Association analysis of metabolic score for visceral fat with cardiovascular disease: a prospective cohort study based on the China health and retirement longitudinal study

Introduction

Cardiovascular disease (CVD), a cluster of heart and vascular conditions, is a major contributor to global mortality (1,2). It was estimated that in 2019, CVD caused an estimated 9.6 million deaths among men and 8.9 million deaths among women globally, making up roughly one-third of all global fatalities. China is the country with the most CVD deaths worldwide (3). As China’s population aging accelerates and lifestyle changes take place, the CVD disease burden is expected to continue escalating, posing significant challenges to individuals, families, and society (4).

Obesity, particularly with visceral fat accumulation, is tightly linked with the onset and development of CVD (5). This association has been confirmed in different races and populations, representing a global health issue. For example, a study on African populations has shown that excessive visceral fat is also significantly associated with insulin resistance and metabolic syndrome, but its distribution is race-specific (6). At the same time, a study on Asian populations has further clarified that visceral fat accumulation is an independent driving factor for CVD risk, and significantly increases disease burden through mechanisms such as promoting inflammation (7). These pieces of evidence collectively indicate that the health hazards of visceral fat are universal. However, population characteristics should be considered when assessing and diagnosing visceral fat accumulation.

Waist circumference and body mass index (BMI) are two traditional obesity assessment indices and are widely employed in epidemiological studies. However, they may not be precise in differentiating between subcutaneous and visceral fat distribution, exhibiting unsatisfactory accuracy in predicting CVD (5,8). Imaging techniques such as computed tomography (CT) and magnetic resonance imaging (MRI) can precisely measure visceral fat area; however, their application in large-scale epidemiological surveys is restricted due to high costs, radiation exposure, and technical complexities (9,10). To overcome the above limitations, previous studies have proposed a novel indicator for evaluating abdominal fat and cardiac metabolic health—the metabolic score for visceral fat (METS-VF). At present, research has confirmed that METS-VF is closely related to various cardiovascular risk factors such as hypertension (11), arteriosclerosis (12), and left ventricular hypertrophy (13). In addition, a recent study based on the US National Health and Nutrition Examination Survey (NHANES) database further confirmed that METS-VF was linearly and positively correlated with the risk of atherosclerotic cardiovascular disease (ASCVD), and its predictive efficacy was better than the traditional visceral fat index (14). However, existing evidence mostly comes from cross-sectional studies or focuses on overall cardiovascular outcomes. Longitudinal studies specifically exploring the association between METS-VF and long-term risk of specific subtypes of CVD [i.e., heart disease (HD) and stroke] are still relatively scarce among the nationally representative middle-aged and older population in China.

Therefore, this study utilized data from the China Health and Retirement Longitudinal Study (CHARLS) to explore the association between METS-VF and the risk of CVD. Through this study, we aim to elucidate the predictive value of METS-VF for CVD risk in middle-aged and older populations. This study may provide important scientific evidence for early identification, personalized risk assessment, and the formulation of preventive strategies against CVD. We present this article in accordance with the STROBE reporting checklist (available at https://cdt.amegroups.com/article/view/10.21037/cdt-2025-472/rc).

Methods

Study population

The data for this prospective cohort study are sourced from CHARLS (http://charls.pku.edu.cn/en). CHARLS used a multi-stage probability proportional to size (PPS) method proportional to population size to select representative samples nationwide to reflect the social, economic, and health status of the middle-aged and older population in China (15). This study encompassed the national baseline survey conducted from May 2011 to March 2012, as well as three subsequent national follow-up assessments conducted in 2013, 2015, and from July to August 2018. The CHARLS study received ethical approval from the Peking University Biomedical Ethics Committee, with all participants providing written informed consent. This study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments.

