Type 1 Diabetes, Sleep, and Hypoglycemia

Interplay among sleep quality, dinner timing, and blood glucose control in users of advanced technologies: A study in a cohort of adults with type 1 diabetes

AIMS

To explore the interplay among sleep quality, dinner timing, and glycemic control in adults with type 1 diabetes (T1D) using advanced diabetes technologies.

METHODS

T1D adults on automated (AID, n = 122) or non-automated (CSII, n = 67) insulin delivery systems completed the Pittsburgh Sleep Quality Index (PSQI) questionnaire. Two-week CGM-metrics, HbA1c, and post-dinner glucose control were compared by independent T-test in Good vs. Bad-sleepers (PSQI-score above 5) or in Early vs. Late-eaters (above median of the cohort's dinner time).

RESULTS

Time-below-range (TBR)70-54 (2.1 ± 2.0 vs. 1.3 ± 1.2 %), TBR54 (0.7 ± 1.0 vs. 0.2 ± 0.4 %), and coefficient of variation (34.4 ± 5.3 vs. 31.8 ± 5.2 %) were significantly higher in Bad-sleepers than Good-sleepers (p < 0.05 for all). Late-eaters, particularly among AID users, showed higher HbA1c and lower TBR70-54, and, after dinner, higher TAR180-250 and lower Time-in-range70-180 than Early-eaters (p < 0.05 for all). At multiple regression analysis, dinner time was a main predictor of HbA1c, and TBR54 a main predictor of sleep quality.

CONCLUSIONS

The rate of hypoglycemia and dinner timing are key factors affecting both sleep quality and glycemic control in adults with T1D. Addressing lifestyle habits, including dinner timing and fear of hypoglycemia, may still be needed in users of AID.

Sleep quality in children with hepatic glycogen storage diseases, a prospective observational pilot study

Background

Hepatic glycogen storage diseases (GSDs) are characterised by enzyme defects affecting liver glycogen metabolism, where carbohydrate supplementation to prevent overnight hypoglycaemia is common. Concerns around sleep quality in hepatic GSDs relate to emerging evidence that overnight dysglycaemia impacts sleep quality.

Methods

This prospective observational study reported sleep quality and duration in children with hepatic GSDs over 7 days utilising: actigraphy (Actiwatch 2 by Phillips Respironics), sleep diaries, proxy reported age-appropriate sleep and quality-of-life (QoL) questionnaires, in the context of nocturnal glycaemic profiles continuous glucose monitor (CGM, Dexcom G6) and nocturnal dietary management strategies. Significant hypo- and hyperglycaemia were defined as ≥1% of sleep diary documented nocturnal period, recording <3.5 and >10.0 mmol/L, respectively.

Results

Seven children with hepatic GSD (aged 1-17 years) participated. Objective sleep quality was poor, with actigraphy demonstrating that no child achieved the minimum sleep duration recommended for age. Subjective sleep quality was also poor, with 4/5 documenting significant daytime sleepiness and 6/6 reporting poor sleep hygiene. Children prescribed overnight bolus feeds (OBF) (n = 2) recorded shorter sleep duration compared to other nocturnal management strategies. Parent-reported QoL suggested poor disease-related QoL outcomes for this cohort.

Conclusion

Objective and subjective sleep disturbances and reduced QoL are common within our sample of children with hepatic GSD. From our observations these outcomes may be linked to nutritional overnight interventions, especially OBFs, rather than overnight glucose levels. Consideration of the impacts of overnight feeding strategies on sleep quality and QoL in children with hepatic GSD should inform future management strategies.

Sleep Disturbances in Type 1 Diabetes and Mitigating Cardiovascular Risk

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

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

INTRODUCTION

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

METHODS

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

RESULTS

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

CONCLUSION

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

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.

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.

Insomnia severity among caregivers of children with type 1 diabetes: A mixed method study

AIM

This research was conducted to assess the severity of insomnia experienced by caregivers of children with type 1 diabetes.

METHOD

In this study, a mixed-method descriptive sequential pattern design was employed, incorporating both qualitative and quantitative research methods. In the study, 152 caregivers were surveyed for quantitative data on survey form and insomnia severity. Additionally, 9 mothers underwent semi-structured interviews for qualitative insights.

RESULTS

Of caregivers, 13.8% were identified to experience clinical insomnia. There were statistically significant differences in insomnia severity index scores in terms of experiencing sleep problems after the child's type 1 diabetes diagnosis, caregivers having adequate sleep duration at night, how they felt on waking in the morning, difficulty managing their child's diabetes the next day due to lack of sleep, difficulty getting to sleep at night, sleeping more than one hour during the day, lack of sleep due to caring and treatment, lack of sleep due to not meeting the desired targets for blood glucose values and receiving support for type 1 diabetes management due to lack of sleep (p < 0.05). Three themes were identified as a result of qualitative analysis: Sleep status of caregivers, effect of sleep problems on daily life and solving sleep problems.

CONCLUSIONS

Caregivers experience insomnia during the process of type 1 diabetes management and this situation affects their daytime care duties.

PRACTICAL IMPLICATIONS

Research shows caregivers of children with type 1 diabetes often face insomnia, highlighting the need for solutions.

