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 examines the association between cardiometabolic index (CMI) and hypertension–diabetes comorbidity among individuals aged 45 years and older in the general population. Clarifying this relationship is important for developing effective prevention and management strategies for hypertension and diabetes in middle-aged and older individuals.
Methods
This study utilized data from NHANES (1999–2020) and CHARLS (2011) to conduct a cross-sectional analysis. The study population comprised adults aged 45 years and older from the general population. The cardiometabolic index (CMI) was calculated using the waist-to-height ratio and the triglycerides-to-high-density lipoprotein cholesterol (HDL-C) ratio. Weighted multivariate logistic regression analyses were employed to assess the association between LnCMI and the likelihood of hypertension–diabetes comorbidity, while adjusting for potential confounders. Subgroup analyses, restricted cubic spline (RCS), interaction tests, threshold effect analyses, and sensitivity analyses were performed.
Results
Associations were observed in both databases between LnCMI and the prevalence of hypertension–diabetes comorbidity in adults aged 45 years and older. In CHARLS, the fully adjusted logistic regression model showed a positive association between higher LnCMI and hypertension–diabetes comorbidity (OR = 1.760, 95% CI: 1.574–1.964), with a threshold value of 0.121. In ROC analyses, CMI showed the highest discriminative performance among the tested indices in CHARLS (AUC = 0.739). In NHANES, the survey-weighted fully adjusted logistic regression model also showed a positive association between higher LnCMI and the odds of hypertension–diabetes comorbidity (OR = 1.995, 95% CI: 1.794–2.218), with a threshold value of 0.286. Subgroup analyses suggested that sex and smoking status modified the association between LnCMI and hypertension–diabetes comorbidity.
Conclusions
In these cross-sectional surveys, LnCMI was associated with hypertension–diabetes comorbidity prevalence and may be a useful marker for population prevalence stratification in China and the United States.
Clinical trial number
Not applicable.
Supplementary Information
The online version contains supplementary material available at 10.1186/s12902-026-02177-2.
Keywords: Cardiometabolic index, Hypertension, Diabetes, NHANES, CHARLS
Background
Hypertension is one of the common risk factors for ischemic heart disease, stroke, other cardiovascular diseases (CVD), chronic kidney disease, and other conditions [1–4]. The latest 2023 NHANES data released in the United States indicates that the prevalence of hypertension among adults aged 18 and older reached 47.7% [5]. Hypertension imposes a heavy burden on healthcare and socioeconomic systems, with related annual expenditures in the United States estimated at approximately $219 billion in 2019 [6]. According to the latest national report from the International Diabetes Federation (IDF), the number of people with diabetes aged 20–79 in the United States reached approximately 38.5 million in 2024, with an age-standardized prevalence rate of 13.7%. In terms of economic burden, diabetes-related healthcare expenditures in the United States reached approximately $404.5 billion in 2024. The number of patients is projected to reach approximately 43 million by 2050 [7]. The American Diabetes Association (ADA) assessment of socioeconomic burden similarly indicates that the total economic cost of diabetes in 2022 was approximately $412.9 billion [8]. Additionally, recent nationwide data analysis in China shows that the prevalence of hypertension among adults aged ≥ 18 years was approximately 31.6% in 2021–2022 [9]. Concurrently, the WHO (2023) Country Profile Report indicates that approximately 256.7 million individuals aged 30–79 in China have hypertension, highlighting its immense scale [10]. Regarding economic burden, a recent study in rural southwest China found that the per capita economic burden of hypertension rose from $1,369 to $2,244 (2021 data) between 2011 and 2021, indicating sustained growth in disease burden and resource consumption [11]. Given that hypertension and diabetes frequently coexist and exert synergistic effects on cardiovascular and renal complications, establishing a simplified indicator for stratifying the prevalence of hypertension–diabetes comorbidity holds significant public health implications.
Obesity is a major risk factor for cardiovascular diseases, hypertension, stroke, diabetes, and other conditions [12–14]. 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 [15]. The Cardiometabolic Index (CMI) is a novel metric that reflects adiposity and lipid profiles [16]. The computation utilizes the ratio of triglyceride-to-high-density lipoprotein cholesterol and the ratio of waist to height [17, 18]. Currently, CMI is being used in research on various diseases, such as cardiovascular disease [19], diabetes [20], and endometriosis [21]. The triglycerides, height, waist circumference, and high-density lipoprotein cholesterol that constitute the CMI are clinically simple and readily obtainable, enhancing the CMI’s universality [22]. The CMI may be used for screening and prevalence stratification, aiding in the earlier identification of high-prevalence populations. This may facilitate the implementation of preventive strategies, potentially reducing subsequent cardiovascular and renal complications and the associated healthcare burden.
Prior research has investigated the association between the cardiometabolic index (CMI) and hypertension or diabetes. Nonetheless, no studies have yet established the association between CMI and hypertension–diabetes comorbidity among individuals with both conditions. In addition, prior research within the American demographic has predominantly concentrated on adults, lacking specific investigations into the association between CMI and hypertension or diabetes among middle-aged and older populations. Moreover, previous studies have not performed direct comparisons between Chinese and American populations. This study investigates the association between CMI and hypertension–diabetes comorbidity among individuals aged 45 years and older in China and the United States.
Methods
Study cohort
NHANES is a cross-sectional study 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. These data can enhance the examination of population aging in China and promote multidisciplinary research on aging-related matters. All respondents provided written informed consent before participation. Interviewers received intensive standardized training and subsequently carried out face-to-face interviews using structured questionnaires. Individuals interviewed in 2011–2012 comprised the baseline cohort and were followed up in 2013, 2015, 2018 and 2020. the present study used data from the 2011 baseline wave and conducted a cross-sectional analysis [25].
NHANES administers standardized questionnaires during in-home interviews and at Mobile Examination Centers (MECs), alongside physical measurements and laboratory testing. CHARLS conducts face-to-face, computer-assisted personal interviews (CAPI) at sampled households with periodic physical or biomarker modules. Because participants were aged 45 years and older, the findings may not be generalizable to younger populations.
For the NHANES study, we analyzed data from 1999 to 2020. Initially, the study included 107,622 participants. We excluded 28,470 individuals lacking hypertension and diabetes data; 47,903 individuals lacking CMI-related data; 17,606 individuals aged < 45 years; and 674 individuals with missing covariate data. A total of 12,996 individuals were ultimately included.
