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 Table of Contents  
ORIGINAL ARTICLE
Year : 2019  |  Volume : 31  |  Issue : 4  |  Page : 620-628

Association of new obesity indices; visceral adiposity index and body adiposity index, with metabolic syndrome parameters in obese patients with or without type 2 diabetes mellitus


Department of Internal Medicine, Faculty of Medicine, Zagazig University, Zagazig, Egypt

Date of Submission03-Jan-2019
Date of Acceptance06-Feb-2019
Date of Web Publication18-Aug-2020

Correspondence Address:
MD Nearmeen M Rashad
Department of Internal Medicine, Faculty of Medicine, Zagazig University, 44519, Zagazig
Egypt
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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/ejim.ejim_4_19

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  Abstract 


Background Obesity is the cornerstone of metabolic syndrome (MetS); it is not possible to use BMI to differentiate between lean mass and fat mass. We aimed to investigate, for the first time, the possible association of new obesity indices; visceral adiposity index (VAI) and body adiposity index (BAI), with parameters of MetS in Egyptian obese patients.
Patients and methods This was a case–control study that included unrelated 150 obese patients and 50 healthy controls. Obese patients were then subdivided into two subgroups, nondiabetic patients (n=85) and 65 patients with type 2 diabetes mellitus. We measured the anthropometric measures; BMI, waist/hip ratio, waist/height ratio, BAI, and VAI.
Results Among obese patients, we found significant positive correlations between parameters of MetS and obesity indices. Among obesity indiced, the highly significant positive correlations were found between VAI and parameters of MetS. After adjusting for the traditional risk factors, logistic regression analysis test found that the VAI value was the best predictor of type 2 diabetes mellitus in comparison with BMI and BAI. Receiver operating characteristic curve was used to assess the power of obesity indices; the sensitivity and the specificity of BMI were 94.7 and 99.9%, for VAI, they were 74.4 and 99.9%, and, for BAI, they were 83.3 and 58%, respectively.
Conclusion BMI is still the most powerful diagnostic tool for obesity. Although, in certain conditions, where there are limitations of using BMI, we can use other obesity indices, VAI and BAI could be used to discriminate cardiovascular risk among obese patients.

Keywords: body adiposity index, BMI, metabolic syndrome, obesity, visceral adiposity index


How to cite this article:
Rashad NM, Emad G. Association of new obesity indices; visceral adiposity index and body adiposity index, with metabolic syndrome parameters in obese patients with or without type 2 diabetes mellitus. Egypt J Intern Med 2019;31:620-8

How to cite this URL:
Rashad NM, Emad G. Association of new obesity indices; visceral adiposity index and body adiposity index, with metabolic syndrome parameters in obese patients with or without type 2 diabetes mellitus. Egypt J Intern Med [serial online] 2019 [cited 2020 Sep 22];31:620-8. Available from: http://www.esim.eg.net/text.asp?2019/31/4/620/292228




  Introduction Top


The metabolic syndrome (MetS) is a set of interrelated risk factors including hypertension, dyslipidemia, obesity, and high blood glucose [1],[2]. Obesity is a key phenotype leading to atherogenic and diabetogenic profiles [3]. Insulin resistance, together with central/abdominal or visceral obesity, have been proposed as key risk factors in the development of the MetS [4],[5],[6].

Obesity is actually an epidemic problem in the world; it has become truly a global problem affecting countries rich and poor. An estimated 500 million adults worldwide are obese, and 1.5 billion are overweight or obese [7]. Particularly the prevalence of obesity in Egypt has increased at an alarming rate during the last three decades, affecting 22% of adult male individuals and 48% of adult female individuals [8]. It is associated with several comorbidities including hypertension, dyslipidemia, type 2 diabetes mellitus (T2DM), coronary heart disease, stroke, osteoarthritis, sleep apnea, and respiratory problems, as well as some types of cancers [9],[10].

BMI is the widely used measure of obesity. However, BMI is unable to differentiate between lean mass and fat mass, and hence it is limited by differences in body adiposity for a given BMI across age, sex, and ethnicity [11]. Waist circumference (WC), waist-to-hip ratio (WHR), and waist-to-height ratio (WHtR) have been developed and studied. WC has been proposed to be the best among these measures, with excellent correlation with abdominal imaging and high association with cardiovascular disease risk factors, especially diabetes [12],[13]. However, WC does not account for differences in height, thereby, potentially, overevaluating and underevaluating risk for tall and short individuals, respectively [14]. Consequently, several researchers independently proposed the WHtR as an alternative to WC.

