Department of Pulmonary Medicine, St. John's National Academy of Health Sciences, Bengaluru, 560034, India.
Department of Pulmonary Medicine, SVS Medical College, Mahbubnagar, Telangana, India.
Sleep Breath. 2023 Jun;27(3):879-886. doi: 10.1007/s11325-022-02683-2. Epub 2022 Jul 15.
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.
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.
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.
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.
在过去的十年中，人们对阻塞性睡眠呼吸暂停（OSA）的病理生理学、临床表现、全身后果和治疗反应的理解有了进步，使得对 OSA 的个体化管理成为可能。作为朝这个方向迈出的第一步，本研究旨在确定 OSA 表型。
纳入在一级多导睡眠图（PSG）上诊断为 OSA 的患者。记录了临床和合并症概况、人体测量学和嗜睡评分。在 PSG 上，记录了呼吸暂停低通气指数、体位指数、睡眠阶段和脱氧饱和度指数（T90）。对包括的 OSA 患者进行聚类分析以确定不同的表型。
纳入了 100 名（66 名男性）平均年龄为 49.5±13.3 岁的患者。94%的患者报告打鼾，50%的患者嗜睡过度。三分之二的患者有合并症，最常见的是高血压（55%）和血脂异常（53%）。在 PSG 上诊断为严重 OSA 的占 42%，轻度和中度 OSA 各占 29%。聚类分析显示有 3 个不同的聚类。聚类 1 由年龄较大、肥胖、无性别倾向、颈围较大、严重 OSA 合并症较多和 T90 较高的患者组成。聚类 2 由年轻、较瘦的男性组成，他们有打鼾、 witnessed 呼吸暂停、中度和仰卧位为主的 OSA。聚类 3 由中年、肥胖的男性组成，他们合并症较少、轻度 OSA 和 T90 较低。
本研究揭示了三种具有不同人口统计学、人体测量学和 PSG 特征的 OSA 聚类。进一步的研究需要更大的样本量和更多的参数，为确定不同的表型和个体化 OSA 管理铺平道路。
