Nonlinear Associations and Gender Disparities Between Cardiometabolic Index and Hypertension in Chinese Adults

Nonlinear Associations and Gender Disparities Between Cardiometabolic Index and Hypertension in Chinese Adults | 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 Nonlinear Associations and Gender Disparities Between Cardiometabolic Index and Hypertension in Chinese Adults Yan Xuan, Dou Tang, Fanfan Zhu, Shujie Wang, Xun Wang, Ying Shen, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6612309/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 The present study aimed to evaluate the relationship between cardiometabolic index (CMI) and hypertension. We undertook a cross-sectional study with a sample of 9013 adults from China. The cardiometabolic index is computed as a product of WHtR and triglyceride-to-HDL cholesterol ratio (TG/HDL ratio). Logistic regression analysis was used to evaluate the association between the CMI and hypertension. Restricted cubic spline (RCS) analysis demonstrated a nonlinear relationship between the CMI and hypertension. The predictive capability of CMI was evaluated using ROC curve analysis. Participants in the highest CMI quartile (Q4) exhibited a 1.34-fold increased risk of hypertension compared to those in the lowest quartile (Q1). Notably, females with CMI > 0.36 exhibited a 52% higher hypertension risk (OR = 1.52, 95% CI:1.15–1.99). Gender-specific analyses revealed that the association was attenuated in males after full adjustment for covariates, suggesting a stronger link between CMI and hypertension risk in females. Restricted cubic spine (RCS) analysis showed a nonlinear relationship, with hypertension risk accelerating beyond a CMI threshold of 0.36. The predictive performance of CMI yielded an AUC of 0.667, indicating moderate discriminatory ability. The study underscores the importance of metabolic health in hypertension pathogenesis and suggests that CMI may serve as a useful marker for early intervention and risk stratification in clinical practice. Further studies are warranted to validate these findings and elucidate the underlying mechanisms. cardiometabolic index hypertension Chinese adults Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction The increasing prevalence of hypertension in China has been largely attributed to dietary Westernization and sedentary lifestyles 1 . Recent epidemiological evidence highlights a substantial hypertension burden in Chinese adults, with awareness, treatment, and control rates demonstrating progressive attrition along the care cascade 2 . Longitudinal cohort studies have consistently identified hypertension as a modifiable risk factor for cardiovascular outcomes, mortality 3 and disability 4 independent of traditional risk covariates. Previous studies demonstrated that lipid levels and obesity indices were associated with hypertension 5 – 7 , chronic kidney disease (CKD) 8 and cardiovascular diseases 9 , Triglyceride metabolism emerges as a central hypertension determinant in Caucasian populations, outperforming conventional lipid measures in predictive models 10 . Our previous investigations have characterized the hypertriglyceridemic waist (HTGW) phenotype as a potent predictor of hypertension progression and diabetes-related cardiovascular sequelae in this aging cohort 11 12 . The Cardiometabolic Index (CMI), originally conceptualized by Wakabayashi et al. operationalizes cardiometabolic risk stratification through the multiplicative product of waist-to-height ratio (WHtR) and triglyceride/HDL-cholesterol ratio (TG/HDL-C), expressed as: CMI = (Waist circumference/Height) × (Triglycerides [mg/dL]/HDL-C [mg/dL]) 13 . Some previous studies showed the strong positive epidemiological associations of the CMI with the risk of cardiovascular events 14 , prediabetes and diabetes 15 , chronic kidney disease (CKD) 16 . WHtR reflects central obesity better than BMI (which fails to distinguish fat distribution). TG/HDL-C is a biomarker of resistance and atherogenic lipid profile. CMI integrates both, which can more comprehensively assess the synergistic effect of visceral fat deposition and li metabolism disorders, especially suitable for predicting metabolic diseases such as hypertension and diabetes. However, to date, few studies have investigated the association of the CMI and hypertension. Despite CMI’s established role in predicting diabetes and CKD, its association with hypertension remains underexplored, particularly in Asian populations with distinct adiposity patterns. China's hypertension care quality chasm persists: 45% prevalence vs 7% control rates in midlife adults, demanding urgent primary care reinforcement 17 18 . Accelerating population aging mandates nationwide hypertension incidence surveillance integrating CMI-based risk stratification to optimize primary prevention in Chinese adults ≥ 45 years, aligning with Healthy China 2030 cardiovascular reduction targets. In this community-based cross-sectional study (2018–2022, n = 9013), we systematically investigated the dose-response relationship between cardiometabolic index (CMI) and hypertension prevalence among adults aged 45–80 years in Shanghai municipality, employing restricted cubic splines to characterize nonlinear associations. Materials and Methods Study population During March to August 2020, about 10,824 participants (aged ≥ 40 years) did health check in the health center of Ruijin Hospital, Luwan branch, Huangpu district, Shanghai. First, from March to August 2020, individuals aged 40 years or older who were natives of Shanghai municipality or those who had lived in Shanghai for at least 5 years who underwent health checks in this health center were enrolled. Second, we invited participants to participate in the study by telephone. Exclusion criteria included inability to provide consent, pregnancy, or critical illnesses such as cancer, organ transplant, or dialysis. At this stage, 10824 participants were called. Third, 9214 participants provided informed consent and were recruited (response rate 85.1%). The exclusion criteria were as follows: second hypertension (caused by an identifiable underlying disorder such as kidney disease, endocrine disease, aortic coarctation, etc.)(n = 107); missing data (n = 94). Finally, 9013 subjects were included in the analysis. (Fig. 1 ) The study protocol (No. LWEC2020024) was approved by the Ethics Committee of the Shanghai Ruijin Hospital, Luwan branch, Shanghai Jiao Tong University School of Medicine. Informed consent was obtained from all participants included in our study. Clinical, anthropometric and laboratory measurements Uniformly trained research staff administered a standardized questionnaire assessing sociodemographic profiles, individual/family medical histories, and lifestyle determinants through structured face-to-face interviews. The body mass index (BMI) was calculated as weight (in kilograms) divided by height (in meters squared). BMI < 24 kg/m 2 was defined as normal weight, while BMI ≥ 24 kg/m 2 was defined as overweight/obese, as determined by the Cooperative Meta-Analysis Group of the Working Group on Obesity in China criteria 19 . Blood pressure measurements were conducted following standardized protocols: Participants underwent seated rest (≥ 5 min) prior to triplicate measurements at 5-minute intervals using a validated Oscillo metric device (Omron HEM-7200, ESH-certified), with final BP calculated as the arithmetic mean. Concurrently, smoking status was classified per CDC criteria, defining current smokers as individuals with ≥ 100 lifetime cigarettes and persistent tobacco use at enrollment 20 . Educational attainment was dichotomized based on high school completion status. Physical activity assessment incorporated both occupational and leisure domains, with participants stratified according to WHO guidelines into those meeting recommended thresholds (≥ 150 min/week moderate-vigorous activity across both domains) versus suboptimal engagement 21 . Following an 8-hour overnight fast, venous blood samples were collected under standardized protocols involving immediate refrigeration at 4°C, centrifugation within 120 minutes, and long-term storage at -80°C in aliquots. Biochemical profiling was performed using certified methodologies: HbA1c levels were quantified via high-performance liquid chromatography (HPLC; MQ-2000PT system, MEDCONN, NGSP-certified), while lipid parameters (triglycerides [TG, AUZ5612 kit, sensitivity 0.01 mmol/L, CV < 7%], total cholesterol, HDL-C, LDL-C), glucose homeostasis markers (fasting plasma glucose), and renal function indices (uric acid, serum creatinine via Jaffé method) were analyzed on an AU680 automated platform (Beckman Coulter). First-morning urine samples underwent prompt albumin-creatinine ratio (ACR) measurement, with estimated glomerular filtration rate (eGFR) calculated using the CKD-EPI China Eq. (2014 adaptation). All procedures adhered to ISO 15189 standards with daily calibration and 5% random duplicate testing (intraassay CV < 8%) 22 . Definition of variables Hypertension was defined as systolic BP ≥ 140 mmHg, diastolic BP ≥ 90 mmHg, or self-reported use of antihypertensive medications within the past 2 weeks, based on the 2023 ACC/AHA guidelines 23 . Chronic kidney disease was defined as an estimated glomerular filtration rate (eGFR) less than 60 mL/min/1.73 m 2 and urinary albumin creatinine ratio (UACR) ≥ 30 mg/g, and it was defined as CKD 24 . The CVD outcome was defined as a previous diagnosis of coronary heart disease, stroke, or peripheral arterial disease and was recorded in the previous studies 11 25 . Definitions of the CMI The WHtR refers to the waist circumference in centimeters divided by the height in centimeters. The TG/HDL-C ratio represents the relationship between TG and HDL-C levels. The WHtR is multiplied by TG/HDL-C to calculate the CMI. On the basis of CMI Quartiles, the participants were categorized into three groups: Q1 group (CMI ≤ 0.33), Q2 group (0.33 < CMI ≤ 0.53), Q3 group (0.53 0.87). Statistical analysis Data analyses were performed using IBM SPSS version 25 statistical software (IBM Corp., Armonk, NY, USA). P < 0.05 indicated significance (two-sided). Continuous variables were presented as the mean ± standard deviation (SD), while non-normally distributed data are expressed as median (interquartile range [IQR]) and categorical variables were presented as percentages (%) when appropriate. Logistic regression was used to assess the association between CMI quartiles and hypertension. Model 1 was adjusted for age, sex, BMI, current smoker. Model 2 was adjusted for Model 1 plus TC, LDL, HbA1c, UA, eGFR, physical activity, education status, systolic blood pressure, diastolic blood pressure, current smoker, diabetes, antidiabetic agents, antihypertensive drugs, physical activity and education status. In addition, stratified analyses by sex, BMI and presence of diabetes, current smoker, LDL and TC were performed.The restricted cubic spline (RCS) analysis was applied to explore potential nonlinear correlations between the CMI and hypertension. A receiver operating characteristic (ROC) curve analysis which was quantified by the area under the ROC curve (AUC) was used to evaluate the value of CMI and other metabolism markers for predicting hypertension by comparing the area under the ROC curve (AUC). Results Baseline clinical characteristics The baseline anthropometric parameters and biochemical indices according to four triglyceride waist phenotypes are shown in Table 1 . Participants in the highest CMI quartile (Q4) were older and had significantly higher BMI, waist circumference (WC), systolic blood pressure (SBP), and diastolic blood pressure (DBP) compared to those in the lowest quartile (Q1); Higher levels of TC, TG, LDL-C, FPG, HbA1c and uACR; and lower levels of HDL-C and eGFR were found in the Q4 group (P < 0.001, Table 1 ). Subjects with the highest CMI level group were more likely to be current smokers, less likely to engage in physical activity and lowest education (P < 0.001). Subjects with Q4 group also had a higher prevalence of a history of diabetes, cardiovascular diseases, and CKD than those in the other three groups (P < 0.001). In Table 1 , the prevalence of hypertension was 65% (5856/9013). Participants in the Q4 group had the highest prevalence of hypertension (1705/2234 (76.3%)) among the four subgroups (P < 0.001). Participants in the Q2 and Q3 groups had a higher prevalence of hypertension than those in the Q1 Group (P < 0.001). Table 1 Characteristics of the participants in each phenotype group (n = 9013). Q1 Q2 Q3 Q4 P for trend No. of participants (%) 2244 2275 2260 2234 Age at baseline (year) 60.46 ± 10.80 62.99 ± 9.61 63.70 ± 9.06 63.91 ± 8.72 0.077 Male (%) 816(36.4) 923(40.6) 1000(44.2) 1064(47.6) 0.001 BMI (kg/m 2 ) 22.65 ± 2.84 24.34 ± 2.95 25.43 ± 3.17 26.14 ± 3.02 < 0.001 WC(cm) 77.22 ± 8.38 82.50 ± 7.95 85.64 ± 8.24 88.11 ± 7.94 < 0.001 SBP (mmHg) 131.92 ± 18.86 135.44 ± 18.45 138.30 ± 18.25 140.65 ± 18.55 0.001 DBP (mmHg) 83.63 ± 10.11 85.17 ± 9.99 86.54 ± 9.97 87.92 ± 10.04 < 0.001 FBG (mmol/L) HbA1C (%) 5.07 ± 1.29 5.59 ± 0.80 5.25 ± 1.33 5.71 ± 0.82 5.46 ± 1.65 5.87 ± 0.97 5.78 ± 1.96 6.07 ± 1.15 < 0.001 < 0.001 TG (mmol/L) 0.83 ± 0.20 1.21 ± 0.24 1.63 ± 0.32 2.94 ± 1.63 < 0.001 TC (mmol/L) 4.92 ± 0.80 5.09 ± 0.86 5.14 ± 0.88 5.18 ± 0.89 < 0.001 HDL-C (mmol/L) 1.75 ± 0.30 1.47 ± 0.24 1.29 ± 0.22 1.07 ± 0.19 < 0.001 LDL-C (mmol/L) 2.98 ± 0.69 3.33 ± 0.75 3.45 ± 0.77 3.26 ± 0.83 < 0.001 eGFR (mL/m/1.73 m 2 ) 91.04 ± 14.17 88.15 ± 13.50 86.63 ± 14.45 85.05 ± 14.97 < 0.001 uACR (mg/g) 46.19 ± 175.07 49.03 ± 241.90 54.93 ± 163.42 96.04 ± 323.56 < 0.001 CKD, (%) 69(3.1) 85(3.7) 76(3.4) 79(3.5) < 0.001 Hypertension, (%) 1138(50.7) 1426(62.7) 1587(70.2) 1705(76.3) < 0.001 Diabetes (%) 191(8.5) 288(12.6) 388(17.2) 543(24.3) < 0.001 CVD (%) 52(2.3) 71(3.1) 84(3.7) 97(4.4) < 