Sage Journals: Discover world-class research

Abstract

Objective

Congestive heart failure (CHF) is a chronic disease marked by impaired cardiac function, and excess visceral fat has been reported to be associated with CHF. While the age-adjusted visceral adiposity index (AVAI) is a novel metric for evaluating visceral fat, its association with CHF has not been well elucidated.

Methods

The association between AVAI and CHF prevalence was assessed using cross-sectional data from the 2013–2018 National Health and Nutrition Examination Survey (NHANES). Logistic regression was used to assess the association between AVAI and CHF prevalence, with sequential adjustment for covariates. Subgroup analyses stratified by demographic variables and lifestyle factors were done to examine the robustness of the results. Lastly, the discriminative ability of VAI and AVAI in distinguishing individuals with and without prevalent CHF was evaluated using receiver operating characteristic (ROC) curve analysis.

Results

A total of 6,695 participants were included in the analysis, comprising 6,470 without CHF and 225 with CHF. Logistic regression revealed a significant positive association between AVAI and CHF prevalence (OR = 1.75, 95% CI: 1.62–2.89), which remained robust after adjusting for confounders (adjusted OR = 1.57, 95% CI: 1.31–1.87). In subgroup studies, AVAI was consistently associated with the prevalence of CHF. ROC analysis indicated that AVAI demonstrated superior discriminative performance compared with VAI in distinguishing individuals with and without prevalent CHF.

Conclusions

This cross-sectional study identified a significant association between AVAI and prevalent CHF. AVAI may represent a clinically relevant marker associated with the prevalence of CHF and may help identify populations with a higher likelihood of prevalent CHF for further prospective investigation.

Keywords

congestive heart failure age-adjusted visceral adiposity index obesity visceral adiposity index NHANES

1. Background

Dyspnea, edema, and exhaustion are common clinical symptoms of congestive heart failure (CHF), a chronic illness marked by compromised cardiac pumping performance that significantly lowers patients’ quality of life.¹ Epidemiological studies indicate that the global prevalence and mortality rates of CHF are steadily rising, particularly in older populations, where these rates remain persistently high.^2,3 CHF has become one of the most common cardiovascular conditions among adults in the United States, according to data from the American Heart Association, and its social cost is steadily rising.⁴ CHF adversely impacts patients’ physical health, leading to a substantial deterioration in quality of life, while also imposing a considerable cost strain on healthcare systems due to recurrent hospitalizations and emergency visits.^5,6 Identifying factors associated with CHF, particularly through the development of effective markers for early identification, remains of important clinical and public health relevance.

The growing prevalence of obesity has coincided with an increasing burden of metabolic and cardiovascular diseases, representing a substantial global public health concern.⁷ Obesity, especially excess visceral fat, is strongly linked to chronic conditions such as metabolic syndrome, type 2 diabetes, and cardiovascular disease.⁸ The visceral adiposity index (VAI) was developed as a non-invasive indicator of visceral fat based on combined anthropometric and metabolic parameters. Several imaging modalities, including computed tomography (CT), magnetic resonance imaging (MRI), dual-energy X-ray absorptiometry (DXA), and ultrasound-based techniques, have been used to assess visceral adiposity.^9–11 Although these methods provide relatively accurate measurements, their use in large-scale population studies is often limited by factors such as cost, accessibility, technical requirements, and, in the case of CT, radiation exposure. In this context, VAI provides a practical, cost-effective, and scalable surrogate for estimating visceral adiposity, particularly in large-scale population studies or resource-limited settings. VAI has been validated against imaging-derived measurements of visceral fat and has demonstrated strong correlations with visceral adiposity assessed by MRI.¹² Beyond its role as a proxy for fat distribution, VAI has also been consistently associated with adverse metabolic profiles and cardiovascular conditions, including insulin resistance, metabolic syndrome, and various cardiovascular diseases such as heart failure.^13–15 A cross-sectional study showed that VAI was positively correlated with CHF. However, a critical limitation of VAI is its inability to adequately account for age, a crucial determinant in visceral fat distribution and metabolism.¹⁶ Given that visceral fat distribution and metabolic function vary with age, the predictive efficacy of VAI may differ across age groups, thereby limiting its clinical applicability.¹⁷ To overcome this limitation, researchers have introduced the age-adjusted visceral adiposity index (AVAI). By incorporating age as an adjustment factor, AVAI offers a more accurate estimation of visceral fat levels and their potential disease impacts across different age groups. AVAI provides a more refined evaluation of visceral fat–related metabolic burden than traditional VAI. Previous studies have demonstrated that age enhances the association of VAI with cardiovascular and all-cause mortality, and AVAI has also been associated with mortality. These findings support its potential role as a clinically relevant marker and its value in informing future prospective studies.¹⁸ Although AVAI has emerged as a refined marker of visceral fat burden, its association with CHF has not been systematically examined. Considering the central role of age in fat redistribution and cardiometabolic dysfunction, it is hypothesized that AVAI may demonstrate superior discriminative ability for prevalent CHF compared to the traditional VAI. This study investigates the association between AVAI and CHF prevalence and compares its discriminative performance with that of VAI in a nationally representative cohort.

