Sleep characteristics in type 1 diabetes and associations with glycemic control: systematic review and meta-analysis

Exploring the effect of adhering to a healthy lifestyle pattern on glycemic control in adults with type 1 diabetes mellitus

BACKGROUND AND AIMS

Diet, physical activity, sleep and smoking have been individually associated with glycemic control (GC) in adults with type 1 diabetes mellitus (T1D). However, the combined effect of these factors has not been investigated. The aim of the present study was to examine the single and combined effects of lifestyle parameters on GC of people with T1D (PwT1D).

METHODS AND RESULTS

Dietary, physical activity and sleep habits were evaluated using validated questionnaires. Diet quality was assessed with two scores (MedDietScore and PURE Diet Score) and two healthy lifestyle indices (HLI) were constructed (MLI based on MedDietScore and PLI based on PURE score). The score of both HLI ranged from 0 to 12 with higher scores indicating greater adherence to the healthy lifestyle pattern. One hundred ninety-two adults [61 % female, median age 42 (34, 51) years] with T1D were included in the analysis. Good GC (defined as HbA1c<7 %) was observed in 31 % of study participants. Examining lifestyle components separately, only smoking was marginally inversely associated with good GC [odds ratio (OR): 0.48, (95 % confidence interval, CI:0.23-1.00; p = 0.050)]. Individuals with better GC had significantly higher HLI scores (both p < 0.05). After adjusting for age, sex, body mass index, wearing an insulin pump and using continuous glucose monitoring, one-unit increase in the PLI was associated with 16 % higher likelihood of good GC (OR:1.16, 95 % CI:1.01-1.35, p = 0.04) and a similar trend was recorded for MLI (p = 0.05).

CONCLUSION

Our results suggest that adherence to a healthy lifestyle, more so than single lifestyle parameters, is associated with better GC in PwT1D.

Chronotype, sleep, and glycemic control in children and adolescents with type 1 diabetes: a case-control study

OBJECTIVES

This study aimed to explore the relationships between sleep parameters, chronotype preferences, and glycemic control in children and adolescents with type 1 diabetes (T1DM), compared to healthy peers.

METHODS

A total of 96 children and adolescents with T1DM and 95 healthy controls aged 8-18 years participated in this case-control study. Anthropometric measurements were collected, and participants completed the Munich Chronotype Questionnaire and the Pittsburgh Sleep Quality Index (PSQI). Glycemic control was assessed using HbA1c levels.

RESULTS

Children with T1DM demonstrated significantly shorter sleep durations, poorer sleep quality, and a later chronotype compared to controls (p<0.05). Poor glycemic control (HbA1c>7.5 %) was observed in 72.9 % of the T1DM group, with 34.3 % exhibiting very poor control (HbA1c>9 %). Logistic regression identified poor sleep quality (PSQI score, OR: 1.47, p<0.001) and later chronotype (OR: 5.14, p<0.01) as independent predictors of poor glycemic control. Generalized linear modeling (GLM) further revealed significant associations between HbA1c levels, insulin dosage (p<0.001), and chronotype (p=0.090).

CONCLUSIONS

Late chronotype and poor sleep quality are closely linked to suboptimal glycemic control in pediatric T1DM populations. These findings underscore the importance of integrating sleep-focused strategies into routine diabetes management.

The effect of sleep quality on glucose variability among adolescents with type 1 diabetes in China: A multi-central temporal longitudinal association analysis

STUDY OBJECTIVES

High glucose variability is common among adolescents with type 1 diabetes mellitus (T1DM). On person level, poor sleep quality has been reported to be an important factor associated with high glucose variability among adolescents with T1DM. However, on day level, the effect of sleep quality on glucose variability remains unclear, limiting temporal adjustment of treatment regimens. This study aimed to concurrently explore associations between sleep quality and glucose variability at both day and person levels among Chinese adolescents with T1DM based on the 24-h Recursive Cycle model.

METHODS

A multi-center, seven-day, temporal longitudinal study was conducted among Chinese adolescents with T1DM. Glucose variability measures were calculated by fingertip blood glucose level at least seven times a day. Subjective sleep quality was measured by total sleep time, wake after sleep onset, number of awakenings, a score of sleep quality, sleep time, and wake time using a sleep diary. Objective sleep quality was assessed by Fitbit Inspire HR and included total sleep time, wake after sleep onset, and number of awakenings, rapid eye movement, light sleep time, deep sleep time, sleep time, wake time, sleep midpoint, and sleep efficiency. A multilevel linear regression model was performed to examine the associations between objective and subjective sleep quality and glucose variability at day and person levels. Gender, age, diabetes duration, complications documented within the preceding 6 months, HbA1c, and insulin pump therapy were controlled at person-level model.

RESULTS

A total of 51 adolescents with T1DM participated in this study, which included 357 records of data. Only 21.57 % (N = 12) of adolescents met the recommended sleep time of 480 min per night measured by Fitbit. About a quarter (N = 11) of adolescents had a coefficient of variation of blood glucose >36 %. At person level, there was no significant association between sleep quality and glucose variability (p > 0.05). Multilevel models found significant associations between sleep quality and glucose variability at day level. Lower score of subjective sleep quality was significantly associated with higher standard deviation of blood glucose (p < 0.05) in the next day. Less Fitbit-measured light sleep time was significantly associated with higher standard deviation of blood glucose, and postprandial of glycemic excursions in the next day (p < 0.05). More Fitbit-measured awakenings and less rapid eye movement were associated with higher postprandial glycemic excursions in the next day (p < 0.05).

CONCLUSION

Nearly 80 % of Chinese adolescents with T1DM did not meet the recommended amount of sleep for their age group. They experienced more wakes after sleep onset at night and poorer sleep quality than their subjective experience. According to the findings on temporal relationships, intervention components targeting reducing wakes after sleep onset and improving subjective sleep quality would reduce glucose variability over time among Chinese adolescents with T1DM.

The Impact of a Multidimensional Physical Activity Intervention on Glycemic Control in Type 1 Diabetes: A Preliminary Study

Objectives: Type 1 diabetes is characterized by hyperglycemic episodes influenced by diet, sleep quality, chronotype, and physical activity, among others. While aerobic exercise is known to improve glycemic control, its effect on blood glucose regulation remains underexplored. Thus, this case study aimed to evaluate the effects of a prolonged and differentiated indoor and outdoor exercise intervention on glycemic control in an individual with type 1 diabetes. Methods: The participant (age: 23 years; weight: 95 kg; height: 1.90 m; Body Mass Index: 26.3 kg/m2; waist to hip ratio: 0.98; basal metabolic rate: 2015 kcal; Heart Rate Maximum (HRmax): 197 beats·min-1) completed two outdoor (~3800 m) and two indoor sessions with self-selected speed, in the initial 2 min stage, at a 0% grade slope. The grade increased by 2% at each stage during the uphill phase until reaching volitional fatigue, followed by a 2% decrease at each stage during the downhill phase until returning to a 0% grade. Blood pressure was assessed before and after each session. Daily nutrition intake, insulin intake, and blood glucose were continuously monitored. Dietary adherence (PREvención con DIeta MEDiterránea), sleep quality (Pittsburgh Sleep Quality Index), chronotype (Morningness-Eveningness Questionnaire), and physical activity levels (International Physical Activity Questionnaire) were assessed before each session. The Physical Activity Enjoyment Scale was used to measure enjoyment after each session. Results: The sessions were completed in ~44 and ~39 min with the participant achieving 84% (outdoor) and 96% (indoor) of their theoretical HRmax. The intervention resulted in glycemic improvements, with time spent in hyperglycemia (>250 mg/dL) decreasing from 56.46% to 0%, while time in the normal range (70-180 mg/dL) increased to 63.96%. A 47% reduction in insulin units showed that insulin sensitivity also improved. Conclusions: Despite differences in intensity, indoor and outdoor activities yielded comparable benefits, with outdoor activities being perceived as more enjoyable (outdoor: 28.5 ± 0.7; indoor: 24.0 ± 5.6) and positively impacting glycemic control, thus supporting the need for tailored strategies in diabetes management.

