The Association Between Cardiometabolic Index and Hypertension in Diabetic Individuals Aged 45 and Above: Evidence from Two National Databases

The Association Between Cardiometabolic Index and Hypertension in Diabetic Individuals Aged 45 and Above: Evidence from Two National Databases | 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 The Association Between Cardiometabolic Index and Hypertension in Diabetic Individuals Aged 45 and Above: Evidence from Two National Databases Dong Yang, Jialiang Huang, Lincheng Duan, Zeping Chen, Yue Feng This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7537879/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 28 Jan, 2026 Read the published version in BMC Endocrine Disorders → Version 1 posted 17 You are reading this latest preprint version Abstract Background This study utilizes data from two principal sources: the National Health And Nutrition Examination Survey (NHANES) conducted in the United States from 1999 to 2020 and the China Health And Retirement Longitudinal Study (CHARLS) from 2011. This study is to examine the correlation between cardiometabolic index and hypertension in diabetic patients aged 45 and older. Comprehending the impact of cardiometabolic index on hypertension in diabetic individuals is crucial for the prevention and management of hypertension in middle-aged and elderly populations. Methods A cross-sectional analysis was performed on individuals aged 45 and older with diabetes utilizing the NHANES (1999–2020) and CHARLS (2011) datasets. The Cardiometabolic Index (CMI) was calculated using the waist-to-height ratio and the triglycerides-to-HDL cholesterol ratio. Multiple logistic regression analyses were employed to assess the link between CMI on the likelihood of hypertension in diabetic patients., while adjusting for clinical factors. Subgroup analyses, curve fitting, and threshold effect investigations were performed. Results An correlation was discovered in both databases between CMI and the prevalence of hypertension in adults aged 45 and older with diabetes. In CHARLS,The modified logistics regression analysis indicated a beneficial relationship between increased LnCMI and the occurrence of hypertension.(OR = 1.87, 95% CI: 1.65–2.12), with a threshold value of LnCMI set at 1.11. The examination of the NHANES database using an adjusted logistic regression model revealed a positive association between elevated LnCMI and the prevalence of hypertension in diabetic patients. Odds Ratio (OR) = 1.78, 95% Confidence Interval (CI): 1.63–1.94, with a LnCMI threshold of -0.73. Subgroup study revealed that education significantly influenced the relationship between CMI and hypertension in diabetic patients. Conclusions These findings underscore the effectiveness of CMI in evaluating hypertension risk among middle-aged and elderly diabetic populations in China and the United States, with particular significance noted in the Chinese demographic. Cardiometabolic index Hypertension Diabetes NHANES CHARLS Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1 Background Hypertension is one of the common risk factors for ischemic heart disease, stroke, other cardiovascular diseases (CVD), chronic kidney disease, and other conditions [01][02][03][04] .It is estimated that 1.39 billion people had hypertension in 2010, which will impose a significant economic burden [05][06][07] . In 2001, the global economic loss due to poor blood pressure control amounted to 370 billion USD, and future increases in blood pressure could result in nearly 1 trillion USD in global healthcare expenditures. Indirect costs could reach as high as 3,600,000,000,000 USD annually [08] . Therefore, controlling the prevalence of hypertension is an urgent priority. In recent years, hypertension among diabetic patients has drawn significant attention [09] .Currently, 50–80% of type 2 diabetes patients develop hypertension, while approximately 30% of type 1 diabetes patients experience hypertension [10] . One study indicates that age-adjusted diabetes incidence rates increase progressively with rising blood pressure [11] . Another study shows that over half of diabetes patients develop hypertension as a complication [12] . Obesity is a leading risk factor for cardiovascular diseases, hypertension, stroke, diabetes, and other conditions [13],[14],[15] . The Body Mass Index, albeit the most prevalent metric for evaluating human obesity, fails to adequately represent body composition or the distribution of visceral fat [16] .The CMI is a novel metric that indicates obesity and lipid concentrations [17] .The CMI is a novel metric that indicates obesity and lipid concentrations. The computation utilizes the the ratio of triglyceride-to-high-density lipoprotein cholesterol and the ratio of waist to height [18][19] .Currently, CMI is being used in research on various diseases, such as cardiovascular disease [20] , diabetes [21] , and endometriosis [22] Prior research has investigated the correlation between the Cardiac Metabolic Index (CMI) and hypertension and diabetes. Nonetheless, no studies have yet established the correlation between CMI and hypertension in diabetic patients who simultaneously experience both conditions.Furthermore, prior research within the American demographic has predominantly concentrated on adults, lacking specific investigations into the correlation between CMI and hypertension/diabetes among middle-aged and elderly populations. Furthermore, previous studies have not performed comparative comparisons between Chinese and American populations. This study primarily investigates the correlation between CMI and hypertension in diabetic patients aged 45 and above in China and the United States. 2 Methods 2.1 Study Cohort: NHANES is a cross-sectional research conducted in the United States that collects health and nutrition information from a nationally representative sample [23] .The National Center for Health Statistics (NCHS) Data Use Policy allows public access to anonymized health survey data for unrestricted analysis [24] . The China Health and Retirement Longitudinal Study (CHARLS) aims to collect high-quality microdata on families and people aged 45 and above in China. This data can enhance the examination of population aging in China and promote multidisciplinary research on aging-related matters [25] . For the NHANES study, we analysed data from 1999 to 2020. Initially, the study included 107,622 participants.Subsequent exclusions were made for the following,27,317 individuals lacking hypertension and diabetes data; 30,901 individuals lacking CMI-related data;17,032 individuals under the age of 45; 5,836 individuals with absent pertinent covariate data. The sum of 8,017 individuals got ultimately included. In the CHARLS, we analysed data from 2011. Of the 17,709 participants, we excluded 369 individuals with missing hypertension and diabetes data, 7,758 individuals with missing CMI-related data, 193 persons aged under 45 and 13 other people with absent pertinent covariate data. The final sample size comprised 9,376 individuals.(As shown in Fig. 1 ) 2.2 Exposure Factors and Outcome Determination TThe CMI measurement for every single participant is calculated using the formula.: CMI = [waist circumference (cm)/height (cm)] x [triglycerides (mg/L)/HDL cholesterol (mg/L)] [18][19] .Hypertension is identified when average blood pressure measurements fulfill any of the subsequent criteria: (1) a systolic blood pressure of no less than 130 mmHg or a diastolic blood pressure of no less than 80 mmHg; (2) a physician-diagnosed past of self-reported hypertension; or (3) the active use of antihypertensive medication. The diagnostic threshold for hypertension is established at 130/80 mmHg, aligning with the criteria set forth by the American Heart Association. The American Diabetes Association guidelines stipulate that diabetes mellitus (DM) may be diagnosed if any of the subsequent criteria are met: A physician has diagnosed you with diabetes. You are presently administering diabetes medication or insulin injections. (3) A random blood glucose measurement of ≥ 11.1 mmol/L; (4) a glycated hemoglobin (HbA1c) level of no less than 6.5%; (5) a fasting blood glucose concentration of at least 7.0 mmol/L; (6) a two-hour glucose measurement of no less than 11.1 mmol/L during an oral glucose tolerance test (OGTT). 2.3 Covariates Screening This study employed data from the National Health and Nutrition Examination Survey (NHANES).included various covariate analyses., including age, gender, ethnicity, household income ratio (PIR), educational attainment, smoking status, alcohol consumption patterns, and history of cardiovascular disease. Smoking status was determined by the following question: “Have you ever smoked at least 100 cigarettes in your lifetime?” The frequency of alcohol consumption was evaluated using the subsequent method: Participants were asked about their drinking frequency over the past twelve months: On average, how often do you drink any type of alcoholic beverage per week, month, or year? This question aimed to confirm the frequency of participants' alcohol consumption.The specific question was: “How many days per week, month, or year do you drink alcohol?” Cardiovascular disease diagnosis was determined by the following question: This inquiry sought to ascertain if the respondent had received a diagnosis of any of the specified cardiovascular diseases: (1)coronary heart disease, (2)heart attack,(3)stroke,(4)angina, or(5)congestive heart failure.The China Household Health Survey (CHARLS) employs the same covariates but lacks data on the family income ratio (PIR) and ethnicity. Smoking behavior is assessed via a binary question where respondents indicate whether they currently smoke. Alcohol consumption is similarly measured through a binary question where respondents state whether they drink alcohol. Cardiovascular disease is characterized by a history of heart disease or stroke. The complete questionnaire is provided as supplementary material accompanying this paper. 2.4Methods of Statistical Analysis As CMI disclosed a biased distribution, we applied a ln transformation to approximate normality. For normally distributed variables, we employed t-tests; for skewed variables, we used the Kruskal-Wallis rank sum test, categorizing results based on ln-CMI quantiles. We examined the relationship between ln-transformed CMI and hypertension in diabetic patients by multivariate logistic regression.. Three independent models were constructed in each database: Model I in the NHANES database was unadjusted for variables; Model II was adjusted for age, sex, and race; Model III incorporated adjustments for age, sex, race, education level, poverty income ratio, smoking, alcohol consumption, and cardiovascular disease. Furthermore, we stratified by sex (male/female), race (Mexican American/other Hispanic/non-Hispanic white/non-Hispanic black/other races—including mixed), educational attainment (below high school/high school/above high school), smoking status (yes/no), alcohol consumption (yes/no), and presence of cardiovascular disease (yes/no), and assessed for interactions.In the CHARLS,Model 1 did not consider any confounding variables.; Model 2 incorporated age, gender, and educational attainment; Model 3 included age, gender, educational attainment, cardiovascular disease, smoking, and alcohol use statistics. Furthermore, stratified analyses were executed based on gender (male or female), and level of education (below, high, or above high school), cardiovascular disease (present/absent), alcohol consumption (yes/no) and smoking status (yes/no), accompanied by interaction tests To ascertain the nonlinear correlation between LnCMI and hypertension in diabetic individuals, smooth curve fitting was employed for both variables. Every analysis of statistics were performed using R software (version 4.3). The level of significance was established at p < 0.05. 