Abstract
Background
Metabolic score for visceral fat (METS-VF) as an effective marker of visceral obesity has been correlated with non-alcoholic fatty liver disease (NAFLD). This study aims to explore the correlation between METS-VF and both all-cause mortality and cardiovascular disease (CVD)-related mortality among individuals with NAFLD.
Methods
A cohort of 6,759 subjects diagnosed with NAFLD was selected from the NHANES during the period from 1999 to 2018. Within this cohort, the prognostic utility of METS-VF for predicting CVD-related and all-cause mortality was assessed.
Results
There was a total of 1254 all-cause deaths (18.6%) and 418 CVD-related deaths (6.2%) at a median follow-up for 9.3 years. Multivariate Cox regression analysis and restricted cubic splines analysis indicated that METS-VF can exhibit a positive non-linearly correlation with CVD mortality (HR: 4.15, 95% CI: 2.31–7.44, p < 0.001) and all-cause mortality (HR: 5.27, 95% CI: 3.75–7.42, p < 0.001), with an identified inflection point at 7.436. Subgroup analyses further revealed a stronger correlation between METS-VF and all-cause mortality among subjects without diabetes. Furthermore, the areas under the curve (AUC) for 1-, 3-, 5-, and 10-year survival rates were 0.756, 0.740, 0.747 and 0.746 for all-cause mortality, and 0.774, 0.751, 0.746 and 0.758 for CVD mortality, respectively, which performs better than the other obesity and IR related index.
Conclusion
Elevated METS-VF independently contributes to an increased risk of both all-cause and CVD mortality in individuals with NAFLD.
Clinical trial number
Not applicable.
Supplementary Information
The online version contains supplementary material available at 10.1186/s12876-025-03833-y.
Keywords: Visceral obesity, NAFLD, Insulin resistance, Obesity, METS-VF
Background
NAFLD is a prevalent chronic hepatic condition marked by the atypical accumulation of lipids within liver. Notably, NAFLD is distinguished by its absence of viral, alcoholic, or autoimmune etiologies [1]. It affects approximately one-third of the global population, thereby imposing significant healthcare and economic burdens [2]. Furthermore, the prevalence of NAFLD has been escalating across all regions worldwide in recent years [3]. In particular, the increasing incidence of NAFLD among younger demographics [4]. Approximately 20% of individuals diagnosed with NAFLD advance to metabolic dysfunction-correlated steatohepatitis (MASH), a condition that subsequently increases the likelihood of future development of liver cirrhosis [5]. Moreover, CVD represents the primary cause of mortality among individuals with NAFLD, underscoring its profound impact on both society and individuals [6]. The incidence of cardiovascular adverse events, such as myocardial infarction and stroke, is notably higher in individuals with NAFLD [7, 8]. Furthermore, it is important to recognize that the risk of CVD is not uniform across all individuals with NAFLD. Recent studies have highlighted the heterogeneity in cardiovascular risk among patients with metabolic dysfunction-associated steatosis liver disease (MASLD) [9], suggesting that factors such as the presence of metabolic syndrome, insulin resistance (IR), and visceral adiposity may further stratify the risk of cardiovascular events. This underscores the need for more precise risk assessment tools to identify high-risk subgroups within the NAFLD population.
Obesity is increasingly acknowledged as a significant global public health concern, linked to the prevalence of non-communicable diseases and premature mortality [10, 11]. Projections indicate that by 2035, 51% of the population will be affected by obesity or overweight, presenting substantial challenges to healthcare systems globally [12]. While BMI is a useful tool for identifying individuals at elevated risk for future diseases, it does not offer a comprehensive evaluation on adipose tissue distribution. Recent research indicates that the distribution of adipose tissues, particularly visceral fat, is a significant determinant of mortality and cardiovascular diseases [13–16].
METS-VF, derived from waist-to-height ratio (WHtR), METS-insulin resistance (METS-IR), gender, and age, represents a novel index for predicting visceral obesity [17]. Moreover, several studies have validated that METS-VF provides a more effective assessment on metabolic risk and is a reliable predictor for the onset of hypertension and diabetes [17–19]. To date, only two longitudinal studies have examined the correlation between METS-VF and all-cause mortality, as well as cardiovascular events, within the general population [20, 21]. However, there is a notable lack of research evaluating the efficacy of METS-VF in predicting all-cause and CVD-related mortality risks among individuals with NAFLD.
To address this gap in literature, data from NHANES were utilized to explore the correlation between METS-VF and both all-cause and CVD-related mortality in individuals with NAFLD.
