通过整合多模态睡眠脑电图解码阻塞性睡眠呼吸暂停表型

文献信息

中文摘要

背景： 阻塞性睡眠呼吸暂停（OSA）是一种异质性疾病，用呼吸暂停低通气指数（AHI）难以充分表征，当前的亚型分类忽视了睡眠神经生理学。我们旨在通过整合多模态睡眠脑电图与临床和生物标志物数据，识别客观的神经生理表型和机制。方法：我们分析了309名OSA患者的多导睡眠图、人口统计学和生物标志物数据，提取了涵盖频谱功率和睡眠微结构的定量脑电图（qEEG）特征。这些特征通过级联多模态变换器融合，并通过共识聚类识别表型。结果：出现了三种稳定、临床上有区别的表型，其中十个最具区分度的特征均为qEEG衍生。表型0（n=52）是最年轻的组，表现出中等睡眠结构，并有自报心血管疾病较高的数值趋势；表型1（n=131）表现出显著的N3睡眠不足和最异常的脑电频谱特征；表型2（n=126）表现出保留的睡眠结构。平均AHI无显著差异，但AHI严重程度类别的分布在表型间存在差异，突显了传统严重程度指标与神经生理亚型之间的分离。结论：我们引入了一种独立于AHI的生理驱动OSA分类法，优先考虑大脑特征，以实现OSA的生物学靶向诊断和治疗。

英文摘要

Obstructive sleep apnea (OSA) is a heterogeneous disorder poorly characterized by the apnea-hypopnea index (AHI), with current subtyping overlooking sleep neurophysiology. We aimed to identify objective neurophysiological phenotypes and mechanisms by integrating multimodal sleep EEG with clinical and biomarker data. We analyzed polysomnographic, demographic, and biomarker data from 309 OSA patients, extracting quantitative EEG (qEEG) features spanning spectral power and sleep microstructure. These were fused via a cascaded multimodal transformer, and phenotypes identified by consensus clustering. Three stable, clinically distinct phenotypes emerged, with the ten most discriminatory features being all qEEG-derived. Phenotype 0 (n = 52) was the youngest group and showed intermediate sleep architecture, with a numerical trend toward higher self-reported cardiovascular disease; Phenotype 1 (n = 131) exhibited marked N3 sleep deficit and the most aberrant EEG spectral profile; and Phenotype 2 (n = 126) showed preserved sleep architecture. Mean AHI did not differ significantly, yet the distribution of AHI severity categories differed across phenotypes, highlighting a dissociation between conventional severity metrics and neurophysiological subtypes. We introduce a physiology-driven OSA taxonomy decoupled from AHI, prioritizing brain signatures to enable biologically targeted diagnosis and therapy of OSA.