Association between Visceral Adipose Index and Cardiovascular Disease in Postmenopausal Women: A Prospective Cohort Study

FAN Zhuanzhuan, LI Wenting, MA Guoliang*

Nanjing Municipal Center for Disease Control and Prevention, Nanjing 210003, China

*Corresponding author: MA Guoliang, Associate Professor; E-mail: guoliang77899@163.com

Abstract

Background: Cardiovascular disease (CVD) represents a major threat to women's health, with postmenopausal women constituting a particularly high-risk population. The decline in estrogen levels following menopause may promote visceral fat accumulation, which is closely linked to insulin resistance, chronic inflammatory responses, and lipid metabolism disorders, potentially elevating CVD risk. However, research examining the relationship between visceral fat index and CVD in postmenopausal women remains limited.

Objective: To explore the association between the Chinese Visceral Adipose Index (CVAI) and cardiovascular disease in postmenopausal women, thereby informing prevention strategies for this high-risk group.

Methods: Using data from the China Health and Retirement Longitudinal Study (CHARLS) spanning 2015–2020, we enrolled 4,743 postmenopausal women aged ≥45 years who were free of CVD at baseline in 2015. Baseline CVAI served as the exposure variable, while incident CVD events identified in the 2018 and 2020 follow-ups constituted the outcome measures. Cox proportional hazards regression models were employed to analyze the relationship between CVAI and CVD risk, and restricted cubic spline (RCS) analysis was used to assess dose-response relationships.

Results: By the end of follow-up in 2020, the incidence rates of CVD, heart disease, and stroke among the 4,743 postmenopausal women were 20.2% (958/4,743), 13.6% (645/4,743), and 8.3% (393/4,743), respectively. Baseline CVAI quartiles were defined as Q1≤84.78, Q2 (84.78–108.49], Q3 (108.49–132.01], and Q4>132.01. After adjusting for confounding factors, Cox regression analysis revealed that compared with the Q1 reference group, women in Q3 and Q4 faced significantly elevated risks: CVD risk increased by 69% (HR=1.69, 95%CI=1.29–2.21) and 82% (HR=1.82, 95%CI=1.38–2.14); stroke risk increased by 76% (HR=1.76, 95%CI=1.10–2.82) and 95% (HR=1.95, 95%CI=1.21–3.14); and heart disease risk increased by 57% (HR=1.57, 95%CI=1.14–2.15) and 68% (HR=1.68, 95%CI=1.21–2.33), respectively. RCS analysis confirmed linear dose-response relationships between CVAI and risks of CVD, heart disease, and stroke (P<0.05). Subgroup analyses demonstrated that the association between CVAI and CVD risk remained significant among women aged <65 years, ≥65 years, those with BMI<24.35 kg/m², and rural residents (P<0.05).

Conclusion: Elevated CVAI significantly increases CVD risk in postmenopausal women, particularly among those with lower BMI and rural populations. Targeted monitoring and management of visceral obesity in these high-risk subgroups are warranted to reduce CVD incidence.

Keywords: Cardiovascular diseases; Chinese visceral adipose index; Coronary disease; Stroke; Postmenopausal women

Introduction

Cardiovascular disease (CVD) is a circulatory system disorder characterized by varying degrees of sclerosis in the heart and blood vessels (microvessels, veins, arteries), accompanied by hypotension, arrhythmia, bradycardia, and cardiac arrest [1]. CVD primarily includes heart disease and stroke, featuring high incidence, mortality, and disease burden [2]. Multiple studies have demonstrated associations between CVD and cognitive decline, depression, and reduced quality of life [3-5], representing a serious public health threat. In 2019, the number of patients with CVD, coronary heart disease, and stroke in China reached 290 million, 11 million, and 13 million, respectively [6], with a continuously rising trend. The standardized incidence of CVD is projected to reach 663.62 per 100,000 by 2050 [7].

