Department of Medicine, Faculty of Medicine, The Ottawa Hospital/University of Ottawa, Ottawa, ON, Canada; Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, ON, Canada; ICES, Ottawa, ON, Canada.
Division of Neurology, Department of Medicine, University of Toronto, Toronto, ON, Canada; Hurvitz Brain Sciences Research Program, Sunnybrook Research Institute, Sunnybrook Health Sciences Centre, Toronto, ON, Canada.
Chest. 2023 Aug;164(2):517-530. doi: 10.1016/j.chest.2023.03.006. Epub 2023 Mar 11.
Many cellular processes are controlled by sleep. Therefore, alterations in sleep might be expected to stress biological systems that could influence malignancy risk.
What is the association between polysomnographic measures of sleep disturbances and incident cancer, and what is the validity of cluster analysis in identifying polysomnography phenotypes?
We conducted a retrospective multicenter cohort study using linked clinical and provincial health administrative data on consecutive adults free of cancer at baseline with polysomnography data collected between 1994 and 2017 in four academic hospitals in Ontario, Canada. Cancer status was derived from registry records. Polysomnography phenotypes were identified by k-means cluster analysis. A combination of validation statistics and distinguishing polysomnographic features was used to select clusters. Cox cause-specific regressions were used to assess the relationship between identified clusters and incident cancer.
Among 29,907 individuals, 2,514 (8.4%) received a diagnosis of cancer over a median of 8.0 years (interquartile range, 4.2-13.5 years). Five clusters were identified: mild (mildly abnormal polysomnography findings), poor sleep, severe OSA or sleep fragmentation, severe desaturations, and periodic limb movements of sleep (PLMS). The associations between cancer and all clusters compared with the mild cluster were significant while controlling for clinic and year of polysomnography. When additionally controlling for age and sex, the effect remained significant only for PLMS (adjusted hazard ratio [aHR], 1.26; 95% CI, 1.06-1.50) and severe desaturations (aHR, 1.32; 95% CI, 1.04-1.66). Further controlling for confounders, the effect remained significant for PLMS, but was attenuated for severe desaturations.
In a large cohort, we confirmed the importance of polysomnographic phenotypes and highlighted the role that PLMS and oxygenation desaturation may play in cancer. Using this study's findings, we also developed an Excel (Microsoft) spreadsheet (polysomnography cluster classifier) that can be used to validate the identified clusters on new data or to identify which cluster a patient belongs to.
ClinicalTrials.gov; Nos.: NCT03383354 and NCT03834792; URL: www.
gov.
许多细胞过程受睡眠控制。因此，睡眠的改变可能会给可能影响恶性肿瘤风险的生物系统带来压力。
多导睡眠图测量的睡眠障碍与癌症发病的关系是什么，聚类分析在识别多导睡眠图表型方面的有效性如何？
我们进行了一项回顾性多中心队列研究，使用加拿大安大略省四所学术医院在 1994 年至 2017 年间收集的连续无癌症基线成年人的临床和省级健康管理数据以及多导睡眠图数据。癌症状态来源于登记记录。通过 K-均值聚类分析确定多导睡眠图表型。使用验证统计数据和区分多导睡眠图特征的组合来选择聚类。使用 Cox 病因特异性回归评估确定的聚类与癌症发病的关系。
在 29907 名参与者中，2514 名（8.4%）在中位数为 8.0 年（四分位距，4.2-13.5 年）内被诊断患有癌症。确定了五个聚类：轻度（轻度异常多导睡眠图发现）、睡眠不佳、严重 OSA 或睡眠片段化、严重低氧血症和周期性肢体运动睡眠（PLMS）。与轻度聚类相比，所有聚类与癌症的关联在控制诊所和多导睡眠图年份后均具有统计学意义。当进一步控制年龄和性别时，PLMS（调整后的危险比[HR]，1.26；95%CI，1.06-1.50）和严重低氧血症（调整后 HR，1.32；95%CI，1.04-1.66）的影响仍然显著。进一步控制混杂因素后，PLMS 的影响仍然显著，但严重低氧血症的影响减弱。
在一个大型队列中，我们证实了多导睡眠图表型的重要性，并强调了 PLMS 和氧合去饱和可能在癌症中的作用。使用本研究的结果，我们还开发了一个 Excel（Microsoft）电子表格（多导睡眠图聚类分类器），可用于在新数据上验证确定的聚类，或确定患者属于哪个聚类。
ClinicalTrials.gov；编号：NCT03383354 和 NCT03834792；网址：www.clinicaltrials.gov。
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