The sleep health composite and chronotype among children and adolescents with type 1 diabetes compared to case-control peers without diabetes

STUDY OBJECTIVES

Our objectives were to compare sleep health composite dimensions and chronotype in children and adolescents with and without type 1 diabetes (T1D) and to explore the relationship between sleep and glycemic variability in T1D.

METHODS

The study comprised 84 participants with T1D aged between 6 to 18 years and age- and sex-matched controls. The sleep health composite was measured using actigraphy, sleep diaries, and self or parental reports. Sleep disturbance was evaluated using the DSM-5 Level 2 Sleep Disorders Scale Short Form. Chronotype was determined using the Children's Chronotype Questionnaire.

RESULTS

The median total sleep health composite score for both the T1D and control groups was 3.0 (3.0-4.0) (P = .485). Sleep quality was reported as good by 89.3% of participants with T1D and 96.4% of controls (P = .072). Objective data from actigraphy indicated poor sleep quality in 56% of participants with T1D and 59.5% of controls (P = .639). Additionally, 88% of participants with T1D and 84.5% of controls had inadequate total age-appropriate sleep duration (P = .501). Among participants with T1D, those with a stable glycemic variability (coefficient of variation < 36%) had an earlier midpoint sleep (P = .008).

CONCLUSIONS

Our study indicates that there are no significant differences in the sleep health composite and chronotype between children and adolescents with and without T1D. Although most participants reported good sleep quality, objective assessments indicated poor sleep quality. These findings suggest that children and adolescents may overestimate their sleep quality.

CLINICAL TRIAL REGISTRATION

Registry: ClinicalTrials.gov; Name: Sleep Patterns and Chronotype in Children With and Without Type 1 Diabetes; URL: https://clinicaltrials.gov/study/NCT06318611; Identifier: NCT06318611.

CITATION

İpar N, Boran P, Barış HE, et al. The sleep health composite and chronotype among children and adolescents with type 1 diabetes compared to case-control peers without diabetes. J Clin Sleep Med. 2025;21(5):825-834.

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.

Towards standardization of person-reported outcomes (PROs) in pediatric diabetes research: A consensus report

BACKGROUND

Diabetes ranks among the most common chronic conditions in childhood and adolescence. It is unique among chronic conditions, in that clinical outcomes are intimately tied to how the child or adolescent living with diabetes and their parents or carers react to and implement good clinical practice guidance. It is widely recognized that the individual's perspective about the impact of trying to manage the disease together with the burden of self-management should be addressed to achieve optimal health outcomes. Standardized, rigorous assessment of behavioural and mental health outcomes is crucial to aid understanding of person-reported outcomes alongside, and in interaction with, physical health outcomes. Whilst tempting to conceptualize person-reported outcomes as a focus on perceived quality of life, the reality is that health-related quality of life is multi-dimensional and covers indicators of physical or functional health status, psychological well-being and social well- being.

METHODS

In this context, this Consensus Statement has been developed by a collection of experts in diabetes to summarize the central themes and lessons derived in the assessment and use of person-reported outcome measures in relation to children and adolescents and their parents/carers, helping to provide a platform for future standardization of these measures for research studies and routine clinical use.

RESULTS

This consensus statement provides an exploration of person-reported outcomes and how to routinely assess and incorporate into clincial research.

The Effect of Sleep Disruption on Cardiometabolic Health

Sleep disruption has emerged as a significant public health concern with profound implications for metabolic health. This review synthesizes current evidence demonstrating the intricate relationships between sleep disturbances and cardiometabolic dysfunction. Epidemiological studies have consistently demonstrated that insufficient sleep duration (<7 h) and poor sleep quality are associated with increased risks of obesity, type 2 diabetes, and cardiovascular disease. The underlying mechanisms are multifaceted, involving the disruption of circadian clock genes, alterations in glucose and lipid metabolism, the activation of inflammatory pathways, and the modulation of the gut microbiome. Sleep loss affects key metabolic regulators, including AMPK signaling and disrupts the secretion of metabolic hormones such as leptin and ghrelin. The latest evidence points to the role of sleep-induced changes in the composition and function of gut microbiota, which may contribute to metabolic dysfunction through modifications in the intestinal barrier and inflammatory responses. The NLRP3 inflammasome and NF-κB signaling pathways have been identified as crucial mediators linking sleep disruption to metabolic inflammation. An understanding of these mechanisms has significant implications for public health and clinical practice, suggesting that improving sleep quality could be an effective strategy for preventing and treating cardiometabolic disorders in modern society.

American Diabetes Association Professional Practice Committee, ElSayed NA, McCoy RG, Aleppo G, Balapattabi K, Beverly EA, Briggs Early K, Bruemmer D, Echouffo-Tcheugui JB, Eichorst B, Ekhlaspour L, Garg R, Hassanein M, Khunti K, Lal R, Lingvay I, Matfin G, Middelbeek RJ, Pandya N, Pekas EJ, Pilla SJ, Polsky S, Segal AR, Seley JJ, Stanton RC, Tanenbaum ML, Urbanski P, Bannuru RR. 5. Facilitating Positive Health Behaviors and Well-being to Improve Health Outcomes: Standards of Care in Diabetes-2025

The American Diabetes Association (ADA) "Standards of Care in Diabetes" includes the ADA's current clinical practice recommendations and is intended to provide the components of diabetes care, general treatment goals and guidelines, and tools to evaluate quality of care. Members of the ADA Professional Practice Committee, an interprofessional expert committee, are responsible for updating the Standards of Care annually, or more frequently as warranted. For a detailed description of ADA standards, statements, and reports, as well as the evidence-grading system for ADA's clinical practice recommendations and a full list of Professional Practice Committee members, please refer to Introduction and Methodology. Readers who wish to comment on the Standards of Care are invited to do so at professional.diabetes.org/SOC.

Obstructive Sleep Apnoea and Type 1 Diabetes Mellitus: A Neglected Relationship?

Obstructive sleep apnoea (OSA) is regarded as a major health condition, progressively affecting an increased number of people around the world. The interplay between OSA and type 2 diabetes mellitus (T2DM) has been extensively studied. However, little is known about the relationship between OSA and type 1 diabetes mellitus (T1DM). This review provides insight into the prevalence of OSA in T1DM and its relationship with diabetic complications. Studies have hitherto yielded contradictory results on the occurrence of OSA in T1DM. Indeed, the risk of OSA in T1DM has ranged from 1 in 10 to more than 1 in 2 T1DM subjects. This high occurrence was confirmed by objective polysomnography as well as widely used subjective questionnaires. Multiple studies revealed the important correlation between OSA and diabetes complications. Both microvascular (nephropathy, neuropathy and retinopathy) and macrovascular complications appear to be associated with OSA occurrence, although some associations were not significant due to inadequate data. In conclusion, T1DM subjects carry a higher risk of undiagnosed OSA. Additional studies are needed to clarify the exact correlation between the two conditions.

Barriers to healthy behaviors: perspectives from teens with comorbid Type 1 diabetes and overweight/obesity, caregivers, and pediatric endocrinologists

OBJECTIVES

This study aimed to understand barriers to engagement in healthy lifestyle behaviors among adolescents with comorbid Type 1 diabetes (T1D) and overweight/obesity (n = 12), their caregivers (n = 12), and pediatric endocrinologists (n = 9).

METHODS

Participants (n = 33) completed individual, semi-structured interviews. Interviews were transcribed verbatim, and applied thematic analysis was used to analyze the interview data.