0.001 Overweight/obesity, (%) 412(18.4) 895(39.3) 1194(52.8) 1207(54.0) < 0.001 Current smoker (%) 246(10.9) 298(13.1) 362(16.0) 436(19.5) < 0.001 Education (≥ high school), % 163(7.3) 127(5.6) 140(6.2) 118(5.3) < 0.001 High physical activity, % 188(8.4) 163(7.2) 108(4.8) 72(3.2) < 0.001 CMI 0.23 ± 0.06 0.43 ± 0.06 0.67 ± 0.09 1.59 ± 1.11 < 0.001 Data are expressed as the mean SD, median value [interquartile range] or as n (%), as appropriate. BMI, body mass index; WC, waist circumference; SBP, systolic blood pressure; DBP, diastolic blood pressure; FPG, fasting blood glucose; TC, total cholesterol; TG, HDL-C, high density lipoprotein cholesterol; LDL-C, low density lipoprotein cholesterol, eGFR, estimated glomerular filtration rate; uACR, urinary albumin creatinine ratio; UA, uric acid; Association of the CMI with hypertension In the overall population, higher CMI quartiles (Q3 and Q4) were significantly associated with an elevated risk of hypertension, even after adjusting for covariates in Model 1 and Model 2. For Q4, the adjusted OR was 1.22 (95% CI: 1.06–1.39) in Model 2. However, the association between CMI and hypertension risk exhibited notable gender differences. In males, the association was less pronounced after full adjustment for covariates, with no significant risk increase in Q4 (OR: 0.79, 95% CI: 0.60–1.05). In contrast, the association remained significant in females, particularly in Q4 (OR: 1.52, 95% CI: 1.15–1.99) in Model 2, suggesting that females with higher CMI levels are at a greater risk of hypertension compared to males. Despite these differences, no significant interaction was observed between CMI and gender (P for interaction > 0.05), indicating that the association between CMI and hypertension risk is consistent across genders, albeit stronger in females. The association between CMI quartiles and hypertension was evaluated using multivariable logistic regression models (Table 2 ). After adjusting for age, sex, BMI, smoking status, and other metabolic variables, Subgroup analyses revealed that this association was particularly pronounced in individuals aged ≥ 60 years (OR: 1.34, 95% CI: 1.07–1.68, P for trend = 0.011) and those with BMI ≥ 24 kg/m² (OR: 1.37, 95% CI: 1.02–1.83, P for trend = 0.035). No significant interaction was observed between CMI and subgroups such as age, BMI, or diabetes status (P for interaction > 0.05)(Figure 2 ). Table 2 Odds ratios for hypertension according to CMI quartiles by various subgroups Subpopulation Cases/Participants Q1 Q2 Q3 Q4 P-trend P-interaction 0.542 Age, years b < 60 2040/9013 1.00(ref) 1.32(0.85,2.04) 0.92(0.57,1.48) 1.11(0.65,1.89) 0.438 ≥ 60 6973/9013 1.00(ref) 1.17(0.95,1.44) 1.30(1.05,1.62) 1.34(1.07,1.68) 0.011 0.678 BMI, kg/m 2 c < 24 4977/9013 1.00(ref) 1.21(0.99,1.46) 1.22(0.99,1.50) 1.36(1.09,1.69) 0.043 ≥ 24 4036/9013 1.00(ref) 1.12(0.84,1.50) 1.13(0.85,1.51) 1.37(1.02,1.83) 0.035 Presence of T2DM d 0.584 No 8064/9013 1.00(ref) 1.57(1.35,1.82) 1.73 (1.44,2.08) 2.36 (1.95,2.86) 0.040 yes 949/9013 1.00(ref) 1.09(0.56,2.16) 0.89(0.45,1.78) 0.82(0.42,1.62) 0.808 Current smoker e 0.358 No 7672/9013 1.00(ref) 1.23(1.02,1.49) 1.30(1.06,1.59) 1.33(1.07,1.64) 0.036 Yes 1341/9013 1.00(ref) 1.21(0.73,1.96) 0.79(0.41,3.76) 1.18(1.08,2.30) 0.001 TC f 0.232 < 5.2 5172/9013 1.00(ref) 1.26(1.00,1.59) 1.29(1.01,1.64) 1.56(1.20,2.02) 0.010 ≥ 5.2 3841/9013 1.00(ref) 1.28 (0.60,2.72) 0.72 (0.31,1.66) 1.12 (1.11,1.13) 0.022 LDL-c g < 3.4 5234/9013 1.00(ref) 1.19(0.93,1.53) 1.40(0.97,2.02) 1.40(1.05,1.87) 0.012 0.394 ≥ 3.4 3379/9013 1.00(ref) 1.12(0.84,1.49) 1.12(1.11,1.13) 1.11(1.01,2.13) 0.011 b for age subgroup: adjusted for sex, BMI, LDL-C, HDL-C, TC, FBG, HbA1c, eGFR, UA, physical activity, education status, current smoker and current drinker, systolic blood pressure, diastolic blood pressure, diabetes, anti-diabetes agents, antihypertension drug; c for BMI subgroup: adjusted for age, sex, LDL-C, HDL-C, TC, FBG, HbA1c, eGFR, UA, physical activity, education status, current smoking and current drink systolic blood pressure, diastolic blood pressure, diabetes, anti-diabetes agents, antihypertension drug; d for T2DM subgroup: adjusted for age, sex, BMI, LDL-C, HDL-C, TC, FBG, HbA1c, eGFR, UA, physical activity, education status, current smoker and current drinker, systolic blood pressure, diastolic blood pressure, anti-diabetes agents(only for diabetes group), antihypertension drug; e for smoke status subgroup: adjusted for age, sex, BMI, LDL-C, HDL-C, TC, FBG, HbA1c, eGFR, UA, physical activity, education status, current drinker, systolic blood pressure, diastolic blood pressure, anti-diabetes agents(only for diabetes group), antihypertension drug; f for TC subgroup: adjusted for age, sex, BMI, HDL-C, TG, LDL, FBG, HbA1c, eGFR, UA, physical activity, education status, current smoker; current drinker; systolic blood pressure, diastolic blood pressure, anti-diabetes agents(only for diabetes group), antihypertension drug ; g for LDL-c subgroup: adjusted for age, sex, BMI, HDL-C, TC, FBG, HbA1c, eGFR, UA, physical activity, education status, current smoker; current drinker; systolic blood pressure, diastolic blood pressure, anti-diabetes agents(only for diabetes group), antihypertension drug; The AUCs (and 95% CIs) of variables in CMI and other measurements for hypertension The predictive ability of CMI for hypertension was assessed using receiver operating characteristic (ROC) curve analysis (Fig. 3 ). The area under the curve (AUC) for CMI was 0.667 (95% CI: 0.655–0.679), with a sensitivity of 0.77 and specificity of 0.60 at the optimal cutoff point of 0.36. CMI demonstrated comparable predictive performance to traditional metabolic indices such as waist circumference (AUC: 0.664, 95% CI: 0.652–0.676) and triglycerides (AUC: 0.626, 95% CI: 0.614–0.638) (Table 3 ). suggesting that CMI may serve as a useful composite marker for hypertension risk assessment. Table 3 Area under the receiver operating characteristic curve (AUC) of CMI and metabolism indexes variables for hypertension Variables AUC 95% CI Cut-off point Sensitivity Specificity Youden index CMI# 0.667 (0.655–0.679) 0.36 0.77 0.60 0.37 TG, mmol/l# 0.626 (0.614–0.638) 1.05 0.76 0.62 0.38 WC, cm# 0.664 (0.652–0.676) 77 0.81 0.60 0.41 BMI, kg/cm 2 # 0.541 (0.529–0.554) 22.53 0.80 0.61 0.41 TC, mmol/l# 0.435 (0.422–0.447) 4.49 0.75 0.70 0.45 LDL-C, mmol/l# 0.496 (0.484–0.508) 2.72 0.76 0.71 0.47 #P < 0.001 Nonlinear Relationship Between CMI and Hypertension RCS analysis demonstrated a nonlinear association between CMI and hypertension risk (Fig. 4 ). After adjusting for age, sex, BMI, and other covariates, the risk of hypertension increased significantly with higher CMI levels, particularly beyond a threshold of 0.36 (P for nonlinearity = 0.015). This trend persisted in the fully adjusted model (P for nonlinearity = 0.001), indicating a robust association between elevated CMI and hypertension risk. Discussion In this study, we investigated the association between the Cardiometabolic Index (CMI) and hypertension risk in a large cohort of 9,013 participants. Our findings demonstrate that higher CMI levels are significantly associated with an increased risk of hypertension, particularly in females, even after adjusting for multiple confounding factors. These results align with previous studies, highlighting the role of metabolic dysregulation in the pathogenesis of hypertension and cardiovascular diseases 11 . The CMI, which integrates triglycerides, HDL and WHtR, provides a comprehensive measure of metabolic health and may serve as a valuable tool for risk stratification in clinical practice. Our analysis revealed a strong positive association between CMI and hypertension risk, with participants in the highest CMI quartile (Q4) exhibiting a 1.34-fold increased risk compared to those in the lowest quartile (Q1). This association was particularly pronounced in individuals aged ≥ 60 years and those with a BMI ≥ 24 kg/m², suggesting that CMI may be especially relevant in older and overweight populations. These findings are consistent with prior research indicating that metabolic abnormalities, such as elevated triglycerides and central obesity, are key contributors to hypertension development 26 27 . The nonlinear relationship identified in our RCS analysis suggests that hypertension risk increases markedly beyond a specific CMI threshold, emphasizing the importance of early intervention in individuals with elevated CMI levels. A notable finding of our study is the gender-specific association between CMI and hypertension risk. While the overall association was significant in the total population, the risk was more pronounced in females, with those in the highest CMI quartile (Q4) having a 1.52-fold increased risk of hypertension compared to their counterparts in Q1. In contrast, the association was attenuated in males after full adjustment for covariates, with no significant risk increase observed in Q4. This gender disparity may be attributed to differences in body composition, hormonal influences, and lipid metabolism between males and females. In our previous researches, we also found worse metabolism dysfunction in female than man 25 28 . On the other hand, we also found that the risk of hypertension in Q4 was significantly higher in non-diabetic participants (OR 2.36 [.95-2.86]) than in diabetic participants (OR 0.82 [0.42–1.62]), the possible reason for may be that diabetic patients often receive hypoglycemic (eg, metformin, SGLT2 inhibitors) or lipid-lowering therapy, statins), and these drugs may directly improve blood pressure or lipid profile, weakening the independent effect of CMI. The biological mechanisms linking CMI and hypertension are still unclear, but the critical role of current acknowledged hypotheses is visceral fat accumulation or visceral obesity 29 Abdominal fat accumulation can induce insulin resistance, which in turn activates the adrenergic system, leading to increased sympathetic nervous system activity and elevated blood pressure. Insulin also inhibits the production of prostacyclin in adipose tissue, reducing its vasodilatory effects and thereby increasing peripheral vascular resistance. Additionally, insulin disrupts intracellular cation regulation by suppressing sodium/potassium ATPase (Na+/K + ATPase) activity and enhancing the Na+/K + pump in vascular smooth muscle cells. This increases their sensitivity to catecholamines and angiotensin II, further elevating peripheral resistance and contributing to hypertension 35 . In addition, insulin resistance is associated with the release of various factors from excess adipose tissue, such as no esterified fatty acids, cytokines, and adiponectin. These factors impair vascular endothelial function, reducing the elasticity of blood vessels and contributing to the development of hypertension 30 . Furthermore, the activation of the Renin-Angiotensin-Aldosterone System (RAAS) plays a critical role in mediating elevated blood pressure in obesity. Studies have demonstrated that abdominal subcutaneous adipose tissue significantly secretes angiotensin II (Ang II) and angiotensinogen (AGT), as evidenced in both animal models and human adipose tissue 31 32 . Thus, it is likely that adipose tissue-derived RAAS components are involved in the regulation of blood pressure. This is attributable not only to sympathetic nervous system overactivity and renal compression but also to dysfunctional adipose tissue 32 . The predictive performance of CMI for hypertension, as assessed by ROC curve analysis, demonstrated an AUC of 0.667, indicating moderate discriminatory ability. While CMI performed comparably to traditional metabolic indices such as waist circumference and triglycerides, its integration of both lipid and anthropometric measures may offer a more holistic assessment of metabolic risk. Our findings have important clinical implications. The strong association between CMI and hypertension risk, particularly in females, suggests that CMI could serve as a useful tool for early identification of individuals at high risk for hypertension. Regular monitoring of CMI in clinical practice may facilitate timely interventions, such as lifestyle modifications or pharmacological treatments, to mitigate the risk of hypertension and its associated complications. Additionally, the gender-specific differences observed in our study highlight the need for tailored approaches in hypertension prevention and management, taking into account the unique metabolic profiles of males and females. Several limitations in this study need to be considered, several limitations should be acknowledged, including the cross-sectional design, potential measurement bias from self-reported data, and the single-center nature of the study, which may limit the generalizability of our findings. However, there are some limitations to consider. First, the cross-sectional design of the study precludes the establishment of causal relationships between CMI and hypertension. Second, the use of self-reported data for certain variables, such as smoking status and physical activity, may introduce measurement bias. Finally, the study population was drawn from a single center in China, which may limit the generalizability of our findings to other populations. Future prospective studies are needed to validate our results and explore the underlying mechanisms linking CMI to hypertension risk. Conclusion In conclusion, our findings indicate that elevated CMI levels are associated with a higher risk of hypertension, particularly among females. The CMI, as a composite measure of metabolic health, may serve as a valuable tool for risk stratification and early intervention in clinical practice. Gender-specific differences in the association between CMI and hypertension risk highlight the need for tailored approaches in hypertension prevention and management. Further studies are needed to