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高级蛋白质组学和聚类分析在持续气道正压通气治疗前后识别新型阻塞性睡眠呼吸暂停亚型。

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

机构信息

Division of Pulmonary, Critical Care and Sleep Medicine.

Human Immune Monitoring Center, Hess Center for Science and Medicine.

出版信息

Ann Am Thorac Soc. 2023 Jul;20(7):1038-1047. doi: 10.1513/AnnalsATS.202210-897OC.

Abstract

Studies have shown elevated inflammatory biomarkers in obstructive sleep apnea (OSA), but data after continuous positive airway pressure (CPAP) treatment are inconsistent. We used the Olink proteomics panel to identify unique OSA clusters on the basis of inflammatory protein expression and assess the impact of CPAP therapy. Adults 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. Among 46 patients, the mean age was 46 ± 12 years (22% women), mean body mass index was 31 ± 5 kg/m, and mean respiratory disturbance index was 33 ± 17 events/hour. Unsupervised cluster and heatmap analysis revealed three unique proteomic clusters, with low ( = 21), intermediate ( = 19), and high ( = 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%;  = 0.02), and the high inflammatory cluster had a significant decrease in protein expression (-20%;  = 0.003), more significant among those compliant with CPAP in the low (25%;  = 0.04) and high (-22%;  = 0.01) clusters. We 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.

摘要

研究表明阻塞性睡眠呼吸暂停(OSA)患者的炎症生物标志物升高,但持续气道正压通气(CPAP)治疗后的相关数据并不一致。我们使用 Olink 蛋白质组学面板根据炎症蛋白表达确定独特的 OSA 聚类,并评估 CPAP 治疗的影响。新诊断为 OSA 的成年人在基线和 CPAP 治疗后 3 至 4 个月时抽取血液样本。使用 Olink 蛋白质组学平台分析样本,该平台使用邻近延伸测定法测量 92 种预先指定的炎症蛋白。使用线性混合效应模型在 CPAP 使用期间对蛋白质表达的变化进行建模,调整批次、年龄和性别。进行无监督层次聚类,根据炎症生物标志物确定独特的炎症 OSA 聚类。评估 CPAP 对炎症蛋白表达的聚类内影响。在 46 名患者中,平均年龄为 46 ± 12 岁(22%为女性),平均体重指数为 31 ± 5 kg/m,平均呼吸紊乱指数为 33 ± 17 事件/小时。无监督聚类和热图分析显示出三种独特的蛋白质组学聚类,低( = 21)、中( = 19)和高( = 6)炎症蛋白表达。CPAP 后,蛋白质表达存在显著的聚类内差异。低炎症聚类的蛋白表达显著增加(16%; = 0.02),高炎症聚类的蛋白表达显著降低(-20%; = 0.003),在低(25%; = 0.04)和高(-22%; = 0.01)炎症聚类中 CPAP 依从性更好的患者中更为显著。我们使用血浆蛋白质组学在 OSA 患者中确定了三个独特的炎症聚类,聚类对 CPAP 的反应存在差异。我们的结果是假设性的,需要在更大的纵向研究中进一步调查,以增强 OSA 的心血管风险分析。

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