Importance of Measured Body Fat,Visceral Adiposity Index,and Lipid Accumulation Product Index in Predicting Cardiometabolic Risk Factors

Abstract

Background:

Obesity, which is defined as excessive fat accumulation in the body, is an important public health problem.

Objective:

The aim of this study was to compare measured body fat and the visceral adiposity index (VAI) and lipid accumulation product (LAP) index used to predict cardiometabolic risk (CMR) factors.

Methods:

This study was performed retrospectively by scanning the files of 817 participants who had bioelectrical impedance analysis (BIA) results and routine blood tests. The VAI and the LAP index were calculated using the appropriate formulas.

Results:

Of the 817 participants included in the study, 67.9% (n = 555) were female, 32.1% (n = 262) were male, and the mean age was 36.14 ± 11.4 (18–65) years. The mean body mass index (BMI) was 29.90 ± 6.6 kg/m² and 24.2% (n = 198) of the participants were of normal weight (BMI <25 kg/m²), while 75.8% (n = 619) were overweight or obese (BMI ≥25 kg/m²). Body fat quantity was higher in females than in males. As BMI increased, the body fat quantity measured by BIA also increased (P < 0.001). The mean LAP index of men was higher than the mean LAP index of women in both the normal-weight group and the obese group (P = 0.025 and P = 0.033, respectively). One unit increase in visceral fat percentage resulted in a 77.9% increase in CMR.

Conclusions:

It may not be sufficient to use only BMI to predict obesity and related cardiometabolic diseases. According to the study findings, it was determined that the increase in visceral fat percentage significantly increases the CMR.

Introduction

Obesity, which has become an increasingly important public health problem in the world, is defined as widespread or localized excess fat accumulation in the body.¹ The intake of energy is higher than the energy consumed and the storage of this energy accumulated in the body as adipose tissue causes obesity.² Sedentary lifestyle, irregular nutrition, genetic predisposition, age, gender, and some psychological and metabolic diseases increase the risk of obesity. Body mass index (BMI) is generally used in defining obesity, grading it, and determining treatment principles. It is calculated as the ratio of body weight to height squared (kg/m²). BMI is classified as overweight if it is between 25 and 29.9 kg/m² and as obese if it is 30 kg/m² or above.^3,4

Body fat varies among people of the same height and body weight, as well as among men and women of the same age group. It is generally known that the weight of body fat is higher in women than in men. In women, 26.9% of total body weight consists of fat mass; in men, this value is 14.7%.⁵ Many different methods are used to measure the amount of visceral fat, such as computed tomography (CT), magnetic resonance imaging, dual-energy X-ray absorptiometry (DEXA), and bioelectrical impedance analysis (BIA).^6,7 CT, magnetic resonance, and DEXA are high cost and time-consuming, cause radiation exposure, and are difficult to apply for morbidly obese patients, preventing the use of these techniques in daily clinical practice. BIA, developed on the basis of resistance to electrical current passing through the body to determine the body fat percentage, muscle, bone weight, and water ratio, is an alternative to other measurement methods.⁸

The visceral adiposity index (VAI) and lipid accumulation product (LAP) index have been used successfully to predict insulin resistance (IR) and cardiometabolic risk (CMR) factors as indicators of visceral adipose tissue functions. The VAI and LAP index are easily calculated with a mathematical formula using anthropometric (BMI and waist circumference [WC]) and biochemical parameters high-density lipoprotein cholesterol (HDL-C) and triglyceride (TG).^9,10 In this study, we aimed to compare measured body fat, the VAI, and LAP index used to predict CMR factors.

Methods

Type, place, and university of the study

This study was applied retrospectively by scanning the files of individuals aged 18 and older who visited our outpatient clinic between 2013 and 2015. BIA measurements for any reason and the files of patients who had routine blood tests during the same period were examined and the study was completed with 817 participants, taking into account the exclusion criteria.

The following individuals were excluded from the study: those with incomplete information; those with congenital or subsequent body abnormalities; patients with cancer; patients with diseases that would affect blood sugar and lipid parameters and those using drugs for this; patients with bone, endocrine, and metabolic diseases; pregnant women and those in puerperium; those receiving medical or surgical obesity treatment; diabetes patients; those receiving treatment for hypertension; and those younger than 18 years.