validate the potential of CMI as a predictive marker for hypertension and other cardiovascular diseases Declarations Funding Sources: This study was supported by Shanghai Municipal Huangpu District Commission (HLQ202205) and Healthy Commission Research Project of Shanghai Huangpu District (HLM202206，HLM202401). The funder played no role in the design or conduct of the study, collection, management, analysis, or interpretation of data, or in the preparation, review, or approval of the article. Institutional Review Board Statement: The study protocol (No. LWEC202024) was approved by the Ethics Committee of the Shanghai Ruijin Hospital, Luwan branch, Shanghai Jiao Tong University School of Medicine. These subjects provided written informed consent, and the study protocol was approved by the institute’s committee on human research. Informed Consent Statement: Informed consent was obtained from all subjects involved in the study Data Availability The raw data supporting the conclusions of this manuscript will be made available by the authors, without undue reservation, to any qualified researcher. Conflicts of Interest: The authors declare no conflicts of interest. Acknowledgment : We gratefully acknowledge the invaluable assistance of the physicians of the Department of Endocrinology, Luwan branch, Ruijin Hospital, School of Medicine, Shanghai Jiaotong University and Ruijin Hospital, School of Medicine, Shanghai Jiaotong University. This study would not have been possible without their support. Clinical trial number: This is not a clinical trial. not applicable Ethics and Consent to Participate declarations ： The research protocol was conducted in accordance with the provisions of the Declaration of Helsinki in 1995 (as revised in Fortaleza, Brazil, October 2013), and approved by the Ethics Committee of Ruijin Hospital Lu Wan Branch, Shanghai Jiaotong University School of Medicine ( Approval No. LWEC202024, date of approval: March 9, 2020 ， by Ruijin Hospital Luwan Branch Ethics Committee Shanghai JiaoTong University School of Medicine) . All participants gave their written informed consent. We preserve the anonymity of our patients. There are no conflicts of interest to disclose. Author Contributions In this study, X. Y., S. Y. and W.S. J. were mainly responsible for the writing of the article, L. L. Q. was mainly responsible for research, Y.X was mainly responsible for data entry, T. D. were mainly responsible for data calculation and correction, and W. X and Z.F.F were mainly responsible for the final data results and additional experiments. The authors would like to thank all the participants for this article. References Jordan J, Yumuk V, Schlaich M, et al. Joint statement of the European Association for the Study of Obesity and the European Society of Hypertension: obesity and difficult to treat arterial hypertension. J Hypertens. 2012;30(6):1047–55. 10.1097/HJH.0b013e3283537347 . [published Online First: 2012/05/11]. Guo QH, Zhang YQ, Wang JG. Asian management of hypertension: Current status, home blood pressure, and specific concerns in China. J Clin Hypertens (Greenwich). 2020;22(3):475–78. 10.1111/jch.13687 . [published Online First: 2019/10/18]. Collaborators GBDRF, Forouzanfar MH, Alexander L, et al. Global, regional, and national comparative risk assessment of 79 behavioural, environmental and occupational, and metabolic risks or clusters of risks in 188 countries, 1990–2013: a systematic analysis for the Global Burden of Disease Study 2013. Lancet. 2015;386(10010):2287–323. 10.1016/S0140-6736(15)00128-2 . [published Online First: 2015/09/15]. Collaborators GBDCoD. Global, regional, and national age-sex specific mortality for 264 causes of death, 1980–2016: a systematic analysis for the Global Burden of Disease Study 2016. Lancet. 2017;390(10100):1151–210. 10.1016/S0140-6736(17)32152-9 . [published Online First: 2017/09/19]. Zhang M, Zhao Y, Sun H, et al. Effect of dynamic change in body mass index on the risk of hypertension: Results from the Rural Chinese Cohort Study. Int J Cardiol. 2017;238:117–22. 10.1016/j.ijcard.2017.03.025 . [published Online First: 2017/03/21]. Zhao Y, Zhang M, Luo X, et al. Association of 6-year waist circumference gain and incident hypertension. Heart. 2017;103(17):1347–52. 10.1136/heartjnl-2016-310760 . [published Online First: 2017/04/09]. Hall ME, do Carmo JM, da Silva AA, et al. Obesity, hypertension, and chronic kidney disease. Int J Nephrol Renovasc Dis. 2014;7:75–88. 10.2147/IJNRD.S39739 . [published Online First: 2014/03/07]. Foster MC, Hwang SJ, Porter SA et al. Fatty kidney, hypertension, and chronic kidney disease: the Framingham Heart Study. Hypertension 2011;58(5):784 – 90. 10.1161/HYPERTENSIONAHA.111.175315 [published Online First: 2011/09/21]. Sowers JR. Obesity as a cardiovascular risk factor. Am J Med 2003;115 Suppl 8A:37S-41S. 10.1016/j.amjmed.2003.08.012 [published Online First: 2003/12/18]. Sanchez-Inigo L, Navarro-Gonzalez D, Pastrana-Delgado J, et al. Association of triglycerides and new lipid markers with the incidence of hypertension in a Spanish cohort. J Hypertens. 2016;34(7):1257–65. 10.1097/HJH.0000000000000941 . [published Online First: 2016/05/03]. Xuan Y, Shen Y, Wang S, et al. The association of hypertriglyceridemic waist phenotype with hypertension: A cross-sectional study in a Chinese middle aged-old population. J Clin Hypertens (Greenwich). 2022;24(2):191–99. 10.1111/jch.14424 . [published Online First: 20220127]. Xuan Y, Zhang W, Wang Y, et al. The Association Between Hypertriglyceridemic-Waist Phenotype and Chronic Kidney Disease in Patients with Type 2 Diabetes: A Cross-Sectional METAL Study. Diabetes Metab Syndr Obes. 2022;15:1885–95. 10.2147/DMSO.S359742 . [published Online First: 2022/06/28]. Wakabayashi I, Daimon T. The cardiometabolic index as a new marker determined by adiposity and blood lipids for discrimination of diabetes mellitus. Clin Chim Acta. 2015;438:274–8. 10.1016/j.cca.2014.08.042 . [published Online First: 2014/09/10]. Zhu XM, Xu Y, Zhang J. Cardiometabolic Index is associated with heart failure: a cross-sectional study based on NHANES. Front Med (Lausanne). 2024;11:1507100. 10.3389/fmed.2024.1507100 . [published Online First: 2024/12/24]. Song J, Li Y, Zhu J, et al. Non-linear associations of cardiometabolic index with insulin resistance, impaired fasting glucose, and type 2 diabetes among US adults: a cross-sectional study. Front Endocrinol (Lausanne). 2024;15:1341828. 10.3389/fendo.2024.1341828 . [published Online First: 2024/02/27]. Miao M, Deng X, Wang Z, et al. Cardiometabolic index is associated with urinary albumin excretion and renal function in aged person over 60: Data from NHANES 2011–2018. Int J Cardiol. 2023;384:76–81. 10.1016/j.ijcard.2023.04.017 . [published Online First: 2023/04/15]. You Y, Teng W, Wang J, et al. Hypertension and physical activity in middle-aged and older adults in China. Sci Rep. 2018;8(1):16098. 10.1038/s41598-018-34617-y . [published Online First: 2018/11/02]. Falkner B, Gidding SS, Baker-Smith CM et al. Pediatric Primary Hypertension: An Underrecognized Condition: A Scientific Statement From the American Heart Association. Hypertension 2023;80(6):e101-e11. 10.1161/HYP.0000000000000228 [published Online First: 2023/03/31]. Bonora E, Targher G, Alberiche M, et al. Homeostasis model assessment closely mirrors the glucose clamp technique in the assessment of insulin sensitivity: studies in subjects with various degrees of glucose tolerance and insulin sensitivity. Diabetes Care. 2000;23(1):57–63. 10.2337/diacare.23.1.57 . Lee SB, Ahn CW, Lee BK, et al. Association between triglyceride glucose index and arterial stiffness in Korean adults. Cardiovasc Diabetol. 2018;17(1):41. 10.1186/s12933-018-0692-1 . [published Online First: 2018/03/23]. Ainsworth BE, Haskell WL, Whitt MC, et al. Compendium of physical activities: an update of activity codes and MET intensities. Med Sci Sports Exerc. 2000;32(9 Suppl):S498–504. 10.1097/00005768-200009001-00009 . [published Online First: 2000/09/19]. Kong X, Ma Y, Chen J, et al. Evaluation of the Chronic Kidney Disease Epidemiology Collaboration equation for estimating glomerular filtration rate in the Chinese population. Nephrol Dial Transpl. 2013;28(3):641–51. 10.1093/ndt/gfs491 . [published Online First: 2012/12/01]. Joglar JA, Chung MK, Armbruster AL, et al. 2023 ACC/AHA/ACCP/HRS Guideline for the Diagnosis and Management of Atrial Fibrillation: A Report of the American College of Cardiology/American Heart Association Joint Committee on Clinical Practice Guidelines. Circulation. 2024;149(1):e1–156. 10.1161/CIR.0000000000001193 . [published Online First: 20231130]. Hashimoto Y, Tanaka M, Okada H, et al. Metabolically healthy obesity and risk of incident CKD. Clin J Am Soc Nephrol. 2015;10(4):578–83. 10.2215/CJN.08980914 . [published Online First: 2015/01/31]. Xuan Y, Zhang W, Wang Y, et al. Association Between Uric Acid to HDL Cholesterol Ratio and Diabetic Complications in Men and Postmenopausal Women. Diabetes Metab Syndr Obes. 2023;16:167–77. 10.2147/DMSO.S387726 . [published Online First: 20230119]. Wang X, Xu W, Song Q, et al. Association between the triglyceride-glucose index and severity of coronary artery disease. Cardiovasc Diabetol. 2022;21(1):168. 10.1186/s12933-022-01606-5 . [published Online First: 20220901]. Hou XZ, Lv YF, Li YS, et al. Association between different insulin resistance surrogates and all-cause mortality in patients with coronary heart disease and hypertension: NHANES longitudinal cohort study. Cardiovasc Diabetol. 2024;23(1):86. 10.1186/s12933-024-02173-7 . [published Online First: 20240228]. Xuan Y, Gao P, Shen Y, et al. Association of hypertriglyceridemic waist phenotype with non-alcoholic fatty liver disease: a cross-sectional study in a Chinese population. Horm (Athens). 2022;21(3):437–46. 10.1007/s42000-022-00374-x . [published Online First: 20220521]. Cunha de Oliveira C, Carneiro Roriz AK, Eickemberg M, et al. Hypertriglyceridemic waist phenotype: association with metabolic disorders and visceral fat in adults. Nutr Hosp. 2014;30(1):25–31. 10.3305/nh.2014.30.1.7411 . [published Online First: 2014/08/20]. Minh HV, Tien HA, Sinh CT, et al. Assessment of preferred methods to measure insulin resistance in Asian patients with hypertension. J Clin Hypertens (Greenwich). 2021;23(3):529–37. 10.1111/jch.14155 . [published Online First: 2021/01/09]. Engeli S, Schling P, Gorzelniak K, et al. The adipose-tissue renin-angiotensin-aldosterone system: role in the metabolic syndrome? Int J Biochem Cell Biol. 2003;35(6):807–25. 10.1016/s1357-2725(02)00311-4 . [published Online First: 2003/04/05]. Hall JE, do Carmo JM, da Silva AA, et al. Obesity-induced hypertension: interaction of neurohumoral and renal mechanisms. Circ Res. 2015;116(6):991–1006. 10.1161/CIRCRESAHA.116.305697 . [published Online First: 2015/03/15]. Additional Declarations No competing interests reported. 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Shanghai Jiao Tong University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Dou","middleName":"","lastName":"Tang","suffix":""},{"id":469317648,"identity":"75cc436d-2199-455d-a995-2d4823601510","order_by":2,"name":"Fanfan Zhu","email":"","orcid":"","institution":"Shanghai Ruijin Hospital, Shanghai Jiao Tong University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Fanfan","middleName":"","lastName":"Zhu","suffix":""},{"id":469317649,"identity":"fe74ace0-6fde-4e82-a53c-f0204a8cbcfc","order_by":3,"name":"Shujie Wang","email":"","orcid":"","institution":"Shanghai Ruijin Hospital, Shanghai Jiao Tong University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Shujie","middleName":"","lastName":"Wang","suffix":""},{"id":469317650,"identity":"d98eaecb-a6c5-49ed-940d-2f395ce92dc1","order_by":4,"name":"Xun Wang","email":"","orcid":"","institution":"Shanghai Ruijin Hospital, Shanghai Jiao Tong University School of 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05:48:01","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":536146,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6612309/v1/baabe2f6c2cd93a10d5a62eb.png"},{"id":84530059,"identity":"cdb5f752-8d4b-42ec-adbd-38ef6af379d6","added_by":"auto","created_at":"2025-06-13 05:56:01","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":290634,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6612309/v1/fd9418024869c8ce5294ad08.png"},{"id":84528951,"identity":"dc1bca96-8a08-4f5b-bf43-571827c20fad","added_by":"auto","created_at":"2025-06-13 05:48:01","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":532961,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-6612309/v1/9da4d8c80e61c759224fa6e9.png"},{"id":84531479,"identity":"4bdde693-d9d1-4a78-9a23-519734fa5d3a","added_by":"auto","created_at":"2025-06-13 06:12:02","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3116060,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6612309/v1/86bbd1b4-2e97-4491-9beb-b1ba01254dae.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Nonlinear Associations and Gender Disparities Between Cardiometabolic Index and Hypertension in Chinese Adults","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe increasing prevalence of hypertension in China has been largely attributed to dietary Westernization and sedentary lifestyles \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. Recent epidemiological evidence highlights a substantial hypertension burden in Chinese adults, with awareness, treatment, and control rates demonstrating progressive attrition along the care cascade\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. Longitudinal cohort studies have consistently identified hypertension as a modifiable risk factor for cardiovascular outcomes, mortality \u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e and disability\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e independent of traditional risk covariates.