The inclusion criteria for this study included: (I) people aged 45 years or older at baseline survey; (II) people having complete baseline biological indicators and physical examination data required for calculating METS-VF. Among the 17,705 participants initially recruited for the baseline, we excluded people based on the following criteria: (I) people aged less than 45 years (n=391); (II) people with undetermined METS-VF (n=9,021); (III) people with undetermined CVD status (n=506); (IV) people with CVD at baseline (n=1,195); and (V) people with missing covariate data (n=91). In total, 6,501 participants were enrolled in the analysis (Figure 1). For participants who have been lost to follow-up in a certain wave, CHARLS still attempts to track them in subsequent wave surveys. In this study, participants were included in the analysis as long as they had at least one valid CVD assessment data from three follow-up visits in 2013, 2015, or 2018. Individuals (n=506) with complete missing CVD follow-up data after baseline survey were excluded from the flowchart.

Figure 1 The flowchart of the screening process for enrolled individuals. CHARLS, China Health and Retirement Longitudinal Study; CVD, cardiovascular disease; METS-VF, Metabolic Score for Visceral Fat.

The calculation of METS-VF

The calculation formula for METS-VF stands as follows: METS-VF = 4.466 + 0.011 × [ln(METS-IR)]³ + 3.239 × [ln waist-to-height ratio (WHtR)]³ + 0.319 × sex + 0.594 × ln(age), wherein METS-IR = ln{[2 × fasting glucose (mg/dL) + fasting triglycerides (mg/dL)] × BMI (kg/m²)}/[ln{high-density lipoprotein cholesterol (mg/dL)]}, BMI = weight (kg)/[height (m)]², WHtR = waist circumference (cm)/height (cm). Gender served as a binary response variable (1 for males and 0 for females) (16).

CVD assessment

In this study, CVD encompassed two aspects: HD and stroke. The determination of HD was based on participants’ self-reporting having been diagnosed by a physician with coronary artery disease, congestive heart failure, myocardial infarction, angina pectoris, or any other heart condition. A stroke was defined as a self-reported stroke (17). The period between baseline and CVD onset was defined as the time elapsed between the first diagnosis of CVD or stroke and the baseline survey. In cases where exact onset times for CVD could not be obtained, an estimation method was employed: the calculation was (time of first reporting CVD in a survey wave − time of previous wave)/2 + (time of previous wave − baseline survey time) (18,19).

Covariates

The covariates included gender, drinking, marital status, age, sleep duration, smoking, hypertension, educational level, and diabetes. Alcohol consumption was categorized as “Yes” if participants reported drinking alcohol at least once a month on average in the past year, and “No” otherwise. The duration of sleep was determined by the question “During the past month, how many hours of actual sleep did you get at night (average hours for one night)” in the CHARLS questionnaire. Diagnosis of hypertension was based on any of the criteria: (I) diastolic blood pressure ≥90 mmHg or systolic blood pressure ≥140 mmHg; (II) self-reported hypertension diagnosed by a physician; or (III) recent use of antihypertensive medication (20). Diabetes was based on any of the following criteria: (I) self-reported having been diagnosed with diabetes by a physician in the past; (II) hemoglobin A1c (HbA1c) level ≥6.5%; (III) fasting plasma glucose ≥126 mg/dL; (IV) recent use of antidiabetic medication (21).

Statistical analysis

Analysis was processed by the R (version 4.1.3) software. The “tableone” package was utilized to construct a baseline table by the quartiles of METS-VF. Categorical variables were presented as sample counts and percentages, with Chi-squared tests employed for inter-group comparison. Continuous variables were depicted by mean values and standard deviations. The Kruskal-Wallis test was used for inter-group comparison. To evaluate the linkage between METS-VF and CVD risk, a Cox regression analysis was conducted utilizing the “survival” and “rms” packages. Three models with different degrees of adjustment for confounding factors were constructed: Model 1, without adjustments; Model 2, with adjustments made in age and gender; Model 3, with adjustments made in all confounders. To reveal the nonlinear linkage between METS-VF and CVD risk through restricted cubic splines (RCS), the RCS curve was plotted using the “ggrcs” package. Lastly, subgroup analysis and interaction testing were performed using the “jstable” package. Subgroup analysis was carried out according to age (<60, 60–74, ≥75 years), gender, smoking, hypertension and diabetes. Age stratification was divided according to the physiological stage characteristics of middle-aged and older people in China to assess the universality of METS-VF in different age groups. In addition, to enhance the robustness of the results, we conducted two sensitivity analyses. Firstly, we introduced the Fine-Gray competitive risk model, which considers death as a competitive event. Secondly, to avoid reverse causal bias, individuals diagnosed with CVD within 2 years after baseline were excluded from repeated analysis. All statistical tests are conducted using a two-sided test. The difference with P<0.05 was considered statistically significant.

