Association between cardiometabolic index and hypertension: National Health and Nutrition Examination Survey (NHANES) 2017–2020

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This study aimed to investigate the relationship between CMI and hypertension and to evaluate the predictive value of CMI for hypertension. Methods This was a cross-sectional study with a sample size of 7897 U.S. adults with hypertension sourced from the NHANES 2017–2020. CMI was calculated by multiplying the ratio of triglycerides and high-density lipoprotein cholesterol (TG/HDL-C) by waist-to-height ratio (WHtR). Multivariate logistic regression analysis was used to systematically evaluate the relationship between CMI and hypertension. To determine whether there was a linear or nonlinear relationship between CMI and hypertension by restricted cubic spline regression.The subgroup analyses were conducted in order to scrutinize the reliability and robustness of the relationship between CMI and hypertension across different subgroups. Results The average age of the 7897 participants was 50.98 years, with males accounting for 48.4% and females 51.6%. Subjects with higher CMI exhibited a significantly increased risk of hypertension. The odds ratio (OR) for a 1-standard-deviation increase in CMI was 3.38(2.69–4.23) after adjusting for various confounding factors. Further subgroup analysis showed that there were significant additive interactions between CMI and hypertension risk in gender, waist circumference(WC), HDL-C, TG and glycohemoglobin ( p for interaction < 0.05). Restricted cubic spline (RCS) analysis identified one significant inflection points: the point at 0.4934. Individuals with a CMI level below 0.4934 had a low risk of developing hypertension. Conclusions: CMI was strongly and positively associated with the risk of hypertension and can be a reference predictor for hypertension. High CMI had excellent diagnostic performance for hypertension, which can enable important clinical value for early identification and screening of hypertension. CMI hypertension NHANES cross-sectional Figures Figure 1 Figure 2 Introduction Hypertension, commonly known as high blood pressure, is a pervasive health issue that affects a significant portion of the global population. It stands as the foremost preventable risk factor for cardiovascular diseases worldwide 1 . The condition is clinically defined by systolic blood pressure (SBP) levels of 140 mmHg or higher, or diastolic blood pressure (DBP) levels of 90 mmHg or higher 2 .According to research conducted by Kario in 2022 3 and Zhou in 2021 1 , the prevalence of hypertension is alarmingly high, with the World Health Organization (WHO) estimating that one out of every five adults across the globe suffers from this condition 4 . This translates to a staggering figure of over 1.3 billion individuals currently living with hypertension 5 . Hypertension also accounts for the largest number of deaths worldwide 3 . In China, about one-third of the adult population had hypertension, particularly the most important causes of CVD mortality and premature death 6 , 7 . The high rates of morbidity and mortality associated with hypertension cause a heavy economic burden on families and society 8 . Consequently, it is both necessary and important to study the risk factors for hypertension, as early detection and proactive modification of these factors are crucial for preventing the progression of the condition. According to the 2018 guidelines, male, advanced age, overweight or obesity, family history of hyperlipidemia, diabetes, cardiovascular disease, cerebrovascular disease, and family history of hypertension are risk factors for hypertension 9 – 12 .Renal insufficiency, dyslipidemia and insulin resistance further increase blood pressure 13 .However,these issues are difficult to change. We suppose if there is an index associated with risk factors of hypertension, which can be early controlled before hypertension that the incidence of high blood pressure will reduce. The cardiac metabolic index (CMI) is a relatively new metabolic marker that has been gaining attention in the medical community to assess the degree of body fat deposited viscerally and provide a more complete understanding of viscerally obese individuals at risk for diabetes and atherosclerotic progression 14 , 15 . It is calculated by taking the product of the triglyceride to high-density lipoprotein cholesterol (TG/HDL-C) ratio and the waist-to-height ratio (WHtR) 16 . WHtR is mainly used to measure the degree of obesity, it has been proven to be a more effective indicator of cardiovascular disease risk factors and infertility when compared to body mass index (BMI) and waist circumference (WC) 17 , 18 . and TG/HDL-C is used to evaluate the level of blood lipids 19 . The CMI has emerged as a pivotal biomarker that is indicative of visceral fat deposition and metabolic dysregulation, and it acts as a quantifiable metric for evaluating central obesity 20 , 21 . In addition, hypertension is associated with a variety of comorbidities, including cardiovascular disease (CVD), type 2 diabetes and metabolic syndrome 22 . The metabolic profile of patients with lipid metabolism disorders worsens over time, making effective management of lipid metabolism disorders in patients with hypertension critical for preventing adverse health outcomes. CMI is seemed as a valuable biomarker for metabolic dysregulation 20 . Hence, we put forward the hypothesis that CMI is associated with hypertension. Since then, several studies have studied the clinical value of CMI in depression 23 , microalbuminuria 19 , arteriosclerosis and type 2 diabetes 24 . However, there is no relevant study to explore the relationship between CMI and hypertension, especially as a predictor of hypertension. Therefore, we conducted a cross-sectional study using data from the 2017–2020 National Health and Nutrition Examination Survey (NHANES) which is a unique source of national data on the health and nutritional status of the US population, collecting data through interviews, standard exams, and biospecimen collection 25 to explore the relationship between CMI and hypertension, with the ultimate goal of providing a reference for the early prediction of hypertension. Materials and methods Study design and participants The data used in the current analysis are publicly available through the NHANES database ( https://www.cdc.gov/nchs/nhanes/index.htm ). The protocols of the NHANES study were authorized by the Research Ethics Review Board of NCHS. Informed consent was obtained from all the NHANES participants. The study was exempt from the approval of the institutional review board as it used de-identified, publicly available data. The study’s inclusion criteria comprised participants who were enrolled in the NHANES Mobile Examination Center in 2017–2020 .NHANES Database Cohort We conducted a comprehensive analysis using the NHANES database, Covariates included age, sex, race, education level, height, waist circumference (WC), Glycohemoglobin, Fasting Plasma Glucose(FPG), high-density lipoprotein cholesterol (HDL-C) and triglycerides(TG).There was a total of 15560 participants in the NHANES during 2017–2020. We have excluded individuals who failed to complete the survey or had missing or unreliable CMI data. Besides, participants without complete information about age, sex, race, education level, height, WC, glycohemoglobin, FPG,HDL-C and TG were also excluded. As a result, a total of 7897 