Association of Sleep Patterns with Type 2 Diabetes Mellitus: A Cross-Sectional Study Based on Latent Class Analysis

Mengdie Liu, Wali Lukman Ahmed, Lang Zhuo, Hui Yuan, Shuo Wang () and Fang Zhou ()
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Mengdie Liu: School of Nursing, Xuzhou Medical University, Xuzhou 221004, China
Wali Lukman Ahmed: School of Nursing, Xuzhou Medical University, Xuzhou 221004, China
Lang Zhuo: School of Public Health, Xuzhou Medical University, Xuzhou 221004, China
Hui Yuan: School of Nursing, Xuzhou Medical University, Xuzhou 221004, China
Shuo Wang: School of Nursing, Xuzhou Medical University, Xuzhou 221004, China
Fang Zhou: School of Nursing, Xuzhou Medical University, Xuzhou 221004, China

IJERPH, 2022, vol. 20, issue 1, 1-13

Abstract: Sleep duration, sleep quality and circadian rhythm disruption indicated by sleep chronotype are associated with type 2 diabetes. Sleep involves multiple dimensions that are closely interrelated. However, the sleep patterns of the population, and whether these sleep patterns are significantly associated with type 2 diabetes, are unknown when considering more sleep dimensions. Our objective was to explore the latent classes of sleep patterns in the population and identify sleep patterns associated with type 2 diabetes. Latent class analysis was used to explore the best latent classes of sleep patterns based on eleven sleep dimensions of the study population. Logistic regression was used to identify sleep patterns associated with type 2 diabetes. A total of 1200 participants were included in the study. There were three classes of sleep patterns in the study population: “circadian disruption with daytime dysfunction” (class 1), “poor sleep status with daytime sleepiness” (class 2), and “favorable sleep status” (class 3). After controlling for all confounding factors, people in class 2 have significantly higher prevalence of type 2 diabetes than those in class 3 (OR: 2.24, 95% CI 1.26–4.00). Sleep problems have aggregated characteristics. People with sleep patterns involving more or worse sleep problems have higher significantly prevalence of T2DM.

Keywords: diabetes mellitus; sleep; latent class analysis (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2022
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