Refining Obesity Metrics for Hypertension Risk Stratification in Macao: A 15-Year Multi- Indicator Comparative Study Based on Four Waves of Population Surveillance

Refining Obesity Metrics for Hypertension Risk Stratification in Macao: A 15-Year Multi- Indicator Comparative Study Based on Four Waves of Population Surveillance | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Refining Obesity Metrics for Hypertension Risk Stratification in Macao: A 15-Year Multi- Indicator Comparative Study Based on Four Waves of Population Surveillance Lupei Jiang, Yibo Gao, Xiang Pan, Xiaoxiao Chen, Mingzhe Li, Deqiang Zhao, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8072827/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract Background : Obesity is a major modifiable risk factor for hypertension, yet the predictive performance of various anthropometric indices differs across populations. Macao lacks population-based evidence on which obesity measures best identify individuals at risk of hypertension. Methods: Data were obtained from four waves of the Macao SAR Physical Fitness Surveillance (2005, 2010, 2015, and 2020), comprising 14,288 adults aged 20–59 years. Seven obesity indices—body mass index (BMI), waist circumference (WC), waist-to-height ratio (WHtR), waist-to-hip ratio (WHR), body roundness index (BRI), a body shape index (ABSI), and conicity index (CI)—were analyzed. Logistic regression models estimated associations with hypertension after adjustment for confounders. Receiver operating characteristic (ROC) curves and the Youden index were used to assess predictive performance and identify optimal cutoffs. Results: WC exhibited the strongest associations with hypertension, with adjusted odds ratios (OR) of 1.89 (95% CI: 1.79–2.00), respectively. Among women, the predictive ability (AUC up to 0.748) exceeded that in men (AUC up to 0.689). WHtR and BRI achieved the highest discriminative accuracy in both sexes. The optimal WHtR cutoff for hypertension was 0.4941 in men and 0.4814 in women—lower than WHO-recommended global thresholds. The impact of central obesity was especially pronounced among younger women. Conclusions: WC and WHtR outperform BMI and novel indices in identifying hypertension risk among Macao adults. The findings support adopting locally optimized WC and WHtR thresholds for early hypertension screening, particularly in younger women. Integrating these simple indicators into community health programs could enhance prevention and early detection strategies for hypertension in Asian populations. Hypertension Obesity indices Anthropometry Cardiometabolic risk Macao Public health surveillance Figures Figure 1 Figure 2 Figure 3 1 Background Hypertension, as a highly prevalent chronic noncommunicable disease (NCD), continues to pose a formidable challenge to global public health. According to data from the World Health Organization (WHO) and the NCD Risk Factor Collaboration (NCD-RisC), between 1990 and 2019, the number of adults aged 30–79 years with hypertension nearly doubled worldwide, rising from approximately 650 million to 1.28 billion. [ 1 , 2 ] Notably, around two-thirds of those with hypertension reside in low- and middle-income countries. [ 1 ] Because hypertension often presents with inconspicuous early symptoms, is burdened by poor adherence to treatment, and has a wide spectrum of complications, it is commonly referred to as the “silent killer.” It is closely associated with cardiovascular disease, cerebrovascular disease, type 2 diabetes, and chronic kidney disease, among other metabolic disorders. In recent years, the prevalence of hypertension among adults in Mainland China remains alarmingly high. National surveys suggest that the overall prevalence of hypertension in Chinese adults is approximately 23%–27%, while awareness, treatment, and control rates stand at about 46%, 41%, and 14%, respectively—indicating substantial room for improvement in hypertension prevention and management. [ 3 , 4 ] The emergence of hypertension is closely linked with westernized dietary patterns, insufficient physical activity, and the rising burden of obesity. In Macao, as a special administrative region of China, rapid economic development, increasing intake of high-fat and high-salt diets, and accelerated life pace may further elevate the risk of metabolic diseases among residents. According to the 2020 Macao Citizens’ Physical Fitness Monitoring Report, the obesity rate (body mass index ≥ 30 kg/m², or per local criteria) in adult men was approximately 9.3%, and in adult women about 6.5%; the obesity rate in men was generally higher than in women. [ 5 ] Obesity is widely recognized as one of the most important modifiable risk factors for hypertension. In particular, central obesity exerts a more pronounced effect among Asian populations. Visceral fat, more so than subcutaneous fat, is prone to promote insulin resistance, chronic inflammation, and activation of the renin–angiotensin system (RAS), thereby contributing to elevated blood pressure. [ 6 ] Moreover, a phenomenon known as “metabolically obese but normal weight” (MONW) is relatively common in Asian populations—that is, individuals may have a normal body mass index (BMI), yet carry excess abdominal fat and thus harbor significantly increased risks for hypertension or metabolic syndrome. [ 7 ] While the conventional body mass index (BMI) is globally accepted as a measure of obesity, it cannot capture differences in fat types or distribution, limiting its predictive utility for metabolic disorder risks such as hypertension. To address this limitation, various anthropometric indices have been proposed to reflect fat distribution more precisely, including waist circumference (WC), waist-to-height ratio (WHtR), waist-to-hip ratio (WHR), body roundness index (BRI), a body shape index (ABSI), and conicity index (CI). The predictive performance of these indices varies across populations. Several systematic reviews suggest that WHtR—and in some studies, BRI and CI—tend to outperform BMI in predicting metabolic disorders (including hypertension) in Asian populations. [ 8 – 11 ] Furthermore, receiver operating characteristic (ROC) curve analysis combined with the Youden index is frequently employed to evaluate diagnostic performance and to determine optimal cutoffs for screening tools. [ 12 , 13 ] Given that Macao currently lacks large, locally based studies examining the relationship between obesity indices and hypertension in adults, the present study draws on four rounds of Macao citizens’ physical fitness monitoring data collected between 2005 and 2020 (cumulative sample size > 14,000). We systematically compare seven commonly used and novel obesity indices (WC, WHtR, WHR, BMI, BRI, ABSI, CI) in their association with and diagnostic performance for hypertension. Using ROC curve analysis and the Youden index, we identify optimal screening thresholds for each index. In addition, we perform stratified analyses by decade of age (20–29, 30–39, 40–49, 50–59 years) to assess whether the predictive utility of each obesity index differs across age groups. Our findings aim to provide localized evidence for early hypertension screening and obesity management strategies tailored to the Macao population. 2 Method 2.1 Participants and Data Sources This study was based on data obtained from four rounds of the Macao SAR Physical Fitness Surveillance, conducted in 2005, 2010, 2015, and 2020. The surveys covered all seven parishes of Macao and adopted a multistage stratified cluster random sampling design. Representative samples of adult residents were selected from government agencies, enterprises, and community settings. Participants were eligible for inclusion if they were 20–59 years of age, had resided in Macao for at least five years, had no history of major diseases, pregnancy, or surgery within the past year, were able to perform basic daily self-care activities, and provided written informed consent. All data collection procedures were carried out by trained investigators in strict accordance with the National Physical Fitness Surveillance Type II Standards. Quality control was supervised throughout the process, and approximately 5% of the samples were re-measured to assess measurement consistency. Participants were divided into four age groups (20–29, 30–39, 40–49, and 50–59 years) to examine differences in the associations between obesity indices and hypertension across different age stages. 2.2 Anthropometric and Blood Pressure Measurements All measurements were conducted by trained staff following standardized protocols in accordance with the National Physical Fitness Surveillance Type II Standards. Height was measured with participants standing barefoot on a stadiometer, back straight and head in the Frankfurt plane, with eyes looking forward. Waist circumference was measured at 0.5–1.0 cm above the umbilicus in a relaxed standing position using a non-elastic tape. Chest circumference was measured with participants standing naturally, arms relaxed at the sides, and feet shoulder-width apart; measurements were taken at the nipple line for men and at the fourth rib level for women, with the tape snug but not compressing the skin. Hip circumference was measured at the level of maximal gluteal protrusion, with the tape applied horizontally and snugly without indenting the skin. All anthropometric measurements were recorded to the nearest 0.1 cm. Blood pressure was measured in the seated position on the right arm at heart level using a calibrated sphygmomanometer, following standard auscultatory procedures. Two readings were taken, and the mean values of systolic and diastolic blood pressure were recorded in mmHg. Hypertension was defined as systolic BP ≥ 140 mmHg, diastolic BP ≥ 90 mmHg, or use of antihypertensive medication within the past two weeks. Seven anthropometric indicators were selected to comprehensively assess overall and central adiposity, including BMI, WC, WHtR, WHR, BRI, ABSI, and CI. All indicators were calculated and standardized according to definitions described in previous studies. BMI = weight (kg) / [height (m)]^2, WC = waist circumference (cm), WHtR = waist circumference (cm) / height (cm), WHR = waist circumference (cm) / hip circumference (cm), BRI = 364.2 − 365.5 × √(1 − (WHtR / π)^2), ABSI = WC (m) / [BMI^(2/3) × height (m)^(1/2)], CI = WC (m) / [0.109 × sqrt(weight (kg) / height (m))]. 2.3 Data Processing and Variable Transformation To standardize the scales of different obesity indices, seven continuous indicators (BMI, WC, WHtR, WHR, BRI, ABSI, and CI) were transformed into Z-scores. Each indicator was also categorized into quartiles (lowest, lower-middle, upper-middle, highest) based on the sample distribution for subsequent dose–response trend analysis. Hypertension was treated as a binary outcome variable (0 = normotensive, 1 = hypertensive). 2.4 Statistical Analysis Continuous variables were expressed as mean ± standard deviation (SD), and categorical variables as frequency and percentage (%). Differences between sexes were assessed using independent-samples t-test for normally distributed continuous variables or Mann–Whitney U test for non-normally distributed variables, while categorical variables were compared using the chi-square test. Associations between obesity indices and hypertension were evaluated using multivariable logistic regression models, and odds ratios (ORs) with 95% confidence intervals (CIs) were calculated. Three models were constructed to control for potential confounders: Model 1, unadjusted; Model 2, adjusted for age and sex; Model 3, further adjusted for educational level and daily occupational activity. Analyses were conducted in the overall sample, as well as stratified by sex and age groups, to assess the consistency and differences of associations across subgroups. Receiver operating characteristic (ROC) curves were used to evaluate the predictive performance of each obesity index, with the area under the curve (AUC) calculated. The optimal cutoff value for each index was determined using the Youden Index, balancing sensitivity and specificity to recommend locally adapted reference standards. All statistical analyses were performed using SPSS version 26.0, R version 4.2.3, and MedCalc version 23.0. Two-sided P < 0.05 was considered statistically significant. 