Notably, the body adiposity index (BAI) was proposed in 2011 [15]. This is a composite index based on hip circumference and height. The authors suggested that this index showed a high correlation with body fat measured using dual-energy radiography absorptiometry. They added that this correlation was higher than the correlation between BMI and body fat, measured using dual-energy radiography absorptiometry among both African–American and Mexican–American men and women [15]. Thus, BAI overcomes the limitation of BMI in differentiating between fat and lean mass. Thus, they concluded that the BAI is a useful predictor of obesity and suggested that it involves more simple measurements because weight is not needed [15].

In addition, visceral adiposity index (VAI) is a mathematical model that uses both anthropometric (BMI and WC) and functional [triglycerides (TG) and high-density lipoprotein (HDL) cholesterol] simple parameters [16]. This index, which could be considered a simple surrogate marker of visceral adipose dysfunction, showed a strong association with both the rate of peripheral glucose utilization during the euglycemic–hyperinsulinemic clamp and with visceral adipose tissue measured with MRI. Interestingly, it showed a strong independent association with both cardiovascular and cerebrovascular events [14] and showed better predictive power for incident diabetes events than its individual components (WC, BMI, TG, and HDL cholesterol) [17].

On the basis of the previous facts, obesity is the cornerstone of the MetS; BMI is unable to differentiate between lean mass and fat mass. Further research is warranted, and, to address this need, we have focused on various anthropometric measures including the recently proposed BAI and VAI, which have not been extensively analyzed and compared with BMI. Therefore, the purpose of this current novel study is to clarify the possible relationships of traditional obesity indices (WC, BMI, and WHtR) and new obesity indices (BAI and VAI) with parameters of MetS in Egyptian obese women.


  Patients and methods Top


Patients

This study included 200 unrelated patients. One hundred fifty obese patients (BMI>30) recruited from diabetes and endocrinology outpatient clinic of Internal Medicine Department of Zagazig University Hospitals and 50 healthy lean controls, who were matched to cases by age, sex, and ethnic origin.

Obese patients were stratified into two subgroups: nondiabetic patients (n=85) and T2DM patients (n=65), The diagnosis of T2DM was according to the American Diabetes Association criteria reported in 2017: fasting plasma glucose levels of more than or equal to 126 mg/dl (7.0 mmol/l) or 2-h postprandial plasma glucose levels of more than or equal to 200 mg/dl (11.1 mmol/l). All patients were subjected to thorough history taking and full clinical assessment including blood pressure, WC (a level midway between the lowest rib and the iliac crest), and hip circumference (widest diameter over the greater trochanters). Anthropometric variables including BMI were calculated as weight (kg)/height (m2), WHR as WC (cm)/hip circumference (cm), and WHtR was calculated as waist (cm)/height (cm). Moreover, VAI in female individuals was calculated as follows:

. It normally equals 1 in healthy nonobese patients with normal adipose distribution and normal TG and HDL levels [15]. Finally, BAI, which is approximately equal to the percentage of body fat for adult men and women of differing ethnicities, was calculated as

[16].

The MetS was diagnosed according to International Diabetes Federation criteriaas WC more than or equal to 80 cm in women and more than or equal to 94 cm in men and the presence of at least two of the following characteristics: (a) fasting blood glucose more than or equal to 100 mg/dl or previously diagnosed impaired fasting glucose; (b) blood pressure more than or equal to 130/85 mmHg or treated for hypertension; (c) TG more than or equal to 150 mg/dl; (d) HDL cholesterol less than 40 mg/dl in men or less than 50 mg/dl in women or taking treatment for low HDL [17].

Patients with cancer, stroke, or liver, kidney, thyroid, and cardiovascular or any active inflammatory diseases were excluded from this study. None of the participants had history of abdominal surgery that could have an impact on abdominal fat distribution, as well as receiving medications that affect endocrine parameters, glucose metabolism, and/or for weight reduction or participating in a dietary or exercise program during the preceding 6 months or the immediately preceding month (anti-inflammatory drugs). There was no concurrent minor infection reported during the study or during the month preceding the study. The ethical committee of Faculty of Medicine, Zagazig University, approved our study protocol, and all participants signed the written informed consent.

