Intra-individual variability in sleep is related to glycaemic control in adults with type 2 diabetes

Sleep irregularity is associated with night-time technology, dysfunctional sleep beliefs and subjective sleep parameters amongst female university students

Sleep irregularity has been linked to multiple deleterious consequences in clinical populations or community adults and adolescents, but little is known about young adults. In this study, we explored the relationships between two measures of sleep regularity and a wide range of factors (lifestyle behaviors, subjective sleep, clinical outcomes, and academic performance) in a sample of female, university students in the United Arab Emirates. A total of 176 participants were recruited. Objective estimates of sleep-wake patterns were obtained using seven-day wrist actigraphy and data were used to calculate daily sleep regularity with the Sleep Regularity Index (SRI) and weekly sleep regularity with the social jetlag (SJL). Subjective sleep measures were also acquired using the Pittsburgh Sleep Quality Index (PSQI), Dysfunctional Beliefs and Attitudes about Sleep (DBAS), and daytime napping frequency. Self-reported night-time technology use frequency was ascertained using the Technology Use Questionnaire (TUQ). Psychological health was assessed using the Hospital Anxiety and Depression Scale. Objective physical health measurements for body mass index, fasting blood glucose and blood pressure were obtained. No significant associations emerged between sleep regularity and psychological physical health, or academic performance. However, significant relationships were detected between SRI and daytime napping frequency (p-value = 0.0017), PSQI (p-value = 0.0337), and DBAS (p-value = 0.0176), suggesting that daily irregular sleep patterns are associated with more frequent daytime napping, greater dysfunctional sleep beliefs, and poorer subjective sleep quality. Conversely, SJL was significantly associated with the DBAS (p-value = 0.0253), and the TUQ (p-value = 0.0208), indicating that weekly irregular sleep patterns are linked to greater dysfunctional sleep beliefs and increased nighttime technology use. In conclusion, efforts to educate and cultivate sustainable and consistent sleep-wake patterns amongst university students are needed, which can be achieved by raising awareness, promoting good sleep health habits, and minimizing excessive bedtime technology.

Personal and Social-Built Environmental Factors of Glucose Variability Among Multiethnic Groups of Adults With Type 2 Diabetes: Research Protocol Using Ecological Momentary Assessment, Continuous Glucose Monitoring, and Actigraphy

Glucose variability (GV)-the degree of fluctuation in glucose levels over a certain period of time-is emerging as an important parameter of dynamic glycemic control. Repeated glycemic oscillations have been reported to be the link to diabetes complications. This prospective observational study aims to: (1) identify multilevel risk factors (personal and social-built environmental factors) associated with high GV; (2) identify "within-person predictors" of high GV leveraging the intra-person data to inform future personalized diabetes interventions; and (3) examine which lifestyle factors either mediate or moderate the relationship between emotional well-being and GV among diverse adults with type 2 diabetes (T2D). We will recruit 200 adults with T2D from the community. All participants will complete baseline surveys assessing demographics, lifestyle, social-built environmental, and clinical factors. Real-time dynamic glucose levels will be measured using continuous glucose monitoring (CGM). Sleep, physical activity, diet/eating, and emotional well-being will be measured with an actigraphy device and a real-time self-report tool (ecological momentary assessment [EMA]) across 14 days. Two 24-h dietary recall data will be collected by online video calls. Generalized linear models, multilevel models, and structural equation models will be developed to achieve the study aims. The findings from the study will identify high-risk groups of high GV who would benefit from CGM to improve diabetes outcomes and inform the future development of personalized just-in-time interventions targeting lifestyle behaviors with an increased understanding of GV and by supporting healthcare providers' clinical decisions.