participants have been included in our final analysis. The flowchart for participant screening was presented in Fig. 1 . Cardiometabolic index CMI was calculated based on the anthropometric data and biochemical data, including height, waist circumference (WC), triglyceride (TG), and high -density lipoprotein cholesterol (HDL -C). The units for height and WC were centimeters (cm) and for TG and HDL -C were milligrams per deciliter (mmol/L), respectively. The computational formula for CMI was presented as follows: Covariates The covariates were selected based on previous literature and substantive reasoning that suggested their potential association with both CMI levels and hypertension prevalence. Standardized questionnaires were utilized to gather information on various factors, including age, gender, race, education level, height, waist circumference (WC), Glycohemoglobin, Fasting Plasma Glucose(FPG), high-density lipoprotein cholesterol (HDL-C) and triglycerides(TG). Race was categorized into five groups: Mexican American, Non -Hispanic Black, Non -Hispanic White, other Hispanic and other races-including Multi-Racial. Education was stratified into five levels: less than 9th grade, 9th -11th grade, high school, some college, and college or above. Detailed information on the specimen collection, processing, quality assurance, and monitoring are described in the section of biospecimen program in NHANES. Statistical analysis All statistical analyses were conducted in accordance with the guidelines of the CDC, and sampling weights were used to produce nationally representative prevalence estimates for the non-institutionalized US population. Continuous variables were presented as mean ± standard deviation (SD), while categorical variables were reported as proportions. A weighted student's t-test (continuous variables) or a weighted chi-square test (categorical variables) was used for comparison of differences between the two groups. Weighted logistic regression model was used to analyze the association between CMI and depression. The performance of CMI and other indicators (including WC, TG and HDL-C) in identifying hypertension was compared by utilizing receiver operating curve (ROC) and calculating area under curve (AUC). Subgroup analysis and interaction tests were used to confirm the stability of the association between CMI and hypertension across different subgroups. Restricted cubic spline (RCS) regression was utilized to explore the nonlinear relationships between CMI and hypertension. The method of multiple imputation was utilized to deal with the variables with missing data. A two -sided P < 0.05 was considered statistically significant. Sample weighting was applied for the unequal probability of individual sampling, resulting from complex multistage survey design. The R software (version 4.4.1) was employed for all analyses, and statistical significance was ascertained by a two -sided P value < 0.05. Results Baseline characteristics of the study population The baseline characteristics of all participants based on grouping of hypertension were presented in Table 1 . The mean age of all enrolled subjects was 50.98 ± 17.48 years, with 51.60% of them being female and 48.4% male.The weighted prevalence of hypertension was 38.56% in the entire US population. Compared with the non -hypertension group, individuals in the hypertension group exhibited a higher level of Age (60.26 ± 14.24 vs. 45.15 ± 16.79), CMI (0.79 ± 1.06 vs. 0.60 ± 0.78), WC(106.71 ± 16.36 vs. 97.58 ± 16.45),, TG (1.37 ± 1.15 vs. 1.17 ± 1.02), as well as higher levels of glycohemoglobin(6.18 ± 1.25 vs. 5.66 ± 0.97) and FPG(6.78 ± 2.43 vs. 5.99 ± 1.78), while a low levels of HDL-C(1.36 ± 0.42 vs. 1.40 ± 0.41). Besides, non -hypertension participants were more likely to have low education levels and there was significant statistical difference in race between people with and without hypertension.The baseline characteristics of all participants based on grouping of hypertension were presented in Table 1 . Observational associations between hypertension and NAFLD in NHANES The weighted logistic regression analysis was utilized to explore the association between CMI and hypertension, and the results were presented in Table 2 . A positive correlation between CMI (as continuous variable or categorical variable) and the occurrence of hypertension was found in all models (including the non -adjusted and adjusted models). When CMI was analyzed as continuous variable, We divided the continuous variable CMI into four groups according to the quartile method.As the level of CMI increased in the four groups, the level of odds ratio in each group also increased,were significantly positively correlated with hypertension,0.26 < CMI ≤ 0.47(OR = 1.79[95%CI,1.52–2.10]),0.47 < CMI ≤ 0.82(OR = 2.46[95%CI,1.93–3.14]),0.82 < CMI ≤ 24.48(OR = 3.38[95% CI, 2.69–4.23]).After converting CMI into a categorical variable, the prevalence of hypertension also increased with increasing tertiles of CMI (P for trend < 0.001, in three models). In model 3, participants in the highest CMI quartile group were more liable to develop hypertension than those in the middle and low CMI quartile group (OR, 1.86 vs. 1.53 vs. 1.33, P for trend < 0.001). Model1 and Model3 both showed a significant increased risk for the development of hypertension in non -adjusted and adjusted models (all P value < 0.05, and all P for trend < 0.05). Table 2 Association between CMI and hpertension Items Model1:OR(95% CI) Model2:OR(95% CI) Model3:OR(95% CI) 0.027 < CMI ≤ 0.26 Reference Reference Reference 0.26 < CMI ≤ 0.47 1.79(1.52–2.10) 1.62(1.37–1.92) 1.33(1.04–1.68) 0.47 < CMI ≤ 0.82 2.46(1.93–3.14) 2.18(1.63–2.91) 1.53(1.05–2.21) 0.82 < CMI ≤ 24.48 3.38(2.69–4.23) 3.15(2.44–4.06) 1.86(1.25–2.75) Model 1 adjusted for: none. Model 2 adjusted for: gender, age, race, and education level. Model 3 adjusted for: gender, age, race, education level, height, waist circumference ,triglycerides, HDL-Cl, glycohemoglobin and FPG. CI: Confidence interval; OR: Odds ratio. Analysis of restricted cubic spline (RCS) regression In restricted cubic spline regression, after the adjustment of potential covariates, a significant nonlinear relationship between CMI and hypertension (P for nonlinearity < 0.001) was detected (Fig. 2). A trend of increased OR for hypertension accompanying by increasing CMI was observed between them. RCS analysis identified one significant inflection points: the point at 0.4934. Individuals with a CMI level below 0.4934 had a low risk of developing hypertension. However, those with a CMI level higher than 0.4934 had a significantly higher risk of experiencing hypertension and there was a gradual increase in the risk of hypertension. Therefore, it is important to maintain a CMI level below 0.4934 in order to potentially reduce the risk of developing depression. Association between CMl and hypertension. The solid red line represents the smooth curve fit between variables. Blue bands represent the 95% of confidence interval from the fit. Association between CMI and hypertension in different subgroups The subgroup analyses were conducted in order to scrutinize the reliability and robustness of the relationship between CMI and hypertension across different subgroups.The summarized results for subgroup analyses were presented in Table 3 . Consistent with the findings obtained from previous analyses in the whole population, a higher level of CMI was associated with increased OR for hypertension, which demonstrated