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The association between visceral fat metabolic score and stroke: mediation by declining kidney function

Diabetology & Metabolic Syndrome volume 17, Article number: 50 (2025) Cite this article

Abstract

Background

Stroke is a leading cause of mortality and disability worldwide. Metabolic Score for Visceral Fat (METS-VF), a metric of visceral obesity, has emerged as a novel predictor of metabolic diseases. However, its association with stroke remains unclear. This study investigates the relationship between METS-VF and the risk of stroke, as well as the potential mediating role of kidney function.

Methods

Data from the 1999–2020 National Health and Nutrition Examination Survey (NHANES) were analyzed, including 19,109 participants. Weighted logistic regression models were used to assess the association between METS-VF and stroke risk, with restricted cubic splines employed to explore their non-linear relationships. Mediation analysis examined the role of kidney function, measured by estimated glomerular filtration rate (eGFR). Subgroup and sensitivity analyses, including propensity score matching (PSM) and multiple imputations, were conducted to ensure the robustness of the findings.

Results

Higher METS-VF was significantly associated with an increased risk of stroke (OR = 2.78, 95% CI: 1.71–4.52, P < 0.001) after adjusting for multiple covariates. A non-linear relationship was observed, with stroke risk sharply increasing when METS-VF exceeded 7.00. Mediation analysis revealed that declining eGFR mediated 26.72% of the METS-VF-stroke association. Subgroup analysis indicated that the association was stronger in men (OR = 5.06, 95% CI: 2.80–9.12, P < 0.001) than in women (OR = 2.01, 95% CI: 1.03–3.92, P = 0.04, P for interaction = 0.01). Sensitivity analyses using PSM and multiple imputations confirmed the robustness of the results.

Conclusions

METS-VF is independently associated with stroke risk, showing a non-linear relationship, with a potential mediating role of declining kidney function.

Introduction

Stroke is a severe cerebrovascular disease, posing a significant threat to life and often leading to irreversible neurological impairments [1, 2]. With the aging population, the incidence of stroke continues to rise. Its high mortality and disability rates make stroke one of the most pressing global public health concerns [3,4,5]. Therefore, identifying modifiable and preventable risk factors of stroke is crucial for reducing the health burden worldwide.

The pathogenesis of stroke is complex, with obesity being recognized as one of the most critical modifiable risk factors [6, 7]. Obesity, particularly visceral obesity, significantly increases the risk of cardiovascular and metabolic diseases, including stroke, diabetes, and non-alcoholic fatty liver disease [8,9,10,11]. Although body mass index (BMI) and waist circumference (WC) are commonly used to assess obesity, they cannot differentiate between fat and muscle mass, limiting their accuracy in assessing fat-related metabolic risk [12,13,14]. Increasing evidence suggests that body composition and fat distribution are more precise predictors of metabolic health [15, 16]. Furthermore, while several visceral adipose tissue (VAT) surrogates, such as waist-to-height ratio (WHtR), waist-to-hip ratio, and visceral adiposity index (VAI), have been developed based on simple anthropometric measures, these surrogates only provide rough estimates of VAT content and do not capture its significant metabolic effects [17, 18]. The Metabolic Score for Visceral Fat (METS-VF) is an advanced index that incorporates triglyceride levels, high-density lipoprotein cholesterol, and the waist-to-height ratio (WHtR), adjusted for age and sex, providing a more comprehensive evaluation of visceral fat and its metabolic consequences [19]. However, its relationship with stroke remains unexplored.

Obesity is not only associated with metabolic diseases but may also contribute to the gradual decline in kidney function [20, 21]. Impaired kidney function, in turn, has been identified as an independent risk factor strongly linked to the occurrence of stroke [22, 23]. Furthermore, a growing body of evidence suggests that kidney function acts as a mediator between obesity and cardiovascular diseases, with some studies even highlighting it as the most significant mediating factor [24]. As a novel obesity index, METS-VF has been shown to correlate with impaired kidney function in previous studies. Thus, it is a plausible hypothesis that METS-VF influences stroke risk through kidney function as a mediator. However, this hypothesis remains underexplored, warranting further investigation.

Therefore, this study aims to utilize data from the 1999–2020 National Health and Nutrition Examination Survey (NHANES) to examine the relationship between METS-VF and stroke and to explore the potential mediating role of kidney function. This research seeks to provide scientific evidence for stroke prevention and early intervention.