Results

Baseline characteristics

A total of 6,501 participants were enrolled (3,420 females and 3,081 males, average age of 58.9±9.2 years). We grouped individuals by their METS-VF quartiles. Those with higher METS-VF levels typically exhibited older age, a higher proportion of single status, lower smoking rates, and a higher prevalence of hypertension and diabetes (Table 1).

Linkage between METS-VF and CVD risk

A total of 1,004 new cases of CVD were documented during the follow-up period, including 669 patients with only HD, 257 patients with only stroke, and 78 patients with both HD and stroke. Table 2 presents a linkage between the METS-VF and CVD risks. In the fully adjusted model, METS-VF had positive correlations with CVD [hazard ratio (HR) =1.59; 95% confidence interval (CI): 1.38–1.83, P<0.001], HD (HR =1.49, 95% CI: 1.26–1.75, P<0.001) and stroke (HR =1.90, 95% CI: 1.47–2.45, P<0.001). In comparison to the lowest quartile array of METS-VF, the CVD risks for the third and fourth quartiles showed a significant increase of 30% (HR =1.30, 95% CI: 1.07–1.58, P=0.009) and 77% (HR =1.77, 95% CI: 1.46–2.14, P<0.001). The results of the trend test had statistical significance (P for trend <0.05). The risks of HD and stroke also significantly increased with rising METS-VF quartiles, exhibiting a similar gradient trend.

We utilized RCS to fit the dose-response curve between METS-VF and CVD risk. The risks of CVD, HD, and stroke (P for overall <0.05) were elevated as the baseline METS-VF level increased. However, no significant nonlinear linkage was detected (P for nonlinear >0.05) (Figure 2).

Figure 2 The dose-response relationship between METS-VF and CVD (A), heart disease (B), and stroke (C). CI, confidence interval; CVD, cardiovascular disease; HR, hazard ratio; METS-VF, Metabolic Score for Visceral Fat.

Subgroup analysis

Subgroup analysis was conducted on participants according to gender, age and smoking status, hypertension and diabetes status at baseline to test the consistency and robustness of METS-VF and CVD risk association. In age stratification analysis, a positive linkage between METS-VF and CVD, HD, and stroke risks persisted in those aged 45–59 and 60–74 years. No such association was observed in individuals above the age of 75 years (P>0.05). Regarding sex stratification analysis, METS-VF exhibited a significant positive linkage with CVD and HD risks in both males and females, while its association with stroke was only significantly positively correlated in males, without statistical significance in females. Age and sex did not show a significant interaction in terms of their association with METS-VF in terms of CVD, HD, and stroke risks (P for interaction >0.05), indicating that the linkage between METS-VF and CVD risk is consistent in different age and gender groups (Figure 3).

Figure 3 Association between METS-VF and CVD, heart disease, stroke, stratified by age and sex. **, P<0.01; ***, P<0.001. CI, confidence interval; CVD, cardiovascular disease; HR, hazard ratio; METS-VF, Metabolic Score for Visceral Fat.

Further subgroup analysis results based on the status of smoking, hypertension, and diabetes at baseline are shown in Table S1. The results showed a positive correlation of METS-VF with the risk of CVD, HD, and stroke. The correlation was stable in both non-smokers and smokers, as well as in non-hypertensive and hypertensive patients (all P<0.05). In patients with diabetes, the intensity of association between METS-VF and CVD (HR =2.13, 95% CI: 1.47–3.08) and HD (HR =2.11, 95% CI: 1.36–3.28) is higher than that in non-diabetes subgroups. The interaction test showed that there was no significant difference in the association between METS-VF and CVD risk among people with different smoking status, hypertension, and diabetes status (all P for interaction >0.05), which further supported that the association had good robustness in subgroups with different metabolic and lifestyle characteristics.

