Nocturnal continuous glucose and sleep stage data in adults with type 1 diabetes in real-world conditions

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

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

Type 1 diabetes-related distress: Current implications in care

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

Gut Microbiota Composition and Functionality Are Associated With REM Sleep Duration and Continuous Glucose Levels

CONTEXT

Sleep disruption is associated with worse glucose metabolic control and altered gut microbiota in animal models.

OBJECTIVE

We aimed to evaluate the possible links among rapid eye movement (REM) sleep duration, continuous glucose levels, and gut microbiota composition.

METHODS

This observational, prospective, real-life, cross-sectional case-control study included 118 (60 with obesity), middle-aged (39.1-54.8 years) healthy volunteers recruited at a tertiary hospital. Glucose variability and REM sleep duration were assessed by 10-day continuous glucose monitoring (CGM) (Dexcom G6) and wrist actigraphy (Fitbit Charge 3), respectively. The coefficient of variation (CV), interquartile range (IQR), and SD of glucose variability was assessed and the percentage of time in range (% TIR), at 126-139 mg/dL (TIR2), and 140-199 mg/dL (TIR3) were calculated. Shotgun metagenomics sequencing was applied to study gut microbiota taxonomy and functionality.

RESULTS

Increased glycemic variability (SD, CV, and IQR) was observed among subjects with obesity in parallel to increased % TIR2 and % TIR3. REM sleep duration was independently associated with % TIR3 (β = -.339; P < .001) and glucose variability (SD, β = -.350; P < .001). Microbial taxa from the Christensenellaceae family (Firmicutes phylum) were positively associated with REM sleep and negatively with CGM levels, while bacteria from Enterobacteriacea family and bacterial functions involved in iron metabolism showed opposite associations.

CONCLUSION

Decreased REM sleep duration was independently associated with a worse glucose profile. The associations of species from Christensenellaceae and Enterobacteriaceae families with REM sleep duration and continuous glucose values suggest an integrated picture of metabolic health.

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.

Evaluating the influence of sleep quality and quantity on glycemic control in adults with type 1 diabetes

BACKGROUND

Sleep quality disturbances are frequent in adults with type 1 diabetes. However, the possible influence of sleep problems on glycemic variability has yet to be studied in depth. This study aims to assess the influence of sleep quality on glycemic control.

MATERIALS AND METHODS

An observational study of 25 adults with type 1 diabetes, with simultaneous recording, for 14 days, of continuous glucose monitoring (Abbott FreeStyle Libre system) and a sleep study by wrist actigraphy (Fitbit Ionic device). The study analyzes, using artificial intelligence techniques, the relationship between the quality and structure of sleep with time in normo-, hypo-, and hyperglycemia ranges and with glycemic variability. The patients were also studied as a group, comparing patients with good and poor sleep quality.

RESULTS

A total of 243 days/nights were analyzed, of which 77% (n = 189) were categorized as poor quality and 33% (n = 54) as good quality. Linear regression methods were used to find a correlation (r =0.8) between the variability of sleep efficiency and the variability of mean blood glucose. With clustering techniques, patients were grouped according to their sleep structure (characterizing this structure by the number of transitions between the different sleep phases). These clusters showed a relationship between time in range and sleep structure.

CONCLUSIONS

This study suggests that poor sleep quality is associated with lower time in range and greater glycemic variability, so improving sleep quality in patients with type 1 diabetes could improve their glycemic control.

Corresponding risk factors between cognitive impairment and type 1 diabetes mellitus: a narrative review

Type 1 diabetes mellitus (T1DM) is a type of diabetes caused by the destruction of pancreatic β cells and the absolute lack of insulin secretion. T1DM usually starts in adolescence or develops directly as a severe disease state of ketoacidosis. T1DM and its complications make many people suffer and have psychological problems, which make us have to pay more attention to the prevention and early control of T1DM. Cognitive impairment (CI) is one of the major complications of T1DM. It can further develop into Alzheimer's disease, which can seriously affect the quality of life of the elderly. Furthermore, the relationship between T1DM and CI is unclear. Hence, we conducted a narrative review of the existing literature through a PubMed search. We summarized some risk factors that may be associated with the cognitive changes in T1DM patients, including onset age and duration, education and gender, glycemic states, microvascular complications, glycemic control, neuropsychology and emotion, intestinal flora, dyslipidemia, sleep quality. We aimed to provide some content related to CI in T1DM, and hoped that it could play a role in early prediction and treatment to reduce the prevalence.