In CHARLS, we analyzed data from 2011. Of the 17,705 participants, we excluded 3,092 individuals with missing hypertension and diabetes data, 4,959 individuals with missing CMI-related data, 186 individuals aged < 45 years, and 108 individuals with missing covariate data. The final sample comprised 9,360 individuals (Fig. 1). The specific number of missing variables is presented in Table S13.
Fig. 1.
Flow chart of study population
Exposure factors and outcome determination
The CMI measurement for each participant was calculated using the formula: CMI = [waist circumference (cm)/height (cm)] x [triglycerides (mg/dL)/high-density lipoprotein cholesterol (mg/dL)] [18, 19]. Waist-to-height ratio (WHtR) = waist circumference (cm)/height (cm); TG/HDL-C = triglycerides (mg/dL)/high-density lipoprotein cholesterol (mg/dL).
Hypertension was defined as any of the following: (1) a systolic blood pressure of no less than 140 mmHg or a diastolic blood pressure of no less than 90 mmHg; (2) a self-reported physician diagnosis of hypertension; or (3) current use of antihypertensive medication. In the primary pooled analysis, to maximize data comparability, both datasets used physician diagnosis reported by participants as the criterion for defining diabetes. Participants who answered “yes” to the question, “Has a doctor or healthcare professional ever told you that you have diabetes?” were identified as having the condition. In the NHANES data, we additionally conducted a sensitivity analysis using glycated hemoglobin and fasting plasma glucose as diagnostic indicators where data were available (see Supplementary Table S7). Hypertension–diabetes comorbidity was defined as having both hypertension and diabetes.
Covariates screening
This study included covariates from NHANES, including age, sex, education level, smoking status, alcohol consumption, kidney disease, and cardiovascular disease. Smoking status was determined by the following question: “Have you ever smoked at least 100 cigarettes in your lifetime?” Alcohol consumption frequency was evaluated using the following question over the past 12 months: “On average, how often do you drink any type of alcoholic beverage per week, month, or year?” Participants were also asked: “Including alcoholic beverages such as whiskey or gin, beer, wine, spirits, and any other type of alcoholic drink, have you consumed at least 12 drinks of any type of alcoholic beverage in any given year?” Cardiovascular disease was determined by whether participants reported a diagnosis of any of the following: (1) coronary heart disease, (2) heart attack, (3) stroke, (4) angina, or (5) congestive heart failure. Kidney disease was determined by the question: “Have you ever been told by a doctor or other health professional that you had weak or failing kidneys?” In CHARLS, alcohol consumption was determined based on the question: “Did you drink any alcoholic beverages, such as beer, wine, or liquor in the past year? How often?” Respondents who answered “(1) Drink more than once a month” or “(2) Drink but less than once a month” were classified as drinkers, while those who answered “no” were classified as non-drinkers. Smoking was determined by the question: “Have you ever chewed tobacco, smoked a pipe, smoked self-rolled cigarettes, or smoked cigarettes or cigars?” Responding “Yes” classified the individual as a smoker; otherwise, the individual was classified as a non-smoker. Cardiovascular disease was assessed based on the question: “Have you been diagnosed with a heart attack, coronary heart disease, angina, congestive heart failure, or other heart problems or stroke by a doctor?”. Kidney disease was determined by the question: “Have you been diagnosed with kidney disease (excluding tumors or cancer) by a doctor?”. Covariates were preselected based on prior literature linking obesity and dyslipidemia to hypertension or diabetes, while considering variables available and harmonizable across both NHANES and CHARLS. Specifically, we adjusted for demographic factors (age, sex, education level) and behavioral or clinical factors (smoking, alcohol consumption, kidney disease, history of cardiovascular disease) to reduce potential confounding of the association between CMI and hypertension–diabetes comorbidity. The specific variable screening methods are shown in Table S8.
Methods of statistical analysis
As CMI showed a skewed distribution, we applied a ln transformation (LnCMI = ln(CMI)) to approximate normality. For normally distributed variables, we employed t-tests and reported means ± standard deviations; for skewed variables, we used the Kruskal–Wallis rank-sum test and reported medians (interquartile ranges), categorizing results by LnCMI tertiles. NHANES data were analyzed using multi-cycle survey weights. The analysis was conducted in the R statistical environment, relying on the survey package, which provides the svyglm function for fitting generalized linear models to complex survey data. We examined the association between LnCMI and the odds of hypertension–diabetes comorbidity. Three models were constructed in each database: Model I was unadjusted; Model II was adjusted for age, sex, and education; and Model III was adjusted for age, sex, education level, smoking status, alcohol consumption, kidney disease, and cardiovascular disease. Furthermore, we stratified by sex (male/female), education level (below high school/high school/above high school), smoking status (yes/no), alcohol consumption (yes/no), kidney disease (yes/no), and cardiovascular disease (yes/no), and assessed for interactions. In CHARLS, Model I was unadjusted; Model II incorporated age, sex, and education; and Model III included age, sex, education, kidney disease, cardiovascular disease, smoking, and alcohol consumption. To investigate nonlinearity, we performed restricted cubic spline (RCS) and threshold effect analyses. To evaluate discriminative ability, receiver operating characteristic (ROC) analysis was used. We compared a base multivariable model (covariates only) versus an extended model adding CMI, and computed AUCs based on predicted probabilities.
Sensitivity analyses: (1) We conducted separate sensitivity analyses excluding CVD for each population to validate the robustness of the results. (2) Since diabetes mellitus (DM) was defined solely by self-report and is prone to misclassification, we added HbA1c and fasting blood glucose measurements to the diabetes diagnosis in NHANES for sensitivity analysis. (3) We employed decision curve analysis and bootstrap internal validation to enhance the robustness of the results.
All statistical analyses were performed using R software (version 4.5.0). A two-tailed P value < 0.05 was considered statistically significant.
Results
Study population
Table 1 delineates the attributes of the study population in the NHANES investigation. A total of 12,996 participants aged 45 and above were enrolled, with a mean age of 62.58 ± 10.97 years. Males constituted 49.9% and females 50.1%. The tertile cut-points for LnCMI were: −2.402 to − 0.053 ( ≤ − 0.053), − 0.053 to 0.639 (≤ 0.639), and 0.639 to 4.519 (≤ 4.519). Overall, the prevalence of HTN–DM comorbidity was 14.7%, increasing across LnCMI tertiles (low: 8.3%, middle: 14.9%, high: 20.7%). Significant differences across tertiles were observed in age, weight, height, waist circumference, TG, HDL-C, LnCMI, sex, education level, smoking status, drinking, CVD and kidney disease (all P < 0.05). Compared with the lowest LnCMI group, individuals in the highest LnCMI group exhibited increased height, weight, waist circumference, TG, CVD prevalence, and kidney disease prevalence; decreased HDL-C and drinking prevalence; and increased smoking prevalence (all P < 0.05). The prevalence of hypertension–diabetes comorbidity increased with rising LnCMI tertiles (P < 0.05).