The Chinese Visceral Adipose Index (CVAI) is a metric developed specifically for Chinese populations based on age, BMI, waist circumference, triglycerides, and high-density lipoprotein cholesterol, demonstrating high predictive value for CVD, hypertension, and diabetes risk [8-10]. Previous research has extensively investigated the correlation between CVAI and CVD, revealing a close relationship [11-12]. However, most studies have focused on middle-aged and elderly populations or the general population, with limited research specifically examining CVAI and CVD risk in postmenopausal women. As a unique population subgroup, postmenopausal women experience significantly increased CVD prevalence, which has become their leading cause of death [13]. Their physiological and psychological characteristics differ substantially from premenopausal women and the general population, making existing findings not necessarily applicable to postmenopausal women. With population aging intensifying, the number of postmenopausal women in China reached 256 million in 2021 and is projected to grow to 280 million by 2030 [14]. Consequently, understanding the association between CVAI and CVD risk in this expanding population is urgently needed. This study utilizes CHARLS 2015–2020 data to explore the relationship between CVAI and CVD risk in postmenopausal women, providing evidence for CVD prevention in this population.

Methods

1.1 Study Population

This study utilized data from the China Health and Retirement Longitudinal Study (CHARLS) project (http://charls.pku.edu.cn/). CHARLS targets middle-aged and elderly populations aged 45 years and older, covering 150 counties/districts across 30 provinces/municipalities and 450 villages/communities nationwide. Survey content includes basic demographics, health status and function, and healthcare utilization. All participants provided written informed consent, and the study was approved by the Peking University Biomedical Ethics Review Committee (IRB00001052-11015) [15].

We selected data from three survey waves (2015, 2018, and 2020), with 2015 serving as baseline. The inclusion criteria were: (1) postmenopausal women aged ≥45 years in 2015 without CVD at baseline. Exclusion criteria were: (1) age <45 years in 2015; (2) missing data on age, BMI, waist circumference, triglycerides, HDL-C, stroke, or heart disease in 2015, or CVAI<0; (3) existing CVD (stroke or heart disease) in 2015; (4) not postmenopausal in 2015; (5) non-participation in either 2018 or 2020 follow-up, or missing CVD outcome data. A total of 4,743 individuals were included in the final analysis.

1.2 Variables

1.2.1 Exposure Factor: Baseline CVAI in 2015 served as the exposure factor. CVAI was calculated using the formula: CVAI = -187.32 + 1.71×age + 4.23×BMI + 1.12×waist circumference + 39.76×log₁₀(triglycerides) - 11.66×HDL-C [8].

1.2.2 Study Outcomes: Outcome events were determined based on participants' responses to the questions: "Has a doctor ever told you that you have heart disease (such as myocardial infarction, coronary heart disease, angina, congestive heart failure, or other heart diseases)?" and "Has a doctor ever told you that you have had a stroke (including cerebral infarction and cerebral hemorrhage)?" Participants who answered "yes" to either question during follow-up were considered to have experienced a CVD outcome event. To differentiate potential associations between CVAI and specific outcomes, heart disease and stroke were analyzed as secondary endpoints.

1.2.3 Covariates: Based on previous research, covariates included sociodemographic, lifestyle, and health status factors: age, education level, marital status, residence, hypertension, self-rated health, sleep duration, smoking, alcohol consumption, cognitive function, and life satisfaction.

1.3 Statistical Analysis

All statistical analyses were performed using R version 4.3.3. Continuous variables were expressed as mean±standard deviation, and categorical data were described using frequencies and percentages. Inter-group comparisons were conducted using chi-square tests or trend chi-square tests. CVAI was categorized into four groups (Q1–Q4) based on quartiles. Cox proportional hazards regression models were used to calculate hazard ratios (HR) and 95% confidence intervals (CI) to explore associations between CVAI quartiles and risks of CVD, heart disease, and stroke. Additionally, CVAI was included as a continuous variable in models, and restricted cubic spline (RCS) analysis was applied to examine dose-response relationships. The significance level was set at α=0.05.

Results

2.1 Baseline Characteristics

The 4,743 postmenopausal women had a mean age of 62.3±8.6 years and mean CVAI of 108.83±36.11 at baseline in 2015. CVAI quartiles were Q1≤84.78, Q2 (84.78–108.49], Q3 (108.49–132.01], and Q4>132.01. Significant differences across CVAI groups were observed for age, marital status, residence, hypertension prevalence, BMI, cognitive function, self-rated health, and alcohol consumption (P<0.05). No significant differences were found for education level, sleep duration, smoking, or life satisfaction (P>0.05) [TABLE:1].