3 Results 3.1 Study population Table 1 delineates the attributes of the study population in the NHANES investigation. A total of 8,017 diabetic individuals aged 45 and above were enrolled., with a mean age of 62.47 ± 11.02 years.Males constituted 49.88% and females 50.12%. The intertertiles ranges for LnCMI were: -2.402 to -0.094 (≤-0.094), -0.094 to 2.212 (≤ 2.212), and 2.212 to 4.52 (≤ 4.52).Overall, 14.21% of participants had hypertension, with prevalence increasing across LnCMI tertiles (low: 8.35%, middle: 13.96%, high: 20.31%).Statistically Substantial disparities were seen among tertile groups for PIR, height, waist circumference, triglycerides, HDL, systolic blood pressure, diastolic blood pressure, gender, ethnicity, education level, and smoking status (all P < 0.05).Compared with the lowest LnCMI group, Individuals in the highest LnCMI group exhibited increased height, waist circumference, triglycerides, systolic and diastolic blood pressure; decreased PIR and HDL levels; and elevated cardiovascular disease risk (all P < 0.05). Individuals with elevated CMI had a greater prevalence of hypertension within the diabetic population (P < 0.05). Table 1 Based on the baseline characteristics of LnCMI(NHANES) LnCMI LOW MIDDLE HIGH P-value N 2672 2672 2673 AGE 62.25 ± 11.32 63.03 ± 11.09 62.13 ± 10.63 0.005 PIR 2.88 ± 1.64 2.67 ± 1.59 2.46 ± 1.57 < 0.001 HEIGHT 166.45 ± 9.85 166.65 ± 10.17 167.22 ± 10.22 0.014 WAIST 93.41 ± 13.38 102.37 ± 13.21 109.04 ± 14.06 < 0.001 TG 72.45 ± 21.17 116.49 ± 28.96 224.53 ± 139.76 < 0.001 HDL 69.19 ± 16.44 53.26 ± 10.74 42.41 ± 9.05 < 0.001 SBP 127.21 ± 19.72 129.37 ± 19.35 129.50 ± 18.63 < 0.001 DBP 68.04 ± 13.37 69.03 ± 13.28 69.76 ± 13.38 < 0.001 GENDER < 0.001 Male 1152 (43.11%) 1342 (50.22%) 1505 (56.30%) Female 1520 (56.89%) 1330 (49.78%) 1168 (43.70%) RACE < 0.001 Mexican American 234 (8.76%) 413 (15.46%) 510 (19.08%) other Hispanic 181 (6.77%) 236 (8.83%) 262 (9.80%) non-Hispanic white 1352 (50.60%) 1309 (48.99%) 1444 (54.02%) non-Hispanic black 711 (26.61%) 520 (19.46%) 300 (11.22%) other races including mixed 194 (7.26%) 194 (7.26%) 157 (5.87%) EDUCATION < 0.001 below high school 602 (22.53%) 748 (27.99%) 898 (33.60%) high school 568 (21.26%) 647 (24.21%) 658 (24.62%) above high school 1502 (56.21%) 1277 (47.79%) 1117 (41.79%) DRINK 0.266 YES 1869 (69.95%) 1816 (67.96%) 1830 (68.46%) NO 803 (30.05%) 856 (32.04%) 843 (31.54%) CVD < 0.001 NO 2299 (86.04%) 2215 (82.90%) 2087 (78.08%) YES 373 (13.96%) 457 (17.10%) 586 (21.92%) SMOKE < 0.001 YES 1279 (47.87%) 1368 (51.20%) 1514 (56.64%) NO 1393 (52.13%) 1304 (48.80%) 1159 (43.36%) DH < 0.001 NO 2449 (91.65%) 2299 (86.04%) 2130 (79.69%) YES 223 (8.35%) 373 (13.96%) 543 (20.31%) Table 2 Baseline characteristics grouped by education EDUCATION below high school high school above high school P-value AGE 64.12 ± 11.00 63.20 ± 11.23 61.17 ± 10.78 < 0.001 PIR 1.69 ± 1.17 2.45 ± 1.46 3.35 ± 1.58 < 0.001 HEIGHT 164.17 ± 10.02 166.31 ± 9.61 168.50 ± 10.00 < 0.001 WAIST 102.06 ± 14.49 101.98 ± 14.89 101.17 ± 15.31 0.039 TG 148.34 ± 113.65 142.15 ± 116.55 129.70 ± 92.55 < 0.001 HDL 52.92 ± 16.07 54.49 ± 16.25 56.34 ± 17.01 < 0.001 SBP 131.34 ± 20.12 130.98 ± 20.13 126.08 ± 17.97 < 0.001 DBP 67.84 ± 13.73 68.90 ± 14.32 69.58 ± 12.61 < 0.001 GENDER 0.114 Male 1148 (51.07%) 897 (47.89%) 1954 (50.15%) Female 1100 (48.93%) 976 (52.11%) 1942 (49.85%) RACE < 0.001 Mexican American 705 (31.36%) 173 (9.24%) 279 (7.16%) other Hispanic 273 (12.14%) 124 (6.62%) 282 (7.24%) non-Hispanic white 745 (33.14%) 1094 (58.41%) 2266 (58.16%) non-Hispanic black 419 (18.64%) 396 (21.14%) 716 (18.38%) other races including mixed 106 (4.72%) 86 (4.59%) 353 (9.06%) DRINKING < 0.001 YES 1408 (62.63%) 1263 (67.43%) 2844 (73.00%) NO 840 (37.37%) 610 (32.57%) 1052 (27.00%) CVD < 0.001 NO 1765 (78.51%) 1515 (80.89%) 3321 (85.24%) YES 483 (21.49%) 358 (19.11%) 575 (14.76%) SMOKE < 0.001 YES 1258 (55.96%) 1023 (54.62%) 1880 (48.25%) NO 990 (44.04%) 850 (45.38%) 2016 (51.75%) DH < 0.001 NO 1839 (81.81%) 1590 (84.89%) 3449 (88.53%) YES 409 (18.19%) 283 (15.11%) 447 (11.47%) Table 3 delineates the attributes of CHARLS. A total of 9,376 people aged 45 and older were enrolled, with a mean age of (59.60 ± 9.41)years.. Males accounted for 46.67% of the study population, while females constituted 53.33%.The intertertiles ranges for LnCMI were: -3.475 to -0.333 (≤-0.333), -0.333 to 2.117 (≤ 2.117), and 2.117 to 4.568 (≤ 4.568).The prevalence of hypertension among diabetic patients was 3.14%, showing an increasing trend with rising LnCMI tertiles (low: 1.41%; middle: 2.24%; high: 5.76%).Statistically significant differences existed across quartile groups for age, height, triglycerides, HDL cholesterol, waist circumference, gender, smoking, alcohol consumption, and cardiovascular disease (all P < 0.05).Compared with the lowest LnCMI group, the highest LnCMI group exhibited greater height, triglycerides, and waist circumference, lower HDL levels, and higher cardiovascular disease risk (all P < 0.05). Notably, The incidence of hypertension in diabetic patients was elevated in the highest LnCMI group (P < 0.05). 3.2 The Correlation Between CMI and Hypertension in Diabetic Patients Table 4 of the NHANES study indicates that the ln-transformed CMI (lnCMI) exhibited a positive correlation with hypertension in diabetes individuals across all three models. In Model 1, every single rise in lnCMI corresponded to a 72% elevated chance of hypertension in diabetic patients(odds ratio = 1.72, 95% CI: 1.58–1.86). In Model 2, each unit increase in lnCMI correlated with an 86% rise in the likelihood of hypertension in diabetic patients (OR = 1.86, 95% CI: 1.70–2.02). In Model 3, each unit increase in the ln-transformed CMI corresponded to a 78% rise in the prevalence of hypertension among diabetes patients (OR = 1.78, 95% CI: 1.63–1.94). In the LnCMI tertile analysis, this association persisted as statistically significant: the fully adjusted model indicated a substantially increased risk of hypertension in the highest tertile group of diabetic patients, 183% greater than that of the lowest tertile group (OR = 2.83, 95% CI: 2.37–3.37) (trend P-value < 0.0001). Additionally, We utilized softening fitting of curves methods to analyze the nonlinear relationship between LnCMI and hypertension in diabetic individuals. Threshold effect analysis revealed a substantial threshold for the influence of LnCMI on hypertension within this cohort. The pivotal barrier was established at -0.73. When the criterion is below LnCMI -0.73), each incremental unit of LnCMI correlated with a 60% rise in hypertension prevalence (OR = 1.60, 95%CI: 1.45–1.77,p < 0.0001), as depicted in Fig. 2 . Table 3 Based on the baseline characteristics of LnCMI(CHARLS) Mean ± SD for continuous variables: the P value was determined using a linear regression model; (%) for categorical variables: the P value was assessed using the chi-square test. PIR refers to the income-to-poverty ratio; HDL denotes high-density lipoprotein cholesterol; TG stands for triglycerides; CVD represents cardiovascular disease.DH, Diabetic patients with hypertension Nonlinear correlation between ln-transformed CMI and hypertension in diabetic patients. The solid red line represents the smoothed curve fit between variables. The blue bars indicate the 95% confidence interval of the fitted results. In the CHALS investigation, Table 5 indicates that ln-transformed CMI (LnCMI) exhibited a positive correlation with hypertension in diabetes patients across all three models. In Model 1, a one-unit rise in LnCMI correlated with a 91% elevated likelihood of hypertension in diabetes patients (OR = 1.91, 95% CI: 1.69–2.16); in Model 2, a unit increase in LnCMI resulted in a 95% elevation in the chance of hypertension in diabetic patients (OR = 1.95, 95%CI: 1.72–2.20). In Model 3, each unit increase in LnCMI elevated the prevalence of hypertension in diabetic individuals by 87% (OR = 1.87, 95% CI: 1.65–2.12). This association retained statistical significance following the ln-transformation of CMI and subsequent tertiary analyses: in the fully adjusted model, the risk of hypertension in diabetic patients within the highest CMI tertiary was 287% greater than in the lowest tertiary (OR = 3.87, 95% CI: 2.76–5.43) (trend P-value < 0.0001). Additionally, Then we examined the nonlinear relationship between LnCMI and hypertension in diabetic patients using smooth curve fitting (shown in Fig. 3 ) and conducted threshold effect analysis to identify the essential threshold at which LnCMI affects hypertension in this population. The LnCMI threshold was determined to be 1.11. When LnCMI falls below 1.11, every supplementary unit of LnCMI correlates with a 144% rise in hypertension prevalence among diabetes individuals (OR = 2.44, 95% CI: 1.96–3.04, P 1.11), the correlation stabilized, yielding an adjusted odds ratio of 1.21 (95% CI: 0.87–1.68, P = 0.2577), signifying no substantial connection. Nonlinear correlation between ln-transformed CMI and hypertension in diabetic patients. The solid red line represents the smoothed curve fit between variables. The blue bars indicate the 95% confidence interval of the fitted results. 3.3. Subgroup analyses In the NHANES study, We conducted subgroup analyses employing interaction testing to evaluate the strength of the association between LnCMI and hypertension in diabetic patients across different cohorts. Figure 4 illustrates that with every 1-unit increment in LnCMI, the prevalence of hypertension escalated by 58% in men (OR = 1.58, 95% CI: 1.16–1.32) and by 87% in women (OR = 1.87, 95% CI: 1.65–2.11). Hispanic Americans exhibited a 58% rise (OR = 1.58, 95% CI: 1.30–1.93), other Latinos a 90% increase (OR = 1.90, 95% CI: 1.45–2.49), and non-Hispanic whites a 98% increase (OR = 1.98, 95% CI: 1.75–2.25). Non-Hispanic Blacks reported a 61% increase (OR = 1.61, 95% CI: 1.34–1.93), but individuals of other races (including multiracial) saw a 68% increase (OR = 1.68, 95% CI: 1.23–2.28). Individuals lacking a high school graduation will see a 45% rise in occurrence (OR = 1.45, 95% CI: 1.27–1.677). High school graduates encounter a 64% elevated risk (OR = 1.64, 95% CI: 1.39–1.93), but individuals with higher education confront a 90% increase (OR = 1.90, 95% CI: 1.68–2.15). Interaction study revealed that the positive link between LnCMI and hypertension in diabetic individuals was affected by educational attainment. Neither smoking nor alcohol use substantially influenced the relationship between LnCMI and hypertension in diabetic patients (interaction P-values > 0.05 for both). Grouping and interaction tests were conducted for LnCMI and hypertension in diabetic patients based on gender, ethnicity, education level, smoking, alcohol consumption, and cardiovascular disease. In the CHARLS investigation, we investigated the correlation between LnCMI and hypertension in diabetic patients across various groups using subgroup analysis and interaction testing. Figure 5 illustrates that with each unit increase in LnCMI, the prevalence of hypertension escalated by 92% in male diabetes patients (OR = 1.92, 95% CI: 1.59–2.31) and by 99% in female patients (OR = 1.99, 95% CI: 1.69–2.35). Interaction tests indicated no substantial influence of gender on the relationship between LnCMI and hypertension. The positive link between LnCMI and hypertension was independent of diabetes, cardiovascular disease, educational level, smoking, or alcohol intake (all interaction P values > 0.05). Grouping and interaction tests for LnCMI and hypertension in diabetic patients by gender, educational attainment, smoking, alcohol consumption, and cardiovascular disease. 