Table 1.
Survey-weighted baseline characteristics of NHANES participants (1999–2020) aged ≥ 45 years by LnCMI tertiles
| LnCMI | Level | Overall | Tertiles Group | P | ||
|---|---|---|---|---|---|---|
| low | middle | high | ||||
| n | 12,996 | 4332 | 4332 | 4332 | ||
| Age (mean (SD)) | 62.58 (10.97) | 62.26 (11.16) | 63.33 (11.06) | 62.16 (10.64) | < 0.001 | |
| Height(cm) (mean (SD)) | 166.60 (10.08) | 166.42 (9.81) | 166.32 (10.09) | 167.06 (10.32) | 0.0011 | |
| Weight (kg) (median [IQR]) | 78.80 [67.60, 92.40] | 71.10 [61.40, 82.80] | 79.50 [69.00, 92.30] | 86.55 [74.40, 100.20] | < 0.001 | |
| Waist (cm) (mean (SD)) | 101.45 (15.03) | 93.51 (13.54) | 102.32 (13.39) | 108.53 (14.18) | < 0.001 | |
| TG (mg/dL) (median [IQR]) | 113.00 [80.00, 165.00] | 70.00 [57.00, 85.25] | 113.00 [96.00, 133.00] | 193.00 [156.00, 248.00] | < 0.001 | |
| HDL-C (mg/dL) (median [IQR]) | 52.00 [43.00, 63.00] | 66.00 [57.00, 78.00] | 52.00 [45.00, 59.00] | 41.00 [36.00, 47.00] | < 0.001 | |
| LnCMI (mean (SD)) | 0.30 (0.80) | -0.56 (0.40) | 0.29 (0.20) | 1.17 (0.47) | < 0.001 | |
| Gender (%) | Female | 6507 (50.1) | 2449 (56.5) | 2187 (50.5) | 1871 (43.2) | < 0.001 |
| Male | 6489 (49.9) | 1883 (43.5) | 2145 (49.5) | 2461 (56.8) | ||
| Education (%) | above high school | l 6211 (47.8) | 2367 (54.6) | 2063 (47.6) | 1781 (41.1) | < 0.001 |
| below high school | l 3739 (28.8) | 972 (22.4) | 1268 (29.3) | 1499 (34.6) | ||
| high school | 3046 (23.4) | 993 (22.9) | 1001 (23.1) | 1052 (24.3) | ||
| Smoking (%) | no | 6294 (48.4) | 2269 (52.4) | 2125 (49.1) | 1900 (43.9) | < 0.001 |
| yes | 6702 (51.6) | 2063 (47.6) | 2207 (50.9) | 2432 (56.1) | ||
| Drinking (%) | no | 3688 (28.4) | 1133 (26.2) | 1266 (29.2) | 1289 (29.8) | < 0.001 |
| yes | 9308 (71.6) | 3199 (73.8) | 3066 (70.8) | 3043 (70.2) | ||
| Hypertension (%) | no | 5120 (39.4) | 1997 (46.1) | 1640 (37.9) | 1483 (34.2) | < 0.001 |
| yes | 7876 (60.6) | 2335 (53.9) | 2692 (62.1) | 2849 (65.8) | ||
| DM (%) | no | 10,564 (81.3) | 3856 (89.0) | 3520 (81.3) | 3188 (73.6) | < 0.001 |
| yes | 2432 (18.7) | 476 (11.0) | 812 (18.7) | 1144 (26.4) | ||
| CVD (%) | no | 10,717 (82.5) | 3735 (86.2) | 3590 (82.9) | 3392 (78.3) | < 0.001 |
| yes | 2279 (17.5) | 597 (13.8) | 742 (17.1) | 940 (21.7) | ||
| HTN–DM comorbidity (%) | no | 11,091 (85.3) | 3971 (91.7) | 3685 (85.1) | 3435 (79.3) | < 0.001 |
| yes | 1905 (14.7) | 361 (8.3) | 647 (14.9) | 897 (20.7) | ||
| Kidney disease (%) | no | 12,437 (95.7) | 4186 (96.6) | 4158 (96.0) | 4093 (94.5) | < 0.001 |
| yes | 559 (4.3) | 146 (3.4) | 174 (4.0) | 239 (5.5) | ||
Continuous variables: P values were determined using linear regression models; categorical variables: P values were assessed using chi-square tests
Abbreviations: CMI, cardiometabolic index; TG, triglycerides; HDL-C, high-density lipoprotein cholesterol; CVD, cardiovascular disease; HTN–DM comorbidity, presence of both hypertension and diabetes; IQR, interquartile range; SD, standard deviation
Table 2 delineates the attributes of CHARLS. A total of 9,360 people aged 45 and older were enrolled, with a median age of 59.00 [52.00–66.00] years. Males accounted for 46.5% of the study population, while females constituted 53.5%. The tertile cut-points for LnCMI were: −3.475 to − 0.214 ( ≤ − 0.214), − 0.214 to 0.485 (≤ 0.485), and 0.485 to 6.070 (≤ 6.070). The prevalence of hypertension–diabetes comorbidity was 3.6%, increasing across LnCMI tertiles (low: 1.6%, middle: 2.6%, high: 6.4%). Compared with the lowest tertile, participants in the highest tertile had higher TG and waist circumference and lower HDL-C, and differed in smoking/drinking status and CVD prevalence (all P < 0.05).
Table 2.