\u003c/p\u003e \u003cp\u003ePrevious studies demonstrated that lipid levels and obesity indices were associated with hypertension\u003csup\u003e\u003cspan additionalcitationids=\"CR6\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e, chronic kidney disease (CKD)\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e and cardiovascular diseases\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e, Triglyceride metabolism emerges as a central hypertension determinant in Caucasian populations, outperforming conventional lipid measures in predictive models \u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. Our previous investigations have characterized the hypertriglyceridemic waist (HTGW) phenotype as a potent predictor of hypertension progression and diabetes-related cardiovascular sequelae in this aging cohort\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. The Cardiometabolic Index (CMI), originally conceptualized by Wakabayashi et al. operationalizes cardiometabolic risk stratification through the multiplicative product of waist-to-height ratio (WHtR) and triglyceride/HDL-cholesterol ratio (TG/HDL-C), expressed as: CMI = (Waist circumference/Height) \u0026times; (Triglycerides [mg/dL]/HDL-C [mg/dL])\u003csup\u003e13\u003c/sup\u003e. Some previous studies showed the strong positive epidemiological associations of the CMI with the risk of cardiovascular events\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e, prediabetes and diabetes\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e, chronic kidney disease (CKD)\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. WHtR reflects central obesity better than BMI (which fails to distinguish fat distribution). TG/HDL-C is a biomarker of resistance and atherogenic lipid profile. CMI integrates both, which can more comprehensively assess the synergistic effect of visceral fat deposition and li metabolism disorders, especially suitable for predicting metabolic diseases such as hypertension and diabetes. However, to date, few studies have investigated the association of the CMI and hypertension. Despite CMI\u0026rsquo;s established role in predicting diabetes and CKD, its association with hypertension remains underexplored, particularly in Asian populations with distinct adiposity patterns. China's hypertension care quality chasm persists: 45% prevalence vs 7% control rates in midlife adults, demanding urgent primary care reinforcement\u003csup\u003e17 18\u003c/sup\u003e. Accelerating population aging mandates nationwide hypertension incidence surveillance integrating CMI-based risk stratification to optimize primary prevention in Chinese adults\u0026thinsp;\u0026ge;\u0026thinsp;45 years, aligning with Healthy China 2030 cardiovascular reduction targets.\u003c/p\u003e \u003cp\u003eIn this community-based cross-sectional study (2018\u0026ndash;2022, n\u0026thinsp;=\u0026thinsp;9013), we systematically investigated the dose-response relationship between cardiometabolic index (CMI) and hypertension prevalence among adults aged 45\u0026ndash;80 years in Shanghai municipality, employing restricted cubic splines to characterize nonlinear associations.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy population\u003c/h2\u003e \u003cp\u003e During March to August 2020, about 10,824 participants (aged\u0026thinsp;\u0026ge;\u0026thinsp;40 years) did health check in the health center of Ruijin Hospital, Luwan branch, Huangpu district, Shanghai. First, from March to August 2020, individuals aged 40 years or older who were natives of Shanghai municipality or those who had lived in Shanghai for at least 5 years who underwent health checks in this health center were enrolled. Second, we invited participants to participate in the study by telephone. Exclusion criteria included inability to provide consent, pregnancy, or critical illnesses such as cancer, organ transplant, or dialysis. At this stage, 10824 participants were called. Third, 9214 participants provided informed consent and were recruited (response rate 85.1%). The exclusion criteria were as follows: second hypertension (caused by an identifiable underlying disorder such as kidney disease, endocrine disease, aortic coarctation, etc.)(n\u0026thinsp;=\u0026thinsp;107); missing data (n\u0026thinsp;=\u0026thinsp;94). Finally, 9013 subjects were included in the analysis. (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe study protocol (No. LWEC2020024) was approved by the Ethics Committee of the Shanghai Ruijin Hospital, Luwan branch, Shanghai Jiao Tong University School of Medicine. Informed consent was obtained from all participants included in our study.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eClinical, anthropometric and laboratory measurements\u003c/h3\u003e\n\u003cp\u003eUniformly trained research staff administered a standardized questionnaire assessing sociodemographic profiles, individual/family medical histories, and lifestyle determinants through structured face-to-face interviews. The body mass index (BMI) was calculated as weight (in kilograms) divided by height (in meters squared). BMI\u0026thinsp;\u0026lt;\u0026thinsp;24 kg/m\u003csup\u003e2\u003c/sup\u003e was defined as normal weight, while BMI\u0026thinsp;\u0026ge;\u0026thinsp;24 kg/m\u003csup\u003e2\u003c/sup\u003e was defined as overweight/obese, as determined by the Cooperative Meta-Analysis Group of the Working Group on Obesity in China criteria\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. Blood pressure measurements were conducted following standardized protocols: Participants underwent seated rest (\u0026ge;\u0026thinsp;5 min) prior to triplicate measurements at 5-minute intervals using a validated Oscillo metric device (Omron HEM-7200, ESH-certified), with final BP calculated as the arithmetic mean. Concurrently, smoking status was classified per CDC criteria, defining current smokers as individuals with \u0026ge;\u0026thinsp;100 lifetime cigarettes and persistent tobacco use at enrollment \u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. Educational attainment was dichotomized based on high school completion status. Physical activity assessment incorporated both occupational and leisure domains, with participants stratified according to WHO guidelines into those meeting recommended thresholds (\u0026ge;\u0026thinsp;150 min/week moderate-vigorous activity across both domains) versus suboptimal engagement\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eFollowing an 8-hour overnight fast, venous blood samples were collected under standardized protocols involving immediate refrigeration at 4\u0026deg;C, centrifugation within 120 minutes, and long-term storage at -80\u0026deg;C in aliquots. Biochemical profiling was performed using certified methodologies: HbA1c levels were quantified via high-performance liquid chromatography (HPLC; MQ-2000PT system, MEDCONN, NGSP-certified), while lipid parameters (triglycerides [TG, AUZ5612 kit, sensitivity 0.01 mmol/L, CV\u0026thinsp;\u0026lt;\u0026thinsp;7%], total cholesterol, HDL-C, LDL-C), glucose homeostasis markers (fasting plasma glucose), and renal function indices (uric acid, serum creatinine via Jaff\u0026eacute; method) were analyzed on an AU680 automated platform (Beckman Coulter). First-morning urine samples underwent prompt albumin-creatinine ratio (ACR) measurement, with estimated glomerular filtration rate (eGFR) calculated using the CKD-EPI China Eq.\u0026nbsp;(2014 adaptation). All procedures adhered to ISO 15189 standards with daily calibration and 5% random duplicate testing (intraassay CV\u0026thinsp;\u0026lt;\u0026thinsp;8%)\u003csup\u003e22\u003c/sup\u003e.\u003c/p\u003e\n\u003ch3\u003eDefinition of variables\u003c/h3\u003e\n\u003cp\u003eHypertension was defined as systolic BP\u0026thinsp;\u0026ge;\u0026thinsp;140 mmHg, diastolic BP\u0026thinsp;\u0026ge;\u0026thinsp;90 mmHg, or self-reported use of antihypertensive medications within the past 2 weeks, based on the 2023 ACC/AHA guidelines\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. Chronic kidney disease was defined as an estimated glomerular filtration rate (eGFR) less than 60 mL/min/1.73 m\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e and urinary albumin creatinine ratio (UACR)\u0026thinsp;\u0026ge;\u0026thinsp;30 mg/g, and it was defined as CKD\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. The CVD outcome was defined as a previous diagnosis of coronary heart disease, stroke, or peripheral arterial disease and was recorded in the previous studies\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\n\u003ch3\u003eDefinitions of the CMI\u003c/h3\u003e\n\u003cp\u003eThe WHtR refers to the waist circumference in centimeters divided by the height in centimeters. The TG/HDL-C ratio represents the relationship between TG and HDL-C levels. The WHtR is multiplied by TG/HDL-C to calculate the CMI. On the basis of CMI Quartiles, the participants were categorized into three groups: Q1 group (CMI\u0026thinsp;\u0026le;\u0026thinsp;0.33), Q2 group (0.33\u0026thinsp;\u0026lt;\u0026thinsp;CMI\u0026thinsp;\u0026le;\u0026thinsp;0.53), Q3 group (0.53\u0026thinsp;\u0026lt;\u0026thinsp;CMI\u0026thinsp;\u0026le;\u0026thinsp;0.87) and Q4 group (CMI\u0026gt;0.87).\u003c/p\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eData analyses were performed using IBM SPSS version 25 statistical software (IBM Corp., Armonk, NY, USA). P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 indicated significance (two-sided). Continuous variables were presented as the mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD), while non-normally distributed data are expressed as median (interquartile range [IQR]) and categorical variables were presented as percentages (%) when appropriate. Logistic regression was used to assess the association between CMI quartiles and hypertension. Model 1 was adjusted for age, sex, BMI, current smoker. Model 2 was adjusted for Model 1 plus TC, LDL, HbA1c, UA, eGFR, physical activity, education status, systolic blood pressure, diastolic blood pressure, current smoker, diabetes, antidiabetic agents, antihypertensive drugs, physical activity and education status. In addition, stratified analyses by sex, BMI and presence of diabetes, current smoker, LDL and TC were performed.The restricted cubic spline (RCS) analysis was applied to explore potential nonlinear correlations between the CMI and hypertension. A receiver operating characteristic (ROC) curve analysis which was quantified by the area under the ROC curve (AUC) was used to evaluate the value of CMI and other metabolism markers for predicting hypertension by comparing the area under the ROC curve (AUC).\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n \u003ch2\u003eBaseline clinical characteristics\u003c/h2\u003e\n \u003cp\u003eThe baseline anthropometric parameters and biochemical indices according to four triglyceride waist phenotypes are shown in Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e. Participants in the highest CMI quartile (Q4) were older and had significantly higher BMI, waist circumference (WC), systolic blood pressure (SBP), and diastolic blood pressure (DBP) compared to those in the lowest quartile (Q1); Higher levels of TC, TG, LDL-C, FPG, HbA1c and uACR; and lower levels of HDL-C and eGFR were found in the Q4 group (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001, Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). Subjects with the highest CMI level group were more likely to be current smokers, less likely to engage in physical activity and lowest education (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Subjects with Q4 group also had a higher prevalence of a history of diabetes, cardiovascular diseases, and CKD than those in the other three groups (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). In Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e, the prevalence of hypertension was 65% (5856/9013). Participants in the Q4 group had the highest prevalence of hypertension (1705/2234 (76.3%)) among the four subgroups (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Participants in the Q2 and Q3 groups had a higher prevalence of hypertension than those in the Q1 Group (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eCharacteristics of the participants in each phenotype group (n\u0026thinsp;=\u0026thinsp;9013).\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eQ1\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eQ2\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eQ3\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eQ4\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP for trend\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo. of participants (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2244\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e2275\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e2260\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e2234\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge at baseline (year)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e60.46\u0026thinsp;\u0026plusmn;\u0026thinsp;10.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e62.99\u0026thinsp;\u0026plusmn;\u0026thinsp;9.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e63.70\u0026thinsp;\u0026plusmn;\u0026thinsp;9.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e63.91\u0026thinsp;\u0026plusmn;\u0026thinsp;8.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.077\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMale (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e816(36.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e923(40.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e1000(44.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e1064(47.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBMI (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22.65\u0026thinsp;\u0026plusmn;\u0026thinsp;2.