Methods
Research subjects and design
NHANES, administered by NCHS [22], is an extensive research initiative aimed at evaluating the correlation among health promotion, disease prevention, and nutrition. This biennial survey is conducted by physical examinations, taking interviews, and a range of sections that include demographic, dietary, laboratory and examination data.
For the present study, data were collected from NHANES during the period from 1999 to 2018. Subjects aged 18 years old or above were included (n = 59204). Subjects were excluded based on the following criteria: Missing data on METS-VF (n = 34,913) or the fatty liver index (FLI) (n = 78), Missing data or positive test results for HCV/HBV infection (n = 454), absence of NAFLD (n = 9,762), heavy alcohol use (n = 7,044), and missing follow-up information (n = 5). Furthermore, 189 pregnant individuals were excluded due to potential alterations in waist circumference (WC) and blood lipid profiles. Finally, the samples in this study consisted of 6759 subjects (as shown in Fig. 1). To evaluate potential selection bias, a comprehensive comparison of baseline characteristics was conducted between the included and excluded cohorts. The baseline characteristics of NAFLD participants aged 18 and above based on inclusion and exclusion criteria are shown in Table S1. Demographic and clinical parameters, including age, gender, BMI, WC, WHtR and prevalence of diabetes and hypertension, both CVD and all-cause mortality demonstrated no statistically significant differences between the two groups.
Fig. 1.
Flowchart of the sample selection from the 1999–2018 NHANES
Covariates
The analysis incorporated a range of covariates, including demographic and socioeconomic factors (poverty-income ratio (PIR), age, marital status, gender, and race), lifestyle variables (physical activity levels, smoking status, BMI, and alcohol consumption), medical history (stroke, CHD, congestive heart failure, angina, and myocardial infarction), as well as laboratory test results (serum triglyceride, creatinine, urine albumin-to-creatinine ratio (UACR), ALT, uric acid, total cholesterol, AST, HDL-C, HbA1c, LDL-C). The definition of drinking and smoking was consistent with those previously reported [23]. The eGFR was calculated using the CKD-EPI formula [24]. CKD was defined according to current clinical guidelines as having a UACR exceeding 30 mg/g, an eGFR less than 60 mL/min/1.73 m², or both conditions [25, 26]. Participants were classified as having CVD if they had a documented medical history of one or more related conditions. BMI was classified into three groups: < 25, 25–30, and ≥ 30. Height was categorized into two groups: one group consisting of males ≥ 175 cm and females ≥ 160 cm, and the other group comprising males < 175 cm and females < 160 cm [27].
Definition of NAFLD
The well-established fatty liver index (FLI) was employed as a surrogate marker for NAFLD, derived from BMI, TG, WC, and GGT using the specified formula:
FLI=(e WC*0.053+loge (TG)*0.953+ loge (GGT) *0.718 + BMI*0.139–15.745) / (1 + e WC*0.053+loge (TG)*0.953+ loge (GGT) *0.718 + BMI*0.139–15.745)×100 [28].
FLI has demonstrated high accuracy in detecting hepatic steatosis, with an AUC of 0.84. NAFLD was suspected when FLI was ≥ 60, following the exclusion of other chronic liver diseases, such as viral hepatitis and excessive alcohol consumption.
Clinical and biochemical evaluation
METS-IR = BMI (kg/m²)×Ln [TG (mg/dL) + 2 × FPG (mg/dL)] / Ln [HDL-C (mg/dL)] [23];
METS-VF = 3.239 × [Ln (WHtR)]³+0.011 × [Ln (METS-IR)]³ + 0.319 × gender (female = 0, male = 1,) + 4.466+[Ln (Age) (year)] ×0.594 [17].
Visceral adiposity index (VAI) (male) = (TG (mg/dL)/1.03/18)×{WC/[1.88×BMI(kg/m2) + 39.68]×{1.31/18/[HDL-C (mg/dL)]};
VAI (female) = (TG (mg/dL)/0.81/18)×{WC/[1.89×BMI (kg/m2) + 36.58]}×{1.52/18/[HDL-C (mg/dL)]} [29].
TyG index = Ln[TG(mg/dL) ×FPG(mg/dL) /2] [30].
Ascertainment of all‑cause and CVD mortality
Data on CVD and all-cause mortality were sourced from the public use mortality files of NDI. The mortality of CVD was defined through the codes I13, I00-I09, I60-I69, I11, and I20-I51 of the International Classification of Diseases and Related Health Problems in the 10th edition (ICD-10). The observation period was determined to start from the date of examination at the mobile examination center and end at the occurrence of the death event or December 31, 2019, whichever occurred first.
