) 1.065(0.624,1.817) 0.531(0.395,0.715) 2.100(1.195,3.688) Q4 0.494(0.411,0.593) 1.840(1.178,2.874) 0.399(0.298,0.536) 1.023(0.514,2.035) 0.388(0.286,0.525) 2.972(1.396,6.331) P trend < 0.001 0.009 < 0.001 0.95 < 0.001 0.054 LAP Q2 1.832(1.519,2.21) 1.673(1.332,2.101) 1.462(1.082,1.976) 1.344(0.937,1.927) 0.643(0.478,0.865) 1.202(0.829,1.744) Q3 2.128(1.765,2.566) 1.811(1.404,2.336) 2.562(1.904,3.448) 1.885(1.253,2.837) 0.477(0.354,0.642) 1.668(1.086,2.564) Q4 2.913(2.414,3.514) 2.059(1.473,2.876) 2.992(2.221,4.032) 2.542(1.506,4.289) 0.376(0.278,0.51) 1.987(1.137,3.474) P trend < 0.001 < 0.001 < 0.001 0.004 < 0.001 0.06 BF% Q2 0.729(0.608,0.875) 1.364(1.045,1.782) 0.736(0.553,0.979) 1.330(0.872,2.029) 1.283(0.946,1.739) 1.617(1.043,2.507) Q3 0.679(0.565,0.815) 1.721(1.213,2.442) 0.517(0.387,0.691) 1.307(0.752,2.272) 1.66(1.228,2.245) 1.954(1.09,3.501) Q4 0.617(0.513,0.742) 1.781(1.120,2.832) 0.399(0.297,0.536) 1.346(0.657,2.757) 3.321(2.444,4.514) 3.113(1.408,6.883) P trend < 0.001 0.225 < 0.001 0.605 < 0.001 0.056 ABSI Q2 1.351(1.117,1.634) 1.308(1.011,1.693) 1.415(1.051,1.905) 0.932(0.619,1.405) 1.638(1.205,2.226) 0.907(0.585,1.405) Q3 1.353(1.119,1.636) 1.460(1.034,2.062) 2.07(1.542,2.78) 0.890(0.516,1.535) 2.529(1.863,3.433) 0.778(0.423,1.43) Q4 2.011(1.667,2.425) 1.345(0.800,2.262) 2.63(1.956,3.534) 0.635(0.279,1.447) 3.125(2.297,4.25) 0.921(0.371,2.287) P trend < 0.001 0.149 < 0.001 0.527 < 0.001 0.591 BRI Q2 1.601(1.32,1.942) 1.425(1.146,1.772) 1.697(1.263,2.28) 1.356(0.961,1.914) 1.035(0.778,1.379) 1.164(0.815,1.663) Q3 1.904(1.571,2.307) 1.616(1.288,2.029) 1.738(1.293,2.338) 1.076(0.75,1.543) 1.076(0.809,1.432) 1.446(0.992,2.108) Q4 2.29(1.893,2.772) 1.966(1.540,2.510) 2.749(2.044,3.697) 1.538(1.041,2.273) 1.602(1.203,2.134) 1.652(1.095,2.492) P trend < 0.001 < 0.001 < 0.001 0.067 0.004 0.08 CI Q2 1.473(1.224,1.772) 1.188(0.963,1.467) 1.302(0.975,1.737) 1.087(0.778,1.518) 1.072(0.796,1.444) 0.834(0.59,1.178) Q3 1.994(1.662,2.392) 1.419(1.146,1.757) 1.197(0.897,1.598) 0.729(0.515,1.032) 1.494(1.112,2.007) 1.033(0.724,1.474) Q4 2.146(1.784,2.581) 1.295(1.035,1.62) 1.682(1.26,2.244) 1.023(0.714,1.467) 1.744(1.294,2.349) 0.906(0.621,1.323) P trend < 0.001 0.014 0.005 0.082 < 0.001 0.586 WTI Q2 1.219(1.005,1.478) 1.302(1.045,1.622) 1.335(0.99,1.799) 1.302(0.907,1.87) 1.547(1.141,2.097) 1.302(0.908,1.867) Q3 1.664(1.377,2.01) 1.799(1.409,2.297) 2.28(1.698,3.063) 2.161(1.449,3.224) 1.92(1.419,2.599) 1.428(0.958,2.127) Q4 2.126(1.762,2.566) 1.867(1.352,2.58) 2.649(1.97,3.562) 2.955(1.742,5.014) 3.078(2.267,4.179) 2.034(1.19,3.476) P trend < 0.001 < 0.001 < 0.001 < 0.001 < 0.001 0.081 CMI Q2 1.235(1.016,1.5) 1.433(1.137,1.807) 1.494(1.104,2.021) 1.489(1.017,2.181) 1.441(1.063,1.953) 1.231(0.848,1.787) Q3 1.922(1.589,2.325) 1.865(1.423,2.443) 3.026(2.243,4.084) 2.858(1.837,4.448) 1.831(1.354,2.477) 1.349(0.867,2.097) Q4 2.334(1.931,2.821) 2.192(1.533,3.135) 2.872(2.128,3.875) 3.235(1.822,5.744) 3.146(2.317,4.272) 2.540(1.417,4.552) P trend < 0.001 < 0.001 < 0.001 < 0.001 < 0.001 0.008 4.3 Comparison of disease risk identification ability Table 4 shows that the AUC of all obesity indicators was higher than 0.58, and the differences between different obesity cycles were statistically significant (P < 0.05).Among the subjects in the normal group, the body roundness index performed best in identifying hypertension risk, with an AUC of (0.6292, 95% CI: 0.612, 0.647), which was larger than the AUC of other indicators. The optimal cut-off values were 3.6765.In the first phase of the study, the lipid accumulation index performed best in identifying hypertension risk, with an AUC of (0.6211, 95% CI: 0.593, 0.649), which was larger than the AUC of other indicators. The optimal cut-off value was 37.87.The AUC of the cardiac metabolic index in the Phase II study subjects was (0.6243, 95% CI: 0.596, 0.653).The optimal cutoff value for the cardiac metabolic index in stage 2 was the lowest, at 0.476, while the optimal cutoff value for the lipid accumulation index in stage 1 was the highest, at 37.87, higher than the 31.91 in stage 0, indicating that as obesity severity increases, lipid accumulation levels in the body gradually rise. ROC curve analysis, as shown in Figs. 2, 3and 4. Table 4 Comparison of the ability to identify disease risk among subjects in different obesity stages Indicators Cut-off AUC 95% CI P value Stage 0 WC 83.75 0.6272 (0.61,0.645) < 0.001 WHtR 0.515 0.6288 (0.611,0.646) < 0.001 VAI 0.405 0.6056 (0.588,0.623) < 0.001 CVAI 952.855 0.5788 (0.561,0.597) < 0.001 LAP 31.91 0.6104 (0.593,0.628) < 0.001 BRI 3.6765 0.6292 (0.612,0.647) < 0.001 CI 0.7145 0.5843 (0.566,0.602) < 0.001 WTI 112.75 0.5842 (0.566,0.602) < 0.001 CMI 0.5495 0.5921 (0.574,0.61) < 0.001 Stage 1 WC 80.25 0.6042 (0.576,0.633) < 0.001 LAP 37.87 0.6211 (0.593,0.649) < 0.001 WTI 109.38 0.6077 (0.579,0.636) < 0.001 CMI 0.568 0.6192 (0.591,0.647) < 0.001 Stage 2 CMI 0.476 0.6243 (0.596,0.653) < 0.001 4.4 Analysis of AUC differences A comparison of the AUC differences in the ability to identify the risk of hypertension under different obesity indicators revealed that the AUC differences for CMI, WC, LAP, WTI, WHtR, VAI, CVAI, BRI, and CI in the normal phase, as well as CMI, WTI, and LAP in phase 1, were statistically significant (all P < 0.05). The pairwise comparisons of AUC differences are presented in Table 5 . Based on the AUC values of obesity indicators at different stages, BRI had the best performance in identifying the risk of hypertension in normal-stage subjects, LAP had the best performance in identifying the risk of hypertension in stage 1 subjects (Table 5 ), and CMI had the best performance in identifying the risk of hypertension in stage 2 subjects. Table 5 Analysis of differences in AUC for identifying hypertension risk under different obesity indicators indicators CMI WC LAP WTI WHtR VAI CVAI BRI CI Stage 0 CMI 1 WC < 0.001 1 LAP < 0.001 0.024 1 WTI 0.006 < 0.001 < 0.001 1 WHtR < 0.001 0.752 0.017 < 0.001 1 VAI < 0.001 0.012 0.211 < 0.001 0.015 1 CVAI 0.265 < 0.001 0.003 0.655 < 0.001 0.005 1 BRI < 0.001 0.681 0.015 < 0.001 0.399 0.014 < 0.001 1 CI 0.467 < 0.001 0.003 0.988 < 0.001 0.049 0.660 < 0.001 1 Stage 1 CMI 1 WC 0.352 1 LAP 0.759 0.164 1 WTI 0.010 0.828 0.008 1 4.5 Analysis of the combined predictive value of two obesity indicators As shown in Table 6 and Figs. 5, 6, and 7, after controlling for confounding factors, analysis of subjects in the normal period, Stage 1, and Stage 2 revealed that, in the normal period, using BRI as the reference, composite indices such as WC + WHtR, WC + BRI, WC + CI, and WC + CMI all exhibited AUC values slightly higher than BRI. with AUC change rates of 0.477% and 0.509% for WC + WHtR and WC + BRI, respectively, and AUC values of 0.6322 and 0.6324, respectively. In Stage 1, using LAP as the reference, the AUC value was 0.6211, with only WC + CI having an AUC higher than that of LAP alone, at 0.6218, but the AUC change rate was only 0.113%; in Stage 2, using CMI as the benchmark, the AUC value was 0.6243. Unlike the normal stage and Stage 1, most combined indicators in the Stage 2 group had higher AUC values than CMI, indicating that in the Stage 2 obesity stage, combined indicators may have better performance in predicting the risk of hypertension in the elderly, particularly WC + VAI, WC + LAP, WC + CI, and WC + CMI, with AUC change rates of 3.011%, 2.355%, 3.524%, and 2.515%, respectively, and AUC values of 0.6431, 0.6390, 0.6463, and 0.6400, respectively, demonstrating a significant predictive advantage. Table 6 Comparison of ROC curve results for combined prediction of hypertension using obesity indicators Indicators AUC (95% CI ) AUC change rate(%) P 值 Stage 0 BRI 0.6292 < 0.001 WC + WHtR 0.6322 0.477 < 0.001 WC + VAI 0.6297 0.079 < 0.001 WC + CVAI 0.6273 -0.302 < 0.001 WC + LAP 0.6289 -0.048 < 0.001 WC + BF% 0.6274 -0.286 < 0.001 WC + ABSI 0.6274 -0.286 < 0.001 WC + BRI 0.6324 0.509 < 0.001 WC + CI 0.6310 0.286 < 0.001 WC + WTI 0.6294 0.032 < 0.001 WC + CMI 0.6306 0.223 < 0.001 Stage 1 LAP 0.6211 < 0.001 WC + WHtR 0.6053 -2.544 < 0.001 WC + VAI 0.607 -2.270 < 0.001 WC + CVAI 0.6102 -1.755 < 0.001 WC + LAP 0.6056 -2.496 < 0.001 WC + BF% 0.6113 -1.578 < 0.001 WC + ABSI 0.6097 -1.835 < 0.001 WC + BRI 0.6054 -2.528 < 0.001 WC + CI 0.6218 0.113 < 0.001 WC + WTI 0.6056 -2.496 < 0.001 WC + CMI 0.6065 -2.351 < 0.001 Stage 2 CMI 0.6243 < 0.001 WC + WHtR 0.6309 1.057 < 0.001 WC + VAI 0.6431 3.011 < 0.001 WC + CVAI 0.6340 1.554 < 0.001 WC + LAP 0.6390 2.355 < 0.001 WC + BF% 0.6350 1.714 < 0.001 WC + ABSI 0.6336 1.490 < 0.001 WC + BRI 0.6308 1.041 < 0.001 WC + CI 0.6463 3.524 < 0.001 WC + WTI 0.6398 2.483 < 0.001 WC + CMI 0.6400 2.515 < 0.001 Discussion This study utilised the ABCD model to categorise obesity stages in the elderly and employed logistic regression analysis to investigate the relationship between various obesity indicators and the risk of hypertension in the elderly. In addition to traditional obesity indicators such as BMI and WC, the study also included emerging obesity indicators such as VAI, CVAI, ABSI, CI, WTI, and CMI. These indicators are characterised by their ability to better reflect body fat distribution and visceral fat accumulation. The results showed that in different stages of obesity, VAI, WHtR, CVAI, ABSI, and LAP all had a significant impact on the risk of hypertension, a finding consistent with existing literature reports on the relationship between obesity indicators and hypertension [ 21 ] . This study further confirms the important role of obesity indicators in predicting the onset of hypertension, consistent with studies in various regions.A study in Nigeria found that traditional obesity indicators such as BMI, WHtR, and WC showed good predictive performance in predicting the risk of hypertension [ 22 ] .The results of a Brazilian study involving 3,143 participants showed that using WHtR to screen for hypertension in women is highly accurate, with a cut-off value of 0.54 [ 23 ] .A cross-sectional study in Malaysia demonstrated the association between ABSI and hypertension. The study found that ABSI predicted hypertension with an AUC value of 0.74, which may be related to the local geographical environment or ethnic characteristics. It is speculated that differences in ABSI predictive ability may be related to regional characteristics [ 24 ] .There have also been related studies in China. A study on risk factors for hypertension in middle-aged and elderly people in China pointed out that the CVAI indicator is more suitable for screening high-risk groups for hypertension in middle-aged and elderly people in China. This indicator takes into account indicators such as WC and TG and has more reliable predictive value [ 21 ] .Another study concluded that as VAI values increase, the 15-year cumulative incidence of hypertension also increases, suggesting that VAI can serve as an independent predictor of hypertension [ 25 ] .In addition, studies have shown that BF% is positively correlated with blood pressure variability in hypertensive patients, meaning that people with higher body fat percentages experience more significant fluctuations in blood pressure [ 26 ] . 5.1 Obesity indicators for predicting hypertension vary at different stages of obesity. This study found that the obesity indicators predicting the occurrence of hypertension vary at different stages of obesity. At stage 0, BRI performed best in identifying the risk of hypertension; while at stages 1 and 2, LAP and CMI became more important predictive indicators. This suggests that as the degree of obesity increases, the accumulation of lipids and metabolic abnormalities in the body have a gradually greater impact on the risk of hypertension, and the probability of developing hypertension also gradually increases. This is consistent with the research results from Fujian Province, Shenzhen City, Anlu City, and other places [ 27 – 29 ] .This finding also indicates that the mechanisms by which lipid accumulation and metabolic abnormalities contribute to hypertension undergo dynamic evolution across different stages of obesity. During the progression of obesity, excessive visceral fat accumulation leads to adipocyte dysfunction, activating both the sympathetic nervous system (SNS) and the renin-angiotensin-aldosterone system (RAAS). Meanwhile, ectopic lipid deposition triggers mitochondrial dysfunction, resulting in excessive production of reactive oxygen species (ROS). This promotes oxidative stress, impairs vascular endothelial function, and ultimately enhances vasoconstriction and elevates blood pressure [ 30 – 32 ]。 This study utilizes straightforward obesity indicators to screen and identify high-risk individuals for hypertension. Implementing weight reduction interventions for this population can contribute to the prevention and management of hypertension. The Body Roundness Index (BRI) primarily reflects overall body fat distribution. It is a novel anthropometric index developed in 2013 by Diana M. Thomas et al. that incorporates both waist circumference and height. By influencing overall fat distribution and visceral adipose tissue accumulation, it indirectly affects blood pressure regulation [ 33 ] . Studies by Kawasoe S et al. demonstrated that BMI, WC, and BRI were significantly associated with the incidence of hypertension in both men and women aged 30–60 years, indicating a certain accuracy of BRI in predicting hypertension [ 34 ] . Calderón-García JF et al. pointed out that BRI outperforms the A Body Shape Index (ABSI) in predicting hypertension among adult women and men from diverse populations. Beyond hypertension, BRI also exhibits a robust predictive capability for cardiovascular diseases and metabolic syndrome [ 35 ] . Research by Cai X et al. proved that BRI is significantly correlated with the risk of cardiovascular disease (CVD) in patients with obstructive sleep apnea (OSA) and concomitant hypertension [ 36 ] . Furthermore, prospective cohort studies have found that BRI trajectories are positively associated with cardiovascular disease incidence, and its predictive value remains significant across subgroups stratified by age and sex [ 37 ] [ 38 ] . Additionally, a study by Tian Ting et al. discovered that BRI possesses a better ability than both BMI and ABSI for predicting metabolic syndrome in female populations [ 39 ] . The Lipid Accumulation Product (LAP) is a significant indicator reflecting visceral and subcutaneous adipose tissue accumulation. Multiple studies have demonstrated a strong association between elevated LAP and an increased risk of hypertension [ 40 ] [ 41 ] [ 42 ] . Increased LAP contributes to systemic insulin resistance. This resistance impairs vascular endothelial glucose uptake via dysfunction in the PI3K/Akt signaling pathway, leading to compensatory hyperinsulinemia which subsequently activates the MAPK pathway. This activation promotes vascular smooth muscle cell proliferation and vascular remodeling, ultimately resulting in elevated blood pressure [ 10 , 43 ] . Wakabayashi et al. found that, compared to the low LAP quartile group, the risk for hypertension was 7.31-fold and 10.66-fold higher in the high LAP quartile group for men and women, respectively [ 40 ] . Similarly, a study by Su Jian et al. reported that the risk of hypertension was 3.65 times higher in men and 3.52 times higher in women within the high LAP group compared to the low LAP group, indicating that hypertension risk escalates with increasing LAP levels in both sexes [ 41 ] . This finding is corroborated by a large prospective study involving 37,333 subjects, which also showed a graded increase in hypertension risk with rising LAP levels in both men and women [ 44 ] . Research by Khanmohammadi S et al. further indicated that LAP offers superior predictive value for hypertension compared to traditional indices like BMI and WC, with even higher predictive accuracy observed in women than in men [ 42 ] . Additionally, a study by Liao Yanping et al. demonstrated [45] that LAP is a valuable and reliable indicator for identifying hypertension risk in elderly populations [ 45 ] . The Cardiometabolic Index (CMI), an indicator reflecting an individual's obesity degree and lipid profile, influences blood pressure regulation by modulating lipid metabolism and cardiac function. This underscores its significant role in assessing the risk of hypertension and cardiovascular disease. A study involving 11,400 Chinese participants demonstrated that CMI, LAP, and BAI were independently associated with higher systolic and diastolic blood pressure [ 46 ] . Specifically, for each standard deviation increase in CMI, the risk of hypertension rose by 31% in men, suggesting CMI serves as a valid marker for hypertension risk assessment. A cross-sectional study further revealed a proportional increase in the prevalence of left ventricular hypertrophy across rising quartiles of both CMI and LAP, reinforcing the link between CMI and adverse cardiac structural alterations [ 47 ] . Additional research indicates the utility of CMI extends to assessing microalbuminuria risk. An analysis based on the NHANES 2011–2018 database showed that among individuals aged ≥ 60 years—encompassing the general population, those with diabetes, and those with hypertension-the microalbuminuria group exhibited significantly higher CMI levels than the normoalbuminuria group, indicating an independent association between CMI and microalbuminuria [ 48 ] . Another study suggested that CMI holds potential usefulness for detecting a reduced glomerular filtration rate in the general Chinese population, implying its value in identifying early-stage chronic kidney disease risk [ 49 ] . As obesity persists and worsens, alterations in body fat distribution and metabolic status occur, which subsequently impact blood pressure levels. Consequently, implementing dynamic and stratified intervention strategies tailored to specific obesity stages is crucial for the effective prevention and control of hypertension. 