Visceral adiposity index is a better predictor of unhealthy metabolic phenotype than traditional adiposity measures: results from a population-based study

Abstract

ObjectiveThe present study aimed to investigate whether the visceral adiposity index (VAI) is an effective predictor to identify unhealthy metabolic phenotype by comparing normal-weight and overweight individuals.DesignA population-based cross-sectional study. Data were collected by interviews, anthropometric evaluation, dietetic, clinical and laboratory tests. The area under the receiver-operating characteristic curve (AUC) and prevalence ratio (PR), obtained from Poisson regression, were used to compare the predictive capacity of the obesity indicators evaluated (VAI, BMI, waist and neck circumference, waist-to-height and waist-to-hip ratios) and their association with the unhealthy metabolic phenotype. All analyses were stratified by sex and by nutritional status.SettingViçosa, Minas Gerais, Brazil.ParticipantsA total of 854 Brazilian adults (20–59 years old) of both sexes.ResultsVAI was the best predictor for unhealthy metabolic phenotype among men (AUC = 0·865) and women (AUC = 0·843) at normal weight. VAI also had the best predictive capacity among overweight women (AUC = 0·903). Among overweight men, its accuracy (AUC = 0·830) was higher than that of waist-to-hip ratio. In the adjusted regression models, VAI was the indicator most strongly associated with the unhealthy metabolic phenotype, especially among those with normal weight (PR = 6·74; 95 % CI 3·15, 14·42 for men; PR = 7·14; 95 % CI 3·79, 13·44 for women).ConclusionsVAI has better predictive capacity in detecting unhealthy metabolic phenotype than conventional anthropometric indicators, regardless of nutritional status and sex.

The Visceral Adipose Index in Relation to Incidence of Hypertension in Chinese Adults: China Health and Nutrition Survey (CHNS)

“…The associations of overweight/obesity and some other adiposity indicators with the risk of hypertension were previously reported in substantial cross-sectional and time-series studies [8][9][10][11], and thus losing weight, as one of the non-pharmacological strategies, is commonly recommended to lower BP [3]. Several simple and low-cost adiposity indicators were developed including general adiposity index (body mass index (BMI) and height-adjusted body weight) and central adiposity indicators such as waist circumference (WC), hip circumference, waist-to-hip ratio, waist-to-height ratio, body adiposity index (BAI), and visceral adiposity index (VAI) [7,11,12]. Though BMI and WC are commonly measured in current research, BMI in some cases is not a suitable predictor for the percentage of body adipose [13] and WC is unable to distinguish visceral from subcutaneous fat [14].…”

“…Instead, VAI was proposed as a reliable and comprehensive indicator of determining visceral adipose associated with cardiometabolic status, since VAI comprehensively takes anthropometric and metabolic factors into account and was reported to be associated with visceral adipose tissue area and volume, but not with subcutaneous adipose tissue [14]. Therefore, VAI was introduced to be a surrogate indicator of adipose tissue function, which can more directly predict the progress and risk of CVDs [12,15].…”

Associations between traditional and non-traditional anthropometric indices and cardiometabolic risk factors among inpatients with type 2 diabetes mellitus: a cross-sectional study

“… 37 – 39 Because individuals with normal BMI are usually not the target of screening programs, they may not receive proper intervention. 38 …”

The mediating role of visceral adiposity in the relationship among schooling, physical inactivity, and unhealthy metabolic phenotype

“…It is a cross‐sectional, population‐based study with adult individuals aged 20 to 59 years, of both sexes, and living in the urban area of the municipality. The sample was calculated using the public domain program Open Epi, online version 3.03a, considering the following parameters: estimated population of 43.431 individuals (Instituto Brasileiro de Geografia e Estatística—IBGE, 2011), 95% confidence level, expected prevalence of 50% (considering multiple outcomes, because this study is part of a survey that aims to evaluate different outcomes) (Barbosa, Pereira, da Cruz, & Leita, 2018; Ferreira et al, 2018a), predicted sample error of 4.5%, and design effect of 1.6. The sample calculated was added with 10% to cover for losses and refusals and 10% more to control confounding factors.…”

“…Figure 1 illustrates the theoretical model used in the analysis of the present study, which was built from evidence described in the literature on the relationship between the studied variables (Du, Yu, Zhang, & Sun, 2015;Oliveira et al, 2007;Rodrigues, Melo, Assis, & Oliveira, 2017;Wardle et al, 2002) and from previous results of the same study population (Ferreira, Juvanhol, et al, 2018;Ferreira, Segheto, da Silva, Pereira, & Longo, 2018). The analyses were also stratified according to nutritional status, since previous results from the same study population (Ferreira et al, 2018a) indicated that the association between VAI and UMP varies according to the nutritional status.…”