The present study used patient data collected by the China Stroke High-risk Population Screening and Intervention Program (CSHPSIP): an ongoing population-based screening project conducted by the China Stroke Prevention Project Committee (30). The program aims to mitigate stroke risk by addressing the prevalence of stroke risk factors through screening, physical examination, and comprehensive interventions. The CSHPSIP enrolled community-dwelling adults who were (1) aged >40 years, (2) resided in the community for >6 months, and (3) provided informed consent (4). The protocol for the program were reviewed and approved by the Institutional Review Board at the Capital Medical University Xuanwu Hospital (No. 2012045).

The present study used data obtained from individuals residing in Hunan Province, a region featuring a relatively elevated incidence of stroke within China (4). 54338 individuals enrolled from twenty-six communities (thirteen urban areas and thirteen rural areas) in the province were administered a baseline survey between January 2017 and December 2018. All eligible participants underwent standardized in-person clinical interviews. After the baseline survey was completed, subsequent surveys were conducted biennially.

A total of 20,487 respondents participated in the follow-up survey two years after completing the baseline survey. Individuals with incomplete sociodemographic information, missing anthropometric measures, or lacking laboratory assay results due to unsuccessful blood collection were excluded from the study.

Of the 18,974 participants included in the final analysis, 6,732 were diagnosed with MetS according to the Chinese Guidelines for the Prevention and Treatment of Type 2 Diabetes (2020 edition). The criteria for defining the detailed components of MetS were as follows (31): (1) abdominal obesity, ascertained by a WC of ≥ 90 cm in males or ≥ 85 cm in females; (2) hypertension, defined as a blood pressure of ≥ 130/85 mmHg or a history of hypertension; (3) hyperglycemia, indicated by a fasting plasma glucose (FBG) level of ≥ 6.10 mmol/L, a 2-h plasma glucose level of ≥7.8 mmol/L, or a diagnosis of type 2 diabetes mellitus; (4) high TG, determined by a fasting TG level of ≥1.70 mmol/L; and (5) low HDL-C, indicated by an HDL-C level of ≤ 1.04 mmol/L. Patients who fulfilled any three of the aforementioned five criteria were diagnosed with MetS.

In parallel with the survey schedule, the study was split into two stages performed at baseline and after two years ( Supplemental Figure 1 ). During the former, we examined the associations between baseline CVAI levels and MetS diagnosis, as well as assessed the diagnostic efficacy of CVAI in detecting MetS. After two years, we investigated the relationship between CVAI and incident stroke risk among patients diagnosed with MetS.

Data on medical, socio-demographic, anthropometric, and lifestyle-related variables were obtained by trained interviewers or medical staff. Demographic information, including age, sex, education level, economic status, lifestyle risk factors (tobacco use, alcohol consumption, and physical activity), medical history (hypertension, diabetes mellitus, dyslipidemia, stroke, and atrial fibrillation), and family medical history of stroke was collected. Education level was classified as “primary school or below,” “middle school,” and “high school or above.” Income was stratified as “<5000 Chinese Yuan (CNY),” “5000–9999 CNY,” “10000–19999 CNY,” and “≥20,000 CNY.” The definition of alcohol consumption in this study was the regular intake of alcoholic beverages at a frequency of three or more times per week, with a minimum of 100 mL per drinking episode. Smoking was defined as the act of smoking continuously or cumulatively for a period exceeding six months. Physical inactivity refers to the absence of moderate-to-vigorous physical activity for >150 minutes/week or vigorous-intensity physical activity for >75 minutes/week (4). Diabetes was defined as a fasting plasma glucose level of ≥ 7.0 mmol/L (126 mg/dL), a previous diagnosis of diabetes mellitus, or the use of antidiabetic medication or insulin (32). Hypertension was defined as a blood pressure of ≥140/90 mmHg, a history of hypertension, or the use of antihypertensive medication (33). Dyslipidemia was defined as serum total cholesterol (TC) concentration ≥ 6.22 mmol/L (240 mg/dL), and/or low-density lipoprotein cholesterol (LDL-C) concentration ≥ 4.14 mmol/L (160 mg/dL), and/or TG concentration ≥ 2.26 mmol/L (200 mg/dL), and/or HDL-C concentration <1.04 mmol/L (40 mg/dL), or previous history of hyperlipidemia and currently taking lipid-lowering drugs (34). Atrial fibrillation was defined as electrocardiographic evidence of atrial fibrillation or treatment for atrial fibrillation.

Each participant underwent a physical examination conducted by a qualified nurse or physician. Height and weight were measured to a precision of 0.1 cm and 0.1 kg, respectively. BMI was calculated by dividing body mass (kilograms) by the height (meters) squared. The WC was measured to a precision of 0.1 cm at the highest point of the iliac crest during minimal respiration. In accordance with previous studies, general obesity was defined as a BMI of ≥25 kg/m² following Asian-specific criteria (35); abdominal obesity as a WC of ≥90 cm for men and a WC of ≥85 cm for women (31). Blood pressure was measured twice by an examining nurse or physician at an interval of 15 min. The average between the two measurements was used as the final datum.