5.2 Synergistic Predictive Analysis of Obesity Indices This study demonstrates that a combined model utilizing multiple obesity indices significantly enhances the predictive capacity for hypertension risk among older adults, particularly in Stage 2 obesity. Combinations such as WC + VAI, WC + LAP, WC + CI, and WC + CMI exhibited superior predictive efficacy compared to any single obesity indicator. These findings align with research by Liu Yan et al. underscoring the clinical value of integrating multiple anthropometric measures to refine hypertension risk stratification [ 50 ] . Similarly, Wang Xin’s team corroborated this conclusion, noting that multi-index models generally outperform single metrics, though the optimal combination may vary across sex and ethnic subgroups, warranting further investigation [ 51 ] . Additionally, Chen Minmin et al. compared single and combined obesity indicators and concluded that integrating WHtR as a key component significantly enhanced hypertension prediction accuracy [ 52 ] . Nevertheless, practical implementation of multi-index models introduces complexities, including increased data collection burdens, higher operational costs, and challenges related to collinearity among variables, which necessitate advanced statistical. Future research should focus on optimizing these models by incorporating machine learning algorithms to refine feature weighting and improve computational efficiency without compromising predictive performance. Such approaches could enhance the practicality and scalability of multi-index predictive tools in clinical and public health settings In summary, this study employed logistic regression to elucidate the complex associations between various obesity indices and hypertension risk in the elderly, highlighting the unique value of the ABCD model and the importance of integrated obesity metrics. Future directions include validation of these findings, identification of novel predictive biomarkers, and the development of targeted interventions—such as dietary modifications and physical activity programs—to improve hypertension prevention and early management in aging populations. Abbreviations Abbreviation Full name ABCD The Adiposity-Based Chronic Disease ROC Receiver Operating Characteristic AUC Area Under the Curve CVAI Chinese Visceral Adipose Index CMI Cardiometabolic Index CI Conicity Index ABSI Body Shape Index BRI Body Roundness Index LAP Lipid Accumulation Product WHO World Health Organization BMI Body Mass Index ACE American College of Endocrinology AACE American Association of Clinical Endocrinologists WC Waist Circumference FBG Fasting Blood Glucose TG Triglyceride TC Total Cholesterol HDL – C High Density Lipoprotein Cholesterol LDL-C Low Density Lipoprotein Cholesterol SBP Systolic Blood Pressure DBP Diastolic Blood Pressure SD Standard Deviation WHtR Waist to Height Ratio VAI Visceral Adipose Index WTI Waist Circumference Triglyceride Index LAP Lipid Accumulation Product BF% Body Fat Percentage Declarations Ethics approval and consent to participate: This study was conducted in strict accordance with the principles outlined in the Declaration of Helsinki . The study protocol was reviewed and approved by the Ethics Review Committee of Tongji Medical College, Huazhong University of Science and Technology. All procedures involving human participants were carried out with the appropriate ethical clearance, and the ethics approval number is 2023-S104. Informed consent was obtained from all individual participants or their legal guardians prior to their involvement in the study, ensuring compliance with the highest ethical standards for research involving human subjects. Consent for publication: Informed consent for publication has been obtained from the patients described in the manuscript. The patients have provided consent to publish their examination results and other relevant clinical information. Every effort has been made to de-identify the patients to protect privacy, and the patients acknowledge and agree to the publication of the de-identified information. Availability of data and materials ： The data supporting the findings of this study are available from Hongshan Street Community Health Service Center, Hongshan District, Wuhan City, Hubei Province, China. Due to restrictions imposed by the license under which the data were used for the current research, these data are not publicly accessible. However, qualified researchers may request access to the data by contacting the corresponding author , provided that such requests are reasonable and have obtained prior permission from Hongshan Street Community Health Service Center, Hongshan District, Wuhan City. Competing interests ： The authors declare that they have no competing interests. Funding: This work was supported by the Ministry of Education 2021 Humanities and Social Sciences Fund in China (Grant No. 21YJA630062); the Research on the Development Path of Smart Healthcare and Health Management in Jiangxia District Communities, Wuhan City (Grant No. H20230163); and the Research on the Collaborative Mechanism of Primary Healthcare Services under the Background of Health Needs (Grant No. H20220099). Authors' contributions ： WH: Conceptualization, Methodology, Investigation, Data Curation, Writing - Original Draft, Writing - Review & Editing. LYQ(Corresponding Author): Conceptualization, Methodology, Supervision, Validation, Writing - Review & Editing, Project Administration. RFF: Investigation, Data Curation, Formal Analysis, Visualization, Writing - Review & Editing. YL: Investigation, Validation, Formal Analysis, Writing - Review & Editing. ZYK: Software, Validation, Formal Analysis, Data Curation. JF: Methodology, Software, Validation, Writing - Review & Editing. DSX: Data Curation, Visualization, Writing - Review & Editing. GLW: Investigation, Resources, Writing - Review & Editing. All authors have read and approved the final manuscript. Acknowledgements: The authors would like to express their sincere gratitude to all individuals and institutions that contributed to the completion of this study. First and foremost, we extend our heartfelt appreciation to the study participants for their voluntary involvement and consistent cooperation throughout the research process. We also gratefully acknowledge the staff of Hongshan Street Community Health Service Center in Hongshan District, Wuhan City, for their invaluable support in data collection and logistical coordination. We are deeply grateful to the funding agencies for their financial support, including the Ministry of Education Humanities and Social Sciences Fund in China (Grant No. 21YJA630062), the Research on the Development Path of Smart Healthcare and Health Management in Jiangxia District Communities, Wuhan City (Grant No. H20230163), and the Research on the Collaborative Mechanism of Primary Healthcare Services under the Background of Health Needs (Grant No. H20220099). Special thanks are due to Professor Liu Yaqing and Professor Liu Chenxi for their expert guidance and constructive feedback during both the study design and manuscript preparation phases. We also acknowledge the contributions of our fellow research team members for their dedicated efforts in data analysis and manuscript refinement. 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Evaluation of the efficiency of different obesity indicators and their combinations in predicting hypertension risk[J]. J. Practical Med. 36 (13), 1823–1828 (2020). Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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1","display":"","copyAsset":false,"role":"figure","size":102331,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart of the residents-selection process.\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8036118/v1/3ed9acf9d3a09b64a7ec1697.jpg"},{"id":100067921,"identity":"f173fbaf-9b62-420b-bb55-f597a6daf21f","added_by":"auto","created_at":"2026-01-12 15:56:49","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":45156,"visible":true,"origin":"","legend":"\u003cp\u003eStage 0 ROC curve\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8036118/v1/fd7e7116e7225c36d3222543.jpg"},{"id":100364731,"identity":"6b0be526-7e8a-4bef-b9e9-0972fa641451","added_by":"auto","created_at":"2026-01-16 07:54:16","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":41351,"visible":true,"origin":"","legend":"\u003cp\u003eStage 1 ROC curve\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8036118/v1/64142cd44a1daeb9234c29a6.jpg"},{"id":100364129,"identity":"509e6b47-0214-41c3-966d-cec1c295a58c","added_by":"auto","created_at":"2026-01-16 07:52:39","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":32536,"visible":true,"origin":"","legend":"\u003cp\u003eStage 2 ROC curve\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8036118/v1/a0b9c85f68b4c9c2918d7aca.jpg"},{"id":100363748,"identity":"401862a0-6159-4e18-bc56-022e3a54b42a","added_by":"auto","created_at":"2026-01-16 07:51:34","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":45119,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of Stage 0 AUCs\u003c/p\u003e","description":"","filename":"5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8036118/v1/6e63a35f9895c9712f3b5446.jpg"},{"id":100364605,"identity":"59c9cc58-8d50-495a-a794-5d97ac7dca04","added_by":"auto","created_at":"2026-01-16 07:54:00","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":43976,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of Stage 1 AUCs\u003c/p\u003e","description":"","filename":"6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8036118/v1/f2e68fd30792295b9b24c1e6.jpg"},{"id":100067946,"identity":"89c76700-621e-4fb8-916e-16112e443281","added_by":"auto","created_at":"2026-01-12 15:56:55","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":41917,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of Stage 2 AUCs\u003c/p\u003e","description":"","filename":"7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8036118/v1/156bf71defed64d22fc9f209.jpg"},{"id":106109747,"identity":"b120622a-9fc9-4e14-af94-efdbc722ac68","added_by":"auto","created_at":"2026-04-03 14:42:07","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2208339,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8036118/v1/ec6f482d-be71-4720-bcf6-14cf448c2197.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Comparison of Hypertension Risk in Elderly Under Adiposity-Based Chronic Disease Model","fulltext":[{"header":"Introduction","content":"\u003cp\u003eObesity and overweight represent major global health challenges. Despite concerted efforts by countries and international organizations, their prevalence has continued to rise steadily over recent decades\u003csup\u003e[1]\u003c/sup\u003e.According to the World Health Organization (WHO) in 2021, over one billion individuals worldwide were obese, comprising approximately 650 million adults, 340 million adolescents, and 39 million children\u003csup\u003e[2]\u003c/sup\u003e.In China, similar trends have been observed over the past two decades, with rapid increases in the prevalence of overweight, obesity, and associated chronic diseases \u003csup\u003e[3]\u003c/sup\u003e.Obesity is not only an independent chronic disease but also a significant risk factor for numerous other chronic conditions, imposing a substantial burden on global health systems and economies. Against the backdrop of China\u0026apos;s accelerating population aging, obesity among the elderly population has emerged as an increasingly critical public health challenge requiring urgent attention.\u003c/p\u003e\n\u003cp\u003eCurrent diagnostic criteria for obesity, as defined by the World Health Organization rely primarily on body mass index (BMI), where a BMI\u0026nbsp;\u0026ge;25 indicates overweight and\u0026nbsp;\u0026ge;30 indicates obesity\u003csup\u003e[4]\u003c/sup\u003e.However, researchers have highlighted significant limitations in using BMI to assess obesity and its associated health risks. These limitations arise from variations in age, sex, and ethnicity; inaccurate stratification of obesity-related disease risk; incomplete characterization of obesity pathophysiology; and perpetuation of weight-related stigma\u003csup\u003e[5]\u003c/sup\u003e.To address these shortcomings, the American College of Endocrinology (ACE) and the American Association of Clinical Endocrinologists (AACE) proposed a novel diagnostic framework: the Adiposity-Based Chronic Disease (ABCD) model. This model aims to enable more comprehensive management and treatment of obesity and its complications by shifting focus from BMI and waist circumference (WC) alone to a complication-centric approach for guiding treatment decisions and outcomes\u003csup\u003e[6]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eThe ABCD model comprises four key dimensions:\u003c/p\u003e\n\u003cp\u003eA (Adiposity): Etiology of excess adiposity\u003c/p\u003e\n\u003cp\u003eB (BMI): Anthropometric classification\u003c/p\u003e\n\u003cp\u003eC (Complications): Obesity-related complications\u003c/p\u003e\n\u003cp\u003eD (Disease Severity): Severity/staging of complications\u003c/p\u003e\n\u003cp\u003eUnlike traditional obesity assessments, the ABCD model evaluates not only elevated total adiposity but also abnormal fat distribution and dysfunction. By staging obesity and its complications, it improves identification of high-risk populations for conditions such as hypertension. Furthermore, the model facilitates early identification and intervention in individuals with lower BMI but significant complications, enabling more precise personalized health management\u003csup\u003e[7]\u003c/sup\u003e.However, systematic research on the ABCD model remains limited globally. To address this gap, this study leverages data from 6,784 adults aged \u0026ge;65 years who underwent health examinations at 17 community in Hongshan District, Wuhan, China. We evaluate the predictive validity of individual and combined obesity assessment indicators for hypertension within the ABCD framework. The findings aim to inform evidence-based strategies for optimizing elderly health management and strengthening early identification/prevention of obesity-related chronic diseases.