Ethics committee approval

Before starting the study, approval of the Necmettin Erbakan University Meram Medical Faculty's Non-interventional Clinical Research Ethics Committee (2020/2272) was obtained.

Data collection

In this period, the files of individuals who applied to our outpatient clinic were examined and sociodemographic characteristics such as age, gender, marital status, educational status, and smoking status, and fasting blood glucose and lipid parameter results, were recorded in a separate program for analysis. Weight, height, and WC were recorded as anthropometric measurements from the study participants' files, and BMI was calculated as weight (kg)/height (m²). BMIs of 18.50–24.99 kg/m² were evaluated as normal weight, 25.0–29.99 kg/m² as overweight, and ≥30.0 kg/m² as obese.

Bioelectrical impedance analysis

The basic working principle of BIA is to measure the electric current at different frequencies in accordance with the structure of that region (fat, muscle, bone, etc.) while passing through different body regions. The alternating current through the tissues shows a voltage drop due to the tissue-specific resistance. It is cheaper than other methods in measuring body compositions; it is simple, safe, and practical to use. The measurement should be carried out at room temperature during the day, while dressed, but with shoes and socks removed, standing still with an empty bladder. In this study, the body weight (kg), muscle weight (kg), bone weight (kg), visceral fat weight (kg), and body fat ratio (%) values of the participants were measured with a TANITA-BC 418 MA segmental body composition analyzer.

CMR factors

The cardiovascular risk factors of the patients were determined by using the Atherosclerotic Cardiovascular Disease (ASCVD) 2013 Risk Calculator from the American Heart Association. According to the calculator used, male gender, age, total cholesterol, HDL-C, systolic blood pressure, smoking status, diabetes mellitus, and being on hypertensive treatment are major risk factors. Cardiovascular risks of the participants (determines 10-year risk of heart disease or stroke for this patient) were calculated with the MD+CALC program https://www.mdcalc.com/ascvd-atherosclerotic-cardiovascular-disease-2013-risk-calculator-aha-acc

Visceral adiposity index

VAI (Women): [WC/(36.58 + 1.89 × BMI)] × (TG/0.81) × (1.52/HDL-C)

VAI (Men): [WC/(39.68 + 1.88 × BMI)] × (TG/1.03) × (1.31/HDL-C)

LAP index

LAP (Men): ([WC (cm) −65] × TG)

LAP (Women): ([WC (cm) −58] × TG)

Statistical analysis

SPSS 20.0 for Windows was used for statistical analysis. Descriptive statistics of continuous variables are given as means and standard deviations, and descriptive statistics of categorical data are given in terms of frequency and percentage. Compliance with normal distribution was evaluated by the Kolmogorov–Smirnov test. Accordingly, one-way ANOVA was used to compare quantitative data showing normal distribution. The post hoc Tukey test was performed when there was a significant difference between groups. The chi-square test was used to compare categorical data. Pearson correlation analysis was used for correlations between parameters. Correlation coefficients (r) of 0.00–0.24 were evaluated as weak relationships, 0.25–0.49 as moderate, 0.50–0.74 as strong, and 0.75–1.00 as very strong. Linear regression analysis was performed between two variables and the regression coefficient was calculated. The results were evaluated with 95% confidence intervals and a significance level of P < 0.05.

Results

A total of 817 participants were included in the study; 32.1% (n = 262) were male, 67.9% (n = 555) were female, and the mean age was 36.14 ± 11.4 years (range: 18–65). The average BMI of the participants was 29.90 ± 6.6 kg/m² and 24.2% (n = 198) of the participants were of normal weight (BMI <25 kg/m²), while 75.8% (n = 619) were overweight or obese (BMI ≥25 kg/m²). The mean age of those who were overweight and obese was 37.8 ± 11.0 years, the mean age of those who were normal was 30.67 ± 10.7 years, and this difference was statistically significant (P < 0.001). The sociodemographic characteristics of the participants are shown in Table 1.

Table 1.