Statistical analyses
Data were presented by the counts (percentages) for categorical variables and means ± standard deviation of normally distributed variables. CVD and all-cause mortality were evaluated in subjects stratified by various METS-VF quartiles, with the log-rank test to evaluate differences in Kaplan–Meier survival curves. Additionally, the Cox regression was conducted on the correlation between METS-VF and all-cause mortality and CVD mortality, respectively. Three models were employed in the analyses: Model 1 was unadjusted. Model 2 was adjusted for gender, BMI group, age group and race. Model 3, the final multivariable model, was given additional adjustments for drinking, smoking, LDL-C, moderate physical activities, CVD, PIR, education level, diabetes, CKD, hypertension, ALT, PIR, AST, marital status, and uric. The dose–response correlation between mortality outcomes and METS-VF was explored through restricted cubic spline (RCS) curves, with a particular focus on potential non-linearity. Moreover, subgroup analyses were conducted to ascertain whether the correlation between METS-VF and both CVD and all-cause mortality differed among subjects with varying characteristics. Data analysis was conducted by using R software and Free Statistics software, with statistical significance defined as a two-sided P of less than 0.05.
Results
Characteristics of research subjects
A total of 6759 subjects (at the mean age of 53.8 years old; 53.0% were males) were included in this study as the analytic samples. Over a median follow-up interval for 9.3 years (interquartile range: 5.0-14.1 years), 1254 subjects (18.6%) died, including 418 (6.2%) for cardiovascular causes. Table 1 delineates the baseline characteristics of subjects stratified by METS-VF. Subjects in the higher METS-VF quartiles, generally older, widowed or divorced, had a higher BMI, WC, all-cause mortality, CVD mortality, and a higher proportion of history of diabetes, CVD, and CKD, compared to those in the lower METS-VF quartile. Significant differences in biochemical indexes could be observed across METS-VF groups (as shown in Table 2). Specifically, the levels of ALT, albumin, eGFR, AST, and lipid parameters in the higher METS-VF quartiles were reduced compared to the lower METS-VF group. Conversely, individuals in the highest quartile had higher levels of TyG, and METS-IR (all p < 0.05).
Table 1.
Baseline characteristics of all participants according to METS-VF quartiles
| Characteristic | Overall N = 6759 |
Q1 N = 1690 |
Q2 N = 1689 |
Q3 N = 1690 |
Q4 N = 1690 |
P |
|---|---|---|---|---|---|---|
| Age, year | 53.8 ± 17.4 | 36.4 ± 14.2 | 52.2 ± 13.8 | 60.7 ± 13.6 | 65.8 ± 11.9 | < 0.001 |
| Gender, n (%) | < 0.001 | |||||
| Male | 3579 (53.0) | 760 (45) | 715 (42.3) | 796 (47.1) | 1308 (77.4) | |
| Female | 3180 (47.0) | 930 (55) | 974 (57.7) | 894 (52.9) | 382 (22.6) | |
| PIR | 2.48 ± 1.59 | 2.39 ± 1.62 | 2.57 ± 1.64 | 2.44 ± 1.56 | 2.51 ± 1.52 | 0.012 |
| Waist circumference, cm | 112.1 ± 12.6 | 103.2 ± 9.0 | 108.1 ± 9.1 | 113.7 ± 10.2 | 123.4 ± 12.0 | < 0.001 |
| BMI, kg/m2 | 34.1 ± 6.1 | 31.5 ± 4.6 | 32.8 ± 5.0 | 34.5 ± 5.7 | 37.5 ± 7.0 | < 0.001 |
