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使用网络科学方法对阻塞性睡眠呼吸暂停综合征患者进行性别表型分析。

Gender Phenotyping of Patients with Obstructive Sleep Apnea Syndrome Using a Network Science Approach.

作者信息

Topîrceanu Alexandru, Udrescu Lucreția, Udrescu Mihai, Mihaicuta Stefan

机构信息

Department of Computer and Information Technology, Politehnica University Timișoara, 300223 Timișoara, Romania.

Department I-Drug Analysis, "Victor Babeș" University of Medicine and Pharmacy Timișoara, 300041 Timișoara, Romania.

出版信息

J Clin Med. 2020 Dec 12;9(12):4025. doi: 10.3390/jcm9124025.

DOI:10.3390/jcm9124025
PMID:33322816
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7764072/
Abstract

We defined gender-specific phenotypes for men and women diagnosed with obstructive sleep apnea syndrome (OSAS) based on easy-to-measure anthropometric parameters, using a network science approach. We collected data from 2796 consecutive patients since 2005, from 4 sleep laboratories in Western Romania, recording sleep, breathing, and anthropometric measurements. For both genders, we created specific apnea patient networks defined by patient compatibility relationships in terms of age, body mass index (BMI), neck circumference (NC), blood pressure (BP), and Epworth sleepiness score (ESS). We classified the patients with clustering algorithms, then statistically analyzed the groups/clusters. Our study uncovered eight phenotypes for each gender. We found that all males with OSAS have a large NC, followed by daytime sleepiness and high BP or obesity. Furthermore, all unique female phenotypes have high BP, followed by obesity and sleepiness. We uncovered gender-related differences in terms of associated OSAS parameters. In males, we defined the pattern large NC-sleepiness-high BP as an OSAS predictor, while in women, we found the pattern of high BP-obesity-sleepiness. These insights are useful for increasing awareness, improving diagnosis, and treatment response.

摘要

我们采用网络科学方法,基于易于测量的人体测量参数,为诊断为阻塞性睡眠呼吸暂停综合征(OSAS)的男性和女性定义了特定性别的表型。自2005年以来,我们从罗马尼亚西部的4个睡眠实验室收集了2796例连续患者的数据,记录睡眠、呼吸和人体测量数据。对于男性和女性,我们根据年龄、体重指数(BMI)、颈围(NC)、血压(BP)和爱泼华嗜睡量表(ESS)方面的患者相容性关系创建了特定的睡眠呼吸暂停患者网络。我们用聚类算法对患者进行分类,然后对这些组/聚类进行统计分析。我们的研究发现了每种性别的8种表型。我们发现,所有患有OSAS的男性颈围都很大,其次是白天嗜睡和高血压或肥胖。此外,所有独特的女性表型都有高血压,其次是肥胖和嗜睡。我们发现了与OSAS相关参数有关的性别差异。在男性中,我们将颈围大-嗜睡-高血压模式定义为OSAS预测指标,而在女性中,我们发现了高血压-肥胖-嗜睡模式。这些见解有助于提高认识、改善诊断和治疗反应。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/85c2/7764072/29976ab89f5b/jcm-09-04025-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/85c2/7764072/5ef709c626c1/jcm-09-04025-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/85c2/7764072/e6d98b33c634/jcm-09-04025-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/85c2/7764072/29976ab89f5b/jcm-09-04025-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/85c2/7764072/5ef709c626c1/jcm-09-04025-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/85c2/7764072/e6d98b33c634/jcm-09-04025-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/85c2/7764072/29976ab89f5b/jcm-09-04025-g003.jpg

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本文引用的文献

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PLoS One. 2018 Sep 5;13(9):e0202042. doi: 10.1371/journal.pone.0202042. eCollection 2018.
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A survey on sleep assessment methods.一项关于睡眠评估方法的调查。
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Personalised medicine in sleep respiratory disorders: focus on obstructive sleep apnoea diagnosis and treatment.睡眠呼吸障碍的个体化医学:以阻塞性睡眠呼吸暂停的诊断和治疗为重点。
利用基于人体测量特征和打鼾事件的监督学习技术筛查阻塞性睡眠呼吸暂停风险。
Digit Health. 2023 Mar 6;9:20552076231152751. doi: 10.1177/20552076231152751. eCollection 2023 Jan-Dec.
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Screening for Obstructive Sleep Apnea Risk by Using Machine Learning Approaches and Anthropometric Features.采用机器学习方法和人体测量特征筛查阻塞性睡眠呼吸暂停风险。
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