consistent outcomes across all subgroups. Besides, interaction tests revealed that such association between CMI and hypertension was not modified by other covariates except for the variable of Race, WC, Glycohemoglobin, HDL-C, TG (all P for interaction > 0.05), which was marginally significant.For the subgroup of gender, a higher CMI level in female(OR 1.765, 95% CI 1.443–2.157, p < 0.001, p for interaction = 0.015) got obviously higher OR for suffering hypertension compared with male(OR 1.251, 95% CI 1.053–1.486, p = 0.017, p for interaction = 0.015).CMI in participants aged ≤ 65 years (OR 1.429, 95% CI 1.211–1.665, p 65 years (OR 1.483, 95% CI 0.969–2.271, p = 0.082, p for interaction = 0.846) was positively linked to hypertension .Significant positive association between CMI and hypertension was observed in the subgroup of Non -Hispanic Black, Non -Hispanic White and other races-including Multi-Racial, but not in the subgroup of Mexican American or other Hispanic .In the NHANES cohort (2017–2020), FPG ≤ 6 participants exhibited a higher linked to hypertension compared to other FPG > 6 groups (OR 1.379, 95% CI 1.133–1.678, p = 0.004, p for interaction = 0.008). HDL-C and TG were grouped according to quartiles and these two measures were strongly correlated with CMI, CMI was strongly associated with hypertension in these two subgroups. Subgroups analysis also revealed that a higher HDL-C level in HDL-C subgroups got obviously higher OR for suffering hypertension compare with the lower HDL-C. Discussion Hypertension is not only one of the main health problems in the world 26 , but a serious condition that significantly increases the risk of heart, brain, kidney, and other diseases 27 – 30 . Several studies have shown a clear relationship between high blood pressure and increased risk of cardiovascular disease 31 , 32 . Dyslipidemia may be associated with hypertension through a variety of mechanisms 33 . Atherosclerosis caused by lipid abnormalities can lead to structural changes in large arteries, resulting in decreased elasticity 34 . In addition, endothelial dysfunction due to dyslipidemia leads to reduced production, release, and activity of nitric oxide, as well as abnormal vasomotor activity, which may manifest as hypertension 35 , 36 . In addition, lipid-mediated damage to the renal microvasculature may manifest as hypertension 37 . So better understanding of anthropometric risk factors and how they correlate was prioritized. In this study, we investigated the relationship between CMI and hypertension using data from 7,897 individuals included in the 2017–2020 NHANES. Our findings are consistent with other studies that the risk of hypertension increases with age 38 . In fact, aging promotes the loss of elasticity of the arteries 39 . The prevalence of hypertension in our study was lower than that of other studies 38.56% vs. 44.8% 40 . However, it is difficult to compare the results of the studies due to the differences in the timing of blood pressure (BP) checks, the methods used to measure BP, the sample sizes, the age of the people studied, their ethnicity, their socioeconomic status and the geographical areas in which they were carried out 41 . Hypertension is one of the major risk factors for the overall global burden of disease in recent years. 42 The recent decline in cardiovascular mortality in high-income countries has been associated with an increase in the number of people with cardiovascular disease and the widespread use of preventive drugs. Therefore, the prevention and monitoring of blood pressure, timely detection and treatment are particularly important to improve and delay the outcome of cardiovascular disease. 43 This study is the first to providing detailed data to show high CMI values are closely related to the presence and severity of hypertension. CMI is calculated by TG/HDL-C and WHtR. It combines lipid levels and visceral obesity 16 . Studies have shown that the TG/HDL-C ratio is a good predictor of cardiovascular disease, while WHtR is a reliable indicator of abdominal obesity. WHtR is considered superior to both BMI and waist circumference for screening cardiovascular disease 44 , 45 . The results showed that a higher level of CMI was associated with an increased risk of hypertension. The relationship between CMI and hypertension was found to be no-linear with a significant inflection point located at 0.4934. Logistic regression analysis confirmed a positive association between higher CMI and hypertension when CMI was grouped in quartiles. Subgroup analyses and interaction tests showed that the association between CMI and hypertension was consistent across subgroups and was not affected by other covariates which support the accuracy of CMI as a predictive indicator for hypertension. Cross-sectional data from the NHANES database was used to explore the relationship between CMI and hypertension in the American population. Although some previous reports have suggested a possible association between CMI and hypertension 7 , 46 – 48 , our study is the first using large-sample to examine the link between CMI and hypertension risk, and provided details. Using multivariate logistic regression and subgroup analysis, we found that CMI can serve as a predictor of hypertension risk. Hypertension often coexists with other components of metabolic syndrome, such as high blood sugar, high blood lipids, and abdominal obesity 49 , 50 . CMI comprehensively considers these factors and can more comprehensively evaluate the metabolic status and cardiovascular health of hypertensive patients 16 . By improving risk factors related to CMI, such as weight control and improving blood sugar and lipid levels, the incidence and severity of hypertension can be effectively reduced. In clinical practice, doctors can use CMI to assess the overall health status of patients and develop personalized treatment plans based on the results of CMI. While treating hypertension, attention should also be paid to other metabolic indicators, including weight control, improving blood sugar and lipid levels, and developing more comprehensive intervention measures to reduce the risk of hypertension 51 , 52 . There were several limitations of our study. First, a limitation of cross-sectional studies is that they capture information at a single point in time, which may affect the timeliness and comprehensiveness of the data. Also, it was not possible to establish a causal association between CMI and hypertension. Second, the participants in this study were all American adults, and the applicability of the results to other geographic and racial groups may be limited due to factors such as geography, genetics, and culture. Moreover, although some covariates were adjusted, there were still potential cofounding factors unadjusted. In conclusion, our study found that incidence of hypertension gradually increased with the increase of CMI. In the field of public health and individual health management, CMI can be used to design health intervention strategies, promote healthy lifestyles, reduce the occurrence of hypertension and cardiovascular disease, and promote overall health improvement. Declarations Acknowledgements Not applicable. Author contributions ZY and YW conceived and planned the study. ZY and JZ wrote the manuscript. JZ and CC carried out the research. ZY, JZ and CC contributed to data preparation. YW and YZ provided critical feedback and helped shape the research, analysis, and manuscript. All authors read and approved the final manuscript. Funding Not applicable. Data availability No datasets were generated or analysed during the current study. 