Visceral Adiposity And Heart Failure Risk
Overview
Obesity is a common risk factor developing for heart failure (HF). In this study, we aimed to explore the potential of the Visceral Adiposity Index (VAI) as a straightforward tool for assessing obesity and its link to HF risk, a relationship that had not been extensively investigated before.
To achieve this, we conducted a cross-sectional study involving 28,764 participants aged 18 and above, using data from the National Health and Nutrition Examination Survey (NHANES) in the United States spanning from 2009 to 2018. VAI, a metric derived from variables such as body mass index (BMI), waist circumference (WC), triglycerides (TG), and high-density lipoprotein cholesterol, was employed as our main indicator. We examined the association between VAI and HF, analyzing VAI as both a continuous and categorical variable, and conducted subgroup analyses.
Our findings revealed that individuals with higher VAI scores (specifically, the fourth quartile, Q4) exhibited greater prevalence of risk factors for HF, including conditions like hypertension, diabetes, and coronary heart disease, as well as lifestyle factors like smoking and medication use. Participants with HF generally displayed higher VAI scores. We observed that for each unit increase in VAI, there was a 4% elevated risk for HF (odds ratio [OR] 1.04 [95% confidence interval (CI) 1.02–1.05]). After accounting for multiple variables, the risk remained significant, with individuals in the third quartile (Q3) having an OR of 1.55 compared to the lowest quartile.
Importantly, our subgroup analyses did not reveal significant variations in the association between VAI and HF risk, suggesting that this connection is relatively consistent across different subpopulations.
In conclusion, our study identified an independent and positive association between VAI and HF risk. VAI, as a noninvasive metric for assessing visceral adiposity, shows promise as a convenient tool for evaluating the risk of HF. This finding suggests that VAI could serve as a novel and efficient marker for the assessment of HF risk, providing valuable clinical insights in a straightforward manner.
Introduction
Heart failure (HF) encompasses a range of complex clinical conditions with various causes, such as myocardial dysfunction, valvular diseases, pericardial and endocardial issues, and heart rhythm disturbances, leading to impaired ventricular function. In the United States, approximately 6.2 million adults aged 20 and older are affected by HF, with around 1 million new cases diagnosed each year, and this prevalence is on the rise. Notably, European data indicates that roughly 1% of HF patients are under 55 years old, while about 10% are aged 70 or above. In developed countries, age-adjusted incidence rates of HF may stabilize due to better cardiovascular disease management, but the overall rate is increasing due to population aging, a trend observed in developing nations as well. Recent epidemiological data reveals that China, for instance, has seen a 44% rise in chronic HF cases over the past 15 years, with approximately 13.7 million individuals affected.
Obesity is prevalent among HF patients, with 29% to 40% being overweight (BMI 25.0–29.9 kg/m²) and 30% to 49% falling into the obese category (BMI ≥ 30 kg/m²). It’s worth noting that obesity is more common in patients with HF with preserved ejection fraction (HFpEF) than in those with HF with reduced ejection fraction (HFrEF), with over 80% of HFpEF patients falling into the overweight or obese BMI range. Nevertheless, the relationship between obesity and HF remains a subject of debate. Some studies point to a significant risk of HF associated with increased BMI, while others propose the “obesity paradox,” suggesting that overweight or mild obesity might improve HF survival rates.
Apart from BMI, visceral adipose tissue (VAT) serves as another indicator of obesity. Research has shown that HF patients, particularly those with HFpEF, tend to have higher VAT levels. However, these studies typically involve computed tomography (CT) or magnetic resonance imaging (MRI) to assess VAT. In contrast, the visceral adiposity index (VAI) is a simpler, more cost-effective, and convenient tool for assessing obesity, calculated using BMI, waist circumference, triglycerides, and high-density lipoprotein cholesterol. VAT has been linked to conditions like diabetes, hyperuricemia, metabolic syndrome, hypertension, atherosclerosis, and vascular calcification. Yet, the relationship between VAI and HF has not been extensively studied.
This study aimed to investigate the connection between VAI and HF among middle-aged and elderly participants in the US National Health and Nutrition Examination Survey (NHANES) from 2009 to 2018, shedding light on a potential link between VAI and HF risk.