Baseline characteristics of CHARLS participants (2011) aged ≥ 45 years by LnCMI tertiles
| LnCMI | Level | Overall | Tertiles Group | P | ||
|---|---|---|---|---|---|---|
| low | middle | high | ||||
| n | 9360 | 3120 | 3120 | 3120 | ||
| Age (median [IQR]) | 59.00 [52.00, 66.00] | 59.00 [52.00, 66.00] | 59.00 [52.00, 66.00] | 58.00 [52.00, 65.00] | 0.0023 | |
| Height(cm) (median [IQR]) | 157.55 [151.90, 164.00] | 157.90 [152.10, 164.10] | 157.40 [151.40, 164.00] | 157.60 [151.90, 164.02] | 0.0372 | |
| Weight(kg) (median [IQR]) | 57.80 [50.90, 65.80] | 53.90 [47.80, 60.02] | 57.60 [50.70, 65.00] | 63.05 [55.50, 70.90] | < 0.001 | |
| Waist(cm) (median [IQR]) | 84.60 [77.88, 92.00] | 78.80 [73.20, 84.40] | 85.00 [78.80, 91.20] | 91.00 [84.30, 97.20] | < 0.001 | |
| TG(mg/dL) (median [IQR]) | 106.20 [75.22, 154.88] | 66.38 [54.87, 79.65] | 105.32 [89.39, 123.90] | 185.41 [146.91, 248.91] | < 0.001 | |
| HDL-C(mg/dL) (median [IQR]) | 49.10 [40.21, 59.92] | 62.24 [54.12, 71.91] | 49.48 [43.69, 56.44] | 38.27 [32.86, 44.46] | < 0.001 | |
| LnCMI (median [IQR]) | 0.12 [-0.40, 0.69] | -0.61 [-0.88, -0.40] | 0.12 [-0.04, 0.30] | 0.96 [0.69, 1.38] | < 0.001 | |
| Gender (%) | Female | 5009 (53.5) | 1468 (47.1) | 1722 (55.2) | 1819 (58.3) | < 0.001 |
| Male | 4351 (46.5) | 1652 (52.9) | 1398 (44.8) | 1301 (41.7) | ||
| Education (%) | above high school | 294 (3.1) | 84 (2.7) | 107 (3.4) | 103 (3.3) | 0.3407 |
| below high school | 8445 (90.2) | 2838 (91.0) | 2809 (90.0) | 2798 (89.7) | ||
| high school | 621 (6.6) | 198 (6.3) | 204 (6.5) | 219 (7.0) | ||
| Smoking (%) | no | 5687 (60.8) | 1751 (56.1) | 1929 (61.8) | 2007 (64.3) | < 0.001 |
| yes | 3673 (39.2) | 1369 (43.9) | 1191 (38.2) | 1113 (35.7) | ||
| Drinking (%) | no | 6295 (67.3) | 1885 (60.4) | 2173 (69.6) | 2237 (71.7) | < 0.001 |
| yes | 3065 (32.7) | 1235 (39.6) | 947 (30.4) | 883 (28.3) | ||
| Hypertension (%) | no | 5464 (58.4) | 2117 (67.9) | 1846 (59.2) | 1501 (48.1) | < 0.001 |
| yes | 3896 (41.6) | 1003 (32.1) | 1274 (40.8) | 1619 (51.9) | ||
| DM (%) | no | 8814 (94.2) | 3016 (96.7) | 2972 (95.3) | 2826 (90.6) | < 0.001 |
| yes | 546 (5.8) | 104 (3.3) | 148 (4.7) | 294 (9.4) | ||
| CVD (%) | no | 8093 (86.5) | 2776 (89.0) | 2729 (87.5) | 2588 (82.9) | < 0.001 |
| yes | 1267 (13.5) | 344 (11.0) | 391 (12.5) | 532 (17.1) | ||
| HTN–DM comorbidity (%) | no | 9026 (96.4) | 3069 (98.4) | 3038 (97.4) | 2919 (93.6) | < 0.001 |
| yes | 334 (3.6) | 51 (1.6) | 82 (2.6) | 201 (6.4) | ||
| Kidney disease (%) | no | 8732 (93.3) | 2888 (92.6) | 2913 (93.4) | 2931 (93.9) | 0.0918 |
| yes | 628 (6.7) | 232 (7.4) | 207 (6.6) | 189 (6.1) | ||
Continuous variables: P values were determined using linear regression models; categorical variables: P values were assessed using chi-square tests
Abbreviations: CMI, cardiometabolic index; TG, triglycerides; HDL-C, high-density lipoprotein cholesterol; CVD, cardiovascular disease; HTN–DM comorbidity, presence of both hypertension and diabetes; IQR, interquartile range; SD, standard deviation
Association between LnCMI and hypertension–diabetes comorbidity
Table 3 of the NHANES study indicates that LnCMI exhibited a positive association with hypertension–diabetes comorbidity across all three models. In Model I, each unit increase in LnCMI was associated with higher odds of hypertension–diabetes comorbidity (OR = 1.994, 95% CI: 1.818–2.186). In Model II, each unit increase in LnCMI was associated with an OR of 2.068 (95% CI: 1.864–2.295). In Model III, each unit increase in LnCMI was associated with an OR of 1.995 (95% CI: 1.794–2.218). In the LnCMI tertile analysis, this association remained statistically significant: in the fully adjusted model, the odds of hypertension–diabetes comorbidity were higher in the highest tertile than in the lowest tertile (OR = 3.696, 95% CI: 3.057–4.468; P for trend < 0.001). In addition, restricted cubic spline (RCS) analyses were used to examine potential nonlinearity between LnCMI and hypertension–diabetes comorbidity. Threshold effect analyses suggested an inflection point in this association. In NHANES, in the fully adjusted model, the inflection point was 0.286. In a two-piecewise model, when LnCMI < 0.286, each unit increase in LnCMI was associated with an OR of 2.068 (95% CI: 1.758–2.444; P < 0.001). When LnCMI ≥ 0.286, each unit increase in LnCMI was associated with an OR of 1.557 (95% CI: 1.405–1.725; P < 0.001). The likelihood ratio test supported the two-piecewise model (P < 0.05), as depicted in Fig. 2 and Table S1.
Table 3.
Weighted multivariable logistic regression of hypertension–diabetes comorbidity on LnCMI, NHANES
| LnCMI | Model I OR (95%CI P value) | Model II OR (95%CI P value) | Model III OR (95%CI P value) |
|---|---|---|---|
| Continue | 1.994(1.818, 2.186), P < 0.001 | 2.068(1.864, 2.295), P < 0.001 | 1.995(1.794, 2.218), P < 0.001 |
| LnCMI Tertile group | |||
| low | 1(Ref) | 1(Ref) | 1(Ref) |
| middle | 2.286(1.947, 2.684), P < 0.001 | 2.195(1.867, 2.579), P < 0.001 | 2.172(1.842, 2.562), P < 0.001 |
| high | 3.8594(3.2404,4.5967) p < 0.001 | 3.939(3.274, 4.738), P < 0.001 | 3.696(3.057, 4.468), P < 0.001 |
| P for trend | < 0.001 | < 0.001 | < 0.001 |
Model I was unadjusted
Model II was adjusted for age, sex and education
Model III was adjusted for age, sex, education, smoking status, alcohol consumption, kidney disease and cardiovascular disease
Fig. 2.
