Abstract
Introduction
A newly recognized condition, the cardiovascular-kidney-metabolic (CKM) syndrome, integrated disease spectrum encompassing interlinked renal, cardiovascular, and metabolic dysfunction. Visceral adiposity plays a pivotal role in driving this multisystem deterioration. Although surrogate markers such as the visceral adiposity index (VAI), metabolic score for visceral fat (METS-VF), body roundness index (BRI), and weight-adjusted waist index (WWI) have been proposed to estimate visceral fat burden, their relationship with advanced CKM syndrome remains poorly defined. This study sought to thoroughly examine the links between these indices and advanced CKM risk and to evaluate their ability to predict such risk.
Methods
In this study, we performed a cross-sectional analysis using data from the National Health and Nutrition Examination Survey (NHANES) spanning 1999–2018, which included 22,019 adults aged 20 years and older. We calculated four indices of visceral fat accumulation (VAI, METS-VF, BRI, WWI) and assessed their associations with advanced CKM syndrome through weighted multivariable logistic regression, restricted cubic spline modeling, and receiver operating characteristic (ROC) curve analysis. Subgroup analyses were also conducted to ensure the robustness of the findings, adjusting for demographic and lifestyle factors.
Results
Advanced CKM syndrome was present in 17.4% of participants. All four indices were significantly associated with advanced CKM (all p < 0.05), with METS-VF showing the strongest association (OR = 1.87, 95% CI: 1.51–2.30). Both METS-VF and VAI demonstrated a nonlinear increase in risk for advanced CKM, whereas BRI and WWI showed a positive linear relationship with the risk. Subgroup analyses provided additional evidence, confirming that these associations remained consistent across multiple population subgroups. In ROC analysis, METS-VF demonstrated the highest predictive accuracy (AUC = 0.79), followed by WWI (AUC = 0.73), outperforming traditional markers such as body mass index and waist circumference.
Conclusion
Elevated VAI, METS-VF, BRI, and WWI levels have been significantly linked to advanced CKM syndrome. METS-VF and WWI, as simple and noninvasive markers, show strong predictive capacity and may serve as effective tools for early detection and intervention in clinical settings.
Keywords: Cardiovascular-kidney-metabolic syndrome, Lipid accumulation, Cross-sectional study, NHANES
Introduction
CKM syndrome, as formally proposed by the American Heart Association (AHA) [1], represents a conceptual framework that integrates the complex pathophysiological interplay among cardiovascular disease (CVD), chronic kidney disease (CKD), and metabolic dysregulation involving glucose and lipid homeostasis. This integrative model underscores the synergistic effects of these conditions on systemic organ damage and adverse clinical outcomes. The CKM framework classifies the syndrome into five stages (0–4), with stage ≥1 indicating the presence of CKM and stage ≥3 defining advanced CKM. The growing global burden of CKM syndrome is largely driven by the increasing prevalence and co-occurrence of its core components. CVD has shown a rising prevalence and now stands as the primary contributor to both global mortality and disability [2]. In 2021, the global prevalence of diabetes among individuals aged 20–79 was estimated at 10.5%, and it is projected to increase to 12.2% by 2045. It is important to note that type 2 diabetes constitutes the overwhelming majority of cases within this group [3]. As of 2017, it was estimated that 9.1% of the global population suffers from CKD [4]. These conditions are not only widespread but also frequently coexist, creating complex pathophysiological interactions that substantially elevate the risk of cardiovascular complications and all-cause mortality – placing an immense burden on healthcare systems [5, 6]. According to a national study, almost 90% of persons in the USA satisfied the requirements for CKM diagnosis, and approximately 15% were in advanced stages – figures that have remained unchanged since 2011 [7]. Such findings highlight the critical need for new and more trustworthy biomarkers to help identify people who are at risk for advanced CKM syndrome.
Obesity has been rising at an alarming pace in recent years. Nowadays, more than 70% of US individuals are categorized as overweight or obese [8]. It is commonly known that this risk factor is linked to a number of metabolic disorders, including type 2 diabetes, hypertension, and dyslipidemia, and for its contribution to vascular complications, including CVD and CKD [9, 10]. Increasing evidence suggests that fat distribution, rather than overall adiposity, serves as a more precise predictor of metabolic risk [11, 12]. Visceral adipose tissue that is metabolically active produces more free fatty acids, thus amplifying cardiovascular risk [13]. In this context, we incorporated four validated indices – VAI, METS-VF, BRI, and WWI – into our analysis [14–17]. These metrics provide a nuanced assessment of visceral and abdominal fat accumulation, addressing the limitations of conventional anthropometric measures such as WC. Compared with traditional markers, these indices offer enhanced accuracy in characterizing body fat distribution and visceral adiposity. In recent years, VAI, METS-VF, BRI, and WWI have emerged as reliable and valuable tools for evaluating visceral fat accumulation and distribution patterns. Compared with traditional markers, these indices provide a more accurate assessment of adipose tissue dysfunction and risks associated with visceral fat. They have shown strong predictive power for cardiovascular events, metabolic syndrome, and all-cause mortality, especially in populations with impaired glucose metabolism or central obesity [18–25].