A Comparison of the Rates of Clock-Based Nocturnal Hypoglycemia and Hypoglycemia While Asleep Among People Living with Diabetes: Findings from the Hypo-METRICS Study

Introduction: Nocturnal hypoglycemia is generally calculated between 00:00 and 06:00. However, those hours may not accurately reflect sleeping patterns and it is unknown whether this leads to bias. We therefore compared hypoglycemia rates while asleep with those of clock-based nocturnal hypoglycemia in adults with type 1 diabetes (T1D) or insulin-treated type 2 diabetes (T2D). Methods: Participants from the Hypo-METRICS study wore a blinded continuous glucose monitor and a Fitbit Charge 4 activity monitor for 10 weeks. They recorded details of episodes of hypoglycemia using a smartphone app. Sensor-detected hypoglycemia (SDH) and person-reported hypoglycemia (PRH) were categorized as nocturnal (00:00-06:00 h) versus diurnal and while asleep versus awake defined by Fitbit sleeping intervals. Paired-sample Wilcoxon tests were used to examine the differences in hypoglycemia rates. Results: A total of 574 participants [47% T1D, 45% women, 89% white, median (interquartile range) age 56 (45-66) years, and hemoglobin A1c 7.3% (6.8-8.0)] were included. Median sleep duration was 6.1 h (5.2-6.8), bedtime and waking time ∼23:30 and 07:30, respectively. There were higher median weekly rates of SDH and PRH while asleep than clock-based nocturnal SDH and PRH among people with T1D, especially for SDH <70 mg/dL (1.7 vs. 1.4, P < 0.001). Higher weekly rates of SDH while asleep than nocturnal SDH were found among people with T2D, especially for SDH <70 mg/dL (0.8 vs. 0.7, P < 0.001). Conclusion: Using 00:00 to 06:00 as a proxy for sleeping hours may underestimate hypoglycemia while asleep. Future hypoglycemia research should consider the use of sleep trackers to record sleep and reflect hypoglycemia while asleep more accurately. The trial registration number is NCT04304963.

Are postprandial glucose responses sufficiently person-specific to use in personalized dietary advice? Design of the RepEAT study: a fully controlled dietary intervention to determine the variation in glucose responses

Introduction

An elevated postprandial glucose response is associated with an increased risk of cardiometabolic diseases. Existing research suggests large heterogeneity in the postprandial glucose responses to identical meals and food products between individuals, but the effect of other consumed meals during the day and the order of meals during the day on the heterogeneity in postprandial glucose responses still needs to be investigated. In addition, the robustness of the glucose responses to meals or foods is still unknown.

Objectives

The overall aim of the project is to assess whether the glucose response to a meal is sufficiently person-specific to use in personalized dietary advice. We aim to answer the question: "How replicable are glucose responses to meals within individuals and how consistent is the variation in glucose responses between individuals?"

Methods

The question will be assessed under standardized conditions of a 9-week fully controlled dietary intervention in which all meals are the same between individuals and consumed in a fixed order at a fixed time. 63 apparently healthy men and women with a BMI of 25-40 kg/m2 and aged 45-75 years were enrolled in the RepEAT study (NCT05456815), of whom 53 participants completed the study. The RepEAT study comprised a fully controlled dietary intervention of nine weeks, consisting of three repetitive periods of three weeks. Within each three-week period, a variety of meals and food products were offered during breakfast, lunch, dinner and in between meal snacks. Throughout the dietary intervention, glucose was continuously monitored using Freestyle Libre Pro IQ monitors. Physical activity was monitored using the ActiGraph and ActivPAL. To measure the association between glucose responses and an individual's phenotype, various measurements were performed before the start of the dietary intervention including an oral glucose tolerance test, a high-fat mixed meal challenge, assessment of body fat distribution including liver fat (MRI/MRS), and cardiometabolic markers.

Discussion

The repetitive and fully controlled nature of the dietary study allows detailed assessment of the replicability of the glucose responses to meals and food products within individuals. Furthermore, the consistency of the variation between individuals independent of insulin resistance will be determined.

The importance of sleep regularity: a consensus statement of the National Sleep Foundation sleep timing and variability panel

OBJECTIVE

To develop and present consensus findings of the National Sleep Foundation sleep timing and variability panel regarding the impact of sleep timing variability on health and performance.