2. Methods

2.1. Study design and population

This cross-sectional study was based on data from the 2013–2018 National Health and Nutrition Examination Survey (NHANES), a nationally representative survey designed to assess the health and nutritional status of the U.S. population. NHANES includes data from physical examinations, laboratory tests, questionnaire interviews, and medical conditions. All participants provided written informed consent, and the NHANES study protocol was approved by the NCHS Institutional Review Board. This study was conducted in accordance with the Declaration of Helsinki of 1975, as revised in 2024. Data from three consecutive NHANES cycles (2013–2014, 2015–2016, and 2017–2018) were included in this analysis. Participants were eligible if they were aged over 20 years, had available self-reported CHF diagnosis data, and had sufficient data for AVAI calculation. Individuals with missing critical variables were excluded to ensure the validity and reliability of the analyses. This study was reported in accordance with the STROBE guidelines.¹⁹

2.2. Calculation of AVAI

In this investigation, AVAI functioned as the main exposure variable and was computed using a method derived from previous research.^18,20 Laboratory data included triglycerides (TG) and high-density lipoprotein (HDL), whilst waist circumference (WC) and body mass index (BMI) were assessed at mobile screening sites. HDL and TG values were quantified in mmol/L, while WC was documented in centimeters. The AVAI calculation formula is presented as follows:

Male : V A I = [W C \div (39.68 + 1.88 * B M I)] * (T G \div 1.03) * (1.31 \div H D L)

Female : V A I = [W C \div (36.58 + 1.89 * B M I)] * (T G \div 0.81) * (1.52 \div H D L)

A V A I = - 16.186 - 1.369 \times H D L + 0.38 \times W C + 0.144 \times A g e - 0.013 \times B M I - 0.151 \times T G

2.3. Definition of congestive heart failure

The diagnosis of CHF was established based on participants’ self-reported answers to the question: “Has a doctor ever informed you that you have congestive heart failure?”

Affirmative responses classified participants as CHF patients. This self-reported diagnostic method has been validated in multiple studies, demonstrating reliable accuracy and feasibility.^21–23

2.4. Covariates

A variety of variables was used to account for any confounding factors that might affect the findings. Demographic factors included sex, race, poverty levels, and level of education. Poverty levels were classified into three categories based on the poverty index: <1, 1–3, and >3.²⁴ Lifestyle determinants including smoking, alcohol intake, and physical exercise. Participants who had consumed more than 12 alcoholic drinks in their lifetime were classified as drinkers, while those who had smoked at least 100 cigarettes were considered smokers.²⁵ Physical activity was assessed using self-reported questionnaires and quantified as metabolic equivalents (METs), with values <600 minutes/week classified as inactivity. Chronic diseases—including hypertension, hypercholesterolemia, chronic kidney disease (CKD), and diabetes—were identified via self-report. Relevant laboratory measures such as blood urea nitrogen (BUN), alanine aminotransferase (ALT), and aspartate aminotransferase (AST) were also included.

2.5. Statistical analysis

A cross-sectional investigation used data from the 2013–2018 NHANES cycles, with individuals chosen based on established inclusion criteria. All analyses in this study were conducted in accordance with the NHANES analysis guidelines, employing a complex sampling design for weighted analysis, incorporating sample weights (WTMEC2YR), stratification variables (SDMVSTRA), and master sampling units (SDMVPSU) to obtain nationally representative estimation results. Baseline characteristics were stratified by CHF status, with continuous variables reported as mean ± standard deviation and categorical variables as frequencies and percentages. Logistic regression was performed to assess the association between AVAI and CHF prevalence. To investigate the correlation between AVAI levels and the occurrence of CHF, AVAI was divided into four quartiles: Q1 (<-9.64), Q2 (-9.64 to -7.52), Q3 (-7.52 to -5.50), and Q4 (>-5.50). Restricted cubic spline (RCS) analysis was employed to explore the potential dose–response relationship between AVAI and CHF prevalence. Subgroup analyses were used to evaluate potential factors that may contribute to the association between AVAI and CHF in various demographics. All statistical analyses were performed using R software, with P-value < 0.05 considered statistically significant.

3. Results

3.1. Baseline characteristics of participants

The participant selection procedure is illustrated in Figure 1, which comprises a total of 6,695 individuals, 225 CHF participants and 6,470 non-CHF participants. Baseline characteristics are summarized in Table 1. Compared to non-CHF participants, those with CHF were generally older, more likely to smoke, and less physically active. Patients with CHF had significantly higher AVAI values compared to non-CHF patients, indicating a potential association between AVAI levels and the prevalence of CHF.

Figure 1.

Flow diagram displaying selection process for including participants. AVAI: age-adjusted visceral adiposity index; CHF: congestive heart failure.