Although interest in visceral adiposity indices has grown in recent years, their association with the chance of developing advanced CKM syndrome remains insufficiently investigated. We used information from the 1999–2018 NHANES in this research, to explore the independent associations of four distinct visceral fat indicators with advanced CKM syndrome. We also assessed their diagnostic performance to determine which index most effectively identifies individuals at high risk for advanced CKM syndrome.
Methods
Study Design and Population
Data used in this study came from the National Center for Health Statistics (NCHS), a component of the Centers for Disease Control and Prevention (CDC), which conducted the publicly available NHANES, a cross-sectional survey. In order to get a nationally representative sample across survey waves, NHANES uses a multistage, stratified probability sampling technique. The NCHS Research Ethics Review Board granted ethical clearance for the study procedure, and each participant provided signed informed permission.
Data from 10 NHANES cycles from 1999 to 2018 were analyzed, encompassing an initial cohort of 101,316 individuals. Following the removal of individuals under the age of twenty, 55,081 subjects were retained for further analysis. We then excluded those with missing data for key variables used to calculate VAI, METS-VF, BRI, and WWI (including WC, BMI, triglycerides [TC], high-density lipoprotein cholesterol [HDL-C], and fasting blood glucose [FBG]), resulting in 25,224 valid cases. After removing cases with incomplete CKM-related data, our final analytic cohort included 22,019 participants. The distribution across CKM stages was as follows: stage 0: 2,077 participants, stage 1: 4,092 participants, stage 2: 12,026 participants, stage 3: 1,294 participants, stage 4: 2,530 participants (Fig. 1). For the purposes of this analysis, the designation of advanced CKM syndrome corresponded to stages 3 and 4, representing 17.37% of the total sample.
Fig. 1.
Study flowchart.
Defining and Categorizing the Stages of CKM Syndrome
Based on previously published definitions and the availability of relevant variables in the NHANES dataset, this study classified CKM syndrome into five stages [6]: stage 0 encompassed individuals without any CKM-related risk factors. Stage 1 referred to those with excessive or dysfunctional adiposity in the absence of additional metabolic risk factors or CKD. Stage 2 included individuals who had developed additional metabolic abnormalities or were classified as having moderate-to-high risk CKD. The existence of subclinical CVD risk equivalents, such as a projected 10-year CVD risk of ≥20% based on the AHA PREVENT equations [26] or extremely high-risk CKD according to Kidney Disease: Improving Global Outcomes (KDIGO) guidelines [27], was used to define stage 3. Stage 4 included participants with established clinical CVD. Comprehensive staging criteria are detailed in the online supplementary methods (for all online suppl. material, see https://doi.org/10.1159/000547533).
Exposure Factors Measurement
This study identified four indices related to visceral adiposity – VAI, METS-VF, BRI, and WWI – designated as exposure variables. The formulas used to calculate each of these indices are illustrated in Figure 2. Laboratory values (HDL-C, TG, and FBG) were obtained from the NHANES laboratory datasets, and the anthropometric measures (WC, height, weight, BMI) came from the Examination Data section.
Fig. 2.
Equations for calculating the four distinct indices related to visceral fat accumulation.
Covariates
To guarantee robustness, the analysis was corrected for a number of covariates: sex, age (years), education level (less than high school, high school or above), marital status (married/living with partner, widowed/divorced/separated, never married), race (Mexican American, other Hispanic, non-Hispanic white, non-Hispanic black, other race), and poverty income ratio (low <1, medium 1.1–3, high >3); lifestyle behaviors: smoking status (current, former, never) and alcohol use, with individuals consuming 4–5 drinks per day categorized as drinkers; laboratory markers: blood urea nitrogen (BUN), serum uric acid (SUA), alanine aminotransferase (ALT), and aspartate aminotransferase (AST).
