Application Value of Cardiometabolic Index for the Screening of Obstructive Sleep Apnea with or Without Metabolic Syndrome

• Published in: Nature and science of sleep Vol. 16; pp. 177 - 191
• Main Authors: Wang, Donghao, Chen, Yating, Ding, Yutong, Tang, Yongkang, Su, Xiaofen, Li, Shiwei, Zhang, Haojie, Zhou, Yanyan, Zhuang, Zhiyang, Gan, Qiming, Wang, Jingcun, Zhang, Yuting, Zhao, Dongxing, Zhang, Nuofu
• Format: Journal Article
• Language: English
• Published: New Zealand Dove Medical Press Limited 29.02.2024; Dove Medical Press
• Subjects: Adipose tissues; Analysis and chemistry; Blood; cardiometabolic index; Chronic diseases; Comorbidity; Comparative analysis; lipid accumulation products; metabolic syndrome; obstructive sleep apnea; screening model; Sleep apnea syndromes; Type 2 diabetes; China; Taiwan; metabolic syndrome; obstructive sleep apnea; cardiometabolic index; lipid accumulation products; screening model
• DOI: 10.2147/NSS.S449862
• ISSN: 1179-1608

Obstructive sleep apnea (OSA) is a common chronic disease with various comorbidities. The cardiometabolic index (CMI) reflects visceral fat tissue distribution and function, assessing the risk of obesity-related conditions such as metabolic syndrome (MetS) and stroke, which are strongly connected to OSA. The relationship between CMI with OSA and OSA combined with MetS (OMS) remains unclear. This study aims to evaluate the screening value of CMI for OSA and OMS, compared to the lipid accumulation product (LAP). A total of 280 participants who underwent polysomnography were finally included, with the measurements of metabolic-related laboratory test results such as total cholesterol and triglyceride. Receiver operating curve (ROC) analysis and calculation of the area under the curve (AUC) were conducted to assess the screening potential of CMI, LAP, and the logistic regression models established based on them for OSA and OMS. The Youden index, sensitivity, and specificity were used to determine the optimal cutoff points. ROC curve analysis revealed that the AUCs for CMI in screening OSA and OMS were 0.808 and 0.797, and the optimal cutoff values were 0.71 (sensitivity 0.797, specificity 0.776) and 0.89 (sensitivity 0.830, specificity 0.662), respectively, showing higher Youden index than LAP. The AUCs of screening models based on CMI for OSA and OMS were 0.887 and 0.824, respectively. CMI and LAP can effectively screen for OSA and OMS, while CMI has more practical cutoff values for identifying the diseased states. Screening models based on CMI demonstrate a high discriminatory ability for OSA and OMS, which needs verification in a large-scale population.