\u003c/p\u003e"},{"header":"Material and methods","content":"\u003cdiv id=\"Sec2\" class=\"Section2\"\u003e \u003ch2\u003e1.1Study design and participants\u003c/h2\u003e \u003cp\u003eA total of 6,784 elderly individuals aged ≥ 65 years who met the inclusion criteria were selected from health examinations conducted at a community health service centre in Wuhan City from January 2022 to December 2023, including 2,964 males and 3,820 females. Inclusion criteria: conscious, aged ≥ 65 years, and able to complete all examinations and questionnaires. Exclusion criteria: aged \u0026lt; 65 years, with severe mental disorders, unconscious, unable to communicate clearly, uncooperative, or unable to complete the survey or examination. This study was approved by the Ethics Review Committee of Tongji Medical College, Huazhong University of Science and Technology. Ethics approval number: 2023-S104.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e1.2 Data collection\u003c/h2\u003e \u003cp\u003eThe target population arrived at the designated screening hospital and completed the screening registration and informed consent form, followed by physical measurements by two experienced epidemiological investigators. A one-to-one epidemiological survey based on a standardised questionnaire administered by trained doctors, nurses or epidemiological investigators, with no cues regarding the answers to the questions. Then, venous blood was collected and tested for laboratory biochemical indices. Finally, ultrasound examinations were performed. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e illustrates the flowchart of the participant selection process.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eQuestionnaires, physical examinations, and laboratory tests are all conducted by staff members of community health service centres who have undergone standardised training. The questionnaire covers personal basic information and lifestyle factors, while the physical examination includes measurements of height, weight, waist circumference, blood pressure, and other parameters. All study participants fasted overnight (for at least 8 hours) and had venous blood drawn by medical staff at the community health service centre for biochemical testing, primarily including fasting blood glucose (FBG),, triglycerides (TG), total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C), and low-density lipoprotein cholesterol (LDL-C).\u003c/p\u003e \u003cp\u003e2. Variable description\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.1The Adiposity-Based Chronic Disease (ABCD) model\u003c/h2\u003e \u003cp\u003eThe ABCD model classifies obesity into three stages based on the presence and severity of obesity-related complications\u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e. The specific staging criteria are detailed in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eStage 0: Individuals who do not meet the criteria for obesity (BMI \u0026lt; 28 kg/m² and WC \u0026lt; 90 cm for men or \u0026lt; 80 cm for women) and have no associated metabolic risk factors; OR individuals who meet the criteria for obesity (BMI ≥ 28 kg/m² or WC ≥ 90 cm for men or WC ≥ 80 cm for women) but have no associated metabolic risk factors.\u003c/p\u003e \u003cp\u003eStage 1: Individuals meeting the criteria for obesity (BMI ≥ 28 kg/m² or WC ≥ 90 cm for men or WC ≥ 80 cm for women) and presenting with 1–2 metabolic risk factors (e.g., elevated blood glucose, dyslipidemia).\u003c/p\u003e \u003cp\u003eStage 2: Individuals meeting the criteria for obesity (BMI ≥ 28 kg/m² or WC ≥ 90 cm for men or WC ≥ 80 cm for women) and presenting with 3 or more metabolic risk factors.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\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\u003eABCD Staging Criteria\u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStage\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBMI and WC Criteria\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNumber of metabolic risk factors\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMetabolic Risk Factors\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNot obese: \u003c/p\u003e \u003cp\u003eBMI \u0026lt; 28 kg/m² and WC \u0026lt; 90 cm (M) / \u0026lt; 80 cm (F)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNone present\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e① Increased WC (M ≥ 112 cm, F ≥ 88 cm)\u003c/p\u003e \u003cp\u003e② Elevated BP (SBP ≥ 130 mmHg and/or DBP ≥ 85 mmHg) or on antihypertensive medication\u003c/p\u003e \u003cp\u003e③ Reduced HDL-C (M \u0026lt; 1.0 mmol/L, F \u0026lt; 1.3 mmol/L) or on medication\u003c/p\u003e \u003cp\u003e④ Elevated fasting serum triglycerides (≥ 1.7 mmol/L) or on medication\u003c/p\u003e \u003cp\u003e⑤ Metabolic syndrome\u003c/p\u003e \u003cp\u003e⑥ Impaired fasting blood glucose (FBG ≥ 5.6 mmol/L)\u003c/p\u003e \u003cp\u003e⑦ Impaired glucose tolerance (2h glucose ≥ 7.8 mmol/L)\u003c/p\u003e \u003cp\u003e⑧ Type 2 diabetes (FBG ≥ 7.0 mmol/L or 2h glucose ≥ 11.1 mmol/L or on diabetes medication)\u003c/p\u003e \u003cp\u003e⑨ Cardiovascular disease (angina or post-event status, e.g., acute coronary syndrome, stent placement, coronary artery bypass grafting, thrombotic stroke, non-traumatic amputation due to peripheral vascular disease)\u003c/p\u003e \u003cp\u003e⑩ Non-alcoholic fatty liver disease (NAFLD) and non-alcoholic steatohepatitis (NASH)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eObese: BMI ≥ 28 kg/m² or WC ≥ 90 cm (M) or WC ≥ 80 cm (F)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1–2\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eObese: BMI ≥ 28 kg/m² or WC ≥ 90 cm (M) or WC ≥ 80 cm (F)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e≥ 3\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003eNote:1 mmHg = 0.133 kPa;HDL-C = high-density lipoprotein cholesterol,LDL-C = low-density lipoprotein cholesterol,TG = triglycerides,FBG = fasting blood glucose.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e \u003cp\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Other variables\u003c/h2\u003e \u003cp\u003e①Diagnostic criteria for hypertension in the elderly\u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e: According to the 2023 Chinese Guidelines for the Management of Hypertension in the Elderly, individuals with systolic blood pressure (SBP) ≥ 140 mmHg and/or diastolic blood pressure (DBP) ≥ 90 mmHg when not taking antihypertensive medications are diagnosed with hypertension;Older adults who have been diagnosed with hypertension and are currently receiving antihypertensive medication should be classified as older adults with hypertension, even if their SBP is less than 140 mmHg or their DBP is less than 90 mmHg. Similarly, older adults who self-report having hypertension should also be included.\u003c/p\u003e \u003cp\u003e②Waist to Height Ratio(WHtR) the calculation formula is as follows\u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e:\u003c/p\u003e\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:\\text{WHtR＝WC(cm)/height(cm)}$$\u003c/div\u003e\u003c/div\u003e;\u003cp\u003e\u003c/p\u003e \u003cp\u003e③Conicity Index(CI) the calculation formula is as follows\u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e:\u003c/p\u003e\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$\\:\\text{CI＝}\\frac{\\text{WC}\\text{(cm)}}{\\text{0.109×}\\sqrt{\\text{weight}\\text{(kg)/}\\text{height}\\text{(m}\\text{)}}}$$\u003c/div\u003e\u003c/div\u003e\u003cp\u003e\u003c/p\u003e \u003cp\u003e④Visceral Adipose Index(VAI) the calculation formula is as follows\u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e:\u003c/p\u003e\u003cdiv id=\"Equc\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equc\" name=\"EquationSource\"\u003e\n$$\\:\\text{Male:}\\text{VAI＝}\\frac{\\text{WC}\\text{(cm)}}{\\text{39.68}\\text{+}\\text{1.88×BMI}}\\text{×}\\frac{\\text{TG(}\\text{mmol/L}\\text{)}}{\\text{1.03}}\\text{×}\\frac{\\text{1.31}}{\\text{HDL-C(}\\text{mmol/L}\\text{)}}$$\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equd\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equd\" name=\"EquationSource\"\u003e\n$$\\:\\text{F}\\text{emale:}\\text{VAI＝}\\frac{\\text{WC}\\text{(cm)}}{\\text{39.68}\\text{+}\\text{1.88×BMI}}\\text{×}\\frac{\\text{TG(}\\text{mmol/L}\\text{)}}{\\text{0.:81}}\\text{×}\\frac{\\text{1.52}}{\\text{HDL-C(}\\text{mmol/L}\\text{)}}\\:$$\u003c/div\u003e\u003c/div\u003e\u003cp\u003e\u003c/p\u003e \u003cp\u003e⑤Chinese Visceral Adipose Index(CVAI) the calculation formula is as follows\u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e:\u003c/p\u003e\u003cdiv id=\"Eque\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Eque\" name=\"EquationSource\"\u003e\n$$\\:\\begin{array}{c}\\text{M}\\text{a}\\text{l}\\text{e}\\text{}\\text{C}\\text{V}\\text{A}\\text{I}\\text{＝}\\text{-}\\text{267.93}\\text{+}\\text{0.68}\\text{×}\\text{a}\\text{g}\\text{e}\\text{+}\\text{0.03}\\text{×}\\text{B}\\text{M}\\text{I}\\text{+}\\text{4.00}\\text{×}\\text{W}\\text{C}\\text{+}\\\\\\:\\text{22.00}\\text{×}\\text{l}\\text{g}\\text{T}\\text{G}\\text{-}\\text{16.32}\\text{×}\\text{H}\\text{D}\\text{L}\\text{-}\\text{C}\\end{array}$$\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equf\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equf\" name=\"EquationSource\"\u003e\n$$\\:\\begin{array}{c}\\text{F}\\text{e}\\text{m}\\text{a}\\text{l}\\text{e}\\text{}\\text{C}\\text{V}\\text{A}\\text{I}\\text{＝}\\text{-}\\text{187.32}\\text{+}\\text{1.71}\\text{×}\\text{a}\\text{g}\\text{e}\\text{+}\\text{4.32}\\text{×}\\text{B}\\text{M}\\text{I}\\text{+}\\text{1.12}\\text{×}\\text{W}\\text{C}\\text{+}\\\\\\:\\text{39.76}\\text{×}\\text{l}\\text{g}\\text{T}\\text{G}\\text{-}\\text{11.66}\\text{×}\\text{H}\\text{D}\\text{L}\\text{-}\\text{C}\\end{array}$$\u003c/div\u003e\u003c/div\u003e\u003cp\u003e\u003c/p\u003e \u003cp\u003e⑥Waist Circumference Triglyceride Index(WTI) the calculation formula is as follows\u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e:\u003c/p\u003e\u003cdiv id=\"Equg\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equg\" name=\"EquationSource\"\u003e\n$$\\:\\text{WTI＝}\\text{WC}\\text{(cm)}\\text{×TG}\\text{(mmol/L)；}$$\u003c/div\u003e\u003c/div\u003e\u003cp\u003e\u003c/p\u003e \u003cp\u003e⑦Lipid Accumulation Product(LAP) the calculation formula is as follows\u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e:\u003c/p\u003e\u003cdiv id=\"Equh\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equh\" name=\"EquationSource\"\u003e\n$$\\:\\text{Male:}\\text{LAP＝[WC(cm)-65]}\\text{×}\\text{TG(mmol/L)}$$\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equi\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equi\" name=\"EquationSource\"\u003e\n$$\\:\\text{F}\\text{emale:}\\text{LAP＝[WC(cm)-58]}\\text{×}\\text{TG(mmol/L)}$$\u003c/div\u003e\u003c/div\u003e\u003cp\u003e\u003c/p\u003e \u003cp\u003e⑧Body Shape Index (ABSI) the calculation formula is as follows\u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e:\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:\\text{ABSI＝}\\text{WC(cm)/(}{\\text{BMI}}^{\\frac{\\text{2}}{\\text{3}}}\\text{×}{\\text{height}}^{\\frac{\\text{1}}{2}}{\\text{(cm)}}^{\\frac{\\text{1}}{2}}\\text{)}\\)\u003c/span\u003e \u003c/span\u003e⑨Body Roundness Index(BRI) the calculation formula is as follows\u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e:\u003c/p\u003e\u003cdiv id=\"Equj\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equj\" name=\"EquationSource\"\u003e\n$$\\:\\text{BRI}\\text{＝364.2-365.5}\\text{×}\\sqrt{\\text{1-}{\\text{(WC}\\text{(cm)}\\text{/2π)}}^{\\text{2}}\\text{/}{\\text{(0.5}\\text{×}\\text{height}\\text{(cm)}\\text{)}}^{\\text{2}}}$$\u003c/div\u003e\u003c/div\u003e\u003cp\u003e\u003c/p\u003e \u003cp\u003e⑩Cardiometabolic Index(CMI) the calculation formula is as follows\u003csup\u003e[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e\u003c/p\u003e\u003cdiv id=\"Equk\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equk\" name=\"EquationSource\"\u003e\n$$\\:\\text{CMI＝}\\frac{\\text{TG(}\\text{mmol/L}\\text{)}}{\\text{HDL-C(}\\text{mmol/L}\\text{)}}\\text{×WHtR}$$\u003c/div\u003e\u003c/div\u003e\u003cp\u003e\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003e3.Statistical analysis\u003c/h3\u003e\n\u003cp\u003eThis study used SPSS 27.0 and MedCalc 19.0 for data organisation and analysis. For continuous variables that followed a normal distribution, the mean ± standard deviation (SD) was used; for those that did not follow a normal distribution, the median (interquartile range) [M (Q1, Q3)] was used; and for categorical variables, the number of cases (percentage) [n (%)] was used. For intergroup comparisons, continuous variables were analysed using analysis of variance (ANOVA) or the Wilcoxon rank-sum test, while categorical variables were analysed using the chi-square test or Fisher's exact probability test. To explore the relationship between various indicators and hypertension, this study used a binary logistic regression model for analysis. ROC curves were plotted to assess the predictive performance of different obesity indicators, and AUC was calculated to determine predictive accuracy and optimal cutoff values. AUC comparisons were performed using MedCalc 19.0 software, with P \u0026lt; 0.05 considered statistically significant.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Participants characteristics at baseline\u003c/h2\u003e \u003cp\u003eA total of 6784 subjects were included in the analysis, including 3077 in the hypertensive group and 3707 in the non - hypertensive group (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Compared with the non-hypertensive group, the hypertensive group had lower proportions of people in the 65\u0026ndash;69 age group, higher proportions in older age groups (70\u0026ndash;74, 75\u0026ndash;79, \u0026ge;\u0026thinsp;80), lower proportions of married individuals, higher proportions of allergic history, family history of hypertension, and family history of diabetes (all P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The hypertensive group had higher SBP, DBP, TC, TG, LDL-C, FBG, weight, WC, BMI, WHR,VAI, CMI,LAP, Body Fat Percentage(BF%), ABSI,BRI, CI, and WTI (all P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). There were no significant differences in gender, educational attainment, eating habits, and whether to exercise between the two groups (all \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05). The baseline characteristics of all participants, stratified by hypertension status, are presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\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\u003eComparison of general characteristics of all study subjects [n (%), M(Q1, Q3)]\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProject Indicators\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHypertensive Group(n\u0026thinsp;=\u0026thinsp;3077)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNon - Hypertensive Group (n\u0026thinsp;=\u0026thinsp;3707)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eX\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e/Z-\u003c/em\u003eValue\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e48.108\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\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\u003e65\u0026ndash;69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1475(47.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2048(55.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e70\u0026ndash;74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1025(33.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1149(31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e75\u0026ndash;79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e403(13.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e375(10.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e174(5.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e135(3.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.981\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.159\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1373(44.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1591(42.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1704(55.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2116(57.