Identifying phenotypes of obstructive sleep apnea using cluster analysis,Sleep and Breathing

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Identifying phenotypes of obstructive sleep apnea using cluster analysis
Sleep and Breathing ( IF 2.1 ) Pub Date : 2022-07-15 , DOI: 10.1007/s11325-022-02683-2
Kavitha Venkatnarayan ₁ , Uma Maheswari Krishnaswamy ₁ , Nithin Kumar Reddy Rajamuri ₂ , Sumithra Selvam ₃ , Chitra Veluthat ₁ , Uma Devaraj ₁ , Priya Ramachandran ₁ , George D'Souza ₁

Affiliation

Purpose

Over the last decade, advances in understanding the pathophysiology, clinical presentation, systemic consequences and treatment responses in obstructive sleep apnea (OSA) have made individualised OSA management plausible. As the first step in this direction, this study was undertaken to identify OSA phenotypes.

Methods

Patients diagnosed with OSA on level 1 polysomnography (PSG) were included. Clinical and co-morbidity profile, anthropometry and sleepiness scores were compiled. On PSG, apnea–hypopnea index, positional indices, sleep stages and desaturation indices (T90) were tabulated. Cluster analysis was performed to identify distinct phenotypes among included patients with OSA.

Results

One hundred patients (66 males) with a mean age of 49.5 ± 13.3 years were included. Snoring was reported by 94% subjects, and 50% were excessively sleepy. Two-thirds of subjects had co-morbidities, the most frequent being hypertension (55%) and dyslipidemia (53%). Severe OSA was diagnosed on PSG in 42%, while 29% each had mild and moderate OSA, respectively. On cluster analysis, 3 distinct clusters emerged. Cluster 1 consisted of older, obese subjects with no gender predilection, higher neck circumference, severe OSA with more co-morbidities and higher T90. Cluster 2 comprised of younger, less obese males with snoring, witnessed apnea, moderate and supine predominant OSA. Cluster 3 consisted of middle-aged, obese males with lesser co-morbidities, mild OSA and lower T90.

Conclusions

This study revealed three OSA clusters with distinct demographic, anthropometric and PSG features. Further research with bigger sample size and additional parameters may pave the way for characterising distinct phenotypes and individualising OSA management.

中文翻译：

使用聚类分析识别阻塞性睡眠呼吸暂停的表型

目的

在过去的十年中，对阻塞性睡眠呼吸暂停 (OSA) 的病理生理学、临床表现、全身性后果和治疗反应的理解取得进展，使得个体化 OSA 管理成为可能。作为朝着这个方向迈出的第一步，本研究旨在确定 OSA 表型。

方法

包括在 1 级多导睡眠图 (PSG) 上诊断为 OSA 的患者。编制了临床和合并症概况、人体测量学和嗜睡评分。在 PSG 上，列出了呼吸暂停低通气指数、体位指数、睡眠阶段和去饱和指数 (T90)。进行聚类分析以确定纳入的 OSA 患者的不同表型。

结果

包括平均年龄为 49.5 ± 13.3 岁的 100 名患者（66 名男性）。94% 的受试者报告打鼾，50% 的受试者过度困倦。三分之二的受试者有合并症，最常见的是高血压 (55%) 和血脂异常 (53%)。42% 的患者通过 PSG 诊断出重度 OSA，而轻度和中度 OSA 患者分别为 29%。在聚类分析中，出现了 3 个不同的聚类。第 1 组由年龄较大、肥胖的受试者组成，没有性别偏好、较高的颈围、严重的 OSA 以及更多的合并症和更高的 T90。第 2 组由年轻、肥胖程度较低的男性组成，他们有打鼾、呼吸暂停、中度和仰卧为主的 OSA。聚类 3 由中年肥胖男性组成，他们有较少的合并症、轻度 OSA 和较低的 T90。