Blood samples were collected after an 8-hour fast and analyzed using the HP-AFS/3 automatic immunoassay system A3 Specific Protein Analyzer with supporting reagents (Shijiazhuang Hebo Biotechnology Co., Ltd., Shijiazhuang, China) on the same day of collection. The biochemical indicators assessed included fasting blood glucose (FBG), Hemoglobin A1c (HbA1c), TC, TG, LDL-C, and HDL-C.

CVAI scores were computed using sex-specific formulas as follows (36):

Males: CVAI = −267.93 + 0.68 × age (years) + 0.03 × BMI (kg/m²) + 4.00 × WC (cm) + 22.00 × log10(TG [mmol/L]) − 16.32 × HDL-C (mmol/L);

Females: CVAI= −187.32 + 1.71 × age (years) + 4.32 × BMI (kg/m²) + 1.12 × WC (cm) + 39.76 × log10(TG [mmol/L]) − 11.66 × HDL-C (mmol/L).

All incidents of stroke, including ischemic stroke, intracerebral hemorrhage, and subarachnoid hemorrhage, were documented during the survey period. The diagnosis of stroke was confirmed either through neurological imaging (brain computed tomography or magnetic resonance imaging) or a diagnosis certificate from a secondary or higher medical unit. However, due to limitations in data collection methods, we were unable to record the exact onset time of stroke.

All continuous variables were non-normally distributed and are presented as medians with interquartile ranges (IQR). Categorical variables are presented as percentages. The baseline characteristics of participants without MetS were compared to those of patients with MetS using the Mann-Whitney test for continuous variables and the Chi-square test for categorical variables. The baseline characteristics of MetS patients with and without stroke were analyzed in the same manner. Furthermore, to demonstrate the baseline characteristics of CVAI, we stratified patients into quartiles based on their initial CVAI levels. Differences in baseline variables between groups were assessed using either the Jonckheere-Terpstra trend test or the Chi-square test for trends.

Multivariate logistic regression models were used to assess the correlation between CAVI and MetS diagnosis. Various multivariable models with different levels of adjustment were employed. Model 1 incorporated individual characteristics, such as age, sex, education level, and economic status. Model 2 further included lifestyle risk factors, like smoking, alcohol consumption, and physical activity; medical history of hypertension, diabetes mellitus, dyslipidemia, and stroke; and family medical history of stroke. The variables included in the models all met the criteria of tolerance > 0.1 and variance inflation factor < 10. The diagnostic performance of CVAI in detecting MetS was compared with that of BMI and WC using receiver operating characteristic (ROC) curve analyses. In addition, we employed a restricted cubic spline to evaluate the dose-response relationship between CVAI and MetS (knots on the 5th, 25th, 75th, and 95th percentiles).

After a 2-year follow-up, multivariate logistic regression models were used to evaluate the correlation between stroke risk in MetS patients and CVAI, as well as other adiposity measures. The multivariable models were consistent with those described above. Model 1 contained individual characteristics (age, sex, education, economic status). Model 2 added lifestyle risk factors and medical history (smoking, alcohol drinking and physical activity, hypertension, diabetes mellitus, dyslipidemia, atrial fibrillation, prior stroke, family medical history of stroke on the base of model 1. The odds ratio (OR) was calculated with a 95% confidence interval (CI) for the presence of incident stroke. The dose-response relationship between CVAI or other adiposity measures and stroke risk was evaluated with a restricted cubic spline. Subgroup analyses were further performed to investigate the relationship between CVAI and stroke in various subgroups based on age (≥60, <60 years), sex (male, female), diabetes (yes, no), hypertension (yes, no), dyslipidemia (yes, no), prior stroke (yes, no), current smoking status (yes, no), current drinking habits (yes, no), and physical activity level (lacking or not lacking exercise). The p-value for an interaction between a subgroup variable and CVAI was assessed in each subgroup analysis.

SPSS version 25.0 (IBM SPSS, Armonk, NY, USA) and R version 4.2.3 (R Development Core Team, Vienna, Austria) were used for all statistical analyses. A two-tailed P-value of <0.05 was considered to indicate statistical significance.

Of the 18,974 participants surveyed at both baseline and the two-year follow-up, 6732 were diagnosed with MetS at baseline. Table 1 outlines the demographic and clinical characteristics of these individuals. Notably, those with MetS had a significantly higher median CVAI compared to their non-MetS counterparts. Supplemental Table 1 presents the characteristics of all study participants stratified by CVAI quartile. The median CVAI for all study participants was 90.49 (IQR, 68.66–114.58).