METHODS

The National Sleep Foundation assembled a panel of sleep and circadian experts to evaluate the scientific evidence and conduct a formal consensus and voting procedure. A systematic literature review was conducted using the NIH National Library of Medicine PubMed database, and panelists voted on the appropriateness of 3 questions using a modified Delphi RAND/UCLA Appropriateness Method with 2 rounds of voting.

RESULTS

The literature search and panel review identified 63 full text publications to inform consensus voting. Panelists achieved consensus on each question: (1) is daily regularity in sleep timing important for (a) health or (b) performance? and (2) when sleep is of insufficient duration during the week (or work days), is catch-up sleep on weekends (or non-work days) important for health? Based on the evidence currently available, panelists agreed to an affirmative response to all 3 questions.

CONCLUSIONS

Consistency of sleep onset and offset timing is important for health, safety, and performance. Nonetheless, when insufficient sleep is obtained during the week/work days, weekend/non-work day catch-up sleep may be beneficial.

Social jet lag and (changes in) glycemic and metabolic control in people with type 2 diabetes

OBJECTIVE

Social jet lag, i.e., the discordance among social and biological rhythms, is associated with poor metabolic control. This study aimed to assess cross-sectional and longitudinal associations among social jet lag and glycemic and metabolic control in people with type 2 diabetes.

METHODS

In a prospective cohort (N = 990) with type 2 diabetes, social jet lag was measured at baseline using daily diaries and was categorized (high, moderate, or low). Metabolic outcomes were assessed at baseline and at 1 and 2 years of follow-up. Associations among social jet lag and glycemic and metabolic control were analyzed using linear regression and linear mixed models adjusted for confounding factors. Analyses were stratified for work status (retired vs. working; p value for interaction = 0.007 for glycated hemoglobin [HbA1c]).

RESULTS

In working people, a cross-sectional association between high social jet lag and HbA1c (1.87 mmol/mol [95% CI: 0.75 to 2.99]) and blood pressure (5.81 mm Hg [95% CI: 4.04 to 7.59]) was observed. For retired people, high social jet lag was negatively associated with HbA1c (-1.58 mmol/mol [95% CI: -2.54 to -0.62]), glucose (-0.19 mmoL/L [95% CI:-0.36 to -0.01]), and blood pressure (-3.70 mm Hg [95% CI: -5.36 to -2.04]), and the association with BMI was positive (1.12 kg/m² [95% CI: 0.74 to 1.51]). Prospective associations had the same direction as cross-sectional findings but were nonsignificant for working or retired people.

CONCLUSIONS

Social jet lag was cross-sectionally, but not prospectively, associated with glycemic and metabolic markers. Interaction with work status was present, and directions of the associations were generally detrimental in the working population, whereas higher social jet lag was associated with improved glycemic and metabolic control for retired people.

Objective sleep characteristics and continuous glucose monitoring profiles of type 2 diabetes patients in real-life settings

AIM

To investigate the association between objective sleep parameters and glycaemic variability determined by continous glucose monitoring (CGM) among patients with type 2 diabetes, given the significant role of sleep in glycaemic control.

METHODS

In this study, CGM was carried out in 28 patients with T2D (aged 62.3 ± 4.8 years, 57% women). Sleep characteristics were assessed by actigraphy within the CGM period. CGM-derived outcomes included glucose level, and percentages of time in range (TIR) and time above range (TAR) during the monitoring period. Associations between intraindividual night-to-night variations in sleep characteristics and overall CGM outcomes were analysed using linear regression. Associations between sleep characteristics during each night and time-matched CGM outcomes were analysed using linear mixed models.

RESULTS

A total of 249 person-days of CGM, coupled with 221 nights of sleep characteristics, were documented. Greater standard deviation (SD) of objective sleep duration (minutes) between measurement nights was associated with higher glucose level (coefficient 0.018 mmol/L [95% confidence interval {CI} 0.004, 0.033], P = 0.017), smaller proportion of TIR (% in observation period; coefficient -0.20% [95% CI -0.36, -0.03], P = 0.023), and greater proportion of TAR (coefficient 0.22% [95% CI 0.06, 0.39], P = 0.011). Later sleep midpoint (minutes from midnight) was associated with greater SD of glucose during the same sleep period (coefficient 0.002 minutes [95% CI 0.0001, 0.003], P = 0.037), longer nocturnal sleep duration was associated with smaller coefficient of variation of glucose level in the upcoming day (-0.015% [95% CI -0.03, -0.001], P = 0.041).