Methods

Study design and data source

This study utilized data from the National Health and Nutrition Examination Survey (NHANES) spanning from 1999 to 2020. NHANES is a cross-sectional survey aimed at assessing the health and nutritional status of adults and children in the United States, employing a multistage probability sampling method to ensure national representativeness. Data were collected through interviews, physical examinations, and laboratory tests. All study protocols were approved by the National Center for Health Statistics (NCHS) Ethics Review Board, and informed consent was obtained from participants. Personal data were anonymized to protect confidentiality [25]. As the data used in this study are publicly available and de-identified, no additional ethical approval was required.

Study population

The study initially included 116,876 participants from the 1999–2020 NHANES dataset. After excluding 52,660 participants with missing stroke data and 37,922 participants missing METS-VF data, an additional 6,152 participants with missing covariate data were excluded. Specifically, the missing covariates included age (n = 0), gender (n = 0), race (n = 0), BMI (n = 0),poverty-to-income ratio (PIR, n = 2,441), education (n = 18), smoking status (n = 15), diabetes status (n = 636), systolic blood pressure (SBP, n = 803), diastolic blood pressure (DBP, n = 94), hypertension(n = 0), alcohol drinking status (n = 2,058), coronary artery disease (CAD, n = 75), and cancer history (n = 12). Furthermore, 62 participants missing glomerular filtration rate (eGFR) and 971 participants with missing fasting weight or weight equal to zero were excluded. After all exclusions, a final sample of 19,109 participants was included in the analysis (Fig. 1).

Fig. 1
figure 1

Flowchart of inclusion and exclusion criteria

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Assessment of stroke

Stroke status was self-reported. Participants were asked, “Has a doctor or other health professional ever told you that you had a stroke?” Those who answered “yes” were classified as having a hitory of stroke [26, 27].

Assessment of METS-VF

METS-VF was calculated using the following formula [28]:

$$\:\text{M}\text{E}\text{T}\text{S}-\text{V}\text{F}=4.466+0.011\left[{\left(\text{l}\text{n}\left(\text{M}\text{E}\text{T}\text{S}-\text{I}\text{R}\right)\right)}^{3}\right]$$
$$\:+3.239\left[{\left(\text{l}\text{n}\left(\text{W}\text{H}\text{t}\text{R}\right)\right)}^{3}\right]+0.319\left(\text{s}\text{e}\text{x}\right)+0.594\left(\text{l}\text{n}\left(\text{a}\text{g}\text{e}\right)\right)$$

METS-IR was calculated as:

$$\eqalign{& {\rm{METS}} - {\rm{IR}} \cr & = {\rm{ln}}\left( {{{\left( {2{\rm{*fasting}}\>{\rm{glucose}} + {\rm{fasting}}\>{\rm{triglycerides}}} \right) \times \>{\rm{BMI}}} \over {{\rm{high}} - {\rm{density}}\>{\rm{lipoprotein}}\>{\rm{cholesterol}}}}} \right) \cr} $$

WHtR was calculated by dividing waist circumference by height.

Covariates and mediator variable

Covariates included age, poverty-to-income ratio (PIR), race, BMI, education level, smoking and alcohol use, history of diabetes, hypertension, coronary artery disease (CAD), cancer, systolic blood pressure (SBP) and diastolic blood pressure (DBP). BMI was calculated by dividing weight by height squared. PIR reflects the ratio of household income to the federal poverty threshold. Smoking status was defined as having smoked at least 100 cigarettes in one’s lifetime, and alcohol use was defined as having consumed at least 12 alcoholic drinks in one’s lifetime. CAD and cancer histories were self-reported. Hypertension was defined by a doctor’s diagnosis or by blood pressure measurements (systolic ≥ 140 mmHg or diastolic ≥ 90 mmHg) or current antihypertensive medication use [29]. Diabetes was diagnosed based on a doctor’s report or laboratory findings that met the criteria of HbA1c ≥ 6.5%, fasting glucose ≥ 126 mg/dL, or OGTT glucose ≥ 200 mg/dL [30].

Estimated glomerular filtration rate (eGFR) was calculated using the CKD-EPI formula [31], which incorporates age, sex, race/ethnicity, and serum creatinine (SCr) levels to adjust for different populations.