Method
The National Health and Nutrition Examination Survey (NHANES) is a comprehensive, nationally representative cross-sectional study in the United States. It employs a carefully designed sampling method to enroll participants, allowing weighted analysis to represent the noninstitutionalized civilian population. Data is collected in two-year cycles, and each participant in the study represents around 50,000 U.S. citizens. All participants provide informed consent, and ethical approval for the study is granted by the Research Ethics Review Board at the National Centre for Health Statistics, composed of medical and health professionals who conduct interviews, health measurements, and laboratory tests. The data from this survey is vital for determining disease prevalence and risk factors.
For this particular study, data was derived from the 2009-2018 NHANES cycle, where 49,693 participants were initially interviewed. Participants under 18 years old (19,341) and those with missing data on heart failure (1,588) were excluded, leaving data from 28,764 participants for the cross-sectional analysis. Individuals with heart failure were defined as those who answered affirmatively to the question regarding a previous diagnosis by a doctor or healthcare professional. Detailed participant selection is outlined in Supporting Information: Figure 1. Ethical review and written informed consent were secured before data collection.
The Visceral Adiposity Index (VAI) score, a measure of visceral adiposity, was calculated for both males and females using established equations. This index reflects the amount of estimated visceral adiposity and is derived from variables such as waist circumference, BMI, triglycerides, and high-density lipoprotein cholesterol.
The study also considered several variables of interest as potential covariates, including demographics, comorbidities, lifestyle factors, BMI, triglycerides, total cholesterol, serum uric acid, estimated glomerular filtration rate, and inflammation markers. These were chosen based on clinical significance and statistical significance. Data on participant characteristics, comorbidities, and lifestyle was collected through questionnaires, while measurements such as BMI and waist circumference were obtained during medical examinations and laboratory assessments conducted at mobile examination centers. Definitions for conditions like hypertension, diabetes, anemia, liver disease, coronary heart disease, kidney disease, and a history of heart attack were provided. Additionally, smoking status, estimated glomerular filtration rate, dietary inflammatory index, and the neutrophil-lymphocyte ratio were considered in the analysis.
Statistical Analysis
The NHANES analytical guidelines were followed in reporting results. Descriptive data is presented as weighted mean with standard error (SE) or median with first and third quartiles (Q2 [Q1, Q3]) for continuous variables and as frequency with weighted percentage for categorical variables. The Visceral Adiposity Index (VAI) was examined both as a continuous variable and categorized into quartiles. Differences in VAI between groups with and without heart failure (HF) were assessed using Student’s t-tests for continuous variables and χ2 tests for categorical variables.
To assess the association between VAI and HF, odds ratios (OR) with corresponding 95% confidence intervals (CI) were calculated. These calculations were done for both a unit increase in VAI and each quartile, with the lowest quartile as the reference. The analysis included univariate and multivariate logistic regression models. To test for linear trends across VAI categories, an independent ordinal variable (0, 1, 2, 3) was used in all models.
The multivariate models were progressively adjusted to account for potential covariates. Model 1 was aligned for for sex, race and age. Model 2 included additional adjustments for factors like hypertension, diabetes, smoking, alcohol consumption, coronary heart disease, kidney disease, and liver disease. Model 3 further incorporated adjustments for variables like estimated glomerular filtration rate (eGFR), systolic blood pressure (SBP), diastolic blood pressure (DBP), serum uric acid (UA), albumin (Alb), hemoglobin (HGB), hematocrit (HCT), and the neutrophil-lymphocyte ratio (NLR).
A restricted cubic spline model was utilized for dose-response analysis. Subgroup analyses were conducted to explore potential variations in the association between VAI and HF according to sex, age, race, smoking status, and comorbidities. The interactions between the stratified variables and VAI were assessed using likelihood ratio tests.
Data extraction and processing were performed using the “nhanesR” package version 0.9.1.9, while statistical analyses were conducted using Free statistics software version 1.4 and the R statistical software package version 4.0.1 from the R Foundation for Statistical Computing in Vienna, Austria. Differences with a two-tailed p-value less than 0.05 was taken to be statistically significant.