Table 1.

Baseline characteristics of the study population.

Characteristic	Group	Overall	CHF	Non-CHF	P-value
n		6695	225	6470
Age (%)	<50	3295 (49.2)	32 (14.2)	3263 (50.4)	<0.001
	>50	3400 (50.8)	193 (85.8)	3207 (49.6)
Sex (%)	Female	3437 (51.3)	101 (44.9)	3336 (51.6)	0.057
	Male	3258 (48.7)	124 (55.1)	3134 (48.4)
Race (%)	Mexican American	974 (14.5)	21 (9.3)	953 (14.7)	0.001
	Non-Hispanic black	1376 (20.6)	58 (25.8)	1318 (20.4)
	Non-Hispanic white	2521 (37.7)	103 (45.8)	2418 (37.4)
	Others	1824 (27.2)	43 (19.1)	1781 (27.5)
Education level (%)	Under high school	1465 (21.9)	73 (32.4)	1392 (21.5)	<0.001
	High school	1482 (22.1)	61 (27.1)	1421 (22.0)
	Above high school	3745 (55.9)	91 (40.4)	3654 (56.5)
	No record	3 (0.0)	0 (0.0)	3 (0.0)
PIR (%)	<1	1245 (20.7)	56 (27.5)	1189 (20.5)	<0.001
	1-3	2556 (42.5)	101 (49.5)	2455 (42.3)
	>3	2212 (36.8)	47 (23.0)	2165 (37.3)
Smoke (%)	No	3744 (55.9)	85 (37.8)	3659 (56.6)	<0.001
	Yes	2946 (44.0)	140 (62.2)	2806 (43.4)
	No record	5 (0.1)	0 (0.0)	5 (0.1)
Drinking (%)	No	1397 (22.2)	48 (22.6)	1349 (22.2)	0.908
	Yes	4880 (77.7)	164 (77.4)	4716 (77.7)
	No record	5 (0.1)	0 (0.0)	5 (0.1)
Activity status (%)	Active	3549 (53.0)	80 (35.6)	3469 (53.6)	<0.001
	Inactive	3146 (47.0)	145 (64.4)	3001 (46.4)
Hypertension (%)	No	4147 (61.9)	39 (17.3)	4108 (63.5)	<0.001
	Yes	2540 (37.9)	186 (82.7)	2354 (36.4)
	No record	8 (0.1)	0 (0.0)	8 (0.1)
Hypercholesterolemia (%)	No	4217 (63.0)	86 (38.2)	4131 (63.8)	<0.001
	Yes	2426 (36.2)	134 (59.6)	2292 (35.4)
	No record	52 (0.8)	5 (2.2)	47 (0.7)
Diabetes (%)	No	5513 (82.3)	116 (51.6)	5397 (83.4)	<0.001
	Yes	972 (14.5)	98 (43.6)	874 (13.5)
	No record	210 (3.1)	11 (4.9)	199 (3.1)
CHD	No	6390 (95.8)	130 (59.9)	6260 (97.0)	<0.001
	Yes	283 (4.2)	87 (40.1)	196 (3.0)
CKD (%)	No	6447 (96.3)	185 (82.2)	6262 (96.8)	<0.001
	Yes	239 (3.6)	40 (17.8)	199 (3.1)
	No record	9 (0.1)	0 (0.0)	9 (0.1)
BUN (mean (SD)) (mmol/L)		5.10 (2.11)	7.08 (3.82)	5.03 (1.99)	<0.001
ALT (mean (SD)) (U/L)		24.19 (16.61)	24.40 (28.54)	24.18 (16.04)	0.844
AST (mean (SD)) (U/L)		24.33 (19.69)	29.16 (57.43)	24.16 (16.90)	<0.001
BMI (mean (SD)) (kg/m²)		29.43 (7.08)	32.16 (8.52)	29.34 (7.00)	<0.001
WC (mean (SD)) (cm)		100.24 (16.70)	109.88 (18.06)	99.91 (16.56)	<0.001
HDL (mean (SD)) (mmol/L)		1.40 (0.43)	1.28 (0.42)	1.40 (0.43)	<0.001
TG (mean (SD)) (mmol/L)		1.32 (1.26)	1.43 (1.11)	1.32 (1.27)	0.203
VAI (mean (SD))		1.61 (2.28)	1.99 (2.16)	1.60 (2.28)	0.012
AVAI (mean (SD))		-7.59 (2.64)	-4.85 (1.83)	-7.68 (2.61)	<0.001
AVAI (%)	Q1 (<-9.64)	1674 (25.0)	3 (1.3)	1671 (25.8)	<0.001
	Q2 (-9.64 to -7.52)	1674 (25.0)	18 (8.0)	1656 (25.6)
	Q3 (-7.52 to -5.50)	1673 (25.0)	51 (22.7)	1622 (25.1)
	Q4 (>5.50)	1674 (25.0)	153 (68.0)	1521 (23.5)

Mean (SD) for continuous variables, % for categorical variables. ALT: alanine aminotransferase; AST: aspartate aminotransferase; AVAI: age-adjusted visceral adiposity index; BMI: body mass index; BUN: Blood Urea Nitrogen; CHD: coronary heart disease; CHF: congestive heart failure; CKD: chronic kidney disease; HDL: high-density lipoprotein; PIR: poverty index ratio; SCR: Serum Creatinine; TG: Triglyceride; VAI: visceral adiposity index; WC: waist circumference.

