Sleep Variability Associated with Glycemic Control Among People with T2D


New research found a correlation between actigraphy determined night-to-night sleep regularity and glycemic control over a 249 person-days CGM period.

New research into the association between sleep and glycemic variability suggested objective-determined sleep duration and sleep midpoint were associated with continuous glucose monitoring (CGM)-derived glucose profiles in people with type 2 diabetes (T2D).

The investigators observed a correlation between actigraphy determined night-to-night sleep regularity and glycemic control over a 249 person-days CGM period. A one-hour increment in the SD of nocturnal sleep duration across multiple nights was associated with approximately 1.1 mmol/L higher mean glucose value, 12% less TIR, and 13% more TAR during the total CGM period.

“Minimizing night-to-night sleep and circadian variability, securing sufficient sleep duration, and promoting an early chronotype may help optimizing glycemic control among patients with T2D,” wrote study authors Xiao Tan, Department of Big Data in Health Science, Zhejiang University School of Public Health and Sir Run Run Shaw Hospital, Zhejiang University School of Medicine and Yan Zhao, School of Sports and Health, Nanjing Sport Institute.

Research has linked insufficient sleep duration, fragmented and disrupted sleep to poor glycemic control among a population with T2D. Most studies calculated either self-reported variables or average values of sleep parameters, but the night-to-night fluctuations of sleep variables had not been reported. These fluctuations were more pronounced among individuals with a disrupted sleep and circadian pattern and correlated with glycemic control.

Current researchers aimed to investigate whether sleep and circadian parameters, as well as their night-to-night variations, were associated with CGM-derived glycemic outcomes among T2D individuals in real-life settings. They additionally explored the potential imminent impact of nocturnal sleep on glucose variation among these patients.

The community-based observational study included middle-aged and older adults from a municipality in China and eligible participants were aged 50 years or older, with a duration of T2D for at least one year.

A CGM system recorded participants’ glucose values for 14 consecutive days in September 2020. CGM-derived outcomes ranged from glucose level to percentages of time in range (TIR) and time above range (TAR) during the monitoring period.

For the objective assessment of sleep, participants were instructed to wear an actigraphy on their non-dominant wrist for at least seven consecutive days during the CGM period. The associations between intraindividual night-to-night variations of sleep characteristics and overall CGM outcomes were analyzed by linear regression. Linear mixed models helped analyze the association between sleep characteristics in each night and time-matched CGM outcomes.

The study population included a total of 28 patients with T2D (16 women, 57.1%) and data from all participants were involved in the analysis. Participants had an average age of 62.3 years old and had a T2D duration of 8.1 years.

The study documented a total of 249 person-days of CGM linked with 221 nights of sleep characteristics. The standard deviation of total sleep time in minutes between measurement nights was correlated with mean glucose value (mmol/L; Coefficient, 0.018 [95% CI, 0.004 - 0.033]; P = .017) and percentage of TAR in the period (0.22 [95% CI, 0.06 - 0.39]; P = .011).

However, this was negatively correlated with the percentage of TIR in the period (-0.20 [95% CI, -0.36 to -0.03]; P = .023).

Longer total sleep time was additionally associated with higher nocturnal glucose variation determined by SD (0.002 [95% CI, 0.001 - 0.003]; P <.001). Meanwhile, longer nocturnal sleep duration was associated with smaller coefficient of variance of glucose level in the subsequent day (-0.015 [95% CI, -0.03 to -0.001]; P = .041).

The study, “Objective sleep characteristics and continuous glucose monitoring profiles of type 2 diabetes patients in real-life settings,” was published in Diabetes, Obesity and Metabolism.

Related Videos
Video 10 - "Future Treatment Landscape for COPD"
Video 9 - "Emerging Treatment Approaches in COPD"
Ghada Bourjeily, MD: Research Gaps on Sleep Issues During Pregnancy
John Winkelman, MD, PhD: When to Use Low-Dose Opioids for Restless Legs Syndrome
Bhanu Prakash Kolla, MBBS, MD: Treating Sleep with Psychiatric Illness
Jennifer Martin, PhD: Boosting CPAP Adherence in Women with Sleep Apnea
Video 2 -  4 KOLs are featured in, "Educating Primary Care Clinicians on Outpatient HE Management and Ammonia Testing"
Video 1 - 4 KOLs are featured in, "Exploring the Impact of Hepatic Encephalopathy on Patients and Their Families "
© 2024 MJH Life Sciences

All rights reserved.