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarital Status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8.625\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.035\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2672(86.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3296(88.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnmarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1(0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4(0.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDivorced\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13(0.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13(0.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWidowed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e391(12.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e394(10.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducational Attainment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.411\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.938\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimary school and below\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e581(18.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e722(19.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJunior high school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1045(34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1253(33.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSecondary vocational or high school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1028(33.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1223(33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCollege degree or above\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e423(13.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e509(13.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAllergic History\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.950\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e336(10.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e329(8.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2741(89.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3378(91.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFamily History of Hypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e699.576\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\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\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1370(44.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e570(15.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1707(55.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3137(84.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFamily History of Diabetes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10.439\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e240(7.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e216(5.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2837(92.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3491(94.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSatisfaction with Current Health Status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e173.814\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\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\u003eSatisfied\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e639(20.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1188(32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBasically Satisfied\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1931(62.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2196(59.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnclear\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e327(10.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e180(4.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNot Very Satisfied\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e115(3.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e102(2.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnsatisfied\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e65(2.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e41(1.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEating Habits\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.599\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.450\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBalanced\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2846(92.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3437(92.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMeat - based\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e52(1.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e49(1.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVegetarian - based\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e179(5.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e221(6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWhether to Exercise\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.175\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.676\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2625(85.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3149(84.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e452(14.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e558(15.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking Status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.758\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.384\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e621(20.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e780(21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2456(79.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2927(79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDrinking Status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.989\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e691(22.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e833(22.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2386(77.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2874(77.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSBP (mmHg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e141.11 (130.00 ,151.00 )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e134.54(123.00,14.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-16.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\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\u003e77.99 (71.00 ,85.00 )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e76.61(69.00,84.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-5.056\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\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(mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.80 (4.10 ,5.49 )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.04(4.38,5.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-9.124\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\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(mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.69 (1.06 ,1.99 )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.53(0.94,1.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-10.590\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\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-C(mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.24 (1.03 ,1.41 )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.33(1.11,1.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-12.087\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\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-C(mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.87 (2.28 ,3.41 )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.04(2.5,3.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-8.253\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\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\u003eFBG(mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.75 (4.92 ,6.03 )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.44(4.77,5.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-13.818\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\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\u003eHeight (cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e160.84 (155.00 ,167.00 )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e160.47(154.50,166.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-1.754\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.079\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\u003e65.01 (57.50 ,71.50 )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e60.7087(54,67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-16.601\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\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\u003e87.34 (81.00 ,93.00 )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e83.2504(77,90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-17.903\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\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\u0026sup2;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25.07 (22.86 ,27.02 )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23.5213(21.46,25.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-19.320\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\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\u003eWHtR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.54 (0.51 ,0.58 )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.5193(0.48,0.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-17.480\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\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\u003eVAI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.63 (0.27 ,0.76 )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.48081(0.19,0.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-16.671\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\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\u003eCVAI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e885.10 (713.95 ,1019.29 )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e961.54(784.19,1097.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-13.246\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\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\u003eLAP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e51.01 (27.77 ,64.18 )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e40.38196(19.68,50.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-16.745\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\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\u003eBF%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e256.40 (207.30 ,294.60 )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e279.77(229.01,319.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-14.093\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\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\u003eABSI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.21 (2.60 ,3.70 )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.88(2.34,3.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-16.326\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\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\u003eBRI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.27 (3.53 ,4.93 )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.79(3.02,4.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-17.519\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\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\u003eCI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.72 (0.70 ,0.75 )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.71(0.69,0.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-10.811\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\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\u003eWTI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e149.26 (90.93 ,178.89 )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e129.03(75.44,152.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-13.496\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\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\u003eCMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.85 (0.43 ,1.01 )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.70(0.33,0.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-14.500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Construction of prediction model\u003c/h2\u003e \u003cp\u003eThe results of the binary logistic regression analysis for each obesity indicator across different ABCD stages are shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. According to the ABCD model, all study subjects were grouped. Hypertension status was used as the dependent variable (no\u0026thinsp;=\u0026thinsp;0, yes\u0026thinsp;=\u0026thinsp;1), and WC, WHtR, VAI, CVAI, LAP, BF%, ABSI, BRI, CI, WTI, and CMI were grouped into quartiles as independent variables for binary logistic regression analysis. Model 1 and Model 2 both used the Q1 group as the reference group. Model 1 did not adjust for confounding factors, while Model 2 adjusted for age, marital status, allergy history, family history of hypertension, family history of diabetes, satisfaction with current health, systolic blood pressure, diastolic blood pressure, total cholesterol (TC), triglycerides (TG), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), fasting blood glucose, weight, and body mass index (BMI).\u003c/p\u003e \u003cp\u003eThe results are shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. Compared with the Q1 group, during the normal period, WC, WHtR, VAI, CVAI, LAP, ABSI, BRI, CI, WTI, and CMI were all significantly associated with an increased risk of hypertension. After adjusting for confounding factors, the risk of hypertension in each group was 1.389, 1.403, and 1.590 times that of the Q1 group, respectively. After adjusting for confounding factors, the relative risks of hypertension for each group were 1.316, 1.406, and 1.563 times that of the Q1 group, respectively.\u003c/p\u003e \u003cp\u003eIn Phase 1, the differences in WC, LAP, WTI, and CMI were all statistically significant (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). LAP was significantly associated with the risk of hypertension in the Q2, Q3, and Q4 groups in Model 2, with odds ratios of 1.626, 1.772, and 2.02 times that of the Q1 group, respectively. This indicates that as LAP increases, the risk of hypertension in Phase 1 significantly rises. Compared to the Q1 group, after adjusting for confounding factors, each unit increase in WTI was associated with a corresponding increase in the risk of hypertension of 1.299, 2.061, and 2.687 times in the Q2, Q3, and Q4 groups, respectively.The increase in CMI in phase 2 was significantly associated with the risk of hypertension. The OR values in model 2 from Q2 to Q4 were 1.377, 1.775, and 2.109, respectively, all indicating that an increase in CMI can increase the risk of hypertension (\u003cem\u003eP\u003c/em\u003e\u003csub\u003etrend\u003c/sub\u003e \u0026lt; 0.05). The results of the binary logistic regression analysis for each obesity indicator across different ABCD stages are shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\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\u003eLogistic regression analysis of various indicators in different obesity cycles and hypertension [\u003cem\u003eOR\u003c/em\u003e(95%\u003cem\u003eCI\u003c/em\u003e)]\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eIndicators\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eStage 0(\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3843)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eStage 1(n\u0026thinsp;=\u0026thinsp;1514)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eStage 2(n\u0026thinsp;=\u0026thinsp;1427)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.672(1.389,2.014)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.413(1.132,1.763)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.752(1.304,2.352)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.346(0.946,1.914)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.382(1.018,1.874)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.071(0.739,1.552)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.155(1.788,2.598)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.553(1.211,1.993)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.944(1.448,2.609)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.001(0.669,1.498)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.043(1.523,2.742)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.127(0.737,1.723)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.179(2.637,3.833)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.726(1.263,2.