RESULTS

Results from adolescents, caregivers, and pediatric endocrinologists revealed three thematic barriers to healthy lifestyle behaviors for adolescents with T1D and overweight/obesity: (1) discomfort with aspects of T1D that draw attention to the self; (2) T1D as a barrier to engagement in healthy lifestyle behaviors; and (3) physiological dysregulation due to T1D impacting health behaviors.

CONCLUSIONS

Results identify perceived limitations to engaging in recommended healthy lifestyle behaviors and diabetes management concurrently. Results may assist research and clinical care in identifying supports and guidance needed to support adolescents in meeting behavioral recommendations for their health.

Sleep Deficiency and Cardiometabolic Disease

Epidemiologic studies have demonstrated that short sleep duration is associated with an increased risk of cardio-metabolic health outcomes including cardiovascular disease mortality, coronary heart disease, type 2 diabetes mellitus, hypertension, and metabolic syndrome. Experimental sleep restriction studies have sought to explain these findings. This review describes the main evidence of these associations and possible mechanisms explaining them. Whether sleep extension reverses these now widely acknowledged adverse health effects and the feasibility of implementing such strategies on a public health level is discussed.

The effect of physical activity on sleep quality in people with diabetes: systematic review and meta-analysis

BACKGROUND

To revise and critically summarize the available scientific evidence regarding the effect of exercise on sleep quality in people with diabetes.

METHODS

Three electronic databases (MEDLINE/PubMed, PEDro Database and Scopus) were searched systematically from their inception until February 2024. The methodological quality of the included studies was assessed using the Physiotherapy Evidence Database and Quality Assessment Tool for Before-After Studies with No Control Group scales.

RESULTS

A total of 7 randomized controlled trials and 3 single-arm studies were included. Most of the studies included patients with type 2 diabetes (n = 8). Self-reported sleep quality (n = 9) and objective sleep status (n = 1) were the main outcomes analysed. A variety of training programs were assessed over durations ranging from 4 to 16 weeks in the studies included. Data from eleven interventions demonstrated a significant improvement in self-reported sleep quality among patients with type 2 diabetes (Hedges' g -1.45; 95% CI -2.6; -0.29, p = 0.005). However, data synthesis indicated that participants who exercised did not obtain significant improvements on their self-reported sleep quality compared to those in the control groups (Hedges' g 1.40; 95% CI -1.36; 4.18, p = 0.111).

CONCLUSIONS

Preliminary evidence suggests that exercise can be prescribed to manage self-reported sleep quality in this population, although its effects may not surpass those of usual care.

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.

Prolonged Use of an Automated Insulin Delivery System Improves Sleep in Long-Standing Type 1 Diabetes Complicated by Impaired Awareness of Hypoglycemia

BACKGROUND

This study assessed changes in actigraphy-estimated sleep and glycemic outcomes after initiating automated insulin delivery (AID).

METHODS

Ten adults with long-standing type 1 diabetes and impaired awareness of hypoglycemia (IAH) participated in an 18-month clinical trial assessing an AID intervention on hypoglycemia and counter-regulatory mechanisms. Data from eight participants (median age = 58 years) with concurrent wrist actigraph and continuous glucose monitoring (CGM) data were used in the present analyses. Actigraphs and CGM measured sleep and glycemic control at baseline (one week) and months 3, 6, 9, 12, 15, and 18 (three weeks) following AID initiation. HypoCount software integrated actigraphy with CGM data to separate wake and sleep-associated glycemic measures. Paired sample t-tests and Cohen's d effect sizes modeled changes and their magnitude in sleep, glycemic control, IAH (Clarke score), hypoglycemia severity (HYPO score), hypoglycemia exposure (CGM), and glycemic variability (lability index [LI]; CGM coefficient-of-variation [CV]) from baseline to 18 months.

RESULTS

Sleep improved from baseline to 18 months (shorter sleep latency [P < .05, d = 1.74], later sleep offset [P < .05, d = 0.90], less wake after sleep onset [P < .01, d = 1.43]). Later sleep onset (d = 0.74) and sleep midpoint (d = 0.77) showed medium effect sizes. Sleep improvements were evident from 12 to 15 months after AID initiation and were preceded by improved hypoglycemia awareness (Clarke score [d = 1.18]), reduced hypoglycemia severity (HYPO score [d = 2.13]), reduced sleep-associated hypoglycemia (percent time glucose was < 54 mg/dL, < 60 mg/dL,< 70 mg/dL; d = 0.66-0.81), and reduced glucose variability (LI, d = 0.86; CV, d = 0.62).

CONCLUSION

AID improved sleep initiation and maintenance. Improved awareness of hypoglycemia, reduced hypoglycemia severity, hypoglycemia exposure, and glucose variability preceded sleep improvements.This trial is registered with ClinicalTrials.gov NCT03215914 https://clinicaltrials.gov/ct2/show/NCT03215914.

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.

Quality of Sleep and Its Determinants Among People with Diabetes Mellitus in Sub-Saharan Africa: A Systematic Review and Meta-Analysis

BACKGROUND

Poor sleep quality is a serious concern among diabetes mellitus patients, adversely affecting glycemic management and the prognosis of diabetic patients. Therefore, the main aim of this study was to estimate the overall pooled prevalence of poor sleep quality and its associated factors among diabetic patients in Sub-Saharan Africa.

METHODS

Several search databases were employed to retrieve the eligible studies. A standardized data extraction format was used to extract the data, and the analysis was conducted using STATA version 14. To determine heterogeneity, the I2 test was conducted, and both the funnel plot and Egger's regression test were used to assess the publication bias. A random effects model meta-analysis was performed to estimate the overall prevalence and associated factors of poor sleep quality.

RESULTS

In the current meta-analysis, a total of 11 studies with 3,766 study participants were included. The overall pooled prevalence of poor sleep quality among diabetic patients was 53.22% (95% CI: 36.26, 70.19). Poor sleep quality was significantly associated with longer duration of diabetes (OR = 2.94, 95% CI: 1.85, 4.67), poor glycemic control (OR = 2.24, 95% CI: 1.51, 3.32), depression (OR = 4.09, 95% CI: 2.07, 8.05) and comorbidity (OR = 1.94, 95% CI: 1.31, 2.88).

CONCLUSION

In this study, more than half of diabetic patients in Sub-Saharan Africa had poor sleep quality. Therefore, healthcare providers should give more attention to sleep disorders when screening diabetic patients to improve their poor sleep quality.

O-GlcNAcylation of circadian clock protein Bmal1 impairs cognitive function in diabetic mice

Neuronal damage in the hippocampus induced by high glucose has been shown to promote the onset and development of cognitive impairment in diabetes, but the underlying molecular mechanism remains unclear. Guided by single-cell RNA sequencing, we here report that high glucose increases O-GlcNAcylation of Bmal1 in hippocampal neurons. This glycosylation promotes the binding of Clock to Bmal1, resulting in the expression of transcription factor Bhlhe41 and its target Dnajb4. Upregulated Dnajb4 in turn leads to ubiquitination and degradation of the mitochondrial Na + /Ca2+ exchanger NCLX, thereby inducing mitochondrial calcium overload that causes neuronal damage and cognitive impairment in mice. Notably, Bhlhe41 downregulation or treatment with a short peptide that specifically blocks O-GlcNAcylation of Bmal1 on Ser424 mitigated these adverse effects in diabetic mouse models. These data highlight the crucial role of O-GlcNAcylation in circadian clock gene expression and may facilitate the design of targeted therapies for diabetes-associated cognitive impairment.