| WHtR | 0.63 ± 0.08 | 0.57 ± 0.05 | 0.61 ± 0.06 | 0.65 ± 0.06 | 0.71 ± 0.08 | < 0.001 |
| Race, n (%) | < 0.001 | |||||
| Mexican American | 1286 (19.0) | 385 (22.8) | 365 (21.6) | 270 (16) | 266 (15.7) | |
| Other Hispanic | 559 (8.3) | 156 (9.2) | 127 (7.5) | 160 (9.5) | 116 (6.9) | |
|
Non-Hispanic White |
3067 (45.4) | 594 (35.1) | 716 (42.4) | 796 (47.1) | 961 (56.9) | |
| Non-Hispanic Black | 1435 (21.2) | 394 (23.3) | 372 (22) | 387 (22.9) | 282 (16.7) | |
| Other Race | 412 (6.1) | 161 (9.5) | 109 (6.5) | 77 (4.6) | 65 (3.8) | |
| Marital group, n (%) | < 0.001 | |||||
| Married or living with partner | 4383 (66.4) | 982 (61.8) | 1150 (69.4) | 1086 (64.8) | 1165 (69.3) | |
| Widowed or divorced | 1334 (20.2) | 170 (10.7) | 305 (18.4) | 449 (26.8) | 410 (24.4) | |
| Never married | 885 (13.4) | 436 (27.5) | 203 (12.2) | 140 (8.4) | 106 (6.3) | |
| Moderate activity, n (%) | < 0.001 | |||||
| Yes | 2345 (34.7) | 682 (40.6) | 632 (37.7) | 527 (31.4) | 504 (30.1) | |
| No | 4414 (65.3) | 1008 (59.4) | 1057 (62.3) | 1163 (68.6) | 1186 (69.9) | |
| Smoking status, n (%) | < 0.001 | |||||
| Current or ever | 2930 (44.9) | 482 (32.4) | 674 (40.5) | 802 (47.5) | 972 (57.5) | |
| Never | 3599 (55.1) | 1005 (67.6) | 992 (59.5) | 885 (52.5) | 717 (42.5) | |
| Drink status, n (%) | < 0.001 | |||||
| Current or ever | 3146 (58.5) | 667 (57.3) | 761 (54.5) | 791 (56.4) | 927 (65.4) | |
| Never | 2235 (41.5) | 498 (42.7) | 635 (45.5) | 612 (43.6) | 490 (34.6) | |
| Diabetes, n (%) | < 0.001 | |||||
| Yes | 1594 (23.6) | 129 (7.6) | 298 (17.6) | 490 (29) | 677 (40.1) | |
| No | 5164 (76.4) | 1561 (92.4) | 1391 (82.4) | 1200 (71) | 1012 (59.9) | |
| Hypertension, n (%) | < 0.001 | |||||
| Yes | 3362 (49.8) | 437 (26) | 759 (45) | 1028 (60.9) | 1138 (67.3) | |
| No | 3384 (50.2) | 1247 (74) | 926 (55) | 659 (39.1) | 552 (32.7) | |
| CVD, n (%) | < 0.001 | |||||
| Yes | 1038 (16.2) | 60 (4.2) | 163 (9.9) | 327 (19.7) | 488 (29.6) | |
| No | 5356 (83.8) | 1383 (95.8) | 1480 (90.1) | 1333 (80.3) | 1160 (70.4) | |
| CKD, n (%) | < 0.001 | |||||
| Yes | 1703 (25.2) | 184 (10.9) | 302 (17.9) | 515 (30.5) | 702 (41.5) | |
| No | 5056 (74.8) | 1506 (89.1) | 1387 (82.1) | 1175 (69.5) | 988 (58.5) | |
| CVD death, n (%) | < 0.001 | |||||
| Yes | 418 (6.2) | 26 (1.5) | 55 (3.3) | 126 (7.5) | 211 (12.5) | |
| No | 6341 (93.8) | 1664 (98.5) | 1634 (96.7) | 1564 (92.5) | 1479 (87.5) | |
| All cause death, n (%) | < 0.001 | |||||
| Yes | 1254 (18.6) | 83 (4.9) | 195 (11.5) | 386 (22.8) | 590 (34.9) | |
| No | 5505 (81.4) | 1607 (95.1) | 1494 (88.5) | 1304 (77.2) | 1100 (65.1) |
Values are mean ± SD or number (%). P < 0.05 was deemed significant. BMI, body mass index; PIR, poverty-income ratio; CVD, cardiovascular disease; CKD, chronic kidney disease
Table 2.
Baseline laboratory characteristics of all participants according to METS-VF quartiles
| Characteristic | Overall N = 6759 |
Q1 N = 1690 |
Q2 N = 1689 |
Q3 N = 1690 |
Q4 N = 1690 |
P |
|---|---|---|---|---|---|---|
| ALT, U/L | 28.6 ± 32.9 | 33.6 ± 58.7 | 28.2 ± 18.3 | 26.6 ± 17.9 | 25.9 ± 13.7 | < 0.001 |
| AST, U/L | 26.1 ± 23.6 | 28.2 ± 38.0 | 25.4 ± 12.3 | 26.0 ± 23.2 | 25.0 ± 9.7 | < 0.001 |