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Corresponding risk factors between cognitive impairment and type 1 diabetes mellitus.

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Duration and age; Education and gender and Glycemic states.

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Diabetic ketoacidosis; Microvascular complications and Glycemic control–HbA1c.

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Neuropsychology and emotion; Intestinal flora; Dyslipidemia and Sleep Quality.

Type 1 Diabetes, Sleep, and Hypoglycemia

PURPOSE OF REVIEW

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

RECENT FINDINGS

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

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

AIMS/HYPOTHESIS

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

METHODS

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

RESULTS

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

CONCLUSIONS/INTERPRETATION

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

Sleep-wake characteristics, daytime sleepiness, and glycemia in young adults with type 1 diabetes

STUDY OBJECTIVES

The purpose of this study was to describe objective sleep-wake characteristics and glycemia over 7 - 14 days in young adults with type 1 diabetes (T1D). Additionally, person level associations among objective sleep-wake characteristics (total sleep time, sleep variability, and sleep fragmentation index), daytime sleepiness, and glycemia (glycemic control and glucose variability) were examined.

METHODS

In this cross-sectional study, objective sleep-wake characteristics were measured via actigraphy and glucose variability via continuous glucose monitoring (CGM) over 6-14 days. At baseline, participants completed a psychomotor vigilance test (PVT), Trail Making Test, and questionnaires on daytime sleepiness, sleep quality, and sleep disturbance including sleep diaries.

RESULTS

Forty-six participants (mean age 22.3 ± 3.2 years) wore a wrist actigraph and CGM concurrently for 6-14 days. Greater sleep variability was directly associated with greater glucose variability (mean of daily differences) (r = 0.33, p = .036). Higher daytime sleepiness was directly associated with greater glucose variability (mean of daily differences) (r = 0.50, p = .001). The association between sleep variability and glucose variability (mean of daily differences) was no longer significant when accounting for daytime sleepiness and controlling for T1D duration (p > .05). A higher sleep fragmentation index was associated with greater glucose variability (B = 1.27, p = .010, pr² = .40) after controlling for T1D duration and accounting for higher daytime sleepiness.

CONCLUSIONS

Sleep-wake variability, sleep fragmentation, daytime sleepiness, and the associations with glycemia are new dimensions to consider in young adults with T1D. Sleep habits in this population may explain higher glucose variability and optimizing sleep may improve overall diabetes management.

Automated meal detection from continuous glucose monitor data through simulation and explanation

Background

Artificial pancreas systems aim to reduce the burden of type 1 diabetes by automating insulin dosing. These systems link a continuous glucose monitor (CGM) and insulin pump with a control algorithm, but require users to announce meals, without which the system can only react to the rise in blood glucose.

Objective

We investigate whether CGM data can be used to automatically infer meals in daily life even in the presence of physical activity, which can raise or lower blood glucose.

Materials and Methods

We propose a novel meal detection algorithm that combines simulations with CGM, insulin pump, and heart rate monitor data. When observed and predicted glucose differ, our algorithm uses simulations to test whether a meal may explain this difference. We evaluated our method on simulated data and real-world data from individuals with type 1 diabetes.

Results

In simulated data, we detected meals earlier and with higher accuracy than was found in prior work (25.7 minutes, 1.2 g error; compared with 48.3 minutes, 17.2 g error). In real-world data, we discovered a larger number of plausible meals than was found in prior work (30 meals, 76.7% accepted; compared with 33 meals, 39.4% accepted).

Discussion

Prior research attempted meal detection from CGM, but had delays and lower accuracy in real data or did not allow for physical activity. Our approach can be used to improve insulin dosing in an artificial pancreas and trigger reminders for missed meal boluses.

Conclusions

We demonstrate that meal information can be robustly inferred from CGM and body-worn sensor data, even in challenging environments of daily life.

