阻塞性睡眠呼吸暂停
医学
睡眠呼吸暂停
内科学
呼吸暂停
心脏病学
作者
Mohamed Abu‐Farha,Fahd Al‐Mulla,Md. Zubbair Malik,Eman Alshawaf,Abdulmohsen Alterki,JEHAD ABUBAKER
出处
期刊:Diabetes
[American Diabetes Association]
日期:2025-06-13
卷期号:74 (Supplement_1)
摘要
Introduction and Objective: OSA is a prevalent sleep disorder characterized by recurrent upper airway obstructions during sleep, leading to intermittent hypoxia and fragmented sleep. Despite numerous biomarker candidates reported for OSA, only a small subset have been validated through cross-sectional and interventional studies. Developing blood-based biomarkers offers a promising alternative to polysomnography (PSG), providing a cost-effective, time-efficient, and patient-friendly diagnostic tool. Such biomarkers could revolutionize OSA screening and follow-up protocols in clinical practice. Methods: In this study, we employed an unbiased mass spectrometry (MS)-based proteomics platform to profile plasma protein biomarkers associated with OSA. Samples were analyzed at baseline and three months post-continuous positive airway pressure (CPAP) therapy. The cohort included 24 individuals with OSA before and after CPAP intervention and their matched controls. Receiver operating characteristic (ROC) analyses were performed to evaluate the diagnostic potential of identified biomarkers. Results: Our findings revealed five plasma proteins—F9 (Coagulation Factor IX), VTN (Vitronectin), CA1 (Carbonic Anhydrase 1), HBA1 (Hemoglobin Subunit 1), and CLSTN1 (Calsyntenin 1)—significantly associated with OSA. These protein levels were modulated following CPAP therapy, indicating their potential role in monitoring treatment response. Validation analyses using enzyme-linked immunosorbent assay (ELISA) on a larger sample size further supported their diagnostic utility. Conclusion: In conclusion, our study highlights the diagnostic potential of the identified proteins, individually or in combination, as reliable biomarkers for OSA detection and therapeutic monitoring. These findings pave the way for integrating proteomics into OSA clinical workflows, enabling improved diagnosis and personalized treatment strategies. Disclosure M. Abu-Farha: None. F. Almulla: None. M. Malik: None. E.M. Alshawaf: None. A. Al-terki: None. J. Abubaker: None. Funding KFAS RAHM2020-014
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