4 Discussion Both datasets demonstrated a connection between CMI and the incidence of hypertension in diabetes persons aged 45 and above.Research demonstrates that the incidence of hypertension in diabetes patients escalates concurrently with increasing CMI values. This trend remains statistically significant irrespective of the analysis of CMI as either a continuous or categorical variable.In NHANES participants, the critical value for LnCMI was − 0.77, while in CHARLS participants, it was 1.11. Subgroup analysis further validated the relevance of this connection across several demographic and clinical categories. The NHANES study revealed an interaction effect of educational attainment, suggesting that the impact of CMI on hypertension differs based on educational level. CMI, as an index reflecting visceral fat and metabolic dysfunction, has demonstrated its predictive value in conditions such as cardiovascular diseases [19] , diabetes [20] , endometriosis [21], kidney stones [26] and osteoporosis [27] .CMI provides a beneficial assessment of visceral fat tissue and exhibits stronger correlations with metabolic conditions, closely linked to obesity [28] .First, excessive visceral fat distribution is associated with various changes, including inflammatory factors and endothelial levels [29] .Second, the renin-angiotensin-aldosterone system can also be activated, leading to impaired sodium excretion and increased sodium reabsorption in the renal tubules [30] .Additionally, visceral fat tissue significantly participates in blood pressure regulation through the secretion of adipokines that regulate arterial tension, such as leptin and adiponectin [31] . Finally, excessive visceral fat tissue increases insulin resistance, leading to metabolic dysfunction, oxidative stress, and vascular dysfunction [32] . The combination of these factors ultimately results in elevated blood pressure. These related pathways further demonstrate that CMI can serve as a predictive index for hypertension and is crucial in forecasting the occurrence of hypertension. This study is the first to investigate the association between CMI and hypertension in diabetic patients, utilizing a direct comparative analysis of databases from China and the United States. Additionally, by specifically focusing on middle-aged and elderly populations, the conclusions are more targeted. Methodologically, data underwent Ln transformation processing to enhance the scientific rigour of the model.The findings confirm that elevated CMI values correlate significantly with a marked increase in hypertension prevalence among diabetic patients. Furthermore, unlike previous studies indicating gender-dependent associations between CMI and hypertension [33] .our analysis reveals that the link between CMI and hypertension in diabetic patients is independent of gender.This study focused on individuals aged 45 and above and conducted threshold effect analysis. Within the NHANES database, the threshold for LnCMI was − 0.73. Below this barrier, the incidence of hypertension escalated with increasing LnCMI; beyond the threshold, the relationship stabilised with no significant association.In the CHARLS database, the LnCMI threshold was 1.11. Beyond this threshold, the relationship stabilised with no significant association. Based on prior research, we hypothesise this may relate to the saturation or maximum efficiency of the body's compensatory mechanisms for maintaining blood pressure homeostasis, highlighting the equilibrium between pathological processes and compensatory responses [33] . Furthermore, the NHANES data revealed a substantial interaction between educational attainment and the association between CMI levels and hypertension among diabetic patients aged 45 and older. This finding suggests that educational attainment exerts a significant influence on The correlation between CMI and hypertension in diabetic individuals, with the observed effect being more pronounced among individuals with higher levels of education compared to those with lower levels. Specifically, the prevalence of hypertension demonstrates a significant positive correlation with educational attainment, indicating that higher educational levels are associated with higher prevalence rates.However, in Table 3 description of the study population grouped by education, higher education levels were associated with lower hypertension prevalence among diabetic patients. This discrepancy may be attributed to the study's focus on individuals aged 45 and older, where the high-education cohort demonstrated a markedly reduced mean age in comparison to the low-education cohort. The elevated prevalence of hypertension in the 45 age and above group, in conjunction with the potential for diagnostic bias and confounding factors within the NHANES database, is likely a contributing factor to the observed results. The principal advantage of this study is its use of NHANES and CHARLS data, both of which feature substantial sample numbers and national representativeness. This represents the inaugural instance in study investigating the correlation between CMI and hypertension in diabetic patients, where in CHARLS and NHANES data have been amalgamated to analyze the link among adults aged 45 and older. Additionally, we validated the findings with subgroup analysis across several populations, curve fitting, interaction testing, and threshold analyses. We recognize the study's limitations: The cross-sectional design constrains causal inference, because residual confounding from unmeasured variables may exist in the data. The respondents, aged 45 and older from China and the United States, render the conclusions challenging to generalize to other groups due to geographical disparities and fluctuating health status. Moreover, diagnostic bias in hypertension may vary among different age demographics, requiring validation of these results through further research. 5 Conclusions This study amalgamated data from 8,017 participants in NHANES and 9,376 people in CHARLS. Findings demonstrate that CMI is a substantial predictor of hypertension risk in diabetes persons aged 45 and above in both China and the United States. The research indicates a nonlinear relationship between CMI and hypertension in diabetic individuals, emphasizing a significant threshold effect. These findings further substantiate the therapeutic relevance of CMI, consequently enhancing hypertension management outcomes among varied populations and significantly contributing to the prediction and management of hypertension in diabetic patients. Abbreviations PIR refers to the income-to-poverty ratio; HDL denotes high-density lipoprotein cholesterol; TG stands for triglycerides; CVD represents cardiovascular disease.DH, Diabetic patients with hypertension Declarations Acknowledgments The authors thank the studies or consortiums referenced and included in the present analysis for providing public datasets Author Contributions DY: Conceptual design, methodology, formal analysis, data organization, and preliminary draft composition; JLH: Formal analysis, data organization, and preliminary draft composition; LCD: Formal analysis, data organization, and preliminary draft composition ZPC: Formal analysis, data organization YF: Oversight of projects, assessment of manuscripts, and editing. All authors participated in the composition of this paper and completed the manuscript. Funding Sichuan Provincial Natural Science Foundation, General Program, Mechanistic Study on the Improvement of Hypertensive Cardiovascular Remodeling by Pushing the Arch of the Aorta via TRPV4-ATP-P2X3 Signaling Pathway Regulation of the Sympathetic Nervous System, 25NSFSC2297, January 1, 2025 to December 31, 2026, ¥200,000 National Natural Science Foundation of China, General Program, Vascular Wall Shear Stress-Mediated TRPV4-ATP-P2X3 Signaling Pathway: Cellular Mechanotransduction Mechanism of Push Bridge Arch in Improving Hypertensive Cardiovascular Remodeling, 82575249, 2026-01-01 to 2029-12-31, ¥510,000 Data availability The data supporting the findings of this research are available in online databases. Details regarding the specific repository names and the associated accession numbers are provided below: The analyzed datasets for this study are accessible to the public and can be retrieved from here: https://www.cdc.gov/nchs/nhanes/.and https://charls.charlsdata.com/pages/Data/2011-charls-wave1/zh-cn.html. Ethics approval and consent to participate NHANES:Data were collected with the approval of the NCHS Research Ethics.Review Board, and were anonymized before being released to the public. All participants provided informed consent prior to participating. All data in CHARLS were generated during the analysis process of this study, which was conducted and approved by the Biomedical Ethics Review Committee of Peking 373 University in accordance with the principles of the Declaration of Helsinki. All participants provided written informed consent to participate in the study (IRB approval number: IRB00001052-11015). This study does not disclose any personal information about the participants, nor does it violate data protection laws; the research has been performed in accordance with the Declaration of Helsinki.The data are available online at http://www.isss.pku.edu.cn/cfps/. Before accessing thedata, you must 378 register as a user on this website. Once your registrationis approved, you may download the dataset by following the provided instructions. Consent for publication Clinical trial number: Not applicable Competing interests The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest . References Tikhonoff, V.; Zhang, H.; Richart, T.; Staessen, J. A. Blood pressure as a prognostic factor after acute stroke. Lancet Neurol 2009 , 8 (10), 938-948. DOI: 10.1016/s1474-4422(09)70184-x From NLM. 