Sensitivity analysis

To verify the robustness of the main results, we conducted two sensitivity analyses. Firstly, after considering all-cause mortality as a competitive risk, the Fine Gray model results showed that METS-VF (as a continuous variable) was still significantly positively correlated with CVD, HD, and stroke risk (HR and 95% CI detailed in Table S2). Secondly, to reduce potential reverse causality, we reanalyzed participants who had CVD events within the two years prior to follow-up and found a significant positive correlation between METS-VF and CVD risk (Table S3). Both analyses indicated that in the highest METS-VF quartile array (Q4, ≥7.05), the increased risks of CVD, HD, and stroke were most significant (all trend P values<0.001). These results collectively confirmed the robustness of the association between METS-VF and CVD risk.

Discussion

Based on longitudinal data representative of the nation, this work systematically evaluated the linkage between METS-VF and the risk of HD, CVD, and stroke in China’s middle-aged and older population. The findings revealed that METS-VF exhibited a positive linkage with all three variables, and this correlation was robust after adjustments for multiple variables. More importantly, the robustness of this association was confirmed by two sensitivity analyses, which considered the competitive risk of all-cause mortality and excluded early onset cases to control for reverse causality. In addition, subgroup analysis revealed consistency in this association across multiple subgroups, suggesting that METS-VF may function as a valid indicator for predicting CVD risk in the middle-aged and older population. At the same time, the population in this study was evenly distributed in some demographic characteristics, such as education level and sleep duration. The association between these demographic characteristics and METS-VF was weak, which provides a clear background for interpreting the core association mentioned above.

The results of this study suggest that METS-VF may be superior to traditional obesity assessment indicators in predicting CVD risk in middle-aged and older populations. Traditional indicators such as BMI and waist circumference mainly reflect the degree of overall obesity or central obesity and cannot effectively distinguish fat types and metabolic states (22,23). METS-VF, as a comprehensive evaluation tool, organically integrates information from multiple dimensions such as insulin resistance metabolic score, WHtR, age, and gender. This integration enables METS-VF to simultaneously capture metabolic abnormalities, visceral fat distribution, and age-related and gender-related risk changes, thereby more comprehensively reflecting the pathophysiological processes associated with visceral fat accumulation (24,25). Previous research evidence has shown that the predictive performance of METS-VF in predicting CVD risk is superior to indicators such as BMI and visceral obesity index (26,27). Therefore, BMI and waist circumference measurements are simple and widely used in clinical practice. Notably, METS-VF, as an equally accessible but more informative indicator, shows greater potential for early risk stratification and precise prevention of CVD.

Past research has extensively confirmed the linkage of METS-VF with CVD and the prognostic implication of METS-VF. In a cross-sectional study, the linkage between METS-VF and atherosclerosis was evaluated, revealing a significant positive correlation between the two when METS-VF was below 8.09; however, this association became insignificant after the threshold point was surpassed (12). Higher METS-VF levels are greatly linked with elevated risks of hypertension and left ventricular hypertrophy (11,13). A positive linkage between METS-VF and all-cause mortality, CVD mortality, and cancer mortality was detected in a prospective cohort study of 11,120 participants (28). Moreover, in individuals with diabetes, pre-diabetes, and normal glucose tolerance, the highest quartile arrays were linked with higher CVD and all-cause mortality risks compared to the lowest quartiles of METS-VF, and METS-VF performed better in predicting disease risk than traditional obesity indicators such as visceral adiposity index (VAI) and BMI (29). This study further validated the positive correlation between METS-VF and the incidence of new CVD events, HD, and stroke, suggesting that an elevation in visceral fat content is significantly associated with an increased risk of CVD.