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Lancet. 2012;380(9859):2224–60. 10.1016/S0140-6736(12)61766-8 . Rapsomaniki E, Timmis A, George J, et al. Blood pressure and incidence of twelve cardiovascular diseases: lifetime risks, healthy life-years lost, and age-specific associations in 1·25 million people. Lancet. 2014;383(9932):1899–911. 10.1016/S0140-6736(14)60685-1 . Kosmas CE, Rodriguez Polanco S, Bousvarou MD, et al. The Triglyceride/High-Density Lipoprotein Cholesterol (TG/HDL-C) Ratio as a Risk Marker for Metabolic Syndrome and Cardiovascular Disease. Diagnostics (Basel). 2023;13(5). 10.3390/diagnostics13050929 . Ashwell M, Gunn P, Gibson S. Waist-to-height ratio is a better screening tool than waist circumference and BMI for adult cardiometabolic risk factors: systematic review and meta-analysis. Obes Rev. 2012;13(3):275–86. 10.1111/j.1467-789X.2011.00952.x . Ezzati M, Riboli E. Behavioral and dietary risk factors for noncommunicable diseases. N Engl J Med. 2013;369(10):954–64. 10.1056/NEJMra1203528 . Cai X, Hu J, Wen W, et al. Associations of the Cardiometabolic Index with the Risk of Cardiovascular Disease in Patients with Hypertension and Obstructive Sleep Apnea: Results of a Longitudinal Cohort Study. Oxid Med Cell Longev. 2022;2022:4914791. 10.1155/2022/4914791 . Li F-E, Luo Y, Zhang F-L, et al. Association Between Cardiometabolic Index and Stroke: A Population- based Cross-sectional Study. Curr Neurovasc Res. 2021;18(3):324–32. 10.2174/1567202618666211013123557 . Ortega FB, Lavie CJ, Blair SN. Obesity and Cardiovascular Disease. Circ Res. 2016;118(11):1752–70. 10.1161/CIRCRESAHA.115.306883 . Chakraborty S, Mandal J, Yang T, et al. Metabolites and Hypertension: Insights into Hypertension as a Metabolic Disorder: 2019 Harriet Dustan Award. Hypertension. 2020;75(6):1386–96. 10.1161/HYPERTENSIONAHA.120.13896 . Rivera SL, Martin J, Landry J. Acute and Chronic Hypertension: What Clinicians Need to Know for Diagnosis and Management. Crit Care Nurs Clin North Am. 2019;31(1). 10.1016/j.cnc.2018.11.008 . Warren HR, Evangelou E, Cabrera CP, et al. Genome-wide association analysis identifies novel blood pressure loci and offers biological insights into cardiovascular risk. Nat Genet. 2017;49(3):403–15. 10.1038/ng.3768 . Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5223581","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":368666740,"identity":"8e8477e4-8e14-4c90-9f65-d8729d09c469","order_by":0,"name":"Zefeng Yan","email":"","orcid":"","institution":"Chinese PLA General Hospital","correspondingAuthor":false,"prefix":"","firstName":"Zefeng","middleName":"","lastName":"Yan","suffix":""},{"id":368666741,"identity":"086938ef-597c-4300-9b70-8cdca9d9fc8f","order_by":1,"name":"Jinxin Zhao","email":"","orcid":"","institution":"Chinese PLA General Hospital","correspondingAuthor":false,"prefix":"","firstName":"Jinxin","middleName":"","lastName":"Zhao","suffix":""},{"id":368666742,"identity":"e2b634d5-4402-4506-967f-c2782598e8bd","order_by":2,"name":"Congzhe Chen","email":"","orcid":"","institution":"Chinese PLA General Hospital","correspondingAuthor":false,"prefix":"","firstName":"Congzhe","middleName":"","lastName":"Chen","suffix":""},{"id":368666743,"identity":"38b0373d-4d2a-483c-b502-8e5c8a214110","order_by":3,"name":"Yu Wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA7UlEQVRIiWNgGAWjYFACxgYkToWEnDwhDTwgLQfg3DMWxoYNOBXDtAABXAtjW0UikgHYgb304ebPH2oYEvtnt1+T5p0nkcDYwPzw0Q18tvAltkkcOMaQOOPOmTJp3m0SeewMbMbGOfi08DC2MRxgY8htuJGTBtJSzNjAwyZNQEvzhwP/GHLng7XMkUhsOEBYS4PEwTaG3A030o9J8zYQo+UMY5vE2T6G+o03cpgt5xyTMDZsJuAX9h72xx8qvjEYy91If3jjTU2dnDx788PH+LRAwX+QhSYSYDYzYeVwCx9/IF7xKBgFo2AUjCQAAAo3SkbzRbzHAAAAAElFTkSuQmCC","orcid":"","institution":"Chinese PLA General Hospital","correspondingAuthor":true,"prefix":"","firstName":"Yu","middleName":"","lastName":"Wang","suffix":""},{"id":368666744,"identity":"22154620-ee4c-4367-82cd-aef21432e36b","order_by":4,"name":"Ying Zhang","email":"","orcid":"","institution":"Chinese PLA General Hospital","correspondingAuthor":false,"prefix":"","firstName":"Ying","middleName":"","lastName":"Zhang","suffix":""}],"badges":[],"createdAt":"2024-10-08 08:53:04","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5223581/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5223581/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":68540315,"identity":"b0795ed3-98f2-4f4d-9db5-68942fc02896","added_by":"auto","created_at":"2024-11-08 10:42:25","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":210553,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart of this study: NHANES cohort (2017-2020)\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-5223581/v1/102cd70de84ece1571bf64ee.jpeg"},{"id":68540314,"identity":"d5e6b602-f930-42c3-b0bb-a42b4d384285","added_by":"auto","created_at":"2024-11-08 10:42:25","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":31188,"visible":true,"origin":"","legend":"\u003cp\u003eCMI to hypertention on RCS regression\u003c/p\u003e\n\u003cp\u003eAssociation between CMl and hypertension. The solid red line represents the smooth curve fit between variables. Blue bands represent the 95 % of confidence interval from the fit.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-5223581/v1/74f96cb24726607986123fff.png"},{"id":85475228,"identity":"f9bd11c3-c6ec-4ca7-b62f-dfd3a727067e","added_by":"auto","created_at":"2025-06-26 09:54:08","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":940365,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5223581/v1/c3b206b2-64b5-4005-9a96-dd6e8cd8f9d1.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Association between cardiometabolic index and hypertension: National Health and Nutrition Examination Survey (NHANES) 2017–2020","fulltext":[{"header":"Introduction","content":"\u003cp\u003eHypertension, commonly known as high blood pressure, is a pervasive health issue that affects a significant portion of the global population. It stands as the foremost preventable risk factor for cardiovascular diseases worldwide\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. The condition is clinically defined by systolic blood pressure (SBP) levels of 140 mmHg or higher, or diastolic blood pressure (DBP) levels of 90 mmHg or higher \u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e.According to research conducted by Kario in 2022\u003csup\u003e3\u003c/sup\u003eand Zhou in 2021\u003csup\u003e1\u003c/sup\u003e, the prevalence of hypertension is alarmingly high, with the World Health Organization (WHO) estimating that one out of every five adults across the globe suffers from this condition\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. This translates to a staggering figure of over 1.3\u0026nbsp;billion individuals currently living with hypertension\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. Hypertension also accounts for the largest number of deaths worldwide\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. In China, about one-third of the adult population had hypertension, particularly the most important causes of CVD mortality and premature death\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e,\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. The high rates of morbidity and mortality associated with hypertension cause a heavy economic burden on families and society\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. Consequently, it is both necessary and important to study the risk factors for hypertension, as early detection and proactive modification of these factors are crucial for preventing the progression of the condition. According to the 2018 guidelines, male, advanced age, overweight or obesity, family history of hyperlipidemia, diabetes, cardiovascular disease, cerebrovascular disease, and family history of hypertension are risk factors for hypertension\u003csup\u003e\u003cspan additionalcitationids=\"CR10 CR11\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e.Renal insufficiency, dyslipidemia and insulin resistance further increase blood pressure\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e.However,these issues are difficult to change. We suppose if there is an index associated with risk factors of hypertension, which can be early controlled before hypertension that the incidence of high blood pressure will reduce.