3 Result 3.1 Basic Characteristics of the Study Population A total of 14,288 participants were included in this study, comprising 6,177 men (43.2%) and 8,111 women (56.8%). The baseline characteristics of all participants are summarized in Table 1 . The mean age of the overall population was 39.65 ± 11.16 years, with no statistically significant difference between men and women (P = 0.136). However, significant sex differences were observed across all anthropometric indicators (P < 0.001). Male participants had significantly higher values of height, weight, chest circumference, WC, hip circumference, WHR, BMI, SBP, and DBP than their female counterparts. In addition, the prevalence of hypertension was markedly higher in men (20.4%) than in women (8.7%) (P < 0.001). Table 1 Baseline Characteristics of Participants Characteristics Total(14288) Male(6177) Female(8111) P value Age (years) 39.65 ± 11.16 39.49 ± 11.37 39.77 ± 11.00 0.136 Height (cm) 162.7 ± 8.3 169.6 ± 6.1 157.5 ± 5.57853 < 0.001 Weight (kg) 60.3 ± 11.3 67.5 ± 10.2 54.8 ± 8.71605 < 0.001 Chest circumference (cm) 88.12 ± 7.8 92.19 ± 6.9 85.03 ± 6.97987 < 0.001 WC (cm) 79.06 ± 9.9 83.37 ± 9.17 75.77 ± 9.20815 < 0.001 Hip circumference (cm) 92.49 ± 6.32 93.16 ± 6.02 91.99 ± 6.49 < 0.001 WHtR 0.486 ± 0.059 0.492 ± 0.056 0.482 ± 0.060829 < 0.001 WHR 0.853 ± 0.075 0.893 ± 0.063 0.823 ± 0.0679904 < 0.001 BMI (kg/m 2 ) 22.69 ± 3.35 23.46 ± 3.21 22.1 ± 3.33531 < 0.001 BRI 3.17 ± 1.16 3.27 ± 1.05 3.09 ± 1.154314 < 0.001 ABSI, m 11/6 ·kg − 2/3 0.0774 ± 0.0044 0.0783 ± 0.0040 0.0768 ± 0.0045415 < 0.001 CI 1.194 ± 0.078 1.213 ± 0.071 1.179 ± 0.0790304 < 0.001 SBP (mmHg) 119.87 ± 15.16 126.08 ± 13.11 115.14 ± 14.904 < 0.001 DBP (mmHg) 74.31 ± 10.19 77.81 ± 9.52 71.64 ± 9.868 < 0.001 Hypertension, n (%) 1971 (13.8) 1262 (20.4) 709 (8.7) < 0.001 3.2 Association Between Obesity Indices and Hypertension Table 2 presents the ORs and 95% CIs for hypertension prevalence according to different obesity indices standardized by Z-scores. In the unadjusted model, all obesity indices were significantly and positively associated with hypertension (P < 0.001). Among them, WHR showed the strongest association (OR = 2.34, 95% CI: 2.22–2.47), followed by WC (OR = 2.28, 95% CI: 2.17–2.40). In Model Ⅱ, after adjusting for age, sex, and other confounders, the ORs of all indices declined to varying degrees but remained statistically significant. After adjustment, WC showed the strongest association with hypertension (OR = 1.89, 95% CI: 1.79–2.00), whereas ABSI exhibited the weakest (OR = 1.14, 95% CI: 1.08–1.20). Table 2 OR and 95% CI for Hypertension According to Different Obesity Indices (Standardized by Z-Score) OR(95%CI) Crude OR Model Ⅰ Model Ⅱ WC 2.28(2.17,2.40) 1.91(1.80,2.02) 1.89(1.79,2.00) WHtR 2.10(2.00,2.20) 1.85(1.75,1.95) 1.83(1.73,1.93) WHR 2.34(2.22,2.47) 1.79(1.68,1.90) 1.75(1.64,1.87) BMI 2.02(1.93,2.12) 1.85(1.76,1.95) 1.84(1.75,1.94) BRI 2.00(1.91,2.09) 1.78(1.69,187) 1.76(1.67,1.85) ABSI 1.52(1.45,1.59) 1.16(1.09,1.22) 1.14(1.08,1.20) CI 1.95(1.85,2.05) 1.52(1.43,1.60) 1.49(1.41,1.58) Figure 1 illustrates the trend in hypertension risk across quartiles of each obesity index. The results showed a clear dose–response relationship between all obesity indices and the prevalence of hypertension, with risk increasing steadily from Q1 to Q4. Among all indices, BMI and WC exhibited the steepest risk-increase curves. In the highest quartile (Q4), the OR for BMI reached 5.673, followed closely by WC with an OR of 5.611. In contrast, ABSI demonstrated the flattest curve, with an OR of only 1.53 in Q4, indicating a relatively weaker association with hypertension risk. Note The quartile cut-off points were as follows: WC: 71.6, 78.3, 85.5; WHtR: 0.4426, 0.4811, 0.5238; WHR: 0.7983, 0.8508, 0.9063; BMI: 20.31, 22.29, 24.58; ABSI: 0.0744, 0.0774, 0.0803; BRI: 2.346, 3.011, 3.816; CI: 1.1383, 1.1927, 1.2472. Confounding factors were adjusted for including age at testing, sex, educational level, and usual occupational activity. Figure 1. Association Between Quartiles of Different Obesity Indices and Hypertension Prevalence 3.3 Subgroup Analysis by Age and Sex In the subgroup analyses stratified by age and sex, the associations between obesity indices and hypertension showed consistent patterns but differed slightly by gender. Among men (Table 3 ), after adjustment for potential confounders, WC, WHtR, WHR, BMI, BRI, and CI were all significant predictors of hypertension across all age groups (20–29, 30–39, 40–49, and 50–59 years) (P < 0.001). The adjusted ORs for these indices ranged from 1.5 to 2.3, indicating that both central and overall obesity were significantly associated with increased hypertension risk in men. The association of ABSI was relatively weaker, reaching statistical significance only in the 40–49 and 50–59 age groups (adjusted OR = 1.22 and 1.31, respectively; P < 0.05). Among women (Table 4 ), after adjusting for confounders, WC, WHtR, WHR, BMI, BRI, and CI were also significantly associated with hypertension across all age groups (P < 0.001). Notably, the ORs in the 20–29 and 30–39 age groups were relatively higher, suggesting that the effect of obesity on elevated blood pressure was more pronounced among younger women. ABSI did not reach statistical significance in any age group. Overall, both abdominal and general obesity indices demonstrated stable predictive performance in both sexes, underscoring the importance of obesity management in hypertension prevention across different age stages. Table 3 OR and 95%CI for Hypertension According to Different Obesity Indices (Standardized by Z-Score) Among Males in Macao by Age Group Age groups(year) 20–29 30–39 40–49 50–59 WC Crude OR 1.96(1.67,2.31)*** 2.31(1.97,2.71) *** 1.75(1.52,2.01) *** 1.66(1.45,1.89) *** Adjusted OR 2.02(1.70,2.39) *** 2.30(1.96,2.70) *** 1.75(1.52,2.02) *** 1.67(1.46,1.91) *** WHtR Crude OR 1.89(1.60,2.23) *** 2.24(1.91,2.61) *** 1.86(1.61,2.14) *** 1.60(1.40,1.82) *** Adjusted OR 1.95(1.64,2.32) *** 2.24(1.91,2.62) *** 1.85(1.60,2.14) *** 1.59(1.39,1.81) *** WHR Crude OR 1.88(1.53,2.30) *** 2.17(1.80,2.61) *** 1.80(1.54,2.11) *** 1.67(1.45,1.93) *** Adjusted OR 1.93(1.57,2.38) *** 2.16(1.79,2.62) *** 1.78(1.52,2.09) *** 1.65(1.42,1.91) *** BMI Crude OR 1.97(1.70,2.30) *** 2.19(1.90,2.52) *** 1.73(1.51,1.97) *** 1.49(1.32,1.69) *** Adjusted OR 2.01(1.72,2.36) *** 2.18(1.89,2.52) *** 1.75(1.53,2.00) *** 1.52(1.34,1.72) *** BRI Crude OR 1.91(1.62,2.25) *** 2.15(1.85,2.49) *** 1.80(1.57,2.06) *** 1.55(1.37,1.76) *** Adjusted OR 1.97(1.66,2.33) *** 2.15(1.85,2.50) *** 1.80(1.56,2.06) *** 1.54(1.36,1.75) *** ABSI Crude OR 1.11(0.93,1.33) 1.15(0.98,1.36) 1.27(1.10,1.46) *** 1.34(1.18,1.53) *** Adjusted OR 1.12(0.93,1.34) 1.13(0.96,1.34) 1.22(1.06,1.42) *** 1.31(1.15,1.50) *** CI Crude OR 1.51(1.27,1.80) *** 1.76(1.48,2.08) *** 1.59(1.37,1.84) *** 1.56(1.36,1.78) *** Adjusted OR 1.53(1.28,1.83) *** 1.74(1.46,2.07) *** 1.56(1.34,1.81) *** 1.53(1.34,1.76) *** Note: Statistical significance: ∗P < 0.05; ∗∗P < 0.01; ∗∗∗P < 0.001. Table 4 OR and 95%CI for Hypertension According to Different Obesity Indices (Standardized by Z-Score) Among Females in Macao by Age Group Age groups(year) 20–29 30–39 40–49 50–59 WC Crude OR 2.61(1.90,3.57) *** 2.47(1.96,3.12) *** 1.88(1.62,2.19) *** 1.69(1.49,1.91) *** Adjusted OR 2.79(2.01,3.87) *** 2.42(1.91,3.07) *** 1.79(1.53,2.09) *** 1.65(1.46,1.87) *** WHtR Crude OR 2.32(1.73,3.09) *** 2.39(1.92,2.97) *** 1.85(1.61,2.13) *** 1.59(1.42,1.77) *** Adjusted OR 2.48(1.82,3.38) *** 2.33(1.86,2.91) *** 1.75(1.51,2.02) *** 1.54(1.38,1.73) *** WHR Crude OR 2.27(1.50,3.42) *** 2.66(2.00,3.54) *** 1.65(1.40,1.94) *** 1.49(1.31,1.70) *** Adjusted OR 2.29(1.52,3.45) *** 2.54(1.89,3.40) *** 1.49(1.26,1.77) *** 1.43(1.26,1.63) *** BMI Crude OR 2.18(1.72,2.77) *** 2.11(1.75,2.55) *** 1.96(1.71,2.24) *** 1.68(1.50,1.88) *** Adjusted OR 2.37(1.83,3.07) *** 2.08(1.71) *** 1.89(1.65,2.17) *** 1.66(1.48,1.86) *** BRI Crude OR 2.18(1.66,2.86) *** 2.20(1.81,2.68) *** 1.78(1.56,2.02) *** 1.52(1.38,1.68) *** Adjusted OR 2.37(1.83,3.07) *** 2.15(1.75) *** 1.68(1.47,1.92) *** 1.49(1.34,1.64) *** ABSI Crude OR 1.04(0.70,1.56) 1.28(0.99,1.65) 1.02(0.89,1.18) 1.10(0.99,1.23) Adjusted OR 1.06(0.71,1.57) 1.26(0.97,1.65) 0.96(0.83,1.10) 1.06(.095,1.19) CI Crude OR 1.72(1.21,2.46) *** 1.91(1.48,2.45) *** 1.35(1.16,1.56) *** 1.33(1.19,1.50) *** Adjusted OR 1.74(1.22,2.48) *** 1.88(1.45,2.44) *** 1.25(1.07,1.45) *** 1.29(1.15,1.45) *** Note: Statistical significance: ∗P < 0.05; ∗∗P < 0.01; ∗∗∗P < 0.001. 3.4 Predictive Value of Different Obesity Indices for Hypertension To evaluate and compare the predictive performance of different obesity indices for hypertension, ROC curves were plotted (Figs. 2 and 3 ), and the AUC, SE, and 95% CI were calculated (Table 5 ). Among men (Fig. 2 and Table 5 ), WHtR and BRI showed the highest AUC values, both at 0.689 (95% CI: 0.677–0.701), indicating the best overall predictive ability. WC ranked next with an AUC of 0.681. ABSI demonstrated the lowest predictive value, with an AUC of 0.602 (95% CI: 0.589–0.614). Among women (Fig. 3 and Table 5 ), the predictive ability of all indices was generally higher than that in men. WHtR and BRI again showed the best predictive performance, both with an AUC of 0.748 (95% CI: 0.738–0.757). WC and BMI also performed well, each with an AUC of 0.734. ABSI remained the weakest predictor in women, with an AUC of 0.619 (95% CI: 0.609–0.630). Table 5 ROC Curve Analysis for Different Obesity Indices in Males and Females in Macao AUC SE 95%CI Male WC 0.681 0.00828 0.669–0.692 WHtR 0.689 0.00815 0.677–0.701 WHR 0.675 0.0083 0.663–0.687 BMI 0.671 0.00836 0.659–0.683 BRI 0.689 0.00815 0.677–0.701 ABSI 0.602 0.00874 0.589–0.614 CI 0.653 0.00844 0.641–0.665 Female WC 0.734 0.00954 0.724–0.744 WHtR 0.748 0.00911 0.738–0.757 WHR 0.722 0.00956 0.721–0.732 BMI 0.734 0.00958 0.724–0.744 BRI 0.748 0.00911 0.738–0.757 ABSI 0.619 0.0108 0.609–0.630 CI 0.691 0.0101 0.681–0.701 3.5 Optimal Cutoff Values of Obesity Indices Table 6 summarizes the optimal cutoff values of each obesity index for hypertension screening, calculated based on the Youden index. Among men, WHtR and BRI showed the highest Youden index values (both 0.2941), with corresponding optimal cutoff points of > 0.4941 and > 3.2490, respectively. Among women, WHtR and BRI also exhibited the highest Youden index values (both 0.3877), with optimal cutoff points of > 0.4814 and > 3.0160, respectively. These cutoff values provide useful reference thresholds for identifying obesity levels associated with elevated hypertension risk in different sex groups. Table 6 Youden Index and Optimal Cutoff Points for Different Obesity Indices in Males and Females in Macao Youden index Optimal cutoff value Male WC 0.2682 >82.4 WHtR 0.2941 >0.4941 WHR 0.2601 >0.9047 BMI 0.2556 >23.84 BRI 0.2941 >3.2490 ABSI 0.1533 >0.0776 CI 0.2412 >1.2231 Female WC 0.3524 >78.1 WHtR 0.3877 >0.4814 WHR 0.3413 >0.8267 BMI 0.3575 >22.69 BRI 0.3877 >3.0160 ABSI 0.1804 >0.0775 CI 0.2973 >1.1927 4 Discussion 4.1 Key Findings Based on data from over 14,000 adults in Macao, this study systematically compared the associations and predictive performance of seven obesity indices with hypertension. The results showed that WC and WHtR outperformed BMI and several novel indices in identifying hypertension risk. ROC analysis indicated that the overall predictive ability was higher in women than in men. Generally, an AUC below 0.6 indicates poor discrimination, 0.7–0.8 is considered acceptable, and values above 0.8 are good [ 13 ]. In this study, WHtR and BRI in females showed AUCs of approximately 0.74–0.75, indicating acceptable discriminative ability, whereas AUCs of around 0.68–0.69 in males suggest borderline performance and should be interpreted with caution. In the 20–29-year-old female group, the adjusted ORs for WC and WHtR reached peak values, suggesting that central obesity in young women may have a particularly pronounced impact on blood pressure. The Youden index reflects the balance between sensitivity and specificity, ranging from 0 to 1, with higher values indicating better discrimination[ 12 , 15 ]. Furthermore, the WC optimal cutoff values determined by the Youden index (82.4 cm for men and 78.1 cm for women) were lower than the World Health Organization (WHO) recommended standards. This indicates that even relatively low levels of obesity in Asian populations may confer a substantial risk of elevated blood pressure. 4.2 Comparison with Previous Studies and Potential Mechanisms Previous studies have generally suggested that central obesity indices have greater value than BMI in predicting cardiometabolic abnormalities. Our findings are consistent with prior systematic reviews and meta-analyses, which reported that WC and WHtR exhibit higher sensitivity and specificity for predicting hypertension and metabolic syndrome [ 16 , 17 ]. In several large-scale population studies in Asia, a WHtR ≥ 0.5 has been widely recognized as a generalizable risk threshold [ 17 , 18 ]. The cutoff values obtained in the present study align closely with these findings, further supporting their applicability in Asian populations. Notably, the WC and BMI cutoff values in our population were lower than those commonly used in Western populations. WHO expert consultations and related guidelines typically recommend a WC threshold of approximately 94 cm for men and 80 cm for women [ 19 ], whereas our study found values of 82.4 cm and 78.1 cm, respectively. This discrepancy likely reflects inter-ethnic differences in fat distribution. Previous research indicates that Asians tend to accumulate visceral fat at lower BMI levels [ 20 ], which may explain why relatively lower WC and WHtR values are significantly associated with hypertension. Mechanistically, central obesity may elevate blood pressure through multiple pathways. Visceral adipose tissue is metabolically active, secreting various pro-inflammatory cytokines and adipokines that can activate the renin–angiotensin–aldosterone system and the sympathetic nervous system, directly contributing to increased blood pressure [ 21 , 22 ]. In addition, visceral fat accumulation is closely associated with insulin resistance, which is considered an important driver of hypertension [ 17 ]. By contrast, BMI merely reflects the weight-to-height ratio and cannot distinguish between visceral and subcutaneous fat, limiting its sensitivity in revealing metabolic risk. It is worth noting that ABSI did not demonstrate satisfactory predictive performance in this study. The external applicability of this index remains inconsistent in the literature, with considerable variability observed between some Asian and Western populations, suggesting that its generalizability across different populations may be limited [ 19 , 23 ]. 