ArticleInternational journal of environmental research and public health2022
Association of Sleep Patterns with Type 2 Diabetes Mellitus: A Cross-Sectional Study Based on Latent Class Analysis.
Article in International journal of environmental research and public health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed, 12 citations in OpenAlex.
- Potential of Triazines as Antidiabetic Agents-A Review of Structures and Pharmacological Activity.Pharmaceuticals (Basel, Switzerland) · 2026Review
- Chronic Stress Destabilizes Ectoplasmic Specialization Dynamics in the Rat Testis via Downregulating the Focal Adhesion Kinase-Associated Protein Complex.Reproductive sciences (Thousand Oaks, Calif.) · 2026Article
- Latent class analysis for quality of life status, sleep quality and anxiety in patients with type 2 diabetes.BMC endocrine disorders · 2025Article
- Genetic predisposition to adiposity, and type 2 diabetes: the role of lifestyle and phenotypic adiposity.European journal of endocrinology · 2025Article
- The U-Shaped Association between Sleep Duration, All-Cause Mortality and Cardiovascular Risk in a Hispanic/Latino Clinically Based Cohort.Journal of clinical medicine · 2023Article
- The Worldwide Prevalence of Sleep Problems Among Medical Students by Problem, Country, and COVID-19 Status: a Systematic Review, Meta-analysis, and Meta-regression of 109 Studies Involving 59427 Participants.Current sleep medicine reports · 2023Review
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Authors and funding
6 authors at 1 institution in 1 country.
Funding
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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.
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