\u003c/p\u003e \u003cp\u003eThe cardiac metabolic index (CMI) is a relatively new metabolic marker that has been gaining attention in the medical community to assess the degree of body fat deposited viscerally and provide a more complete understanding of viscerally obese individuals at risk for diabetes and atherosclerotic progression\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e,\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. It is calculated by taking the product of the triglyceride to high-density lipoprotein cholesterol (TG/HDL-C) ratio and the waist-to-height ratio (WHtR) \u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. WHtR is mainly used to measure the degree of obesity, it has been proven to be a more effective indicator of cardiovascular disease risk factors and infertility when compared to body mass index (BMI) and waist circumference (WC)\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e,\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. and TG/HDL-C is used to evaluate the level of blood lipids \u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. The CMI has emerged as a pivotal biomarker that is indicative of visceral fat deposition and metabolic dysregulation, and it acts as a quantifiable metric for evaluating central obesity\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e,\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn addition, hypertension is associated with a variety of comorbidities, including cardiovascular disease (CVD), type 2 diabetes and metabolic syndrome\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. The metabolic profile of patients with lipid metabolism disorders worsens over time, making effective management of lipid metabolism disorders in patients with hypertension critical for preventing adverse health outcomes. CMI is seemed as a valuable biomarker for metabolic dysregulation\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. Hence, we put forward the hypothesis that CMI is associated with hypertension. Since then, several studies have studied the clinical value of CMI in depression\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e, microalbuminuria\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e, arteriosclerosis and type 2 diabetes\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. However, there is no relevant study to explore the relationship between CMI and hypertension, especially as a predictor of hypertension. Therefore, we conducted a cross-sectional study using data from the 2017\u0026ndash;2020 National Health and Nutrition Examination Survey (NHANES) which is a unique source of national data on the health and nutritional status of the US population, collecting data through interviews, standard exams, and biospecimen collection\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003eto explore the relationship between CMI and hypertension, with the ultimate goal of providing a reference for the early prediction of hypertension.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003eStudy design and participants\u003c/h2\u003e\n \u003cp\u003eThe data used in the current analysis are publicly available through the NHANES database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.cdc.gov/nchs/nhanes/index.htm\u003c/span\u003e\u003c/span\u003e). The protocols of the NHANES study were authorized by the Research Ethics Review Board of NCHS. Informed consent was obtained from all the NHANES participants. The study was exempt from the approval of the institutional review board as it used de-identified, publicly available data.\u003c/p\u003e\n \u003cp\u003eThe study\u0026rsquo;s inclusion criteria comprised participants who were enrolled in the NHANES Mobile Examination Center in 2017\u0026ndash;2020 .NHANES Database Cohort We conducted a comprehensive analysis using the NHANES database, Covariates included age, sex, race, education level, height, waist circumference (WC), Glycohemoglobin, Fasting Plasma Glucose(FPG), high-density lipoprotein cholesterol (HDL-C) and triglycerides(TG).There was a total of 15560 participants in the NHANES during 2017\u0026ndash;2020. We have excluded individuals who failed to complete the survey or had missing or unreliable CMI data. Besides, participants without complete information about age, sex, race, education level, height, WC, glycohemoglobin, FPG,HDL-C and TG were also excluded. As a result, a total of 7897 participants have been included in our final analysis. The flowchart for participant screening was presented in Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\n \u003cp\u003eCardiometabolic index\u003c/p\u003e\n \u003cp\u003eCMI was calculated based on the anthropometric data and biochemical data, including height, waist circumference (WC), triglyceride (TG), and high -density lipoprotein cholesterol (HDL -C). The units for height and WC were centimeters (cm) and for TG and HDL -C were milligrams per deciliter (mmol/L), respectively. The computational formula for CMI was presented as follows:\u003c/p\u003e\n \u003cp\u003e\u003cimg src=\"data:image/png;base64,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\" width=\"278\" height=\"134\"\u003e\u003c/p\u003e\n \u003ch2\u003eCovariates\u003c/h2\u003e\n \u003cp\u003eThe covariates were selected based on previous literature and substantive reasoning that suggested their potential association with both CMI levels and hypertension prevalence. Standardized questionnaires were utilized to gather information on various factors, including age, gender, race, education level, height, waist circumference (WC), Glycohemoglobin, Fasting Plasma Glucose(FPG), high-density lipoprotein cholesterol (HDL-C) and triglycerides(TG). Race was categorized into five groups: Mexican American, Non -Hispanic Black, Non -Hispanic White, other Hispanic and other races-including Multi-Racial. Education was stratified into five levels: less than 9th grade, 9th -11th grade, high school, some college, and college or above. Detailed information on the specimen collection, processing, quality assurance, and monitoring are described in the section of biospecimen program in NHANES.