ArticleNature and science of sleep2024
Application Value of Cardiometabolic Index for the Screening of Obstructive Sleep Apnea with or Without Metabolic Syndrome.
Article in Nature and science of sleep, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis that pooled it.
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Who cites it
18 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Pooled it
- Article
- Association between modified cardiometabolic index and cardiometabolic multimorbidity in middle-aged and older adults: evidence from two nationwide cohort studies.Scientific reports · 2026Article
- An online interpretable machine learning model for predicting cardiometabolic multimorbidity risk in patients with type 2 diabetes mellitus.Scientific reports · 2026Article
- Sleep Disorders and Perioperative Stroke: A Global Bibliometric Analysis from 1999 to 2024.Nature and science of sleep · 2026Article
- The Cardiometabolic Index (CMI) is associated with an increased risk of obstructive sleep apnea: a population-based study.European journal of medical research · 2025Article
- Article
- Associations of metabolic heterogeneity of obesity, cardiometabolic index with sleep trajectory among middle-aged and older adults: a longitudinal study.BMC public health · 2025Article
- Risk assessment and prevention of cardiovascular events in patients with obstructive sleep apnea syndrome: A narrative review.International journal of cardiology. Cardiovascular risk and prevention · 2025Review
- Association between cardiometabolic index and self-reported symptoms of obstructive sleep apnea: a cross-sectional study.Scientific reports · 2025Article
- Pulmonary Function and Nocturnal Hypoxemia Patterns in Patients with Obstructive Sleep Apnea.Journal of clinical medicine · 2025Article
- Association between cardiometabolic Index and obstructive sleep apnea and the mediating role of smoking: a cross-sectional study.Frontiers in endocrinology · 2025Article
- Nonlinear association of cardiometabolic index with hyperuricemia: insights from the NHANES 1999-2018 study.Frontiers in endocrinology · 2025Article
- Sleep-disordered breathing and metabolic syndrome across gender, age, and sleep subtypes in East Asians.Frontiers in endocrinology · 2025Article
- Cardiometabolic index and mortality risks: elevated cancer and reduced cardiovascular mortality risk in a large cohort.Lipids in health and disease · 2024Article
- The relationship between composite lipid indices and baveno classification in patients with obstructive sleep apnoea.Scientific reports · 2024Article
- Association of cardiometabolic index with sleep quality in adults: a population‑based study.Scientific reports · 2024Article
- Association between obstructive sleep apnea and visceral adiposity index and lipid accumulation product: NHANES 2015-2018.Lipids in health and disease · 2024Article
Corrections and comments
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Authors and funding
14 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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