Statistical Analysis
For all statistical studies, R software (version 4.4.3) was used. Given the complex sampling design of NHANES, sample weights (WTSAF4YR/WTSAF2YR) were applied using the “survey” package, following official NHANES guidance for fasting data. Missing covariates were imputed using multiple imputation via the “mice” package. For categorical variables, baseline characteristics were expressed as counts (percentages) and for continuous variables as means (standard deviations). Group differences were assessed using weighted chi-square tests and ANOVA, respectively. Associations between the four visceral fat indices (VAI, METS-VF, BRI, WWI) and advanced CKM syndrome were examined using weighted multivariable logistic regression, with three models: model 1: unadjusted, model 2: adjusted for demographic variables, model 3: further adjusted for lifestyle and laboratory variables (smoking, alcohol, SUA, BUN, ALT, AST). Variance inflation factors were used to assess multicollinearity (online suppl. Tables 1–8). In order to explore possible nonlinear dose-response correlations, RCS models were utilized, with knots positioned at the 5th, 35th, 65th, and 95th percentiles of the exposure distribution. For subgroup analysis, participants were categorized by age, sex, marital status, poverty income ratio (PIR), smoking, and drinking habits. The predictive ability of the four visceral adiposity indicators, BMI, and WC were assessed and compared using receiver operating characteristic (ROC) curves. The study provided information on the sensitivity, specificity, appropriate cutoff values, and area under the curve (AUC). Statistical significance was defined as p < 0.05.
Results
Characteristics of Study Participants
Among the NHANES 1999–2018 fasting subsample, 22,019 US adults aged 20 years or older were examined. Table 1 indicates that 3,824 patients (17.37%) were found to have advanced CKM syndrome. The average age of the research sample was 46.71 ± 16.73 years, with a relatively balanced sex distribution – 48.29% were male. In the full study cohort, the mean values for VAI, METS-VF, BRI, and WWI were 2.04 (±2.65), 6.20 (±0.64), 5.29 (±2.28), and 10.93 (±0.82). Participants with advanced CKM syndrome were typically older, more likely male, non-Hispanic white, less educated, widowed/divorced/separated, more prone to smoke or drink, and their PIR was lower. They also had higher BMI and waist circumference and elevated levels of systolic blood pressure, SCR, SUA, BUN, AST, FBG, and glycated hemoglobin A1c (HbA1c). Furthermore, participants with advanced CKM syndrome exhibited significantly higher values of VAI, METS-VF, BRI, and WWI (each p < 0.001).
Table 1.
Baseline characteristics of participants by CKM from the NHANES, 1999–2018
| Character | Overall (n = 22,019) | Advanced CKM syndrome (stages 3 or 4) | p value | |
|---|---|---|---|---|
| no (n = 18,195) | yes (n = 3,824) | |||
| Age, mean±SD, years | 46.71±16.73 | 43.84±15.10 | 67.27±13.00 | <0.001 |
| Sex, n (%) | <0.001 | |||
| Male | 10,634 (48.29) | 8,418 (46.27) | 2,216 (57.95) | |
| Female | 11,385 (51.61) | 9,777 (53.73) | 1,608 (42.05) | |
| Race, n (%) | <0.001 | |||
| Mexican American | 3,974 (18.05) | 3,493 (19.20) | 481 (12.58) | |
| Other Hispanic | 1,865 (8.20) | 1,617 (8.89) | 248 (6.49) | |
| Non-Hispanic white | 9,870 (44.82) | 7,727 (42.47) | 2,143 (56.04) | |
| Non-Hispanic black | 4,320 (19.62) | 3,581 (19.35) | 739 (19.33) | |
| Other race | 1,990 (9.31) | 1,777 (10.09) | 213 (5.56) | |