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n \u003ch2\u003eStatistical analysis\u003c/h2\u003e\n \u003cp\u003eAll statistical analyses were conducted in accordance with the guidelines of the CDC, and sampling weights were used to produce nationally representative prevalence estimates for the non-institutionalized US population. Continuous variables were presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD), while categorical variables were reported as proportions. A weighted student\u0026apos;s t-test (continuous variables) or a weighted chi-square test (categorical variables) was used for comparison of differences between the two groups. Weighted logistic regression model was used to analyze the association between CMI and depression. The performance of CMI and other indicators (including WC, TG and HDL-C) in identifying hypertension was compared by utilizing receiver operating curve (ROC) and calculating area under curve (AUC). Subgroup analysis and interaction tests were used to confirm the stability of the association between CMI and hypertension across different subgroups. Restricted cubic spline (RCS) regression was utilized to explore the nonlinear relationships between CMI and hypertension. The method of multiple imputation was utilized to deal with the variables with missing data. A two -sided P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant. Sample weighting was applied for the unequal probability of individual sampling, resulting from complex multistage survey design. The R software (version 4.4.1) was employed for all analyses, and statistical significance was ascertained by a two -sided P value\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eBaseline characteristics of the study population\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe baseline characteristics of all participants based on grouping of hypertension were presented in Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e. The mean age of all enrolled subjects was 50.98\u0026thinsp;\u0026plusmn;\u0026thinsp;17.48 years, with 51.60% of them being female and 48.4% male.The weighted prevalence of hypertension was 38.56% in the entire US population. Compared with the non -hypertension group, individuals in the hypertension group exhibited a higher level of Age (60.26\u0026thinsp;\u0026plusmn;\u0026thinsp;14.24 vs. 45.15\u0026thinsp;\u0026plusmn;\u0026thinsp;16.79), CMI (0.79\u0026thinsp;\u0026plusmn;\u0026thinsp;1.06 vs. 0.60\u0026thinsp;\u0026plusmn;\u0026thinsp;0.78), WC(106.71\u0026thinsp;\u0026plusmn;\u0026thinsp;16.36 vs. 97.58\u0026thinsp;\u0026plusmn;\u0026thinsp;16.45),, TG (1.37\u0026thinsp;\u0026plusmn;\u0026thinsp;1.15 vs. 1.17\u0026thinsp;\u0026plusmn;\u0026thinsp;1.02), as well as higher levels of glycohemoglobin(6.18\u0026thinsp;\u0026plusmn;\u0026thinsp;1.25 vs. 5.66\u0026thinsp;\u0026plusmn;\u0026thinsp;0.97) and FPG(6.78\u0026thinsp;\u0026plusmn;\u0026thinsp;2.43 vs. 5.99\u0026thinsp;\u0026plusmn;\u0026thinsp;1.78), while a low levels of HDL-C(1.36\u0026thinsp;\u0026plusmn;\u0026thinsp;0.42 vs. 1.40\u0026thinsp;\u0026plusmn;\u0026thinsp;0.41). Besides, non -hypertension participants were more likely to have low education levels and there was significant statistical difference in race between people with and without hypertension.The baseline characteristics of all participants based on grouping of hypertension were presented in Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cimg 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\" width=\"581\" height=\"389\"\u003e\u003c/p\u003e\n\u003cp\u003eObservational associations between hypertension and NAFLD in NHANES\u003c/p\u003e\n\u003cp\u003eThe weighted logistic regression analysis was utilized to explore the association between CMI and hypertension, and the results were presented in Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e. A positive correlation between CMI (as continuous variable or categorical variable) and the occurrence of hypertension was found in all models (including the non -adjusted and adjusted models). When CMI was analyzed as continuous variable, We divided the continuous variable CMI into four groups according to the quartile method.As the level of CMI increased in the four groups, the level of odds ratio in each group also increased,were significantly positively correlated with hypertension,0.26\u0026thinsp;\u0026lt;\u0026thinsp;CMI\u0026thinsp;\u0026le;\u0026thinsp;0.47(OR\u0026thinsp;=\u0026thinsp;1.79[95%CI,1.52\u0026ndash;2.10]),0.47\u0026thinsp;\u0026lt;\u0026thinsp;CMI\u0026thinsp;\u0026le;\u0026thinsp;0.82(OR\u0026thinsp;=\u0026thinsp;2.46[95%CI,1.93\u0026ndash;3.14]),0.82\u0026thinsp;\u0026lt;\u0026thinsp;CMI\u0026thinsp;\u0026le;\u0026thinsp;24.48(OR\u0026thinsp;=\u0026thinsp;3.38[95% CI, 2.69\u0026ndash;4.23]).After converting CMI into a categorical variable, the prevalence of hypertension also increased with increasing tertiles of CMI (P for trend\u0026thinsp;\u0026lt;\u0026thinsp;0.001, in three models). In model 3, participants in the highest CMI quartile group were more liable to develop hypertension than those in the middle and low CMI quartile group (OR, 1.86 vs. 1.53 vs. 1.33, P for trend\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Model1 and Model3 both showed a significant increased risk for the development of hypertension in non -adjusted and adjusted models (all P value\u0026thinsp;\u0026lt;\u0026thinsp;0.05, and all P for trend\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u0026nbsp;\u003c/p\u003e\n\u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eAssociation between CMI and hpertension\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eItems\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eModel1:OR(95% CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eModel2:OR(95% CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eModel3:OR(95% CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.027\u0026thinsp;\u0026lt;\u0026thinsp;CMI\u0026thinsp;\u0026le;\u0026thinsp;0.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.26\u0026thinsp;\u0026lt;\u0026thinsp;CMI\u0026thinsp;\u0026le;\u0026thinsp;0.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.79(1.52\u0026ndash;2.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.62(1.37\u0026ndash;1.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.33(1.04\u0026ndash;1.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.47\u0026thinsp;\u0026lt;\u0026thinsp;CMI\u0026thinsp;\u0026le;\u0026thinsp;0.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.46(1.93\u0026ndash;3.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.18(1.63\u0026ndash;2.91)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.53(1.05\u0026ndash;2.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.82\u0026thinsp;\u0026lt;\u0026thinsp;CMI\u0026thinsp;\u0026le;\u0026thinsp;24.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.38(2.69\u0026ndash;4.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.15(2.44\u0026ndash;4.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.86(1.25\u0026ndash;2.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eModel 1 adjusted for: none. Model 2 adjusted for: gender, age, race, and education level. Model 3 adjusted for: gender, age, race, education level, height, waist circumference ,triglycerides, HDL-Cl, glycohemoglobin and FPG. CI: Confidence interval; OR: Odds ratio.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAnalysis of restricted cubic spline (RCS) regression\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn restricted cubic spline regression, after the adjustment of potential covariates, a significant nonlinear relationship between CMI and hypertension (P for nonlinearity\u0026thinsp;\u0026lt;\u0026thinsp;0.001) was detected (Fig. 2). A trend of increased OR for hypertension accompanying by increasing CMI was observed between them. RCS analysis identified one significant inflection points: the point at 0.4934. Individuals with a CMI level below 0.4934 had a low risk of developing hypertension. However, those with a CMI level higher than 0.4934 had a significantly higher risk of experiencing hypertension and there was a gradual increase in the risk of hypertension. Therefore, it is important to maintain a CMI level below 0.4934 in order to potentially reduce the risk of developing depression.