As the CVAI increased, there was a corresponding increase in the prevalence of MetS ( Figure 1A ). Table 2 shows a correlation between CVAI and MetS. In the unadjusted model, for each additional SD increase in CVAI, the risk of MetS increased by 1.59. In all two adjusted models, CVAI demonstrated an independent association with MetS; the adjusted odds ratios are 2.68 (95% CI: 2.57-2.79), and 2.05 (95% CI: 1.95-2.16), respectively. When assessed as quartiles, CVAI was significantly associated with MetS in the second, third, and fourth quartiles—even after adjustment for all confounding factors (adjusted OR: 1.54, 95% CI: 1.34, 1.76; adjusted OR: 2.83, 95% CI: 2.48, 3.24; adjusted OR: 6.29, 95% CI: 5.47,7.23, respectively). Participants in the third and fourth CVAI quartiles were associated with a significantly higher risk of MetS compared to their counterparts in the first and second quartiles (adjusted OR: 3.13, 95% CI: 2.86, 3.44). Additionally, dose-response relationships between CVAI and MetS were evaluated by restricted cubic splines ( Figure 1B ). CVAI increases the risk of MetS when higher than 90.94.

ROC curve analysis was used to compare the diagnostic efficacy of CVAI with that of BMI and WC in detecting MetS ( Figure 2 ). In the diagnosis of MetS, the CVAI demonstrated the highest AUC values (AUC: 0.741, 95% CI: 0.734-0.749), exceeding those of WC (AUC: 0.627, 95% CI: 0.619-0.635) and BMI (AUC: 0.631, 95% CI: 0.623-0.639). Table 3 presents the diagnostic performance of each anthropometric index in identifying MetS, encompassing sensitivity, specificity, and corresponding optimal cut-off values. CVAI exhibited the highest Youden indices (0.376) for identifying MetS, with an optimal cut-off of 99.15.

Of the 6,732 patients with MetS, 72 experienced strokes during the two-year follow-up period; these individuals were more likely to be of advanced age and have a lower income at baseline relative to those without a history of stroke. Hypertension, previous history of stroke, and family history of stroke were more prevalent among individuals with a stroke incident. Additionally, the stroke group exhibited significantly higher SBP levels, BMI, WC, and CVAI at baseline ( Supplement Table 2 ).

Table 4 shows the characteristics of participants with MetS categorized by CVAI quartile. The median CVAI was 110.79 (IQR, 88.45–133.32). Participants in the upper CVAI quartile tended to be older, female, less educated, and have a higher income. They were also less physically active, and hypertension, dyslipidemia, prior stroke history, and family history of stroke were more prevalent among them. Furthermore, we observed a significant trend towards increasing SBP, TG, and TC levels, as well as decreasing HDL-C levels with increasing CVAI, among the participants who developed MetS.

There was a positive association between CVAI and the risk of incident stroke ( Figure 3A ). Table 5 presents the association between CVAI and incident stroke. In the unadjusted model, the risk of incident stroke increased by 64% (95% CI: 1.30, 2.06) for each SD of CVAI. In both adjusted model 1 and model 2, there was a significant association between the increase in CVAI and the occurrence of stroke, as evidenced by the adjusted ORs of 1.61 (95% CI: 1.26, 2.04) and 1.52 (95% CI: 1.18, 1.95), respectively. When assessing CVAI as quartiles, the associations between incident stroke and the second, third, and fourth CVAI quartiles were 3.22 (95% CI: 1.17-8.81), 4.04 (95% CI: 1.51-10.78), and 6.30 (95% CI: 2.44-16.24), respectively, relative to the first CVAI quartile. These association estimators did not change significantly after additional adjustment for individual characteristics, medical history, and risk factors. Consistently, participants in the third and fourth CVAI quartiles showed a significantly higher risk of incident stroke than those in the first and second quartiles (adjusted OR: 2.14, 95% CI: 1.25, 3.69).

As demonstrated by the logistic regression models, our findings also indicate a positive correlation between WC and incident stroke in individuals with MetS. Participants exhibiting abdominal obesity had a relative risk of 2.73 (95% CI: 1.67, 4.46) for developing an incident stroke compared to their counterparts without abdominal obesity ( Supplement Table 3 ). The fully adjusted model revealed significant associations between incident stroke and the third and fourth BMI quartiles in patients with MetS, but not the second quartile ( Supplement Table 4 ). Although general obesity may be as-sociated with an elevated risk of stroke, the association did not reach statistical significance after adjustment for confounding factors (adjusted OR: 1.60; 95% CI: 0.99, 2.59). We further used restricted cubic splines to visualize the relation of CVAI, WC, and BMI with incident stroke in patients with MetS. The risk of incident stroke was non-linearly associated with CVAI ( Figure 3B ) and followed an inverted U-shaped curve with respect to BMI ( Supplement Figure 2A ). A decreased risk of stroke was observed when the CVAI was below 110.91.

During the two-year follow-up period, 57 strokes occurred among the 12,242 patients without MetS. However, no significant correlation was detected between CVAI and stroke in non-MetS patients ( Supplement Table 5 ).

To evaluate any potential modifying effects of stroke risk factors on the association between CVAI (3-4 quartiles vs. 1-2 quartiles) and incident stroke, subgroup analyses were conducted ( Table 6 ). We found no significant interaction between CVAI and covariates in relation to incident stroke.