Institute of Computer Science, University of Bern, Bern, Switzerland.
Center for Experimental Neurology, Department of Neurology, Inselspital, Bern University Hospital and University of Bern, Bern, Switzerland.
Eur J Neurol. 2024 Jan;31(1):e16026. doi: 10.1111/ene.16026. Epub 2023 Aug 13.
The diagnosis of sleep-wake disorders (SWDs) is challenging because of the existence of only few accurate biomarkers and the frequent coexistence of multiple SWDs and/or other comorbidities. The aim of this study was to assess in a large cohort of well-characterized SWD patients the potential of a data-driven approach for the identification of SWDs.
We included 6958 patients from the Bernese Sleep Registry and 300 variables/biomarkers including questionnaires, results of polysomnography/vigilance tests, and final clinical diagnoses. A pipeline, based on machine learning, was created to extract and cluster the clinical data. Our analysis was performed on three cohorts: patients with central disorders of hypersomnolence (CDHs), a full cohort of patients with SWDs, and a clean cohort without coexisting SWDs.
A first analysis focused on the cohort of patients with CDHs and revealed four patient clusters: two clusters for narcolepsy type 1 (NT1) but not for narcolepsy type 2 or idiopathic hypersomnia. In the full cohort of SWDs, nine clusters were found: four contained patients with obstructive and central sleep apnea syndrome, one with NT1, and four with intermixed SWDs. In the cohort of patients without coexisting SWDs, an additional cluster of patients with chronic insomnia disorder was identified.
This study confirms the existence of clear clusters of NT1 in CDHs, but mainly intermixed groups in the full spectrum of SWDs, with the exception of sleep apnea syndromes and NT1. New biomarkers are needed for better phenotyping and diagnosis of SWDs.
由于睡眠-觉醒障碍（SWD）仅有少数准确的生物标志物，且常合并多种 SWD 和/或其他合并症，因此其诊断具有挑战性。本研究旨在评估在一个具有良好特征的 SWD 患者大队列中，数据驱动方法在识别 SWD 方面的潜力。
我们纳入了伯尔尼睡眠登记处的 6958 名患者和 300 个变量/生物标志物，包括问卷、多导睡眠图/警觉测试结果和最终临床诊断。基于机器学习的流水线用于提取和聚类临床数据。我们的分析在三个队列中进行：中枢性嗜睡障碍（CDH）患者、SWD 患者的全队列和无共存 SWD 的清洁队列。
首次分析集中在 CDH 患者队列，揭示了四个患者聚类：两个聚类为 1 型发作性睡病（NT1），但不是 2 型发作性睡病或特发性嗜睡症。在 SWD 的全队列中，发现了九个聚类：四个聚类包含阻塞性和中枢性睡眠呼吸暂停综合征患者，一个聚类包含 NT1 患者，四个聚类包含混合性 SWD 患者。在无共存 SWD 的患者队列中，还发现了一个慢性失眠障碍患者的聚类。
本研究证实了 CDH 中 NT1 存在明确的聚类，但在 SWD 的全谱中主要是混合性群组，除了睡眠呼吸暂停综合征和 NT1。需要新的生物标志物来更好地对 SWD 进行表型和诊断。