\u003c/p\u003e\n\u003cp\u003eAssociation between CMl and hypertension. The solid red line represents the smooth curve fit between variables. Blue bands represent the 95% of confidence interval from the fit.\u003c/p\u003e\n\u003cp\u003eAssociation between CMI and hypertension in different subgroups\u003c/p\u003e\n\u003cp\u003eThe subgroup analyses were conducted in order to scrutinize the reliability and robustness of the relationship between CMI and hypertension across different subgroups.The summarized results for subgroup analyses were presented in Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e. Consistent with the findings obtained from previous analyses in the whole population, a higher level of CMI was associated with increased OR for hypertension, which demonstrated consistent outcomes across all subgroups. Besides, interaction tests revealed that such association between CMI and hypertension was not modified by other covariates except for the variable of Race, WC, Glycohemoglobin, HDL-C, TG (all P for interaction\u0026thinsp;\u0026gt;\u0026thinsp;0.05), which was marginally significant.For the subgroup of gender, a higher CMI level in female(OR 1.765, 95% CI 1.443\u0026ndash;2.157, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, p for interaction\u0026thinsp;=\u0026thinsp;0.015) got obviously higher OR for suffering hypertension compared with male(OR 1.251, 95% CI 1.053\u0026ndash;1.486, p\u0026thinsp;=\u0026thinsp;0.017, p for interaction\u0026thinsp;=\u0026thinsp;0.015).CMI in participants aged\u0026thinsp;\u0026le;\u0026thinsp;65 years (OR 1.429, 95% CI 1.211\u0026ndash;1.665, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, p for interaction\u0026thinsp;=\u0026thinsp;0.846) and aged\u0026thinsp;\u0026gt;\u0026thinsp;65 years (OR 1.483, 95% CI 0.969\u0026ndash;2.271, p\u0026thinsp;=\u0026thinsp;0.082, p for interaction\u0026thinsp;=\u0026thinsp;0.846) was positively linked to hypertension .Significant positive association between CMI and hypertension was observed in the subgroup of Non -Hispanic Black, Non -Hispanic White and other races-including Multi-Racial, but not in the subgroup of Mexican American or other Hispanic .In the NHANES cohort (2017\u0026ndash;2020), FPG\u0026thinsp;\u0026le;\u0026thinsp;6 participants exhibited a higher linked to hypertension compared to other FPG\u0026thinsp;\u0026gt;\u0026thinsp;6 groups (OR 1.379, 95% CI 1.133\u0026ndash;1.678, p\u0026thinsp;=\u0026thinsp;0.004, p for interaction\u0026thinsp;=\u0026thinsp;0.008). HDL-C and TG were grouped according to quartiles and these two measures were strongly correlated with CMI, CMI was strongly associated with hypertension in these two subgroups. Subgroups analysis also revealed that a higher HDL-C level in HDL-C subgroups got obviously higher OR for suffering hypertension compare with the lower HDL-C.\u003c/p\u003e\n\u003cp\u003e\u003cimg 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\" width=\"581\" height=\"525\"\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eHypertension is not only one of the main health problems in the world\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e, but a serious condition that significantly increases the risk of heart, brain, kidney, and other diseases\u003csup\u003e\u003cspan additionalcitationids=\"CR28 CR29\" citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. Several studies have shown a clear relationship between high blood pressure and increased risk of cardiovascular disease\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e,\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. Dyslipidemia may be associated with hypertension through a variety of mechanisms\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. Atherosclerosis caused by lipid abnormalities can lead to structural changes in large arteries, resulting in decreased elasticity\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. In addition, endothelial dysfunction due to dyslipidemia leads to reduced production, release, and activity of nitric oxide, as well as abnormal vasomotor activity, which may manifest as hypertension\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e,\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e. In addition, lipid-mediated damage to the renal microvasculature may manifest as hypertension\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e. So better understanding of anthropometric risk factors and how they correlate was prioritized.\u003c/p\u003e \u003cp\u003eIn this study, we investigated the relationship between CMI and hypertension using data from 7,897 individuals included in the 2017\u0026ndash;2020 NHANES. Our findings are consistent with other studies that the risk of hypertension increases with age\u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e. In fact, aging promotes the loss of elasticity of the arteries\u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e. The prevalence of hypertension in our study was lower than that of other studies 38.56% vs. 44.8%\u003csup\u003e40\u003c/sup\u003e. However, it is difficult to compare the results of the studies due to the differences in the timing of blood pressure (BP) checks, the methods used to measure BP, the sample sizes, the age of the people studied, their ethnicity, their socioeconomic status and the geographical areas in which they were carried out\u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e. Hypertension is one of the major risk factors for the overall global burden of disease in recent years.\u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003eThe recent decline in cardiovascular mortality in high-income countries has been associated with an increase in the number of people with cardiovascular disease and the widespread use of preventive drugs. Therefore, the prevention and monitoring of blood pressure, timely detection and treatment are particularly important to improve and delay the outcome of cardiovascular disease.\u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eThis study is the first to providing detailed data to show high CMI values are closely related to the presence and severity of hypertension. CMI is calculated by TG/HDL-C and WHtR. It combines lipid levels and visceral obesity\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. Studies have shown that the TG/HDL-C ratio is a good predictor of cardiovascular disease, while WHtR is a reliable indicator of abdominal obesity. WHtR is considered superior to both BMI and waist circumference for screening cardiovascular disease \u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e,\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e. The results showed that a higher level of CMI was associated with an increased risk of hypertension. The relationship between CMI and hypertension was found to be no-linear with a significant inflection point located at 0.4934. Logistic regression analysis confirmed a positive association between higher CMI and hypertension when CMI was grouped in quartiles. Subgroup analyses and interaction tests showed that the association between CMI and hypertension was consistent across subgroups and was not affected by other covariates which support the accuracy of CMI as a predictive indicator for hypertension.