Restricted cubic spline (RCS) analysis of the association between LnCMI and hypertension–diabetes comorbidity in NHANES. Survey-weighted logistic regression models were used to estimate odds ratios (ORs) and 95% confidence intervals (CIs) across three models (Model I–III, as defined in Table 3). Solid lines represent adjusted ORs, shaded areas denote 95% CIs, and the dashed line indicates OR = 1
In the CHARLS investigation, Table 4 indicates that LnCMI exhibited a positive association with hypertension–diabetes comorbidity across all three models. In Model I, each unit increase in LnCMI was associated with an OR of 1.790 (95% CI: 1.610–1.988). In Model II, each unit increase in LnCMI was associated with an OR of 1.808 (95% CI: 1.623–2.012). In Model III, each unit increase in LnCMI was associated with an OR of 1.760 (95% CI: 1.574–1.964). This association remained statistically significant in tertile analyses: in the fully adjusted model, the odds of hypertension–diabetes comorbidity were higher in the highest tertile than in the lowest tertile (OR = 3.788, 95% CI: 2.782–5.249; P for trend < 0.001). We also examined potential nonlinearity using restricted cubic spline (RCS) (Fig. 3 and Table S2) and performed threshold effect analysis. In the fully adjusted model, the LnCMI inflection point was 0.121. In a two-piecewise model, when LnCMI < 0.121, each unit increase in LnCMI was associated with an OR of 2.683 (95% CI: 1.757–4.253; P < 0.001). When LnCMI ≥ 0.121, each unit increase in LnCMI was associated with an OR of 1.599 (95% CI: 1.374–1.848; P < 0.001). The likelihood ratio test supported the two-piecewise model (P < 0.05).
Table 4.
Multivariable logistic regression of hypertension–diabetes comorbidity on LnCMI, CHARLS
| LnCMI | Model I OR(95%CI P value) | Model II OR(95%CI P value) | Model III OR(95%CI P value) |
|---|---|---|---|
| Continue | 1.790(1.610, 1.988), P < 0.001 | 1.808(1.623, 2.012), P < 0.001 | 1.760(1.574, 1.964), P < 0.001 |
| LnCMI Tertile group | |||
| low | 1(Ref) | 1(Ref) | 1(Ref) |
| middle | 1.624(1.145, 2.325), P = 0.007 | 1.573(1.108, 2.255), P = 0.012 | 1.549(1.089, 2.225), P = 0.016 |
| high | 4.144(3.061, 5.714), P < 0.001 | 4.098(3.020, 5.661), P < 0.001 | 3.788(2.782, 5.249), P < 0.001 |
| P for trend | < 0.001 | < 0.001 | < 0.001 |
Model I was unadjusted
Model II was adjusted for age, sex and education
Model III was adjusted for age, sex, education, smoking status, alcohol consumption, kidney disease and cardiovascular disease
Fig. 3.
Restricted cubic spline (RCS) analysis of the association between LnCMI and hypertension–diabetes comorbidity in CHARLS. Odds ratios (ORs) and 95% confidence intervals (CIs) were estimated across three models. Solid lines represent adjusted ORs, shaded areas denote 95% CIs, and the dashed line indicates OR = 1
Subgroup analyses
In the NHANES study, we conducted subgroup analyses with interaction testing to evaluate whether the association between LnCMI and hypertension–diabetes comorbidity differed across subgroups. Figure 4 illustrates that with every 1-unit increment in LnCMI, the odds of hypertension–diabetes comorbidity increased by 75.3% in men (OR = 1.753, 95% CI: 1.572–1.954) and by 136.4% in women (OR = 2.136, 95% CI: 2.009–2.782). Among smokers, the association was OR = 1.868 (95% CI: 1.650–2.114), and among non-smokers, the association was OR = 2.191 (95% CI: 1.879–2.555). Interaction testing suggested that sex and smoking status modified the association between LnCMI and hypertension–diabetes comorbidity. No statistically significant interactions were observed for age, education level, alcohol consumption, kidney disease, or CVD (all P for interaction > 0.05).
Fig. 4.
Subgroup analysis of the association between LnCMI and hypertension–diabetes comorbidity in NHANES. ORs and 95% CIs were estimated using survey-weighted logistic regression (Model III). Interaction P values were obtained by including interaction terms between LnCMI and subgroup indicators. The vertical dashed line indicates OR = 1
In the CHARLS investigation, we examined the association between LnCMI and hypertension–diabetes comorbidity across subgroups using interaction testing. Figure 5 illustrates that among men, the association between LnCMI and hypertension–diabetes comorbidity was OR = 1.668 (95% CI: 1.402–1.974), and among women, the association was OR = 1.807 (95% CI: 1.557–2.096). Interaction tests indicated no substantial effect modification by sex on the relationship between LnCMI and hypertension–diabetes comorbidity. The positive association between LnCMI and hypertension–diabetes comorbidity was also consistent across CVD, education level, smoking, kidney disease, age, and alcohol intake subgroups (all interaction P values > 0.05).
Fig. 5.
Subgroup analysis of the association between LnCMI and hypertension–diabetes comorbidity in CHARLS. ORs and 95% CIs were estimated using logistic regression (Model III). Interaction P values were obtained by including product terms between LnCMI and subgroup indicators. The vertical dashed line indicates OR = 1
ROC analysis
To assess the discriminative ability of various indicators (TG/HDL-C, BMI, WHtR, and CMI) for hypertension–diabetes comorbidity, we performed receiver operating characteristic (ROC) curve analyses in both databases. As shown in Tables S3–S4 and Fig. 6, CMI demonstrated a higher AUC than the other indicators in CHARLS, while WHtR slightly exceeded CMI in NHANES. These discrepancies may reflect differences in population composition, measurement modules, and cardiometabolic characteristics between the two surveys.
Fig. 6.
Receiver operating characteristic (ROC) curves comparing the discriminatory ability of CMI, TG/HDL-C, WHtR, and BMI for hypertension–diabetes comorbidity in NHANES and CHARLS. Calculations in NHANES were performed using survey weights. Adjusted variables included age, sex, education, smoking status, alcohol consumption, kidney disease, and cardiovascular disease
Sensitivity analysis
The association between LnCMI and hypertension–diabetes comorbidity remained robust in sensitivity analyses using the fully adjusted model (Model III) after excluding CVD (Tables S5–S6). In the NHANES sensitivity analysis, diabetes was defined as HbA1c ≥ 6.5%, fasting plasma glucose ≥ 126 mg/dL, or self-reported diabetes; results showed a consistent association between LnCMI and hypertension–diabetes comorbidity (OR = 2.218, 95% CI: 1.940–2.333; P < 0.001) (Table S7). Decision curve analysis suggested a higher net benefit of the CMI-extended model than the base model across clinically relevant threshold probabilities (Figure S2; Tables S9–S10). Bootstrap internal validation showed minimal optimism and stable calibration (Table S11).
