Study on the differences in BMI-oxygen saturation-sleep position-heart rate variability between OSA and non-OSA populations based on a network model
10.11886/scjsws20250303001
- VernacularTitle:基于网络模型的OSA与非OSA人群BMI-血氧饱和度-睡眠体位-心率变异性的差异研究
- Author:
Yao LUO
1
;
Anlin WANG
1
;
Tingting WANG
1
;
Xuemei LIANG
1
;
Bo XIANG
1
;
Kezhi LIU
1
Author Information
1. Affiliated Hospital of Southwest Medical University， Luzhou 646000， China
- Publication Type:Journal Article
- Keywords:
Obstructive sleep apnea;
Sleep monitoring;
Body mass index;
Heart rate variability;
Network analysis
- From:
Sichuan Mental Health
2025;38(5):405-413
- CountryChina
- Language:Chinese
-
Abstract:
BackgroundIn recent years， the prevalence of obstructive sleep apnea （OSA） is escalating in China， leading to a serious disease burden. However， previous studies on the influencing factors of OSA， such as obesity and sleep position， were mostly cross-sectional studies. This approach inherently hinders the identification of dynamic interaction mechanism among multiple variables， consequently obstructing the formulation of individualized intervention strategies. ObjectiveTo investigate the differences in body mass index （BMI）-oxygen saturation-sleep position-heart rate variability （HRV） network models between OSA and non-OSA populations， thereby offering a reference for the early detection and management of OSA. MethodsA total of 384 adult participants undergoing sleep monitoring at the Affiliated Hospital of Southwest Medical University from July 12， 2022 to October 11， 2023 were included. Subjects were categorized into OSA group （n=203） and control group （n=181） based on an apnea-hypopnea index （AHI） threshold of 5 events per hour. Subsequently， BMI-oxygen saturation-sleep position-HRV networks were constructed and compared between two groups. ResultsThere was no significant difference in the overall edge weight （P=0.55） and overall strength （P=0.28） of the network model between control group and OSA group. Notable differences emerged in both the node connection strength （e.g.， minimum oxygen saturation with BMI， sleep in prone position， and mean RR interval） and node centrality indices （mean oxygen saturation， minimum oxygen saturation， AHI in upright position， AHI in right lateral position and mean heart rate） within the two network models （P<0.05）. ConclusionSignificant differences are observed between the non-OSA and OSA populations in specific factors， including sleep position， heart rate and oxygen saturation.
