Advanced Proteomics and Cluster Analysis for Identifying Novel Obstructive Sleep Apnea Subtypes before and after Continuous Positive Airway Pressure Therapy

医学 蛋白质组学 持续气道正压 内科学 睡眠呼吸暂停 体质指数 阻塞性睡眠呼吸暂停 呼吸暂停 生物信息学 生物 生物化学 基因
作者
Vaishnavi Kundel,Oren Cohen,Samira Khan,Manishkumar Patel,Seunghee Kim‐Schulze,Jason C. Kovacic,Mayte Suárez‐Fariñas,Neomi Shah
出处
期刊:Annals of the American Thoracic Society [American Thoracic Society]
卷期号:20 (7): 1038-1047 被引量:5
标识
DOI:10.1513/annalsats.202210-897oc
摘要

RationaleStudies have shown elevated inflammatory biomarkers in obstructive sleep apnea (OSA), but data after continuous positive airway pressure (CPAP) treatment are inconsistent. ObjectivesWe used the Olink proteomics panel to identify unique OSA clusters on the basis of inflammatory protein expression and assess the impact of CPAP therapy. MethodsAdults with newly diagnosed OSA had blood drawn at baseline and three to four months after CPAP. Samples were analyzed using the Olink proteomics platform, which measures 92 prespecified inflammatory proteins using proximity extension assay. Linear mixed-effects models were used to model changes in protein expression during the period of CPAP use, adjusting for batch, age, and sex. Unsupervised hierarchical clustering was performed to identify unique inflammatory OSA clusters on the basis of inflammatory biomarkers. Within-cluster impact of CPAP on inflammatory protein expression was assessed. ResultsAmong 46 patients, the mean age was 46 ± 12 years (22% women), mean body mass index was 31 ± 5 kg/m2, and mean respiratory disturbance index was 33 ± 17 events/hour. Unsupervised cluster and heatmap analysis revealed three unique proteomic clusters, with low (n = 21), intermediate (n = 19), and high (n = 6) inflammatory protein expression. After CPAP, there were significant within-cluster differences in protein expression. The low inflammatory cluster had a significant increase in protein expression (16%; P = 0.02), and the high inflammatory cluster had a significant decrease in protein expression (−20%; P = 0.003), more significant among those compliant with CPAP in the low (25%; P = 0.04) and high (−22%; P = 0.01) clusters. ConclusionsWe identified three unique inflammatory clusters in patients with OSA using plasma proteomics, with a differential response to CPAP by cluster. Our results are hypothesis generating and require further investigation in larger longitudinal studies for enhanced cardiovascular risk profiling in OSA.
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