4.3 Sex- and Age-Specific Differences and Public Health Implications This study further found that the predictive performance of obesity indices was generally higher in women than in men, with particularly pronounced risk in young women. This difference may be related to sex hormones and patterns of fat distribution. Estrogen typically improves vascular function and promotes subcutaneous fat deposition, which may reduce blood pressure risk to some extent; however, when central obesity occurs in young women, this protective effect may be attenuated, thereby exposing more pronounced metabolic abnormalities [ 24 , 25 ]. Age also plays an important role. Although younger individuals generally have greater vascular compliance and theoretically stronger compensatory capacity, the presence of visceral fat accumulation at this stage suggests impaired metabolic homeostasis, resulting in relatively higher ORs [ 26 ]. These findings have important implications for public health and clinical practice. First, while BMI is widely used, it may underestimate hypertension risk in Asian populations, highlighting the need to prioritize WC and WHtR in screening and health management [ 16 ]. Second, young women emerge as a high-risk group, suggesting that public health interventions should be implemented earlier, targeting lifestyle, dietary patterns, and physical activity. Finally, the localized cutoff values identified in this study are lower than WHO international standards, indicating that applying Western thresholds may delay the identification of high-risk individuals. WHtR is simple to calculate, cost-effective, and suitable for primary care settings, and thus may serve as a key regional tool for hypertension risk assessment in the future. 4.4 Strengths and Limitations The large sample size and robust findings of this study, together with the systematic comparison of multiple obesity indices, provide evidence-based guidance for hypertension screening in Macao. However, several limitations should be noted. First, the cross-sectional design precludes causal inference, and prospective cohort studies are needed for validation. Second, potential confounders such as diet, physical activity, genetics, and medication use were not included, which may affect the interpretation of results. Third, the study population was limited to adults in Macao, and the generalizability of the findings to other populations should be considered with caution. Finally, novel indices such as ABSI are computationally complex and show considerable inter-ethnic variation, limiting their widespread clinical applicability. 5 Conclusion Based on a large adult population in Macao, this study systematically compared multiple obesity indices in relation to hypertension. The results indicated that WC and WHtR outperformed BMI and several novel indices in risk identification, with WHtR demonstrating particularly strong predictive performance in women. Further analyses showed that central obesity in young women was associated with a relatively greater increase in hypertension risk. The findings also suggested that the optimal cutoff values of WHtR and WC determined by the Youden index were lower than the WHO-recommended international standards, indicating that even relatively low levels of obesity in Asian populations may confer substantial blood pressure risk. This study not only further validates previous conclusions regarding WC cutoffs but also provides new empirical evidence supporting the application of WHtR in the Macao population. In clinical and public health practice, WC and WHtR should be incorporated into routine risk assessment, particularly in community and primary care settings, to facilitate the early identification of individuals at high risk. Young women should be prioritized for intervention to achieve early prevention of hypertension. Future research should integrate prospective cohorts and metabolic biomarkers to develop more precise risk prediction tools and further explore the applicability of obesity indices across diverse populations. Abbreviations AUC(area under the receiver operating characteristic curve) BMI( body mass index) BRI(body roundness index) CI(conicity index) WC(waist circumference) WHR(waist to-hip ratio) WHtR(waist to-height ratio) ABSI(a body shape index) SBP(systolic blood pressure) DBP(diastolic blood pressure) ROC(receiver operating characteristic) OR(odds ratio) 95%CI(95% confidence interval) WHO(World Health Organization) NCD RisC(Non-Communicable Disease Risk Factor Collaboration). Declarations Ethics approval and consent to participate：This study was conducted in accordance with the Declaration of Helsinki and approved by the Ethics Committee of the China Institute of Sport Science (approval number: CISS-20190607). Written informed consent was obtained from individual or guardian participants. Consent for publication：Not applicable. Availability of data and materials: The datasets generated and analysed during the current study are not publicly available but are available from the corresponding author on reasonable request. Competing interests：The authors declare that they have no competing interests. Funding: This study was supported by the 2025 Macao Residents’ Physical Fitness Surveillance Technical Support Service Project (No. B2425) and the Fourth Macao Residents’ Physical Fitness Surveillance in 2020 (No. B2014). Authors' contributions：LP J: Writing - Original Draft and Conceptualization. YB G: Writing - Review & Editing. X P: Writing - Review & Editing and Visualization. XX C: Formal analysis. MZ L: Visualization and Data Curation. DQ Z: Validation. CM W: Review. JX C: Data Curation. YB W: Data Curation .K S: Project administration. YF Z: Supervision. Acknowledgements: We appreciated Ms. Chuanrui Cui for contributing to the revision of the Visualization of this manuscript. References World Health Organization. Global Health Observatory data: Raised blood pressure. Geneva: WHO; 2021. NCD Risk Factor Collaboration (NCD-RisC). Worldwide trends in hypertension prevalence and progress in treatment and control from 1990 to 2019: a pooled analysis of 1,201 population-representative studies with 104 million participants. Lancet. 2021;398(10304):957–980. doi:10.1016/S0140-6736(21)01330-1. PMID:34450083. Lu J, Lu Y, Wang X, et al. Prevalence, awareness, treatment, and control of hypertension in China: data from 1·7 million adults in a population-based screening study (China PEACE Million Persons Project). Lancet. 2017;390:2549–2558. doi:10.1016/S0140-6736(17)32478-9. PMID:29102084. Wang Z, Chen Z, Zhang L, et al. Status of hypertension in China: results from the China Hypertension Survey, 2012–2015. Circulation. 2018;137:2344–2356. doi:10.1161/CIRCULATIONAHA.117.032380. PMID:29449338. Macao SAR Sports Bureau. 2020 Physical Fitness Report of Macao SAR Residents [Internet]. Macao: Sports Bureau; 2021. (in Chinese) Hall JE, do Carmo JM, da Silva AA, Wang Z, Hall ME. Obesity-induced hypertension: interaction of neurohumoral and renal mechanisms. Circ Res. 2015;116:991–1006. doi:10.1161/CIRCRESAHA.116.305697. PMID:25767285. Wildman RP, Muntner P, Reynolds K, et al. The obese without cardiometabolic risk factor clustering and the normal weight with cardiometabolic risk factor clustering. Obesity (Silver Spring). 2008;16:1891–1900. doi:10.1038/oby.2008.293. PMID:18695075. Ashwell M, Gunn P, Gibson S. Waist-to-height ratio is a better screening tool than waist circumference and BMI for adult cardiometabolic risk factors: systematic review and meta-analysis. Obes Rev. 2012;13:275–286. doi:10.1111/j.1467-789X.2011.00952.x. PMID:22106927. Thomas DM, Bredlau C, Bosy-Westphal A, et al. Relationships between body roundness with body fat and visceral adipose tissue emerging from a new geometrical model. Obesity (Silver Spring). 2013;21:2264–2271. doi:10.1002/oby.20408. PMID:23519954. Krakauer NY, Krakauer JC. A new body shape index predicts mortality hazard independently of body mass index. PLoS One. 2012;7:e39504. doi:10.1371/journal.pone.0039504. PMID:22815607. Valdez R. A simple model-based index of abdominal adiposity. J Clin Epidemiol. 1991;44:955–956. doi:10.1016/0895-4356(91)90059-I. PMID:1890438. Fluss R, Faraggi D, Reiser B. Estimation of the Youden index and its associated cutoff point. Biometrical Journal. 2005;47:458–472. doi:10.1002/bimj.200410135. PMID:16161804. Ashwell M, Gibson S. Waist-to-height ratio as an indicator of 'early health risk': simpler and more predictive than BMI. BMJ Open. 2016;6:e010159. doi:10.1136/bmjopen-2015-010159. PMID:26975935. Mandrekar JN. Receiver operating characteristic curve in diagnostic test assessment. J Thorac Oncol. 2010;5(9):1315–1316. doi:10.1097/JTO.0b013e3181ec173d. PMID:20736804. Perkins NJ, Schisterman EF. The inconsistency of “optimal” cutpoints using two criteria based on the receiver operating characteristic curve. Am J Epidemiol. 2006;163(7):670–675. doi:10.1093/aje/kwj063. PMID:16410346. Browning LM, Hsieh SD, Ashwell M. A systematic review of waist-to-height ratio as a screening tool for the prediction of cardiovascular disease and diabetes: 0.5 could be a suitable global boundary value. Nutr Res Rev. 2010;23:247–269. doi:10.1017/S0954422410000144. PMID:20819243. Yoo EG. Waist-to-height ratio as a screening tool for obesity and cardiometabolic risk. Clin Chim Acta. 2016;459:157–163. doi:10.1016/j.cca.2016.07.006. PMID:27591527. Zhang X, Ye R, Sun L, et al. Relationship between novel anthropometric indices and the incidence of hypertension in Chinese individuals: a prospective cohort study based on the CHNS from 1993 to 2015. BMC Public Health. 2023;23:436. doi:10.1186/s12889-023-15208-7. World Health Organization. Waist circumference and waist–hip ratio: Report of a WHO Expert Consultation. Geneva: WHO; 2011. ISBN: 9789241501491. Després JP. Body fat distribution and risk of cardiovascular disease: an update. Circulation. 2012;126:1301–1313. doi:10.1161/CIRCULATIONAHA.111.067264. PMID:22949540. Lee DY, et al. Prediction of mortality with a body shape index in young Asians: comparison with BMI and WC. Obesity (Silver Spring). 2018;26:1096–1103. doi:10.1002/oby.22193. PMID:29719128. Taylor LE, Sullivan JC. Sex differences in obesity-induced hypertension and vascular dysfunction: a protective role for estrogen in adipose tissue inflammation? Am J Physiol Regul Integr Comp Physiol. 2016;311:R714–R720. doi:10.1152/ajpregu.00202.2016. PMID:27547012. Choi JR, Ahn SV, Kim JY, et al. Comparison of various anthropometric indices for the identification of a predictor of incident hypertension: The ARIRANG study. J Hum Hypertens. 2018;32:294–300. doi:10.1038/s41371-018-0043-4. Regensteiner JG, Reusch JEB. Sex differences in cardiovascular consequences of hypertension, obesity, and diabetes: JACC Focus Seminar 4/7. J Am Coll Cardiol. 2022;79:1492–1505. doi:10.1016/j.jacc.2022.02.010. PMID:35422246. Ueda K, Okuda K, Yamashita T. Sex differences and regulatory actions of estrogen in cardiovascular physiology. Front Physiol. 2021;12:738218. doi:10.3389/fphys.2021.738218. McClements L, Kautzky-Willer A, Kararigas G, et al. The role of sex differences in cardiovascular, metabolic, and immune functions in health and disease: a review for “Sex Differences in Health Awareness Day”. Biol Sex Differ. 2025;16:33. doi:10.1186/s13293-025-00714-1. Additional Declarations No competing interests reported. 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1","display":"","copyAsset":false,"role":"figure","size":83142,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAssociation Between Quartiles of Different Obesity Indices and Hypertension Prevalence\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNote: The quartile cut-off points were as follows: WC: 71.6, 78.3, 85.5; WHtR: 0.4426, 0.4811, 0.5238; WHR: 0.7983, 0.8508, 0.9063; BMI: 20.31, 22.29, 24.58; ABSI: 0.0744, 0.0774, 0.0803; BRI: 2.346, 3.011, 3.816; CI: 1.1383, 1.1927, 1.2472. Confounding factors were adjusted for including age at testing, sex, educational level, and usual occupational activity.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8072827/v1/c4f0e265185f0946a23ef329.png"},{"id":98600436,"identity":"aaed3803-24ff-45d0-b2c4-51acb662a354","added_by":"auto","created_at":"2025-12-19 12:29:42","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":133611,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eComparison of ROC Curves of Different Obesity Indices in Males in Macao\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8072827/v1/e06ff461800e93bfefb0b0c5.png"},{"id":98600435,"identity":"32886189-74c9-4070-b2c2-ac4ad9cdca14","added_by":"auto","created_at":"2025-12-19 12:29:42","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":140040,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eComparison of ROC Curves of Different Obesity Indices in Females in Macao\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-8072827/v1/7408cd63bd4e26aafc842d3e.png"},{"id":98775536,"identity":"4350fbca-9d46-4f72-b6d7-ec83f4c86c3e","added_by":"auto","created_at":"2025-12-22 12:20:14","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1450897,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8072827/v1/839719b6-6b7c-4577-bc6c-23cca8bd5e37.