4.5 Article

Cut-off points of the visceral adiposity index (VAI) identifying a visceral adipose dysfunction associated with cardiometabolic risk in a Caucasian Sicilian population

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LIPIDS IN HEALTH AND DISEASE
卷 10, 期 -, 页码 -

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BMC
DOI: 10.1186/1476-511X-10-183

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Obesity; cardiometabolic risk; visceral adipose dynsfunction; VAI

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Background:The Visceral Adiposity Index (VAI) is a sex-specific mathematical index, based on Waist Circumference (WC), Body Mass Index (BMI), triglycerides (TG) and HDL cholesterol (HDL) levels, indirectly expressing visceral adipose function and insulin sensitivity. Our aim was to find the optimal cut-off points of VAI identifying a visceral adipose dysfunction (VAD) associated with cardiometabolic risk in a Caucasian Sicilian population. Methods:Medical check-up data of 1,764 Primary Care patients (PC patients) were retrospectively and cross-sectionally examined using a receiver-operating characteristic (ROC) curve to determine appropriate stratified-forage cut-off of VAI, for the identification of PC patients with Metabolic Syndrome (MetS) according to the NCEP-ATP III criteria. The PC patients with higher VAI scores were subdivided into three groups according to VAI tertiles (i.e. PC patients with mild VAD, moderate VAD or severe VAD). Finally, VAD classes were compared to classical cardio- and cerebrovascular risk factors as independent predictors of coronary heart disease and/or myocardial infarction, transient ischemic attack and/or ischemic stroke. Results:Moderate and severe VADs proved to be independently associated with cardiovascular events [(OR:5.35; 95% CI:1.92-14.87; p = 0.001) and (OR:7.46; 95% CI:2.64-21.05; p < 0.001) respectively]. Mild, moderate and severe VADs were found to be independently associated with cerebrovascular events [(OR:2.73; 95% CI:1.12-6.65; p = 0.027), (OR:4.20; 95% CI:1.86-9.45; p = 0.001) and (OR:5.10; 95% CI:2.14-12.17; p < 0.001) respectively]. Conclusions:Our study suggests that among Caucasian Sicilian subjects there are clear cut-off points of VAI able to identify a VAD strongly associated with cardiometabolic risk.

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