Departamento de Psicobiologia, Universidade Federal de São Paulo, Rua Napoleão de Barros, 925, Vila Clementino, São Paulo, SP, 04024-002, Brazil.
Sleep Breath. 2022 Sep;26(3):1253-1263. doi: 10.1007/s11325-021-02470-5. Epub 2022 Jan 5.
The identification of subgroups of obstructive sleep apnea (OSA) is critical to understand disease outcome and treatment response and ultimately develop optimal care strategies customized for each subgroup. In this sense, we aimed to perform a cluster analysis to identify subgroups of individuals with OSA based on clinical parameters in the Epidemiological Sleep Study of São Paulo city (EPISONO). We aimed to analyze whether or not subgroups remain after 8 years, since there is not any evidence showing if these subtypes of clinical presentation of OSA in the same population can change overtime.
We used data derived from EPISONO cohort, which was followed over 8 years after baseline evaluation. All individuals underwent polysomnography, answered questionnaires, and had their blood collected for biochemical examinations. OSA was defined according to AHI ≥ 15 events/h. Cluster analysis was performed using latent class analysis (LCA).
Of the 1042 individuals in the EPISONO cohort, 68% agreed to participate in the follow-up study (n = 712), and 704 were included in the analysis. We were able to replicate the OSA 3-cluster solution observed in previous studies: disturbed sleep, minimally symptomatic and excessively sleepy in both baseline (36%, 45% and 19%, respectively) and follow-up studies (42%, 43%, and 15%, respectively). The optimal cluster solution for our sample based on Bayesian information criterion (BIC) was 2 cluster for baseline (disturbed sleep and excessively sleepy) and 3 clusters for follow-up (disturbed sleep, minimally symptomatic, and excessively sleepy). A total of 45% of the participants migrated clusters between the two evaluations (and the factor associated with this was a greater delta-AHI (B = - 0.033, df = 1, p = 0.003).
The results replicate and confirm previously identified clinical clusters in OSA which remain in the longitudinal analysis, with some percentage of migration between clusters.
识别阻塞性睡眠呼吸暂停（OSA）亚组对于了解疾病结局和治疗反应至关重要，最终可以为每个亚组制定最佳的护理策略。在这方面，我们旨在进行聚类分析，根据圣保罗市流行病学睡眠研究（EPISONO）中的临床参数识别 OSA 患者的亚组。我们旨在分析这些 OSA 临床表型的亚组是否在 8 年后仍然存在，因为目前尚无任何证据表明同一人群中的这些 OSA 亚组在不同时间是否会发生变化。
我们使用了 EPISONO 队列研究的数据，该队列在基线评估后进行了 8 年的随访。所有参与者都接受了多导睡眠图检查、问卷调查，并采集了血液进行生化检查。OSA 根据 AHI≥15 次/小时来定义。聚类分析采用潜在类别分析（LCA）。
在 EPISONO 队列的 1042 名个体中，有 68%（n=712）同意参加随访研究，其中 704 名被纳入分析。我们能够复制之前研究中观察到的 OSA 3 聚类解决方案：在基线和随访研究中，分别有 36%、45%和 19%的个体存在睡眠障碍、轻度症状和过度嗜睡（分别为 42%、43%和 15%）。基于贝叶斯信息准则（BIC）的最佳聚类解决方案是基线时的 2 个聚类（睡眠障碍和过度嗜睡）和随访时的 3 个聚类（睡眠障碍、轻度症状和过度嗜睡）。在两次评估之间，共有 45%的参与者出现聚类迁移（与这种迁移相关的因素是 AHI 差值较大（B=-0.033，df=1，p=0.003）。
这些结果复制并证实了先前在 OSA 中确定的临床聚类，这些聚类在纵向分析中仍然存在，并且在聚类之间存在一定比例的迁移。