2.2 Outcome Events

After 5 years of follow-up, total follow-up time accumulated to 21,957 person-years (average 4.62 years per person). The incidence of the primary outcome CVD was 20.2% (958/4,743), while secondary outcomes heart disease and stroke occurred in 13.6% (645/4,743) and 8.3% (393/4,743) of participants, respectively. Specifically, CVD incidence rates across CVAI quartiles Q1, Q2, Q3, and Q4 were 12.9% (96/742), 16.8% (210/1,249), 21.4% (313/1,463), and 26.3% (339/1,289), respectively. Heart disease incidence was 9.8% (73/742), 12.6% (157/1,249), 14.0% (204/1,463), and 18.1% (233/1,289), while stroke incidence was 4.2% (31/742), 6.3% (78/1,249), 8.6% (126/1,463), and 12.3% (158/1,289). Trend chi-square tests revealed that risks of CVD (χ² trend=64.21, P<0.001), heart disease (χ² trend=30.46, P<0.001), and stroke (χ² trend=50.28, P<0.001) all increased significantly with higher CVAI.

2.3 Association between CVAI and CVD Risk

Using CVD, heart disease, and stroke as outcome variables (yes=1, no=0) with follow-up time in years and CVAI quartiles as the exposure, multivariate Cox proportional hazards regression was performed adjusting for age, education, marital status, residence, hypertension, self-rated health, sleep duration, smoking, alcohol consumption, cognitive function, and life satisfaction. Results showed that risks of CVD, heart disease, and stroke increased progressively with CVAI level (P trend<0.001).

Model 1 (unadjusted) showed that Q3 and Q4 had higher heart disease risk than Q1 (P<0.05), while Q2, Q3, and Q4 had higher stroke and CVD risk (P<0.05). Model 2 adjusted for age (≤54=0, 55-64=1, 65-74=2, ≥75=3), education (below primary=0, primary=1, secondary=2, high school+=3), marital status (married=1, other=0), and residence (rural=0, urban=1), revealing significantly elevated risks in Q3 and Q4 for all three outcomes (P<0.05). Model 3 further adjusted for hypertension (no=0, yes=1), self-rated health (good=0, fair=1, poor=2), sleep duration (≤5h=0, 6-7h=1, 7-8h=2, ≥8h=3), smoking (quit=0, never=1, current=2), alcohol (no=0, yes=1), cognitive function (normal=0, impaired=1), and life satisfaction (good=0, fair=1, poor=2), with Q3 and Q4 maintaining significantly higher risks for all outcomes compared with Q1 (P<0.05) [TABLE:2].

2.4 Dose-Response Relationship

When CVAI was entered as a continuous variable, RCS curve fitting revealed linear dose-response relationships between CVAI and risks of CVD, heart disease, and stroke after adjusting for confounders (P for overall<0.05, P for nonlinear>0.05). As CVAI increased, the risks of CVD, heart disease, and stroke in postmenopausal women rose accordingly [FIGURE:1].

2.5 Subgroup Analysis

Stratified analyses by age, BMI, and residence showed that among women aged <65 years, those with BMI<24.35 kg/m², and rural residents, CVAI was significantly associated with risks of CVD, heart disease, and stroke (P<0.05). In women aged ≥65 years, elevated CVAI was associated only with CVD risk (P<0.05). Among urban residents, CVAI elevation was associated only with heart disease risk (P<0.05) [TABLE:3].

Discussion

This nationwide prospective cohort study examined the association between CVAI and CVD risk in postmenopausal women. The incidence rates of CVD (20.2%), heart disease (13.6%), and stroke (8.3%) in our postmenopausal sample exceeded those reported for the general middle-aged and elderly population (14.31%, 10.74%, and 4.71%, respectively) [16]. This disparity may be attributed to estrogen decline after menopause [13] and cumulative increases in CVD risk factors [17]. Estrogen enhances vascular tone and inhibits atherosclerosis development; its reduction diminishes vascular protection and increases CVD risk [18]. Furthermore, numerous studies indicate that postmenopausal women experience metabolic abnormalities including dyslipidemia, altered fat distribution, elevated blood pressure, and insulin resistance, with significant increases in total cholesterol, triglycerides, total cholesterol/HDL-C ratio, and HDL-C—all established CVD risk factors [13,19-20].

