Sweet dreams or bitter nightmare: a narrative review of 25 years of research on the role of sleep in diabetes and the contributions of behavioural science

Sleep quality in relation to perceived psychological stress in patients with type 2 diabetes and in age- and sex-matched control individuals

AIM

To assess sleep quality in relation to perceived stress in patients with type 2 diabetes (T2DM) and age- and sex-matched controls.

METHODS

Perceived stress level and sleep quality assessed in 154 patients with T2DM (58 men, 96 women, age 58.3 ± 11.9 years), 154 matched controls (58 men, 96 women, age 56.8 ± 12.2 years) using Perceived Stress Scale and Pittsburgh Sleep Quality Index.

RESULTS

Patients with T2DM had worse subjective sleep quality (p < 0.001), sleep latency (p = 0.047) than controls. Patients with high stress level had worse subjective sleep quality (p = 0.027), higher use of sleeping medication (p = 0.023), daytime dysfunction (p < 0.001) than those with low stress level. No significant differences in sleep quality between controls with high and low perceived stress level. Perceived stress level in patients with T2DM correlated with subjective sleep quality (r = 0.260, p = 0.002), sleep duration (r = 0.228, p = 0.005), use of sleep medication (r = 0.245, p = 0.004), daytime dysfunction (r = 0.326, p < 0.001), in age- and sex-matched controls-to daytime dysfunction (r = 0.191, p = 0.037).

CONCLUSION

Sleep quality (subjective sleep quality, sleep latency) is worse in patients with type 2 diabetes than in age- and sex-matched controls. Patients with high perceived stress level have worse subjective sleep quality, higher use of sleeping medication, daytime dysfunction than patients with low perceived stress level; no significant differences in sleep quality between controls with high and low stress level. Perceived stress level in patients with type 2 diabetes is related to subjective sleep quality, sleep duration, use of sleep medication, daytime dysfunction, in age- and sex-matched controls-to daytime dysfunction.

Sleep disturbances in children and adolescents with type 1 diabetes mellitus: Prevalence, and relationship with diabetes management

OBJECTIVE

A decline in sleep quality and regularity has been reported in patients with type 1 diabetes mellitus (T1D) in many studies. However, research on medical-based sleep disorders in patients with T1D is limited. Diagnosing sleep disorders is crucial, as it negatively impacts academic performance, cardiovascular health, and cognitive functions among children as well as essential skills for effective diabetes management. Our objective was to assess sleep disturbances in patients diagnosed with T1D and explore whether these patients experience significantly more sleep disturbances compared to their healthy peers.

METHODS

This study, designed as a cross-sectional case-control investigation, involved a cohort of 250 participants (144 T1D, 106 control cases) aged 6-15 years. The Sleep Disturbance Scale for Children (SDCS) scores of the T1D group were compared with those of the control group. Furthermore, the study explored the correlation between clinical/biochemical parameters and SDCS scores within the T1D group.

RESULTS

The mean age of individuals in the T1D group was 10.27 ± 3.25 years, while the control group had a comparable mean age of 10.48 ± 3.5 years (P = 0.303). Within the T1D group, the median duration of diabetes was 5 (1-15) years, and the median glycosylated hemoglobin A1c (HbA1c) level for the past one year was 8.4 %. Although there was no significant difference in total SDSC scores between the T1D and control groups, both groups exhibited average scores that remained close to the threshold indicative of sleep disturbances (>39). Notably, individuals with total SDSC scores surpassing 39 were identified at rates of 48.6 % in the T1D group and 47.6 % in the control group, respectively. Furthermore, disorders of arousal nightmares (DA) were more prevalent in T1D patients compared to their healthy peers (P = 0.049). Additionally, HbA1c showed a positive correlation with scores for disorders of excessive somnolence (DOES) and total scores (P < 0.001, R = 0.368; P = 0.003, R = 0.243).

CONCLUSION

Our study found that the prevalence of sleep disturbances among children and adolescents with T1D was not significantly higher than that observed in their healthy peers. Nevertheless, it is crucial to note that a notable portion, 48.6 % of T1D cases and 47.6 % of healthy cases, displayed sleep disturbances based on SDSC scores. To optimize diabetes management and proactively address potential challenges, incorporating routine screening for sleep disturbances in the monitoring of T1D patients can yield valuable benefits.

Waking Up to the Importance of Sleep in Type 2 Diabetes Management: A Narrative Review

For the first time, the latest American Diabetes Association/European Association for the Study of Diabetes (ADA/EASD) consensus guidelines have incorporated a growing body of evidence linking health outcomes associated with type 2 diabetes to the movement behavior composition over the whole 24-h day. Of particular note, the importance of sleep as a key lifestyle component in the management of type 2 diabetes is promulgated and presented using three key constructs: quantity, quality, and timing (i.e., chronotype). In this narrative review we highlight some of the key evidence justifying the inclusion of sleep in the latest consensus guidelines by examining the associations of quantity, quality, and timing of sleep with measures of glycemia, cardiovascular disease risk, and mortality. We also consider potential mechanisms implicated in the association between sleep and type 2 diabetes and provide practical advice for health care professionals about initiating conversations pertaining to sleep in clinical care. In particular, we emphasize the importance of measuring sleep in a free-living environment and provide a summary of the different methodologies and targets. In summary, although the latest ADA/EASD consensus report highlights sleep as a central component in the management of type 2 diabetes, placing it, for the first time, on a level playing field with other lifestyle behaviors (e.g., physical activity and diet), the evidence base for improving sleep (beyond sleep disorders) in those living with type 2 diabetes is limited. This review should act as a timely reminder to incorporate sleep into clinical consultations, ongoing diabetes education, and future interventions.