3.2. Association between AVAI and prevalence of Congestive heart failure

Table 2 summarizes the logistic regression results examining the associations of VAI and AVAI with CHF prevalence. A strong positive association was observed between AVAI and CHF (OR = 1.75, 95% CI: 1.62–2.89), which remained statistically significant after adjusting for multiple covariates (adjusted OR = 1.57, 95% CI: 1.31–1.87). When AVAI was classified into quartiles, the highest quartile of individuals exhibited a significantly stronger correlation with CHF prevalence than the lowest quartile (OR: 19.30, 95% CI: 4.24–88.30). A positive dose–response relationship between AVAI and the prevalence of CHF was observed in the RCS analysis, as illustrated in Figure 2. These findings further support a robust positive association between AVAI and CHF prevalence.

Table 2.

Logistic regression analysis of the association between AVAI and CHF prevalence.

		Model 1 OR (95%CI) P-value	Model 2 OR (95%CI) P-value	Model 3 OR (95%CI) P-value
CHF	AVAI	1.75 (1.62, 1.89) <0.001	1.89 (1.65, 2.16) <0.001	1.57 (1.31, 1.87) <0.001
	Q1	[Reference]	[Reference]	[Reference]
	Q2	4.51 (1.12, 18.1) 0.035	4.73 (1.20, 18.6) 0.027	2.83 (0.69, 11.7) 0.140
	Q3	14.1 (3.88, 51.5) <0.001	17.3 (4.34, 69.2) <0.001	7.55 (1.95, 29.30) 0.005
	Q4	47.8 (14.0, 163) <0.001	63.2 (14.5, 276) <0.001	19.30 (4.24, 88.30) <0.001
	P for trend	<0.001	<0.001	<0.001

AVAI: age-adjusted visceral adiposity index; CI: confidence interval; CHF: congestive heart failure; OR: odds ratio; Q: quartiles.

Model 1: no covariates adjusted; Model 2: adjusted for sex and race; Model 3: adjusted for sex, race, PIR, educational level, smoke, drinking, activity status, diabetes, hypertension, CKD, hypercholesterolemia, BUN, ALT, AST.

Figure 2.

RCS curve illustrating the dose-response relationship between AVAI levels and CHF prevalence. Adjusted for sex, race, PIR, educational level, smoke, drinking, activity status, diabetes, hypertension, CKD, hypercholesterolemia, BUN, ALT, AST. AVAI: age-adjusted visceral adiposity index; CI: confidence interval; CHF: congestive heart failure; OR: odds ratio.

3.3. Subgroup analysis

Subgroup analyses were conducted to validate the robustness of the study results, categorized by demographic characteristics and lifestyle factors. The subgroup analysis results, shown in Table 3, indicated that the positive association between AVAI and CHF prevalence remained statistically significant across all categories, hence validating the primary findings of this study. Interaction tests revealed no statistically significant interaction between AVAI and CHF, suggesting that the positive association was consistent across various subgroups.

Table 3.

Subgroup analysis between AVAI and CHF.

Characteristic	Group	OR (95%CI) P-value	P for interaction
Age	<50	1.77 (1.06, 2.97) 0.031	0.500
	>50	1.57 (1.34, 1.84) <0.001
Sex	Male	1.58 (1.22, 2.04) 0.001	0.140
	Female	1.64 (1.35, 2.00) <0.001
Race	Mexican American	1.52 (0.77, 3.00) 0.200	0.500
	Non-Hispanic black	1.77 (1.32, 2.38) <0.001
	Non-Hispanic white	1.59 (1.27, 1.99) <0.001
	Others	1.72 (1.29, 2.31) <0.001
Smoke	No	1.75 (1.30, 2.35) <0.001	0.073
	Yes	1.55 (1.28, 1.87) <0.001
Drinking	No	1.74 (1.20, 2.53) 0.005	0.300
	Yes	1.61 (1.38, 1.87) <0.001
Activity Status	Active	1.84 (1.42, 2.39) <0.001	0.300
	Inactive	1.50 (1.23, 1.83) <0.001

3.4. ROC analysis

Figure 3 presents the ROC curves and corresponding AUC values for the discrimination of prevalent CHF. AVAI demonstrated a higher AUC (0.8070) compared with VAI (0.5726), indicating superior discriminative ability. To further ensure a fair comparison, we additionally evaluated a VAI model incorporating age (VAI + age), which showed an improved AUC of 0.7143; however, AVAI still exhibited better performance. These findings suggest that AVAI is more strongly associated with prevalent CHF and may better distinguish individuals with and without CHF in this cross-sectional setting. Given the established association between visceral adiposity and cardiometabolic disorders, AVAI may serve as a simple and non-invasive indicator associated with prevalent CHF in clinical practice.