Supporting healthy sleep: a qualitative assessment of adolescents with type 1 diabetes and their parents

OBJECTIVE

Poor sleep health is common in adolescence due to a combination of physiological, psychosocial, and environmental factors. Adolescents with type 1 diabetes (T1D) may be at increased risk for poor sleep health due to physiological and behavioral aspects of diabetes and its management. This article describes a qualitative analysis of interviews with adolescents with T1D and their parents about facilitators and barriers to sleep health and family strategies to balance teens' sleep with competing demands.

METHODS

Separate interviews were conducted with 20 adolescents with T1D and 20 parents. Interviews were recorded and transcribed verbatim and analyzed thematically. Participants were on average 15.8 ± 1.2 years old, 45% female, and 85% non-Hispanic White.

RESULTS

Overnight diabetes management was the most frequently reported barrier to sleep. Families reported different strategies for taking responsibility of overnight diabetes management, which differentially impacted sleep. Families worked to balance diabetes management and sleep with other aspects of adolescent life, including school demands, social activities, and electronics use. Facilitators to healthy sleep identified by families included diabetes assistive technology and maintaining a consistent sleep/wake schedule. Both adolescents and parents voiced beliefs that their diabetes care team is not able to help with sleep health.

CONCLUSIONS

Pediatric psychologists should be aware of the specific sleep barriers experienced by adolescents with T1D and their parents. A focus on overnight diabetes management strategies may facilitate psychologists' support of families in the adolescent's transition to independent diabetes management. Research is needed on the impact of optimizing sleep health in adolescents with T1D.

Sleep quality and glucose control in adults with type 1 diabetes during the seasonal daylight saving time shifts

AIM

There is a bidirectional relationship between glucose control and sleep quality and timing in type 1 diabetes (T1D). The aim of the study was to investigate the sleep quality and the glucose metrics in people with T1D at the seasonal clock adjustment.

METHODS

This observational study retrospectively compared the continuous glucose monitoring (CGM) derived metrics and sleep quality observed before (Time 0) and after (Time 1) transition in autumn and before (Time 2) and after (Time 3) transition in spring. We included adults with T1D, treated with CGM systems, who completed the Pittsburgh Sleep Quality Index questionnaire. The main outcome measure was the change in glucose monitoring indicator (GMI), time in range (TIR), time above range (TAR) and time below range.

RESULTS

Sixty-two participants showed no changes in sleep quality at time transitions. GMI values increased during both time transitions and the percentage of TIR decreased from Time 0 to Time 1 and from Time 2 to Time 3. The percentage of level 2 TAR increased during the observation.

CONCLUSIONS

At similar level of sleep quality, adults with T1D underwent the worsening of most of CGM-derived glucose control metrics during the transition time.

Sleep Quality Assessment and Its Predictors Among Saudi Adults with Type 1 and Type 2 Diabetes: A Cross-Sectional Study

Background: Poor sleep quality is prevalent among adults with diabetes, impacting their physical, psychological, and functional well-being. Our goals were to assess the prevalence of poor sleep quality and its association with glycemic control and to identify predictors of poor sleep quality among adults with diabetes mellitus. Methods: This cross-sectional study took place at a tertiary hospital, from October 2022 to March 2023, including 192 adults with type 1 and type 2 diabetes. We collected demographic and clinical data and utilized the Pittsburgh Sleep Quality Index (PSQI) scale to evaluate sleep quality. Results: A total of 65.9% of the participants experienced poor sleep quality (PSQI score > 5), with an average global sleep quality score of 7.36 ± 3.53 for all patients. Poor sleep quality was higher among older and married patients, those with lower education levels, housewives, and those with type 2 diabetes mellitus and associated comorbidities, such as hypertension and dyslipidemia. None of these factors were significantly associated with sleep quality in a multiple linear regression analysis. The mean glycated hemoglobin was 8.68 ± 1.91% and did not correlate with the overall PSQI score and its components. Conclusions: This study revealed a notably high prevalence of poor sleep quality among Saudi adults with diabetes, potentially associated with specific sociodemographic and clinical factors. These findings emphasize the importance of integrating sleep quality education into diabetes management strategies.

Closed-Loop Therapy and Sleep in Young People Newly Diagnosed With T1D and Their Parents

BACKGROUND

A diagnosis of type 1 diabetes in a young person can create vulnerability for sleep. Historically it has been rare for young people to be offered a closed-loop system soon after diagnosis meaning that studies examining sleep under these circumstances in comparison with standard treatment have not been possible. In this study, we examine sleep in young people (and their parents) who were provided with hybrid closed-loop therapy at diagnosis of type 1 diabetes versus those who receive standard treatment over a 2-year period.

METHODS

The sample comprised 97 participants (mean age = 12.0 years; SD = 1.7) from a multicenter, open-label, randomized, parallel trial, where young people were randomized to either hybrid closed-loop insulin delivery or standard care at diagnosis. Sleep was measured using actigraphy and the Pittsburgh Sleep Quality Index (PSQI) in the young people, and using the PSQI in parents.

RESULTS

Sleep in young people using hybrid closed-loop insulin delivery did not differ significantly compared with those receiving standard care (although there were nonsignificant trends for better sleep in the closed-loop group for 4 of the 5 sleep actigraphy measures and PSQI). Similarly, there were nonsignificant differences for sleep between the groups at 24 months (with mixed direction of effects).

CONCLUSIONS

This study assessed for the first time sleep in young people using a closed-loop system soon after diagnosis. Although sleep was not significantly different for young people using closed-loop insulin delivery as compared with those receiving standard care, the direction of effects of the nonsignificant results indicates a possible tendency for better sleep quality in the hybrid closed-loop insulin delivery group at the beginning of the treatment.

Neighbourhood Environments and Lifestyle Behaviours in Adolescents With Type 1 Diabetes

OBJECTIVES

Early prevention strategies are needed to mitigate the high risk of cardiovascular disease in adolescents with type 1 diabetes (T1D). Residential neighbourhood features can promote healthy lifestyle behaviours and reduce cardiovascular risk, but less is known about their role in lifestyle behaviours in adolescents with T1D, and no studies used comparisons to healthy controls.

METHODS

We examined associations between residential neighbourhood features and lifestyle behaviours in adolescents with T1D and healthy controls. Data were analyzed from the CARdiovascular Disease risk factors in pEdiatric type 1 diAbetes (CARDEA) study, a cross-sectional investigation of 100 adolescents with T1D (14 to 18 years) from a pediatric diabetes clinic in Montréal, Canada, and 97 healthy controls. Outcomes included physical activity and sedentary behaviour (accelerometry), screen time and sleep duration (questionnaires), and dietary habits (24-hour recalls). Cluster analysis of selected neighbourhood indicators computed for participants' postal codes resulted in 2 neighbourhood types: central urban and peri-urban. Central urban neighbourhoods were characterized by very high population density, high active living index, numerous points of interest, higher social deprivation, higher residential mobility, and lower median household income compared with peri-urban neighbourhoods. Associations of neighbourhood type with lifestyle behaviours were estimated with multiple linear regressions and interactions by T1D status were tested.

RESULTS

Living in central urban neighbourhoods was associated with greater daily minutes of moderate-to-vigorous physical activity (beta = 8.61, 95% confidence interval 1.79 to 15.44) compared with living in peri-urban neighbourhoods. No associations were observed for other lifestyle behaviours, and no statistically significant interactions were found between neighbourhood type and T1D status.

CONCLUSION

Features that characterize central urban built environments appear to promote physical activity in adolescents, regardless of T1D status.

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.

Adherence to the Mediterranean diet and sleep quality are inter-correlated with flash glucose monitoring (FGM)-measured glycemia among children with type 1 diabetes

BACKGROUND

We examined the inter-correlation between diet quality, objectively measured sleep duration, and subjectively measured sleep quality with flash glucose monitoring (FGM)-measured glycemia among young patients with type 1 diabetes (T1D).