Statistical analysis

The analysis accounted for NHANES’ complex survey design and applied appropriate weighting methods to ensure national representativeness. Continuous variables were expressed as means with standard errors, and categorical variables were reported as frequencies and percentages. The association between METS-VF and stroke was examined using weighted logistic regression models, with odds ratios (ORs) and 95% confidence intervals (CIs) reported. To explore potential nonlinear relationships, a weighted restricted cubic spline (RCS) model was employed. Subgroup analyses were conducted to examine potential moderating effects of covariates on the METS-VF–stroke association. Mediation analysis was performed to explore the potential mediating role of eGFR using the “mediation” package (version 4.5.0). Two sensitivity analyses were conducted to assess the robustness of the results. First, propensity score matching (PSM) was applied with 1:2 nearest neighbor matching using the “MatchIt” package (version 4.5.5) to control for confounding variables, and the association between METS-VF and stroke was re-evaluated using weighted logistic regression. Second, multiple imputations were used to handle missing covariate and mediator data, followed by reanalysis. Receiver Operating Characteristic (ROC) curves and the area under the curve (AUC) were generated and calculated using the “pROC” package (version 1.18.5). All statistical analyses were conducted using R software (version 4.2.2), with the “rms” package (version 6.8-1) used for weighting. A two-sided P-value of < 0.05 was considered statistically significant.

Results

Participant characteristics

Participants were divided into four quartile groups (Q1 to Q4) based on their METS-VF(Table 1). Baseline characteristics revealed that as METS-VF increased, the incidence of stroke significantly increased (P < 0.001), accompanied by a progressive decrease in eGFR (P < 0.001). Furthermore, both SBP and DBP showed significant increases with higher METS-VF quartiles (P < 0.001 for both). Additionally, participants in the highest quartile (Q4) were more likely to be aged ≥ 65 years, male, white, smokers, and have a higher prevalence of diabetes, hypertension, and CAD compared to the other groups. Other characteristics did not show significant differences across the groups.

Table 1 Baseline characteristics of participants
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Association between METS-VF and stroke

In the analysis of the association between METS-VF and stroke (Table 2), after adjusting for multiple covariates, METS-VF (both continuous and quartile forms) remained significantly associated with stroke risk. After adjusting for multiple covariates, METS-VF, analyzed as both a continuous variable and by quartiles, was significantly associated with stroke risk (Table 2). Participants in the highest METS-VF quartile had the highest stroke risk (OR = 3.18, 95% CI: 1.74–5.82, P < 0.001), compared to those in the lowest quartile.

Table 2 Associations between METS-VF and Stroke
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Non-linear relationship between METS-VF and stroke

Figure 2 illustrates a significant non-linear association between METS-VF and stroke risk (P for overall < 0.001, P for nonlinear < 0.001). Stroke risk increased progressively with higher METS-VF values, showing a sharp rise when METS-VF exceeded 7.00.

Fig. 2
figure 2

Restricted cubic spline (RCS) analysis of the association between METS-VF and stroke

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Diagnostic value of METS-VF and obesity indicators for stroke

METS-VF demonstrated the highest AUC for stroke prediction (0.687, 95% CI: 0.668–0.706), outperforming BMI, WHtR, VAI, and Cardiometabolic Index (CMI) (Fig. 3).

Fig. 3
figure 3

Diagnostic value of METS-VF and obesity indicators for strok

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Mediation analysis

A mediation analysis was conducted to examine the mediating role of low eGFR in the relationship between METS-VF and stroke (Fig. 4). Both direct and indirect effects were statistically significant, with the indirect effect accounting for 26.72% of the total effect.

Fig. 4
figure 4

Mediation analysis of the association between METS-VF and stroke

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Subgroup analysis

Figure 5 shows the association between METS-VF and stroke risk across subgroups. The trend was consistent with the main analysis. Subgroup analysis revealed a significant interaction by sex (P for interaction = 0.01). Men had a higher stroke risk (OR = 5.06, 95% CI: 2.80–9.12, P < 0.001) compared to women (OR = 2.01, 95% CI: 1.03–3.92, P = 0.04), suggesting that METS-VF may have a stronger effect on stroke risk in men.