Design of Wearable EEG Devices Specialized for Passive Brain-Computer Interface Applications

Owing to the increased public interest in passive brain–computer interface (pBCI) applications, many wearable devices for capturing electroencephalogram (EEG) signals in daily life have recently been released on the market. However, there exists no well-established criterion to determine the electrode configuration for such devices. Herein, an overall procedure is proposed to determine the optimal electrode configurations of wearable EEG devices that yield the optimal performance for intended pBCI applications. We utilized two EEG datasets recorded in different experiments designed to modulate emotional or attentional states. Emotion-specialized EEG headsets were designed to maximize the accuracy of classification of different emotional states using the emotion-associated EEG dataset, and attention-specialized EEG headsets were designed to maximize the temporal correlation between the EEG index and the behavioral attention index. General purpose electrode configurations were designed to maximize the overall performance in both applications for different numbers of electrodes (2, 4, 6, and 8). The performance was then compared with that of existing wearable EEG devices. Simulations indicated that the proposed electrode configurations allowed for more accurate estimation of the users’ emotional and attentional states than the conventional electrode configurations, suggesting that wearable EEG devices should be designed according to the well-established EEG datasets associated with the target pBCI applications.

Sleep, self-management, neurocognitive function, and glycemia in emerging adults with Type 1 diabetes mellitus: A research protocol

Type 1 Diabetes (T1D) affects 1.6 million Americans, and only 14% of emerging adults ages 18-25 years achieve targets for glycemic control (A1C < 7.0%). Sleep deficiency, including habitual short sleep duration (<6.5 hr total sleep time and high within-person variability in total sleep time), is associated with poorer glycemic control. Emerging adults with T1D have a more pronounced sleep extension on weekends compared with matched controls, consistent with sleep deficiency; however, associations among sleep variability and glycemic control have not been explored in this population. Sleep deficiency may affect the complex higher-order neurocognitive functioning needed for successful diabetes self-management (DSM). We report the protocol for an ongoing study designed to characterize sleep and the associations among sleep deficiency, neurocognitive function, DSM, diabetes quality of life, and glycemia among a sample of 40 emerging adults with T1D. We monitor sleep via wrist-worn actigraphy and glucose via continuous glucose monitoring concurrently over 14 days. We are collecting data on self-report and objective sleep, a 10-min psychomotor vigilance test on a PVT-192 device, a 3-min Trail Making Test on paper, and questionnaires, including twice-daily Pittsburgh sleep diaries using Research Electronic Data Capture (REDCap)^TM . Results from this study will be used to support the development and testing of the efficacy of a tailored sleep self-management intervention that may improve total sleep time, sleep variability, neurocognitive function, DSM, glycemic control, and glucose variability among emerging adults with T1D.

Sleep-related disorders in patients with type 1 diabetes mellitus: current insights

Type 1 diabetes mellitus (T1DM) is an autoimmune condition that results from destruction of beta cells in the pancreas. Several reviews have concluded that sleep contributes to poor glycemic control, diabetes management, and diabetes-related complications in individuals with T1DM and represents an untapped opportunity for intervention. However, at the current juncture, the American Diabetes Association's Standards of Medical Care are devoid of recommendations about how to address sleep in the management of T1DM. This article summarizes reviews of sleep in youth and adults with T1DM and empirical studies that have examined various sleep parameters ranging from sleep disturbances (general, perceived sleep quality, sleepiness, awakenings, and sleep efficiency), sleep duration, sleep consistency, sleep-disordered breathing (SDB), and sleep architecture. The data show that many individuals with T1DM sleep less than recommendations; individuals with the poorest sleep have difficulties with diabetes management; and sleep deficiency including SDB often corresponds to several disease morbidities (neuropathy, nephropathy, etc). Mixed findings exist regarding direct associations of various sleep parameters and glycemic control. SDB appears to be just as prevalent, if not more, than other conditions that have been recommended for universal screening in individuals with T1DM. The article concludes with recommendations for collaborative research efforts to further elucidate the role of sleep in diabetes-related outcomes; investigations to test behavioral strategies to increase sleep quantity and consistency; and considerations for clinical care to address sleep.

Using Domain Knowledge to Overcome Latent Variables in Causal Inference from Time Series

Increasingly large observational datasets from healthcare and social media may allow new types of causal inference. However, these data are often missing key variables, increasing the chance of finding spurious causal relationships due to confounding. While methods exist for causal inference with latent variables in static cases, temporal relationships are more challenging, as varying time lags make latent causes more difficult to uncover and approaches often have significantly higher computational complexity. To address this, we make the key observation that while a variable may be latent in one dataset, it may be observed in another, or we may have domain knowledge about its effects. We propose a computationally efficient method that overcomes latent variables by using prior knowledge to reconstruct data for unobserved variables, while remaining robust to cases when the knowledge is wrong or does not apply. On simulated data, our approach outperforms the state of the art with a lower false discovery rate for causal inference. On real-world data from individuals with Type 1 diabetes, we show that our approach can discover causal relationships involving unmeasured meals and exercise.