Figure 3.

ROC curve assessing the diagnostic performance of VAI, VAI+Age and AVAI for predicting CHF prevalence.

4. Discussion

This study, based on NHANES 2013–2018 data, examined the association between AVAI and CHF. The results demonstrated a significant association between AVAI and the prevalence of CHF, and this relationship remained consistent across multiple subgroups. ROC curve analysis indicated that AVAI showed better discriminative ability than traditional VAI for distinguishing individuals with and without prevalent CHF. These findings highlight the potential of AVAI as a reliable surrogate for visceral adiposity, a key factor in metabolic and cardiovascular disorders. Given the high burden of CHF and its clinical implications, AVAI may serve as a marker associated with adverse cardiometabolic profiles and prevalent CHF and serve as a basis for future prospective and mechanistic studies.

This study demonstrated a significant association between AVAI and prevalent CHF, providing new insight into the relationship between visceral adiposity and CHF. Prior research has demonstrated a significant association between visceral fat and cardiovascular diseases, particularly in the presence of metabolic abnormalities such as metabolic syndrome, diabetes, and hypertension, with excessive visceral fat accumulation being linked to these conditions.^26–28 Compared with traditional VAI, AVAI introduced in this study demonstrates improved discriminative ability for prevalent CHF. This enhancement may be related to the important role of age in determining visceral fat distribution and metabolic activity. Aging is associated with a progressive redistribution of fat from subcutaneous to visceral compartments, along with increased adipose tissue inflammation, insulin resistance, and metabolic dysfunction, which have been associated with CHF.^29–31 However, traditional VAI does not account for age-related metabolic changes, which may lead to underestimation in older adults. By incorporating age as an adjustment factor, AVAI may better capture age-related differences in visceral fat burden and cardiometabolic status across different age groups. This refinement strengthens the pathophysiological relevance of the index and supports the relevance of AVAI as a more tailored marker associated with prevalent CHF. There is a significant association between AVAI and CHF, and this finding is consistent with existing literature. Previous studies suggest that visceral fat accumulation is strongly associated with insulin resistance, inflammation, and metabolic dysregulation, all of which contribute to cardiovascular disease development and progression.^32–34 Visceral fat has been associated with insulin resistance and inflammation and is considered to play an important role in cardiovascular pathology. In this study, AVAI was significantly associated with the prevalence of CHF across all subgroups, with no significant interaction effects observed. This consistency suggests that the observed association remains stable across different populations. The absence of interaction further supports the robustness of this association. ROC analysis indicated that AVAI demonstrated better discriminative ability than VAI for distinguishing individuals with and without prevalent CHF in this cross-sectional analysis. The higher AUC of AVAI may have potential clinical relevance, as it suggests improved cross-sectional discrimination of prevalent CHF compared with VAI; however, the practical impact of this difference remains to be established in prospective studies. Therefore, AVAI may serve as a valuable complementary marker associated with cardiometabolic status and prevalent CHF. While its prognostic or predictive utility cannot be determined from this cross-sectional study, its non-invasive and easily accessible nature supports its potential application in clinical research. Rather than functioning as a standalone diagnostic or prognostic tool, AVAI may provide additional cross-sectional information when used alongside conventional biomarkers, imaging modalities, and clinical indices. Prospective studies are warranted to clarify its temporal relationship with CHF and to determine whether AVAI has prognostic relevance in longitudinal settings.

The possible biological pathways between AVAI and CHF may be investigated via the various functions of visceral fat. Visceral fat operates as an active endocrine organ, releasing pro-inflammatory agents, which provoke systemic chronic low-grade inflammation and detrimentally affect heart structure and function.^35–38 Excess visceral fat is closely linked to insulin resistance, which increases systemic metabolic stress and impairs cardiac metabolism, thereby contributing to metabolic dysfunction and worsening heart failure.^36,39–41 Increased visceral fat also promotes cardiac remodeling, exacerbates myocardial fibrosis, and induces structural changes in the heart, thereby advancing the progression of CHF.^42,43 Studies have also shown that visceral fat alters sympathetic nervous system activity, increasing cardiac workload and subsequently impairing heart function.⁴⁴ Additionally, metabolic byproducts of visceral fat exacerbate oxidative stress and myocardial cell damage, contributing to the onset of heart failure.^41,45,46 These findings suggest that visceral fat may be associated with CHF through multiple pathways, including inflammatory responses, metabolic disturbances, cardiac remodeling, and neural regulation. However, although existing evidence indicates a close association between visceral adiposity and CHF, the underlying mechanisms remain unclear. Further studies are needed to better elucidate the mechanisms linking AVAI and CHF.