Discussion
Both datasets showed an association between LnCMI and the prevalence of hypertension–diabetes comorbidity in persons aged 45 years and older. In both populations, the prevalence of hypertension–diabetes comorbidity increased with increasing LnCMI, and this pattern remained statistically significant whether LnCMI was modeled as a continuous or categorical variable. In NHANES, the inflection point for LnCMI was 0.286, while in CHARLS it was 0.121. Subgroup analyses further supported the consistency of this association across several demographic and clinical categories. In NHANES, interaction testing suggested effect modification by sex and smoking status, indicating that the association between LnCMI and hypertension–diabetes comorbidity may differ by these factors. ROC analyses indicated that WHtR had the highest discriminative ability in NHANES (AUC = 0.739), while CMI showed the highest discrimination in CHARLS (AUC = 0.739). Sensitivity analyses supported the robustness of the association between LnCMI and hypertension–diabetes comorbidity. Compared with WHtR and TG/HDL-C alone, LnCMI incorporates both central adiposity (WHtR) and lipid traits (TG/HDL-C), which may capture complementary cardiometabolic information. In our subgroup analysis, the association between LnCMI and hypertension–diabetes comorbidity was stronger in women and non-smokers in NHANES (P for interaction = 0.003), suggesting potential subgroup-specific utility.
CMI, as an index reflecting visceral fat and metabolic dysfunction, has demonstrated predictive value in conditions such as cardiovascular diseases [19], diabetes [20], endometriosis [21], kidney stones [26] and osteoporosis [27]. CMI provides a useful proxy for visceral adiposity and shows strong correlations with metabolic conditions, closely linked to obesity [28]. First, excessive visceral fat distribution is associated with changes in inflammatory mediators and endothelial function [29]. Second, the renin-angiotensin-aldosterone system can be activated, leading to impaired sodium excretion and increased sodium reabsorption in the renal tubules [30]. Additionally, visceral fat tissue participates in blood pressure regulation through the secretion of adipokines that regulate arterial tone, such as leptin and adiponectin [31]. Finally, excessive visceral fat tissue increases insulin resistance, leading to metabolic dysfunction, oxidative stress, and vascular dysfunction [32]. Together, these pathways support the biological plausibility that higher CMI is associated with hypertension and may help prevalence stratification. To our knowledge, this study is the first to examine the association between LnCMI and hypertension–diabetes comorbidity using a direct comparison of databases from China and the United States. By specifically focusing on middle-aged and older populations, the findings are more targeted. Methodologically, data underwent ln transformation to improve model fit. Our findings indicate that higher LnCMI was associated with higher odds of hypertension–diabetes comorbidity. Furthermore, consistent with previous studies indicating sex-dependent associations between CMI and hypertension [33], our analysis suggests that the association between LnCMI and hypertension–diabetes comorbidity may differ by sex [33]. Estrogen modulates key cardiovascular processes, including vascular nitric oxide production, endothelial function, sympathetic activity, and the renin-angiotensin system. A reduction in estrogen levels can impair endothelial relaxation, increase vascular stiffness, and promote salt sensitivity. By exerting potent pro-inflammatory and pro-oxidative effects, triglyceride-rich remnants and triglyceride-displaced HDL-C disrupt key vascular protective mechanisms, including reverse cholesterol transport and native HDL-C antioxidant capacity. This HDL-C dysfunction may be particularly detrimental for women, as sex hormones and their receptors differentially regulate the endothelial–HDL-C interface, potentially heightening vascular risk during this transition [34–37]. Unlike previous studies, the association between LnCMI and hypertension–diabetes comorbidity may differ by smoking status because smoking drives chronic inflammation [38], oxidative stress [39], impaired endothelial function [40], sympathetic activation [41], and insulin resistance [42]. When baseline risk pathways in smokers are already elevated by tobacco exposure, the relative incremental association of higher CMI may be less pronounced compared with non-smokers. Among non-smokers, elevated CMI levels often more strongly reflect central obesity and dyslipidemia, aligning with pathways such as the renin-angiotensin-aldosterone system [43], adipokines [44], insulin resistance [45], and oxidative stress [46].
Differences in ROC performance between NHANES and CHARLS suggest that the discriminatory ability of CMI may depend on population context. Our conjecture is that in NHANES, central fat distribution captured by WHtR likely concentrated much of the discriminative information for comorbidity, whereas in CHARLS, TG/HDL-C combined with central fat distribution and an atherogenic lipid profile may provide additional differentiation. Differences in measurement patterns and baseline prevalence distributions may also contribute. Furthermore, our primary findings are based on multivariable association models and nonlinear associations observed in both populations.
There was a marked difference in baseline comorbidity prevalence (NHANES 14.7% vs. CHARLS 3.6%). This may help explain the differing inflection points of LnCMI between the two cohorts (NHANES 0.286 vs. CHARLS 0.121) and the variation in discriminative rankings (WHtR was highest in NHANES, while CMI performed best in CHARLS). Furthermore, we observed more pronounced associations among women and non-smokers in NHANES, potentially attributable to environmental, geographic, and demographic factors. Additionally, the consistent positive associations across both datasets support the robustness of LnCMI as a population-level marker for prevalence stratification.
A principal advantage of this study is the use of NHANES and CHARLS data, both of which have large sample sizes and national representativeness. To our knowledge, this is the first study to investigate the association between LnCMI and hypertension–diabetes comorbidity in adults aged 45 years and older using harmonized analyses across China and the United States. We validated the findings using subgroup analyses, RCS, interaction testing, threshold analyses, ROC analysis, decision curve analysis, and bootstrap internal validation. Another key strength is cross-national verification using two representative surveys. Consistent associations across populations with distinct sociocultural backgrounds, healthcare systems, and cardiometabolic profiles enhance external validity. Because CMI derives from routine anthropometric and lipid measurements, it may serve as a practical tool for screening and prevalence stratification in community and primary care settings.