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Refining Obesity Metrics for Hypertension Risk Stratification in Macao: A 15-Year Multi- Indicator Comparative Study Based on Four Waves of Population Surveillance","fulltext":[{"header":"1 Background","content":"\u003cp\u003eHypertension, as a highly prevalent chronic noncommunicable disease (NCD), continues to pose a formidable challenge to global public health. According to data from the World Health Organization (WHO) and the NCD Risk Factor Collaboration (NCD-RisC), between 1990 and 2019, the number of adults aged 30\u0026ndash;79 years with hypertension nearly doubled worldwide, rising from approximately 650\u0026nbsp;million to 1.28\u0026nbsp;billion. [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e] Notably, around two-thirds of those with hypertension reside in low- and middle-income countries. [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e] Because hypertension often presents with inconspicuous early symptoms, is burdened by poor adherence to treatment, and has a wide spectrum of complications, it is commonly referred to as the \u0026ldquo;silent killer.\u0026rdquo; It is closely associated with cardiovascular disease, cerebrovascular disease, type 2 diabetes, and chronic kidney disease, among other metabolic disorders.\u003c/p\u003e \u003cp\u003eIn recent years, the prevalence of hypertension among adults in Mainland China remains alarmingly high. National surveys suggest that the overall prevalence of hypertension in Chinese adults is approximately 23%\u0026ndash;27%, while awareness, treatment, and control rates stand at about 46%, 41%, and 14%, respectively\u0026mdash;indicating substantial room for improvement in hypertension prevention and management. [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e] The emergence of hypertension is closely linked with westernized dietary patterns, insufficient physical activity, and the rising burden of obesity. In Macao, as a special administrative region of China, rapid economic development, increasing intake of high-fat and high-salt diets, and accelerated life pace may further elevate the risk of metabolic diseases among residents. According to the 2020 Macao Citizens\u0026rsquo; Physical Fitness Monitoring Report, the obesity rate (body mass index\u0026thinsp;\u0026ge;\u0026thinsp;30 kg/m\u0026sup2;, or per local criteria) in adult men was approximately 9.3%, and in adult women about 6.5%; the obesity rate in men was generally higher than in women. [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eObesity is widely recognized as one of the most important modifiable risk factors for hypertension. In particular, central obesity exerts a more pronounced effect among Asian populations. Visceral fat, more so than subcutaneous fat, is prone to promote insulin resistance, chronic inflammation, and activation of the renin\u0026ndash;angiotensin system (RAS), thereby contributing to elevated blood pressure. [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e] Moreover, a phenomenon known as \u0026ldquo;metabolically obese but normal weight\u0026rdquo; (MONW) is relatively common in Asian populations\u0026mdash;that is, individuals may have a normal body mass index (BMI), yet carry excess abdominal fat and thus harbor significantly increased risks for hypertension or metabolic syndrome. [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eWhile the conventional body mass index (BMI) is globally accepted as a measure of obesity, it cannot capture differences in fat types or distribution, limiting its predictive utility for metabolic disorder risks such as hypertension. To address this limitation, various anthropometric indices have been proposed to reflect fat distribution more precisely, including waist circumference (WC), waist-to-height ratio (WHtR), waist-to-hip ratio (WHR), body roundness index (BRI), a body shape index (ABSI), and conicity index (CI). The predictive performance of these indices varies across populations. Several systematic reviews suggest that WHtR\u0026mdash;and in some studies, BRI and CI\u0026mdash;tend to outperform BMI in predicting metabolic disorders (including hypertension) in Asian populations. [\u003cspan additionalcitationids=\"CR9 CR10\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] Furthermore, receiver operating characteristic (ROC) curve analysis combined with the Youden index is frequently employed to evaluate diagnostic performance and to determine optimal cutoffs for screening tools. [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eGiven that Macao currently lacks large, locally based studies examining the relationship between obesity indices and hypertension in adults, the present study draws on four rounds of Macao citizens\u0026rsquo; physical fitness monitoring data collected between 2005 and 2020 (cumulative sample size\u0026thinsp;\u0026gt;\u0026thinsp;14,000). We systematically compare seven commonly used and novel obesity indices (WC, WHtR, WHR, BMI, BRI, ABSI, CI) in their association with and diagnostic performance for hypertension. Using ROC curve analysis and the Youden index, we identify optimal screening thresholds for each index. In addition, we perform stratified analyses by decade of age (20\u0026ndash;29, 30\u0026ndash;39, 40\u0026ndash;49, 50\u0026ndash;59 years) to assess whether the predictive utility of each obesity index differs across age groups. Our findings aim to provide localized evidence for early hypertension screening and obesity management strategies tailored to the Macao population.\u003c/p\u003e"},{"header":"2 Method","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Participants and Data Sources\u003c/h2\u003e \u003cp\u003eThis study was based on data obtained from four rounds of the Macao SAR Physical Fitness Surveillance, conducted in 2005, 2010, 2015, and 2020. The surveys covered all seven parishes of Macao and adopted a multistage stratified cluster random sampling design. Representative samples of adult residents were selected from government agencies, enterprises, and community settings. Participants were eligible for inclusion if they were 20\u0026ndash;59 years of age, had resided in Macao for at least five years, had no history of major diseases, pregnancy, or surgery within the past year, were able to perform basic daily self-care activities, and provided written informed consent. All data collection procedures were carried out by trained investigators in strict accordance with the National Physical Fitness Surveillance Type II Standards. Quality control was supervised throughout the process, and approximately 5% of the samples were re-measured to assess measurement consistency. Participants were divided into four age groups (20\u0026ndash;29, 30\u0026ndash;39, 40\u0026ndash;49, and 50\u0026ndash;59 years) to examine differences in the associations between obesity indices and hypertension across different age stages.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Anthropometric and Blood Pressure Measurements\u003c/h2\u003e \u003cp\u003eAll measurements were conducted by trained staff following standardized protocols in accordance with the National Physical Fitness Surveillance Type II Standards.\u003c/p\u003e \u003cp\u003eHeight was measured with participants standing barefoot on a stadiometer, back straight and head in the Frankfurt plane, with eyes looking forward. Waist circumference was measured at 0.5\u0026ndash;1.0 cm above the umbilicus in a relaxed standing position using a non-elastic tape. Chest circumference was measured with participants standing naturally, arms relaxed at the sides, and feet shoulder-width apart; measurements were taken at the nipple line for men and at the fourth rib level for women, with the tape snug but not compressing the skin. Hip circumference was measured at the level of maximal gluteal protrusion, with the tape applied horizontally and snugly without indenting the skin. All anthropometric measurements were recorded to the nearest 0.1 cm.\u003c/p\u003e \u003cp\u003eBlood pressure was measured in the seated position on the right arm at heart level using a calibrated sphygmomanometer, following standard auscultatory procedures. Two readings were taken, and the mean values of systolic and diastolic blood pressure were recorded in mmHg. Hypertension was defined as systolic BP\u0026thinsp;\u0026ge;\u0026thinsp;140 mmHg, diastolic BP\u0026thinsp;\u0026ge;\u0026thinsp;90 mmHg, or use of antihypertensive medication within the past two weeks.\u003c/p\u003e \u003cp\u003eSeven anthropometric indicators were selected to comprehensively assess overall and central adiposity, including BMI, WC, WHtR, WHR, BRI, ABSI, and CI. All indicators were calculated and standardized according to definitions described in previous studies. BMI\u0026thinsp;=\u0026thinsp;weight (kg) / [height (m)]^2, WC\u0026thinsp;=\u0026thinsp;waist circumference (cm), WHtR\u0026thinsp;=\u0026thinsp;waist circumference (cm) / height (cm), WHR\u0026thinsp;=\u0026thinsp;waist circumference (cm) / hip circumference (cm), BRI\u0026thinsp;=\u0026thinsp;364.2\u0026thinsp;\u0026minus;\u0026thinsp;365.5 \u0026times; \u0026radic;(1 \u0026minus; (WHtR / π)^2), ABSI\u0026thinsp;=\u0026thinsp;WC (m) / [BMI^(2/3) \u0026times; height (m)^(1/2)], CI\u0026thinsp;=\u0026thinsp;WC (m) / [0.109 \u0026times; sqrt(weight (kg) / height (m))].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Data Processing and Variable Transformation\u003c/h2\u003e \u003cp\u003eTo standardize the scales of different obesity indices, seven continuous indicators (BMI, WC, WHtR, WHR, BRI, ABSI, and CI) were transformed into Z-scores. Each indicator was also categorized into quartiles (lowest, lower-middle, upper-middle, highest) based on the sample distribution for subsequent dose\u0026ndash;response trend analysis. Hypertension was treated as a binary outcome variable (0\u0026thinsp;=\u0026thinsp;normotensive, 1\u0026thinsp;=\u0026thinsp;hypertensive).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Statistical Analysis\u003c/h2\u003e \u003cp\u003eContinuous variables were expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD), and categorical variables as frequency and percentage (%). Differences between sexes were assessed using independent-samples t-test for normally distributed continuous variables or Mann\u0026ndash;Whitney U test for non-normally distributed variables, while categorical variables were compared using the chi-square test.\u003c/p\u003e \u003cp\u003eAssociations between obesity indices and hypertension were evaluated using multivariable logistic regression models, and odds ratios (ORs) with 95% confidence intervals (CIs) were calculated. Three models were constructed to control for potential confounders: Model 1, unadjusted; Model 2, adjusted for age and sex; Model 3, further adjusted for educational level and daily occupational activity. Analyses were conducted in the overall sample, as well as stratified by sex and age groups, to assess the consistency and differences of associations across subgroups.\u003c/p\u003e \u003cp\u003eReceiver operating characteristic (ROC) curves were used to evaluate the predictive performance of each obesity index, with the area under the curve (AUC) calculated. The optimal cutoff value for each index was determined using the Youden Index, balancing sensitivity and specificity to recommend locally adapted reference standards.\u003c/p\u003e \u003cp\u003eAll statistical analyses were performed using SPSS version 26.0, R version 4.2.3, and MedCalc version 23.0. Two-sided P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"3 Result","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Basic Characteristics of the Study Population\u003c/h2\u003e \u003cp\u003eA total of 14,288 participants were included in this study, comprising 6,177 men (43.2%) and 8,111 women (56.8%). The baseline characteristics of all participants are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The mean age of the overall population was 39.65\u0026thinsp;\u0026plusmn;\u0026thinsp;11.16 years, with no statistically significant difference between men and women (P\u0026thinsp;=\u0026thinsp;0.136). However, significant sex differences were observed across all anthropometric indicators (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Male participants had significantly higher values of height, weight, chest circumference, WC, hip circumference, WHR, BMI, SBP, and DBP than their female counterparts. In addition, the prevalence of hypertension was markedly higher in men (20.4%) than in women (8.7%) (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline Characteristics of Participants\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal(14288)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMale(6177)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFemale(8111)\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 \u003cth align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e39.65\u0026thinsp;\u0026plusmn;\u0026thinsp;11.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e39.49\u0026thinsp;\u0026plusmn;\u0026thinsp;11.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e39.77\u0026thinsp;\u0026plusmn;\u0026thinsp;11.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.136\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\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\u003e162.7\u0026thinsp;\u0026plusmn;\u0026thinsp;8.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e169.6\u0026thinsp;\u0026plusmn;\u0026thinsp;6.