Blood sampling

Blood samples were drawn from all patients after an overnight fast and divided into three portions: 1 ml of whole blood was collected into evacuated tubes containing EDTA for glycated hemoglobin (HbA1c), and 1 ml of whole blood was collected into evacuated tubes containing potassium oxalate and sodium fluoride (2 : 1) for fasting blood plasma glucose (FPG). Serum were separated immediately from the remaining part of the sample and stored at −20°C until analysis.

Biochemical analysis

We determined FPG levels using the glucose oxidase method (Spinreact, Girona, Spain). Total cholesterol, HDL cholesterol, and TG levels were measured by routine enzymatic methods (Spinreac). The low-density lipoprotein (LDL) cholesterol level was calculated using the Friedewald formula [18].

Immunochemical assays

Fasting serum insulin (FSI) concentrations were measured using high-sensitivity linked immunosorbent assay kit provided by (Biosource Europe S.A., Nivelles, Belgium). Homeostasis model assessments of insulin resistance (HOMA-IR) were calculated as follows: [FSI (mU/ml)×FPG (mg/dl)/405], and β-cell function (HOMA-β) was calculated as {20×[FSI (lU/ml)]/[FPG (mmol/l)−3.5]} [19].

Statistical analysis

Statistical analyses were performed using the statistical package for the social sciences for Windows (version 17.0; SPSS Inc., Chicago, Illinois, USA). Data were expressed using descriptive statistics (mean±SD) and were analyzed using the t test. Pearson’s correlation coefficient was used to assess the association between obesity indices and other studied metabolic parameters in obese women. Receiver operating characteristic (ROC) analysis was performed to assess the potential accuracy of BMI, VAI, and BAI, the area under the curve (AUC), and the cutoff values for diagnosis of T2DM among obese patients. A stepwise multiple linear regression analysis was performed to detect the main predictors of VAI and BAI values in the obese group. Logistic regression analysis was performed to assess the predictive powers of VAI as well as BAI in the diagnosis of MetS in obese patients with and without T2DM. We considered P to be significant at less than 0.05 with a 95% confidence interval (CI).


  Results Top


Anthropometric and biochemical characteristics of the studied groups

Anthropometric and biochemical characteristics of the study patients are summarized in [Table 1]. Obese patients had significantly higher values of systolic blood pressure, diastolic blood pressure, fasting blood glucose, HbA1c values, FSI, HOMA-IR, total cholesterol, LDL, and TG levels compared with lean controls. Moreover, all obesity indices and parameters (WC, BMI, WHR, WHtR, BAI, and VAI) were significantly higher in obese women compared with lean patients. On the contrary, obese patients had significantly lower levels of HDL cholesterol and HOMA-B than healthy lean individuals (P<0.001).
Table 1 Anthropometric and biochemical characteristics of the studied groups

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General characteristics of obese patients stratified by fasting blood plasma glucose as diabetic and nondiabetic obese patients

We found statistically significant higher values of obesity indices BMI, WC, WHR, WHtR VAI, and BAI in obese T2DM patients than in nondiabetic obese patients (P<0.001). Moreover, obese T2DM patients had statistically significant higher values of systolic blood pressure, diastolic blood pressure, TC, LDL, FPG, FSI, HbA1c, and HOMA-IR. On the contrary, obese T2DM patients had significantly lower levels of HOMA-β than nondiabetic obese patients (P<0.001).

Correlations between anthropometric measures and parameters of metabolic syndrome in obese patients

Our results showed significant positive correlations between parameters of MetS, including WC, systolic and diastolic blood pressure, low HDL as well as TG, and all anthropometric measures in obese cases (BMI, WHR, WHtR, BAI, and VAI). Interestingly, among obesity indices, the highest positive correlation was found between VAI and parameters of MetS (P<0.001) ([Table 2] and [Table 3]).
Table 2 Laboratory and anthropometric parameters in nondiabetic obese and type 2 diabetes mellitus obese patients’ groups

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Table 3 Pearson’s correlation coefficient between anthropometric indices and parameters of metabolic syndrome among obese patients

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Multiple stepwise linear regression analyses in obese patients to assess the main independent parameters associated with visceral adiposity index