Social jetlag, sleep-related parameters, and glycemic control in adults with type 1 diabetes: Results of a cross-sectional study

BACKGROUND

Social jetlag (SJL) is a small recurrent circadian rhythm disruption and the most frequent form of circadian rhythm misalignment. The main aim of this study was to investigate the effect of SJL on glycemic control, as assessed by HbA1c, in real-life settings.

METHODS

In all, 115 consecutive patients with type 1 diabetes (T1D) were analyzed cross-sectionally. Data on bedtime, sleep onset latency, and wake up time on weekdays and weekends during the previous month were collected from all participants and used to calculate SJL, chronotype, and sleep duration. Sleep quality was assessed by the Pittsburgh Sleep Quality Index (PSQI). A PSQI score > 5 was considered as an indicator of poor sleep quality.

RESULTS

Patients with SJL ≥ 1 hour had significantly higher adjusted values of HbA1c than those with SJL <1 hour (8.7% vs 8.0%; P = 0.029). In unadjusted multivariate regression analysis, SJL ≥ 1 hour and poor sleep quality were significant predictors of HbA1c values, explaining 22.7% and 23.5%, respectively, of the increase in HbA1c. After adjusting for age, sex, diabetes duration, insulin dose (kg/d), insulin regimen and body mass index, only SJL ≥ 1 hour remained associated with HbA1c (β = 0.253; P = 0.026). There was no significant interaction between SJL ≥ 1 hour and poor sleep quality in either the unadjusted or adjusted models (P_interaction  = 0.914).

CONCLUSIONS

In patients with T1D, SJL is associated with poor glycemic control, acting independently of sleep quality, sleep duration, and chronotype to exert a deleterious effect on glycemic control.

Does sleep duration, napping, and social jetlag predict hemoglobin A1c among college students with type 1 diabetes mellitus?

AIMS

The first aim examined the relationship between sleep behaviors (duration, napping, and social jetlag) and hemoglobin A1c (HbA1c) among emerging young adults (EYAs) with T1DM between 18 and 25 years old, who are living on a college campus. The second aim characterized the gender differences in glucose management, sleep behaviors, caffeine intake, and nighttime technology.

METHODS

A cross-sectional study of eligible participants used a convenience sample of eligible participants. Using Research Electronic Data Capture (REDCap), participants completed surveys about diabetes management, caffeine intake, nighttime technology use, and sleep-related behaviors. Data were analyzed using correlation and multiple linear regression to predict HbA1c from sleep behaviors, adjusting for covariates.

RESULTS

Participants (N = 76) average years with T1DM was 10.25 ± 5.70. Compared to females, males had a longer sleep duration lower HbA1c levels. HbA1c levels were negatively correlated with weekday sleep (r = -0.24, p = 0.03) and positively correlated with napping (r = 0.34, p = 0.003). After adjusting for covariates, participants who napped had a higher HbA1c level (β = 0.74, p = 0.03) compared with non-nappers.

CONCLUSIONS

Higher HbA1c levels were found among EYAs with T1DM in college who were nappers and had a longer sleep duration. Modifying sleep behaviors may be an appropriate target to improve glycemic control.

Poor sleep quality is associated with nocturnal glycaemic variability and fear of hypoglycaemia in adults with type 1 diabetes

AIMS

To examine sleep quality and its associations with glycaemic control, glycaemic variability (GV), and fear of hypoglycaemia (FOH) in adults with type 1 diabetes.

BACKGROUND

Poor sleep quality has negative health consequences and is a frequent complaint among adults with type 1 diabetes. Sleep quality in adults with type 1 diabetes is likely affected by glucose levels as well as stressors associated with managing a chronic condition.

DESIGN

A retrospective secondary analysis of pooled data from two previous cross-sectional studies was conducted.

METHODS

We examined subjective sleep quality, FOH; objective measures of glycaemic control (HbA1c); and GV (3-day continuous glucose monitoring) in 48 men and women aged 18-45 years with type 1 diabetes. The data were collected over 3 years in 2013-2016.

RESULTS/FINDINGS

Poor sleep quality was reported by 46% of patients. Those with poor sleep quality had significantly greater nocturnal GV and FOH. Nocturnal GV and FOH were significantly associated with poor sleep quality. The interaction effect of GV and FOH was significant.

