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

The association between glycaemic variability and sleep quality and quantity in adults with type 1 and type 2 diabetes: A systematic review

AIMS

Individuals with diabetes frequently encounter sleep disturbances, which can detrimentally impact glycaemic management. We reviewed the relationship between sleep outcomes and glycaemic variability in adults with diabetes.

METHODS

We systematically searched Medline, EMBASE and Cochrane Library (2002-March 2023) for studies evaluating sleep and glycaemic variability in adults with type 1 and type 2 diabetes. Among the 3049 records, 27 met the inclusion criteria (type 1 diabetes studies = 22). Due to methodological heterogeneity, a qualitative analysis was conducted.

RESULTS

Most studies measuring sleep quality (5 out 7; 71%) reported a significant association with glycaemic variability in type 1 and type 2 diabetes. Sleep duration was not significantly associated with glycaemic variability in type 1 diabetes, whereas other sleep metrics yielded inconclusive results. Hybrid closed-loop pump interventions (n = 12) demonstrated varying sleep outcomes with improved glycaemic variability. Similarly, sleep interventions (n = 3) consistently enhanced sleep but not glycaemic variability. Limitations included moderate to high risk of study bias, confounders, methodological heterogeneity and limited type 2 diabetes data.

CONCLUSIONS

A potential association between sleep quality and glycaemic variability exists. However, associations with other sleep metrics remain elusive, with no discernible association between sleep duration and glycaemic variability in type 1 diabetes. Despite advancements in continuous glucose monitoring and ambulatory sleep monitoring, standardised sleep assessment methodologies are lacking in real-world studies. Establishing standard protocols for sleep assessment and defining optimal sleep targets are crucial for meaningful comparisons between studies. Understanding the complex interplay between sleep and glycaemic variability holds promise in improving diabetes management and sleep health.

Sleep extension and cardiometabolic health: what it is, possible mechanisms and real-world applications

Short sleep duration is associated with heightened cardiometabolic disease risk and has reached epidemic proportions among children, adolescents and adults. Potential mechanisms underlying this association are complex and multifaceted, including disturbances in circadian timing, food intake and appetitive hormones, brain regions linked to control of hedonic eating, physical activity, an altered microbiome and impaired insulin sensitivity. Sleep extension, or increasing total sleep duration, is an emerging and ecologically relevant intervention with significant potential to advance our understanding of the mechanisms underlying the association between short sleep duration and the risk of cardiometabolic disease. If effective, sleep extension interventions have potential to improve cardiometabolic health across the lifespan. Existing data show that sleep extension is feasible and might have potential cardiometabolic health benefits, although there are limitations that the field must overcome. Notably, most existing studies are short term (2-8 weeks), use different sleep extension strategies, analyse a wide array of cardiometabolic health outcomes in different populations and, frequently, lack adequate statistical power, thus limiting robust scientific conclusions. Overcoming these limitations will require fully powered, randomized studies conducted in people with habitual short sleep duration and existing cardiometabolic risk factors. Additionally, randomized controlled trials comparing different sleep extension strategies are essential to determine the most effective interventions. Ongoing and future research should focus on elucidating the potential cardiometabolic health benefits of sleep extension. Such studies have high potential to generate crucial knowledge with potential to improve health and quality of life for those struggling with short sleep duration.

Sleep Disturbances in Type 1 Diabetes and Mitigating Cardiovascular Risk

Cardiovascular disease (CVD) is a major cause of morbidity and mortality in persons with type 1 diabetes (T1D). Despite control of known cardiovascular (CV) risk factors and better glycemic management, persons with T1D still face heightened CVD risk, suggesting additional contributing factors. Sleep has recently been recognized as a CV risk factor; however; the role of sleep in CVD specifically in T1D population has only started to emerge. Extensive evidence suggests that persons with T1D often encounter sleep disturbances. This review aims to comprehensively explore the relationship between sleep disturbances and CVD in T1D, proposed possible mediators including glycemic control, which has been studied more extensively, and less studied factors such as blood pressure, lipid metabolism, and weight management. Stress and self-care behaviors likely also play a role in the relationship between sleep disturbances and CVD. The evidence regarding sleep interventions in the context of T1D in mitigating these CV risk factors has recently been shown in early, small-scale studies. Sleep assessments should be a part of the standard of care in persons with T1D. Further research should focus on understanding the impact and mechanistic pathways of sleep disturbances on CV risk and developing T1D-specific sleep interventions to reduce CVD burden in this population.