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Supplementary Files SupplementaryQuestionnaire.pdf Cite Share Download PDF Status: Published Journal Publication published 28 Jan, 2026 Read the published version in BMC Endocrine Disorders → Version 1 posted Editorial decision: Revision requested 29 Oct, 2025 Reviews received at journal 28 Oct, 2025 Reviews received at journal 28 Oct, 2025 Reviewers agreed at journal 26 Oct, 2025 Reviewers agreed at journal 25 Oct, 2025 Reviewers agreed at journal 23 Oct, 2025 Reviewers agreed at journal 23 Oct, 2025 Reviewers agreed at journal 23 Oct, 2025 Reviews received at journal 12 Oct, 2025 Reviewers agreed at journal 01 Oct, 2025 Reviewers agreed at journal 29 Sep, 2025 Reviewers agreed at journal 29 Sep, 2025 Reviewers invited by journal 29 Sep, 2025 Editor assigned by journal 29 Sep, 2025 Editor invited by journal 09 Sep, 2025 Submission checks completed at journal 09 Sep, 2025 First submitted to journal 08 Sep, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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16:10:30","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2913428,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7537879/v1/a53d32c3-3a19-4e91-a2ba-c1dceea21729.pdf"},{"id":93338562,"identity":"9e58e6a4-4735-456e-b41c-83a3779c93d0","added_by":"auto","created_at":"2025-10-12 14:21:45","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":129832,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryQuestionnaire.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7537879/v1/76cfb947fd8641db8a7296aa.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"The Association Between Cardiometabolic Index and Hypertension in Diabetic Individuals Aged 45 and Above: Evidence from Two National Databases","fulltext":[{"header":"1 Background","content":"\u003cp\u003eHypertension is one of the common risk factors for ischemic heart disease, stroke, other cardiovascular diseases (CVD), chronic kidney disease, and other conditions\u003csup\u003e[01][02][03][04]\u003c/sup\u003e.It is estimated that 1.39\u0026nbsp;billion people had hypertension in 2010, which will impose a significant economic burden\u003csup\u003e[05][06][07]\u003c/sup\u003e. In 2001, the global economic loss due to poor blood pressure control amounted to 370\u0026nbsp;billion USD, and future increases in blood pressure could result in nearly 1 trillion USD in global healthcare expenditures. Indirect costs could reach as high as 3,600,000,000,000 USD annually\u003csup\u003e[08]\u003c/sup\u003e. Therefore, controlling the prevalence of hypertension is an urgent priority. In recent years, hypertension among diabetic patients has drawn significant attention\u003csup\u003e[09]\u003c/sup\u003e.Currently, 50\u0026ndash;80% of type 2 diabetes patients develop hypertension, while approximately 30% of type 1 diabetes patients experience hypertension\u003csup\u003e[10]\u003c/sup\u003e. One study indicates that age-adjusted diabetes incidence rates increase progressively with rising blood pressure\u003csup\u003e[11]\u003c/sup\u003e. Another study shows that over half of diabetes patients develop hypertension as a complication\u003csup\u003e[12]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eObesity is a leading risk factor for cardiovascular diseases, hypertension, stroke, diabetes, and other conditions\u003csup\u003e[13],[14],[15]\u003c/sup\u003e. The Body Mass Index, albeit the most prevalent metric for evaluating human obesity, fails to adequately represent body composition or the distribution of visceral fat\u003csup\u003e[16]\u003c/sup\u003e.The CMI is a novel metric that indicates obesity and lipid concentrations\u003csup\u003e[17]\u003c/sup\u003e.The CMI is a novel metric that indicates obesity and lipid concentrations. The computation utilizes the the ratio of triglyceride-to-high-density lipoprotein cholesterol and the ratio of waist to height\u003csup\u003e[18][19]\u003c/sup\u003e.Currently, CMI is being used in research on various diseases, such as cardiovascular disease\u003csup\u003e[20]\u003c/sup\u003e, diabetes\u003csup\u003e[21]\u003c/sup\u003e, and endometriosis\u003csup\u003e[22]\u003c/sup\u003e\u003c/p\u003e\u003cp\u003ePrior research has investigated the correlation between the Cardiac Metabolic Index (CMI) and hypertension and diabetes. Nonetheless, no studies have yet established the correlation between CMI and hypertension in diabetic patients who simultaneously experience both conditions.Furthermore, prior research within the American demographic has predominantly concentrated on adults, lacking specific investigations into the correlation between CMI and hypertension/diabetes among middle-aged and elderly populations. Furthermore, previous studies have not performed comparative comparisons between Chinese and American populations. This study primarily investigates the correlation between CMI and hypertension in diabetic patients aged 45 and above in China and the United States.\u003c/p\u003e"},{"header":"2 Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Study Cohort:\u003c/h2\u003e\u003cp\u003eNHANES is a cross-sectional research conducted in the United States that collects health and nutrition information from a nationally representative sample\u003csup\u003e[23]\u003c/sup\u003e.The National Center for Health Statistics (NCHS) Data Use Policy allows public access to anonymized health survey data for unrestricted analysis\u003csup\u003e[24]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eThe China Health and Retirement Longitudinal Study (CHARLS) aims to collect high-quality microdata on families and people aged 45 and above in China. This data can enhance the examination of population aging in China and promote multidisciplinary research on aging-related matters\u003csup\u003e[25]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eFor the NHANES study, we analysed data from 1999 to 2020. Initially, the study included 107,622 participants.Subsequent exclusions were made for the following,27,317 individuals lacking hypertension and diabetes data; 30,901 individuals lacking CMI-related data;17,032 individuals under the age of 45; 5,836 individuals with absent pertinent covariate data. The sum of 8,017 individuals got ultimately included.\u003c/p\u003e\u003cp\u003eIn the CHARLS, we analysed data from 2011. Of the 17,709 participants, we excluded 369 individuals with missing hypertension and diabetes data, 7,758 individuals with missing CMI-related data, 193 persons aged under 45 and 13 other people with absent pertinent covariate data. The final sample size comprised 9,376 individuals.(As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e)\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Exposure Factors and Outcome Determination\u003c/h2\u003e\u003cp\u003eTThe CMI measurement for every single participant is calculated using the formula.: CMI = [waist circumference (cm)/height (cm)] x [triglycerides (mg/L)/HDL cholesterol (mg/L)]\u003csup\u003e[18][19]\u003c/sup\u003e .Hypertension is identified when average blood pressure measurements fulfill any of the subsequent criteria: (1) a systolic blood pressure of no less than 130 mmHg or a diastolic blood pressure of no less than 80 mmHg; (2) a physician-diagnosed past of self-reported hypertension; or (3) the active use of antihypertensive medication. The diagnostic threshold for hypertension is established at 130/80 mmHg, aligning with the criteria set forth by the American Heart Association. The American Diabetes Association guidelines stipulate that diabetes mellitus (DM) may be diagnosed if any of the subsequent criteria are met: A physician has diagnosed you with diabetes. You are presently administering diabetes medication or insulin injections. (3) A random blood glucose measurement of \u0026ge;\u0026thinsp;11.1 mmol/L; (4) a glycated hemoglobin (HbA1c) level of no less than 6.5%; (5) a fasting blood glucose concentration of at least 7.0 mmol/L; (6) a two-hour glucose measurement of no less than 11.1 mmol/L during an oral glucose tolerance test (OGTT).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Covariates Screening\u003c/h2\u003e\u003cp\u003eThis study employed data from the National Health and Nutrition Examination Survey (NHANES).included various covariate analyses., including age, gender, ethnicity, household income ratio (PIR), educational attainment, smoking status, alcohol consumption patterns, and history of cardiovascular disease. Smoking status was determined by the following question: \u0026ldquo;Have you ever smoked at least 100 cigarettes in your lifetime?\u0026rdquo; The frequency of alcohol consumption was evaluated using the subsequent method: Participants were asked about their drinking frequency over the past twelve months: On average, how often do you drink any type of alcoholic beverage per week, month, or year? This question aimed to confirm the frequency of participants' alcohol consumption.The specific question was: \u0026ldquo;How many days per week, month, or year do you drink alcohol?\u0026rdquo; Cardiovascular disease diagnosis was determined by the following question: This inquiry sought to ascertain if the respondent had received a diagnosis of any of the specified cardiovascular diseases: (1)coronary heart disease, (2)heart attack,(3)stroke,(4)angina, or(5)congestive heart failure.The China Household Health Survey (CHARLS) employs the same covariates but lacks data on the family income ratio (PIR) and ethnicity. Smoking behavior is assessed via a binary question where respondents indicate whether they currently smoke. Alcohol consumption is similarly measured through a binary question where respondents state whether they drink alcohol. Cardiovascular disease is characterized by a history of heart disease or stroke. The complete questionnaire is provided as supplementary material accompanying this paper.