Nocturnal hypoglycemia is associated with next day cognitive performance in adults with type 1 diabetes: Pilot data from the GluCog study

OBJECTIVE

 Individuals with type 1 diabetes (T1D) have increased risk for cognitive dysfunction and high rates of sleep disturbance. Despite associations between glycemia and cognitive performance using cross-sectional and experimental methods few studies have evaluated this relationship in a naturalistic setting, or the impact of nocturnal versus daytime hypoglycemia. Ecological Momentary Assessment (EMA) may provide insight into the dynamic associations between cognition, affective, and physiological states. The current study couples EMA data with continuous glucose monitoring (CGM) to examine the within-person impact of nocturnal glycemia on next day cognitive performance in adults with T1D. Due to high rates of sleep disturbance and emotional distress in people with T1D, the potential impacts of sleep characteristics and negative affect were also evaluated.

METHODS

 This pilot study utilized EMA in 18 adults with T1D to examine the impact of glycemic excursions, measured using CGM, on cognitive performance, measured via mobile cognitive assessment using the TestMyBrain platform. Multilevel modeling was used to test the within-person effects of nocturnal hypoglycemia and hyperglycemia on next day cognition.

RESULTS

 Results indicated that increases in nocturnal hypoglycemia were associated with slower next day processing speed. This association was not significantly attenuated by negative affect, sleepiness, or sleep quality.

CONCLUSIONS

 These results, while preliminary due to small sample size, showcase the power of intensive longitudinal designs using ambulatory cognitive assessment to uncover novel determinants of cognitive fluctuation in real world settings, an approach that may be utilized in other populations. Findings suggest reducing nocturnal hypoglycemia may improve cognition in adults with T1D.

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.

Coordination of glucose monitoring, self-care behaviour and mental health: achieving precision monitoring in diabetes

Monitoring of glucose plays an essential role in the management of diabetes. However, to fully understand and meaningfully interpret glucose levels, additional information on context is necessary. Important contextual factors include data on behaviours such as eating, exercise, medication-taking and sleep, as well as data on mental health aspects such as stress, affect, diabetes distress and depressive symptoms. This narrative review provides an overview of the current state and future directions of precision monitoring in diabetes. Precision monitoring of glucose has made great progress over the last 5 years with the emergence of continuous glucose monitoring (CGM), automated analysis of new glucose variables and visualisation of CGM data via the ambulatory glucose profile. Interestingly, there has been little progress in the identification of subgroups of people with diabetes based on their glycaemic profile. The integration of behavioural and mental health data could enrich such identification of subgroups to stimulate precision medicine. There are a handful of studies that have used innovative methodology such as ecological momentary assessment to monitor behaviour and mental health in people's everyday life. These studies indicate the importance of the interplay between behaviour, mental health and glucose. However, automated integration and intelligent interpretation of these data sources are currently not available. Automated integration of behaviour, mental health and glucose could lead to the identification of certain subgroups that, for example, show a strong association between mental health and glucose in contrast to subgroups that show independence of mental health and glucose. This could inform precision diagnostics and precision therapeutics. We identified just-in-time adaptive interventions as a potential means by which precision monitoring could lead to precision therapeutics. Just-in-time adaptive interventions consist of micro-interventions that are triggered in people's everyday lives when a certain problem is identified using monitored behaviour, mental health and glucose variables. Thus, these micro-interventions are responsive to real-life circumstances and are adaptive to the specific needs of an individual with diabetes. We conclude that, with current developments in big data analysis, there is a huge potential for precision monitoring in diabetes.

Type 1 Diabetes, Sleep, and Hypoglycemia

PURPOSE OF REVIEW

To review the relationship between sleep and hypoglycemia, sleep characteristics, and their associations with glycemic control in persons with type 1 diabetes (T1D). The effects of sleep interventions and diabetes technology on sleep are summarized.

RECENT FINDINGS

Nocturnal hypoglycemia affects objective and subjective sleep quality and is related to behavioral, psychological, and physiological factors. Sleep disturbances are common, including inadequate sleep, impaired sleep efficiency, poor subjective satisfaction, irregular timing, increased daytime sleepiness, and sleep apnea. Some have a bidirectional relationship with glycemic control. Preliminary evidence supports sleep interventions (e.g., sleep extension and sleep coach) in improving sleep and glycemic control, while diabetes technology use could potentially improve sleep. Hypoglycemia and sleep disturbances are common among persons with T1D. There is a need to develop sleep promotion programs and test their effects on sleep, glucose, and related outcomes (e.g., self-care, psychological health).