Fig. 5
figure 5

Subgroup analysis of the association between METS-VF and stroke

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Sensitivity analysis

To assess the robustness of the findings, two sensitivity analyses were conducted. First, propensity score matching (PSM) was performed with 1:2 matching, resulting in well-balanced groups for stroke (n = 661) and control (n = 1,322) based on covariates. No significant differences in baseline characteristics were observed. However, METS-VF remained significantly higher in the stroke group (7.20 vs. 7.11, P = 0.03), suggesting a potential association with stroke risk (Table 3). The regression analysis after PSM (Table 4) showed that METS-VF, both continuous and quartile forms, remained significantly associated with stroke risk across different models, with a clear trend across quartiles (P for trend < 0.001). The second sensitivity analysis addressed missing covariate and mediator data using multiple imputations. The results for baseline characteristics, regression, and mediation analysis after multiple imputations were consistent with the main findings (see Supplementary Tables 1–2 and Supplementary Fig. 1).

Table 3 Participant characteristics based on propensity score matching results
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Table 4 Associations of METS-VF and Stroke based on propensity score matching
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Discussion

This study, utilizing NHANES data from 1999 to 2020, is the first to investigate the association between METS-VF and stroke risk. Our findings indicate a significant association between METS-VF and stroke, with a notable nonlinear relationship. This association was partially mediated by declining kidney function. Subgroup analyses confirmed the robustness of this positive association and revealed a moderating effect of sex. Additionally, sensitivity analyses using PSM and multiple imputations further validated the robustness of the results, providing new evidence for METS-VF as a predictive tool for stroke risk.

We found, for the first time, a significant association between METS-VF and stroke. Baseline comparisons revealed that higher METS-VF was associated with a higher prevalence of stroke. Individuals with higher METS-VF also had higher BMI, were older, and had a greater proportion of males, smokers, and individuals with diabetes and hypertension. Previous studies have confirmed that obesity, advanced age, male sex, diabetes, and hypertension are independent risk factors for stroke and are associated with its severity [32]. Obesity, particularly visceral fat accumulation, has been shown to correlate with stroke occurrence [33, 34]. Furthermore, individuals with higher METS-VF were more likely to have modifiable cardiovascular risk factors, such as smoking, diabetes, and hypertension, consistent with prior research. Interestingly, we found that participants with higher METS-VF tended to have lower education levels, aligning with earlier studies that found poor hypertension control in lower-educated populations, contributing to an increased stroke risk [35]. Therefore, this study underscores the link between METS-VF and various cardiovascular risk factors and supports the importance of early intervention in stroke prevention.

Our research also supports METS-VF as an effective marker for assessing stroke risk. METS-VF is a powerful tool for assessing stroke risk as it effectively reflects metabolic status. It had also been suggested that the vagus nerve regulates metabolic homeostasis and mediates gut-brain communication, with potential implications for ischemic stroke [36]. Additionally, the Controlling Nutritional Status (CONUT) score at admission is linked to outcomes after ischemic stroke, highlighting the importance of metabolic and nutritional indicators in stroke prognosis [37]. These data suggested that metabolic dysregulation may offer new therapeutic avenues for stroke treatment.

Obesity and visceral fat metabolism are closely related to kidney function and cardiovascular diseases. Previous studies have shown that the pathophysiological mechanisms of metabolism, cardiovascular health, and kidney function are interrelated and mutually reinforcing [38,39,40,41,42,43,44,45,46]. Furthermore, kidney function indicators are crucial for assessing obesity, metabolic abnormalities, and heart function [47, 48]. Prospective studies have found that obesity increases the risk of atherosclerotic cardiovascular disease (ASCVD), with kidney function being the strongest mediating factor in ASCVD risk [44]. Notably, METS-VF is significantly associated with impaired kidney function [43], suggesting that kidney function may mediate the impact of METS-VF on cardiovascular diseases. In this study, our mediation analysis found that kidney function decline mediated approximately 30% of the association between METS-VF and cardiovascular diseases. Additionally, we found a significant gender-modulating effect on the relationship between METS-VF and stroke risk, consistent with previous literature [45].