| Education status, n (%) | <0.001 | |||
| Less than high school | 5,929 (26.93) | 4,506 (24.77) | 1,423 (37.21) | |
| High school or above | 16,090 (73.07) | 13,689 (75.23) | 2,401 (62.79) | |
| Marital status, n (%) | <0.001 | |||
| Married or living with a partner | 13,595 (61.74) | 11,373 (62.51) | 2,222 (58.11) | |
| Widowed/divorced/separated | 4,689 (21.30) | 3,280 (18.03) | 1,409 (36.85) | |
| Never married | 3,735 (16.96) | 3,542 (19.46) | 193 (5.04) | |
| PIR, n (%) | <0.001 | |||
| ≤1 | 4,577 (20.79) | 3,731 (20.51) | 846 (22.12) | |
| 1.1–3 | 9,213 (41.84) | 7,316 (40.21) | 1,897 (49.61) | |
| >3 | 8,299 (37.37) | 7,148 (39.28) | 1,081 (28.27) | |
| Alcohol, n (%) | <0.001 | |||
| Yes | 3,510 (15.94) | 2,715 (14.92) | 795 (20.79) | |
| No | 18,509 (84.06) | 15,480 (85.08) | 3,029 (79.21) | |
| Smoking, n (%) | <0.001 | |||
| Current | 4,570 (20.75) | 3,847 (21.14) | 723 (18.91) | |
| Former | 5,506 (25.01) | 3,999 (21.98) | 1,507 (39.41) | |
| Never | 11,943 (54.24) | 10,349 (56.88) | 1,594 (41.68) | |
| Hypertension, n (%) | <0.001 | |||
| Yes | 7,498 (34.05) | 4,953 (27.22) | 2,545 (66.55) | |
| No | 14,521 (65.95) | 13,242 (72.78) | 1,279 (34.45) | |
| Diabetes, n (%) | <0.001 | |||
| Yes | 3,998 (18.16) | 2,318 (12.74) | 1,680 (43.93) | |
| No | 18,021 (81.84) | 15,877 (87.26) | 2,144 (56.07) | |
| CKD, n (%) | <0.001 | |||
| Yes | 3,680 (16.71) | 1,879 (10.33) | 1,801 (47.10) | |
| No | 18,339 (83.29) | 16,316 (89.67) | 2,023 (52.90) | |
| MetS, n (%) | <0.001 | |||
| Yes | 8,733 (39.66) | 6,534 (35.91) | 2,199 (57.51) | |
| No | 13,286 (60.34) | 11,661 (64.09) | 1,625 (42.49) | |
| BMI, mean±SD, kg/m2 | 28.81±6.69 | 28.71±6.69 | 29.58±6.68 | <0.001 |
| WC, mean±SD, cm | 98.78±16.40 | 97.97±16.31 | 104.58±15.82 | <0.001 |
| SBP, mean±SD, mm Hg | 121.68±16.68 | 120.09±15.15 | 133.07±21.92 | <0.001 |
| DBP, mean±SD, mm Hg | 70.03±11.94 | 70.47±11.29 | 66.90±15.44 | <0.001 |
| TC, mean±SD, mg/dL | 193.93±41.55 | 195.29±40.89 | 184.20±44.78 | <0.001 |
| HDL-C, mean±SD, mg/dL | 54.32±16.23 | 54.68±16.22 | 51.77±16.01 | <0.001 |
| LDL-C, mean±SD, mg/dL | 114.16±35.69 | 115.65±35.21 | 103.47±37.22 | <0.001 |
| SCR, mean±SD, mg/dL | 0.88±0.33 | 0.85±0.20 | 1.09±0.76 | <0.001 |
| SUA, mean±SD, mg/dL | 5.45±1.39 | 5.39±1.36 | 5.90±1.54 | <0.001 |
| BUN, mean±SD, mg/dL | 13.25±5.23 | 12.69±4.26 | 17.28±8.66 | <0.001 |
| ALT, mean±SD, U/L | 25.69±21.41 | 25.95±20.95 | 23.83±24.37 | <0.001 |
| AST, mean±SD, U/L | 25.32±19.20 | 25.27±19.25 | 25.71±18.83 | <0.001 |
| FBG, mean±SD, mg/dL | 104.98±29.44 | 102.92±26.83 | 119.74±40.90 | <0.001 |
| HbA1c, mean±SD, % | 5.58±0.91 | 5.50±0.83 | 6.12±1.21 | <0.001 |
| VAI, mean±SD | 2.04±2.65 | 1.97±2.39 | 2.54±4.05 | <0.001 |
| METS-VF, mean±SD | 6.20±0.64 | 6.13±0.64 | 6.68±0.43 | <0.001 |
| BRI, mean±SD | 5.29±2.28 | 5.16±2.25 | 6.19±2.28 | <0.001 |
| WWI, mean±SD | 10.93±0.82 | 10.84±0.80 | 11.54±0.74 | <0.001 |
CKD, chronic kidney disease; MetS, metabolic syndrome; BMI, body mass index; WC, waist circumference; SBP, systolic blood pressure; DBP, diastolic blood pressure; TC, total cholesterol; HDL-C, high-density lipoprotein cholesterol; LDL-C, low-density lipoprotein cholesterol; SCR, serum creatinine; SUA, serum uric acid; BUN, blood urea nitrogen; ALT, alanine aminotransferase; AST, aspartate aminotransferase; FBG, fasting blood glucose; HbA1C, glycated hemoglobin A1c; VAI, visceral adiposity index; METS-VF, metabolic score for visceral fat; BRI, body roundness index; WWI, weight-adjusted waist index.