5. Study strengths and limitations

The principal strength of this study lies in the use of NHANES, a nationally representative large-scale dataset, in conjunction with AVAI to examine its association with congestive heart failure (CHF). By introducing AVAI as a novel marker, this study incorporates age-related factors, providing a more refined characterization of visceral fat distribution across age groups and its potential clinical relevance compared with traditional VAI. Nevertheless, several limitations should be acknowledged. The cross-sectional design precludes causal inference. Although a significant association between AVAI and CHF was observed, its role requires further validation in longitudinal studies. Secondly, the self-reported diagnosis of CHF by participants may be subject to recall bias or inaccuracies. Since the NHANES dataset relies on self-reported questionnaire data and is designed for large-scale epidemiological surveys using non-invasive methods, it lacks professional diagnostic imaging data. Moreover, while the NHANES dataset includes a broad and varied population, the lack of long-term individual follow-up data may restrict the generalizability of the results. Subsequent research should, thus, include long-term follow-up data to better clarify the possible processes and prediction capabilities of AVAI in cardiovascular illnesses. Additionally, the NHANES dataset does not distinguish between HFpEF (Heart Failure with preserved Ejection Fraction) and HFrEF(Heart Failure with reduced Ejection Fraction), precluding evaluation of subtype-specific associations. Given the plausible mechanistic relevance of visceral adiposity in HFpEF, future studies incorporating echocardiographic data are warranted.

6. Conclusion

Footnotes

Acknowledgements

The National Health and Nutrition Examination Survey was made accessible to us by the CDC’s National Center for Health Statistics.

ORCID iDs

Guohao Li

Dingli Xu

Ethical considerations

The National Center for Health Statistics Research Ethics Review Board looked over the NHANES study plan and gave its approval. Each subject also signed a form saying they understood what the study was about. This study did not directly test on humans or involve any other treatments. Secondary analyses were based on a de-identified, publicly available dataset, and this type of research did not need any extra ethical approval.

Author contributions

DX conceived the study and planned the study. GL and XL analyzed the data and wrote the paper. SJ, DG and QZ performed the literature search. XW and TL made the critical revision of the paper. All authors have contributed significantly to the manuscript to be published.

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

Declaration of conflicting interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Data Availability Statement

The NHANES data are useful for academics and people who use data all over the world. All of the data can be found at .

References

Chen

Aronowitz

. Congestive Heart Failure. The Medical clinics of North America 2022; 106(3): 447–458. https://doi.org/10.1016/j.mcna.2021.12.002

Roger

. Epidemiology of Heart Failure: A Contemporary Perspective. Circulation research 2021; 128(10): 1421–1434. https://doi.org/10.1161/CIRCRESAHA.121.318172

Vogels

Scheltens

Schroeder-Tanka

, et al. Cognitive impairment in heart failure: a systematic review of the literature. European journal of heart failure 2007; 9(5): 440–449. https://doi.org/10.1016/j.ejheart.2006.11.001

Heidenreich

Bozkurt

Aguilar

, et al. AHA/ACC/HFSA Guideline for the Management of Heart Failure: Executive Summary: A Report of the American College of Cardiology/American Heart Association Joint Committee on Clinical Practice Guidelines. Circulation 2022; 145(18): e876–e894. https://doi.org/10.1161/CIR.0000000000001062

Yan

Zhu

Yin

, et al. Burden, Trends, and Inequalities of Heart Failure Globally, 1990 to 2019: A Secondary Analysis Based on the Global Burden of Disease 2019 Study. Journal of the American Heart Association 2023; 12(6): e027852. https://doi.org/10.1161/JAHA.122.027852

Savarese

Becher

Lund

, et al. Coats AJJCr: Global burden of heart failure: a comprehensive and updated review of epidemiology. Cardiovasc Res 2022; 118(17): 3272–3287. https://doi.org/10.1093/cvr/cvac013

Chew

NWS

Tan

DJH

, et al. The global burden of metabolic disease: Data from 2000 to 2019. Cell metabolism 2023; 35(3): 414–428.e413. https://doi.org/10.1016/j.cmet.2023.02.003

Piché

Tchernof

Després

. Obesity Phenotypes, Diabetes, and Cardiovascular Diseases. Circulation research 2020; 126(11): 1477–1500. https://doi.org/10.1161/CIRCRESAHA.120.316101

Mina

Xie

Low

, et al. Adiposity and metabolic health in Asian populations: an epidemiological study using dual-energy x-ray absorptiometry in Singapore. The lancet Diabetes & endocrinology 2024; 12(10): 704–715. https://doi.org/10.1016/S2213-8587(24)00195-5

10.

Tan

Misur

, et al. Relationship of computed tomography quantified visceral adiposity with the severity and complications of acute pancreatitis: a systematic review. Japanese journal of radiology 2023; 41(10): 1104–1116. https://doi.org/10.1007/s11604-023-01430-1

11.