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e24.34\u0026thinsp;\u0026plusmn;\u0026thinsp;2.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e25.43\u0026thinsp;\u0026plusmn;\u0026thinsp;3.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e26.14\u0026thinsp;\u0026plusmn;\u0026thinsp;3.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWC(cm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e77.22\u0026thinsp;\u0026plusmn;\u0026thinsp;8.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e82.50\u0026thinsp;\u0026plusmn;\u0026thinsp;7.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e85.64\u0026thinsp;\u0026plusmn;\u0026thinsp;8.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e88.11\u0026thinsp;\u0026plusmn;\u0026thinsp;7.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSBP (mmHg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e131.92\u0026thinsp;\u0026plusmn;\u0026thinsp;18.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e135.44\u0026thinsp;\u0026plusmn;\u0026thinsp;18.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e138.30\u0026thinsp;\u0026plusmn;\u0026thinsp;18.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e140.65\u0026thinsp;\u0026plusmn;\u0026thinsp;18.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDBP (mmHg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e83.63\u0026thinsp;\u0026plusmn;\u0026thinsp;10.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e85.17\u0026thinsp;\u0026plusmn;\u0026thinsp;9.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e86.54\u0026thinsp;\u0026plusmn;\u0026thinsp;9.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e87.92\u0026thinsp;\u0026plusmn;\u0026thinsp;10.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFBG (mmol/L)\u003c/p\u003e\n \u003cp\u003eHbA1C (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.07\u0026thinsp;\u0026plusmn;\u0026thinsp;1.29\u003c/p\u003e\n \u003cp\u003e5.59\u0026thinsp;\u0026plusmn;\u0026thinsp;0.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e5.25\u0026thinsp;\u0026plusmn;\u0026thinsp;1.33\u003c/p\u003e\n \u003cp\u003e5.71\u0026thinsp;\u0026plusmn;\u0026thinsp;0.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e5.46\u0026thinsp;\u0026plusmn;\u0026thinsp;1.65\u003c/p\u003e\n \u003cp\u003e5.87\u0026thinsp;\u0026plusmn;\u0026thinsp;0.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e5.78\u0026thinsp;\u0026plusmn;\u0026thinsp;1.96\u003c/p\u003e\n \u003cp\u003e6.07\u0026thinsp;\u0026plusmn;\u0026thinsp;1.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTG (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.83\u0026thinsp;\u0026plusmn;\u0026thinsp;0.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e1.21\u0026thinsp;\u0026plusmn;\u0026thinsp;0.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e1.63\u0026thinsp;\u0026plusmn;\u0026thinsp;0.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e2.94\u0026thinsp;\u0026plusmn;\u0026thinsp;1.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTC (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.92\u0026thinsp;\u0026plusmn;\u0026thinsp;0.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e5.09\u0026thinsp;\u0026plusmn;\u0026thinsp;0.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e5.14\u0026thinsp;\u0026plusmn;\u0026thinsp;0.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e5.18\u0026thinsp;\u0026plusmn;\u0026thinsp;0.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHDL-C (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.75\u0026thinsp;\u0026plusmn;\u0026thinsp;0.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e1.47\u0026thinsp;\u0026plusmn;\u0026thinsp;0.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e1.29\u0026thinsp;\u0026plusmn;\u0026thinsp;0.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e1.07\u0026thinsp;\u0026plusmn;\u0026thinsp;0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLDL-C (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.98\u0026thinsp;\u0026plusmn;\u0026thinsp;0.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e3.33\u0026thinsp;\u0026plusmn;\u0026thinsp;0.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e3.45\u0026thinsp;\u0026plusmn;\u0026thinsp;0.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e3.26\u0026thinsp;\u0026plusmn;\u0026thinsp;0.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eeGFR (mL/m/1.73 m\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e91.04\u0026thinsp;\u0026plusmn;\u0026thinsp;14.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e88.15\u0026thinsp;\u0026plusmn;\u0026thinsp;13.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e86.63\u0026thinsp;\u0026plusmn;\u0026thinsp;14.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e85.05\u0026thinsp;\u0026plusmn;\u0026thinsp;14.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003euACR (mg/g)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e46.19\u0026thinsp;\u0026plusmn;\u0026thinsp;175.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e49.03\u0026thinsp;\u0026plusmn;\u0026thinsp;241.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e54.93\u0026thinsp;\u0026plusmn;\u0026thinsp;163.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e96.04\u0026thinsp;\u0026plusmn;\u0026thinsp;323.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCKD, (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e69(3.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e85(3.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e76(3.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e79(3.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHypertension, (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1138(50.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e1426(62.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e1587(70.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e1705(76.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDiabetes (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e191(8.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e288(12.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e388(17.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e543(24.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCVD (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e52(2.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e71(3.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e84(3.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e97(4.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOverweight/obesity, (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e412(18.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e895(39.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e1194(52.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e1207(54.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCurrent smoker (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e246(10.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e298(13.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e362(16.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e436(19.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEducation (\u0026ge;\u0026thinsp;high school), %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e163(7.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e127(5.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e140(6.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e118(5.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh physical activity, %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e188(8.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e163(7.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e108(4.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e72(3.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.23\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.43\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.67\u0026thinsp;\u0026plusmn;\u0026thinsp;0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e1.59\u0026thinsp;\u0026plusmn;\u0026thinsp;1.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"9\"\u003eData are expressed as the mean SD, median value [interquartile range] or as n (%), as appropriate. BMI, body mass index; WC, waist circumference; SBP, systolic blood pressure; DBP, diastolic blood pressure; FPG, fasting blood glucose; TC, total cholesterol; TG, HDL-C, high density lipoprotein cholesterol; LDL-C, low density lipoprotein cholesterol, eGFR, estimated glomerular filtration rate; uACR, urinary albumin creatinine ratio; UA, uric acid;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003eAssociation of the CMI with hypertension\u003c/h3\u003e\n\u003cp\u003eIn the overall population, higher CMI quartiles (Q3 and Q4) were significantly associated with an elevated risk of hypertension, even after adjusting for covariates in Model 1 and Model 2. For Q4, the adjusted OR was 1.22 (95% CI: 1.06\u0026ndash;1.39) in Model 2. However, the association between CMI and hypertension risk exhibited notable gender differences. In males, the association was less pronounced after full adjustment for covariates, with no significant risk increase in Q4 (OR: 0.79, 95% CI: 0.60\u0026ndash;1.05). In contrast, the association remained significant in females, particularly in Q4 (OR: 1.52, 95% CI: 1.15\u0026ndash;1.99) in Model 2, suggesting that females with higher CMI levels are at a greater risk of hypertension compared to males. Despite these differences, no significant interaction was observed between CMI and gender (P for interaction\u0026thinsp;\u0026gt;\u0026thinsp;0.05), indicating that the association between CMI and hypertension risk is consistent across genders, albeit stronger in females.\u003c/p\u003e\n\u003cp\u003eThe association between CMI quartiles and hypertension was evaluated using multivariable logistic regression models (Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). After adjusting for age, sex, BMI, smoking status, and other metabolic variables, Subgroup analyses revealed that this association was particularly pronounced in individuals aged\u0026thinsp;\u0026ge;\u0026thinsp;60 years (OR: 1.34, 95% CI: 1.07\u0026ndash;1.68, P for trend\u0026thinsp;=\u0026thinsp;0.011) and those with BMI\u0026thinsp;\u0026ge;\u0026thinsp;24 kg/m\u0026sup2; (OR: 1.37, 95% CI: 1.02\u0026ndash;1.83, P for trend\u0026thinsp;=\u0026thinsp;0.035). No significant interaction was observed between CMI and subgroups such as age, BMI, or diabetes status (P for interaction\u0026thinsp;\u0026gt;\u0026thinsp;0.05)(Figure \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"left\" class=\"colspec\"\u003e\u0026nbsp;\u003c/div\u003e\u0026nbsp;\u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eOdds ratios for hypertension according to CMI quartiles by various subgroups\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSubpopulation\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eCases/Participants\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eQ1\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eQ2\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eQ3\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eQ4\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP-trend\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP-interaction\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"4\"\u003e\n \u003cp\u003e0.542\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge, years \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e2040/9013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00(ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.32(0.85,2.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.92(0.57,1.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.11(0.65,1.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.438\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ge;\u0026thinsp;60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e6973/9013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00(ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.17(0.95,1.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.30(1.05,1.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.34(1.07,1.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"4\"\u003e\n \u003cp\u003e0.678\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eBMI, kg/m\u003csup\u003e2 c\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e4977/9013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00(ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.21(0.99,1.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.22(0.99,1.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.36(1.09,1.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.043\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ge;\u0026thinsp;24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e4036/9013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00(ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.12(0.84,1.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.13(0.85,1.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.37(1.02,1.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.035\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003ePresence of T2DM \u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003e0.584\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e8064/9013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00(ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.57(1.35,1.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.73 (1.44,2.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.36 (1.95,2.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.040\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eyes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e949/9013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00(ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.09(0.56,2.