The Associations Between Sleep Architecture and Metabolic Parameters in Patients With Obstructive Sleep Apnea: A Hospital-Based Cohort Study

Background and Objectives: The associations between objective sleep architecture and metabolic parameters have been rarely studied in patients with obstructive sleep apnea (OSA). Here, we evaluated the associations between objective sleep measures derived via polysomnography (PSG) and metabolic parameters. Methods: A total of 2,308 subjects with suspected OSA were included. We measured common metabolic parameters such as body mass index (BMI) and glucose, insulin, blood pressure, total cholesterol (TC), triglyceride (TG), high-density lipoprotein cholesterol (HDL-C), and low-density lipoprotein cholesterol (LDL-C) levels. All subjects underwent full-night PSG. PSG sleep parameters included total sleep time (TST), time spent in slow-wave sleep (SWS) and rapid eye movement (REM) sleep, sleep efficiency, and the microarousal index (MAI). Results: The TST correlated with the BMI, glucose level, and systolic blood pressure. The SWS/TST ratio correlated with BMI and glucose, TC, and TG levels. The REM/TST ratio correlated with BMI, glucose, insulin, and TG levels, and diastolic blood pressure. We found significant relationships between sleep efficiency and BMI, glucose levels, and TG levels. The MAI was significantly correlated with all metabolic parameters. After adjustment for age, gender, smoking status, alcohol use, apnea hypopnea index, and oxygen desaturation index (ODI), multiple linear regression analysis showed that the MAI was independently associated with glucose level, TC, HDL, and LDL. REM/TST ratio was positively associated with diastolic blood pressure but negatively associated with glucose metabolism. Conclusions: Though some independent correlation between sleep and metabolic parameters was confirmed, only weak associations were observed, suggesting a clinically negligible influence of sleep structure. Further prospective studies are warranted to confirm our findings.

Duke Scholars

Author Fan Wang Neurobiology