| Albumin, g/L | 41.6 ± 3.3 | 42.4 ± 3.4 | 41.7 ± 3.0 | 41.2 ± 3.2 | 40.9 ± 3.2 | < 0.001 |
| Uric acid, mmol/L | 357.4 ± 85.5 | 337.1 ± 77.7 | 344.4 ± 81.4 | 362.4 ± 84.9 | 385.8 ± 89.3 | < 0.001 |
| FPG, mg/dL | 118.9 ± 44.3 | 104.1 ± 31.6 | 114.6 ± 41.0 | 123.7 ± 47.1 | 133.1 ± 50.0 | < 0.001 |
| TC, mmol/L | 5.02 (4.34, 5.77) | 5.09 (4.45, 5.87) | 5.25 (4.55, 5.92) | 5.04 (4.40, 5.79) | 4.71 (4.03, 5.42) | < 0.001 |
| TG, mmol/L | 1.63 (1.15, 2.31) | 1.64 (1.12, 2.37) | 1.61 (1.15, 2.26) | 1.61 (1.14, 2.31) | 1.63 (1.17, 2.35) | 0.673 |
| HDL, mmol/L | 1.16 (0.98, 1.37) | 1.16 (1.00, 1.37) | 1.19 (1.01, 1.45) | 1.19 (1.01, 1.42) | 1.11 (0.93, 1.27) | < 0.001 |
| LDL, mmol/L | 2.97 (2.38, 3.62) | 3.05 (2.48, 3.65) | 3.15 (2.59, 3.75) | 2.97 (2.35, 3.65) | 2.71 (2.07, 3.36) | < 0.001 |
| eGFR, mL/min/1.73 m2 | 86.1 (70.6, 104.0) | 100.3 (85.1, 117.4) | 88.3 (74.5, 103.8) | 80.7 (65.9, 98.3) | 74.8 (60.5, 91.8) | < 0.001 |
| TyG | 9.03 ± 0.66 | 8.91 ± 0.63 | 8.99 ± 0.64 | 9.06 ± 0.67 | 9.15 ± 0.68 | < 0.001 |
| VAI | 3.12 ± 3.57 | 3.09 ± 2.93 | 3.05 ± 2.97 | 3.19 ± 4.75 | 3.15 ± 3.32 | 0.643 |
| METS-IR | 53.6 ± 10.3 | 48.8 ± 7.2 | 50.9 ± 8.3 | 54.0 ± 9.3 | 60.4 ± 12.0 | < 0.001 |
| METS-VF | 7.38 ± 0.35 | 6.91 ± 0.21 | 7.29 ± 0.07 | 7.52 ± 0.06 | 7.81 ± 0.13 | < 0.001 |
Values are mean ± SD or number (%). P < 0.05 was deemed significant. FPG, fasting plasma glucose; TC, total cholesterol; TG, triglyceride; HDL-c, High density lipoprotein cholesterol; LDL-c, Low density lipoprotein cholesterol; ALT, alanine aminotransferase; AST, aspartate transaminase; eGFR, estimated glomerular filtration rate; METS-VF, metabolic score for visceral fat; TyG, triglyceride-glucose index; VAI, visceral adiposity index; METS-IR, metabolic score for insulin resistance
Correlation between METS-VF and mortality in subjects with NAFLD
Kaplan-Meier survival curves indicated decreased all-cause and CVD-related survival in the higher METS-VF quartiles group (p < 0.001) (as shown in Fig. 2). To further evaluate the correlation between mortality and METS-VF, a Cox regression model was conducted. Table 3 presents the impact of METS-VF on all-cause and CVD mortality across various statistical models in individuals with NAFLD. In Model 3, METS-VF as a continuous variable demonstrated a positive correlation with CVD and all-cause mortality, with HRs and 95% CIs of 4.15 (2.31, 7.44) and 5.27 (3.75, 7.42), respectively. In the totally adjusted Model 3, HR and 95% CI for subjects in the METS-VF quartiles of Q2, Q3, Q4 were 1.46 (1.07, 1.99), 2.18 (1.59, 2.98), and 3.46 (2.47, 4.86), compared to the control group with Q1 group, respectively, for all-cause mortality (p < 0.001), and 1.01 (0.58, 1.76), 1.64 (0.95, 2.82), and 2.53 (1.41, 4.51), for CVD mortality (p < 0.001), respectively. RCS analysis demonstrated a non-linearly correlation between a higher METS-VF levels and an elevated risk of mortality (as shown in Fig. 3). With a two-piecewise Cox regression model, a threshold METS-VF value of 7.436 was identified (Table 4).
Fig. 2.
Kaplan–Meier curves depicting survival rate and the number (%) of a NAFLD population stratified by METS-VF groups. A. All-cause mortality. B. CVD mortality
Table 3.