Stepwise linear regression analysis test revealed that VAI was independently correlated with TGs, HDL, fasting blood glucose, HOMA-IR, and HOMA-B (P<0.001) ([Table 4]).
Table 4 Multiple stepwise linear regression analysis testing the influence of the main independent variables against visceral adiposity index (dependent variable) in obese women

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Multiple stepwise linear regression analyses in obese patients to assess the main independent parameters associated with body adiposity index

Stepwise linear regression analysis test found that BAI was independently correlated with fasting blood glucose, BMI, total cholesterol TGs, and diastolic blood pressure (P<0.001) ([Table 5]).
Table 5 Multiple stepwise linear regression analysis testing the influence of the main independent variables against body adiposity index (dependent variable) in obese women

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Logistic regression analysis evaluating the association of BMI, visceral adiposity index, and body adiposity index with type 2 diabetes mellitus among obese patients

After adjusting for the traditional risk factors, logistic regression analysis test was carried out to evaluate the predictor of T2DM among obese patients. VAI, BAI, and BMI were statistically significant predictors of T2DM among obese patients (P<0.001) ([Table 6]), whereas the odds ratio of VAI (odds ratio=3.498, 95% CI=1.987–6.158) was higher than that of BAI and BMI (odds ratio=1.149, 95% CI=1.085–1.218; odds ratio=0.791, 95% CI=0.728–0.860, respectively).
Table 6 Logistic regression analysis of body adiposity index, visceral adiposity index and BMI in type 2 diabetic versus nondiabetic obese women

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Accuracy of visceral adiposity index and BMI for discriminating type 2 diabetes risk among obese patients by receiver operating characteristic analysis

We further investigated the potential diagnostic value of VAI and BMI by ROC curves, which are presented in [Figure 1]. In obese patients, when we discriminate type 2 diabetes among nondiabetic patients, the cutoff values of BMI and VAI were 28.82 and 7.31, and the AUC values were 0.958 (95% CI=0.931–0.985) and 0.952 (95% CI=0.923–0.980), respectively. In addition, the sensitivity and the specificity of BMI were 94.7 and 99.9% and those of VAI were 74.4 and 99.9%, respectively. Thus VAI as well as BMI could be useful diagnostic tests to discriminate T2DM from nondiabetic obese patients.
Figure 1 ROC curve for both BMI and VAI in discriminating obese women with T2DM from those without diabetes. ROC, receiver operating characteristic; T2DM, type 2 diabetes mellitus; VAI, visceral adiposity index.

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Accuracy of body adiposity index and BMI for discriminating type 2 diabetes risk among obese patients by receiver operating characteristic analysis

We further investigated the potential diagnostic value of BAI and BMI using ROC curves, which are presented in [Figure 2]. In obese patients, when we discriminate type 2 diabetes among nondiabetic patients, the cutoff values of BMI and BAI were 28.82 and 27.58 and the AUC values were 0.958 (95% CI=0.931–0.985) and 0.770 (95% CI=0.703–0.837), respectively. In addition, the sensitivity and the specificity of BMI were 94.7 and 99.9% and those of BAI were 83.3 and 58%, respectively.
Figure 2 ROC curve for both BMI and BAI in discriminating obese women with T2DM from those without diabetes. BAI, body adiposity index; ROC, receiver operating characteristic; T2DM, type 2 diabetes mellitus.

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  Discussion Top


Obesity plays a key role in the pathophysiology of all MetS parameters and can be considered as an independent predictor of MetS [6]. Pradeep et al. [20] suggested that central obesity as an indicator of body fat can be easily and cost-effectively estimated by measuring BMI and WC, which might discriminate MetS from non-MetS status.

BMI was not a good indicator of MetS risk, particularly when it is the only indicator, because it is not able to differentiate between adipose and muscle tissue. Taken together, we investigated, in the present study, for the first time, the possible association of new obesity indices, BAI and VAI, as well as traditional obesity indices (WC, BMI, WHtR) and parameters of MetS in Egyptian obese patients.

The results presented herein are innovative, as this study performs a robust evaluation of new obesity indices VAI and BAI as diagnostic tests of MetS. Noteworthy, our results confirmed that VAI and BAI values were significantly higher in obese patients compared with the lean group. Moreover, in the T2DM group, the values of VAI and BAI were higher compared with the nondiabetic group.

