{"title":"Application Value of Cardiometabolic Index for the Screening of Obstructive Sleep Apnea with or Without Metabolic Syndrome","authors":"Donghao Wang, Yating Chen, Yutong Ding, Yongkang Tang, Xiaofen Su, Shiwei Li, Haojie Zhang, Yanyan Zhou, Zhiyang Zhuang, Qiming Gan, Jingcun Wang, Yuting Zhang, Dongxing Zhao, Nuofu Zhang","doi":"10.2147/nss.s449862","DOIUrl":null,"url":null,"abstract":"<strong>Background:</strong> 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).<br/><strong>Methods:</strong> 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.<br/><strong>Results:</strong> 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.<br/><strong>Conclusion:</strong> 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.<br/><br/>","PeriodicalId":18896,"journal":{"name":"Nature and Science of Sleep","volume":null,"pages":null},"PeriodicalIF":3.0000,"publicationDate":"2024-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Nature and Science of Sleep","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.2147/nss.s449862","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"CLINICAL NEUROLOGY","Score":null,"Total":0}
引用次数: 0
Abstract
Background: 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). Methods: 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. Results: 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. Conclusion: 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.
期刊介绍:
Nature and Science of Sleep is an international, peer-reviewed, open access journal covering all aspects of sleep science and sleep medicine, including the neurophysiology and functions of sleep, the genetics of sleep, sleep and society, biological rhythms, dreaming, sleep disorders and therapy, and strategies to optimize healthy sleep.
Specific topics covered in the journal include:
The functions of sleep in humans and other animals
Physiological and neurophysiological changes with sleep
The genetics of sleep and sleep differences
The neurotransmitters, receptors and pathways involved in controlling both sleep and wakefulness
Behavioral and pharmacological interventions aimed at improving sleep, and improving wakefulness
Sleep changes with development and with age
Sleep and reproduction (e.g., changes across the menstrual cycle, with pregnancy and menopause)
The science and nature of dreams
Sleep disorders
Impact of sleep and sleep disorders on health, daytime function and quality of life
Sleep problems secondary to clinical disorders
Interaction of society with sleep (e.g., consequences of shift work, occupational health, public health)
The microbiome and sleep
Chronotherapy
Impact of circadian rhythms on sleep, physiology, cognition and health
Mechanisms controlling circadian rhythms, centrally and peripherally
Impact of circadian rhythm disruptions (including night shift work, jet lag and social jet lag) on sleep, physiology, cognition and health
Behavioral and pharmacological interventions aimed at reducing adverse effects of circadian-related sleep disruption
Assessment of technologies and biomarkers for measuring sleep and/or circadian rhythms
Epigenetic markers of sleep or circadian disruption.