We recognize several limitations. First, the cross-sectional design constrains causal inference, and residual confounding from unmeasured variables may exist. Second, because participants were aged 45 years and older from China and the United States, the findings may not be generalizable to other age groups or settings. Third, diagnostic bias in hypertension and diabetes may vary across age groups and survey settings. In addition, because we relied on parent survey instruments, we did not conduct independent re-validation of the questionnaires. Potential measurement error and mode differences between NHANES (home/MEC) and CHARLS (CAPI) may remain. Therefore, we conducted sensitivity analyses using NHANES laboratory data and provided reconciliation tables to enhance transparency (Table S8). Future longitudinal studies should evaluate temporal relationships, assess incremental predictive value beyond established measures, and determine whether CMI-guided strategies improve preventive outcomes.
Conclusions
We conducted parallel analyses in NHANES and CHARLS and compared the consistency of associations across countries. Overall, higher LnCMI (log-transformed CMI) was associated with a higher prevalence of hypertension–diabetes comorbidity in persons aged 45 years and older in both China and the United States. We also observed evidence of nonlinearity and potential inflection points in the association between LnCMI and hypertension–diabetes comorbidity. CMI may be a practical marker for prevalence stratification, but longitudinal studies are needed to evaluate predictive utility and clinical impact.
Supplementary Information
Below is the link to the electronic supplementary material.
Acknowledgements
The authors thank the studies or consortiums referenced and included in the present analysis for providing public datasets.
Abbreviations
- CMI
Cardiometabolic Index
- HDL-C
High-density lipoprotein cholesterol
- DM
Diabetes Mellitus
- CVD
Cardiovascular Disease
- TG
Triglycerides
- Waist
Waist circumference
- Hypertension–Diabetes Comorbidity
Participants with both hypertension and diabetes
- WHtR
Waist-to-Height Ratio
- RCS
Restricted Cubic Spline
- AUC
Area Under the Curve
- DCA
Decision Curve Analysis
- IQR
Interquartile Range
- SD
Standard Deviation
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-P2 × 3 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-P2 × 3 Signaling Pathway: Cellular Mechan transduction 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).
Declarations
Ethics approval and consent to participate
NHANES: Data were collected with the approval of the NCHS Research Ethics. Review Board, and NHANES data were collected following approval by the National Center for Health Statistics Research Ethics Review Committee and anonymized prior to public release. All participants signed informed consent forms before participating in the study. CHARLS data were collected by the CHARLS research team and are publicly available. This study was approved by the Biomedical Ethics Review Committee of Peking University in accordance with the principles of the Declaration of Helsinki (IRB00001052-11015). This secondary analysis utilizes de-identified public data and therefore does not require additional ethical review.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
References
- 1.Tikhonoff V, Zhang H, Richart T, Staessen JA. Blood pressure as a prognostic factor after acute stroke. Lancet Neurol. 2009;8(10):938–48. [DOI] [PubMed] [Google Scholar]
- 2.Wang C, Yuan Y, Zheng M, Pan A, Wang M, Zhao M, Li Y, Yao S, Chen S, Wu S, et al. Association of age of onset of hypertension with cardiovascular diseases and mortality. J Am Coll Cardiol. 2020;75(23):2921–30. [DOI] [PubMed] [Google Scholar]
- 3.Burnier M, Damianaki A. Hypertension as cardiovascular risk factor in chronic kidney disease. Circ Res. 2023;132(8):1050–63. [DOI] [PubMed] [Google Scholar]
- 4.Cardiovascular disease. Chronic kidney disease, and diabetes mortality burden of cardiometabolic risk factors from 1980 to 2010: a comparative risk assessment. Lancet Diabetes Endocrinol. 2014;2(8):634–47. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5.Fryar CD, Kit B, Carroll MD, Afful J. Hypertension Prevalence, Awareness, Treatment, and Control Among Adults Age 18 and Older: United States, August 2021-August 2023. NCHS Data Brief 2024(511).
- 6.Wang Y, Lee JS, Pollack LM, Kumar A, Honeycutt S, Luo F. Health care expenditures and use associated with hypertension among U.S. Adults. Am J Prev Med. 2024;67(6):820–31. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7.Number of adults (20–79 years) with diabetes in United States of America (US) https://diabetesatlas.org/data-by-location/country/united-states-of-america/
- 8.Parker ED, Lin J, Mahoney T, Ume N, Yang G, Gabbay RA, ElSayed NA, Bannuru RR. Economic costs of diabetes In the U.S. In 2022. Diabetes Care. 2024;47(1):26–43. [DOI] [PubMed] [Google Scholar]
- 9.Cao X, Wang X, Tian Y, Tuerdi N, Zheng C, Li W, Pei X, Li F, Chang C, Yang J, et al. Trends and sociodemographic patterns in hypertension prevalence and treatment in China. Med. 2025;6(11):100808. [DOI] [PubMed] [Google Scholar]
- 10.Organization WH. 2023. https://www.who.int/publications/m/item/hypertension-chn-2023-country-profile
- 11.Fan L, Liu L, Zhao Y, Mo Y, Li J, Cai L. Trends in the prevalence and economic burden of hypertension and its socioeconomic disparities in rural Southwestern china: two repeated cross-sectional studies. BMJ Open. 2023;13(11):e076694. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12.Powell-Wiley TM, Poirier P, Burke LE, Després JP, Gordon-Larsen P, Lavie CJ, Lear SA, Ndumele CE, Neeland IJ, Sanders P, et al. Obesity and cardiovascular disease: A scientific statement from the American heart association. Circulation. 2021;143(21):e984–1010. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13.Lu Y, Hajifathalian K, Ezzati M, Woodward M, Rimm EB, Danaei G. Metabolic mediators of the effects of body-mass index, overweight, and obesity on coronary heart disease and stroke: a pooled analysis of 97 prospective cohorts with 1·8 million participants. Lancet. 2014;383(9921):970–83. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14.Piché ME, Tchernof A, Després JP. Obesity Phenotypes, Diabetes, and cardiovascular diseases. Circ Res. 2020;126(11):1477–500. [DOI] [PubMed] [Google Scholar]
- 15.Xia X, Chen S, Tian X, Xu Q, Zhang Y, Zhang X, Li J, Wang P, Wu S, Wang A. Roles of general and central adiposity in cardiometabolic multimorbidity: revisiting the obesity paradox using a multistate model. Obes (Silver Spring). 2024;32(4):810–21. [Google Scholar]
- 16.Wakabayashi I, Daimon T. The cardiometabolic index as a new marker determined by adiposity and blood lipids for discrimination of diabetes mellitus. Clin Chim Acta. 2015;438:274–8. [DOI] [PubMed] [Google Scholar]
- 17.Wakabayashi I, Sotoda Y, Hirooka S, Orita H. Association between cardiometabolic index and atherosclerotic progression in patients with peripheral arterial disease. Clin Chim Acta. 2015;446:231–6. [DOI] [PubMed] [Google Scholar]
- 18.Chen F, Niu Y, Wu R, Jiang H, Zhu J, Wang C, Xia X, Jin Y. Association between cardiometabolic index and cardiovascular disease: evidence from the NHANES 2007–2018. Front Cardiovasc Med. 2025;12:1516591. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19.Li J, Wei X. Baseline and changes in cardiometabolic index and incident cardiovascular disease in two prospective cohorts. Am J Prev Cardiol. 2025;23:101046. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20.Wei Y, Yu J. Association between cardiometabolic index and frailty among patients with diabetes mellitus: a cross-sectional study. Front Nutr. 2024;11:1495792. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21.Wang J, Wang B, Liu T, Shang J, Gu X, Zhang T, Cong H. Association between cardiometabolic index (CMI) and endometriosis: a cross-sectional study on NHANES. Lipids Health Dis. 2024;23(1):328. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22.Huang X, Wen S, Huang Y, Zhang B, Xia Z, Huang Z. Association between cardiometabolic index and the incidence of stroke: a prospective nationwide cohort study in China. J Diabetes Metab Disord. 2025;24(1):26. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23.Zipf G, Chiappa M, Porter KS, Ostchega Y, Lewis BG, Dostal J. National health and nutrition examination survey: plan and operations, 1999–2010. Vital Health Stat 1 2013(56):1–37.