As regards correlation of anthropometric measures with the parameters of MetS, we found significant positive correlations between parameters of MetS, including WC, systolic and diastolic blood pressure, low HDL, as well as TG, and all anthropometric measures in obese cases (BMI, WHR, WHtR, BAI, and VAI). Interestingly, among obesity indices, the highest positive correlation was found between VAI and parameters of MetS.

In agreement with our finding, Amato et al. [15], among an age-stratified Caucasian Sicilian population, found that the cut-off points of VAI were proved to be strongly associated with MetS; they explained that VAI represents physical (BMI and WC) and metabolic parameters (TG and HDL cholesterol), and may indirectly reflect other nonclassical risk factors, that is, cytokines and plasma-free fatty acids.

In the current study, we found a significant positive correlation between BAI and all parameters of MetS, except WC and diastolic blood pressure among obese cases.

Similar to our results, Bergman et al. [16] reported that BAI is a good tool to estimate adiposity in the Caucasian population. Furthermore, they suggested that it is more practical and easier than other complex mechanical methods.

As regards traditional obesity indices (WC, BMI, and WHR), our results revealed that, among them, BMI had the best positive correlation with MetS parameters. Nonetheless, the new obesity indices had the best positive correlation with MetS parameters.

Gharipour et al. [21] suggested that WC could be considered as a useful anthropometric assessment for prediction of T2DM and MetS. These results were in accordance with two prospective studies showing that WC and WHtR performed equally well in their ability to predict T2DM in Pima Indians [22],[23],[24].

In this study, we attempted to point out the association between MetS and WHtR and to compare between WHtR and BMI. Our results revealed that BMI had a strong positive correlation with parameters of MetS compared with WHtR.

On the contrary, study by Hsieh et al. [25] found that WHtR was a practical and simple anthropometric measurement for identifying patients of both sexes with higher metabolic risk.

In contrast to our results, previous studies found that WHtR was the most predictive measure of T2DM, followed by BMI [26],[27],[28],[29],[30]. The diverse results summarized above contributed to differences in race/ethnicity.

Although the BMI could be used as a diagnostic test of obesity and its related metabolic complications, we still indeed need other obesity indices to overcome the limitations of BMI with regard to measurement of excess fat. Accordingly, we analyzed our data by ROC to estimate the sensitivity and specificity of VAI as well as BAI. Our results detected that the cutoff values of BMI, VAI, and BAI were 28.82, 7.31, and 27.58 and the AUC values were 0.958 (95% CI=0.931–0.985), 0.952 (95% CI=0.923–0.980), and 0.770 (95% CI=0.703–0.837), respectively. In addition, the sensitivity and specificity of BMI were 94.7 and 99.9%, those of VAI were 74.4 and 99.9%, and those of BAI were 83.3 and 58%.

The main finding in the current study is that BMI is still the most powerful diagnostic tool for obesity. Although, in certain conditions where there are limitations of using BMI, for example, heart failure and ascites, we can use other obesity indices; VAI and BAI could be used to discriminate cardiovascular risk among obese patients. Interestingly, the power of VAI was sensitive and specific in parallel to BMI in the diagnosis of metabolic risk among T2DM patients. Our study explored that after adjusting for the traditional risk factors, the logistic regression analysis test found that the odds ratio of VAI value was the best predictor of T2DM in comparison with BMI and BAI among obese patients, P value less than 0.05.

In a study by Amato et al. [16] among an age-stratified Caucasian Sicilian population, the cut-off points of VAI were proved to be strongly associated with MetS.

On the contrary, Bennasar-Veny and colleagues reported that BAI does not overcome the limitations of BMI and that it is not a good tool to measure metabolic risk in the Caucasian population. They concluded that BAI is less useful not only than BMI but also than other adiposity indexes such as WHtR, WHR, and WC [31].


  Conclusion Top


In conclusion, unusual anthropometric measurements such as BAI and VAI, which represent physical (BMI and WC) and metabolic parameters (TG and HDL cholesterol), may indirectly reflect other nonclassical risk factors associated with the development of obesity-related comorbidities. Further future multicenter studies with bigger sample size are needed to validate our findings.

Financial support and sponsorship

Nil.

Conflicts of interest

There are no conflicts of interest.



 
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    Figures

  [Figure 1], [Figure 2]
 
 
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  [Table 1], [Table 2], [Table 3], [Table 4], [Table 5], [Table 6]



 

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