MATERIALS AND METHODS

Following cross-sectional design, Fitbit® accelerometers were used to objectively assess sleep duration, while the validated questionnaires Pittsburgh sleep quality index and Mediterranean diet (MD) adherence were used to subjectively assess sleep quality and diet quality, respectively. Glycated hemoglobin (HbA1c) and FGM-reported glycemia components among children with T1D were assessed as well.

RESULTS

Of the 47 participants surveyed (25 boys, 22 girls, 9.31 ± 2.88 years), the majority reported high HbA1c, good sleep quality, and high adherence to the MD. However, only one-third of the participants reported a healthy sleep duration. Only the sleep latency was significantly (P < 0.05) associated with the time above range level 2 and time below range level 2 (P = 0.048) components of the FGM. A positive correlation (r = 0.309, P = 0.035) was reported between adherence to MD and time in range of the FGM.

CONCLUSIONS

Diet quality and sleep quality are variably inter-correlated with FGM-measured glycemia among young patients with T1D and are suggested to be considered influential factors in FGM-monitored diabetes research on this age group.

Sleep duration and association with cardiometabolic health in adolescents and adults with type 1 diabetes: Results from the BCQR-T1D study

AIM

Type 1 diabetes (T1D) increases the risk of morbidity and mortality from cardiovascular disease, and insufficient sleep is prevalent. Emerging evidence suggests a link between sleep and cardiometabolic health, but this has not been examined across the lifespan in individuals with T1D. We aimed to examine associations between sleep and cardiometabolic health in adolescents and adults with T1D in a secondary analysis of data from a 4-week double-blind, random-order, placebo-controlled crossover trial of bromocriptine quick release (BCQR) therapy with a 4-week washout in between conditions.

MATERIALS AND METHODS

Forty-two adults (19-60 years) and 42 adolescents (12-18 years) with T1D >9 months completed 1 week of home monitoring with wrist-worn actigraphy to estimate sleep duration and continuous glucose monitoring, anthropometrics, arterial stiffness, magnetic resonance imaging (adolescents only), and fasting laboratory testing at each treatment phase.

RESULTS

Sixty-two per cent of adolescents and 74% of adults obtained <7 h of sleep per night at baseline. After adjustment for age, sex and diabetes duration, baseline sleep <7 h per night was associated with a higher body mass index, a higher waist circumference, a higher systolic blood pressure, worse arterial stiffness and a lower estimated insulin sensitivity (all p < .05). When examined by age group, associations between sleep duration and cardiometabolic health outcomes remained significant, predominantly for adolescents. In adolescents only, wake time was significantly later (p = .027) and time in bed was significantly longer with BCQR versus placebo (p = .049).

CONCLUSIONS

Objectively measured sleep <7 h per night was prevalent in adolescents and adults with T1D and associated with poorer cardiometabolic health markers. Small changes in sleep were seen following BCQR treatment in adolescents only. Sleep may be an important and novel target for improving cardiometabolic health in individuals with T1D.

Eating habits and sleep quality in individuals with type 1 diabetes on continuous glucose monitoring and insulin pump

BACKGROUND AND AIMS

Sleep disorders are bidirectionally linked with eating behaviors and glucose metabolism, which could be clinically relevant in type 1 diabetes (T1D). We investigated the relationship between dietary habits and sleep quality in individuals with T1D on insulin pumps and continuous glucose monitoring (CGM).

METHODS AND RESULTS

In a cross-sectional study, dietary habits (7-day food diary, EPIC questionnaire) and sleep quality (Pittsburgh Sleep Quality Index questionnaire) were assessed in 59 men and 58 women with T1D, aged 19-79 years, using CGM and insulin pump. Differences in dietary habits and blood glucose after dinner (6 h) between participants differing in sleep quality, sleep duration, and sleep onset latency were evaluated. Bad Sleepers (n = 81) were twice as prevalent as Good Sleepers (n = 36) and had a significantly higher intake of fat than Good Sleepers (dinner: 30.7 ± 10.7 vs. 24.0 ± 10.5 g, p = 0.004). Short sleepers had a significantly higher usual intake (g/1000 kcal) of coffee and tea (90.4 ± 71.7 vs. 62.0 ± 35.6), alcoholic (47.8 ± 51.1 vs. 28.9 ± 31.5) and carbonated beverages (21.8 ± 38.1 vs. 9.3 ± 17.2) (p < 0.05 for all) than Long Sleepers. Long Sleep Onset Latency was associated with a significantly higher fat intake at dinner (41.8 ± 7.4 vs. 38.1 ± 9.1 % total energy, p = 0.029) than Short Sleep Onset Latency. No significant differences in post-dinner blood glucose levels were detected between participants with good or bad sleep quality.

CONCLUSION

Sleep disruption is common in T1D and is associated with unhealthy dietary choices, especially at dinner, independently of post-dinner blood glucose control.

The Impact of Dietary Habits on Sleep Deprivation and Glucose Control in School-Aged Children with Type 1 Diabetes: A Cross-Sectional Study

Diet plays a crucial role in managing type 1 diabetes (T1DM). Background/Objectives:This study aimed to determine the impact of nutritional habits on sleep deprivation and glucose control in school-aged children with T1DM. Methods: In this cross-sectional study, nutritional habits and sleep deprivation were assessed in 100 school-aged children with T1DM, aged 7-13 years. The Dietary Habits Index and the Sleep Deprivation Scale for Children and Adolescents were used to evaluate nutritional habits and the level of sleep deprivation. Patients' sociodemographic and nutritional variables were collected through researcher-composed questionnaires. HbA1c levels over the past 6 months were obtained from the patient data system. Results: The study found a moderately strong positive correlation between the Dietary Habits Index score and HbA1c (p < 0.001), with 28% of the variation in HbA1c explained by changes in the Dietary Habits Index score. However, no correlation was found between the Dietary Habits Index score and the level of sleep deprivation. Conclusions: The nutritional habits of school-aged children with T1DM may affect glucose control and sleep deprivation. Therefore, it is important to educate children with T1DM on making healthy food choices to manage their condition effectively.

Eating Disorders and Sleep Disturbance as Determinants of Metabolic Control in Adolescents with Type 1 Diabetes

This cross-sectional study examined the effect of sleep disturbance and eating disorders on metabolic control in adolescents with Type 1 Diabetes. The study was conducted with adolescents with T1DM treated at a university hospital in Turkey between October 2023 and January 2024. The study sample consisted of 120 adolescents with T1DM between the ages of 10-18. Data were collected online using the Adolescent Information Form, Dutch Eating Behavior Questionnaire (DEBQ), and DSM-5 Sleep Disorder Scale (SDS). Mean, percentage, and regression analyses were used to analyze the data. Ethics committee, institutional permission and written permission from the adolescents with Type 1 Diabetes and their parents were obtained for the study. In the current study, sleep disturbance and eating disorders explained 38.5% and 40.2% of HbA1c, respectively, and were found to have a significant effect (respectively: F = 73.737, p ≤ .001; F = 19.353, p ≤ .001). This study provides evidence that eating disorders and sleep disturbance explain approximately half of HbA1c. The results of the study revealed that sleep disturbance and eating disorders were significant predictors of metabolic control in adolescents with type 1 diabetes.