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e157.5\u0026thinsp;\u0026plusmn;\u0026thinsp;5.57853\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=\"left\" colname=\"c6\"\u003e\u0026nbsp;\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\u003e60.3\u0026thinsp;\u0026plusmn;\u0026thinsp;11.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e67.5\u0026thinsp;\u0026plusmn;\u0026thinsp;10.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e54.8\u0026thinsp;\u0026plusmn;\u0026thinsp;8.71605\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=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChest circumference\u003c/p\u003e \u003cp\u003e(cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e88.12\u0026thinsp;\u0026plusmn;\u0026thinsp;7.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e92.19\u0026thinsp;\u0026plusmn;\u0026thinsp;6.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e85.03\u0026thinsp;\u0026plusmn;\u0026thinsp;6.97987\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=\"left\" colname=\"c6\"\u003e\u0026nbsp;\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\u003e79.06\u0026thinsp;\u0026plusmn;\u0026thinsp;9.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e83.37\u0026thinsp;\u0026plusmn;\u0026thinsp;9.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e75.77\u0026thinsp;\u0026plusmn;\u0026thinsp;9.20815\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=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHip circumference\u003c/p\u003e \u003cp\u003e(cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e92.49\u0026thinsp;\u0026plusmn;\u0026thinsp;6.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e93.16\u0026thinsp;\u0026plusmn;\u0026thinsp;6.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e91.99\u0026thinsp;\u0026plusmn;\u0026thinsp;6.49\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=\"left\" colname=\"c6\"\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\u003e0.486\u0026thinsp;\u0026plusmn;\u0026thinsp;0.059\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.492\u0026thinsp;\u0026plusmn;\u0026thinsp;0.056\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.482\u0026thinsp;\u0026plusmn;\u0026thinsp;0.060829\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=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWHR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.853\u0026thinsp;\u0026plusmn;\u0026thinsp;0.075\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.893\u0026thinsp;\u0026plusmn;\u0026thinsp;0.063\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.823\u0026thinsp;\u0026plusmn;\u0026thinsp;0.0679904\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=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22.69\u0026thinsp;\u0026plusmn;\u0026thinsp;3.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23.46\u0026thinsp;\u0026plusmn;\u0026thinsp;3.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22.1\u0026thinsp;\u0026plusmn;\u0026thinsp;3.33531\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=\"left\" colname=\"c6\"\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\u003e3.17\u0026thinsp;\u0026plusmn;\u0026thinsp;1.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.27\u0026thinsp;\u0026plusmn;\u0026thinsp;1.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.09\u0026thinsp;\u0026plusmn;\u0026thinsp;1.154314\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=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eABSI, m\u003csup\u003e11/6\u003c/sup\u003e\u0026middot;kg\u003csup\u003e\u0026minus;\u0026thinsp;2/3\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0774\u0026thinsp;\u0026plusmn;\u0026thinsp;0.0044\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0783\u0026thinsp;\u0026plusmn;\u0026thinsp;0.0040\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0768\u0026thinsp;\u0026plusmn;\u0026thinsp;0.0045415\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=\"left\" colname=\"c6\"\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\u003e1.194\u0026thinsp;\u0026plusmn;\u0026thinsp;0.078\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.213\u0026thinsp;\u0026plusmn;\u0026thinsp;0.071\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.179\u0026thinsp;\u0026plusmn;\u0026thinsp;0.0790304\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=\"left\" colname=\"c6\"\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\u003e119.87\u0026thinsp;\u0026plusmn;\u0026thinsp;15.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e126.08\u0026thinsp;\u0026plusmn;\u0026thinsp;13.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e115.14\u0026thinsp;\u0026plusmn;\u0026thinsp;14.904\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=\"left\" colname=\"c6\"\u003e\u0026nbsp;\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\u003e74.31\u0026thinsp;\u0026plusmn;\u0026thinsp;10.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e77.81\u0026thinsp;\u0026plusmn;\u0026thinsp;9.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e71.64\u0026thinsp;\u0026plusmn;\u0026thinsp;9.868\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=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1971 (13.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1262 (20.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e709 (8.7)\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=\"left\" colname=\"c6\"\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=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Association Between Obesity Indices and Hypertension\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents the ORs and 95% CIs for hypertension prevalence according to different obesity indices standardized by Z-scores. In the unadjusted model, all obesity indices were significantly and positively associated with hypertension (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Among them, WHR showed the strongest association (OR\u0026thinsp;=\u0026thinsp;2.34, 95% CI: 2.22\u0026ndash;2.47), followed by WC (OR\u0026thinsp;=\u0026thinsp;2.28, 95% CI: 2.17\u0026ndash;2.40). In Model Ⅱ, after adjusting for age, sex, and other confounders, the ORs of all indices declined to varying degrees but remained statistically significant. After adjustment, WC showed the strongest association with hypertension (OR\u0026thinsp;=\u0026thinsp;1.89, 95% CI: 1.79\u0026ndash;2.00), whereas ABSI exhibited the weakest (OR\u0026thinsp;=\u0026thinsp;1.14, 95% CI: 1.08\u0026ndash;1.20).\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\u003eOR and 95% CI for Hypertension According to Different Obesity Indices (Standardized by Z-Score)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eOR(95%CI)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCrude OR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eModel Ⅰ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eModel Ⅱ\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.28(2.17,2.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.91(1.80,2.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.89(1.79,2.00)\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\u003e2.10(2.00,2.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.85(1.75,1.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.83(1.73,1.93)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWHR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.34(2.22,2.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.79(1.68,1.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.75(1.64,1.87)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.02(1.93,2.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.85(1.76,1.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.84(1.75,1.94)\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\u003e2.00(1.91,2.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.78(1.69,187)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.76(1.67,1.85)\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.52(1.45,1.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.16(1.09,1.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.14(1.08,1.20)\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\u003e1.95(1.85,2.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.52(1.43,1.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.49(1.41,1.58)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eFigure 1 illustrates the trend in hypertension risk across quartiles of each obesity index. The results showed a clear dose\u0026ndash;response relationship between all obesity indices and the prevalence of hypertension, with risk increasing steadily from Q1 to Q4. Among all indices, BMI and WC exhibited the steepest risk-increase curves. In the highest quartile (Q4), the OR for BMI reached 5.673, followed closely by WC with an OR of 5.611. In contrast, ABSI demonstrated the flattest curve, with an OR of only 1.53 in Q4, indicating a relatively weaker association with hypertension risk.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eNote\u003c/strong\u003e \u003cp\u003eThe quartile cut-off points were as follows: WC: 71.6, 78.3, 85.5; WHtR: 0.4426, 0.4811, 0.5238; WHR: 0.7983, 0.8508, 0.9063; BMI: 20.31, 22.29, 24.58; ABSI: 0.0744, 0.0774, 0.0803; BRI: 2.346, 3.011, 3.816; CI: 1.1383, 1.1927, 1.2472. Confounding factors were adjusted for including age at testing, sex, educational level, and usual occupational activity.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eFigure 1. Association Between Quartiles of Different Obesity Indices and Hypertension Prevalence\u003c/b\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Subgroup Analysis by Age and Sex\u003c/h2\u003e \u003cp\u003eIn the subgroup analyses stratified by age and sex, the associations between obesity indices and hypertension showed consistent patterns but differed slightly by gender. Among men (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), after adjustment for potential confounders, WC, WHtR, WHR, BMI, BRI, and CI were all significant predictors of hypertension across all age groups (20\u0026ndash;29, 30\u0026ndash;39, 40\u0026ndash;49, and 50\u0026ndash;59 years) (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The adjusted ORs for these indices ranged from 1.5 to 2.3, indicating that both central and overall obesity were significantly associated with increased hypertension risk in men. The association of ABSI was relatively weaker, reaching statistical significance only in the 40\u0026ndash;49 and 50\u0026ndash;59 age groups (adjusted OR\u0026thinsp;=\u0026thinsp;1.22 and 1.31, respectively; P\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003eAmong women (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e), after adjusting for confounders, WC, WHtR, WHR, BMI, BRI, and CI were also significantly associated with hypertension across all age groups (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Notably, the ORs in the 20\u0026ndash;29 and 30\u0026ndash;39 age groups were relatively higher, suggesting that the effect of obesity on elevated blood pressure was more pronounced among younger women. ABSI did not reach statistical significance in any age group. Overall, both abdominal and general obesity indices demonstrated stable predictive performance in both sexes, underscoring the importance of obesity management in hypertension prevention across different age stages.\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\u003eOR and 95%CI for Hypertension According to Different Obesity Indices (Standardized by Z-Score) Among Males in Macao by Age Group\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\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 \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c6\" namest=\"c3\"\u003e \u003cp\u003eAge groups(year)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20\u0026ndash;29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30\u0026ndash;39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e40\u0026ndash;49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e50\u0026ndash;59\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eWC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCrude OR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.96(1.67,2.31)***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.31(1.97,2.71) ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.75(1.52,2.01) ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.66(1.45,1.89) ***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAdjusted OR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.02(1.70,2.39) ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.30(1.96,2.70) ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.75(1.52,2.02) ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.67(1.46,1.91) ***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eWHtR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCrude OR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.89(1.60,2.23) ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.24(1.91,2.61) ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.86(1.61,2.14) ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.60(1.40,1.82) ***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAdjusted OR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.95(1.64,2.32) ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.24(1.91,2.62) ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.85(1.60,2.14) ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.59(1.39,1.81) ***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eWHR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCrude OR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.88(1.53,2.30) ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.17(1.80,2.61) ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.80(1.54,2.11) ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.67(1.45,1.93) ***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAdjusted OR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.93(1.57,2.38) ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.16(1.79,2.62) ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.78(1.52,2.09) ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.65(1.42,1.91) ***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eBMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCrude OR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.97(1.70,2.30) ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.19(1.90,2.52) ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.73(1.51,1.97) ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.49(1.32,1.69) ***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAdjusted OR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.01(1.72,2.36) ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.18(1.89,2.52) ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.75(1.53,2.00) ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.52(1.34,1.72) ***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eBRI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCrude OR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.91(1.62,2.25) ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.15(1.85,2.49) ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.80(1.57,2.06) ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.55(1.37,1.76) ***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAdjusted OR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.97(1.66,2.33) ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.15(1.85,2.50) ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.80(1.56,2.06) ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.54(1.36,1.75) ***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eABSI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCrude OR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.11(0.93,1.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.15(0.98,1.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.27(1.10,1.46) ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.34(1.18,1.53) ***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAdjusted OR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.12(0.93,1.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.13(0.96,1.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.22(1.06,1.42) ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.31(1.15,1.50) ***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCrude OR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.51(1.27,1.80) ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.76(1.48,2.08) ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.59(1.37,1.84) ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.56(1.36,1.78) ***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAdjusted OR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.53(1.28,1.83) ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.74(1.46,2.07) ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.56(1.34,1.81) ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.53(1.34,1.76) ***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eNote: Statistical significance: \u0026lowast;P\u0026thinsp;\u0026lt;\u0026thinsp;0.05; \u0026lowast;\u0026lowast;P\u0026thinsp;\u0026lt;\u0026thinsp;0.01; \u0026lowast;\u0026lowast;\u0026lowast;P\u0026thinsp;\u0026lt;\u0026thinsp;0.001.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \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\u003eOR and 95%CI for Hypertension According to Different Obesity Indices (Standardized by Z-Score) Among Females in Macao by Age Group\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\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 \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c6\" namest=\"c3\"\u003e \u003cp\u003eAge groups(year)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20\u0026ndash;29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30\u0026ndash;39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e40\u0026ndash;49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e50\u0026ndash;59\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eWC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCrude OR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.61(1.90,3.57) ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.47(1.96,3.12) ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.88(1.62,2.19) ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.69(1.49,1.91) ***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAdjusted OR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.79(2.01,3.87) ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.42(1.91,3.07) ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.79(1.53,2.09) ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.65(1.46,1.87) ***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eWHtR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCrude OR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.32(1.73,3.09) ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.39(1.92,2.97) ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.85(1.61,2.13) ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.59(1.42,1.77) ***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAdjusted OR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.48(1.82,3.38) ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.33(1.86,2.91) ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.75(1.51,2.02) ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.54(1.38,1.73) ***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eWHR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCrude OR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.27(1.50,3.42) ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.66(2.00,3.54) ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.65(1.40,1.94) ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.49(1.31,1.70) ***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAdjusted OR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.29(1.52,3.45) ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.54(1.89,3.40) ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.49(1.26,1.77) ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.43(1.26,1.63) ***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eBMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCrude OR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.18(1.72,2.77) ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.11(1.75,2.55) ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.96(1.71,2.24) ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.68(1.50,1.88) ***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAdjusted OR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.37(1.83,3.07) ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.08(1.71) ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.89(1.65,2.17) ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.66(1.48,1.86) ***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eBRI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCrude OR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.18(1.66,2.86) ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.20(1.81,2.68) ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.78(1.56,2.02) ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.52(1.38,1.68) ***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAdjusted OR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.37(1.83,3.07) ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.15(1.75) ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.68(1.47,1.92) ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.49(1.34,1.64) ***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eABSI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCrude OR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.04(0.70,1.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.28(0.99,1.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.02(0.89,1.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.10(0.99,1.23)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAdjusted OR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.06(0.71,1.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.26(0.97,1.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.96(0.83,1.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.06(.095,1.19)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCrude OR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.72(1.21,2.46) ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.91(1.48,2.45) ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.35(1.16,1.56) ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.33(1.19,1.50) ***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAdjusted OR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.74(1.22,2.48) ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.88(1.45,2.44) ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.25(1.07,1.45) ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.29(1.15,1.45) ***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eNote: Statistical significance: \u0026lowast;P\u0026thinsp;\u0026lt;\u0026thinsp;0.05; \u0026lowast;\u0026lowast;P\u0026thinsp;\u0026lt;\u0026thinsp;0.01; \u0026lowast;\u0026lowast;\u0026lowast;P\u0026thinsp;\u0026lt;\u0026thinsp;0.001.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Predictive Value of Different Obesity Indices for Hypertension\u003c/h2\u003e \u003cp\u003eTo evaluate and compare the predictive performance of different obesity indices for hypertension, ROC curves were plotted (Figs.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003e and \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003e), and the AUC, SE, and 95% CI were calculated (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAmong men (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003e and Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e), WHtR and BRI showed the highest AUC values, both at 0.689 (95% CI: 0.677\u0026ndash;0.701), indicating the best overall predictive ability. WC ranked next with an AUC of 0.681. ABSI demonstrated the lowest predictive value, with an AUC of 0.602 (95% CI: 0.589\u0026ndash;0.614).\u003c/p\u003e \u003cp\u003eAmong women (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003e and Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e), the predictive ability of all indices was generally higher than that in men. WHtR and BRI again showed the best predictive performance, both with an AUC of 0.748 (95% CI: 0.738\u0026ndash;0.757). WC and BMI also performed well, each with an AUC of 0.734. ABSI remained the weakest predictor in women, with an AUC of 0.619 (95% CI: 0.609\u0026ndash;0.630).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \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\u003eROC Curve Analysis for Different Obesity Indices in Males and Females in Macao\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAUC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e95%CI\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMale\u003c/b\u003e\u003c/p\u003e \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\u003e0.681\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00828\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.669\u0026ndash;0.692\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\u003e0.689\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00815\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.677\u0026ndash;0.701\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWHR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.675\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0083\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.663\u0026ndash;0.687\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.671\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00836\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.659\u0026ndash;0.683\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\u003e0.689\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00815\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.677\u0026ndash;0.701\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\u003e0.602\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00874\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.589\u0026ndash;0.614\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\u003e0.653\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00844\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.641\u0026ndash;0.665\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFemale\u003c/b\u003e\u003c/p\u003e \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\u003e0.734\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00954\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.724\u0026ndash;0.744\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\u003e0.748\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00911\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.738\u0026ndash;0.757\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWHR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.722\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00956\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.721\u0026ndash;0.732\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.734\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00958\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.724\u0026ndash;0.744\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\u003e0.748\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00911\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.738\u0026ndash;0.757\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\u003e0.619\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0108\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.609\u0026ndash;0.630\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\u003e0.691\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0101\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.681\u0026ndash;0.701\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=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Optimal Cutoff Values of Obesity Indices\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e summarizes the optimal cutoff values of each obesity index for hypertension screening, calculated based on the Youden index. Among men, WHtR and BRI showed the highest Youden index values (both 0.2941), with corresponding optimal cutoff points of \u0026gt;\u0026thinsp;0.4941 and \u0026gt;\u0026thinsp;3.2490, respectively. Among women, WHtR and BRI also exhibited the highest Youden index values (both 0.3877), with optimal cutoff points of \u0026gt;\u0026thinsp;0.4814 and \u0026gt;\u0026thinsp;3.0160, respectively. These cutoff values provide useful reference thresholds for identifying obesity levels associated with elevated hypertension risk in different sex groups.\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\u003eYouden Index and Optimal Cutoff Points for Different Obesity Indices in Males and Females in Macao\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\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=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYouden index\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOptimal cutoff value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"1\" nameend=\"c4\" namest=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMale\u003c/b\u003e\u003c/p\u003e \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\u003eWC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.2682\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026gt;82.4\u003c/p\u003e \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\u003eWHtR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.2941\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026gt;0.4941\u003c/p\u003e \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\u003eWHR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.2601\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026gt;0.9047\u003c/p\u003e \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\u003eBMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.2556\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026gt;23.84\u003c/p\u003e \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\u003e0.2941\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026gt;3.2490\u003c/p\u003e \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\u003eABSI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.1533\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026gt;0.0776\u003c/p\u003e \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\u003eCI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.2412\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026gt;1.2231\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFemale\u003c/b\u003e\u003c/p\u003e \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\u003eWC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.3524\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026gt;78.1\u003c/p\u003e \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\u003eWHtR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.3877\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026gt;0.4814\u003c/p\u003e \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\u003eWHR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.3413\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026gt;0.8267\u003c/p\u003e \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\u003eBMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.3575\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026gt;22.69\u003c/p\u003e \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\u003e0.3877\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026gt;3.0160\u003c/p\u003e \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\u003eABSI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.1804\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026gt;0.0775\u003c/p\u003e \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\u003eCI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.2973\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026gt;1.1927\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\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"},{"header":"4 Discussion","content":"\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Key Findings\u003c/h2\u003e \u003cp\u003eBased on data from over 14,000 adults in Macao, this study systematically compared the associations and predictive performance of seven obesity indices with hypertension. The results showed that WC and WHtR outperformed BMI and several novel indices in identifying hypertension risk. ROC analysis indicated that the overall predictive ability was higher in women than in men. Generally, an AUC below 0.6 indicates poor discrimination, 0.7\u0026ndash;0.8 is considered acceptable, and values above 0.8 are good [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. In this study, WHtR and BRI in females showed AUCs of approximately 0.74\u0026ndash;0.75, indicating acceptable discriminative ability, whereas AUCs of around 0.68\u0026ndash;0.69 in males suggest borderline performance and should be interpreted with caution. In the 20\u0026ndash;29-year-old female group, the adjusted ORs for WC and WHtR reached peak values, suggesting that central obesity in young women may have a particularly pronounced impact on blood pressure. The Youden index reflects the balance between sensitivity and specificity, ranging from 0 to 1, with higher values indicating better discrimination[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Furthermore, the WC optimal cutoff values determined by the Youden index (82.4 cm for men and 78.1 cm for women) were lower than the World Health Organization (WHO) recommended standards. This indicates that even relatively low levels of obesity in Asian populations may confer a substantial risk of elevated blood pressure.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Comparison with Previous Studies and Potential Mechanisms\u003c/h2\u003e \u003cp\u003ePrevious studies have generally suggested that central obesity indices have greater value than BMI in predicting cardiometabolic abnormalities. Our findings are consistent with prior systematic reviews and meta-analyses, which reported that WC and WHtR exhibit higher sensitivity and specificity for predicting hypertension and metabolic syndrome [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. In several large-scale population studies in Asia, a WHtR\u0026thinsp;\u0026ge;\u0026thinsp;0.5 has been widely recognized as a generalizable risk threshold [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. The cutoff values obtained in the present study align closely with these findings, further supporting their applicability in Asian populations.\u003c/p\u003e \u003cp\u003eNotably, the WC and BMI cutoff values in our population were lower than those commonly used in Western populations. WHO expert consultations and related guidelines typically recommend a WC threshold of approximately 94 cm for men and 80 cm for women [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], whereas our study found values of 82.4 cm and 78.1 cm, respectively. This discrepancy likely reflects inter-ethnic differences in fat distribution. Previous research indicates that Asians tend to accumulate visceral fat at lower BMI levels [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], which may explain why relatively lower WC and WHtR values are significantly associated with hypertension.\u003c/p\u003e \u003cp\u003eMechanistically, central obesity may elevate blood pressure through multiple pathways. Visceral adipose tissue is metabolically active, secreting various pro-inflammatory cytokines and adipokines that can activate the renin\u0026ndash;angiotensin\u0026ndash;aldosterone system and the sympathetic nervous system, directly contributing to increased blood pressure [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. In addition, visceral fat accumulation is closely associated with insulin resistance, which is considered an important driver of hypertension [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. By contrast, BMI merely reflects the weight-to-height ratio and cannot distinguish between visceral and subcutaneous fat, limiting its sensitivity in revealing metabolic risk.\u003c/p\u003e \u003cp\u003eIt is worth noting that ABSI did not demonstrate satisfactory predictive performance in this study. The external applicability of this index remains inconsistent in the literature, with considerable variability observed between some Asian and Western populations, suggesting that its generalizability across different populations may be limited [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Sex- and Age-Specific Differences and Public Health Implications\u003c/h2\u003e \u003cp\u003eThis study further found that the predictive performance of obesity indices was generally higher in women than in men, with particularly pronounced risk in young women. This difference may be related to sex hormones and patterns of fat distribution. Estrogen typically improves vascular function and promotes subcutaneous fat deposition, which may reduce blood pressure risk to some extent; however, when central obesity occurs in young women, this protective effect may be attenuated, thereby exposing more pronounced metabolic abnormalities [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Age also plays an important role. Although younger individuals generally have greater vascular compliance and theoretically stronger compensatory capacity, the presence of visceral fat accumulation at this stage suggests impaired metabolic homeostasis, resulting in relatively higher ORs [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThese findings have important implications for public health and clinical practice. First, while BMI is widely used, it may underestimate hypertension risk in Asian populations, highlighting the need to prioritize WC and WHtR in screening and health management [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Second, young women emerge as a high-risk group, suggesting that public health interventions should be implemented earlier, targeting lifestyle, dietary patterns, and physical activity. Finally, the localized cutoff values identified in this study are lower than WHO international standards, indicating that applying Western thresholds may delay the identification of high-risk individuals. WHtR is simple to calculate, cost-effective, and suitable for primary care settings, and thus may serve as a key regional tool for hypertension risk assessment in the future.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e4.4 Strengths and Limitations\u003c/h2\u003e \u003cp\u003eThe large sample size and robust findings of this study, together with the systematic comparison of multiple obesity indices, provide evidence-based guidance for hypertension screening in Macao. However, several limitations should be noted. First, the cross-sectional design precludes causal inference, and prospective cohort studies are needed for validation. Second, potential confounders such as diet, physical activity, genetics, and medication use were not included, which may affect the interpretation of results. Third, the study population was limited to adults in Macao, and the generalizability of the findings to other populations should be considered with caution. Finally, novel indices such as ABSI are computationally complex and show considerable inter-ethnic variation, limiting their widespread clinical applicability.\u003c/p\u003e \u003c/div\u003e"},{"header":"5 Conclusion","content":"\u003cp\u003eBased on a large adult population in Macao, this study systematically compared multiple obesity indices in relation to hypertension. The results indicated that WC and WHtR outperformed BMI and several novel indices in risk identification, with WHtR demonstrating particularly strong predictive performance in women. Further analyses showed that central obesity in young women was associated with a relatively greater increase in hypertension risk. The findings also suggested that the optimal cutoff values of WHtR and WC determined by the Youden index were lower than the WHO-recommended international standards, indicating that even relatively low levels of obesity in Asian populations may confer substantial blood pressure risk.