Cox regression analyses for the association between all-cause mortality, CVD mortality and METS-VF
| subgroups | Model1 | Model2 | Model3 | |||
|---|---|---|---|---|---|---|
| HR (95%CI) | P-value | HR (95%CI) | P-value | HR (95%CI) | P-value | |
| All-cause mortality | ||||||
| METS-VF | 12.86 (10.64, 15.53) | < 0.001 | 9.60 (7.06, 13.05) | < 0.001 | 5.27 (3.75, 7.42) | < 0.001 |
| METS-VF (category) | ||||||
| Q1 | 1(Ref) | 1(Ref) | 1(Ref) | |||
| Q2 | 2.66 (2.06, 3.44) | < 0.001 | 1.49 (1.12, 1.98) | 0.006 | 1.46 (1.07, 1.99) | 0.018 |
| Q3 | 5.98 (4.72, 7.58) | < 0.001 | 2.74 (2.06, 3.65) | < 0.001 | 2.18 (1.59, 2.98) | < 0.001 |
| Q4 | 11.26 (8.94, 14.18) | < 0.001 | 5.14 (3.79, 6.97) | < 0.001 | 3.46 (2.47, 4.86) | < 0.001 |
| P for trend | 2.14 (2.02, 2.27) | < 0.001 | 1.80 (1.66, 1.96) | < 0.001 | 1.53 (1.40, 1.68) | < 0.001 |
| CVD mortality | ||||||
| METS-VF | 16.70 (11.97, 23.31) | < 0.001 | 10.75 (6.31, 18.31) | < 0.001 | 4.15 (2.31, 7.44) | < 0.001 |
| METS-VF (category) | ||||||
| Q1 | 1(Ref) | 1(Ref) | 1(Ref) | |||
| Q2 | 2.40 (1.50, 3.83) | < 0.001 | 1.15 (0.69, 1.91) | 0.586 | 1.01 (0.58, 1.76) | 0.977 |
| Q3 | 6.24 (4.09, 9.52) | < 0.001 | 2.3 (1.40, 3.79) | 0.001 | 1.64 (0.95, 2.82) | 0.074 |
| Q4 | 12.82 (8.52, 19.3) | < 0.001 | 4.52 (2.66, 7.67) | < 0.001 | 2.53 (1.41, 4.51) | 0.002 |
| P for trend | 2.30 (2.08, 2.56) | < 0.001 | 1.84 (1.59, 2.13) | < 0.001 | 1.48 (1.26, 1.74) | < 0.001 |
Model 1: None covariates were adjusted; Model 2: gender and age group, race, BMI group were adjusted; Model 3, gender, age group, race, drinking, BMI group, smoking, LDL-C, PIR, moderate physical activities, CVD, education level, diabetes, CKD, hypertension, ALT, AST, marital status, and uric were adjusted
Fig. 3.
Dose-response curve of METS-VF and all-cause mortality (A) and CVD mortality (B) using restricted cubic splines
Table 4.
Threshold effect analysis of METS-VF on mortality using the two-piecewise Cox regression model
| Adjusted HR (95% CI) | P value | |
|---|---|---|
| All-cause mortality | ||
| Inflection point | 7.436 | |
| METS-VF < 7.436 | 3.46 (1.48, 8.08) <0.001 | |
| METS-VF > 7.436 | 4.01 (2.46, 6.53) <0.001 | |
| Log likelihood ratio | 0.375 | |
| CVD mortality | ||
| Inflection point | 7.436 | |
| METS-VF < 7.436 | 2.43 (0.49, 12.1) 0.277 | |
| METS-VF > 7.436 | 2.69 (1.20, 6.03) 0.016 | |
| Log likelihood ratio | <0.001 | |
Subgroup analysis
As shown in Fig. 4, subgroup analyses indicated a correlation between METS-VF and CVD mortality remaining unaltered by subjects’ age, gender, BMI, height, history of diabetes, CVD, hypertension and CKD (p > 0.05). However, the correlation between all-cause mortality with a higher METS-VF was stronger among individuals without a history of diabetes (as shown in Fig. 4A).
Fig. 4.
Exploratory stratified analysis of the associations between METS-VF and All-cause(A) or CVD mortality(B)
Predictive capacity of METS-VF in correlation to all‑cause and CVD mortality among individuals with NAFLD
Time-dependent ROC analysis revealed AUC values for METS-VF of 0.756, 0.740, 0.747 and 0.746 for 1-year, 3-year, 5-year, and 10-year all-cause mortality, respectively (as shown in Fig. 5A). Additionally, the AUC values for METS-VF concerning CVD mortality were 0.774, 0.751, 0.746 and 0.758 for the same intervals (as shown in Fig. 5B). Furthermore, the predictive efficacy of utilizing METS-IR, VAI, TyG, and BMI was assessed independently for predicting both all-cause and CVD mortality in individuals with NAFLD. The findings demonstrated the predictive capability of METS-IR, VAI, TyG, BMI for mortality markedly inferior to that of METS-VF across intervals of 1 year, 3 years, 5 years, and 10 years (as shown in Supplementary Fig. 1).
Fig. 5.