Modifiable lifestyle risk factors for overweight and obesity in children and adolescents with type 1 diabetes: A systematic review

This review aims to identify and report epidemiological associations between modifiable lifestyle risk factors for overweight or obesity in children and adolescents with type 1 diabetes (T1D). A systematic literature search of medical databases from 1990 to 2023 was undertaken. Inclusion criteria were observational studies reporting on associations between dietary factors, disordered eating, physical activity, sedentary and sleep behaviours and measures of adiposity in children and adolescents (<18 years) with T1D. Thirty-seven studies met inclusion criteria. Studies were mostly cross-sectional (89 %), and 13 studies included adolescents up to 19 years which were included in this analysis. In adolescents with T1D, higher adiposity was positively associated with disordered eating behaviours (DEB) and a higher than recommended total fat and lower carbohydrate intake. A small amount of evidence suggested a positive association with skipping meals, and negative associations with diet quality and sleep stage. There were no published associations between overweight and physical activity, sedentary behaviours and eating disorders. Overall, the findings infer relationships between DEB, fat and carbohydrate intake and adiposity outcomes in people with T1D. Prospective studies are needed to determine causal relationships and to investigate sleep stages. High quality studies objectively measuring physical activity and include body composition outcomes are needed.

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.

Person-reported outcomes in diabetes care: What are they and why are they so important?

In this review, we aim to show how person-reported outcomes (PROs) and person-reported experiences (PREs) can significantly contribute to the way diabetes care is delivered, the involvement of people with diabetes in diabetes care, and the collaboration between health care professionals and people with diabetes. This review focuses on the definition and measurement of PROs and PREs, the importance of PROs and PREs for person-centred diabetes care, and integrating the perspectives of people with diabetes in the evaluation of medical, psychological and technological interventions. PROs have been increasingly accepted by Health Technology Assessment bodies and are therefore valued in the context of reimbursement decisions and consequently by regulators and other health care stakeholders for the allocation of health care resources. Furthermore, the review identified current challenges to the assessment and use of PROs and PREs in clinical care and research. These challenges relate to the combination of questionnaires and ecological momentary assessment for measuring PROs and PREs, lack of consensus on a core outcome set, limited sensitivity to change within many measures and insufficient standardization of what can be considered a minimal clinically important difference. Another issue that has not been sufficiently addressed is the involvement of people with diabetes in the design and development of measures to assess PROs and PREs.

Clustering of risk behaviours and associations between risk behaviours and cardio-metabolic risk factors in adult individuals with type 1 diabetes

AIMS

To assess clustering of risk behaviours and their health determinants.

METHODS

Cross-sectional health behaviour and health data were collected from individuals with type 1 diabetes, in the FinnDiane Study. Clustering of risk behaviours was assessed and associations between behaviours and health variables were investigated.

RESULTS

Data were available from 956 participants (40 % men, mean age 46 years). Altogether, 4.3 % individuals reported no risk behaviours, while 25.7 %, 37.4 %, 24.7 %, 6.8 %, and 1.0 % reported 1, 2, 3, 4, and 5 risk behaviours, respectively. Reporting ≥4 risk behaviours occurred more frequently than expected by chance. Dietary non-adherence was most frequently reported (84.4 %), followed by low LTPA (54.4 %), poor sleep (41.9 %), high alcohol consumption (15.2 %), and smoking (11.2 %). Adjusted for confounders, relative to ≤1 risk behaviour, reporting ≥2 risk behaviours was associated with higher BMI, waist circumference, and diastolic blood pressure. Having ≥3 risk behaviours was associated with larger waist-hip ratio, and higher HbA1c and triglyceride concentration; ≥4 risk behaviours was associated with higher cholesterol concentration. Of the health behaviours, low LTPA had the highest number of deleterious health associations.

CONCLUSIONS

Accumulation of risk behaviors increases negative health outcomes. Exhibiting ≥2 risk behaviours or low LTPA was associated with multiple adverse outcomes.

American Diabetes Association Professional Practice Committee, ElSayed NA, Aleppo G, Bannuru RR, Beverly EA, Bruemmer D, Collins BS, Darville A, Ekhlaspour L, Hassanein M, Hilliard ME, Johnson EL, Khunti K, Lingvay I, Matfin G, McCoy RG, Perry ML, Pilla SJ, Polsky S, Prahalad P, Pratley RE, Segal AR, Seley JJ, Stanton RC, Gabbay RA. 5. Facilitating Positive Health Behaviors and Well-being to Improve Health Outcomes: Standards of Care in Diabetes-2024

The American Diabetes Association (ADA) "Standards of Care in Diabetes" includes the ADA's current clinical practice recommendations and is intended to provide the components of diabetes care, general treatment goals and guidelines, and tools to evaluate quality of care. Members of the ADA Professional Practice Committee, an interprofessional expert committee, are responsible for updating the Standards of Care annually, or more frequently as warranted. For a detailed description of ADA standards, statements, and reports, as well as the evidence-grading system for ADA's clinical practice recommendations and a full list of Professional Practice Committee members, please refer to Introduction and Methodology. Readers who wish to comment on the Standards of Care are invited to do so at professional.diabetes.org/SOC.

Impact of 6-months of an advanced hybrid closed-loop system on sleep and psychosocial outcomes in youth with type 1 diabetes and their parents

INTRODUCTION

Youth with type 1 diabetes (T1D) and parents experience reduced quality of life and sleep quality due to nocturnal monitoring, hypoglycemia fear, and diabetes-related disruptions. This study examined the sleep and quality of life impact of advanced technology.

METHODS

Thirty-nine youth with T1D, aged 2-17 years, starting an advanced hybrid closed-loop (HCL) system and a parent participated in an observational study. Surveys, actigraphy, sleep diaries, and glycemic data (youth) were captured prior to HCL, at one week, 3 months, and 6 months. Outcomes were modeled using linear mixed effects models with random intercepts to account for within-subject correlation, with least-squares means at each timepoint compared to baseline.

RESULTS

Parents and youth reported improvements in health-related quality of life and fear of hypoglycemia after HCL initiation. Concurrently, nocturnal glycemia improved. Actigraphy-derived sleep outcomes showed improved 6 month adolescent efficiency and 3 and 6 month parent wake after sleep onset. Additionally, parents reported improved subjective sleep quality and child sleep-related impairment at 3 months.

CONCLUSIONS

With nocturnal glycemic improvements in youth using HCL technology, some aspects of parent and youth sleep and quality of life improved. This may reflect decreased parental monitoring and worry and highlights benefits for youth beyond glycemia.

Psychosocial determinants of healthy sleep habits in adults with type 1 and type 2 diabetes

Background

Many adults with type 1 (T1D) and type 2 diabetes (T2D) have inadequate sleep increasing their risk of hyperglycemia and developing complications. The objective was to identify psychosocial determinants of healthy sleep habits (HSH) among adults with T1D and T2D.

Methods

The two HSH were: avoiding screen use in bed and having sleep regularity. Adults (≥18 years) with T1D and T2D were invited to complete an anonymous online survey. The questionnaires were based on the Reasoned Action Approach and formative qualitative research previously conducted in 56 adults with T1D and T2D. Habit was included as an additional variable for screen use in bed.

Results

In total, 320 adults with diabetes (T1D: 39%; T2D: 61%) completed the questionnaires (screen use in bed: 174; sleep timing: 146). Close to 75% of participants reported screen use in bed and close to 90% reported sleep timing variability in the last month. Perceived behavioral control (PBC) to avoid screen use in bed (β = -0.4486, p < 0.0001), habit of using screens in bed (β = 0.4002; p < 0.0001), and age (β = -0.0202; p = 0.0086) were determinants of screen use in bed, and this model explained 71% of the variance. PBC for sleep regularity (β = -0.2909; p = 0.0004) and being female (β = 0.5057; p = 0.0069) were determinants of sleep timing variability, and this model explained 28% of the variance. The most important beliefs associated with each HSH were identified to obtain information to design targeted interventions.

Conclusions

Few adults with diabetes have HSH. Screen use in bed was strongly influenced by habit and the results suggest that both HSH are not easy to adopt among adults with diabetes. Younger adults with diabetes should be prioritized for screen use in bed, while females with diabetes should be prioritized for sleep timing variability. Adults with diabetes should have access to behavior change interventions to encourage them to adopt HSH.