Associations between VAI, METS-VF, BRI, WWI and Advanced CKM Syndrome
To investigate the relationships between visceral adiposity indicators and advanced CKM syndrome, three multivariable logistic regression models were created. As shown in Table 2, all four indices (VAI, METS-VF, BRI, and WWI) were strongly correlated in model 1 with the probability of advanced CKM syndrome. These associations remained significant after partial (model 2) and full (model 3) adjustment (all p < 0.05). In model 3, METS-VF showed the strongest association with advanced CKM syndrome. The risks of acquiring advanced CKM syndrome were shown to be 87% higher for every unit rise in METS-VF (95% CI: 1.51–2.30; p = 0.019). To enhance interpretability and robustness, all indices were further categorized into quartiles (Q1–Q4), where Q1 was designated as the reference group. Participants in the highest VAI quartile (Q4) had 110% higher odds of developing advanced CKM syndrome than those in the lowest quartile (95% CI: 1.63–2.70; p < 0.001). For METS-VF, those in Q4 had 100% higher odds than Q1 (95% CI: 1.44–2.79; p < 0.001). For BRI, the top quartile was associated with 104% increased odds (95% CI: 1.60–2.62; p < 0.001). The strongest association was observed for WWI, where individuals in Q4 had 170% higher odds of advanced CKM syndrome compared to Q1 (95% CI: 2.07–3.52; p < 0.001).
Table 2.
Association of VAI, METS-VF, BRI, WWI, and advanced CKM syndrome
| | Model 1 | Model 2 | Model 3 | |||
|---|---|---|---|---|---|---|
| OR (95% CI) | p value | OR (95% CI) | p value | OR (95% CI) | p value | |
| VAI | 1.06 (1.04, 1.08) | <0.001 | 1.08 (1.05, 1.11) | <0.001 | 1.07 (1.04, 1.09) | <0.001 |
| VAI (quartile) | ||||||
| Quartile 1 | Reference | | Reference | | Reference | |
| Quartile 2 | 1.31 (1.10, 1.57) | 0.003 | 1.21 (0.96, 1.51) | 0.105 | 1.21 (0.95, 1.53) | 0.114 |
| Quartile 3 | 1.73 (1.48, 2.02) | <0.001 | 1.42 (1.16, 1.73) | <0.001 | 1.41 (1.13, 1.76) | 0.002 |
| Quartile 4 | 2.33 (1.97, 2.75) | <0.001 | 2.23 (1.79, 2.79) | <0.001 | 2.10 (1.63, 2.70) | <0.001 |
| METS-VF | 11.22 (9.17, 13.70) | <0.001 | 1.84 (1.52, 2.24) | <0.001 | 1.87 (1.51, 2.30) | 0.019 |
| METS-VF (quartile) | ||||||
| Quartile 1 | Reference | | Reference | | Reference | |
| Quartile 2 | 2.65 (1.93, 3.62) | <0.001 | 1.20 (0.82, 1.74) | 0.347 | 1.25 (0.85, 1.82) | 0.257 |
| Quartile 3 | 5.17 (3.92, 6.80) | <0.001 | 1.31 (0.94, 1.82) | 0.110 | 1.35 (0.97, 1.89) | 0.074 |
| Quartile 4 | 18.13 (14.17, 23.26) | <0.001 | 1.95 (1.43, 2.66) | <0.001 | 2.00 (1.44, 2.79) | <0.001 |
| BRI | 1.19 (1.16, 1.21) | <0.001 | 1.14 (1.11, 1.18) | <0.001 | 1.14 (1.10, 1.18) | <0.001 |
| BRI (quartile) | ||||||
| Quartile 1 | Reference | | Reference | | Reference | |
| Quartile 2 | 2.19 (1.81, 2.65) | <0.001 | 1.14 (0.90, 1.46) | 0.281 | 1.18 (0.92, 1.50) | 0.195 |
| Quartile 3 | 3.16 (2.60, 3.84) | <0.001 | 1.33 (1.06, 1.67) | 0.013 | 1.40 (1.11, 1.77) | 0.006 |
| Quartile 4 | 4.41 (3.66, 5.32) | <0.001 | 2.01 (1.61, 2.52) | <0.001 | 2.04 (1.60, 2.62) | <0.001 |
| WWI | 2.95 (2.74, 3.17) | <0.001 | 1.61 (1.46, 1.78) | <0.001 | 1.56 (1.40, 1.75) | <0.001 |
| WWI (quartile) | ||||||
| Quartile 1 | Reference | | Reference | | Reference | |
| Quartile 2 | 3.08 (2.41, 3.94) | <0.001 | 1.57 (1.19, 2.07) | 0.001 | 1.60 (1.21, 2.12) | 0.001 |
| Quartile 3 | 6.18 (4.91, 7.78) | <0.001 | 1.92 (1.50, 2.46) | <0.001 | 1.93 (1.50, 2.48) | <0.001 |
| Quartile 4 | 13.94 (11.01, 17.4) | <0.001 | 2.78 (2.17, 3.56) | <0.001 | 2.70 (2.07, 3.52) | <0.001 |
Model 1: unadjusted.