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4Methods of Statistical Analysis\u003c/h2\u003e\u003cp\u003eAs CMI disclosed a biased distribution, we applied a ln transformation to approximate normality. For normally distributed variables, we employed t-tests; for skewed variables, we used the Kruskal-Wallis rank sum test, categorizing results based on ln-CMI quantiles. We examined the relationship between ln-transformed CMI and hypertension in diabetic patients by multivariate logistic regression.. Three independent models were constructed in each database: Model I in the NHANES database was unadjusted for variables; Model II was adjusted for age, sex, and race; Model III incorporated adjustments for age, sex, race, education level, poverty income ratio, smoking, alcohol consumption, and cardiovascular disease. Furthermore, we stratified by sex (male/female), race (Mexican American/other Hispanic/non-Hispanic white/non-Hispanic black/other races\u0026mdash;including mixed), educational attainment (below high school/high school/above high school), smoking status (yes/no), alcohol consumption (yes/no), and presence of cardiovascular disease (yes/no), and assessed for interactions.In the CHARLS,Model 1 did not consider any confounding variables.; Model 2 incorporated age, gender, and educational attainment; Model 3 included age, gender, educational attainment, cardiovascular disease, smoking, and alcohol use statistics. Furthermore, stratified analyses were executed based on gender (male or female), and level of education (below, high, or above high school), cardiovascular disease (present/absent), alcohol consumption (yes/no) and smoking status (yes/no), accompanied by interaction tests To ascertain the nonlinear correlation between LnCMI and hypertension in diabetic individuals, smooth curve fitting was employed for both variables. Every analysis of statistics were performed using R software (version 4.3). The level of significance was established at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e\u003c/div\u003e"},{"header":"3 Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Study population\u003c/h2\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e delineates the attributes of the study population in the NHANES investigation. A total of 8,017 diabetic individuals aged 45 and above were enrolled., with a mean age of 62.47\u0026thinsp;\u0026plusmn;\u0026thinsp;11.02 years.Males constituted 49.88% and females 50.12%. The intertertiles ranges for LnCMI were: -2.402 to -0.094 (\u0026le;-0.094), -0.094 to 2.212 (\u0026le;\u0026thinsp;2.212), and 2.212 to 4.52 (\u0026le;\u0026thinsp;4.52).Overall, 14.21% of participants had hypertension, with prevalence increasing across LnCMI tertiles (low: 8.35%, middle: 13.96%, high: 20.31%).Statistically Substantial disparities were seen among tertile groups for PIR, height, waist circumference, triglycerides, HDL, systolic blood pressure, diastolic blood pressure, gender, ethnicity, education level, and smoking status (all P\u0026thinsp;\u0026lt;\u0026thinsp;0.05).Compared with the lowest LnCMI group, Individuals in the highest LnCMI group exhibited increased height, waist circumference, triglycerides, systolic and diastolic blood pressure; decreased PIR and HDL levels; and elevated cardiovascular disease risk (all P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Individuals with elevated CMI had a greater prevalence of hypertension within the diabetic population (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eBased on the baseline characteristics of LnCMI(NHANES)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"1\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLnCMI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLOW\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMIDDLE\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eHIGH\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eP-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eN\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2672\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2672\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2673\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAGE\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e62.25\u0026thinsp;\u0026plusmn;\u0026thinsp;11.32\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e63.03\u0026thinsp;\u0026plusmn;\u0026thinsp;11.09\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e62.13\u0026thinsp;\u0026plusmn;\u0026thinsp;10.63\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.005\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePIR\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.88\u0026thinsp;\u0026plusmn;\u0026thinsp;1.64\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.67\u0026thinsp;\u0026plusmn;\u0026thinsp;1.59\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.46\u0026thinsp;\u0026plusmn;\u0026thinsp;1.57\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHEIGHT\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e166.45\u0026thinsp;\u0026plusmn;\u0026thinsp;9.85\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e166.65\u0026thinsp;\u0026plusmn;\u0026thinsp;10.17\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" 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colname=\"c4\"\u003e\u003cp\u003e42.41\u0026thinsp;\u0026plusmn;\u0026thinsp;9.05\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSBP\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e127.21\u0026thinsp;\u0026plusmn;\u0026thinsp;19.72\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e129.37\u0026thinsp;\u0026plusmn;\u0026thinsp;19.35\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e129.50\u0026thinsp;\u0026plusmn;\u0026thinsp;18.63\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDBP\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e68.04\u0026thinsp;\u0026plusmn;\u0026thinsp;13.37\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e69.03\u0026thinsp;\u0026plusmn;\u0026thinsp;13.28\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e69.76\u0026thinsp;\u0026plusmn;\u0026thinsp;13.38\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGENDER\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1152 (43.11%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1342 (50.22%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1505 (56.30%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1520 (56.89%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1330 (49.78%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1168 (43.70%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRACE\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMexican American\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e234 (8.76%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e413 (15.46%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e510 (19.08%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eother Hispanic\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e181 (6.77%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e236 (8.83%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e262 (9.80%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003enon-Hispanic white\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1352 (50.60%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1309 (48.99%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1444 (54.02%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003enon-Hispanic black\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e711 (26.61%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e520 (19.46%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e300 (11.22%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eother races including mixed\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e194 (7.26%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e194 (7.26%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e157 (5.87%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEDUCATION\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003ebelow high school\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e602 (22.53%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e748 (27.99%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e898 (33.60%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003ehigh school\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e568 (21.26%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e647 (24.21%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e658 (24.62%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eabove high school\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1502 (56.21%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1277 (47.79%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1117 (41.79%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDRINK\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.266\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1869 (69.95%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1816 (67.96%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1830 (68.46%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNO\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e803 (30.05%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e856 (32.04%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e843 (31.54%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCVD\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNO\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2299 (86.04%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2215 (82.90%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2087 (78.08%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e373 (13.96%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e457 (17.10%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e586 (21.92%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSMOKE\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1279 (47.87%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1368 (51.20%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1514 (56.64%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNO\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1393 (52.13%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1304 (48.80%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1159 (43.36%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDH\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNO\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2449 (91.65%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2299 (86.04%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2130 (79.69%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e223 (8.35%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e373 (13.96%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e543 (20.31%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eBaseline characteristics grouped by education\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"1\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Tabb\" border=\"1\"\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEDUCATION\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003ebelow high school\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003ehigh school\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eabove high