Psychological Alterations in Youths with Type I Diabetes: Associations with Salivary Cortisol Concentration

Background and Objectives: Type 1 diabetes mellitus (T1DM) is one of the most common chronic diseases in children and adolescents, and is associated with stress and other psychological alterations. This study aims to assess psychological and sleep disorders and health-related quality of life in young people with T1DM and to determine the relationship between these parameters and levels of salivary cortisol, a hormone widely associated with stress and several psychological symptoms. Materials and Methods: In our cross-sectional study performed in 60 Moroccan children and adolescents with T1DM, detailed psychological evaluations were performed to assess symptoms of anxiety, attention-deficit hyperactivity disorder (ADHD), sleep quality and diabetes-specific quality of life (using the RCMAS-2, ADHD rating scale, Pittsburgh scale and the DQoL scale, respectively), and cortisol concentration was measured from saliva samples taken mid-morning. Results: A total of 60 children and adolescents with T1DM were recruited. The mean age was 11.05 ± 0.35 (6-17). The mean salivary cortisol level in ng/mL was 4.7 ± 0.49 (0.7-20.2) and was significantly associated with an anxiety RCMAS2 score for the Worry subdomain and DQoL subdomain "Anxiety". Linear regression analysis showed that salivary cortisol was significantly higher in girls compared to boys (p = 0.004) (beta coefficient: 3.384 CI95%: 1.137-5.630) and with Hb1AC level as a continuous variable (p = 0.0001) (beta coefficient: 1.135 CI95%: 0.509-1.760). The other variables included in the model were not significant (p > 0.05). There was an association between salivary cortisol concentration with anxiety RCMAS2 score for Worry subdomain and QoL sub-domain "Anxiety". Still, a significant (p = 0.018) association emerged for anxiety RCMAS2 score Worry subdomain and QoL anxiety subdomain (p = 0.044). Conclusions: Children and adolescents with T1DM experienced significantly elevated symptoms of anxiety and sleep disturbances, particularly in girls, and frequent symptoms of ADHD, particularly in boys. Salivary cortisol concentration collected in the morning is associated with anxiety burden but not with other psychological alterations. Further studies are needed to clarify the associations between salivary cortisol concentration and anxiety in type 1 diabetes in order to propose the hormone as a biomarker for interventions aimed to reduce anxiety levels in these patients.

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.

Within-person relationships of sleep duration with next-day stress and affect in the daily life of adults with Type-1 diabetes

OBJECTIVE

The objective of this study is to examine the within-person relationships between sleep duration and next-day stress and affect in the daily life of individuals with T1D.

METHODS

Study participants were recruited in the Function and Emotion in Everyday Life with Type 1 Diabetes (FEEL-T1D) study. Sleep duration was derived by synthesizing objective (actigraphy) and self-report measures. General and diabetes-specific stress and positive and negative affect were measured using ecological momentary assessment. Multilevel regression was used to examine the within-person relationships between sleep duration and next-day stress and affect. Cross-level interactions were used to explore whether gender and baseline depression and anxiety moderated these within-person relationships.

RESULTS

Adults with T1D (n = 166) completed measurements for 14 days. The average age was 41.0 years, and 91 participants (54.8%) were female. The average sleep duration was 7.3 h (SD = 1.2 h). Longer sleep was significantly associated with lower general stress (p < 0.001) but not diabetes-specific stress (p = 0.18) on the next day. There were significant within-person associations of longer sleep with lower levels on next-day negative affect (overall, p = 0.002, disappoint, p = 0.05; sad, p = 0.05; tense, p < 0.001; upset, p = 0.008; anxious, p = 0.04). There were no significant associations with positive affect. Examination of the interaction effects did not reveal significant differential relationships for men and women and for individuals with and without depression or anxiety at baseline.

CONCLUSION

Findings from this study suggest optimizing sleep duration as an important interventional target for better managing general stress and improving daily emotional wellbeing of individuals with T1D.

Frequency and Causes of Nocturnal Alarms in Youth and Young Adults With Type 1 Diabetes Using a First-Generation Hybrid Closed-Loop System

Background

Meeting glycemic recommendations is challenging for youth with type 1 diabetes. Diabetes technology, including continuous glucose monitoring (CGM) and hybrid closed-loop (HCL) automated insulin delivery systems, significantly increase achievement of glycemic targets; however, many youth struggle to sustain use of early HCL systems. Nocturnal alarm fatigue contributes to disrupted sleep and device discontinuation.

Methods

We examined the frequency and causes of nocturnal (10:00 p.m. to 6:00 a.m.) alarms in pediatric patients (N = 76, median age 14.5 years [interquartile range 11.8-17.0 years, range 7-24 years]) starting on a first-generation HCL system in a prospective observational study. Device data were analyzed with linear mixed-effects models to examine change across time at 3-month intervals for 12 months.

Results

At baseline (HCL system in nonautomated mode), participants averaged 3.3 ± 0.6 alarms per night. In the 2 weeks after starting HCL (automated) mode, alarm frequency significantly increased to 5.4 ± 0.5 times per night (P <0.001). Alarm frequency decreased through the remainder of the observational period; however, CGM sensor and HCL system use also declined. The types of alarms were evenly distributed among sensor maintenance, sensor threshold, pump, and HCL-specific alarms.

Conclusion

These data show that HCL system nocturnal alarms are frequent and may be barriers to sleep quality and device use. Further research is needed to assess the impact of diabetes technology on sleep and to determine method to improve sleep quality with technology use.

Sleep Deficiency and Cardiometabolic Disease

Epidemiologic studies have demonstrated that short sleep duration is associated with an increased risk of cardio-metabolic health outcomes including cardiovascular disease mortality, coronary heart disease, type 2 diabetes mellitus, hypertension, and metabolic syndrome. Experimental sleep restriction studies have sought to explain these findings. This review describes the main evidence of these associations and possible mechanisms explaining them. Whether sleep extension reverses these now widely acknowledged adverse health effects and the feasibility of implementing such strategies on a public health level is discussed.

Associations between sleep characteristics and glycemic variability in youth with type 1 diabetes

OBJECTIVE

This study aimed to determine sleep characteristics and their associations with glycemic variability in youth with type 1 diabetes (T1D).

MATERIAL AND METHODS

This cross-sectional study conducted at two pediatric diabetes centers in Istanbul, Turkey, included 84 children with T1D (mean age 10.5 years). Sleep characteristics and glycemic variability were determined by actigraphy, DSM-5 Level 2-Sleep Disturbance Scale Short Form and continuous glucose monitoring. Circadian preference was evaluated by the Children's Chronotype Questionnaire. Sleep disturbances were assessed by the. The sleep quality was determined by actigraphy-derived sleep measures.

RESULTS

Eighty-eight percent of participants had insufficient age-appropriate total sleep time (TST) (<9 h for 6-13-year-olds and <8 h for 14-17-year-olds). Chronotype was classified as intermediate in 50%, evening in 45.2%, and morning in 4.8%. A higher chronotype score indicating a stronger eveningness preference was associated with more time spent in hypoglycemia (β = 0.433, p = 0.002). On nights when participants had lower sleep efficiency and longer sleep onset latency, they had significantly higher overnight glycemic variability (β = -0.343, p = 0.016, β = 0.129, p = 0.017, respectively). Prolonged nocturnal wake duration was significantly associated with more time spent in daytime hypoglycemia (β = 0.037, p = 0.046) and higher overnight glycemic variability (J index, β = 0.300, p = 0.015). The associations between TST and glycemic variability indices were not significant.

CONCLUSIONS

Sleep quality rather than TST was significantly associated with glycemic variability in children with T1D. Eveningness preference might contribute to an increased risk of hypoglycemia. Addressing sleep patterns and chronotypes can be crucial in management plans for youth with T1D.