Jung

Raghu

Reisert

, et al. Deep learning-based body composition analysis from whole-body magnetic resonance imaging to predict all-cause mortality in a large western population. EBioMedicine 2024; 110: 105467. https://doi.org/10.1016/j.ebiom.2024.105467

12.

Amato

Giordano

Galia

, et al. Visceral Adiposity Index: a reliable indicator of visceral fat function associated with cardiometabolic risk. Diabetes care 2010; 33(4): 920–922. https://doi.org/10.2337/dc09-1825

13.

Zhang

Zhao

, et al. Association between Chinese visceral adiposity index and risk of stroke incidence in middle-aged and elderly Chinese population: evidence from a large national cohort study. Journal of translational medicine 2023; 21(1): 518. https://doi.org/10.1186/s12967-023-04309-x

14.

Zhang

Sun

, et al. Association between visceral adiposity index and heart failure: A cross-sectional study. Clinical cardiology 2023; 46(3): 310–319. https://doi.org/10.1002/clc.23976

15.

Yuan

Jin

, et al. Transition of visceral adiposity index and risk of cardiovascular disease in middle-aged and older Chinese adults. Archives of gerontology and geriatrics 2024; 121: 105356. https://doi.org/10.1016/j.archger.2024.105356

16.

Fan

Yang

Song

, et al. Correlation between Visceral Adiposity Index (VAI) and Congestive Heart Failure (CHF) in patients with hypertension: a study based on NHANES 2001-2018. BMC cardiovascular disorders 2025; 25(1): 452. https://doi.org/10.1186/s12872-025-04913-3

17.

Baarts

Jensen

Hansen

, et al. Age- and sex-specific changes in visceral fat mass throughout the life-span. Obesity (Silver Spring, Md) 2023; 31(7): 1953–1961. https://doi.org/10.1002/oby.23779

18.

Liu

Weng

Chen

, et al. Age-adjusted visceral adiposity index (VAI) is superior to VAI for predicting mortality among US adults: an analysis of the NHANES 2011-2014. Aging clinical and experimental research 2024; 36(1): 24. https://doi.org/10.1007/s40520-023-02660-z

19.

Erik von Elm, Douglas G Altman, Matthias Egger , et al. The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: guidelines for reporting observational studies. Ann Intern Med 2007; 147: 573–577. https://doi.org/10.7326/0003-4819-147-8-200710160-00010

20.

Kuang

. Association between the age-adjusted visceral adiposity index (AVAI) and female infertility status: a cross-sectional analysis of the NHANES 2013-2018. Lipids in health and disease 2024; 23(1): 314. https://doi.org/10.1186/s12944-024-02295-7

21.

Liu

Zhang

Fang

, et al. Association Between Noninvasive Liver Fibrosis Scores and Heart Failure in a General Population. Journal of the American Heart Association 2024; 13(22): e035371. https://doi.org/10.1161/JAHA.123.035371

22.

Wang

Liu

, et al. The potential of platelet to high-density lipoprotein cholesterol ratio (PHR) as a novel biomarker for heart failure. Scientific reports 2024; 14(1): 23283. https://doi.org/10.1038/s41598-024-75453-7

23.

Luo

Cai

. Association between cardiometabolic index and congestive heart failure among US adults: a cross-sectional study. Frontiers in cardiovascular medicine 2024; 11: 1433950. https://doi.org/10.3389/fcvm.2024.1433950

24.

Lin

Deng

, et al. Association of visceral adiposity index with sarcopenia based on NHANES data. Scientific reports 2024; 14(1): 21169. https://doi.org/10.1038/s41598-024-72218-0

25.

Xiao

Pan

Huang

, et al. Association of hemoglobin-to-red blood cell distribution width ratio and bone mineral density in older adults. BMC musculoskeletal disorders 2024; 25(1): 866. https://doi.org/10.1186/s12891-024-07984-z

26.

Yang

Ren

, et al. The interaction between triglyceride-glucose index and visceral adiposity in cardiovascular disease risk: findings from a nationwide Chinese cohort. Cardiovascular diabetology 2024; 23(1): 427. https://doi.org/10.1186/s12933-024-02518-2

27.

Huo

. Association between visceral adiposity index, lipid accumulation product and type 2 diabetes mellitus in US adults with hypertension: a cross-sectional analysis of NHANES from 2005 to 2018. BMC endocrine disorders 2024; 24(1): 216. https://doi.org/10.1186/s12902-024-01750-x

28.

Huang

, et al. Exploring the Relationship Between Visceral Fat and Coronary Artery Calcification Risk Using Metabolic Score for Visceral Fat (METS-VF). Life (Basel, Switzerland) 2024; 14(11): 1399. https://doi.org/10.3390/life14111399

29.

Nguyen

Corvera

. Adipose tissue as a linchpin of organismal ageing. Nature metabolism 2024; 6(5): 793–807. https://doi.org/10.1038/s42255-024-01046-3

30.