school\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eP-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAGE\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e64.12\u0026thinsp;\u0026plusmn;\u0026thinsp;11.00\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e63.20\u0026thinsp;\u0026plusmn;\u0026thinsp;11.23\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e61.17\u0026thinsp;\u0026plusmn;\u0026thinsp;10.78\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePIR\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.69\u0026thinsp;\u0026plusmn;\u0026thinsp;1.17\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.45\u0026thinsp;\u0026plusmn;\u0026thinsp;1.46\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.35\u0026thinsp;\u0026plusmn;\u0026thinsp;1.58\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHEIGHT\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e164.17\u0026thinsp;\u0026plusmn;\u0026thinsp;10.02\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e166.31\u0026thinsp;\u0026plusmn;\u0026thinsp;9.61\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e168.50\u0026thinsp;\u0026plusmn;\u0026thinsp;10.00\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWAIST\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e102.06\u0026thinsp;\u0026plusmn;\u0026thinsp;14.49\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e101.98\u0026thinsp;\u0026plusmn;\u0026thinsp;14.89\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e101.17\u0026thinsp;\u0026plusmn;\u0026thinsp;15.31\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.039\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTG\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e148.34\u0026thinsp;\u0026plusmn;\u0026thinsp;113.65\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e142.15\u0026thinsp;\u0026plusmn;\u0026thinsp;116.55\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e129.70\u0026thinsp;\u0026plusmn;\u0026thinsp;92.55\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHDL\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e52.92\u0026thinsp;\u0026plusmn;\u0026thinsp;16.07\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e54.49\u0026thinsp;\u0026plusmn;\u0026thinsp;16.25\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e56.34\u0026thinsp;\u0026plusmn;\u0026thinsp;17.01\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSBP\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e131.34\u0026thinsp;\u0026plusmn;\u0026thinsp;20.12\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e130.98\u0026thinsp;\u0026plusmn;\u0026thinsp;20.13\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e126.08\u0026thinsp;\u0026plusmn;\u0026thinsp;17.97\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDBP\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e67.84\u0026thinsp;\u0026plusmn;\u0026thinsp;13.73\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e68.90\u0026thinsp;\u0026plusmn;\u0026thinsp;14.32\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e69.58\u0026thinsp;\u0026plusmn;\u0026thinsp;12.61\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGENDER\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.114\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1148 (51.07%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e897 (47.89%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1954 (50.15%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1100 (48.93%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e976 (52.11%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1942 (49.85%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRACE\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMexican American\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e705 (31.36%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e173 (9.24%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e279 (7.16%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eother Hispanic\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e273 (12.14%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e124 (6.62%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e282 (7.24%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003enon-Hispanic white\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e745 (33.14%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1094 (58.41%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2266 (58.16%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003enon-Hispanic black\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e419 (18.64%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e396 (21.14%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e716 (18.38%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eother races including mixed\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e106 (4.72%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e86 (4.59%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e353 (9.06%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDRINKING\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1408 (62.63%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1263 (67.43%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2844 (73.00%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNO\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e840 (37.37%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e610 (32.57%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1052 (27.00%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCVD\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNO\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1765 (78.51%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1515 (80.89%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3321 (85.24%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e483 (21.49%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e358 (19.11%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e575 (14.76%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSMOKE\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1258 (55.96%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1023 (54.62%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1880 (48.25%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNO\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e990 (44.04%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e850 (45.38%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2016 (51.75%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDH\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNO\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1839 (81.81%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1590 (84.89%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3449 (88.53%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e409 (18.19%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e283 (15.11%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e447 (11.47%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e delineates the attributes of CHARLS. A total of 9,376 people aged 45 and older were enrolled, with a mean age of (59.60\u0026thinsp;\u0026plusmn;\u0026thinsp;9.41)years.. Males accounted for 46.67% of the study population, while females constituted 53.33%.The intertertiles ranges for LnCMI were: -3.475 to -0.333 (\u0026le;-0.333), -0.333 to 2.117 (\u0026le;\u0026thinsp;2.117), and 2.117 to 4.568 (\u0026le;\u0026thinsp;4.568).The prevalence of hypertension among diabetic patients was 3.14%, showing an increasing trend with rising LnCMI tertiles (low: 1.41%; middle: 2.24%; high: 5.76%).Statistically significant differences existed across quartile groups for age, height, triglycerides, HDL cholesterol, waist circumference, gender, smoking, alcohol consumption, and cardiovascular disease (all P\u0026thinsp;\u0026lt;\u0026thinsp;0.05).Compared with the lowest LnCMI group, the highest LnCMI group exhibited greater height, triglycerides, and waist circumference, lower HDL levels, and higher cardiovascular disease risk (all P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Notably, The incidence of hypertension in diabetic patients was elevated in the highest LnCMI group (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e\n\u003ch2\u003e3.2 The Correlation Between CMI and Hypertension in Diabetic Patients\u003c/h2\u003e\n\u003cp\u003eTable \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e of the NHANES study indicates that the ln-transformed CMI (lnCMI) exhibited a positive correlation with hypertension in diabetes individuals across all three models. In Model 1, every single rise in lnCMI corresponded to a 72% elevated chance of hypertension in diabetic patients(odds ratio\u0026thinsp;=\u0026thinsp;1.72, 95% CI: 1.58\u0026ndash;1.86). In Model 2, each unit increase in lnCMI correlated with an 86% rise in the likelihood of hypertension in diabetic patients (OR\u0026thinsp;=\u0026thinsp;1.86, 95% CI: 1.70\u0026ndash;2.02). In Model 3, each unit increase in the ln-transformed CMI corresponded to a 78% rise in the prevalence of hypertension among diabetes patients (OR\u0026thinsp;=\u0026thinsp;1.78, 95% CI: 1.63\u0026ndash;1.94). In the LnCMI tertile analysis, this association persisted as statistically significant: the fully adjusted model indicated a substantially increased risk of hypertension in the highest tertile group of diabetic patients, 183% greater than that of the lowest tertile group (OR\u0026thinsp;=\u0026thinsp;2.83, 95% CI: 2.37\u0026ndash;3.37) (trend P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). Additionally, We utilized softening fitting of curves methods to analyze the nonlinear relationship between LnCMI and hypertension in diabetic individuals. Threshold effect analysis revealed a substantial threshold for the influence of LnCMI on hypertension within this cohort. The pivotal barrier was established at -0.73. When the criterion is below LnCMI \u0026lt; -0.73, the odds ratio is 0.72 (95% CI: 0.37\u0026ndash;1.38, P\u0026thinsp;=\u0026thinsp;0.3227), signifying a lack of statistical significance. When values surpassed the threshold (LnCMI \u0026gt;-0.73), each incremental unit of LnCMI correlated with a 60% rise in hypertension prevalence (OR\u0026thinsp;=\u0026thinsp;1.60, 95%CI: 1.45\u0026ndash;1.77,p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), as depicted in Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e\n\u003cp\u003eTable 3 Based on the baseline characteristics of LnCMI(CHARLS)\u003c/p\u003e\n\u003cp\u003e\u003cimg 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\"\u003e\u003c/p\u003e\n\u003cp\u003eMean \u0026plusmn; SD for continuous variables: the P value was determined using a linear regression model; (%) for categorical variables: the P value was assessed using the chi-square test.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePIR refers to the income-to-poverty ratio; HDL denotes high-density lipoprotein cholesterol; TG stands for triglycerides; CVD represents cardiovascular disease.DH, Diabetic patients with hypertension\u003cimg 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\"\u003e\u003c/p\u003e\n\u003cp\u003eNonlinear correlation between ln-transformed CMI and hypertension in diabetic patients. The solid red line represents the smoothed curve fit between variables. The blue bars indicate the 95% confidence interval of the fitted results.\u003c/p\u003e\n\u003cp\u003eIn the CHALS investigation, Table \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e indicates that ln-transformed CMI (LnCMI) exhibited a positive correlation with hypertension in diabetes patients across all three models. In Model 1, a one-unit rise in LnCMI correlated with a 91% elevated likelihood of hypertension in diabetes patients (OR\u0026thinsp;=\u0026thinsp;1.91, 95% CI: 1.69\u0026ndash;2.16); in Model 2, a unit increase in LnCMI resulted in a 95% elevation in the chance of hypertension in diabetic patients (OR\u0026thinsp;=\u0026thinsp;1.95, 95%CI: 1.72\u0026ndash;2.20). In Model 3, each unit increase in LnCMI elevated the prevalence of hypertension in diabetic individuals by 87% (OR\u0026thinsp;=\u0026thinsp;1.87, 95% CI: 1.65\u0026ndash;2.12). This association retained statistical significance following the ln-transformation of CMI and subsequent tertiary analyses: in the fully adjusted model, the risk of hypertension in diabetic patients within the highest CMI tertiary was 287% greater than in the lowest tertiary (OR\u0026thinsp;=\u0026thinsp;3.87, 95% CI: 2.76\u0026ndash;5.43) (trend P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). Additionally, Then we examined the nonlinear relationship between LnCMI and hypertension in diabetic patients using smooth curve fitting (shown in Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e) and conducted threshold effect analysis to identify the essential threshold at which LnCMI affects hypertension in this population. The LnCMI threshold was determined to be 1.11. When LnCMI falls below 1.11, every supplementary unit of LnCMI correlates with a 144% rise in hypertension prevalence among diabetes individuals (OR\u0026thinsp;=\u0026thinsp;2.44, 95% CI: 1.96\u0026ndash;3.04, P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). Exceeding this threshold (LnCMI\u0026thinsp;\u0026gt;\u0026thinsp;1.11), the correlation stabilized, yielding an adjusted odds ratio of 1.21 (95% CI: 0.87\u0026ndash;1.68, P\u0026thinsp;=\u0026thinsp;0.2577), signifying no substantial connection.