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.708(2.006,3.656)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.995(0.593,1.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.377(2.47,4.617)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.328(0.749,2.357)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003csub\u003etrend\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.043\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=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.791\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWHtR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.543(1.286,1.852)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.514(1.222,1.874)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.574(1.137,2.011)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.133(0.812,1.581)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.859(1.39,2.486)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.231(0.874,1.733)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.972(1.624,2.393)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.643(1.315,2.053)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.832(1.296,2.367)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.112(0.772,1.602)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.112(1.567,2.846)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.244(0.857,1.803)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.32(1.924,2.797)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.092(1.625,2.693)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.731(1.948,3.514)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.441(0.978,2.122)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.338(2.42,4.606)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.680(1.093,2.581)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003csub\u003etrend\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.281\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=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.127\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVAI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.326(1.092,1.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.595(1.251,2.034)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.571(1.305,1.891)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.351(0.917,1.992)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.562(1.152,2.118)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.318(0.886,1.961)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.758(1.457,2.122)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.766(1.309,2.383)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.073(1.722,2.496)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.840(1.147,2.951)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.51(1.851,3.404)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.691(1.033,2.77)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.509(2.081,3.025)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.008(1.337,3.015)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.693(2.234,3.247)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.450(1.307,4.593)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.641(2.674,4.959)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.944(0.991,3.812)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003csub\u003etrend\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.077\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=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.202\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCVAI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.753(0.63,0.901)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.351(1.041,1.754)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.708(0.532,0.943)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.097(0.725,1.659)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.674(0.501,0.905)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.631(1.064,2.499)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.669(0.559,0.801)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.795(1.279,2.518)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.516(0.387,0.689)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.065(0.624,1.817)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.531(0.395,0.715)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.100(1.195,3.688)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.494(0.411,0.593)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.840(1.178,2.874)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.399(0.298,0.536)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.023(0.514,2.035)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.388(0.286,0.525)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.972(1.396,6.331)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003csub\u003etrend\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.95\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=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.054\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLAP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.832(1.519,2.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.673(1.332,2.101)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.462(1.082,1.976)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.344(0.937,1.927)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.643(0.478,0.865)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.202(0.829,1.744)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.128(1.765,2.566)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.811(1.404,2.336)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.562(1.904,3.448)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.885(1.253,2.837)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.477(0.354,0.642)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.668(1.086,2.564)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.913(2.414,3.514)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.059(1.473,2.876)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.992(2.221,4.032)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.542(1.506,4.289)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.376(0.278,0.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.987(1.137,3.474)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003csub\u003etrend\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.004\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=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBF%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.729(0.608,0.875)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.364(1.045,1.782)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.736(0.553,0.979)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.330(0.872,2.029)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.283(0.946,1.739)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.617(1.043,2.507)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.679(0.565,0.815)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.721(1.213,2.442)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.517(0.387,0.691)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.307(0.752,2.272)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.66(1.228,2.245)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.954(1.09,3.501)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.617(0.513,0.742)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.781(1.120,2.832)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.399(0.297,0.536)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.346(0.657,2.757)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.321(2.444,4.514)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e3.113(1.408,6.883)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003csub\u003etrend\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.225\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.605\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=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.056\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eABSI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.351(1.117,1.634)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.308(1.011,1.693)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.415(1.051,1.905)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.932(0.619,1.405)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.638(1.205,2.226)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.907(0.585,1.405)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.353(1.119,1.636)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.460(1.034,2.062)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.07(1.542,2.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.890(0.516,1.535)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.529(1.863,3.433)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.778(0.423,1.43)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.011(1.667,2.425)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.345(0.800,2.262)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.63(1.956,3.534)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.635(0.279,1.447)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.125(2.297,4.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.921(0.371,2.287)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003csub\u003etrend\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.149\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.527\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=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.591\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBRI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.601(1.32,1.942)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.425(1.146,1.772)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.697(1.263,2.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.356(0.961,1.914)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.035(0.778,1.379)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.164(0.815,1.663)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.904(1.571,2.307)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.616(1.288,2.029)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.738(1.293,2.338)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.076(0.75,1.543)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.076(0.809,1.432)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.446(0.992,2.108)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.29(1.893,2.772)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.966(1.540,2.510)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.749(2.044,3.697)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.538(1.041,2.273)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.602(1.203,2.134)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.652(1.095,2.492)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003csub\u003etrend\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.067\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.473(1.224,1.772)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.188(0.963,1.467)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.302(0.975,1.737)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.087(0.778,1.518)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.072(0.796,1.444)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.834(0.59,1.178)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.994(1.662,2.392)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.419(1.146,1.757)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.197(0.897,1.598)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.729(0.515,1.032)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.494(1.112,2.007)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.033(0.724,1.474)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.146(1.784,2.581)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.295(1.035,1.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.682(1.26,2.244)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.023(0.714,1.467)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.744(1.294,2.349)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.906(0.621,1.323)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003csub\u003etrend\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.082\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=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.586\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWTI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.219(1.005,1.478)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.302(1.045,1.622)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.335(0.99,1.799)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.302(0.907,1.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.547(1.141,2.097)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.302(0.908,1.867)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.664(1.377,2.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.799(1.409,2.297)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.28(1.698,3.063)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.161(1.449,3.224)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.92(1.419,2.599)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.428(0.958,2.127)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.126(1.762,2.566)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.867(1.352,2.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.649(1.97,3.562)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.955(1.742,5.014)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.078(2.267,4.179)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.034(1.19,3.476)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003csub\u003etrend\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\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=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.081\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.235(1.016,1.