Result
This study examined the basic characteristics of a population of 28,764 participants, with an average age of 50 years. Participants were categorized into Visceral Adiposity Index (VAI) quartiles, and those in the highest quartile (Q4) demonstrated higher values in various factors such as gender, race (including Mexican Americans, other Hispanics, and non-Hispanic whites), body mass index (BMI), blood pressure (systolic and diastolic), waist circumference (WC), hypertension, diabetes, liver diseases, coronary heart disease (CHD), smoking, total cholesterol (TC), triglycerides (TG), and serum uric acid. Moreover, a larger proportion of participants in Q4 were using medications like β-receptor blockers, angiotensin-converting enzyme inhibitors/angiotensin II receptor blockers/angiotensin receptor-neprilysin inhibitor, calcium channel blockers, antidiabetic medication, and antihyperlipidemic medications. In contrast, non-Hispanic Blacks and alcohol consumption were notably lower in Q4. This analysis revealed significant differences in sex, race, BMI, blood pressure, lifestyle factors, disease prevalence, laboratory results, and medication usage among the four groups (p < 0.001).
When assessing the association between VAI and heart failure (HF), the study observed that participants with HF had higher VAI values compared to those without HF (p < 0.001). The analysis considered VAI both as a continuous and categorical variable. In the univariate logistic regression model, each unit increase in VAI resulted in a higher risk for HF (OR 1.04 [95% CI 1.02–1.05]). This association remained statistically significant in all multivariate logistic regression models after adjusting for various covariates, including demographics, comorbidities, and relevant clinical measurements. When VAI was analyzed as a categorical variable, subjects in the third quartile (Q3) had the highest risk for HF compared to the highest VAI quartile (OR 1.55 [95% CI 1.24–1.94]). There was a linear relationship between VAI and the odds ratio for HF, as shown in the restricted cubic spline model.
Subgroup analyses were conducted to examine the trend in effect size across different strata, considering variables such as sex, age, race, smoking status, hypertension, diabetes, CHD, liver disease, serum uric acid, and estimated glomerular filtration rate (eGFR). The results indicated positive associations in most subgroups, except for Mexican Americans and participants without hypertension. Importantly, there were no significant interactions between VAI and these stratified variables.
Conclusion
This study, utilizing data from a nationally representative sample of middle-aged and elderly individuals in the United States, revealed a significant association between the Visceral Adiposity Index (VAI) and heart failure (HF), which exhibited a nearly linear dose-response relationship. Subgroup analyses supported these findings, showing consistent relationships between VAI and HF across different populations.
The overall prevalence of HF in this study, approximately 2.4%, aligned with previous research. Previous studies have linked obesity with HF and identified obesity as a primary risk factor for conditions like hypertension, cardiovascular disease, and left ventricular hypertrophy, which are known precursors of HF. These studies have shown that even modest increases in body mass index (BMI) can elevate the risk of HF, with higher BMI values resulting in amplified risk. Moreover, abdominal obesity, often measured by waist circumference (WC), has been associated with cardiac metabolic diseases and cardiovascular risk.
However, traditional obesity measures, like BMI, may not adequately capture abdominal obesity, which can also play a crucial role in HF risk. This study demonstrated the utility of VAI as a simple, non-invasive metric for assessing abdominal obesity and its association with HF risk. Unlike more complex and expensive methods like abdominal CT or MRI, VAI can be calculated using readily available data such as WC, height, weight, and blood lipid levels (TG and HDL-c).
The findings from this study contribute to the understanding of the relationship between VAI and HF and underline the potential of VAI as a practical tool for assessing HF risk in clinical settings. By identifying individuals with high VAI, healthcare providers can offer targeted interventions to prevent HF, which is of particular importance given the rising prevalence and health impact of HF.
It is worth noting that while the study provided valuable insights, it had limitations, including its cross-sectional nature, reliance on self-reported HF diagnosis, and the inability to distinguish between different types of HF. Further research is needed to establish causality and investigate the relationship between VAI and specific HF subtypes.
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