\u003c/p\u003e\n\u003cp\u003e\u003cimg 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\"\u003e\u003c/p\u003e\n\u003cp\u003eNonlinear correlation between ln-transformed CMI and hypertension in diabetic patients. The solid red line represents the smoothed curve fit between variables. The blue bars indicate the 95% confidence interval of the fitted results.\u003c/p\u003e\n\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n \u003ch2\u003e3.3. Subgroup analyses\u003c/h2\u003e\n \u003cp\u003eIn the NHANES study, We conducted subgroup analyses employing interaction testing to evaluate the strength of the association between LnCMI and hypertension in diabetic patients across different cohorts. Figure \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e illustrates that with every 1-unit increment in LnCMI, the prevalence of hypertension escalated by 58% in men (OR\u0026thinsp;=\u0026thinsp;1.58, 95% CI: 1.16\u0026ndash;1.32) and by 87% in women (OR\u0026thinsp;=\u0026thinsp;1.87, 95% CI: 1.65\u0026ndash;2.11). Hispanic Americans exhibited a 58% rise (OR\u0026thinsp;=\u0026thinsp;1.58, 95% CI: 1.30\u0026ndash;1.93), other Latinos a 90% increase (OR\u0026thinsp;=\u0026thinsp;1.90, 95% CI: 1.45\u0026ndash;2.49), and non-Hispanic whites a 98% increase (OR\u0026thinsp;=\u0026thinsp;1.98, 95% CI: 1.75\u0026ndash;2.25). Non-Hispanic Blacks reported a 61% increase (OR\u0026thinsp;=\u0026thinsp;1.61, 95% CI: 1.34\u0026ndash;1.93), but individuals of other races (including multiracial) saw a 68% increase (OR\u0026thinsp;=\u0026thinsp;1.68, 95% CI: 1.23\u0026ndash;2.28). Individuals lacking a high school graduation will see a 45% rise in occurrence (OR\u0026thinsp;=\u0026thinsp;1.45, 95% CI: 1.27\u0026ndash;1.677). High school graduates encounter a 64% elevated risk (OR\u0026thinsp;=\u0026thinsp;1.64, 95% CI: 1.39\u0026ndash;1.93), but individuals with higher education confront a 90% increase (OR\u0026thinsp;=\u0026thinsp;1.90, 95% CI: 1.68\u0026ndash;2.15). Interaction study revealed that the positive link between LnCMI and hypertension in diabetic individuals was affected by educational attainment. Neither smoking nor alcohol use substantially influenced the relationship between LnCMI and hypertension in diabetic patients (interaction P-values\u0026thinsp;\u0026gt;\u0026thinsp;0.05 for both).\u003c/p\u003e\n \u003cp\u003eGrouping and interaction tests were conducted for LnCMI and hypertension in diabetic patients based on gender, ethnicity, education level, smoking, alcohol consumption, and cardiovascular disease.\u003c/p\u003e\n \u003cp\u003eIn the CHARLS investigation, we investigated the correlation between LnCMI and hypertension in diabetic patients across various groups using subgroup analysis and interaction testing. Figure \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e illustrates that with each unit increase in LnCMI, the prevalence of hypertension escalated by 92% in male diabetes patients (OR\u0026thinsp;=\u0026thinsp;1.92, 95% CI: 1.59\u0026ndash;2.31) and by 99% in female patients (OR\u0026thinsp;=\u0026thinsp;1.99, 95% CI: 1.69\u0026ndash;2.35). Interaction tests indicated no substantial influence of gender on the relationship between LnCMI and hypertension. The positive link between LnCMI and hypertension was independent of diabetes, cardiovascular disease, educational level, smoking, or alcohol intake (all interaction P values\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e\u003cp\u003eGrouping and interaction tests for LnCMI and hypertension in diabetic patients by gender, educational attainment, smoking, alcohol consumption, and cardiovascular disease.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"4 Discussion","content":"\u003cp\u003eBoth datasets demonstrated a connection between CMI and the incidence of hypertension in diabetes persons aged 45 and above.Research demonstrates that the incidence of hypertension in diabetes patients escalates concurrently with increasing CMI values. This trend remains statistically significant irrespective of the analysis of CMI as either a continuous or categorical variable.In NHANES participants, the critical value for LnCMI was \u0026minus;\u0026thinsp;0.77, while in CHARLS participants, it was 1.11. Subgroup analysis further validated the relevance of this connection across several demographic and clinical categories. The NHANES study revealed an interaction effect of educational attainment, suggesting that the impact of CMI on hypertension differs based on educational level.\u003c/p\u003e\u003cp\u003eCMI, as an index reflecting visceral fat and metabolic dysfunction, has demonstrated its predictive value in conditions such as cardiovascular diseases\u003csup\u003e[19]\u003c/sup\u003e, diabetes\u003csup\u003e[20]\u003c/sup\u003e, endometriosis\u003csup\u003e[21],\u003c/sup\u003e kidney stones\u003csup\u003e[26]\u003c/sup\u003eand osteoporosis\u003csup\u003e[27]\u003c/sup\u003e.CMI provides a beneficial assessment of visceral fat tissue and exhibits stronger correlations with metabolic conditions, closely linked to obesity\u003csup\u003e[28]\u003c/sup\u003e.First, excessive visceral fat distribution is associated with various changes, including inflammatory factors and endothelial levels\u003csup\u003e[29]\u003c/sup\u003e.Second, the renin-angiotensin-aldosterone system can also be activated, leading to impaired sodium excretion and increased sodium reabsorption in the renal tubules\u003csup\u003e[30]\u003c/sup\u003e .Additionally, visceral fat tissue significantly participates in blood pressure regulation through the secretion of adipokines that regulate arterial tension, such as leptin and adiponectin\u003csup\u003e[31]\u003c/sup\u003e. Finally, excessive visceral fat tissue increases insulin resistance, leading to metabolic dysfunction, oxidative stress, and vascular dysfunction\u003csup\u003e[32]\u003c/sup\u003e. The combination of these factors ultimately results in elevated blood pressure. These related pathways further demonstrate that CMI can serve as a predictive index for hypertension and is crucial in forecasting the occurrence of hypertension.\u003c/p\u003e\u003cp\u003eThis study is the first to investigate the association between CMI and hypertension in diabetic patients, utilizing a direct comparative analysis of databases from China and the United States. Additionally, by specifically focusing on middle-aged and elderly populations, the conclusions are more targeted. Methodologically, data underwent Ln transformation processing to enhance the scientific rigour of the model.The findings confirm that elevated CMI values correlate significantly with a marked increase in hypertension prevalence among diabetic patients. Furthermore, unlike previous studies indicating gender-dependent associations between CMI and hypertension\u003csup\u003e[33]\u003c/sup\u003e.our analysis reveals that the link between CMI and hypertension in diabetic patients is independent of gender.This study focused on individuals aged 45 and above and conducted threshold effect analysis. Within the NHANES database, the threshold for LnCMI was \u0026minus;\u0026thinsp;0.73. Below this barrier, the incidence of hypertension escalated with increasing LnCMI; beyond the threshold, the relationship stabilised with no significant association.In the CHARLS database, the LnCMI threshold was 1.11. Beyond this threshold, the relationship stabilised with no significant association. Based on prior research, we hypothesise this may relate to the saturation or maximum efficiency of the body's compensatory mechanisms for maintaining blood pressure homeostasis, highlighting the equilibrium between pathological processes and compensatory responses\u003csup\u003e[33]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eFurthermore, the NHANES data revealed a substantial interaction between educational attainment and the association between CMI levels and hypertension among diabetic patients aged 45 and older. This finding suggests that educational attainment exerts a significant influence on The correlation between CMI and hypertension in diabetic individuals, with the observed effect being more pronounced among individuals with higher levels of education compared to those with lower levels. Specifically, the prevalence of hypertension demonstrates a significant positive correlation with educational attainment, indicating that higher educational levels are associated with higher prevalence rates.However, in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e description of the study population grouped by education, higher education levels were associated with lower hypertension prevalence among diabetic patients. This discrepancy may be attributed to the study's focus on individuals aged 45 and older, where the high-education cohort demonstrated a markedly reduced mean age in comparison to the low-education cohort. The elevated prevalence of hypertension in the 45 age and above group, in conjunction with the potential for diagnostic bias and confounding factors within the NHANES database, is likely a contributing factor to the observed results.\u003c/p\u003e\u003cp\u003eThe principal advantage of this study is its use of NHANES and CHARLS data, both of which feature substantial sample numbers and national representativeness. This represents the inaugural instance in study investigating the correlation between CMI and hypertension in diabetic patients, where in CHARLS and NHANES data have been amalgamated to analyze the link among adults aged 45 and older. Additionally, we validated the findings with subgroup analysis across several populations, curve fitting, interaction testing, and threshold analyses. We recognize the study's limitations: The cross-sectional design constrains causal inference, because residual confounding from unmeasured variables may exist in the data. The respondents, aged 45 and older from China and the United States, render the conclusions challenging to generalize to other groups due to geographical disparities and fluctuating health status. Moreover, diagnostic bias in hypertension may vary among different age demographics, requiring validation of these results through further research.