Multivariate Cox regression demonstrated a positive association between CVAI and CVD risk. RCS analysis confirmed linear dose-response relationships. Compared with Q1, Q3 and Q4 showed significantly increased risks of CVD, heart disease, and stroke (P<0.05), consistent with previous findings [11,21]. This association may be mediated by harmful substances released from abdominal adipose tissue. Research indicates that increased abdominal fat induces production of inflammatory cytokines and oxidized LDL [21], promotes insulin resistance and metabolic syndrome [22], and disrupts the balance between leptin and adiponectin expression. Elevated leptin or reduced adiponectin can induce oxidative stress and endothelial injury, increasing CVD risk [23]. Abdominal fat accumulation also triggers hypertension, hyperglycemia, and hyperlipidemia—established CVD risk factors [24].

Subgroup analyses revealed that the association between CVAI and CVD outcomes was particularly pronounced in women aged <65 years, those with lower BMI, and rural residents, while in urban women, CVAI was associated only with heart disease risk. These findings may reflect heterogeneous pathophysiological mechanisms or risk factor profiles across populations. The stronger predictive value of CVAI in the <65 age group may be explained by "cumulative effect" theory: older individuals often have multiple cardiovascular risk factors, and the cumulative effects of non-metabolic factors (e.g., arterial stiffness, chronic inflammation) may obscure CVAI's independent contribution [25-26]. In younger individuals, metabolic abnormalities may more directly reflect visceral fat's pathological impact with less interference from aging-related confounders. The association between CVAI and CVD risk in those ≥65 years may relate to age-related vascular degeneration.

The robust association in the lower BMI subgroup is particularly noteworthy. While obesity is traditionally considered a major CVD risk factor, our findings support the "metabolically obese normal weight" (MONW) hypothesis, which posits that metabolic status rather than BMI determines obesity-related health risks. MONW individuals may have normal BMI but abnormal visceral fat distribution leading to insulin resistance and metabolic dysregulation. Their reduced metabolic compensatory capacity means that equivalent visceral fat accumulation may cause more severe lipotoxicity, oxidative stress, and vascular endothelial injury, conferring CVD risk comparable to or exceeding that of overtly obese individuals [27]. Conversely, some individuals with higher BMI but without metabolic abnormalities may not experience increased CVD risk.

The pronounced association in rural populations highlights health disparities consistent with a "Matthew effect" in health outcomes. Rural areas face relative scarcity of medical resources, lower health literacy, and suboptimal dietary patterns, predisposing residents to visceral fat accumulation with limited opportunities for early intervention. Once metabolic abnormalities develop, delayed treatment may accelerate cardiovascular damage and increase CVD risk [28-29]. In contrast, urban populations with better access to healthcare and higher health awareness can utilize health screening services more frequently, enabling early intervention that mitigates visceral fat accumulation and reduces CVAI-related cardiovascular harm [30]. The association between CVAI and heart disease risk specifically in urban populations may result from combined effects of heavier air pollution and greater psychosocial stress.

This study has several limitations. First, its observational design precluded measurement of molecular markers such as leptin and adiponectin, limiting mechanistic insights. Second, we did not capture dynamic CVAI trajectories, preventing examination of how changes in CVAI over time relate to CVD risk. Future research should incorporate molecular biology studies of leptin, adiponectin, and inflammatory factors, as well as longitudinal CVAI trajectory analyses, to more comprehensively elucidate the relationship between CVAI and CVD.

In conclusion, elevated CVAI levels are positively associated with increased CVD risk in postmenopausal women, particularly among those with lower BMI and rural residents. Primary prevention of CVD in postmenopausal women should monitor CVAI levels, with enhanced surveillance and management of visceral obesity in these high-risk subgroups to reduce CVD incidence.

Acknowledgments

We thank the China Center for Economic Research at Peking University and the Institute of Social Science Survey at Peking University for providing the CHARLS data.

Author Contributions

FAN Zhuanzhuan contributed to study conception and design, data analysis, and manuscript writing. LI Wenting contributed to data collection and organization and manuscript revision. MA Guoliang was responsible for quality control and review of the article, overall supervision, and project administration.

Conflict of Interest

The authors declare no conflict of interest.

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Received: March 20, 2024; Revised: June 25, 2025

Edited by: JIA Mengmeng