Yook Soonhyun, Park Hea Ree, Seo Dongjin, Joo Eun Yeon, Kim Hosung
USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, 90033, USA.
Department of Neurology, Inje University College of Medicine, Ilsan Paik Hospital, Goyang, 10380, Republic of Korea.
Comput Biol Med. 2025 Feb;185:109604. doi: 10.1016/j.compbiomed.2024.109604. Epub 2024 Dec 24.
Conventional metrics such as the apnea-hypopnea index (AHI) may not fully capture the diverse clinical manifestations of obstructive sleep apnea (OSA). This study aims to establish a novel OSA subtype classification based on the patterns of apneic and hypopneic hypoxic burden (HB), a potential biomarker that more accurately reflects the severity and duration of respiratory events. We further examined the associations of these HB-based subtypes with cardiometabolic risk and brain health outcomes.
We retrospectively analyzed polysomnography data from 1000 participants including normal, mild, moderate, and severe OSA patients. We performed hierarchical clustering based on apneic and hypopneic HB to identify OSA subtypes. We then compared the prevalence of cardiometabolic syndrome (CMS) and brain health outcomes using the brain age index (BAI) among these subtypes.
Five distinct subtypes were identified: 'good sleepers' (subtype 1), 'light hypopneic HB' (subtype 2), 'mild HB' (subtype 3), 'moderate HB' (subtype 4), and 'severe HB with marked apneic HB' (subtype 5). The prevalence of CMS (particularly hypertension) was significantly higher in subtypes 2-5 (p < 0.001) compared to subtype 1. BAI was higher in subtypes 4 (3.2 years, p < 0.0001) and 5 (11.1 years, p < 0001) compared to subtype 1. Greater daytime sleepiness was observed in HB-based subtypes 2 and 5 compared to subtype 1 (p < 0.001), whereas no significant differences were found among groups classified by OSA severity using AHI.
Our study demonstrates that the HB-based subtypes of OSA are significantly associated with various clinical features and outcomes. These insights could be utilized to improve risk stratification and guide the design of future OSA studies.
诸如呼吸暂停低通气指数（AHI）等传统指标可能无法完全捕捉阻塞性睡眠呼吸暂停（OSA）的多种临床表现。本研究旨在基于呼吸暂停和低通气低氧负荷（HB）模式建立一种新的OSA亚型分类，HB是一种潜在的生物标志物，能更准确地反映呼吸事件的严重程度和持续时间。我们进一步研究了这些基于HB的亚型与心血管代谢风险和脑健康结局之间的关联。
我们回顾性分析了1000名参与者的多导睡眠图数据，包括正常、轻度、中度和重度OSA患者。我们基于呼吸暂停和低通气HB进行层次聚类以识别OSA亚型。然后我们比较了这些亚型中心血管代谢综合征（CMS）的患病率以及使用脑年龄指数（BAI）评估的脑健康结局。
识别出五种不同的亚型：“良好睡眠者”（亚型1）、“轻度低通气HB”（亚型2）、“轻度HB”（亚型3）、“中度HB”（亚型4）和“伴有明显呼吸暂停HB的重度HB”（亚型5）。与亚型1相比，亚型2至5中CMS（尤其是高血压）的患病率显著更高（p < 0.001）。与亚型1相比，亚型4（3.2岁，p < 0.0001）和亚型5（11.1岁，p < 0.0001）的BAI更高。与亚型1相比，基于HB的亚型2和5中白天嗜睡情况更严重（p < 0.001），而使用AHI按OSA严重程度分类的各组之间未发现显著差异。
我们的研究表明，基于HB 的OSA亚型与各种临床特征和结局显著相关。这些见解可用于改善风险分层并指导未来OSA研究的设计。