\u003c/p\u003e"},{"header":"5 Conclusions","content":"\u003cp\u003eThis study amalgamated data from 8,017 participants in NHANES and 9,376 people in CHARLS. Findings demonstrate that CMI is a substantial predictor of hypertension risk in diabetes persons aged 45 and above in both China and the United States. The research indicates a nonlinear relationship between CMI and hypertension in diabetic individuals, emphasizing a significant threshold effect. These findings further substantiate the therapeutic relevance of CMI, consequently enhancing hypertension management outcomes among varied populations and significantly contributing to the prediction and management of hypertension in diabetic patients.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003ePIR refers to the income-to-poverty ratio; HDL denotes high-density lipoprotein cholesterol; TG stands for triglycerides; CVD represents cardiovascular disease.DH, Diabetic patients with hypertension\u003c/p\u003e\n"},{"header":"Declarations","content":"\u003cp\u003eAcknowledgments\u003c/p\u003e\n\u003cp\u003eThe authors thank the studies or consortiums referenced and included in the present analysis for providing public datasets\u003c/p\u003e\n\u003cp\u003eAuthor Contributions\u003c/p\u003e\n\u003cp\u003eDY: Conceptual design, methodology, formal analysis, data organization, and preliminary draft composition; JLH: Formal analysis, data organization, and preliminary draft composition; LCD: Formal analysis, data organization, and preliminary draft composition ZPC: Formal analysis, data organization YF: Oversight of projects, assessment of manuscripts, and editing. All authors participated in the composition of this paper and completed the manuscript.\u003c/p\u003e\n\u003cp\u003eFunding\u003c/p\u003e\n\u003cp\u003eSichuan Provincial Natural Science Foundation, General Program, Mechanistic Study on the Improvement of Hypertensive Cardiovascular Remodeling by Pushing the Arch of the Aorta via TRPV4-ATP-P2X3 Signaling Pathway Regulation of the Sympathetic Nervous System, 25NSFSC2297, January 1, 2025 to December 31, 2026, \u0026yen;200,000\u003c/p\u003e\n\u003cp\u003eNational Natural Science Foundation of China, General Program, Vascular Wall Shear Stress-Mediated TRPV4-ATP-P2X3 Signaling Pathway: Cellular Mechanotransduction Mechanism of Push Bridge Arch in Improving Hypertensive Cardiovascular Remodeling, 82575249, 2026-01-01 to 2029-12-31, \u0026yen;510,000\u003c/p\u003e\n\u003cp\u003eData availability\u003c/p\u003e\n\u003cp\u003eThe data supporting the findings of this research are available in online databases. Details regarding the specific repository names and the associated accession numbers are provided below: The analyzed datasets for this study are accessible to the public and can be retrieved from here: https://www.cdc.gov/nchs/nhanes/.and https://charls.charlsdata.com/pages/Data/2011-charls-wave1/zh-cn.html.\u003c/p\u003e\n\u003cp\u003eEthics approval and consent to participate\u003c/p\u003e\n\u003cp\u003eNHANES:Data were collected with the approval of the NCHS Research Ethics.Review Board, and were anonymized before being released to the public. All participants provided informed consent prior to participating. All data in CHARLS were generated during the analysis process of this study, which was conducted and approved by the Biomedical Ethics Review Committee of Peking 373 University in accordance with the principles of the Declaration of Helsinki. All participants provided written informed consent to participate in the study (IRB approval number: IRB00001052-11015). This study does not disclose any personal information about the participants, nor does it violate data protection laws; the research has been performed in accordance with the Declaration of Helsinki.The data are available online at http://www.isss.pku.edu.cn/cfps/. Before accessing thedata, you must 378 register as a user on this website. Once your registrationis approved, you may download the dataset by following the provided instructions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number: Not applicable\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eThe authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest\u003c/em\u003e.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eTikhonoff, V.; Zhang, H.; Richart, T.; Staessen, J. A. Blood pressure as a prognostic factor after acute stroke. \u003cem\u003eLancet Neurol \u003c/em\u003e\u003cstrong\u003e2009\u003c/strong\u003e, \u003cem\u003e8\u003c/em\u003e (10), 938-948. 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Adipokines and blood pressure control. \u003cem\u003eCurr Opin Nephrol Hypertens \u003c/em\u003e\u003cstrong\u003e2010\u003c/strong\u003e, \u003cem\u003e19\u003c/em\u003e (2), 195-200. DOI: 10.1097/MNH.0b013e3283366cd0 From NLM.\u003c/li\u003e\n\u003cli\u003eReaven, G. M. Banting lecture 1988. Role of insulin resistance in human disease. \u003cem\u003eDiabetes \u003c/em\u003e\u003cstrong\u003e1988\u003c/strong\u003e, \u003cem\u003e37\u003c/em\u003e (12), 1595-1607. DOI: 10.2337/diab.37.12.1595 From NLM.\u003c/li\u003e\n\u003cli\u003eGuo, T.; Zhou, Y.; Yang, G.; Sheng, L.; Chai, X. Association between cardiometabolic index and hypertension among US adults from NHANES 1999-2020. \u003cem\u003eSci Rep \u003c/em\u003e\u003cstrong\u003e2025\u003c/strong\u003e, \u003cem\u003e15\u003c/em\u003e (1), 4007. DOI: 10.1038/s41598-025-87029-0 From NLM.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-endocrine-disorders","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bend","sideBox":"Learn more about [BMC Endocrine Disorders](http://bmcendocrdisord.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bend/default.aspx","title":"BMC Endocrine Disorders","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Cardiometabolic index, Hypertension, Diabetes, NHANES, CHARLS","lastPublishedDoi":"10.21203/rs.3.rs-7537879/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7537879/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eThis study utilizes data from two principal sources: the National Health And Nutrition Examination Survey (NHANES) conducted in the United States from 1999 to 2020 and the China Health And Retirement Longitudinal Study (CHARLS) from 2011. This study is to examine the correlation between cardiometabolic index and hypertension in diabetic patients aged 45 and older. Comprehending the impact of cardiometabolic index on hypertension in diabetic individuals is crucial for the prevention and management of hypertension in middle-aged and elderly populations.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eA cross-sectional analysis was performed on individuals aged 45 and older with diabetes utilizing the NHANES (1999\u0026ndash;2020) and CHARLS (2011) datasets. The Cardiometabolic Index (CMI) was calculated using the waist-to-height ratio and the triglycerides-to-HDL cholesterol ratio. Multiple logistic regression analyses were employed to assess the link between CMI on the likelihood of hypertension in diabetic patients., while adjusting for clinical factors. Subgroup analyses, curve fitting, and threshold effect investigations were performed.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eAn correlation was discovered in both databases between CMI and the prevalence of hypertension in adults aged 45 and older with diabetes. In CHARLS,The modified logistics regression analysis indicated a beneficial relationship between increased LnCMI and the occurrence of hypertension.(OR\u0026thinsp;=\u0026thinsp;1.87, 95% CI: 1.65\u0026ndash;2.12), with a threshold value of LnCMI set at 1.11. The examination of the NHANES database using an adjusted logistic regression model revealed a positive association between elevated LnCMI and the prevalence of hypertension in diabetic patients. Odds Ratio (OR)\u0026thinsp;=\u0026thinsp;1.78, 95% Confidence Interval (CI): 1.63\u0026ndash;1.94, with a LnCMI threshold of -0.73. Subgroup study revealed that education significantly influenced the relationship between CMI and hypertension in diabetic patients.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e\u003cp\u003eThese findings underscore the effectiveness of CMI in evaluating hypertension risk among middle-aged and elderly diabetic populations in China and the United States, with particular significance noted in the Chinese demographic.\u003c/p\u003e","manuscriptTitle":"The Association Between Cardiometabolic Index and Hypertension in Diabetic Individuals Aged 45 and Above: Evidence from Two National Databases","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-12 14:21:40","doi":"10.21203/rs.3.rs-7537879/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-10-29T04:53:37+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-28T14:06:41+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-28T08:50:48+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"168570161539619674633918341873517938733","date":"2025-10-26T10:45:32+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"80134320757681309936559544369568979610","date":"2025-10-25T07:05:36+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"51568895061008810176512943916061347321","date":"2025-10-23T09:57:48+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"120248353985192819860003337971297337893","date":"2025-10-23T07:12:44+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"281250053750597595479023068915721051445","date":"2025-10-23T06:32:33+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-12T13:18:19+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"96657092091206261898138126805599400254","date":"2025-10-01T23:56:02+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"78547409140541459430002856606128106053","date":"2025-09-30T01:20:03+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"127688226131116124336691544657505337163","date":"2025-09-29T22:40:15+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-09-29T13:42:49+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-29T12:34:47+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-09-09T09:48:51+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-09-09T06:02:27+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Endocrine Disorders","date":"2025-09-09T03:40:58+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-endocrine-disorders","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bend","sideBox":"Learn more about [BMC Endocrine Disorders](http://bmcendocrdisord.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bend/default.aspx","title":"BMC Endocrine Disorders","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"785cea38-eda9-4853-a238-3b68057a9448","owner":[],"postedDate":"October 12th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-02-02T16:06:46+00:00","versionOfRecord":{"articleIdentity":"rs-7537879","link":"https://doi.org/10.1186/s12902-026-02177-2","journal":{"identity":"bmc-endocrine-disorders","isVorOnly":false,"title":"BMC Endocrine Disorders"},"publishedOn":"2026-01-28 15:58:20","publishedOnDateReadable":"January 28th, 2026"},"versionCreatedAt":"2025-10-12 14:21:40","video":"","vorDoi":"10.1186/s12902-026-02177-2","vorDoiUrl":"https://doi.org/10.1186/s12902-026-02177-2","workflowStages":[]},"version":"v1","identity":"rs-7537879","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7537879","identity":"rs-7537879","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}