Comparison of Participants' Sociodemographic Characteristics and Body Mass Index

	Normal BMI <25 kg/m², mean ± SD		Overweight-obese BMI ≥25 kg/m², mean ± SD		P
Age, years	30.67 ± 10.7		37.8 ± 11.0		<0.001
	n	%	n	%	Total	P
Gender
Male	81	30.9	181	69.1	262	0.003
Female	117	21.1	438	78.9	555	0.003
Marital status
Married	111	17.6	518	82.4	629	<0.001
Single	87	46.3	101	53.7	188	<0.001
Occupational
Employed	146	37.8	240	62.2	386	<0.001
Unemployed/retired	52	12.1	379	87.9	431	<0.001
Education status
≤Primary school	22	7.2	283	92.8	305	<0.001
Secondary school	68	30.9	152	69.1	220
University educated	108	37.0	184	63.0	292
Smoking status
Smoker	89	38.7	141	61.3	230	<0.001
Nonsmoker	109	18.6	478	81.4	587	<0.001

BMI, body mass index; SD, standard deviation.

The mean WC of the women participating in our study was 93.89 ± 15.5 cm, mean body fat percentage was 37.39% ± 8.1%, mean visceral fat amount was 7.52 ± 4.0 kg, mean VAI score was 4.56 ± 2.5, and mean LAP index was 48.01 ± 33.2. The mean WC of the men was 97.78 ± 14.6 cm, mean body fat percentage was 22.92% ± 7.7%, mean visceral fat amount was 8.67 ± 5.3 kg, mean VAI score was 4.73 ± 2.5, and mean LAP index was 51.14 ± 33.7. As a result of BIA measurements, the average body fat percentage of underweight participants was 13.41% ± 5.5%, of normal-weight participants was 23.64% ± 8.0%, of overweight participants was 30.45% ± 7.6%, and of obese participants was 39.71% ± 7.2%. As BMI increased, the amount of body fat also increased (P < 0.001). According to the BMI, the body fat amount of females was higher in both the normal-weight group and the obese group compared with the body fat amount of men, and this difference was statistically significant (P < 0.001). The mean LAP index of men was higher than the mean of females in both the normal-weight group and the obese group (P = 0.025 and P = 0.033, respectively). There was no statistically significant relationship between the VAI score and gender or BMI (P = 0.322 and P = 0.210, respectively) (Table 2).

Table 2.

According to Body Mass Index and Gender of the Participants, Bioelectrical Impedance Analysis, Visceral Adiposity Index, and Lipid Accumulation Product

	Normal BMI <25.0 kg/m², mean ± SD/median (min–max)		Overweight-obese BMI ≥25.0 kg/m², mean ± SD/median (min–max)
	Female	Male	Female	Male
WC, cm	76.74 ± 8.7	83.91 ± 9.0	98.4 ± 13.6	103.98 ± 12.1
	P < 0.001 ^a		P < 0.001 ^a
Body fat, %	26.63 ± 6.5	15.67 ± 6.6	40.27 ± 5.8	26.17 ± 5.8
	P < 0.001 ^a		P < 0.001 ^a
Body water, kg	53.17 ± 6.6	60.30 ± 8.1	44.29 ± 4.1	52.8 ± 4.7
	P < 0.001 ^a		P < 0.001 ^a
Bone mass, kg	2.17 ± 0.2	2.92 ± 0.4	2.55 ± 0.2	3.37 ± 0.4
	P < 0.001 ^a		P < 0.001 ^a
Muscle mass, kg	40.64 ± 4.0	54.07 ± 7.3	47.68 ± 5.5	64.70 ± 8.3
	P < 0.001 ^a		P < 0.001 ^a
Visceral fat	2.0 (1 to 10)	3.0 (1 to 10)	8.0 (2 to 29)	11.0 (1.6 to 32)
	P = 0.059 ^b		P < 0.001 ^b
VAI	3.01 (1 to 13.8)	3.3 (0.7 to 11)	4.3 (0.6 to 15.5)	4.5 (1.0 to 14.9)
	P = 0.322 ^b		P = 0.210 ^b
LAP	16.8 (0.5 to 75)	25.1 (−2.8 to 79.2)	48.7 (−38 to 186)	54.0 (6.48 to 160)
	P = 0.025 ^b		P = 0.033 ^b
CMR	0.4 (0.1 to 7.6)	1.5 (0.3 to 28.8)	0.6 (0.1 to 8.6)	1.9 (0.3 to 34.8)
	P < 0.001 ^b		P < 0.001 ^b

Bold values are p < 0.05 significance level.

