Applicability of visceral adiposity index in predicting metabolic syndrome in adults with obstructive sleep apnea: a cross-sectional study

Background: Obstructive sleep apnea (OSA) is severely affected by visceral adiposity (VA) that correlates to another disorder-metabolic syndrome (MetS). However, little is known concerning the relation of visceral adiposity index (VAI)-a novel and simple indicator of VA, with OSA and MetS. The objective of the study was to analyze the association of VAI with both disorders and applicability to identify OSA patients at risk of MetS. Methods: Consecutive individuals undergoing polysomnography and biochemical tests were enrolled, and differences in all subjects grouped by apnea-hypopnea index (AHI) were analyzed. Spearman correlation was performed for assessing the relationship between VAI, OSA-related indices and metabolic score-total number of the positive diagnostic criteria of MetS. Receiver operating characteristic (ROC) curve was conducted to obtain a cut-off value of VAI for predicting incident MetS by sex. Then, the risk of MetS in OSA patients according to the cut-offs was attained by logistic regression. Results: A total of 411 individuals were enrolled. Of whom, 361 subjects were diagnosed OSA (mild in 67 patients, moderate in 89 and severe in 205, respectively). A significant increasing trend based on AHI was observed in the variables of blood pressure, triglycerides, fasting glucose, incident MetS, metabolic score and VAI (all p < 0.05). Irrespective of gender, VAI was all significantly correlated with PSG characteristics as AHI, mean nocturnal oxygen saturation, the lowest oxygen saturation, metabolic score(all p < 0.05). A VAI of 2.282, 2.105, 2.511 (for all subjects, males and females, separately) were calculated to determine the occurrence of MetS. According to the cut-offs, OSA patients tended to suffer from greater risk in MetS (odds ratio [OR] = 10.237, p = 0.000; OR = 13.556, p = 0.000; OR = 21.458, p = 0.000). Conclusions: The present study suggested that VAI was significantly associated with MetS and OSA. As a simple and alternative approach obtained in everyday practice, it may offer a powerful tool to identify patients with OSA at risk of MetS.