An Examination of the Associations between Nutritional Composition, Social Jet Lag and Temporal Sleep Variability in Young Adults

While dietary intake has previously been related to various indices of poor sleep (e.g., short sleep duration, poor sleep quality), to date, few studies have examined chrononutrition from the perspectives of the relationship between dietary intake and social jet lag and temporal sleep variability. Moreover, recently it has been suggested that previous methods of measuring social jet lag have the potential to lead to large overestimations. Together, this precludes a clear understanding of the role of nutritional composition in the pathophysiology of poor sleep, via social jet lag and temporal sleep variability, or vice versa. The aim of the present study was to determine the relationships between nutrient intake and social jet lag (using a revised index, taking account of intention to sleep and sleep onset and offset difficulties), and temporal sleep variability. Using a cross-sectional survey, 657 healthy participants (mean age 26.7 ± 6.1 years), without sleep disorders, were recruited via an online platform and completed measures of weekly dietary intake, social jet lag, temporal sleep variability, stress/sleep reactivity and mood. Results showed limited associations between nutritional composition and social jet lag. However, levels of temporal sleep variability were predicted by consumption of polyunsaturated fats, sodium, chloride and total energy intake. The results suggest further examinations of specific nutrients are warranted in a first step to tailoring interventions to manage diet and temporal variabilities in sleep patterns.

Genetics of diabetes

Diabetes mellitus is a complicated disease characterized by a complex interplay of genetic, epigenetic, and environmental variables. It is one of the world's fastest-growing diseases, with 783 million adults expected to be affected by 2045. Devastating macrovascular consequences (cerebrovascular disease, cardiovascular disease, and peripheral vascular disease) and microvascular complications (like retinopathy, nephropathy, and neuropathy) increase mortality, blindness, kidney failure, and overall quality of life in individuals with diabetes. Clinical risk factors and glycemic management alone cannot predict the development of vascular problems; multiple genetic investigations have revealed a clear hereditary component to both diabetes and its related complications. In the twenty-first century, technological advancements (genome-wide association studies, next-generation sequencing, and exome-sequencing) have led to the identification of genetic variants associated with diabetes, however, these variants can only explain a small proportion of the total heritability of the condition. In this review, we address some of the likely explanations for this "missing heritability", for diabetes such as the significance of uncommon variants, gene-environment interactions, and epigenetics. Current discoveries clinical value, management of diabetes, and future research directions are also discussed.

Sleep Quality and Quantity in Caregivers of Children with Type 1 Diabetes Using Closed-Loop Insulin Delivery or a Sensor-Augmented Pump

Introduction

Parents of children living with type 1 diabetes (T1D) often report short and/or poor quality sleep. The development of closed-loop systems promises to transform the management of T1D. This study compared sleep quality and quantity in caregivers of children using a closed-loop system (CL) or sensor-augmented pump (SAP) therapy.

Method

Data from sleep diaries, accelerometers, and questionnaires were provided by forty parents (classified as caregiver 1 (main analyses) or 2 (supplementary analyses) based on their contribution towards treatment management) of 21 very young children aged 1 to 7 years living with T1D (mean age: 4.7 (SD = 1.7)). Assessments were made at a single post-randomisation time point when the child was completing either the 16-week CL arm (n = 10) or the 16-week SAP arm (n = 11) of the main study.

Results

Overall, there was a mixed pattern of results and group differences were not statistically significant at the p < 0.05 level. However, when we consider the direction of results and results from caregiver 1, sleep diary data showed that parents of the CL (as compared to the SAP) group reported a shorter sleep duration but better sleep quality, fewer awakenings, and less wake after sleep onset (WASO). Actiwatch data showed that caregiver 1 of the CL (as compared to the SAP) group had a shorter sleep latency; greater sleep efficiency; and less wake after sleep onset. Results from the Pittsburgh Sleep Quality Index also showed better sleep quality for caregiver 1 of the CL group as compared to the SAP group.

Conclusions

Results from this study suggest that sleep quality and quantity in parents of children using CL were not significantly different to those using SAP. Considering effect sizes and the direction of the non-significant results, CL treatment could be associated with better sleep quality in the primary caregiver. However, further research is needed to confirm these findings. This trial is registered with NCT05158816.

A composite measure of sleep health is associated with glycaemic target achievement in young adults with type 1 diabetes

We investigated whether sleep health (each individual dimension and a composite measure) was associated with better glycaemia among a cohort of young adults with type 1 diabetes (mean age 21.5 years, mean body mass index 24.55 kg m-2 ). Multiple validated self-report questionnaires were completed, and raw continuous glucose monitor data were shared. One self-reported sleep characteristic for each of the five sleep health dimensions was selected. A composite score was calculated by summing the number of "good" sleep health dimensions. We evaluated the associations between sleep health and glycaemia, and whether covariates, including age, type 1 diabetes duration and sleep apnea risk, influenced the relationships among the study variables using multivariable linear regression. Individual dimensions of sleep satisfaction (β = 0.380, p = 0.019; β = -0.414, p = 0.010), timing (β = 0.392, p = 0.015; β = -0.393, p = 0.015) and sleep efficiency (β = 0.428, p = 0.007) were associated with higher achievement of glycaemic targets (J-index and time in range); however, these associations did not persist after considering covariates. A better Sleep Health Composite score was associated with higher achievement of glycaemic targets even after considering covariates. Using a multidimensional framework can guide future research on causal pathways between sleep and diabetes health, interventions to target sleep health profiles, and may improve sleep screening in routine diabetes care.

The relationship between sleep and salivary and serum inflammatory biomarkers in adolescents

Objectives

Poor sleep behavior can trigger an inflammatory response and contribute to the development of inflammatory diseases. Cytokines can act as indicators of inflammation and may precede the onset of inflammatory diseases. This study aimed to determine the association between sleep timing parameters (bedtime, sleep duration, sleep debt, and social jetlag) and the levels of nine serum and salivary inflammatory and metabolic biomarkers.

Methods

Data were collected from 352 adolescents aged 16-19 years enrolled in Kuwait's public high schools. The levels of C-reactive protein (CRP), interleukin-6 (IL-6), interleukin-8 (IL-8), interleukin-10 (IL-10), vascular endothelial growth factor (VEGF), monocyte chemoattractant protein-1 (MCP-1), adiponectin, leptin, and insulin were measured from saliva and serum samples. We conducted mixed-effect multiple linear regression modeling to account for the school variable as a random effect to assess the relationship between the sleep variables and salivary and serum biomarkers. Mediation analysis was conducted to check if BMI was a mediator between bedtime and the biomarkers.

Results

There was a statistically significant elevation in serum IL-6 level associated with later bedtime (0.05 pg./mL, p = 0.01). Adolescents with severe sleep debt of ≥2 h had an increase in salivary IL-6 biomarker levels (0.38 pg./mL, p = 0.01) compared to those who had sleep debt of <1 h. Adolescents with sleep debt of ≥2 h had significantly higher levels of serum CRP (0.61 μg/mL, p = 0.02) than those without sleep debt. Additionally, we found that the inflammatory biomarkers (CRP, IL-6, IL-8, IL-10, VEGF, and MCP-1) and metabolic biomarkers (adiponectin, leptin, and insulin) had more statistically significant associations with the bedtime variables than with sleep duration variables. CRP, IL-6, and IL-8 were associated with sleep debt, and IL-6, VEGF, adiponectin, and leptin levels were associated with social jetlag. BMIz was a full mediator in the relationship between late bedtime and increased serum levels of CRP, IL-6, and insulin.

Conclusion

Adolescents who go to bed at or later than midnight had dysregulated levels of salivary and serum inflammatory biomarkers, suggesting that disrupted circadian rhythm can trigger higher levels of systemic inflammation and potentially exacerbate chronic inflammation and the risk of metabolic diseases.