As a composite indicator of visceral fat content, the linkage between METS-VF and CVD may involve multiple pathophysiological mechanisms. Adipose tissue with its heightened fat degradation activity can release free fatty acids and glycerol into the portal venous circulation, which are transported into the liver, thereby promoting insulin resistance and abnormal glucose metabolism (30,31). Insulin resistance not only serves as the core mechanism in type 2 diabetes but also holds an instrumental role in CVD (32,33). Insulin resistance promotes an increase in the activity of serum and glucocorticoid-regulated kinase-1 (SGK-1), as well as sodium flux, through serine/threonine kinases, ultimately contributing to the augmentation of vascular stiffness and the development of CVD (34). This also explains why the group with higher METS-VF levels in the baseline had a higher prevalence of hypertension and diabetes. Moreover, macrophages infiltrate enlarged adipocytes, resulting in increased pro-inflammatory cytokines such as tumor necrosis factor-alpha (TNF-α) and interleukin-6 (IL-6), while inhibiting the production of adiponectin (ADPN), which has anti-atherosclerotic effects (35,36). This inflammatory milieu further impairs endothelial function, thus exacerbating CVD.

In the subgroup analysis, a significant linkage was observed between METS-VF and CVD, HD, and stroke among those aged 45–75 years. However, no significant association was found in individuals over the age of 75 years. Comparing the baseline characteristics of different age groups in this study, we found that the average BMI of individuals aged ≥75 years (21.7 kg/m²) was lower than that of the younger group (45–59 years old: 23.9 kg/m²; 60–74 years old: 23.1 kg/m²). This disparity may reflect physiological changes associated with aging. The basal metabolic rate and dietary intake in individuals aged ≥75 years exhibit significant reductions (37,38). According to research, the prevalence of obesity and overweight among individuals aged 75 years or older in China is lower than that among those aged 45–75 years, suggesting a lesser fat-related metabolic burden in the elderly population (39). Furthermore, there may exist a “survivor bias” among those aged 75 years and above, with individuals who have survived to advanced age potentially possessing a more protective genotypic profile for CVD health, stronger metabolic adaptability, and healthier lifestyle choices that could mitigate the predictive power of METS-VF for CVD risk. As regards the gender disparities in the linkage between METS-VF and stroke risk, it may be induced by epidemiological characteristics and the protective effects of estrogen. A large-scale epidemiological survey in China covering 480,687 adults across 31 provinces revealed that the standardized prevalence of stroke among males aged ≥40 years was significantly higher than that among females (40). This disparity may partly be owed to the cardiovascular protective effects of estrogen prior to menopause. Estrogens possess anti-inflammatory and immunomodulatory functions that can reduce inflammatory factor expression, thereby lowering the risk of stroke (41). Consequently, METS-VF’s capacity to predict ischemic stroke in females may be less than that in males. Though the association strength varied across certain subgroups, the interaction analysis revealed that age and gender had no significant interaction effects on the relationship between METS-VF and CVD, HD, and stroke risks. This suggests that the association exhibits good robustness and consistency across different age and gender populations.

Regarding the gender difference between METS-VF and stroke risk, this study revealed that the association is more significant in males, which is consistent with the epidemiological feature of a generally higher incidence of stroke among males in China. For example, a large-scale epidemiological survey covering 480,687 adults in 31 provinces of China showed that the age-standardized prevalence of stroke in males aged 40 years and above was significantly higher than that in females (40). This gender difference may be caused by a combination of complex physiological and social behavioral factors. At the physiological level, endogenous estrogen in premenopausal women is believed to provide cardiovascular protection through various pathways such as promoting vasodilation, anti-inflammatory, antioxidant, and regulating lipid metabolism profiles (41). However, this protective effect will weaken with the decrease in estrogen levels after menopause. At the socio-cultural and behavioral levels, Chinese men typically have a higher exposure rate to risky behaviors. For example, significantly higher rates of smoking and alcohol abuse (42), as well as delayed medical treatment due to cultural beliefs (43), have led to poor management of pre-stroke diseases such as hypertension. Therefore, the gender differences observed in this study may be due to the overlapping or interactive effects between the visceral fat metabolism risk represented by METS-VF and the already higher behavioral risk factors in the male population, resulting in a stronger statistical association. However, interaction analysis showed that the modification effect was not significant, indicating that the positive correlation between METS-VF and stroke risk is essentially similar in both males and females. This result strongly supports METS-VF as a CVD risk warning indicator with universal and consistent predictive value in different gender populations.