- 24.Flegal KM, Kruszon-Moran D, Carroll MD, Fryar CD, Ogden CL. Trends in obesity among adults in the united States, 2005 to 2014. JAMA. 2016;315(21):2284–91. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25.Zhao Y, Hu Y, Smith JP, Strauss J, Yang G. Cohort profile: the China health and retirement longitudinal study (CHARLS). Int J Epidemiol. 2014;43(1):61–8. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26.Wang Q, Wang Z, Tang Z, Liu C, Pan Y, Zhong S. Association between cardiometabolic index and kidney stone from NHANES: a population-based study. Front Endocrinol (Lausanne). 2024;15:1408781. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27.Li D, Li J, Li Y, Dong W, Lin Z. Association between the cardiometabolic index and osteoporosis: a cross-sectional study of the NHANES. Front Public Health. 2024;12:1462169. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28.Rico-Martín S, Calderón-García JF, Sánchez-Rey P, Franco-Antonio C, Martínez Alvarez M. Sánchez Muñoz-Torrero JF: effectiveness of body roundness index in predicting metabolic syndrome: A systematic review and meta-analysis. Obes Rev. 2020;21(7):e13023. [DOI] [PubMed] [Google Scholar]
- 29.Seravalle G, Grassi G. Obesity and hypertension. Pharmacol Res. 2017;122:1–7. [DOI] [PubMed] [Google Scholar]
- 30.Hall JE, Brands MW, Henegar JR. Mechanisms of hypertension and kidney disease in obesity. Ann N Y Acad Sci. 1999;892:91–107. [DOI] [PubMed] [Google Scholar]
- 31.Yiannikouris F, Gupte M, Putnam K, Cassis L. Adipokines and blood pressure control. Curr Opin Nephrol Hypertens. 2010;19(2):195–200. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 32.Reaven GM. Banting lecture 1988. Role of insulin resistance in human disease. Diabetes. 1988;37(12):1595–607. [DOI] [PubMed] [Google Scholar]
- 33.Guo T, Zhou Y, Yang G, Sheng L, Chai X. Association between cardiometabolic index and hypertension among US adults from NHANES 1999–2020. Sci Rep. 2025;15(1):4007. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 34.Rogers JR, Lee J, Zhou Z, Cheung YK, Hripcsak G, Weng C. Contemporary use of real-world data for clinical trial conduct in the united states: a scoping review. J Am Med Inf Assoc. 2021;28(1):144–54. [Google Scholar]
- 35.Dietrich E, Jomard A, Osto E. Crosstalk between high-density lipoproteins and endothelial cells in health and disease: insights into sex-dependent modulation. Front Cardiovasc Med. 2022;9:989428. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 36.Higashi Y. Endothelial function in dyslipidemia: roles of LDL-Cholesterol, HDL-Cholesterol and triglycerides. Cells 2023, 12(9).
- 37.Sabbatini AR, Kararigas G. Estrogen-related mechanisms in sex differences of hypertension and target organ damage. Biol Sex Differ. 2020;11(1):31. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 38.Yanbaeva DG, Dentener MA, Creutzberg EC, Wesseling G, Wouters EF. Systemic effects of smoking. Chest. 2007;131(5):1557–66. [DOI] [PubMed] [Google Scholar]
- 39.Seo YS, Park JM, Kim JH, Lee MY. Cigarette Smoke-Induced reactive oxygen species formation: A concise review. Antioxid (Basel) 2023, 12(9).
- 40.Higashi Y. Smoking cessation and vascular endothelial function. Hypertens Res. 2023;46(12):2670–8. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 41.Middlekauff HR, Park J, Moheimani RS. Adverse effects of cigarette and noncigarette smoke exposure on the autonomic nervous system: mechanisms and implications for cardiovascular risk. J Am Coll Cardiol. 2014;64(16):1740–50. [DOI] [PubMed] [Google Scholar]
- 42.Bergman BC, Perreault L, Hunerdosse D, Kerege A, Playdon M, Samek AM, Eckel RH. Novel and reversible mechanisms of smoking-induced insulin resistance in humans. Diabetes. 2012;61(12):3156–66. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 43.Segura J, Ruilope LM. Obesity, essential hypertension and renin-angiotensin system. Public Health Nutr. 2007;10(10a):1151–5. [DOI] [PubMed] [Google Scholar]
- 44.Landecho MF, Tuero C, Valentí V, Bilbao I, de la Higuera M, Frühbeck G. Relevance of leptin and other adipokines in Obesity-Associated cardiovascular risk. Nutrients 2019, 11(11).
- 45.Kahn BB, Flier JS. Obesity and insulin resistance. J Clin Invest. 2000;106(4):473–81. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 46.Masenga SK, Kabwe LS, Chakulya M, Kirabo A. Mechanisms of oxidative stress in metabolic syndrome. Int J Mol Sci 2023, 24(9).
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
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).