Despite the several strengths of this study, including the use of nationally representative data, extensive adjustment for confounders, and multiple sensitivity analyses, there are certain limitations. First, as this study is based on cross-sectional data, it cannot establish causality. Second, stroke assessment relied on participants’ self-reports, which may be subject to recall bias, and it did not distinguish between hemorrhagic and ischemic strokes. Future research should consider using a prospective design to further validate the predictive ability of METS-VF for stroke risk.

Conclusion

This study suggests that METS-VF is independently associated with stroke risk, with evidence supporting a non-linear relationship. Kidney function decline may partially mediate this association, and sex appears to play a moderating role, with men showing a potentially higher risk.

Data availability

No datasets were generated or analysed during the current study.

Abbreviations

METS-VF:

Metabolic Score for Visceral Fat

NHANES:

National Health and Nutrition Examination Survey

eGFR:

Estimated Glomerular Filtration Rate

BMI:

Body Mass Index

WC:

Waist Circumference

VAT:

Visceral Adipose Tissue

WHtR:

Waist-to-Height Ratio

VAI:

Visceral Adiposity Index

METS-IR:

Metabolic Score for Insulin Resistance

SCr:

Serum Creatinine

PIR:

Poverty-to-Income Ratio

CAD:

Coronary Artery Disease

RCS:

Restricted Cubic Splines

OR:

Odds Ratio

CI:

Confidence Interval

PSM:

Propensity Score Matching

AUC:

Area Under the Curve

ASCVD:

Atherosclerotic Cardiovascular Disease

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Acknowledgements

Not applicable.

Funding

This work was supported by the Guangzhou Science and Technology Plan Project (2023B01J1011), Guangdong Medical Research Foundation Project (B2023061), Guangdong Medical Research Foundation (A2024382), Guangdong Province Basic and Applied Basic Research Fund (2023B1515120088), GuangDong Basic and Applied Basic Research Foundation (2019B1515120044), Guangdong Provincial Bureau of Traditional Chinese Medicine Research Project (20231321), Scientific Research Start Plan of Shunde Hospital, Southern Medical University (SRSP2022016, SRSP2022012, SRSP2023017).

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Authors and Affiliations

  1. Department of Cardiology, Shunde Hospital, Southern Medical University (The First People’s Hospital of Shunde), NO. 1 Jiazi Road, Lunjiao, Shunde District, Foshan City, Guangdong, 528308, China

    Yue Cao, Weixing Wen, Hao Zhang, Weiwen Li, Guolin Huang & Yuli Huang

  2. The George Institute for Global Health, Faculty of Medicine, University of New South Wales, Sydney, Australia

    Yuli Huang

Authors
  1. Yue Cao
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  2. Weixing Wen
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  3. Hao Zhang
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  4. Weiwen Li
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  5. Guolin Huang
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  6. Yuli Huang
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Contributions

Y. Cao and W.X. Wen: Conceptualization, data curation, validation, visualization, and writing– original draft. Y. Cao, H. Zhang and W.W. Li: Investigation and validation. Y. Cao, G.L. Huang and Y.L. Huang: Validation and visualization. Y.L. Huang: Supervision and writing– review & editing. All authors have reviewed and approved the final manuscript and agreed to the author order.

Corresponding author

Correspondence to Yuli Huang.

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This study followed the guidelines of the Declaration of Helsinki and received approval from the NCHS Ethics Review Board. All participants provided written informed consent.

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The authors declare no competing interests.

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13098_2025_1608_MOESM1_ESM.docx

Supplementary Material 1: Supplement figure 1. Mediation analysis of the association between METS-VF and stroke after Multiple Imputation for Missing Covariates Supplement figure 1. Mediation analysis of the association between METS-VF and stroke after Multiple Imputation for Missing Covariates

13098_2025_1608_MOESM2_ESM.docx

Supplementary Material 2: Supplement table 1. Participant Characteristics by METS-VF Quartiles After Multiple Imputation for Missing Covariates.Supplement table 2. Associations of METS-VF and Stroke After Multiple Imputation for Missing Covariates. Supplement table 3. Collinearity Analysis

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Cao, Y., Wen, W., Zhang, H. et al. The association between visceral fat metabolic score and stroke: mediation by declining kidney function. Diabetol Metab Syndr 17, 50 (2025). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s13098-025-01608-9

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  • DOI: https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s13098-025-01608-9

Diabetology & Metabolic Syndrome

ISSN: 1758-5996

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