The positive correlation between METS-VF and CVD risk is stable in smokers and non-smokers, as well as in hypertensive and non-hypertensive patients, and the interaction test is not significant. This finding reflects the universal applicability of this indicator in populations with different behavioral and metabolic characteristics. It is worth noting that the correlation strength (HR ≈2.1) of METS-VF with CVD and HD in diabetes patients is significantly higher than that in non-diabetes patients. We speculate that this phenomenon reveals a synergistic amplification effect between visceral fat accumulation and hyperglycemia in driving cardiovascular damage. Diabetes patients are usually in a vicious circle of insulin resistance, oxidative stress and chronic inflammation. In this context, a large amount of visceral fat releases excessive free fatty acids through the portal vein system, further exacerbating systemic insulin resistance and lipotoxicity. Stronger inflammatory reactions can be reactivated and a second strike is formed on the vascular endothelium, significantly accelerating the process of atherosclerosis (44-46). Therefore, for patients with diabetes, the management of visceral fat may have more important vascular protection significance than the simple control of blood sugar. Although an increase in correlation strength was observed in diabetes patients, the formal interaction test did not show statistical significance. This may be related to the limited sample size of subgroups or the essential similarity of the core pathophysiological pathways represented by METS-VF in different populations. In any case, the absence of significant interaction conveys a key clinical message: regardless of an individual’s smoking status, blood pressure, or blood glucose levels, METS-VF is a universal and undeniable CVD risk factor. This discovery reinforces the potential value of using METS-VF as a routine screening tool for populations with different risk characteristics.

However, there are still certain limitations in this work. The first and most important point is that there is a potential selection bias in this study. A considerable number of individuals were excluded from the CHARLS baseline participants due to missing blood biochemical indicators or physical examination data required for calculating METS-VF. This large-scale data loss may not occur randomly, and participants who can provide complete biological sample information may have higher health awareness or better socio-economic status, which may lead to systematic differences between the final included population and the excluded population. Therefore, the extrapolation of research results among the middle-aged and older population in China may be limited. Secondly, the determination of CVD and its subtypes is based on self-report by participants. Although it is a common method in large-scale social population health studies such as CHARLS, it still cannot completely avoid recall bias and misclassification. If such misclassification is nondifferential, it may weaken the observed association strength, indicating that the results of this study may underestimate the true relationship between METS-VF and CVD risk. Secondly, despite our efforts in adjusting multiple confounders within the model, we were unable to collect some crucial confounders, such as detailed dietary patterns, intensity, and frequency of physical activity. In theory, an unhealthy diet and sedentary lifestyle are both related to visceral fat accumulation and independent risk factors for CVD. If such factors are associated with higher METS-VF in the study population, the model’s failure to fully control their impact may lead to an overestimation of the METS-VF effect. Lastly, the sample for this study was derived from a Chinese population, whose metabolic characteristics, lifestyle, and genetic background may differ from those of other ethnicities, thereby potentially limiting the generalizability of the findings. In the future, research across various ethnicities and regions is required to further investigate the application value and related mechanisms of METS-VF in predicting CVD risk.

Conclusions

A strong positive linkage exists between METS-VF and the CVD risk in middle-aged and older adults. This association remains stable across subgroups of individuals with smoking, hypertension, and diabetes, though no significant correlation was found in participants aged over 75 years or in female participants with stroke risk.

Acknowledgments

We thank Valentin Fuster for his assistance in polishing our paper.

Footnote

Reporting Checklist: The authors have completed the STROBE reporting checklist. Available at https://cdt.amegroups.com/article/view/10.21037/cdt-2025-472/rc

Peer Review File: Available at https://cdt.amegroups.com/article/view/10.21037/cdt-2025-472/prf

Funding: This work was supported by the Ningbo Clinical Research Center Member for Emergency and Critical Diseases (grant No. 2024L003).

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://cdt.amegroups.com/article/view/10.21037/cdt-2025-472/coif). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The CHARLS study received ethical approval from the Peking University Biomedical Ethics Committee, with all participants providing written informed consent. This study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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