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.433(1.137,1.807)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.494(1.104,2.021)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.489(1.017,2.181)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.441(1.063,1.953)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.231(0.848,1.787)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.922(1.589,2.325)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.865(1.423,2.443)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.026(2.243,4.084)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.858(1.837,4.448)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.831(1.354,2.477)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.349(0.867,2.097)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.334(1.931,2.821)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.192(1.533,3.135)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.872(2.128,3.875)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.235(1.822,5.744)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.146(2.317,4.272)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.540(1.417,4.552)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003csub\u003etrend\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\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=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Comparison of disease risk identification ability\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e shows that the AUC of all obesity indicators was higher than 0.58, and the differences between different obesity cycles were statistically significant (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05).Among the subjects in the normal group, the body roundness index performed best in identifying hypertension risk, with an AUC of (0.6292, 95% CI: 0.612, 0.647), which was larger than the AUC of other indicators. The optimal cut-off values were 3.6765.In the first phase of the study, the lipid accumulation index performed best in identifying hypertension risk, with an AUC of (0.6211, 95% CI: 0.593, 0.649), which was larger than the AUC of other indicators. The optimal cut-off value was 37.87.The AUC of the cardiac metabolic index in the Phase II study subjects was (0.6243, 95% CI: 0.596, 0.653).The optimal cutoff value for the cardiac metabolic index in stage 2 was the lowest, at 0.476, while the optimal cutoff value for the lipid accumulation index in stage 1 was the highest, at 37.87, higher than the 31.91 in stage 0, indicating that as obesity severity increases, lipid accumulation levels in the body gradually rise. ROC curve analysis, as shown in Figs.\u0026nbsp;2, 3and 4.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison of the ability to identify disease risk among subjects in different obesity stages\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndicators\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCut-off\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAUC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e95%\u003cem\u003eCI\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eStage 0\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e83.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.6272\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e(0.61,0.645)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\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\u003eWHtR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.515\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.6288\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e(0.611,0.646)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\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\u003eVAI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.405\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.6056\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e(0.588,0.623)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\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\u003eCVAI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e952.855\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.5788\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e(0.561,0.597)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\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\u003eLAP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e31.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.6104\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e(0.593,0.628)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\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\u003eBRI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.6765\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.6292\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e(0.612,0.647)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\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\u003eCI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.7145\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.5843\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e(0.566,0.602)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\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\u003eWTI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e112.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.5842\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e(0.566,0.602)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\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\u003eCMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.5495\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.5921\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e(0.574,0.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eStage 1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e80.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.6042\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e(0.576,0.633)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\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\u003eLAP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e37.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.6211\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e(0.593,0.649)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\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\u003eWTI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e109.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.6077\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e(0.579,0.636)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\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\u003eCMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.568\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.6192\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e(0.591,0.647)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eStage 2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.476\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.6243\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e(0.596,0.653)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e4.4 Analysis of AUC differences\u003c/h2\u003e \u003cp\u003eA comparison of the AUC differences in the ability to identify the risk of hypertension under different obesity indicators revealed that the AUC differences for CMI, WC, LAP, WTI, WHtR, VAI, CVAI, BRI, and CI in the normal phase, as well as CMI, WTI, and LAP in phase 1, were statistically significant (all P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The pairwise comparisons of AUC differences are presented in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e. Based on the AUC values of obesity indicators at different stages, BRI had the best performance in identifying the risk of hypertension in normal-stage subjects, LAP had the best performance in identifying the risk of hypertension in stage 1 subjects (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e), and CMI had the best performance in identifying the risk of hypertension in stage 2 subjects.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAnalysis of differences in AUC for identifying hypertension risk under different obesity indicators\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eindicators\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCMI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLAP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eWTI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eWHtR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eVAI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eCVAI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eBRI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eCI\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eStage 0\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLAP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWTI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWHtR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.752\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVAI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.211\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCVAI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.265\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.655\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBRI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.681\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.399\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.467\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.988\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.049\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.660\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eStage 1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.352\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLAP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.759\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.164\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWTI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.828\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e4.5 Analysis of the combined predictive value of two obesity indicators\u003c/h2\u003e \u003cp\u003eAs shown in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e and Figs.\u0026nbsp;5, 6, and 7, after controlling for confounding factors, analysis of subjects in the normal period, Stage 1, and Stage 2 revealed that, in the normal period, using BRI as the reference, composite indices such as WC\u0026thinsp;+\u0026thinsp;WHtR, WC\u0026thinsp;+\u0026thinsp;BRI, WC\u0026thinsp;+\u0026thinsp;CI, and WC\u0026thinsp;+\u0026thinsp;CMI all exhibited AUC values slightly higher than BRI. with AUC change rates of 0.477% and 0.509% for WC\u0026thinsp;+\u0026thinsp;WHtR and WC\u0026thinsp;+\u0026thinsp;BRI, respectively, and AUC values of 0.6322 and 0.6324, respectively. In Stage 1, using LAP as the reference, the AUC value was 0.6211, with only WC\u0026thinsp;+\u0026thinsp;CI having an AUC higher than that of LAP alone, at 0.6218, but the AUC change rate was only 0.113%; in Stage 2, using CMI as the benchmark, the AUC value was 0.6243. Unlike the normal stage and Stage 1, most combined indicators in the Stage 2 group had higher AUC values than CMI, indicating that in the Stage 2 obesity stage, combined indicators may have better performance in predicting the risk of hypertension in the elderly, particularly WC\u0026thinsp;+\u0026thinsp;VAI, WC\u0026thinsp;+\u0026thinsp;LAP, WC\u0026thinsp;+\u0026thinsp;CI, and WC\u0026thinsp;+\u0026thinsp;CMI, with AUC change rates of 3.011%, 2.355%, 3.524%, and 2.515%, respectively, and AUC values of 0.6431, 0.6390, 0.6463, and 0.6400, respectively, demonstrating a significant predictive advantage.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison of ROC curve results for combined prediction of hypertension using obesity indicators\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndicators\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAUC (95%\u003cem\u003eCI\u003c/em\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAUC change rate(%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e值\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eStage 0\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBRI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.6292\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\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\u0026thinsp;+\u0026thinsp;WHtR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.6322\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.477\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\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\u0026thinsp;+\u0026thinsp;VAI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.6297\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.079\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\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\u0026thinsp;+\u0026thinsp;CVAI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.6273\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.302\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\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\u0026thinsp;+\u0026thinsp;LAP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.6289\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.048\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\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\u0026thinsp;+\u0026thinsp;BF%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.6274\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.286\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\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\u0026thinsp;+\u0026thinsp;ABSI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.6274\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.286\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\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\u0026thinsp;+\u0026thinsp;BRI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.6324\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.509\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\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\u0026thinsp;+\u0026thinsp;CI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.6310\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.286\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\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\u0026thinsp;+\u0026thinsp;WTI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.6294\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.032\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\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\u0026thinsp;+\u0026thinsp;CMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.6306\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.223\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eStage 1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLAP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.6211\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\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\u0026thinsp;+\u0026thinsp;WHtR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.6053\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-2.544\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\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\u0026thinsp;+\u0026thinsp;VAI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.607\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-2.270\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\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\u0026thinsp;+\u0026thinsp;CVAI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.6102\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-1.755\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\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\u0026thinsp;+\u0026thinsp;LAP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.6056\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-2.496\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\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\u0026thinsp;+\u0026thinsp;BF%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.6113\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-1.578\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\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\u0026thinsp;+\u0026thinsp;ABSI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.6097\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-1.835\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\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\u0026thinsp;+\u0026thinsp;BRI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.6054\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-2.528\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\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\u0026thinsp;+\u0026thinsp;CI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.6218\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.113\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\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\u0026thinsp;+\u0026thinsp;WTI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.6056\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-2.496\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\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\u0026thinsp;+\u0026thinsp;CMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.6065\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-2.351\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eStage 