Zhang

Tan

, et al. Adipose tissue aging: mechanisms and therapeutic implications. Cell death & disease 2022; 13(4): 300. https://doi.org/10.1038/s41419-022-04752-6

31.

Wang

. Adipose Tissue Aging and Metabolic Disorder, and the Impact of Nutritional Interventions. Nutrients 2022; 14(15): 3134. https://doi.org/10.3390/nu14153134

32.

Pan

Zhou

. A Nomogram Incorporating Inflammation and Nutrition Indexes for Predicting Outcomes in Patients with Acute Coronary Syndrome and Chronic Kidney Disease. Journal of inflammation research 2024; 17: 8181–8198. https://doi.org/10.2147/JIR.S488674

33.

Guo

Shi

, et al. Prevalence and prognostic value of malnutrition in patients with acute coronary syndrome and chronic kidney disease. Frontiers in nutrition 2023; 10: 1187672. https://doi.org/10.3389/fnut.2023.1187672

34.

Jiang

, et al. Association between insulin resistance indices and outcomes in patients with heart failure with preserved ejection fraction. Cardiovascular diabetology 2025; 24(1): 32. https://doi.org/10.1186/s12933-025-02595-x

35.

Rana

Neeland

. Adipose Tissue Inflammation and Cardiovascular Disease: An Update. Current diabetes reports 2022; 22(1): 27–37. https://doi.org/10.1007/s11892-021-01446-9

36.

Kawai

Autieri

Scalia

. Adipose tissue inflammation and metabolic dysfunction in obesity. American journal of physiology Cell physiology 2021; 320(3): C375–c391. https://doi.org/10.1152/ajpcell.00379.2020

37.

Kolb

. Obese visceral fat tissue inflammation: from protective to detrimental? BMC medicine 2022; 20(1): 494. https://doi.org/10.1186/s12916-022-02672-y

38.

Chait

den Hartigh

. Adipose Tissue Distribution, Inflammation and Its Metabolic Consequences, Including Diabetes and Cardiovascular Disease. Frontiers in cardiovascular medicine 2020; 7: 22. https://doi.org/10.3389/fcvm.2020.00022

39.

Mashayekhi

Sheng

Bailin

, et al. The subcutaneous adipose transcriptome identifies a molecular signature of insulin resistance shared with visceral adipose. Obesity (Silver Spring, Md) 2024; 32(8): 1526–1540. https://doi.org/10.1002/oby.24064

40.

Vasamsetti

Natarajan

Sadaf

, et al. Regulation of cardiovascular health and disease by visceral adipose tissue-derived metabolic hormones. The Journal of physiology 2023; 601(11): 2099–2120. https://doi.org/10.1113/JP282728

41.

Suresh

Mohamed

Geetha

, et al. Cardiovascular Implications in Metabolic Dysfunction-Associated Steatotic Liver Disease (MASLD): A State-of-the-Art Review. Korean circulation journal 2026; 56(2): 103–130. https://doi.org/10.4070/kcj.2025.0212

42.

Koenen

Hill

Cohen

, et al. Obesity, Adipose Tissue and Vascular Dysfunction. Circulation research 2021; 128(7): 951–968. https://doi.org/10.1161/CIRCRESAHA.121.318093

43.

Zou

Zhang

, et al. Role of epicardial adipose tissue in cardiac remodeling. Diabetes research and clinical practice 2024; 217: 111878. https://doi.org/10.1016/j.diabres.2024.111878

44.

Lambert

Straznicky

Dixon

, et al.

Should the sympathetic nervous system be a target to improve cardiometabolic risk in obesity?

American journal of physiology Heart and circulatory physiology 2015; 309(2): H244–H258. https://doi.org/10.1152/ajpheart.00096.2015

45.

Rossi

Gruebler

Monzo

, et al. The Different Pathways of Epicardial Adipose Tissue across the Heart Failure Phenotypes: From Pathophysiology to Therapeutic Target. International journal of molecular sciences 2023; 24(7): 6838. https://doi.org/10.3390/ijms24076838

46.

Zhao

Guo

Wang

, et al. Proteomics of epicardial adipose tissue in patients with heart failure. Journal of cellular and molecular medicine 2020; 24(1): 511–520. https://doi.org/10.1111/jcmm.14758

Association between age-adjusted visceral fat index (AVAI) and congestive heart failure: A cross-sectional study

Abstract

Objective

Methods

Results

Conclusions

Keywords

1. Background

2. Methods

2.1. Study design and population

2.2. Calculation of AVAI

2.3. Definition of congestive heart failure

2.4. Covariates

2.5. Statistical analysis

3. Results

3.1. Baseline characteristics of participants

3.2. Association between AVAI and prevalence of Congestive heart failure

3.3. Subgroup analysis

3.4. ROC analysis

4. Discussion

5. Study strengths and limitations

6. Conclusion

Footnotes

Acknowledgements

ORCID iDs

Ethical considerations

Author contributions

Funding

Declaration of conflicting interests

Data Availability Statement

References