Visceral adiposity index is a better predictor to discriminate metabolic syndrome than other classical adiposity indices among young adults

Abstract

Background: Visceral adiposity index (VAI) has been identified as a cardiometabolic risk marker in children and adolescents which reflects abdominal fat distribution. The aim of the present study was to evaluated the predictive capacity of VAI, a body shape index (ABSI), atherogenic index of plasma (AIP), and triglycerides and glucose index (TyG index) compared with classical anthropometric measurements to discriminate metabolic syndrome (MetS). Methods: This retrospective study included 1372 individuals. Anthropometric, clinical, and biochemical measurements were used to screen the prevalence of MetS components and to calculate VAI, ABSI, TyG index, and AIP. Results:The discriminatory capacity among the variables was assessed by the area under the receiver operating characteristic (ROC) curve (AUC). VAI was the variable with the highest AUC with 0.932 CI 95% (0.917-0.948), followed by AIP with 0.914 CI 95% (0.897-0.931), and TyG index with 0.889 CI 95% (0.871-0.908). Conclusion:VAI is a promising tool to identify MetS in the late adolescence setting. Among the novel adiposity indexes VAI, AIP, TyG index are able to determine MetS presence, while ABSI is not capable. | INTRODUCTIONMetabolic syndrome (MetS) is a convergence of intercorrelated metabolic risk factors including high blood pressure, hyperglycemia, abdominal obesity, elevated triglyceride (TG) levels, and low high-density lipoprotein cholesterol (HDL-C). MetS is closely linked with type 2 diabetes mellitus (T2D) and promotes cardiovascular disease (CVD) development (Sarrafzadegan et al., 2013). The presence of the MetS increase the risks for CVD 2.88 in men and 2.25 in women. In individuals with MetS, the risk for T2D is up to five times higher compared with

Comparison of atherogenic indices for predicting the risk of metabolic syndrome in Southwest Iran: results from the Hoveyzeh Cohort Study (HCS)

“…compared the predictive power of AIP, non-HDL-c, LDL-c/HDL-c, TG/HDL-c, and TC/HDL-c in discriminating MetS in the Chinese population and revealed that AIP was a better index to identify MetS than the other lipid parameters [ 10 ]. Vega-Cardenas and colleagues found that AIP had a strong predictive ability for MetS (AUC = 0.91), and the optimal cutoff point was 0.44 [ 41 ]. Furthermore, a study conducted among various ethnicities in China showed that AIP has a good predictive performance for MetS, and the cutoff values for AIP ranged from − 0.1 to 0/07 [ 40 ].…”

“…The discrepancy between the studies’ results may be due to differences in participants’ age, ethnicity, and MetS diagnosis criteria. For instance, in our study, IDF criteria were used to define MetS, while American Heart Association (AHA) criteria were used in Vega-Cardenas et al’s study [ 41 ]. The results of logistic regression exhibited a significant association between the AIP and the MetS risk in both sexes.…”

Dietary Intake of Capsaicin and Its Association with Markers of Body Adiposity and Fatty Liver in a Mexican Adult Population of Tijuana

“…All anthropometric measurements were collected by trained nutritionists in a conditioned/equipped clinic in CAIS. The body adiposity index (BAI) was calculated as BAI = [hip circumference (cm) ÷ height (m) 1.5] − 18 [28], whereas the visceral adiposity index (VAI) was estimated as VAI = (WC (cm)/(39.68 + (1.88 × BMI))) × (triglycerides/1.03) × (1.31/high-density lipoprotein cholesterol or HDL-c) for males and VAI = (WC (cm)/(36.58 + (1.89*BMI))) × (triglycerides/0.81) × (1.52/HDL-c) for females [29]. The BAI is a useful measure of body fat percentage, which can be calculated from the HC and height only, with an accessible application in a clinical setting [28].…”

“…Furthermore, the BAI has been validated in individuals of Mexican ancestry with widely varying adiposity levels [28]. Likewise, the VAI has been identified as a reliable parameter reflecting abdominal fat distribution and a promising tool to identify metabolic syndrome and cardiometabolic risk [29]. This index also uses conventional issues for its calculation, being simple to apply in epidemiological research.…”

miR-21, miR-221, miR-29 and miR-34 are distinguishable molecular features of a metabolically unhealthy phenotype in young adults

“…However, the BMI by itself does not distinguish lean and fat tissues, which explains previous observations that a significant proportion of overweight (~50%) and obese (~30%) individuals do not show any metabolic alterations or health complications [ 5 , 6 ]. Of the components of MetS, HDL-cholesterol [ 7 ] and triglycerides (TG) [ 8 , 9 ] levels have greater predictive power for classifying metabolic health over waist circumference (WC), systolic and diastolic blood pressure (SBP/DBP), and glucose. Therefore, there is a need for a more accurate assessment of metabolic health at the individual level and the categorization of individuals beyond their BMI.…”