CONCLUSION

These findings suggest that glycaemic control and FOH are targets for intervention to improve sleep quality in those with type 1 diabetes.

Multivariable Adaptive Artificial Pancreas System in Type 1 Diabetes

PURPOSE OF REVIEW

The review summarizes the current state of the artificial pancreas (AP) systems and introduces various new modules that should be included in future AP systems.

RECENT FINDINGS

A fully automated AP must be able to detect and mitigate the effects of meals, exercise, stress and sleep on blood glucose concentrations. This can only be achieved by using a multivariable approach that leverages information from wearable devices that provide real-time streaming data about various physiological variables that indicate imminent changes in blood glucose concentrations caused by meals, exercise, stress and sleep. The development of a fully automated AP will necessitate the design of multivariable and adaptive systems that use information from wearable devices in addition to glucose sensors and modify the models used in their model-predictive alarm and control systems to adapt to the changes in the metabolic state of the user. These AP systems will also integrate modules for controller performance assessment, fault detection and diagnosis, machine learning and classification to interpret various signals and achieve fault-tolerant control. Advances in wearable devices, computational power, and safe and secure communications are enabling the development of fully automated multivariable AP systems.

Subjective sleep disturbances and glycemic control in adults with long-standing type 1 diabetes: The Pittsburgh's Epidemiology of Diabetes Complications study

AIMS

To date, studies on sleep disturbances in type 1 diabetes (T1D) have been limited to youth and/or small samples. We therefore assessed the prevalence of subjective sleep disturbances and their associations with glycemia and estimated insulin sensitivity in individuals with long-standing T1D.

METHODS

We conducted a cross-sectional study including 222 participants of the Epidemiology of Diabetes Complications study of childhood-onset T1D attending the 25-year examination (mean age=52years, diabetes duration=43years). The Berlin Questionnaire (risk of obstructive sleep apnea, OSA), the Epworth Sleepiness Scale (daytime sleepiness), and the Pittsburgh Sleep Quality Index (sleep quality, bad dreams presence, and sleep duration) were completed. Associations between sleep disturbances and poor glycemic control (HbA1c⩾7.5%/58mmol/mol), log-transformed HbA1c, and estimated insulin sensitivity (estimated glucose disposal rate, eGDR, squared) were assessed in multivariable regression.

RESULTS

The prevalences of high OSA risk, excessive daytime sleepiness, poor sleep quality, and bad dreams were 23%, 13%, 41%, and 26%, respectively, with more women (51%) reporting poor sleep quality than men (30%, p=0.004). Participants under poor glycemic control were twice as likely to report bad dreams (p=0.03), but not independently (p=0.07) of depressive symptomatology. Sleep duration was directly associated with HbA1c among individuals with poor glycemic control, but inversely in their counterparts (interaction p=0.002), and inversely associated with eGDR (p=0.002).

CONCLUSIONS

These findings suggest important interrelationships between sleep, gender, depressive symptomatology, and glycemic control, which may have important clinical implications. Further research is warranted to examine the mechanism of the interaction between sleep duration and glycemic control.

Effect of Slow Wave Sleep Disruption on Metabolic Parameters in Adolescents

STUDY OBJECTIVES

Cross-sectional studies report a correlation between slow wave sleep (SWS) duration and insulin sensitivity (SI) in children and adults. Suppression of SWS causes insulin resistance in adults but effects in children are unknown. This study was designed to determine the effect of SWS fragmentation on SI in children.

METHODS

Fourteen pubertal children (11.3-14.1 y, body mass index 29(th) to 97(th) percentile) were randomized to sleep studies and mixed meal (MM) tolerance tests with and without SWS disruption. Beta-cell responsiveness (Φ) and SI were determined using oral minimal modeling.

RESULTS

During the disruption night, auditory stimuli (68.1 ± 10.7/night; mean ± standard error) decreased SWS by 40.0 ± 8.0%. SWS fragmentation did not affect fasting glucose (non-disrupted 76.9 ± 2.3 versus disrupted 80.6 ± 2.1 mg/dL), insulin (9.2 ± 1.6 versus 10.4 ± 2.0 μIU/mL), or C-peptide (1.9 ± 0.2 versus 1.9 ± 0.1 ng/mL) levels and did not impair SI (12.9 ± 2.3 versus 10.1 ± 1.6 10(-4) dL/kg/min per μIU/mL) or Φ (73.4 ± 7.8 versus 74.4 ± 8.4 10(-9) min(-1)) to a MM challenge. Only the subjects in the most insulin-sensitive tertile demonstrated a consistent decrease in SI after SWS disruption.