Sleep Health Composite and Diabetes Symptom Burden in Young Adults With Type 1 Diabetes

INTRODUCTION

Multiple individual sleep health dimensions (satisfaction, regularity, and duration) are associated with diabetes symptoms, precursors to micro-and macrovascular complications, among young adults with type 1 diabetes mellitus (T1DM). Nearly half of young adults with T1DM develop vascular complications; however, modifiable contributors of diabetes symptoms, including sleep health, have been understudied.

METHODS

This cross-sectional quantitative descriptive study involved the completion of multiple validated self-report questionnaires and the collection of raw continuous glucose monitor and diary data over a 14-day period. The sleep health composite score was calculated by summing the number of dimensions with "good" sleep health. Chronotype was estimated with the 19-item Morningness-Eveningness Questionnaire. Multiple linear regression analyses were conducted to evaluate the associations between the independent variables (sleep health composite and chronotype) and diabetes symptom burden. Covariates, including age, race, sex at birth, T1D duration, continuous subcutaneous insulin infusion use, and hemoglobin A1C (HbA1C), were considered to determine their contribution to these relationships.

RESULTS

One hundred nineteen young adults with T1DM who were aged 18 to 26 years were included in this study from 2 cohorts. Higher sleep health composite scores were associated with a lower overall diabetes symptom burden, even after adjusting for covariates in the linear regression models. Initially, a later chronotype was linked to a higher diabetes symptom burden, but this association became insignificant after accounting for HbA1C levels.

CONCLUSION

Improving multiple dimensions of sleep health may alleviate the diabetes symptom burden among young adults with T1DM.

Impact of irregular sleep pattern, and sleep quality on glycaemic parameters and endothelial function in adolescents and young adults with type 1 diabetes

This study investigated the impact of comprehensive sleep patterns on glycaemic parameters and endothelial function in adolescents and young adults with type 1 diabetes (T1D). Thirty subjects with type 1 diabetes (aged 13-25) without chronic complications participated. For 1 week, glucose levels were monitored by real-time continuous glucose monitoring (CGM) and sleep was simultaneously assessed by actigraphy. Subjective sleep quality was assessed using the Pittsburgh Sleep Quality Index (PSQI). Flow-mediated dilatation (FMD) measured endothelial function at the brachial artery. Insulin sensitivity was determined by calculated estimated glucose disposal rate (eGDR). Glycaemic control was assessed using haemoglobin A1C (HbA1C) levels. To address potential confounding by metabolic syndrome on the FMD results, three affected subjects were excluded from FMD correlation analyses. Participants with PSQI scores >5 had a lower %FMD compared with those with scores ≤5 (4.6 ± 3.7% vs. 7.6 ± 3.0%, p = 0.03). Multivariate analysis indicated that lower sleep efficiency and higher sleep duration variability were associated with higher HbA1C levels (β = -0.076, 95%CI [-0.145, -0.008], p = 0.029; β = 0.012, 95%CI [0.001, 0.023], p = 0.033). Irregular sleep timing and lower sleep efficiency were related to decreased insulin sensitivity (sleep midpoint irregularity β = -1.581, 95%CI [-2.661, -0.502], p = 0.004, and sleep efficiency β = 0.147, 95%CI [0.060, 0.235], p = 0.001). No significant associations were found between glycaemic parameters and FMD. Our study demonstrated that sleep irregularity in type 1 diabetes was associated with glycaemic control and insulin resistance, while poor subjective sleep quality was linked to endothelial dysfunction. Promoting healthy sleep habits, including consistent sleep timing could benefit metabolic and cardiovascular health in type 1 diabetes.