Time-dependent ROC curves and time-dependent AUC values of the METS-VF for predicting all-cause mortality (A, B) and CVD mortality (C, D)
Discussion
This study provides evidence that METS-VF as a novel measurement index for visceral fat was positively correlated with the all-cause and CVD related mortality in individuals with NAFLD. It performs better than the other obesity-related indices (BMI and VAI), IR indices (TyG and METS-IR) in predicting these outcomes. The correlation between METS-VF and all-cause as well as CVD mortality follows a non-linear pattern, with an identified inflection point at 7.436. Subgroup analyses revealed that the correlation between METS-VF and all-cause mortality was particularly pronounced in individuals without a history of diabetes.
Obesity is a highly heterogeneous disease, and the metabolic and CVD risks vary according to the distribution of body adipose tissue (subcutaneous and visceral) [31]. In conditions of positive energy balance, surplus free fatty acids are initially stored subcutaneously, leading to the enlargement of subcutaneous adipose tissue (SAT) through both adipocyte hyperplasia and hypertrophy. Nevertheless, once the storage capacity of SAT is depleted, lipid accumulation shifts to visceral adiposity, which is primarily situated in the abdominal cavity, as well as in other tissues and organs, notably the liver, pancreas, and heart [10]. A substantial body of literature has been published concerning the role of visceral adiposity in the progression and onset of NAFLD. For example, Chung et al. identified a dose-dependent and independent correlation between visceral fat and elevated ALT levels in healthy individuals [32]. Furthermore, Yu et al. corroborated in a histologically diagnostic study of NAFLD that visceral fat was independently linked to significant hepatic fibrosis and non-alcoholic steatohepatitis [33, 34]. Additionally, visceral fat has been demonstrated to be linked with heart failure [35], coronary artery disease [35], frailty [36], cognitive impairment [37], an elevated risk of all-cause mortality [38], and adverse prognostic outcomes [39, 40]. The findings from these studies indicate that precise evaluation of visceral fat may hold significant clinical relevance for the prognosis of individuals with NAFLD.
In comparison to direct imaging techniques for assessing visceral fat, such as magnetic resonance imaging and computed tomography, METS-VF emerges as an innovative alternative index that integrates experimental markers with anthropometric measurements. Prior research has indicated that METS-VF outperforms conventional surrogates of visceral fat, including lipid accumulation product, BMI, waist-to-hip ratio, visceral adiposity index, and waist [17, 41]. Furthermore, previous studies have demonstrated that METS-VF is independently correlated with the progression of diabetes [18], hypertension [19], coronary artery calcification [42], NAFLD [43], chronic kidney disease [44, 45], and hyperuricemia [46]. A cohort study conducted in China monitored 6,827 individuals over the age of 40 for a duration of five years, revealing a dose-response correlation between METS-VF and both all-cause mortality and cardiovascular events across various glucose tolerance statuses [21]. Jia et al. demonstrated that METS-VF serves as an independent risk factor for mortality among the general adult population in the United States [20]. This study represents the first investigation into the correlation between METS-VF and mortality in individuals with NAFLD.
METS-VF incorporates multiple metabolic and anthropometric parameters, including WHtR, METS-IR, gender, and age, making it a comprehensive tool for assessing visceral obesity and its associated metabolic risks. In contrast, the VAI primarily focuses on gender-specific relationships between TG, WC, BMI, and HDL-C. While VAI is useful for estimating visceral fat, it may not fully capture the complex interplay between IR and other metabolic factors [29]. The TyG index, which is calculated based on TG and glucose levels [47], is a simpler marker of IR. However, it does not account for body composition or gender differences, which are critical factors in the development of NAFLD and its complications. In our study, METS-VF demonstrated superior predictive performance compared to VAI and TyG, with higher AUC for both all-cause and CVD mortality (Fig. 5 and Supplementary Fig. 1). This suggests that METS-VF provides a more nuanced and accurate assessment of mortality risk in NAFLD patients.
Furthermore, stratified analysis demonstrated that the correlation between METS-VF and both all-cause and CVD-related mortality were consistent and persistent across most subgroups. Notably, the risk of all-cause mortality correlated with METS-VF was more pronounced in individuals without a history of diabetes, which may be attributed to the fact that subjects with normal glucose tolerance were at an elevated risk of developing severe metabolic disorders, such as dyslipidemia, hypertension, impaired renal function, and weight gain, as visceral fat increases, thereby leading to varied outcome events [48–51]. Importantly, visceral obesity is often neglected among the above population. Accordingly, METS-VF should be regarded as an important factor to predicting mortality, especially among the above population.
