| Background The number of people diagnosed with obstructive sleep apnea (OSA) in China continues to rise, which has caused a serious disease burden. However, most of the previous studies on the influencing factors of OSA (such as obesity and sleep position) have been cross-sectional studies, which are difficult to reveal the dynamic interaction mechanism between multiple factors, which restricts the formulation of individualized intervention programs. Objective By exploring the differences in body mass index (BMI)-oxygen saturation-sleep position-heart rate variability (HRV) network models between non-OSA and OSA populations, this paper provides a reference for the early diagnosis and intervention of OSA. Methods Adult subjects who went to the Affiliated Hospital of Southwest Medical University for sleep monitoring from July 12, 2022 to October 11, 2023 were included, and AHI=5 times/h was used as the diagnostic threshold, and they were divided into control group and OSA group, and BMI-oxygen saturation-sleep position-HRV networks were constructed and compared, respectively. Results There was no significant difference in the overall edge weight (P=0.55) and overall strength (P=0.28) of the network model between the control group and the OSA group. There were significant differences in the connection strength between the two network models (e.g., between “minimum oxygen saturation” and “BMI”, “prone position”, “mean interval”) and centrality index of specific nodes [“mean oxygen saturation”, “minimum oxygen saturation”, “upright apnea-hypopnea index (AHI)”, “right decubitus AHI”, and “mean heart rate”)] (P<0.05). Conclusion There are differences between the non-OSA population and the OSA population in specific factors (e.g., body position, heart rate, oxygen saturation), and these differences may provide a theoretical basis for the early prediction of OSA. |