CONCLUSION

Objectively determined sleep duration and sleep midpoint, as well as their daily variability, are associated with CGM-derived glucose profiles in T2D patients.

Sleep-Opt-In: A Randomized Controlled Pilot Study to Improve Sleep and Glycemic Variability in Adults With Type 1 Diabetes

PURPOSE

The purpose of this study was to evaluate the feasibility and acceptability of a technology-assisted behavioral sleep intervention (Sleep-Opt-In) and to examine the effects of Sleep-Opt-In on sleep duration and regularity, glucose indices, and patient-reported outcomes. Short sleep duration and irregular sleep schedules are associated with reduced glycemic control and greater glycemic variability.

METHODS

A randomized controlled parallel-arm pilot study was employed. Adults with type 1 diabetes (n = 14) were recruited from the Midwest and randomized 3:2 to the sleep-optimization (Sleep-Opt-In) or Healthy Living attention control group. Sleep-Opt-In was an 8-week, remotely delivered intervention consisting of digital lessons, sleep tracker, and weekly coaching phone calls by a trained sleep coach. Assessments of sleep (actigraphy), glucose (A1C, continuous glucose monitoring), and patient-reported outcomes (questionnaires for daytime sleepiness, fatigue, diabetes distress, and depressive mood) were completed at baseline and at completion of the intervention.

RESULTS

Sleep-Opt-In was feasible and acceptable. Those in Sleep-Opt-In with objectively confirmed short or irregular sleep demonstrated an improvement in sleep regularity (25 minutes), reduced glycemic variability (3.2%), and improved time in range (6.9%) compared to the Healthy Living attention control group. Patient-reported outcomes improved only for the Sleep-Opt-In group. Fatigue and depressive mood improved compared to the control.

CONCLUSIONS

Sleep-Opt-In is feasible, acceptable, and promising for further evaluation as a means to improve sleep duration or regularity in the population of people with type 1 diabetes.

Irregular sleep and cardiometabolic risk: Clinical evidence and mechanisms

Sleep regularity is an essential part of the multidimensional sleep health framework. The phenomenon of irregular sleep patterns is widespread in contemporary lifestyles. This review synthesizes clinical evidence to summarize the measures of sleep regularity and discusses the role of different sleep regularity indicators in developing cardiometabolic diseases (coronary heart disease, hypertension, obesity, and diabetes). Existing literature has proposed several measurements to assess sleep regularity, mainly including the standard deviation (SD) of sleep duration and timing, sleep regularity index (SRI), interdaily stability (IS), and social jetlag (SJL). Evidence on associations between sleep variability and cardiometabolic diseases varies depending on the measure used to characterize variability in sleep. Current studies have identified a robust association between SRI and cardiometabolic diseases. In comparison, the association between other metrics of sleep regularity and cardiometabolic diseases was mixed. Meanwhile, the associations of sleep variability with cardiometabolic diseases differ across the population. SD of sleep characteristics or IS may be more consistently associated with HbA1c in patients with diabetes compared with the general population. The association between SJL and hypertension for patients with diabetes was more accordant than in the general population. Interestingly, the age-stratified association between SJL and metabolic factors was observed in the present studies. Furthermore, the relevant literature was reviewed to generalize the potential mechanisms through which irregular sleep increases cardiometabolic risk, including circadian dysfunction, inflammation, autonomic dysfunction, hypothalamic-pituitary-adrenal (HPA) axis disorder, and gut dysbiosis. Health-related practitioners should give more attention to the role of sleep regularity on human cardiometabolic in the future.