2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.6243\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\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\u0026thinsp;+\u0026thinsp;WHtR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.6309\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.057\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\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\u0026thinsp;+\u0026thinsp;VAI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.6431\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\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\u0026thinsp;+\u0026thinsp;CVAI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.6340\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.554\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\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\u0026thinsp;+\u0026thinsp;LAP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.6390\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.355\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\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\u0026thinsp;+\u0026thinsp;BF%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.6350\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.714\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\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\u0026thinsp;+\u0026thinsp;ABSI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.6336\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.490\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\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\u0026thinsp;+\u0026thinsp;BRI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.6308\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.041\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\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\u0026thinsp;+\u0026thinsp;CI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.6463\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.524\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\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\u0026thinsp;+\u0026thinsp;WTI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.6398\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.483\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\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\u0026thinsp;+\u0026thinsp;CMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.6400\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.515\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study utilised the ABCD model to categorise obesity stages in the elderly and employed logistic regression analysis to investigate the relationship between various obesity indicators and the risk of hypertension in the elderly. In addition to traditional obesity indicators such as BMI and WC, the study also included emerging obesity indicators such as VAI, CVAI, ABSI, CI, WTI, and CMI. These indicators are characterised by their ability to better reflect body fat distribution and visceral fat accumulation. The results showed that in different stages of obesity, VAI, WHtR, CVAI, ABSI, and LAP all had a significant impact on the risk of hypertension, a finding consistent with existing literature reports on the relationship between obesity indicators and hypertension\u003csup\u003e[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThis study further confirms the important role of obesity indicators in predicting the onset of hypertension, consistent with studies in various regions.A study in Nigeria found that traditional obesity indicators such as BMI, WHtR, and WC showed good predictive performance in predicting the risk of hypertension\u003csup\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e.The results of a Brazilian study involving 3,143 participants showed that using WHtR to screen for hypertension in women is highly accurate, with a cut-off value of 0.54\u003csup\u003e[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003e.A cross-sectional study in Malaysia demonstrated the association between ABSI and hypertension. The study found that ABSI predicted hypertension with an AUC value of 0.74, which may be related to the local geographical environment or ethnic characteristics. It is speculated that differences in ABSI predictive ability may be related to regional characteristics\u003csup\u003e[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/sup\u003e.There have also been related studies in China. A study on risk factors for hypertension in middle-aged and elderly people in China pointed out that the CVAI indicator is more suitable for screening high-risk groups for hypertension in middle-aged and elderly people in China. This indicator takes into account indicators such as WC and TG and has more reliable predictive value\u003csup\u003e[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e.Another study concluded that as VAI values increase, the 15-year cumulative incidence of hypertension also increases, suggesting that VAI can serve as an independent predictor of hypertension\u003csup\u003e[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/sup\u003e.In addition, studies have shown that BF% is positively correlated with blood pressure variability in hypertensive patients, meaning that people with higher body fat percentages experience more significant fluctuations in blood pressure\u003csup\u003e[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e5.1 Obesity indicators for predicting hypertension vary at different stages of obesity.\u003c/h2\u003e \u003cp\u003eThis study found that the obesity indicators predicting the occurrence of hypertension vary at different stages of obesity. At stage 0, BRI performed best in identifying the risk of hypertension; while at stages 1 and 2, LAP and CMI became more important predictive indicators. This suggests that as the degree of obesity increases, the accumulation of lipids and metabolic abnormalities in the body have a gradually greater impact on the risk of hypertension, and the probability of developing hypertension also gradually increases. This is consistent with the research results from Fujian Province, Shenzhen City, Anlu City, and other places\u003csup\u003e[\u003cspan additionalcitationids=\"CR28\" citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]\u003c/sup\u003e.This finding also indicates that the mechanisms by which lipid accumulation and metabolic abnormalities contribute to hypertension undergo dynamic evolution across different stages of obesity. During the progression of obesity, excessive visceral fat accumulation leads to adipocyte dysfunction, activating both the sympathetic nervous system (SNS) and the renin-angiotensin-aldosterone system (RAAS). Meanwhile, ectopic lipid deposition triggers mitochondrial dysfunction, resulting in excessive production of reactive oxygen species (ROS). This promotes oxidative stress, impairs vascular endothelial function, and ultimately enhances vasoconstriction and elevates blood pressure\u003csup\u003e[\u003cspan additionalcitationids=\"CR31\" citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]。\u003c/sup\u003eThis study utilizes straightforward obesity indicators to screen and identify high-risk individuals for hypertension. Implementing weight reduction interventions for this population can contribute to the prevention and management of hypertension.\u003c/p\u003e \u003cp\u003eThe Body Roundness Index (BRI) primarily reflects overall body fat distribution. It is a novel anthropometric index developed in 2013 by Diana M. Thomas et al. that incorporates both waist circumference and height. By influencing overall fat distribution and visceral adipose tissue accumulation, it indirectly affects blood pressure regulation\u003csup\u003e[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]\u003c/sup\u003e. Studies by Kawasoe S et al. demonstrated that BMI, WC, and BRI were significantly associated with the incidence of hypertension in both men and women aged 30\u0026ndash;60 years, indicating a certain accuracy of BRI in predicting hypertension\u003csup\u003e[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]\u003c/sup\u003e. Calder\u0026oacute;n-Garc\u0026iacute;a JF et al. pointed out that BRI outperforms the A Body Shape Index (ABSI) in predicting hypertension among adult women and men from diverse populations. Beyond hypertension, BRI also exhibits a robust predictive capability for cardiovascular diseases and metabolic syndrome\u003csup\u003e[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]\u003c/sup\u003e. Research by Cai X et al. proved that BRI is significantly correlated with the risk of cardiovascular disease (CVD) in patients with obstructive sleep apnea (OSA) and concomitant hypertension\u003csup\u003e[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]\u003c/sup\u003e. Furthermore, prospective cohort studies have found that BRI trajectories are positively associated with cardiovascular disease incidence, and its predictive value remains significant across subgroups stratified by age and sex\u003csup\u003e[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e] [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]\u003c/sup\u003e. Additionally, a study by Tian Ting et al. discovered that BRI possesses a better ability than both BMI and ABSI for predicting metabolic syndrome in female populations\u003csup\u003e[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe Lipid Accumulation Product (LAP) is a significant indicator reflecting visceral and subcutaneous adipose tissue accumulation. Multiple studies have demonstrated a strong association between elevated LAP and an increased risk of hypertension\u003csup\u003e[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e] [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e] [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]\u003c/sup\u003e. Increased LAP contributes to systemic insulin resistance. This resistance impairs vascular endothelial glucose uptake via dysfunction in the PI3K/Akt signaling pathway, leading to compensatory hyperinsulinemia which subsequently activates the MAPK pathway. This activation promotes vascular smooth muscle cell proliferation and vascular remodeling, ultimately resulting in elevated blood pressure\u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]\u003c/sup\u003e. Wakabayashi et al. found that, compared to the low LAP quartile group, the risk for hypertension was 7.31-fold and 10.66-fold higher in the high LAP quartile group for men and women, respectively\u003csup\u003e[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]\u003c/sup\u003e. Similarly, a study by Su Jian et al. reported that the risk of hypertension was 3.65 times higher in men and 3.52 times higher in women within the high LAP group compared to the low LAP group, indicating that hypertension risk escalates with increasing LAP levels in both sexes\u003csup\u003e[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]\u003c/sup\u003e. This finding is corroborated by a large prospective study involving 37,333 subjects, which also showed a graded increase in hypertension risk with rising LAP levels in both men and women\u003csup\u003e[\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]\u003c/sup\u003e. Research by Khanmohammadi S et al. further indicated that LAP offers superior predictive value for hypertension compared to traditional indices like BMI and WC, with even higher predictive accuracy observed in women than in men\u003csup\u003e[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]\u003c/sup\u003e. Additionally, a study by Liao Yanping et al. demonstrated [45] that LAP is a valuable and reliable indicator for identifying hypertension risk in elderly populations\u003csup\u003e[\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe Cardiometabolic Index (CMI), an indicator reflecting an individual's obesity degree and lipid profile, influences blood pressure regulation by modulating lipid metabolism and cardiac function. This underscores its significant role in assessing the risk of hypertension and cardiovascular disease. A study involving 11,400 Chinese participants demonstrated that CMI, LAP, and BAI were independently associated with higher systolic and diastolic blood pressure\u003csup\u003e[\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]\u003c/sup\u003e. Specifically, for each standard deviation increase in CMI, the risk of hypertension rose by 31% in men, suggesting CMI serves as a valid marker for hypertension risk assessment. A cross-sectional study further revealed a proportional increase in the prevalence of left ventricular hypertrophy across rising quartiles of both CMI and LAP, reinforcing the link between CMI and adverse cardiac structural alterations\u003csup\u003e[\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]\u003c/sup\u003e. Additional research indicates the utility of CMI extends to assessing microalbuminuria risk. An analysis based on the NHANES 2011\u0026ndash;2018 database showed that among individuals aged\u0026thinsp;\u0026ge;\u0026thinsp;60 years\u0026mdash;encompassing the general population, those with diabetes, and those with hypertension-the microalbuminuria group exhibited significantly higher CMI levels than the normoalbuminuria group, indicating an independent association between CMI and microalbuminuria\u003csup\u003e[\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]\u003c/sup\u003e. Another study suggested that CMI holds potential usefulness for detecting a reduced glomerular filtration rate in the general Chinese population, implying its value in identifying early-stage chronic kidney disease risk\u003csup\u003e[\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eAs obesity persists and worsens, alterations in body fat distribution and metabolic status occur, which subsequently impact blood pressure levels. Consequently, implementing dynamic and stratified intervention strategies tailored to specific obesity stages is crucial for the effective prevention and control of hypertension.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e5.2 Synergistic Predictive Analysis of Obesity Indices\u003c/h2\u003e \u003cp\u003eThis study demonstrates that a combined model utilizing multiple obesity indices significantly enhances the predictive capacity for hypertension risk among older adults, particularly in Stage 2 obesity. Combinations such as WC\u0026thinsp;+\u0026thinsp;VAI, WC\u0026thinsp;+\u0026thinsp;LAP, WC\u0026thinsp;+\u0026thinsp;CI, and WC\u0026thinsp;+\u0026thinsp;CMI exhibited superior predictive efficacy compared to any single obesity indicator. These findings align with research by Liu Yan et al. underscoring the clinical value of integrating multiple anthropometric measures to refine hypertension risk stratification\u003csup\u003e[\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]\u003c/sup\u003e. Similarly, Wang Xin\u0026rsquo;s team corroborated this conclusion, noting that multi-index models generally outperform single metrics, though the optimal combination may vary across sex and ethnic subgroups, warranting further investigation\u003csup\u003e[\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]\u003c/sup\u003e. Additionally, Chen Minmin et al. compared single and combined obesity indicators and concluded that integrating WHtR as a key component significantly enhanced hypertension prediction accuracy\u003csup\u003e[\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]\u003c/sup\u003e. Nevertheless, practical implementation of multi-index models introduces complexities, including increased data collection burdens, higher operational costs, and challenges related to collinearity among variables, which necessitate advanced statistical. Future research should focus on optimizing these models by incorporating machine learning algorithms to refine feature weighting and improve computational efficiency without compromising predictive performance. Such approaches could enhance the practicality and scalability of multi-index predictive tools in clinical and public health settings\u003c/p\u003e \u003cp\u003eIn summary, this study employed logistic regression to elucidate the complex associations between various obesity indices and hypertension risk in the elderly, highlighting the unique value of the ABCD model and the importance of integrated obesity metrics. Future directions include validation of these findings, identification of novel predictive biomarkers, and the development of targeted interventions\u0026mdash;such as dietary modifications and physical activity programs\u0026mdash;to improve hypertension prevention and early management in aging populations.