Model 2: adjusted for sex, age, race, marital status, education, and PIR.
Model 3: adjusted for sex, age, race, marital status, education, PIR, smoking, alcohol, SUA, BUN, ALT, and AST.
Restrained Cubic Spline Regression Analysis
To better understand how visceral fat accumulation relates to advanced CKM syndrome, we conducted a regression analysis using RCS. In order to strengthen the validity of our results, we eliminated the lowest and highest 2.5% of values for each visceral adiposity index. Figure 3 shows the dose-response connections between each adiposity index and the chance of developing advanced CKM syndrome. Upon full adjustment for potential confounders, we found statistically significant associations for all four indices (each p overall < 0.001). Notably, both the BRI and WWI showed linear relationships with advanced CKM syndrome (with p nonlinear = 0.209 for BRI and p nonlinear = 0.310 for WWI), while VAI and METS-VF exhibited significant non-linear associations (both p nonlinear < 0.001). Elevations in each of the four indices were clearly associated with an increased probability of advanced CKM syndrome. The inflection points indicating increased risk were identified as 1.934 for VAI, 6.470 for METS-VF, 8.811 for BRI, and 11.869 for WWI.
Fig. 3.
RCS analysis with multivariate-adjusted associations of VAI, METS-VF, BRI, and WWI with advanced CKM syndrome.
Subgroup Analysis
To assess differences in the relationships between each of the four visceral adiposity indices and the possibility of advanced CKM syndrome across various demographic groupings, subgroup analyses were conducted (Fig. 4). With the exception of the other race subgroup in the VAI analysis, all indices remained significantly associated with advanced CKM syndrome risk across the remaining subgroups (p < 0.05), consistently acting as risk indicators. Interaction testing revealed significant modification effects by age for VAI (p for interaction = 0.019), by sex for METS-VF (p = 0.004), by age for BRI (p < 0.001), and by both sex and age for WWI (p < 0.001 for both). None of the additional stratified variables exhibited significant interaction effects, indicating the associations were largely consistent across subgroups (p for interaction > 0.05).
Fig. 4.
Subgroup analysis for the association between the VAI, METS-VF, BRI, WWI, and advanced CKM syndrome. All models were adjusted for the aforementioned covariates, excluding those used for stratification. Results are reported as weighted odds ratios along with their corresponding 95% confidence intervals. a VAI. b METS-VF. c BRI. d WWI.
ROC Curve Analysis
To evaluate visceral adiposity’s predictive significance for advanced CKM syndrome in more detail, we evaluated the performance of four visceral fat indices along with WC and BMI. AUCs were as follows: VAI, 0.58 (95% CI: 0.57–0.59); METS-VF, 0.79 (95% CI: 0.78–0.80); BRI, 0.63 (95% CI: 0.62–0.64); WWI, 0.73 (95% CI: 0.72–0.74); BMI, 0.53 (95% CI: 0.52–0.54); and WC, 0.62 (95% CI: 0.61–0.63), as shown in Figure 5 and Table 3. A higher AUC denotes stronger predictive performance. Among these, METS-VF showed the highest predictive accuracy for advanced CKM syndrome, followed by WWI, with optimal thresholds of 6.64 and 11.14. All pairwise comparisons of AUC revealed significant differences (all p < 0.05 by DeLong test).
Fig. 5.
ROC curves of VAI, METS-VF, BRI, WWI, WC, BMI in relation to advanced CKM syndrome.
Table 3.
ROC analysis results
| Index | AUC | 95% CI | Cutoff point | Specificity | Sensitivity |
|---|---|---|---|---|---|
| VAI | 0.58 | 0.57, 0.59 | 1.41 | 0.64 | 0.49 |
| METS-VF | 0.79 | 0.78, 0.80 | 6.64 | 0.76 | 0.68 |
| BRI | 0.63 | 0.62, 0.64 | 4.84 | 0.50 | 0.71 |
| WWI | 0.73 | 0.72, 0.74 | 11.14 | 0.62 | 0.72 |
| WC | 0.62 | 0.61, 0.63 | 94.00 | 0.44 | 0.74 |
| BMI | 0.53 | 0.52, 0.54 | 24.61 | 0.29 | 0.77 |
Discussion
This is the first study that we are aware of that examines the relationships between four anthropometric indices associated with visceral adiposity and advanced CKM syndrome in a nationally representative cohort of adults in the USA. Analysis of cross-sectional data from the 1999–2018 NHANES revealed that 17.37% of US adults satisfied the criteria for advanced CKM syndrome. Across unadjusted, partially adjusted, and fully adjusted weighted logistic regression models, consistently, higher levels of VAI, METS-VF, BRI, and WWI were associated with an increased chance of developing advanced CKM syndrome. Individuals in the highest quartiles of all indices exhibited significantly greater odds of advanced CKM syndrome compared to those in the bottom quartiles. In addition, the RCS analysis indicated nonlinear dose-response patterns for VAI and METS-VF, while BRI and WWI exhibited linear associations with disease risk. Subgroup analyses further indicated that the associations for WWI and BRI were stronger in participants under 60 years of age and that METS-VF and WWI had more pronounced effects among males. Finally, ROC curve analyses demonstrated that METS-VF and WWI were the most effective predictors of advanced CKM syndrome in this population.