Independent t-test.

Mann–Whitney U test.

CMR, cardiometabolic risk; LAP, lipid accumulation product; VAI, visceral adiposity index; WC, waist circumference.

When the correlations between BMI, BIA measurements, VAI, and LAP index were examined, a very strong positive correlation was found between BMI and body fat percentage and visceral fat amount (r = 0.750, r = 0.777, P < 0.001, respectively). While there was a strong positive correlation between BMI and LAP index, there was a weak correlation detected between BMI and VAI (r = 0.619, r = 0.256, P < 0.001, respectively). Similarly, there was a moderate positive correlation between body fat percentage and LAP index, whereas a weakly significant relationship was found with VAI (r = 0.391, r = 0.154, P < 0.001, respectively) (Table 3).

Table 3.

The Correlation Between Bioelectrical Impedance Analysis, Visceral Adiposity Index, and Lipid Accumulation Product

	WC	BMI	Body fat	Visceral fat	VAI	LAP	CMR
WC
r	1
P	1
BMI
r	0.729^**	1
P	<0.001	1
Body fat
r	0.470^**	0.750^**	1
P	<0.001	<0.001	1
Visceral fat
r	0.719^**	0.779^**	0.513^**	1
P	<0.001	<0.001	<0.001	1
VAI
r	0.410^**	0.256^**	0.154^**	0.288^**	1
P	<0.001	<0.001	<0.001	<0.001	1
LAP
r	0.787^**	0.619^**	0.391^**	0.610^**	0.769^**	1
P	<0.001	<0.001	<0.001	<0.001	<0.001	1
CMR
R	0.196^**	0.022^**	0.200^**	0.296^**	0.233^**	0.261^**	1
P	<0.001	0.531	<0.001	<0.001	<0.001	<0.001	1

Bold values are p < 0.05 significance level.

Correlation is significant at the 0.01 level (2-tailed).

Univariate and multivariate linear regression analysis was performed to determine the independent risk factors of BMI. According to the univariate model results, the effect of visceral fat on CMR was positive and statistically significant (β = 0.779, P < 0.001). One unit increase in visceral fat results in 77.9% increase in CMR. Visceral fat explains 60.7% of the change in CMR. According to the results of multiple linear regression analysis, the effect of only visceral fat on CMR was found to be positive and statistically significant (β = 0.296, P < 0.001) (Table 4).

Table 4.

Linear Regression Analysis of Variables for Cardiometabolic Risk

	One variable model				Multivariate model (stepwise)
	R²	β	t	P	R²	β	t	P
WC, cm	0.039	0.196	5.721	<0.001
Body fat, %	0.040	0.200	−5.836	<0.001
Body water, kg	0.033	0.181	5.236	<0.001
Bone mass, kg	0.087	0.259	7.661	<0.001
Muscle mass, kg	0.093	0.015	8.368	<0.001
Visceral fat	0.607	0.779	8.832	<0.001		0.296	8.832	<0.001
VAI	0.066	0.256	6.842	<0.001
LAP	0.384	0.619	3.783	<0.001
BMI	0.022	−0.022	−0.626	0.531

Bold values are p < 0.05 significance level.

Discussion

The results of this study are important because it is one of the few studies comparing the results of the VAI, the LAP index, and BIA, which are visceral fat markers of obesity, an important public health problem worldwide. The average age of overweight and obese individuals included in this study was higher than the average age of normal-weight individuals. In a case/control study conducted by Tiryaki et al. with 99 obese, overweight, and normal-weight women between the ages of 55 and 70, body compositions were evaluated and weight and BMI increased as age progressed.¹¹ The increasing frequency of obesity with age may be due to the loss of energy as age increases, the decrease of basal metabolic rate with age, and the decrease in physical activity as age progresses.