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.89(0.45,1.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.82(0.42,1.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.808\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eCurrent smoker \u003csup\u003ee\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003e0.358\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7672/9013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00(ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.23(1.02,1.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.30(1.06,1.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.33(1.07,1.64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.036\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1341/9013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00(ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.21(0.73,1.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.79(0.41,3.76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.18(1.08,2.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eTC\u003csup\u003ef\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003e0.232\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;5.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5172/9013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00(ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.26(1.00,1.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.29(1.01,1.64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.56(1.20,2.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.010\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026ge;\u0026thinsp;5.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3841/9013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00(ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.28 (0.60,2.72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.72 (0.31,1.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.12 (1.11,1.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.022\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eLDL-c\u003csup\u003eg\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;3.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5234/9013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00(ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.19(0.93,1.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.40(0.97,2.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.40(1.05,1.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e0.394\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026ge;\u0026thinsp;3.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3379/9013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00(ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.12(0.84,1.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.12(1.11,1.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.11(1.01,2.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"9\"\u003e\u003csup\u003eb\u003c/sup\u003e for age subgroup: adjusted for sex, BMI, LDL-C, HDL-C, TC, FBG, HbA1c, eGFR, UA, physical activity, education status, current smoker and current drinker, systolic blood pressure, diastolic blood pressure, diabetes, anti-diabetes agents, antihypertension drug; \u003csup\u003ec\u003c/sup\u003e for BMI subgroup: adjusted for age, sex, LDL-C, HDL-C, TC, FBG, HbA1c, eGFR, UA, physical activity, education status, current smoking and current drink systolic blood pressure, diastolic blood pressure, diabetes, anti-diabetes agents, antihypertension drug; \u003csup\u003ed\u003c/sup\u003e for T2DM subgroup: adjusted for age, sex, BMI, LDL-C, HDL-C, TC, FBG, HbA1c, eGFR, UA, physical activity, education status, current smoker and current drinker, systolic blood pressure, diastolic blood pressure, anti-diabetes agents(only for diabetes group), antihypertension drug; \u003csup\u003ee\u003c/sup\u003e for smoke status subgroup: adjusted for age, sex, BMI, LDL-C, HDL-C, TC, FBG, HbA1c, eGFR, UA, physical activity, education status, current drinker, systolic blood pressure, diastolic blood pressure, anti-diabetes agents(only for diabetes group), antihypertension drug; \u003csup\u003ef\u003c/sup\u003e for TC subgroup: adjusted for age, sex, BMI, HDL-C, TG, LDL, FBG, HbA1c, eGFR, UA, physical activity, education status, current smoker; current drinker; systolic blood pressure, diastolic blood pressure, anti-diabetes agents(only for diabetes group), antihypertension drug\u003csub\u003e;\u003c/sub\u003e \u003csup\u003eg\u003c/sup\u003e for LDL-c subgroup: adjusted for age, sex, BMI, HDL-C, TC, FBG, HbA1c, eGFR, UA, physical activity, education status, current smoker; current drinker; systolic blood pressure, diastolic blood pressure, anti-diabetes agents(only for diabetes group), antihypertension drug;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003eThe AUCs (and 95% CIs) of variables in CMI and other measurements for hypertension\u003c/h2\u003e\n \u003cp\u003eThe predictive ability of CMI for hypertension was assessed using receiver operating characteristic (ROC) curve analysis (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). The area under the curve (AUC) for CMI was 0.667 (95% CI: 0.655\u0026ndash;0.679), with a sensitivity of 0.77 and specificity of 0.60 at the optimal cutoff point of 0.36. CMI demonstrated comparable predictive performance to traditional metabolic indices such as waist circumference (AUC: 0.664, 95% CI: 0.652\u0026ndash;0.676) and triglycerides (AUC: 0.626, 95% CI: 0.614\u0026ndash;0.638) (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). suggesting that CMI may serve as a useful composite marker for hypertension risk assessment.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"left\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\n \u003ctable id=\"Tab3\" border=\"1\" style=\"margin-right: calc(0%); width: 100%;\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eArea under the receiver operating characteristic curve (AUC) of CMI and metabolism indexes variables for hypertension\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" style=\"width: 7.6622%;\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" style=\"width: 12.2752%;\"\u003e\n \u003cp\u003eAUC\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" style=\"width: 6.5676%;\"\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" style=\"width: 6.4113%;\"\u003e\n \u003cp\u003eCut-off point\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" style=\"width: 8.0532%;\"\u003e\n \u003cp\u003eSensitivity\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" style=\"width: 8.0532%;\"\u003e\n \u003cp\u003eSpecificity\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" style=\"width: 6.6458%;\"\u003e\n \u003cp\u003eYouden index\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 7.6622%;\"\u003e\n \u003cp\u003eCMI#\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 12.2752%;\"\u003e\n \u003cp\u003e0.667\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 6.5676%;\"\u003e\n \u003cp\u003e(0.655\u0026ndash;0.679)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 6.4113%;\"\u003e\n \u003cp\u003e0.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 8.0532%;\"\u003e\n \u003cp\u003e0.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 8.0532%;\"\u003e\n \u003cp\u003e0.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 6.6458%;\"\u003e\n \u003cp\u003e0.37\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 7.6622%;\"\u003e\n \u003cp\u003eTG, mmol/l#\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 12.2752%;\"\u003e\n \u003cp\u003e0.626\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 6.5676%;\"\u003e\n \u003cp\u003e(0.614\u0026ndash;0.638)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 6.4113%;\"\u003e\n \u003cp\u003e1.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 8.0532%;\"\u003e\n \u003cp\u003e0.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 8.0532%;\"\u003e\n \u003cp\u003e0.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 6.6458%;\"\u003e\n \u003cp\u003e0.38\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 7.6622%;\"\u003e\n \u003cp\u003eWC, cm#\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 12.2752%;\"\u003e\n \u003cp\u003e0.664\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 6.5676%;\"\u003e\n \u003cp\u003e(0.652\u0026ndash;0.676)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 6.4113%;\"\u003e\n \u003cp\u003e77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 8.0532%;\"\u003e\n \u003cp\u003e0.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 8.0532%;\"\u003e\n \u003cp\u003e0.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 6.6458%;\"\u003e\n \u003cp\u003e0.41\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 7.6622%;\"\u003e\n \u003cp\u003eBMI, kg/cm\u003csup\u003e2\u003c/sup\u003e#\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 12.2752%;\"\u003e\n \u003cp\u003e0.541\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 6.5676%;\"\u003e\n \u003cp\u003e(0.529\u0026ndash;0.554)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 6.4113%;\"\u003e\n \u003cp\u003e22.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 8.0532%;\"\u003e\n \u003cp\u003e0.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 8.0532%;\"\u003e\n \u003cp\u003e0.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 6.6458%;\"\u003e\n \u003cp\u003e0.41\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 7.6622%;\"\u003e\n \u003cp\u003eTC, mmol/l#\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 12.2752%;\"\u003e\n \u003cp\u003e0.435\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 6.5676%;\"\u003e\n \u003cp\u003e(0.422\u0026ndash;0.447)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 6.4113%;\"\u003e\n \u003cp\u003e4.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 8.0532%;\"\u003e\n \u003cp\u003e0.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 8.0532%;\"\u003e\n \u003cp\u003e0.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 6.6458%;\"\u003e\n \u003cp\u003e0.45\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 10.2424%;\"\u003e\n \u003cp\u003eLDL-C, mmol/l#\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 12.2752%;\"\u003e\n \u003cp\u003e0.496\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 6.5676%;\"\u003e\n \u003cp\u003e(0.484\u0026ndash;0.508)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 6.4113%;\"\u003e\n \u003cp\u003e2.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 8.0532%;\"\u003e\n \u003cp\u003e0.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 8.0532%;\"\u003e\n \u003cp\u003e0.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 6.6458%;\"\u003e\n \u003cp\u003e0.47\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" style=\"width: 58.2486%;\"\u003e#P\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003eNonlinear Relationship Between CMI and Hypertension\u003c/h2\u003e\n \u003cp\u003eRCS analysis demonstrated a nonlinear association between CMI and hypertension risk (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e). After adjusting for age, sex, BMI, and other covariates, the risk of hypertension increased significantly with higher CMI levels, particularly beyond a threshold of 0.36 (P for nonlinearity\u0026thinsp;=\u0026thinsp;0.015). This trend persisted in the fully adjusted model (P for nonlinearity\u0026thinsp;=\u0026thinsp;0.001), indicating a robust association between elevated CMI and hypertension risk.\u003c/p\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, we investigated the association between the Cardiometabolic Index (CMI) and hypertension risk in a large cohort of 9,013 participants. Our findings demonstrate that higher CMI levels are significantly associated with an increased risk of hypertension, particularly in females, even after adjusting for multiple confounding factors. These results align with previous studies, highlighting the role of metabolic dysregulation in the pathogenesis of hypertension and cardiovascular diseases\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. The CMI, which integrates triglycerides, HDL and WHtR, provides a comprehensive measure of metabolic health and may serve as a valuable tool for risk stratification in clinical practice.\u003c/p\u003e \u003cp\u003eOur analysis revealed a strong positive association between CMI and hypertension risk, with participants in the highest CMI quartile (Q4) exhibiting a 1.34-fold increased risk compared to those in the lowest quartile (Q1). This association was particularly pronounced in individuals aged\u0026thinsp;\u0026ge;\u0026thinsp;60 years and those with a BMI\u0026thinsp;\u0026ge;\u0026thinsp;24 kg/m\u0026sup2;, suggesting that CMI may be especially relevant in older and overweight populations. These findings are consistent with prior research indicating that metabolic abnormalities, such as elevated triglycerides and central obesity, are key contributors to hypertension development\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. The nonlinear relationship identified in our RCS analysis suggests that hypertension risk increases markedly beyond a specific CMI threshold, emphasizing the importance of early intervention in individuals with elevated CMI levels.