The ROC curve analysis identified METS-VF as the most robust predictor of advanced CKM syndrome. As a relatively novel and comprehensive metric, METS-VF combines indicators of body composition (height, waist circumference, BMI), glucose metabolism (FPG), insulin resistance (METS-IR), and lipid metabolism (TG, HDL-C), while taking age and sex into account. Previous research has demonstrated that METS-IR is a valid indicator of fat distribution [28] and mounting data point to insulin resistance as a key factor in the development of both CVD and CKD [29–31]. Inappropriate renin-angiotensin-aldosterone system (RAAS) activation is one such explanation, which contributes significantly to both cardiovascular and renal injury in patients with metabolic dysfunction, including obesity [32]. In addition, insulin resistance promotes hypertension, endothelial dysfunction, and metabolic dysregulation [33–37], reinforcing the notion that METS-VF offers superior predictive performance over traditional indices in identifying individuals at risk for advanced CKM syndrome. In our logistic regression analysis, people in the top METS-VF quartile had double the prevalence of advanced CKM syndrome as people in the bottom quartile. The RCS curve revealed a distinct inflection point: prior to this threshold (METS-VF = 6.470), the chance of getting advanced CKM syndrome steadily rose; beyond it, the risk rose sharply. These findings are consistent with earlier studies. For example, a prospective study with 41,756 participants reported a 1,023% higher frequency of CVD events in the top quartile of METS-VF relative to the bottom [21]. In addition, Tourn et al. [38] reported that METS-VF values of 6.4 for men and 6.5 for women represent critical cutoff points for identifying elevated visceral fat accumulation. In our study, the optimal cutoff value determined by ROC analysis was 6.64. We therefore recommend that individuals exceeding this threshold receive closer clinical monitoring and undergo timely assessments of cardiovascular, renal, and metabolic health as they are at significantly higher risk of advanced CKM syndrome. Subgroup analyses further suggest that METS-VF warrants particular attention in men, potentially due to sex hormone- related differences in fat distribution and metabolism [39, 40].
The WWI, a novel index of body fat distribution, also demonstrated strong predictive value for advanced CKM syndrome. Derived from waist circumference and body weight, WWI serves as a more reliable indicator for evaluating central obesity. Research has consistently shown that WWI is negatively correlated with muscle mass and positively correlated with fat mass, as measured by various techniques [41, 42]. In contrast, BMI does not account for the distribution of fat, and although WC is a measure of abdominal fat, it does not capture other important factors, such as overall body weight – limitations that have been widely acknowledged in the literature [43, 44]. Accordingly, WWI offers a more accurate representation of body composition, capturing both muscle and fat distribution, and has outperformed BMI and waist circumference in predicting metabolic disorders across multiple studies [45, 46]. Numerous metabolic diseases and hypertension have been positively correlated with WWI, according to a large body of research [47, 48]. However, its role in predicting advanced CKM syndrome had not been previously investigated. Our results show that a strong correlation exists between increasing WWI levels and a greater chance of advanced CKM syndrome. Moreover, our multivariable logistic regression analysis revealed that VAI and BRI were also significantly linked with the likelihood of developing advanced CKM syndrome. These results further support the strong connection between visceral fat accumulation and the development of advanced CKM. While VAI and BRI showed relatively modest predictive performance compared to other indices, they still outperformed traditional single-dimension indicators such as WC and BMI.