BMI, WC, and waist/hip ratio are frequently used in the diagnosis of adiposity. Although obesity is related to total body fat tissue, BMI does not provide sufficient information about the distribution of fat in the body. The BIA method, which has been developed in recent years based on different lean tissue masses and the electrical permeability of adipose tissue, has begun to be used more frequently, since it provides quick results with a portable device and without requiring user experience.⁷ In the present study, the BIA method was used with an appropriate device to evaluate body composition. As a result of this measurement, body fat and visceral fat were found to be higher in women and those with higher BMI. Similarly, Lazzer et al., in a study investigating the relationship between age, gender, and body composition in 8780 obese patients, found that body fat percentage was higher in women.¹² There are many factors that affect body composition, particularly genetic factors, lifestyle, dietary habits, and family structure. Body composition and segmental distribution of fat and muscle mass may vary with age, but they also vary by gender.¹³

In the literature, there is a positive correlation between visceral fat amount and WC and BMI.¹⁴ In a study comparing different methods for visceral fat measurement, it was found that the amount of fat measured by BIA was similar to the amount of fat measured by CT and ultrasonography (USG), and that the percentage of body fat was positively correlated with BMI in both genders.¹⁵ In the present study, it was shown that there is a positive correlation between body fat percentage and visceral fat amount and BMI in accordance with the literature. In a cohort study in which 78 patients who underwent surgery due to obesity were followed for 2 years, it was found that the body fat percentages measured by DEXA rapidly decreased in patients whose BMI decreased after the operation. The authors concluded that body fat percentage is more important than BMI in diagnosing obesity and selecting patients for surgery.¹⁶

It has been proven in several studies that excessive increase in body fat can cause an increase in the prevalence of multiple cardiovascular risk factors such as hyperinsulinemia, IR, and dyslipidemia, thereby inducing cardiovascular diseases.^17,18 The LAP index and VAI are mathematical models that use both anthropometric and metabolic parameters and are recommended as sensitive markers of visceral obesity and useful indices in the evaluation of IR and CMR in clinical practice. The VAI is an indicator of visceral adipose tissue and has been associated with CMR, cardiovascular disease, and mortality in the general population. It is associated with visceral adiposity, increased adipocytokine production, proinflammatory activity, impaired insulin sensitivity, increased risk of developing diabetes, dyslipidemia (increased TG levels, decreased HDL-C), hypertension, atherosclerosis, and high mortality rate.^19

–22 In a study examining VAI scores in patients with hypothyroidism, weight, BMI, and VAI levels were found to be higher in patients compared with the control group, and a positive correlation was found between VAI and thyroid-stimulating hormone.²³ The LAP index, which is easily calculated and reliable, is suggested to be a useful marker for women with polycystic ovary syndrome (PCOS) in relation to homeostasis model assessment of insulin resistance (HOMA-IR) and for screening prediabetic individuals.²⁴ In another study, it was suggested that the LAP index could better predict the incidence of cardiovascular disease compared with BMI.¹⁰ In the present study, there was a strong positive correlation between both body fat amount and BMI and LAP index, while a weakly significant relationship with VAI was detected.

Limitations

In the presented study, the files of patients were retrospectively scanned. Our results cannot be generalized, as the study was conducted with a limited number of participants in a particular community. Furthermore, risk factors for cardiovascular diseases and diabetes could not be evaluated due to missing information in the files. However, it is recommended that future studies be cohort type studies that will cover a long follow-up period and take risk factors into consideration with larger numbers of participants.

Conclusions

In the presented study, it was determined that the amount of body fat measured by BIA correlated positively with CMR, and a one unit increase in body fat percentage resulted in a 77% increase in CMR. The amount of body fat was higher in females than males regardless of BMI. While there was a moderate positive relationship between BMI and body fat percentage and the LAP index, a weakly significant relationship was detected with VAI. While evaluating obesity in every age group in primary health care, which is the first point of contact of individuals, using not only BMI but also other methods to determine the amount of body fat will enable early detection of metabolic and cardiovascular diseases in these individuals.

Footnotes

Author Disclosure Statement

No conflicting financial interests exist.

Funding Information

No funding was received for this work.

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