Type 1 diabetes-related distress: Current implications in care

Type 1 diabetes (T1D) is a complex chronic disease associated with major health and economic consequences, also involving important issues in the psychosocial sphere. In this regard, T1D-related distress, defined as the emotional burden of living with T1D, has emerged as a specific entity related to the disease. Diabetes distress (DD) is an overlooked but prevalent condition in people living with T1D, and has significant implications in both glycemic control and mental health in this population. Although overlapping symptoms may be found between DD and mental health disorders, specific approaches should be performed for the diagnosis of this problem. In recent years, different DD-targeted interventions have been postulated, including behavioral and psychosocial strategies. Moreover, new technologies in this field may be helpful to address DD in people living with T1D. In this article, we summarize the current knowledge on T1D-related distress, and we also discuss the current approaches and future perspectives in its management.

Greater sleep variability is associated with higher systemic inflammation in type 2 diabetes

Sleep irregularity and variability have been shown to be detrimental to cardiometabolic health. The present pilot study explored if higher day-to-day sleep irregularity and variability were associated with systemic inflammation, as assessed by high-sensitivity C-reactive protein, in type 2 diabetes. Thirty-five patients with type 2 diabetes (mean age 54.3 years, 54.3% female) who were not shift-workers participated. The presence of diabetic retinopathy was determined. The standard deviation of sleep duration and sleep midpoint across all recorded nights were used to quantify sleep variability and regularity, respectively, assessed by 14-day actigraphy. The presence and severity of sleep apnea were assessed using an overnight home monitor. Low-density lipoprotein, haemoglobin A1C and high-sensitivity C-reactive protein were collected. Multiple regression analysis using natural-log-transformed values was performed to establish an independent association between sleep variability and high-sensitivity C-reactive protein. Twenty-two (62.9%) patients had diabetic retinopathy. The median (interquartile range) of high-sensitivity C-reactive protein was 2.4 (1.4, 4.6) mg L-1. Higher sleep variability was significantly associated with higher high-sensitivity C-reactive protein (r = 0.342, p = 0.044), as was haemoglobin A1C (r = 0.431, p = 0.010) and low-density lipoprotein (r = 0.379, p = 0.025), but not sleep regularity, sleep apnea severity or diabetic retinopathy. Multiple regression analysis showed that higher sleep variability (B = 0.907, p = 0.038) and higher HbA1c (B = 1.519, p = 0.035), but not low-density lipoprotein, contributed to higher high-sensitivity C-reactive protein. In conclusion, higher sleep variability in patients with type 2 diabetes who were not shift-workers was independently associated with higher systemic inflammation, conferring increased cardiovascular risk. Whether sleep interventions to reduce sleep variability can reduce systemic inflammation and improve cardiometabolic health should be investigated.

Exploring Technology's Influence on Health Behaviours and Well-being in Type 1 Diabetes: a Review

PURPOSE OF REVIEW

Maintaining positive health behaviours promotes better health outcomes for people with type 1 diabetes (T1D). However, implementing these behaviours may also lead to additional management burdens and challenges. Diabetes technologies, including continuous glucose monitoring systems, automated insulin delivery systems, and digital platforms, are being rapidly developed and widely used to reduce these burdens. Our aim was to review recent evidence to explore the influence of these technologies on health behaviours and well-being among adults with T1D and discuss future directions.

RECENT FINDINGS

Current evidence, albeit limited, suggests that technologies applied in diabetes self-management education and support (DSME/S), nutrition, physical activity (PA), and psychosocial care areas improved glucose outcomes. They may also increase flexibility in insulin adjustment and eating behaviours, reduce carb counting burden, increase confidence in PA, and reduce mental burden. Technologies have the potential to promote health behaviours changes and well-being for people with T1D. More confirmative studies on their effectiveness and safety are needed to ensure optimal integration in standard care practices.

Association between sleep variability and time in range of glucose levels in patients with type 1 diabetes: Cross-sectional study

OBJECTIVE

Sleep and circadian disturbances emerge as novel factors influencing glycemic control in type 1 diabetes (T1D). We aimed to explore the associations among sleep, behavioral circadian parameters, self-care, and glycemic parameters in T1D.

