Promoting Healthy Sleep Patterns for Diabetes Prevention: A Study of Sleep Regularity and Duration
This retrospective cohort study, published in BMC Public Health, investigated the relationship between sleep patterns - specifically sleep regularity and duration – and the risk of developing type 2 diabetes (T2D) within a prospective Chinese cohort. The research utilized routinely collected medical records and wearable device data, making clinical trial registration unnecessary.
Researchers defined daily sleep duration as the average sleep time across valid days, categorizing it as adequate (7-9 hours/day for adults aged 18-64 years, and 7-8 hours/day for those aged 65 years or older) or inadequate based on age-specific recommendations. Sleep regularity was assessed using data from accelerometers processed through GGIR (v2.7-1), generating a Sleep regularity Index (SRI). Participants with fewer than five valid days of wear data or invalid data were excluded from the analysis.
The primary outcome measured was incident T2D, identified through hospital electronic medical records and insurance billing data using ICD-10 code E11, physician-confirmed diagnoses, or prescriptions for antidiabetic medications like metformin or insulin.Individuals with pre-existing T2D records prior to baseline or within the frist year after baseline were excluded to mitigate reverse causation.
Baseline covariates considered included age, sex, moderate-to-vigorous physical activity (MVPA) in minutes/day, screen time, smoking status (never, former, current), alcohol consumption frequency (times/week), coffee intake (cups/day), fruit and vegetable intake (servings/day), presence of sleep problems, medication use, mental health issues, family history of cardiovascular disease (CVD), and employment status (retired, employed with or without shift work). Thes covariates were selected based on established links to both sleep and T2D.
Statistical analysis employed Cox proportional hazards regression to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) for incident T2D, using age as the time scale. The proportional hazards assumption was verified using Schoenfeld residuals. Restricted cubic spline models were used to explore potential non-linear associations between SRI and T2D risk.A joint exposure analysis was also conducted, cross-classifying SRI categories with sleep duration adequacy, resulting in six distinct groups. complete-case analysis was used to address missing covariate data (less than 5%). all analyses were performed using R (version 4.2.3), with a p-value of less than 0.05 considered statistically notable.