\u003c/p\u003e \u003cp\u003eA notable finding of our study is the gender-specific association between CMI and hypertension risk. While the overall association was significant in the total population, the risk was more pronounced in females, with those in the highest CMI quartile (Q4) having a 1.52-fold increased risk of hypertension compared to their counterparts in Q1. In contrast, the association was attenuated in males after full adjustment for covariates, with no significant risk increase observed in Q4. This gender disparity may be attributed to differences in body composition, hormonal influences, and lipid metabolism between males and females. In our previous researches, we also found worse metabolism dysfunction in female than man\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. On the other hand, we also found that the risk of hypertension in Q4 was significantly higher in non-diabetic participants (OR 2.36 [.95-2.86]) than in diabetic participants (OR 0.82 [0.42\u0026ndash;1.62]), the possible reason for may be that diabetic patients often receive hypoglycemic (eg, metformin, SGLT2 inhibitors) or lipid-lowering therapy, statins), and these drugs may directly improve blood pressure or lipid profile, weakening the independent effect of CMI.\u003c/p\u003e \u003cp\u003eThe biological mechanisms linking CMI and hypertension are still unclear, but the critical role of current acknowledged hypotheses is visceral fat accumulation or visceral obesity\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e Abdominal fat accumulation can induce insulin resistance, which in turn activates the adrenergic system, leading to increased sympathetic nervous system activity and elevated blood pressure. Insulin also inhibits the production of prostacyclin in adipose tissue, reducing its vasodilatory effects and thereby increasing peripheral vascular resistance. Additionally, insulin disrupts intracellular cation regulation by suppressing sodium/potassium ATPase (Na+/K\u0026thinsp;+\u0026thinsp;ATPase) activity and enhancing the Na+/K\u0026thinsp;+\u0026thinsp;pump in vascular smooth muscle cells. This increases their sensitivity to catecholamines and angiotensin II, further elevating peripheral resistance and contributing to hypertension\u003csup\u003e35\u003c/sup\u003e. In addition, insulin resistance is associated with the release of various factors from excess adipose tissue, such as no esterified fatty acids, cytokines, and adiponectin. These factors impair vascular endothelial function, reducing the elasticity of blood vessels and contributing to the development of hypertension\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. Furthermore, the activation of the Renin-Angiotensin-Aldosterone System (RAAS) plays a critical role in mediating elevated blood pressure in obesity. Studies have demonstrated that abdominal subcutaneous adipose tissue significantly secretes angiotensin II (Ang II) and angiotensinogen (AGT), as evidenced in both animal models and human adipose tissue\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. Thus, it is likely that adipose tissue-derived RAAS components are involved in the regulation of blood pressure. This is attributable not only to sympathetic nervous system overactivity and renal compression but also to dysfunctional adipose tissue\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe predictive performance of CMI for hypertension, as assessed by ROC curve analysis, demonstrated an AUC of 0.667, indicating moderate discriminatory ability. While CMI performed comparably to traditional metabolic indices such as waist circumference and triglycerides, its integration of both lipid and anthropometric measures may offer a more holistic assessment of metabolic risk.\u003c/p\u003e \u003cp\u003eOur findings have important clinical implications. The strong association between CMI and hypertension risk, particularly in females, suggests that CMI could serve as a useful tool for early identification of individuals at high risk for hypertension. Regular monitoring of CMI in clinical practice may facilitate timely interventions, such as lifestyle modifications or pharmacological treatments, to mitigate the risk of hypertension and its associated complications. Additionally, the gender-specific differences observed in our study highlight the need for tailored approaches in hypertension prevention and management, taking into account the unique metabolic profiles of males and females.\u003c/p\u003e \u003cp\u003eSeveral limitations in this study need to be considered, several limitations should be acknowledged, including the cross-sectional design, potential measurement bias from self-reported data, and the single-center nature of the study, which may limit the generalizability of our findings. However, there are some limitations to consider. First, the cross-sectional design of the study precludes the establishment of causal relationships between CMI and hypertension. Second, the use of self-reported data for certain variables, such as smoking status and physical activity, may introduce measurement bias. Finally, the study population was drawn from a single center in China, which may limit the generalizability of our findings to other populations. Future prospective studies are needed to validate our results and explore the underlying mechanisms linking CMI to hypertension risk.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn conclusion, our findings indicate that elevated CMI levels are associated with a higher risk of hypertension, particularly among females. The CMI, as a composite measure of metabolic health, may serve as a valuable tool for risk stratification and early intervention in clinical practice. Gender-specific differences in the association between CMI and hypertension risk highlight the need for tailored approaches in hypertension prevention and management. Further studies are needed to validate the potential of CMI as a predictive marker for hypertension and other cardiovascular diseases\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding Sources:\u0026nbsp;\u003c/strong\u003eThis study was supported by Shanghai Municipal Huangpu District Commission (HLQ202205) and Healthy Commission Research Project of Shanghai Huangpu District (HLM202206，HLM202401). The funder played no role in the design or conduct of the study, collection, management, analysis, or interpretation of data, or in the preparation, review, or approval of the article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInstitutional Review Board Statement:\u0026nbsp;\u003c/strong\u003eThe study protocol (No. LWEC202024) was approved by the Ethics Committee of the Shanghai Ruijin Hospital, Luwan branch, Shanghai Jiao Tong University School of Medicine. These subjects provided written informed consent, and the study protocol was approved by the institute\u0026rsquo;s committee on human research.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInformed Consent Statement:\u003c/strong\u003e Informed consent was obtained from all subjects involved in the study\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe raw data supporting the conclusions of this manuscript will be made available by the authors, without undue reservation, to any qualified researcher.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of Interest:\u003c/strong\u003e The authors\u0026nbsp;declare no conflicts\u0026nbsp;of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgment\u003c/strong\u003e\u003cstrong\u003e:\u003c/strong\u003e We gratefully acknowledge the invaluable assistance of the physicians of the Department of Endocrinology, Luwan branch, Ruijin Hospital, School of Medicine, Shanghai Jiaotong University and Ruijin Hospital, School of Medicine, Shanghai Jiaotong University. This study would not have been possible without their support.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number:\u003c/strong\u003e This is not a clinical trial. not applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics and Consent to Participate declarations\u003c/strong\u003e\u003cstrong\u003e：\u003c/strong\u003eThe research protocol was conducted in accordance with the provisions of the Declaration of Helsinki in 1995 (as revised in Fortaleza, Brazil, October 2013), and approved by the Ethics Committee of Ruijin Hospital Lu Wan Branch, Shanghai Jiaotong University School of Medicine (\u003cstrong\u003eApproval No. LWEC202024, date of approval: March 9, 2020\u003c/strong\u003e\u003cstrong\u003e，\u003c/strong\u003e\u003cstrong\u003eby Ruijin Hospital Luwan Branch Ethics Committee Shanghai JiaoTong University School of Medicine)\u003c/strong\u003e. All participants gave their written informed consent. We preserve the anonymity of our patients. There are no conflicts of interest to disclose.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn this study, X. Y., S. Y. and W.S. J. were mainly responsible for the writing of the article, L. L. Q. was mainly responsible for research, Y.X was mainly responsible for data entry, T. D. were mainly responsible for data calculation and correction, and W. X and Z.F.F were mainly responsible for the final data results and additional experiments. The authors would like to thank all the participants for this article.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eJordan J, Yumuk V, Schlaich M, et al. Joint statement of the European Association for the Study of Obesity and the European Society of Hypertension: obesity and difficult to treat arterial hypertension. J Hypertens. 2012;30(6):1047\u0026ndash;55. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1097/HJH.0b013e3283537347\u003c/span\u003e\u003cspan address=\"10.1097/HJH.0b013e3283537347\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. [published Online First: 2012/05/11].\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGuo QH, Zhang YQ, Wang JG. Asian management of hypertension: Current status, home blood pressure, and specific concerns in China. J Clin Hypertens (Greenwich). 2020;22(3):475\u0026ndash;78. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1111/jch.13687\u003c/span\u003e\u003cspan address=\"10.1111/jch.13687\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. [published Online First: 2019/10/18].\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCollaborators GBDRF, Forouzanfar MH, Alexander L, et al. Global, regional, and national comparative risk assessment of 79 behavioural, environmental and occupational, and metabolic risks or clusters of risks in 188 countries, 1990\u0026ndash;2013: a systematic analysis for the Global Burden of Disease Study 2013. Lancet. 2015;386(10010):2287\u0026ndash;323. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/S0140-6736(15)00128-2\u003c/span\u003e\u003cspan address=\"10.1016/S0140-6736(15)00128-2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. [published Online First: 2015/09/15].\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCollaborators GBDCoD. Global, regional, and national age-sex specific mortality for 264 causes of death, 1980\u0026ndash;2016: a systematic analysis for the Global Burden of Disease Study 2016. Lancet. 2017;390(10100):1151\u0026ndash;210. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/S0140-6736(17)32152-9\u003c/span\u003e\u003cspan address=\"10.1016/S0140-6736(17)32152-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. [published Online First: 2017/09/19].\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang M, Zhao Y, Sun H, et al. Effect of dynamic change in body mass index on the risk of hypertension: Results from the Rural Chinese Cohort Study. Int J Cardiol. 2017;238:117\u0026ndash;22. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.ijcard.2017.03.025\u003c/span\u003e\u003cspan address=\"10.1016/j.ijcard.2017.03.025\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. [published Online First: 2017/03/21].\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhao Y, Zhang M, Luo X, et al. Association of 6-year waist circumference gain and incident hypertension. Heart. 2017;103(17):1347\u0026ndash;52. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1136/heartjnl-2016-310760\u003c/span\u003e\u003cspan address=\"10.1136/heartjnl-2016-310760\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. [published Online First: 2017/04/09].\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHall ME, do Carmo JM, da Silva AA, et al. Obesity, hypertension, and chronic kidney disease. Int J Nephrol Renovasc Dis. 2014;7:75\u0026ndash;88. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.2147/IJNRD.S39739\u003c/span\u003e\u003cspan address=\"10.2147/IJNRD.S39739\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. [published Online First: 2014/03/07].\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFoster MC, Hwang SJ, Porter SA et al. Fatty kidney, hypertension, and chronic kidney disease: the Framingham Heart Study. \u003cem\u003eHypertension\u003c/em\u003e 2011;58(5):784\u0026thinsp;\u0026ndash;\u0026thinsp;90. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1161/HYPERTENSIONAHA.111.175315\u003c/span\u003e\u003cspan address=\"10.1161/HYPERTENSIONAHA.111.175315\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e [published Online First: 2011/09/21].\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSowers JR. Obesity as a cardiovascular risk factor. \u003cem\u003eAm J Med\u003c/em\u003e 2003;115 Suppl 8A:37S-41S. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.amjmed.2003.08.012\u003c/span\u003e\u003cspan address=\"10.1016/j.amjmed.2003.08.012\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e [published Online First: 2003/12/18].\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSanchez-Inigo L, Navarro-Gonzalez D, Pastrana-Delgado J, et al. Association of triglycerides and new lipid markers with the incidence of hypertension in a Spanish cohort. J Hypertens. 2016;34(7):1257\u0026ndash;65. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1097/HJH.0000000000000941\u003c/span\u003e\u003cspan address=\"10.1097/HJH.0000000000000941\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. [published Online First: 2016/05/03].\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXuan Y, Shen Y, Wang S, et al. The association of hypertriglyceridemic waist phenotype with hypertension: A cross-sectional study in a Chinese middle aged-old population. J Clin Hypertens (Greenwich). 2022;24(2):191\u0026ndash;99. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1111/jch.14424\u003c/span\u003e\u003cspan address=\"10.1111/jch.14424\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. [published Online First: 20220127].\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXuan Y, Zhang W, Wang Y, et al. The Association Between Hypertriglyceridemic-Waist Phenotype and Chronic Kidney Disease in Patients with Type 2 Diabetes: A Cross-Sectional METAL Study. Diabetes Metab Syndr Obes. 2022;15:1885\u0026ndash;95. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.2147/DMSO.S359742\u003c/span\u003e\u003cspan address=\"10.2147/DMSO.S359742\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. [published Online First: 2022/06/28].\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWakabayashi I, Daimon T. The cardiometabolic index as a new marker determined by adiposity and blood lipids for discrimination of diabetes mellitus. Clin Chim Acta. 2015;438:274\u0026ndash;8. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.cca.2014.08.042\u003c/span\u003e\u003cspan address=\"10.1016/j.cca.2014.08.042\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. [published Online First: 2014/09/10].\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhu XM, Xu Y, Zhang J. Cardiometabolic Index is associated with heart failure: a cross-sectional study based on NHANES. Front Med (Lausanne). 2024;11:1507100. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fmed.2024.1507100\u003c/span\u003e\u003cspan address=\"10.3389/fmed.2024.1507100\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. [published Online First: 2024/12/24].\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSong J, Li Y, Zhu J, et al. Non-linear associations of cardiometabolic index with insulin resistance, impaired fasting glucose, and type 2 diabetes among US adults: a cross-sectional study. Front Endocrinol (Lausanne). 2024;15:1341828. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fendo.2024.1341828\u003c/span\u003e\u003cspan address=\"10.3389/fendo.2024.1341828\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. [published Online First: 2024/02/27].\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMiao M, Deng X, Wang Z, et al. Cardiometabolic index is associated with urinary albumin excretion and renal function in aged person over 60: Data from NHANES 2011\u0026ndash;2018. Int J Cardiol. 2023;384:76\u0026ndash;81. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.ijcard.2023.04.017\u003c/span\u003e\u003cspan address=\"10.1016/j.ijcard.2023.04.017\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. [published Online First: 2023/04/15].\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYou Y, Teng W, Wang J, et al. Hypertension and physical activity in middle-aged and older adults in China. Sci Rep. 2018;8(1):16098. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41598-018-34617-y\u003c/span\u003e\u003cspan address=\"10.1038/s41598-018-34617-y\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. [published Online First: 2018/11/02].\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFalkner B, Gidding SS, Baker-Smith CM et al. Pediatric Primary Hypertension: An Underrecognized Condition: A Scientific Statement From the American Heart Association. \u003cem\u003eHypertension\u003c/em\u003e 2023;80(6):e101-e11. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1161/HYP.0000000000000228\u003c/span\u003e\u003cspan address=\"10.1161/HYP.0000000000000228\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e [published Online First: 2023/03/31].\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBonora E, Targher G, Alberiche M, et al. Homeostasis model assessment closely mirrors the glucose clamp technique in the assessment of insulin sensitivity: studies in subjects with various degrees of glucose tolerance and insulin sensitivity. Diabetes Care. 2000;23(1):57\u0026ndash;63. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.2337/diacare.23.1.57\u003c/span\u003e\u003cspan address=\"10.2337/diacare.23.1.57\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLee SB, Ahn CW, Lee BK, et al. Association between triglyceride glucose index and arterial stiffness in Korean adults. Cardiovasc Diabetol. 2018;17(1):41. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s12933-018-0692-1\u003c/span\u003e\u003cspan address=\"10.1186/s12933-018-0692-1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. [published Online First: 2018/03/23].\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAinsworth BE, Haskell WL, Whitt MC, et al. Compendium of physical activities: an update of activity codes and MET intensities. Med Sci Sports Exerc. 2000;32(9 Suppl):S498\u0026ndash;504. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1097/00005768-200009001-00009\u003c/span\u003e\u003cspan address=\"10.1097/00005768-200009001-00009\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. [published Online First: 2000/09/19].\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKong X, Ma Y, Chen J, et al. Evaluation of the Chronic Kidney Disease Epidemiology Collaboration equation for estimating glomerular filtration rate in the Chinese population. Nephrol Dial Transpl. 2013;28(3):641\u0026ndash;51. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/ndt/gfs491\u003c/span\u003e\u003cspan address=\"10.1093/ndt/gfs491\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. [published Online First: 2012/12/01].\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJoglar JA, Chung MK, Armbruster AL, et al. 2023 ACC/AHA/ACCP/HRS Guideline for the Diagnosis and Management of Atrial Fibrillation: A Report of the American College of Cardiology/American Heart Association Joint Committee on Clinical Practice Guidelines. Circulation. 2024;149(1):e1\u0026ndash;156. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1161/CIR.0000000000001193\u003c/span\u003e\u003cspan address=\"10.1161/CIR.0000000000001193\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. [published Online First: 20231130].\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHashimoto Y, Tanaka M, Okada H, et al. Metabolically healthy obesity and risk of incident CKD. Clin J Am Soc Nephrol. 2015;10(4):578\u0026ndash;83. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.2215/CJN.08980914\u003c/span\u003e\u003cspan address=\"10.2215/CJN.08980914\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. [published Online First: 2015/01/31].\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXuan Y, Zhang W, Wang Y, et al. Association Between Uric Acid to HDL Cholesterol Ratio and Diabetic Complications in Men and Postmenopausal Women. Diabetes Metab Syndr Obes. 2023;16:167\u0026ndash;77. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.2147/DMSO.S387726\u003c/span\u003e\u003cspan address=\"10.2147/DMSO.S387726\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. [published Online First: 20230119].\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang X, Xu W, Song Q, et al. Association between the triglyceride-glucose index and severity of coronary artery disease. Cardiovasc Diabetol. 2022;21(1):168. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s12933-022-01606-5\u003c/span\u003e\u003cspan address=\"10.1186/s12933-022-01606-5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. [published Online First: 20220901].\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHou XZ, Lv YF, Li YS, et al. Association between different insulin resistance surrogates and all-cause mortality in patients with coronary heart disease and hypertension: NHANES longitudinal cohort study. Cardiovasc Diabetol. 2024;23(1):86. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s12933-024-02173-7\u003c/span\u003e\u003cspan address=\"10.1186/s12933-024-02173-7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. [published Online First: 20240228].\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXuan Y, Gao P, Shen Y, et al. Association of hypertriglyceridemic waist phenotype with non-alcoholic fatty liver disease: a cross-sectional study in a Chinese population. Horm (Athens). 2022;21(3):437\u0026ndash;46. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s42000-022-00374-x\u003c/span\u003e\u003cspan address=\"10.1007/s42000-022-00374-x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. [published Online First: 20220521].\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCunha de Oliveira C, Carneiro Roriz AK, Eickemberg M, et al. Hypertriglyceridemic waist phenotype: association with metabolic disorders and visceral fat in adults. Nutr Hosp. 2014;30(1):25\u0026ndash;31. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3305/nh.2014.30.1.7411\u003c/span\u003e\u003cspan address=\"10.3305/nh.2014.30.1.7411\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. [published Online First: 2014/08/20].\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMinh HV, Tien HA, Sinh CT, et al. Assessment of preferred methods to measure insulin resistance in Asian patients with hypertension. J Clin Hypertens (Greenwich). 2021;23(3):529\u0026ndash;37. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1111/jch.14155\u003c/span\u003e\u003cspan address=\"10.1111/jch.14155\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. [published Online First: 2021/01/09].\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEngeli S, Schling P, Gorzelniak K, et al. The adipose-tissue renin-angiotensin-aldosterone system: role in the metabolic syndrome? Int J Biochem Cell Biol. 2003;35(6):807\u0026ndash;25. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/s1357-2725(02)00311-4\u003c/span\u003e\u003cspan address=\"10.1016/s1357-2725(02)00311-4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. [published Online First: 2003/04/05].\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHall JE, do Carmo JM, da Silva AA, et al. Obesity-induced hypertension: interaction of neurohumoral and renal mechanisms. Circ Res. 2015;116(6):991\u0026ndash;1006. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1161/CIRCRESAHA.116.305697\u003c/span\u003e\u003cspan address=\"10.1161/CIRCRESAHA.116.305697\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. [published Online First: 2015/03/15].\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-cardiovascular-disorders","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcar","sideBox":"Learn more about [BMC Cardiovascular Disorders](http://bmccardiovascdisord.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcar/default.aspx","title":"BMC Cardiovascular Disorders","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"cardiometabolic index, hypertension, Chinese adults","lastPublishedDoi":"10.21203/rs.3.rs-6612309/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6612309/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe present study aimed to evaluate the relationship between cardiometabolic index (CMI) and hypertension. We undertook a cross-sectional study with a sample of 9013 adults from China. The cardiometabolic index is computed as a product of WHtR and triglyceride-to-HDL cholesterol ratio (TG/HDL ratio). Logistic regression analysis was used to evaluate the association between the CMI and hypertension. Restricted cubic spline (RCS) analysis demonstrated a nonlinear relationship between the CMI and hypertension. The predictive capability of CMI was evaluated using ROC curve analysis. Participants in the highest CMI quartile (Q4) exhibited a 1.34-fold increased risk of hypertension compared to those in the lowest quartile (Q1). Notably, females with CMI\u0026thinsp;\u0026gt;\u0026thinsp;0.36 exhibited a 52% higher hypertension risk (OR\u0026thinsp;=\u0026thinsp;1.52, 95% CI:1.15\u0026ndash;1.99). Gender-specific analyses revealed that the association was attenuated in males after full adjustment for covariates, suggesting a stronger link between CMI and hypertension risk in females. Restricted cubic spine (RCS) analysis showed a nonlinear relationship, with hypertension risk accelerating beyond a CMI threshold of 0.36. The predictive performance of CMI yielded an AUC of 0.667, indicating moderate discriminatory ability. The study underscores the importance of metabolic health in hypertension pathogenesis and suggests that CMI may serve as a useful marker for early intervention and risk stratification in clinical practice. Further studies are warranted to validate these findings and elucidate the underlying mechanisms.\u003c/p\u003e","manuscriptTitle":"Nonlinear Associations and Gender Disparities Between Cardiometabolic Index and Hypertension in Chinese Adults","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-13 05:47:56","doi":"10.21203/rs.3.rs-6612309/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewersInvited","content":"","date":"2025-06-10T11:30:10+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-06-10T06:09:44+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-05-16T08:11:10+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-05-16T06:47:54+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Cardiovascular Disorders","date":"2025-05-16T06:46:46+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-cardiovascular-disorders","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcar","sideBox":"Learn more about [BMC Cardiovascular Disorders](http://bmccardiovascdisord.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcar/default.aspx","title":"BMC Cardiovascular Disorders","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"cdebe589-0467-418f-8cc2-583e5fbbedc3","owner":[],"postedDate":"June 13th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-06-13T05:47:56+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-13 05:47:56","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6612309","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6612309","identity":"rs-6612309","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}