METHODS

Seventy-six non-shift-working adult T1D patients participated. Blinded 7-day continuous glucose monitoring (CGM) and hemoglobin A1C (A1C) were collected. Percentages of time-in-range (glucose levels 70-180 mg/dL) and glycemic variability (measured by the coefficient of variation [%CV]) were calculated from CGM. Sleep (duration and efficiency) was recorded using 7-day actigraphy. Variability (standard deviation) of midsleep time was used to represent sleep variability. Nonparametric behavioral circadian variables were derived from actigraphy activity recordings. Self-care was measured by diabetes self-management questionnaire-revised. Multiple regression analyses were performed to identify independent predictors of glycemic parameters.

RESULTS

Median (interquartile range) age was 34.0 (27.2, 43.1) years, 48 (63.2%) were female, and median (interquartile range) A1C was 6.8% (6.2, 7.4). Sleep duration, efficiency, and nonparametric behavioral circadian variables were not associated with glycemic parameters. After adjusting for age, sex, insulin delivery mode/CGM use, and ethnicity, each hour increase in sleep variability was associated with 9.64% less time-in-range (B = -9.64, 95% confidence interval [-16.29, -2.99], p ≤ .001). A higher diabetes self-management questionnaire score was an independent predictor of lower A1C (B = -0.18, 95% confidence interval [-0.32, -0.04]).

CONCLUSION

Greater sleep timing variability is independently associated with less time spent in the desirable glucose range in this T1D cohort. Reducing sleep timing variability could potentially lead to improved metabolic control and should be explored in future research.

DATA AVAILABILITY STATEMENT

Data are available upon a reasonable request to the corresponding author.

Stress and human health in diabetes: A report from the 19th Chicago Biomedical Consortium symposium

Stress and diabetes coexist in a vicious cycle. Different types of stress lead to diabetes, while diabetes itself is a major life stressor. This was the focus of the Chicago Biomedical Consortium's 19th annual symposium, "Stress and Human Health: Diabetes," in November 2022. There, researchers primarily from the Chicago area met to explore how different sources of stress - from the cells to the community - impact diabetes outcomes. Presenters discussed the consequences of stress arising from mutant proteins, obesity, sleep disturbances, environmental pollutants, COVID-19, and racial and socioeconomic disparities. This symposium showcased the latest diabetes research and highlighted promising new treatment approaches for mitigating stress in diabetes.

Glucose variability and mood in people with type 1 diabetes using ecological momentary assessment

OBJECTIVE

Mood fluctuations related to blood glucose excursions are a commonly reported source of diabetes-distress, but research is scarce. We aimed to assess the relationship between real-time glucose variability and mood in adults with type 1 diabetes (T1D) using ecological momentary assessments.

METHODS

In this prospective observational study, participants wore a masked continuous glucose monitor and received prompts on their smartphone 6 times a day to answer questions about their current mood (Profile Of Mood States (POMS)-SF (dimensions: Anxiety, Depressive symptoms, Anger, Fatigue, Vigor)) for 14 days. Mixed model analyses examined associations over time between daily Coefficient of Variation (CV) of blood glucose and mean and variability (CV) of POMS scores. Further, within-person differences in sleep and nocturnal hypoglycemia were explored.

RESULTS

18 people with T1D (10 female, mean age 44.3 years) participated. A total of 264 out of 367 days (70.2%) could be included in the analyses. No overall significant associations were found between CV of blood glucose and mean and CV of POMS scores, however, nocturnal hypoglycemia moderated the associations between CV of blood glucose and POMS scales (mean Fatigue Estimate 1.998, p < .006, mean Vigor Estimate -3.308, p < .001; CV Anger Estimate 0.731p = 0.02, CV Vigor Estimate -0.525, p = .006).

CONCLUSION

We found no overall relationship between real-time glycemic variability and mood per day. Further research into within-person differences such as sleep and nocturnal hypoglycemia is warranted.