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阻塞性睡眠呼吸暂停患者症状谱与内型之间的关系:潜在类别分析。

Relationship between Symptom Profiles and Endotypes among Patients with Obstructive Sleep Apnea: A Latent Class Analysis.

机构信息

Department of Psychiatry and.

Department of Public Health and.

出版信息

Ann Am Thorac Soc. 2023 Sep;20(9):1337-1344. doi: 10.1513/AnnalsATS.202212-1054OC.

Abstract

Obstructive sleep apnea (OSA) is a heterogeneous syndrome with various endotypic traits and symptoms. A link among symptoms, endotypes, and disease prognosis has been proposed but remains unsupported by empirical data. To link symptom profiles and endotypes by clustering endotypic traits estimated using polysomnographic signals. We recruited 509 patients with moderate to severe OSA from a single sleep center. Polysomnographic data were collected between May 2020 and January 2022. Endotypic traits, namely arousal threshold, upper airway collapsibility, loop gain, and upper airway muscle compensation, were retrieved using polysomnographic signals during non-rapid eye movement periods. We used latent class analysis to group participants into endotype clusters. Demographic and polysomnographic parameter differences were compared between clusters, and associations between endotype clusters and symptom profiles were examined using logistic regression analyses. Three endotype clusters were identified, characterized by high collapsibility/loop gain, low arousal threshold, and low compensation, respectively. Patients in each cluster exhibited similar demographic characteristics, but those in the high collapsibility/loop gain cluster had the highest proportion of obesity and severe oxygen desaturation observed in polysomnographic studies. The low compensation cluster was characterized by fewer sleepy symptoms and exhibited a lower rate of diabetes mellitus. Compared with the excessively sleepy group, disturbed sleep symptoms were associated with the low arousal threshold cluster (odds ratio, 1.89; 95% confidence interval, 1.16-3.10). Excessively sleepy symptoms were associated with the high collapsibility/loop gain cluster (odds ratio, 2.16; 95% confidence interval, 1.39-3.37) compared with the minimally symptomatic group. Three pathological endotype clusters were identified among patients with moderate to severe OSA, each exhibiting distinct polysomnographic characteristics and clinical symptom profiles.

摘要

阻塞性睡眠呼吸暂停(OSA)是一种具有多种表型特征和症状的异质性综合征。已经提出了症状、表型和疾病预后之间的联系,但仍然没有得到实证数据的支持。为了通过聚类使用多导睡眠图信号估计的表型特征来连接症状谱和表型。我们从一个单一的睡眠中心招募了 509 名中重度 OSA 患者。多导睡眠图数据于 2020 年 5 月至 2022 年 1 月期间采集。使用多导睡眠图信号在非快速眼动期间检索表型特征,即觉醒阈值、上气道塌陷性、环路增益和上气道肌肉补偿。我们使用潜在类别分析将参与者分为表型聚类。比较聚类之间的人口统计学和多导睡眠图参数差异,并使用逻辑回归分析检查表型聚类与症状谱之间的关联。确定了三个表型聚类,分别表现为高塌陷/环路增益、低觉醒阈值和低补偿。每个聚类的患者表现出相似的人口统计学特征,但在高塌陷/环路增益聚类中,肥胖和严重的氧减饱和在多导睡眠图研究中观察到的比例最高。低补偿聚类的特征是嗜睡症状较少,糖尿病发生率较低。与过度嗜睡组相比,睡眠障碍症状与低觉醒阈值聚类相关(优势比,1.89;95%置信区间,1.16-3.10)。与最小症状组相比,过度嗜睡症状与高塌陷/环路增益聚类相关(优势比,2.16;95%置信区间,1.39-3.37)。在中重度 OSA 患者中确定了三个病理性表型聚类,每个聚类表现出不同的多导睡眠图特征和临床症状谱。

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