\u003c/p\u003e \u003cp\u003eThis study not only further validates previous conclusions regarding WC cutoffs but also provides new empirical evidence supporting the application of WHtR in the Macao population. In clinical and public health practice, WC and WHtR should be incorporated into routine risk assessment, particularly in community and primary care settings, to facilitate the early identification of individuals at high risk. Young women should be prioritized for intervention to achieve early prevention of hypertension. Future research should integrate prospective cohorts and metabolic biomarkers to develop more precise risk prediction tools and further explore the applicability of obesity indices across diverse populations.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eAUC(area under the receiver operating characteristic curve)\u003c/p\u003e\n\u003cp\u003eBMI( body mass index)\u003c/p\u003e\n\u003cp\u003eBRI(body roundness index)\u003c/p\u003e\n\u003cp\u003eCI(conicity index)\u003c/p\u003e\n\u003cp\u003eWC(waist circumference)\u003c/p\u003e\n\u003cp\u003eWHR(waist\u003c/p\u003e\n\u003cp\u003eto-hip ratio)\u003c/p\u003e\n\u003cp\u003eWHtR(waist\u003c/p\u003e\n\u003cp\u003eto-height ratio)\u003c/p\u003e\n\u003cp\u003eABSI(a body shape index)\u003c/p\u003e\n\u003cp\u003eSBP(systolic blood pressure)\u003c/p\u003e\n\u003cp\u003eDBP(diastolic blood pressure)\u003c/p\u003e\n\u003cp\u003eROC(receiver operating characteristic)\u003c/p\u003e\n\u003cp\u003eOR(odds ratio)\u003c/p\u003e\n\u003cp\u003e95%CI(95% confidence interval)\u003c/p\u003e\n\u003cp\u003eWHO(World Health Organization)\u003c/p\u003e\n\u003cp\u003eNCD\u003c/p\u003e\n\u003cp\u003eRisC(Non-Communicable Disease Risk Factor Collaboration).\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eEthics approval and consent to participate：This study was conducted in accordance with the Declaration of Helsinki and approved by the Ethics Committee of the China Institute of Sport Science (approval number: CISS-20190607). Written informed consent was obtained from individual or guardian participants.\u003c/p\u003e\n\u003cp\u003eConsent for publication：Not applicable.\u003c/p\u003e\n\u003cp\u003eAvailability of data and materials: The datasets generated and analysed during the current study are not publicly available but are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003eCompeting interests：The authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003eFunding:\u0026nbsp;This study was supported by the 2025 Macao Residents’ Physical Fitness Surveillance Technical Support Service Project (No. B2425) and the Fourth Macao Residents’ Physical Fitness Surveillance in 2020 (No. B2014).\u003c/p\u003e\n\u003cp\u003eAuthors' contributions：LP J: Writing - Original Draft and Conceptualization. YB G: Writing - Review \u0026amp; Editing. X P: Writing - Review \u0026amp; Editing and Visualization. XX C:\u0026nbsp;Formal analysis. MZ L: Visualization and Data Curation. DQ Z:\u0026nbsp;Validation.\u0026nbsp;CM W:\u0026nbsp;Review. JX C: Data Curation. YB W: Data Curation .K S: Project administration. YF Z: Supervision.\u003c/p\u003e\n\u003cp\u003eAcknowledgements:\u0026nbsp;We appreciated Ms. Chuanrui Cui for contributing to the revision of the Visualization of this manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eWorld Health Organization. Global Health Observatory data: Raised blood pressure. Geneva: WHO; 2021.\u003c/li\u003e\n\u003cli\u003eNCD Risk Factor Collaboration (NCD-RisC). Worldwide trends in hypertension prevalence and progress in treatment and control from 1990 to 2019: a pooled analysis of 1,201 population-representative studies with 104 million participants. Lancet. 2021;398(10304):957\u0026ndash;980. doi:10.1016/S0140-6736(21)01330-1. PMID:34450083.\u003c/li\u003e\n\u003cli\u003eLu J, Lu Y, Wang X, et al. Prevalence, awareness, treatment, and control of hypertension in China: data from 1\u0026middot;7 million adults in a population-based screening study (China PEACE Million Persons Project). Lancet. 2017;390:2549\u0026ndash;2558. doi:10.1016/S0140-6736(17)32478-9. PMID:29102084.\u003c/li\u003e\n\u003cli\u003eWang Z, Chen Z, Zhang L, et al. Status of hypertension in China: results from the China Hypertension Survey, 2012\u0026ndash;2015. Circulation. 2018;137:2344\u0026ndash;2356. doi:10.1161/CIRCULATIONAHA.117.032380. PMID:29449338.\u003c/li\u003e\n\u003cli\u003eMacao SAR Sports Bureau. 2020 Physical Fitness Report of Macao SAR Residents [Internet]. Macao: Sports Bureau; 2021. (in Chinese) \u003c/li\u003e\n\u003cli\u003eHall JE, do Carmo JM, da Silva AA, Wang Z, Hall ME. Obesity-induced hypertension: interaction of neurohumoral and renal mechanisms. Circ Res. 2015;116:991\u0026ndash;1006. doi:10.1161/CIRCRESAHA.116.305697. PMID:25767285.\u003c/li\u003e\n\u003cli\u003eWildman RP, Muntner P, Reynolds K, et al. The obese without cardiometabolic risk factor clustering and the normal weight with cardiometabolic risk factor clustering. Obesity (Silver Spring). 2008;16:1891\u0026ndash;1900. doi:10.1038/oby.2008.293. PMID:18695075.\u003c/li\u003e\n\u003cli\u003eAshwell M, Gunn P, Gibson S. Waist-to-height ratio is a better screening tool than waist circumference and BMI for adult cardiometabolic risk factors: systematic review and meta-analysis. Obes Rev. 2012;13:275\u0026ndash;286. doi:10.1111/j.1467-789X.2011.00952.x. PMID:22106927.\u003c/li\u003e\n\u003cli\u003eThomas DM, Bredlau C, Bosy-Westphal A, et al. Relationships between body roundness with body fat and visceral adipose tissue emerging from a new geometrical model. Obesity (Silver Spring). 2013;21:2264\u0026ndash;2271. doi:10.1002/oby.20408. PMID:23519954.\u003c/li\u003e\n\u003cli\u003eKrakauer NY, Krakauer JC. A new body shape index predicts mortality hazard independently of body mass index. PLoS One. 2012;7:e39504. doi:10.1371/journal.pone.0039504. PMID:22815607.\u003c/li\u003e\n\u003cli\u003eValdez R. A simple model-based index of abdominal adiposity. J Clin Epidemiol. 1991;44:955\u0026ndash;956. doi:10.1016/0895-4356(91)90059-I. PMID:1890438.\u003c/li\u003e\n\u003cli\u003eFluss R, Faraggi D, Reiser B. Estimation of the Youden index and its associated cutoff point. Biometrical Journal. 2005;47:458\u0026ndash;472. doi:10.1002/bimj.200410135. PMID:16161804.\u003c/li\u003e\n\u003cli\u003eAshwell M, Gibson S. Waist-to-height ratio as an indicator of \u0026apos;early health risk\u0026apos;: simpler and more predictive than BMI. BMJ Open. 2016;6:e010159. doi:10.1136/bmjopen-2015-010159. PMID:26975935.\u003c/li\u003e\n\u003cli\u003eMandrekar JN. Receiver operating characteristic curve in diagnostic test assessment. J Thorac Oncol. 2010;5(9):1315\u0026ndash;1316. doi:10.1097/JTO.0b013e3181ec173d. PMID:20736804.\u003c/li\u003e\n\u003cli\u003ePerkins NJ, Schisterman EF. The inconsistency of \u0026ldquo;optimal\u0026rdquo; cutpoints using two criteria based on the receiver operating characteristic curve. Am J Epidemiol. 2006;163(7):670\u0026ndash;675. doi:10.1093/aje/kwj063. PMID:16410346.\u003c/li\u003e\n\u003cli\u003eBrowning LM, Hsieh SD, Ashwell M. A systematic review of waist-to-height ratio as a screening tool for the prediction of cardiovascular disease and diabetes: 0.5 could be a suitable global boundary value. Nutr Res Rev. 2010;23:247\u0026ndash;269. doi:10.1017/S0954422410000144. PMID:20819243.\u003c/li\u003e\n\u003cli\u003eYoo EG. Waist-to-height ratio as a screening tool for obesity and cardiometabolic risk. Clin Chim Acta. 2016;459:157\u0026ndash;163. doi:10.1016/j.cca.2016.07.006. PMID:27591527.\u003c/li\u003e\n\u003cli\u003eZhang X, Ye R, Sun L, et al. Relationship between novel anthropometric indices and the incidence of hypertension in Chinese individuals: a prospective cohort study based on the CHNS from 1993 to 2015. BMC Public Health. 2023;23:436. doi:10.1186/s12889-023-15208-7.\u003c/li\u003e\n\u003cli\u003eWorld Health Organization. Waist circumference and waist\u0026ndash;hip ratio: Report of a WHO Expert Consultation. Geneva: WHO; 2011. ISBN: 9789241501491.\u003c/li\u003e\n\u003cli\u003eDespr\u0026eacute;s JP. Body fat distribution and risk of cardiovascular disease: an update. Circulation. 2012;126:1301\u0026ndash;1313. doi:10.1161/CIRCULATIONAHA.111.067264. PMID:22949540.\u003c/li\u003e\n\u003cli\u003eLee DY, et al. Prediction of mortality with a body shape index in young Asians: comparison with BMI and WC. Obesity (Silver Spring). 2018;26:1096\u0026ndash;1103. doi:10.1002/oby.22193. PMID:29719128.\u003c/li\u003e\n\u003cli\u003eTaylor LE, Sullivan JC. Sex differences in obesity-induced hypertension and vascular dysfunction: a protective role for estrogen in adipose tissue inflammation? Am J Physiol Regul Integr Comp Physiol. 2016;311:R714\u0026ndash;R720. doi:10.1152/ajpregu.00202.2016. PMID:27547012.\u003c/li\u003e\n\u003cli\u003eChoi JR, Ahn SV, Kim JY, et al. Comparison of various anthropometric indices for the identification of a predictor of incident hypertension: The ARIRANG study. J Hum Hypertens. 2018;32:294\u0026ndash;300. doi:10.1038/s41371-018-0043-4.\u003c/li\u003e\n\u003cli\u003eRegensteiner JG, Reusch JEB. Sex differences in cardiovascular consequences of hypertension, obesity, and diabetes: JACC Focus Seminar 4/7. J Am Coll Cardiol. 2022;79:1492\u0026ndash;1505. doi:10.1016/j.jacc.2022.02.010. PMID:35422246.\u003c/li\u003e\n\u003cli\u003eUeda K, Okuda K, Yamashita T. Sex differences and regulatory actions of estrogen in cardiovascular physiology. Front Physiol. 2021;12:738218. doi:10.3389/fphys.2021.738218.\u003c/li\u003e\n\u003cli\u003eMcClements L, Kautzky-Willer A, Kararigas G, et al. The role of sex differences in cardiovascular, metabolic, and immune functions in health and disease: a review for \u0026ldquo;Sex Differences in Health Awareness Day\u0026rdquo;. Biol Sex Differ. 2025;16:33. doi:10.1186/s13293-025-00714-1.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Hypertension, Obesity indices, Anthropometry, Cardiometabolic risk, Macao, Public health surveillance","lastPublishedDoi":"10.21203/rs.3.rs-8072827/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8072827/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e: Obesity is a major modifiable risk factor for hypertension, yet the predictive performance of various anthropometric indices differs across populations. Macao lacks population-based evidence on which obesity measures best identify individuals at risk of hypertension.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e Data were obtained from four waves of the Macao SAR Physical Fitness Surveillance (2005, 2010, 2015, and 2020), comprising 14,288 adults aged 20–59 years. Seven obesity indices—body mass index (BMI), waist circumference (WC), waist-to-height ratio (WHtR), waist-to-hip ratio (WHR), body roundness index (BRI), a body shape index (ABSI), and conicity index (CI)—were analyzed. Logistic regression models estimated associations with hypertension after adjustment for confounders. Receiver operating characteristic (ROC) curves and the Youden index were used to assess predictive performance and identify optimal cutoffs.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eWC exhibited the strongest associations with hypertension, with adjusted odds ratios (OR) of 1.89 (95% CI: 1.79–2.00), respectively. Among women, the predictive ability (AUC up to 0.748) exceeded that in men (AUC up to 0.689). WHtR and BRI achieved the highest discriminative accuracy in both sexes. The optimal WHtR cutoff for hypertension was 0.4941 in men and 0.4814 in women—lower than WHO-recommended global thresholds. The impact of central obesity was especially pronounced among younger women.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions:\u003c/strong\u003e WC and WHtR outperform BMI and novel indices in identifying hypertension risk among Macao adults. The findings support adopting locally optimized WC and WHtR thresholds for early hypertension screening, particularly in younger women. Integrating these simple indicators into community health programs could enhance prevention and early detection strategies for hypertension in Asian populations.\u003c/p\u003e","manuscriptTitle":"Refining Obesity Metrics for Hypertension Risk Stratification in Macao: A 15-Year Multi- Indicator Comparative Study Based on Four Waves of Population Surveillance","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-19 12:29:33","doi":"10.21203/rs.3.rs-8072827/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewersInvited","content":"","date":"2025-12-17T12:17:02+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-11-17T09:22:21+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-11-15T08:47:01+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-11-15T08:45:42+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Public Health","date":"2025-11-10T05:02:30+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"2293ac9c-6421-4537-bba6-fcc28e4c07df","owner":[],"postedDate":"December 19th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-12-19T12:29:33+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-19 12:29:33","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8072827","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8072827","identity":"rs-8072827","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}