为什么睡眠在COPD中很重要：聚类衍生的睡眠表型揭示不同的生理、共病和生存特征

文献信息

中文摘要

背景： 睡眠障碍在慢性阻塞性肺疾病（COPD）中非常普遍，但未被纳入当前的临床表型分类。我们检验了多维睡眠数据是否能在COPD中识别具有临床相关性的模式，以及这些模式是否与全因死亡率相关。
方法： 在一个205例COPD患者的临床队列中，使用WatchPAT™设备进行家庭睡眠评估。采用围绕中心点的划分聚类分析15个反映睡眠时长与结构、夜间氧合、睡眠呼吸障碍和日间嗜睡性的互补变量。所得表型通过肺功能、气体交换、功能能力、全身生物标志物和共病负担进行表征。199名参与者可获得纵向随访；使用Kaplan-Meier分析和校正年龄、性别、支气管舒张剂后FEV1及气道正压（PAP）治疗的Cox回归评估生存。
结果： 识别出五种不同的睡眠表型：睡眠生理保留（睡眠良好者，20.5%）、睡眠时长缩短（短睡眠者，33%）、伴REM抑制的睡眠结构改变（浅睡眠者，18.5%）、以夜间低氧血症为主（低氧睡眠者，20%）和严重睡眠呼吸障碍（呼吸暂停睡眠者，8%）。尽管气流受限和COPD症状负担相似，各表型在气体交换、功能能力、全身炎症和心脏代谢共病方面存在差异。在中位随访4.2年期间，56名参与者（28.1%）死亡。与睡眠良好者相比，低氧睡眠者在校正年龄、性别和FEV1后死亡风险更高（HR 2.80，95% CI 1.05-7.44）。在校正PAP治疗后，未治疗的呼吸暂停睡眠者也观察到更高的死亡风险（HR 6.16，95% CI 1.56-24.38）。
结论： 多维睡眠表型分析在传统肺量计严重程度和呼吸暂停低通气指数（AHI）定义的睡眠呼吸障碍之外，识别出COPD患者中具有临床差异的聚类。这些发现支持睡眠是COPD异质性的一个临床相关维度，并值得对睡眠相关特征及其临床结局进行前瞻性验证。

英文摘要

BACKGROUND: Sleep disturbances are highly prevalent in chronic obstructive pulmonary disease (COPD) but are not incorporated into current phenotypic classifications. We examined whether multidimensional sleep data identify clinically relevant patterns in COPD and whether these patterns are associated with all-cause mortality.
METHODS: In a clinical cohort of 205 patients with COPD, home-based sleep assessment was performed using the WatchPAT™ device. Fifteen complementary variables reflecting sleep duration and architecture, nocturnal oxygenation, sleep-disordered breathing, and daytime sleepiness were analysed using Partitioning Around Medoids clustering. The resulting phenotypes were characterised by pulmonary function, gas exchange, functional capacity, systemic biomarkers, and comorbidity burden. Longitudinal follow-up was available for 199 participants; survival was assessed using Kaplan-Meier analysis and Cox regression adjusted for age, sex, post-bronchodilator FEV1 and positive airway pressure (PAP) treatment.
RESULTS: Five distinct sleep phenotypes were identified: preserved sleep physiology (good sleepers, 20.5%), curtailed sleep duration (short sleepers, 33%), altered sleep architecture with REM suppression (light sleepers, 18.5%), predominant nocturnal hypoxaemia (hypoxic sleepers, 20%), and severe sleep-disordered breathing (apnoeic sleepers, 8%). Despite similar airflow limitation and COPD symptom burden, phenotypes differed in gas exchange, functional capacity, systemic inflammation, and cardiometabolic comorbidities. During a median follow-up of 4.2 years, 56 participants (28.1%) died. Compared with good sleepers, hypoxic sleepers had a higher hazard of death after adjustment for age, sex, and FEV1 (HR 2.80, 95% CI 1.05-7.44). Following adjustment for PAP treatment, a higher mortality hazard was also observed among untreated apnoeic sleepers (HR 6.16, 95% CI 1.56-24.38).
CONCLUSION: Multidimensional sleep phenotyping identified clinically distinct clusters of patients with COPD beyond conventional spirometric severity and apnoea-hypopnoea index (AHI)-defined sleep-disordered breathing. These findings support sleep as a clinically relevant dimension of COPD heterogeneity, and warrant prospective validation of sleep-related traits and their clinical outcomes.