Notably, this study reveals a non-linear correlation between the METS-VF and mortality, with a turning point at 7.436, which may be attributed to compensatory mechanisms, such as augmented insulin secretion, which can sustain normoglycemia during the initial phases of obesity [52]. Nonetheless, when METS-VF surpasses the critical threshold, these metabolic mechanisms become insufficient, resulting in impaired metabolic regulation and an elevated risk of cardiovascular complications. These findings emphasize the importance for clinicians to pay particular attention to individuals with METS-VF > 7.436. Hence, it is advantageous to identify individuals at early risk of death by evaluating METS-VF, as this facilitates to implement preventive measures and treatment before the onset of the disease.
Recently, the term “Metabolic Dysfunction-Associated Steatotic Liver Disease” (MASLD) has been proposed to better reflect the underlying metabolic dysfunction associated with this condition [53]. This new definition emphasizes the importance of metabolic factors such as IR, obesity, and T2DM in the pathogenesis of liver steatosis, which aligns with our findings that METS-VF, an index of visceral fat, is positively correlated with mortality in individuals with NAFLD.
The precise mechanisms underlying the strong correlation between METS-VF and mortality in individuals with NAFLD remain incompletely understood. Nevertheless, it is recognized that factors such as IR, inflammation, endothelial dysfunction, and dysregulated glycolipid metabolism can play contributory roles [43, 54]. Considering that the majority of visceral fat tissue is drained through the portal vein, the hyperlipidemia correlated with visceral obesity exposes the liver to increased levels of free fatty acids and glycerol [48], leading to metabolic dysfunctions in liver, resulting in reduced insulin clearance (aggravated hyperinsulinemia), increased synthesis of triglyceride-rich lipoproteins, and enhanced hepatic glucose production [48]. Meanwhile, excessive fat accumulation in the liver promotes the generation of reactive oxygen species (ROS) in the mitochondria, resulting in large amounts of oxidized low-density lipoproteins in the circulation, leading to endothelial oxidative stress injury and impaired vascular function [10]. Additionally, visceral fat tissues are capable of producing inflammatory mediators, such as TNF-α and IL-6, which may further provoke low-grade systemic inflammation [55, 56], which can subsequently contribute to the development of atherosclerosis [57] and elevate cardiovascular risk [58], ultimately culminating in various cardiovascular diseases.
In this study, a large sample derived from national databases was utilized and an extended follow-up period was included, thereby strengthening the reliability of the research findings. These findings are of considerable reference value for understanding the prognosis of individuals with NAFLD. However, several limitations should be recognized. The study primarily did not account for the dynamic changes in METS-VF. Additionally, while METS-VF is a convenient and practical method for assessing visceral fat, it cannot differentiate between visceral fat and liver fat accumulation. Since liver fat accumulation in NAFLD often occurs independently of visceral obesity, future research is needed to evaluate liver fat content as a influencing factor [59]. Furthermore, the findings from NHANES are predominantly applicable to the American population due to variations in disease characteristics among different racial groups. Additionally, the NHANES database uses death certificates and the level of accuracy in coding cases of death is susceptible to human reporting errors including, but not limited to, inaccurate cause of death assessments, compilation errors, and demographic classification errors. Finally, the diagnosis of NAFLD mainly depends on the FLI, potentially introducing selection bias. Consequently, future research should address these limitations.
Conclusion
Elevated levels of METS-VF, an index of visceral fat, are correlated with an increased risk of mortality among individuals with NAFLD in the United States. METS-VF demonstrates superior predictive capability compared to other indices of obesity and IR in forecasting these outcomes. Therefore, it is possible to manage the prognosis of patients with NAFLD according to METS-VF and to facilitate the development of individualized treatment plans, including lifestyle modifications or pharmacological interventions, to enhance survival outcomes. In the future, the METS-VF methodology may be integrated with imaging technologies, such as ultrasound and MRI, to offer more comprehensive assessments of visceral fat content and associated metabolic risks.
Electronic supplementary material
Below is the link to the electronic supplementary material.
Acknowledgements
We would like to thank the NHANES database for providing the data source for this study.
Author contributions
HL designed the study; XPC, JKZ, XQJ and JX collected biochemical data; CMX drafted the manuscript. All authors read and approved the final manuscript.
Funding
This study was supported by the Wenzhou Municipal Science and Technology Bureau (Y20240231 to Jing Xu).
Data availability
The datasets generated and analysis during the current study are available in the NHANES, www.cdc.gov/nchs/NHANEs/.
Declarations
Ethics approval and consent to participate
The National Center for Health Statistics Ethics Review Board has approved the implementation of NHANES, and every participant signed informed consent.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Chunming Xie and Xianpei Chen contributed equally to this work.
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Associated Data
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Supplementary Materials
Data Availability Statement
The datasets generated and analysis during the current study are available in the NHANES, www.cdc.gov/nchs/NHANEs/.