The link between visceral fat accumulation and the likelihood of developing advanced CKM syndrome is likely driven by multiple interrelated pathophysiological mechanisms. One of the key features of visceral obesity is a state of chronic low-grade inflammation, driven by adipocyte hypertrophy, macrophage infiltration, and elevated secretion of pro-inflammatory cytokines, including TNF-α and IL-6, may lead to metabolic dysfunction [49, 50]. This inflammatory environment promotes the formation of type 2 diabetes by aiding in the development of insulin resistance [37]. The interplay, inflammation, insulin resistance, and obesity forms a vicious cycle that further amplifies the possibility of developing advanced CKM syndrome. In addition to its role in fat storage, visceral adipose tissue operates as an active endocrine organ, secreting both protective mediators (e.g., adiponectin, FGF-21) and deleterious factors (e.g., leptin, chemokines) [51, 52]. Evidence suggests that in populations with metabolic abnormalities or coronary artery disease, the secretion of adiponectin is impaired [53], while elevated leptin levels are frequently observed in patients with CKD [54, 55]. In CKM populations, this imbalance – reduced protective and increased harmful adipokines – may accelerate disease progression. These multifaceted and interwoven mechanisms emphasize the biological complexity of visceral fat in CKM syndrome and underscore the critical need for early identification and targeted intervention.
This study has several important strengths. First, to our knowledge, it is the first cross-sectional analysis to evaluate the relationship between visceral adiposity indices and the prevalence of advanced CKM syndrome, leveraging a large, nationally representative sample from the NHANES database. Second, by including four distinct indices – VAI, METS-VF, BRI, and WWI – we were able to comprehensively assess this relationship across multiple validated markers of visceral fat distribution, all of which yielded consistent results, reinforcing the robustness of our findings. Notably, our results suggest that the most reliable proxy for predicting advanced CKM syndrome could be METS-VF. WWI also proved to be a strong predictor, and thanks to its simplicity and easy accessibility, it holds significant potential in clinical practice. We propose that WWI be adopted as an initial screening tool in primary care settings. Patients with values above the recommended threshold could then be referred for standard metabolic evaluations, including blood glucose, lipid profile, and blood pressure. These assessments not only offer a basic overview of the individual’s metabolic health but also enable accurate calculation of the METS-VF index. For those with elevated METS-VF scores, further cardiovascular and renal assessments are advised. Such a tiered approach could support earlier detection and intervention, help contain healthcare costs, and ultimately improve long-term health outcomes. However, a number of restrictions must be noted. Initially, due to the inherent nature of cross-sectional studies, causal inferences cannot be established. Second, although the adiposity indices used in this study are convenient and widely validated, the lack of direct imaging modalities such as MRI or CT in the NHANES dataset limits our ability to confirm the accuracy of visceral fat estimation, which may result in misclassification bias. Third, CKM staging was assigned retrospectively using NHANES variables as proxies, which may not capture the full clinical nuance of the original staging framework. This could lead to potential misclassification in a subset of participants. Hence, future prospective cohort studies with more granular clinical and imaging data are need to confirm and build on these findings.
Conclusion
Our results provide compelling evidence of a significant positive association between visceral lipid accumulation indicators and advanced CKM syndrome among US adults. This relationship was consistently demonstrated across four validated indices – VAI, METS-VF, BRI, and WWI. Notably, METS-VF and WWI emerged as the most effective noninvasive surrogates for identifying individuals at elevated risk. These indices offer promising clinical utility for the early detection and management of advanced CKM syndrome, potentially enabling more timely interventions and improving patient outcomes.
Statement of Ethics
The NHANES data used in this study were publicly available and collected with approval from the National Center for Health Statistics Review Board, with all participants providing written informed consent beforehand. As such, no further ethical review or approval was required for this research.
Conflict of Interest Statement
The authors have no competing financial interests to declare.
Funding Sources
This research was supported by the Zhejiang Province Chinese Medicine Program (Grant No. 2023ZR063) and the General Project of the Medical and Health of Zhejiang Province (Grant No. 2023KY533; 2023ZL793).
Author Contributions
Z.F. and J.Y. designed the study, extracted and collated the data, and performed the analyses. Z.F., J.Y., and J.Q. wrote the manuscript. Q.L., R.H., and D.Z. made significant contributions to data collection and analysis, critically revised the manuscript, and participated in the interpretation of the findings. As corresponding authors, J.J. and Q.H. oversaw the study, contributed their intellectual expertise, and ensured the work’s accuracy and integrity. All authors have reviewed and approved the final version of the manuscript and accepted responsibility for every aspect of the work.
Funding Statement
This research was supported by the Zhejiang Province Chinese Medicine Program (Grant No. 2023ZR063) and the General Project of the Medical and Health of Zhejiang Province (Grant No. 2023KY533; 2023ZL793).
Data Availability Statement
This study was based on publicly accessible data from the National Health and Nutrition Examination Survey (NHANES), available at https://www.cdc.gov/nchs/nhanes/index.htm.
Supplementary Material.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
This study was based on publicly accessible data from the National Health and Nutrition Examination Survey (NHANES), available at https://www.cdc.gov/nchs/nhanes/index.htm.