Psychological profiles of individuals with type 2 diabetes and their association with physical and psychological outcomes: a cluster analysis

OBJECTIVES

This study aimed to identify clusters of participants with Type 2 diabetes mellitus (T2DM) at risk for developing psychological and somatic distress symptoms. Moreover, we investigated whether the different clusters were associated with glycemic control, sleep, and physical activity levels.Design and main outcome measures. In a cross-sectional design, participants with T2DM (n = 269) completed questionnaires on psychological and somatic distress, sleep disorders and physical activity.

RESULTS

Cluster analyses yielded three groups: a) "high self-confident and low demoralised"; b) "low support and low involvement"; c) "high consequences, high demoralisation and nagging". The groups were distinguished by the social, cognitive, and vital exhaustion variables and significant differences in diabetes-related psychological distress and physical activity. The measure of glycemic control did not differ between clusters. The "high self-confident and low demoralised" group displayed the lowest scores on psychological distress compared to the other clusters.

CONCLUSIONS

Results suggest that social cognitive dimensions and affective states play a key role in defining clusters in participants with T2DM. Thus, we need to consider the psychological profiles of participants with T2DM when designing interventions to improve self-management strategies.

Sleep quality and glycaemic variability in a real-life setting in adults with type 1 diabetes

AIMS/HYPOTHESIS

Suboptimal subjective sleep quality is very common in adults with type 1 diabetes. Reducing glycaemic variability is a key objective in this population. To date, no prior studies have examined the associations between objectively measured sleep quality and overnight glycaemic variability in adults with type 1 diabetes. We aimed to test the hypothesis that poor sleep quality would be associated with greater overnight glycaemic variability.

METHODS

Data were collected in the home setting from 20 adults (ten male and ten female participants) with type 1 diabetes who were current insulin pump users. Simultaneous assessments of objective sleep quality (Zmachine Insight+) and continuous glucose monitoring (CGM) were performed over multiple nights (up to 15 nights) in each participant. Due to the real-life nature of this study, the participants kept their usual CGM alerts for out-of-range glucose values. Sleep quality was categorised as 'good' or 'poor' using a composite of objective sleep features (i.e. sleep efficiency, wake after sleep onset and number of awakenings) based on the National Sleep Foundation's consensus criteria. Glycaemic variability was quantified using SD and CV of overnight glucose values based on overnight CGM profiles.

RESULTS

A total of 170 nights were analysed. Overall, 86 (51%) nights were categorised as good sleep quality, and 84 (49%) nights were categorised as poor sleep quality. Using linear mixed-effects models, poor sleep quality was significantly associated with greater glycaemic variability as quantified by SD (coefficient: 0.39 [95% CI 0.10, 0.67], p = 0.009) and CV (coefficient: 4.35 [95% CI 0.8, 7.9], p = 0.02) of overnight glucose values, after accounting for age, sex, BMI and overnight insulin dose. There was large inter- and intra-individual variability in sleep and glycaemic characteristics. Both pooled and individual-level data revealed that the nights with poor sleep quality had larger SDs and CVs, and, conversely, the nights with good sleep quality had smaller SDs and CVs. No associations were found between sleep quality and time spent in the target glucose range, or above or below the target glucose range, where CGM alarms are most likely to occur.

CONCLUSIONS/INTERPRETATION

Objectively measured sleep quality is associated with overnight glycaemic variability in adults with type 1 diabetes. These findings highlight an important role of sleep quality in overnight glycaemic control in type 1 diabetes. They also provide a strong incentive to both patients and healthcare providers for considering sleep quality in personalised type 1 diabetes glycaemic management plans. Future studies should investigate the mechanisms linking sleep quality to glycaemic variability.

Is There an Association between Sleep Disorders and Diabetic Foot? A Scoping Review

Diabetic foot is associated with a low quality of life since physical disabilities, mood disturbances and psychological disorders are frequent. One of the most important biological processes to ensure quality of life is sleep. Sleep disorders can impair glycemic control in patients with diabetes mellitus or even cause long-term type 2 diabetes mellitus. The aim of this study is to carry out a scoping review about the association between sleep cycle disorders and diabetic foot. PubMed, Scopus, CINAHL, PEDro, Cochrane Library, SCIELO and EMBASE databases were chosen for the search and the following terms were used: "diabetic foot","sleep*","rest-activity","mood" and"behavior". All the studies should include outcome variables about sleep and diabetic foot. Finally, 12 articles were selected, all of whichwere observational. The most frequent variables were those regarding diabetic foot ulcer aspects and diabetic neuropathy on one side, and obstructive sleep apnea, sleep duration and sleep quality on the other side. The results suggest that there is a possible association between obstructive sleep apnea and the presence or history of diabetic foot ulcers. No direct associations between sleep quality or sleep duration and diabetic foot or diabetic foot ulcer variables have been found.