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.

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.

Sleep quality and sex-related factors in adult patients with immune-mediated diabetes: a large cross-sectional study

AIM

To analyze sleep quality and its relationships with clinical and biochemical features in a large cohort of adults with autoimmune diabetes.

METHODS

We administered to 553 patients with autoimmune diabetes the questionnaires: Pittsburgh Sleep Quality Index (PSQI), diabetes distress scale, diabetes-related quality of life and diabetes treatment satisfaction questionnaire. We excluded patients with missing HbA1c ± 4 months from PSQI administration or incorrect PSQI compilation (n = 110).

RESULTS

Altered sleep quality was recorded in 142/443 subjects (32%), insufficient total sleep time in 177/443 (40%). The altered sleep quality group had higher HbA1c (median 56 mmol/mol [interquartile range-IQR 49-62] vs 59 [IQR 52-68]; P < 0.001), higher average HbA1c in the previous 36 months (59 mmol/mol [IQR 54-68] vs 56 [IQR 51-62]; P < 0.001), and more individuals with HbA1c > 53 mmol/mol (74.6% vs 62.8%; P = 0.014). Diabetes duration (P = 0.63), type of insulin delivery (P = 0.48) and glucose monitoring (P = 0.35) were uninfluential. Patients with altered sleep quality showed higher prevalence of autoimmune (42 vs 28%; P = 0.005) and mental diseases (12 vs 4%; P = 0.002); there were greater emotional distress, and lower quality of life and treatment satisfaction (P < 0.001 for all), irrespective of sex. Men with altered sleep quality had higher HbA1c and prevalence of autoimmune diseases. Women showed greater prevalence of psychiatric disorders. Average HbA1c of the previous 36 months, autoimmune or psychiatric disorders were independent predictive factors for altered sleep quality.

CONCLUSION

One-third of the patients with autoimmune diabetes showed altered sleep quality, which associates with worse glycemic control, and autoimmune and mental disorders, with sex-specific differences.

Objective and Subjective Sleep Patterns in Adults With Maturity-Onset Diabetes of the Young (MODY)

OBJECTIVE

To examine sleep patterns in adults with maturity-onset diabetes of the young (MODY).

RESEARCH DESIGN AND METHODS

Adults with glucokinase (GCK)-MODY and transcription factor (TF)-related MODY (HNF1A, HNF1B, HNF4A) were recruited (n = 24; age 46.0 years, 79% women, BMI 24.7 kg/m2) from The University of Chicago's Monogenic Diabetes Registry. Sleep patterns were assessed by 2-week wrist actigraphy (total 315 nights), one night of a home sleep apnea test, and validated surveys.

RESULTS

Overall, compared with established criteria, 29% of participants had sleep latency ≥15 min, 38% had sleep efficiency ≤85%, 46% had wake after sleep onset >40 min, all indicating poor objective sleep quality. Among all participants, 54% had a sleep duration below the recommended minimum of 7 h, 88% reported poor sleep quality, 58% had obstructive sleep apnea, and 71% reported insomnia. Compared with GCK-MODY, participants with TF-related MODY had poorer objective sleep quality and increased night-to-night variability in sleep patterns.

CONCLUSIONS

Sleep disturbances appear to be common in adults with MODY despite absent traditional risk factors for sleep disorders. Future research investigating the sleep-diabetes relationship is warranted in this population.

Pathophysiology and management of hypoglycemia in diabetes

In the century since the discovery of insulin, diabetes has changed from an early death sentence to a manageable chronic disease. This change in longevity and duration of diabetes coupled with significant advances in therapeutic options for patients has fundamentally changed the landscape of diabetes management, particularly in patients with type 1 diabetes mellitus. However, hypoglycemia remains a major barrier to achieving optimal glycemic control. Current understanding of the mechanisms of hypoglycemia has expanded to include not only counter-regulatory hormonal responses but also direct changes in brain glucose, fuel sensing, and utilization, as well as changes in neural networks that modulate behavior, mood, and cognition. Different strategies to prevent and treat hypoglycemia have been developed, including educational strategies, new insulin formulations, delivery devices, novel technologies, and pharmacologic targets. This review article will discuss current literature contributing to our understanding of the myriad of factors that lead to the development of clinically meaningful hypoglycemia and review established and novel therapies for the prevention and treatment of hypoglycemia.

Study of the Economic, Environmental, and Social Factors Affecting Chinese Residents' Health Based on Machine Learning

The Healthy China Strategy puts realistic demands for residents' health levels, but the reality is that various factors can affect health. In order to clarify which factors have a great impact on residents' health, based on China's provincial panel data from 2011 to 2018, this paper selects 17 characteristic variables from the three levels of economy, environment, and society and uses the XG boost algorithm and Random forest algorithm based on recursive feature elimination to determine the influencing variables. The results show that at the economic level, the number of industrial enterprises above designated size, industrial added value, population density, and per capita GDP have a greater impact on the health of residents. At the environmental level, coal consumption, energy consumption, total wastewater discharge, and solid waste discharge have a greater impact on the health level of residents. Therefore, the Chinese government should formulate targeted measures at both economic and environmental levels, which is of great significance to realizing the Healthy China strategy.