\u003c/p\u003e \u003c/div\u003e"},{"header":"Abbreviations","content":"\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"439\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 112px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAbbreviation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 328px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFull name\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eABCD\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eThe Adiposity-Based Chronic Disease\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eROC \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eReceiver Operating Characteristic\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eAUC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eArea Under the Curve\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eCVAI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eChinese Visceral Adipose Index\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eCMI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eCardiometabolic Index\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eCI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eConicity Index\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eABSI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eBody Shape Index\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eBRI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eBody Roundness Index\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eLAP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eLipid Accumulation Product\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eWHO\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eWorld Health Organization\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eBMI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eBody Mass Index\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eACE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eAmerican College of Endocrinology\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eAACE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eAmerican Association of Clinical Endocrinologists\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eWC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eWaist Circumference\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eFBG\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eFasting Blood Glucose\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eTG\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eTriglyceride\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eTC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eTotal Cholesterol\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eHDL \u0026ndash; C\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eHigh Density Lipoprotein Cholesterol\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eLDL-C\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eLow Density Lipoprotein Cholesterol\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eSBP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eSystolic Blood Pressure\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eDBP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eDiastolic Blood Pressure\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eSD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eStandard Deviation\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eWHtR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eWaist to Height Ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eVAI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eVisceral Adipose Index\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eWTI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eWaist Circumference Triglyceride Index\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eLAP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eLipid Accumulation Product\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eBF%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eBody Fat Percentage\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was conducted in strict accordance with the principles outlined in the\u0026nbsp;\u003cem\u003eDeclaration of Helsinki\u003c/em\u003e. The study protocol was reviewed and approved by the Ethics Review Committee of Tongji Medical College, Huazhong University of Science and Technology. All procedures involving human participants were carried out with the appropriate ethical clearance, and the ethics approval number is 2023-S104. Informed consent was obtained from all individual participants or their legal guardians prior to their involvement in the study, ensuring compliance with the highest ethical standards for research involving human subjects.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eInformed consent for publication has been obtained from the patients described in the manuscript. The patients have provided consent to publish their examination results and other relevant clinical information. Every effort has been made to de-identify the patients to protect privacy, and the patients acknowledge and agree to the publication of the de-identified information.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003cstrong\u003e：\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data supporting the findings of this study are available from Hongshan Street Community Health Service Center, Hongshan District, Wuhan City, Hubei Province, China. Due to restrictions imposed by the license under which the data were used for the current research, these data are not publicly accessible. However, qualified researchers may request access to the data by contacting the corresponding author , provided that such requests are reasonable and have obtained prior permission from Hongshan Street Community Health Service Center, Hongshan District, Wuhan City.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003cstrong\u003e：\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the Ministry of Education 2021 Humanities and Social Sciences Fund in China (Grant No. 21YJA630062); the Research on the Development Path of Smart Healthcare and Health Management in Jiangxia District Communities, Wuhan City (Grant No. H20230163); and the Research on the Collaborative Mechanism of Primary Healthcare Services under the Background of Health Needs (Grant No. H20220099).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors' contributions\u003c/strong\u003e\u003cstrong\u003e：\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWH: Conceptualization, Methodology, Investigation, Data Curation, Writing - Original Draft, Writing - Review \u0026amp; Editing.\u003c/p\u003e\n\u003cp\u003eLYQ(Corresponding Author): Conceptualization, Methodology, Supervision, Validation, Writing - Review \u0026amp; Editing, Project Administration.\u003c/p\u003e\n\u003cp\u003eRFF: Investigation, Data Curation, Formal Analysis, Visualization, Writing - Review \u0026amp; Editing.\u003c/p\u003e\n\u003cp\u003eYL: Investigation, Validation, Formal Analysis, Writing - Review \u0026amp; Editing.\u003c/p\u003e\n\u003cp\u003eZYK: Software, Validation, Formal Analysis, Data Curation.\u003c/p\u003e\n\u003cp\u003eJF: Methodology, Software, Validation, Writing - Review \u0026amp; Editing.\u003c/p\u003e\n\u003cp\u003eDSX: Data Curation, Visualization, Writing - Review \u0026amp; Editing.\u003c/p\u003e\n\u003cp\u003eGLW: Investigation, Resources, Writing - Review \u0026amp; Editing.\u003c/p\u003e\n\u003cp\u003eAll authors have read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would like to express their sincere gratitude to all individuals and institutions that contributed to the completion of this study. First and foremost, we extend our heartfelt appreciation to the study participants for their voluntary involvement and consistent cooperation throughout the research process. We also gratefully acknowledge the staff of Hongshan Street Community Health Service Center in Hongshan District, Wuhan City, for their invaluable support in data collection and logistical coordination.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe are deeply grateful to the funding agencies for their financial support, including the Ministry of Education Humanities and Social Sciences Fund in China (Grant No. 21YJA630062), the Research on the Development Path of Smart Healthcare and Health Management in Jiangxia District Communities, Wuhan City (Grant No. H20230163), and the Research on the Collaborative Mechanism of Primary Healthcare Services under the Background of Health Needs (Grant No. H20220099).\u003c/p\u003e\n\u003cp\u003eSpecial thanks are due to Professor Liu Yaqing and Professor Liu Chenxi for their expert guidance and constructive feedback during both the study design and manuscript preparation phases. We also acknowledge the contributions of our fellow research team members for their dedicated efforts in data analysis and manuscript refinement.\u003c/p\u003e\n\u003cp\u003eFinally, we sincerely thank the editorial board and the anonymous reviewers for their time, thoughtful evaluation, and insightful comments, which significantly enhanced the quality and clarity of this manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eABAD-JIM\u0026Eacute;NEZ Z, V. E. Z. Z. A. T. \u0026amp; Obesity A Global Health Challenge Demanding Urgent Action[J]. \u003cem\u003eBiomedicines\u003c/em\u003e, \u003cb\u003e13\u003c/b\u003e(2). (2025).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWorld Health Organization Obesity and Overweight [Internet]. [(accessed on 27 December 2024)]. 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T. Predictive effect of combined obesity indicators on hypertension risk in the elderly[J]. \u003cem\u003eChin. J. Gerontol.\u003c/em\u003e \u003cb\u003e44\u003c/b\u003e (17), 4097\u0026ndash;4102 (2024).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang, X. et al. Predictive value of different obesity evaluation indicators for hypertension in Dong and Miao adults in Guizhou Province[J]. \u003cem\u003eChin. J. Prev. Med.\u003c/em\u003e \u003cb\u003e25\u003c/b\u003e (1), 32\u0026ndash;39 (2024).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen, M. M. et al. Evaluation of the efficiency of different obesity indicators and their combinations in predicting hypertension risk[J]. \u003cem\u003eJ. Practical Med.\u003c/em\u003e \u003cb\u003e36\u003c/b\u003e (13), 1823\u0026ndash;1828 (2020).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Chronic disease models caused by excessive adipose tissue, hypertension in the elderly, obesity indicators, risk identification, chronic disease management","lastPublishedDoi":"10.21203/rs.3.rs-8036118/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8036118/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003eThe Adiposity-Based Chronic Disease (ABCD) model, introduced by the American College of Endocrinology, provides a novel, complication-centric framework for assessing obesity with enhanced pathophysiological relevance. However, its practical value for predicting specific health outcomes, such as hypertension in the elderly, remains largely unexamined in the Chinese population.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eObjective:\u003c/strong\u003eThis study is anchored in the theoretical framework of ABCD models related to excessive adipose tissue accumulation. Its primary objective is to systematically assess the relationship between various obesity indicators and the risk of hypertension among older adults. Additionally, it aims to compare the predictive efficacy of individual obesity indicators against combined indicators, with the goal of identifying the most effective and stage-specific predictors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eThis study focused on elderly individuals aged 65 years or older at a community health service centre in Wuhan City, with a total of 6,784 eligible elderly individuals included in the study. Basic information such as age, gender, family history, smoking, and alcohol consumption was collected for all study participants, along with biochemical indicators such as lipid levels and blood glucose. Obesity was classified into three stages—stage 0, stage 1, and stage 2—using the ABCD model. The performance of the model was evaluated using receiver operating characteristic (ROC) curves and area under the curve (AUC) values. the efficacy of obesity-related indicators such as the Chinese Visceral Adipose Index (CVAI), Cardiometabolic Index (CMI), Conicity Index (CI), and Body Shape Index (ABSI) in predicting hypertension risk was assessed; the predictive value of single obesity indicators and combined indicators was compared and analysed.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eThis study found that the optimal indicators for predicting hypertension in the elderly vary across different stages of obesity. During the normal stage, the Body Roundness Index (BRI) demonstrated the best predictive performance, with an AUC value of 0.6292. In stages 1 and 2, the Lipid Accumulation Product (LAP) and CMI showed more significant predictive effects, with AUC values of 0.6211 and 0.6243, respectively. Further multi-indicator combined predictive analysis showed that combining multiple obesity-related indicators for prediction can enhance the accuracy of predicting hypertension risk. the AUC value for the combined prediction of WC+AVI in the normal stage was 0.6311, higher than the predictive performance of any single obesity indicator; the AUC value for the combined prediction of WC+BRI in stage 1 reached 0.6354; while the AUC values for the combined predictions of WC+LAP, WC+CI, and WC+CMI in stage 2 were significantly higher than those of single obesity indicators, with the highest AUC value of 0.6478 for WC+LAP, at 0.6478.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions: \u003c/strong\u003eThis study found that as obesity levels change, obesity indicators predicting hypertension in older adults also change, indicating that different prevention and intervention measures should be adopted for different stages of obesity in hypertension management. In addition, the combined use of multiple obesity indicators can improve the predictive ability of hypertension risk in older adults.\u003c/p\u003e","manuscriptTitle":"Comparison of Hypertension Risk in Elderly Under Adiposity-Based Chronic Disease Model","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-12 15:56:44","doi":"10.21203/rs.3.rs-8036118/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"29e24fd4-c103-4278-a4a2-e96014a19cf1","owner":[],"postedDate":"January 12th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":60806537,"name":"Health sciences/Biomarkers"},{"id":60806538,"name":"Health sciences/Diseases"},{"id":60806539,"name":"Health sciences/Endocrinology"},{"id":60806540,"name":"Health sciences/Health care"},{"id":60806541,"name":"Health sciences/Medical research"},{"id":60806542,"name":"Health sciences/Risk factors"}],"tags":[],"updatedAt":"2026-04-03T14:41:07+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-12 15:56:44","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8036118","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8036118","identity":"rs-8036118","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}