Associations between sleep variability and cardiometabolic health: A systematic review

This review explored the associations between sleep variability and cardiometabolic health. It was performed following PRISMA guidelines. We identified 63 studies. Forty-one studies examined the association between sleep variability and body composition, with 29 examined body mass index (BMI). Thirteen studies used social jet lag (SJL), n = 30,519, with nine reporting a null association. Eight studies used variability in sleep duration (n = 33,029), with five reporting a correlation with BMI. Fourteen studies (n = 133,403) focused on overweight/obesity; significant associations with sleep variability were found in 11 (n = 120,168). Sleep variability was associated with weight gain (seven studies; n = 79,522). Twenty-three studies examined glucose outcomes. The association with hemoglobin A1c (16 studies, n = 11,755) differed depending on populations, while associations with diabetes or glucose were mixed, and none were seen with insulin resistance (five studies; n = 6416). Sixteen studies examined cardiovascular-related outcomes, with inconsistent results. Overall significant associations were found in five studies focusing on metabolic syndrome (n = 7413). In summary, sleep variability was likely associated with obesity, weight gain, and metabolic syndrome. It might be associated with hemoglobin A1c in people with type 1 diabetes. The associations with other outcomes were mixed. This review highlighted the possible association between sleep variability and cardiometabolic health.

Sleep and Circadian Rhythm Disturbances in Diabetes: A Narrative Review

Sleep and circadian rhythm disturbances are less-known risk factors for the development and suboptimal outcomes of diabetes. The goal of this narrative review is to highlight the importance of sleep and circadian rhythm disturbances in the development and outcomes of type 1 diabetes (T1D) and type 2 diabetes (T2D), assess current treatment options and the possible mediating mechanisms. We performed a literature search using PubMed and selected relevant English and Dutch papers. Disturbances of sleep and circadian rhythm are common in people with diabetes. They are associated with an increased risk of developing T2D as well as with suboptimal diabetes outcomes (including higher HbA1c levels and reduced quality of life) for T1D and T2D. Preliminary data suggest that treatment of sleep and circadian rhythm disturbances could improve diabetes outcomes in people with T1D and T2D. Finally, the association with medical parameters appears to be mediated by disturbance in hormones, and by suboptimal self-care including forgetting or postponing glucose monitoring or medication use as well as higher consumption of high fat/high sugary foods. Diabetes may also disturb sleep, for example through nocturnal hypoglycemia and nocturia. We concluded that sleep and circadian rhythm disturbances are closely linked with diabetes. More attention to sleep in regular diabetes care is warranted, while further research is needed on treatment of sleep and circadian rhythm disturbances in the prevention of diabetes and its suboptimal outcomes.

Long-term variability and change trend of systolic blood pressure and risk of type 2 diabetes mellitus in middle-aged Japanese individuals: findings of the Aichi Workers' Cohort Study

Studies have reported that short-term blood pressure (BP) variability (BPV) is associated with type 2 diabetes mellitus (T2DM) incidence, but the association with long-term BPV remains unclear. The present study investigated the associations of long-term BPV as well as the time trend of BP changes over time with the incidence of T2DM. This study followed a cohort of 3017 Japanese individuals (2446 male, 571 female) aged 36-65 years from 2007 through March 31, 2019. The root-mean-square error (RMSE) and the slope of systolic BP (SBP) change regressed on year were calculated individually using SBP values obtained from 2003 to baseline (2007). A multivariable Cox proportional hazard model was applied to estimate hazard ratios (HRs) and corresponding 95% confidence intervals (CIs) for tertiles of SBP RMSE and continuous SBP slopes adjusted for age, sex, smoking status, regular exercise, sodium intake, family history of diabetes, sleep disorder, body mass index (BMI), SBP, and fasting blood glucose (FBG) at baseline, and BMI slope from 2003 to 2007. The highest RMSE tertile compared to the lowest was associated with a significantly higher incidence of T2DM after adjusting for covariates (HR: 1.79, 95% CI: 1.15, 2.78). The slope was also significantly associated with T2DM incidence until baseline SBP and FBG were adjusted (HR: 1.03, 95% CI: 0.99, 1.07). In conclusion, long-term SBP variability was significantly associated with an increased incidence of T2DM independent of baseline age, sex, BMI, SBP, FBG, lifestyle factors and BMI slope from 2003 until baseline.