\u003c/p\u003e \u003cp\u003eCross-sectional data from the NHANES database was used to explore the relationship between CMI and hypertension in the American population. Although some previous reports have suggested a possible association between CMI and hypertension\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e,\u003cspan additionalcitationids=\"CR47\" citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e, our study is the first using large-sample to examine the link between CMI and hypertension risk, and provided details. Using multivariate logistic regression and subgroup analysis, we found that CMI can serve as a predictor of hypertension risk.\u003c/p\u003e \u003cp\u003eHypertension often coexists with other components of metabolic syndrome, such as high blood sugar, high blood lipids, and abdominal obesity\u003csup\u003e\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e,\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e. CMI comprehensively considers these factors and can more comprehensively evaluate the metabolic status and cardiovascular health of hypertensive patients\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. By improving risk factors related to CMI, such as weight control and improving blood sugar and lipid levels, the incidence and severity of hypertension can be effectively reduced.\u003c/p\u003e \u003cp\u003eIn clinical practice, doctors can use CMI to assess the overall health status of patients and develop personalized treatment plans based on the results of CMI. While treating hypertension, attention should also be paid to other metabolic indicators, including weight control, improving blood sugar and lipid levels, and developing more comprehensive intervention measures to reduce the risk of hypertension\u003csup\u003e\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e,\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThere were several limitations of our study. First, a limitation of cross-sectional studies is that they capture information at a single point in time, which may affect the timeliness and comprehensiveness of the data. Also, it was not possible to establish a causal association between CMI and hypertension. Second, the participants in this study were all American adults, and the applicability of the results to other geographic and racial groups may be limited due to factors such as geography, genetics, and culture. Moreover, although some covariates were adjusted, there were still potential cofounding factors unadjusted.\u003c/p\u003e \u003cp\u003eIn conclusion, our study found that incidence of hypertension gradually increased with the increase of CMI. In the field of public health and individual health management, CMI can be used to design health intervention strategies, promote healthy lifestyles, reduce the occurrence of hypertension and cardiovascular disease, and promote overall health improvement.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u0026nbsp;\u003c/strong\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e ZY and YW conceived and planned the study. ZY and JZ wrote the manuscript. JZ and CC carried out the research. ZY, JZ and CC contributed to data preparation. YW and YZ provided critical feedback and helped shape the research, analysis, and manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e No datasets were generated or analysed during the current study.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e Not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e Not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u0026nbsp; \u0026nbsp;The authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eZhou B, Perel P, Mensah GA, Ezzati M. Global epidemiology, health burden and effective interventions for elevated blood pressure and hypertension. 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Nat Genet. 2017;49(3):403\u0026ndash;15. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/ng.3768\u003c/span\u003e\u003cspan address=\"10.1038/ng.3768\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"CMI, hypertension, NHANES, cross-sectional","lastPublishedDoi":"10.21203/rs.3.rs-5223581/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5223581/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cb\u003eObjective\u003c/b\u003e\u003c/p\u003e \u003cp\u003eCardiometabolic index (CMI) is a well promising indicator for predicting obesity-related diseases, but its predictive value for hypertension is unclear. This study aimed to investigate the relationship between CMI and hypertension and to evaluate the predictive value of CMI for hypertension.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMethods\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThis was a cross-sectional study with a sample size of 7897 U.S. adults with hypertension sourced from the NHANES 2017\u0026ndash;2020. CMI was calculated by multiplying the ratio of triglycerides and high-density lipoprotein cholesterol (TG/HDL-C) by waist-to-height ratio (WHtR). Multivariate logistic regression analysis was used to systematically evaluate the relationship between CMI and hypertension. To determine whether there was a linear or nonlinear relationship between CMI and hypertension by restricted cubic spline regression.The subgroup analyses were conducted in order to scrutinize the reliability and robustness of the relationship between CMI and hypertension across different subgroups.\u003c/p\u003e\u003cp\u003e\u003cb\u003eResults\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThe average age of the 7897 participants was 50.98 years, with males accounting for 48.4% and females 51.6%. Subjects with higher CMI exhibited a significantly increased risk of hypertension. The odds ratio (OR) for a 1-standard-deviation increase in CMI was 3.38(2.69\u0026ndash;4.23) after adjusting for various confounding factors. Further subgroup analysis showed that there were significant additive interactions between CMI and hypertension risk in gender, waist circumference(WC), HDL-C, TG and glycohemoglobin ( \u003cem\u003ep\u003c/em\u003e for interaction\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Restricted cubic spline (RCS) analysis identified one significant inflection points: the point at 0.4934. Individuals with a CMI level below 0.4934 had a low risk of developing hypertension. Conclusions: CMI was strongly and positively associated with the risk of hypertension and can be a reference predictor for hypertension. High CMI had excellent diagnostic performance for hypertension, which can enable important clinical value for early identification and screening of hypertension.\u003c/p\u003e","manuscriptTitle":"Association between cardiometabolic index and hypertension: National Health and Nutrition Examination Survey (NHANES) 2017–2020","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-11-08 10:42:20","doi":"10.21203/rs.3.rs-5223581/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"8426a6f4-164a-45f5-b823-d44347f4e357","owner":[],"postedDate":"November 8th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-06-26T09:53:51+00:00","versionOfRecord":[],"versionCreatedAt":"2024-11-08 10:42:20","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5223581","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5223581","identity":"rs-5223581","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}