CONCLUSION

Pubertal children across a range of body mass indices may be resistant to the adverse metabolic effects of acute SWS disruption. Only those subjects with high SI (i.e., having the greatest "metabolic reserve") demonstrated a consistent decrease in SI. These results suggest that adolescents may have a unique ability to adapt to metabolic stressors, such as acute SWS disruption, to maintain euglycemia. Additional studies are necessary to confirm that this resiliency is maintained in settings of chronic SWS disruption.

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

Objectives:

The association between inadequate sleep and type 2 diabetes has garnered much attention, but little is known about sleep and type 1 diabetes (T1D). Our objectives were to conduct a systematic review and meta-analysis comparing sleep in persons with and without T1D, and to explore relationships between sleep and glycemic control in T1D.

Methods:

Studies were identified from Medline and Scopus. Studies reporting measures of sleep in T1D patients and controls, and/or associations between sleep and glycemic control, were selected.

Results:

A total of 22 studies were eligible for the meta-analysis. Children with T1D had shorter sleep duration (mean difference [MD] = −26.4 minutes; 95% confidence interval [CI] = −35.4, −17.7) than controls. Adults with T1D reported poorer sleep quality (MD in standardized sleep quality score = 0.51; 95% CI = 0.33, 0.70), with higher scores reflecting worse sleep quality) than controls, but there was no difference in self-reported sleep duration. Adults with TID who reported sleeping >6 hours had lower hemoglobin A1c (HbA1c) levels than those sleeping ≤6 hours (MD = −0.24%; 95% CI = −0.47, −0.02), and participants reporting good sleep quality had lower HbA1c than those with poor sleep quality (MD = −0.19%; 95% CI = −0.30, −0.08). The estimated prevalence of obstructive sleep apnea (OSA) in adults with TID was 51.9% (95% CI = 31.2, 72.6). Patients with moderate-to-severe OSA had a trend toward higher HbA1c (MD = 0.39%, 95% CI = −0.08, 0.87).

Conclusion:

T1D was associated with poorer sleep and high prevalence of OSA. Poor sleep quality, shorter sleep duration, and OSA were associated with suboptimal glycemic control in T1D patients.

Impact of Chronic Sleep Disturbance for People Living With T1 Diabetes

AIM

The aim was to explore personal experiences and to determine the impact of impaired sleep on well-being and diabetes-related activities/decision making among a cohort of people living with T1D.

METHOD

Adults with T1D over the age of 18 and parents/carers of children with T1D were invited to complete an online questionnaire about their quality and quantity of sleep. Questions included impact of sleep on diabetes-related decision making, effective calculation of bolus doses, important aspects of psychosocial functioning, and frequency of waking. Diasend download data were used to objectively determine frequency of nocturnal blood glucose testing in children.

RESULTS

A total of 258 parent/carer participants (n = 221 female, 85.6%) and 192 adults with T1D (n = 145, 75.5% female, age range 19 to 89 years) took part. In all, 239 parents/carers and 160 adults believed waking in the night has an impact on their usual daily functioning. Of these, 236 parents/carers and 151 (64%) adults reported the impact as negative. Chronic sleep interruption was associated with detrimental impact on mood, work, family relationships, ability to exercise regularly, ability to eat healthily, and happiness.

CONCLUSION

Chronic sleep interruption is highly prevalent in adults with T1D and parents/carers of children with T1D with negative effects on daily functioning and well-being. Appropriate interventions are required to alleviate this burden of T1D, address modifiable risk factors for nocturnal hypoglycemia, and reduce the (perceived) need for nocturnal waking.

Type 1 Diabetes and Sleep

IN BRIEF In people with type 1 diabetes, sleep may be disrupted as a result of both behavioral and physiological aspects of diabetes and its management. This sleep disruption may negatively affect disease progression and development of complications. This review highlights key research findings regarding sleep in people with type 1 diabetes.

Sleep Optimization and Diabetes Control: A Review of the Literature

Pre-diabetes and diabetes occur secondary to a constellation of pathophysiological abnormalities that culminate in insulin resistance, which results in reduced cellular glucose uptake and increased glucose production. Although pre-diabetes and diabetes have a strong genetic basis, they are largely environmentally driven through lifestyle factors. Traditional lifestyle factors such as diet and physical activity do not fully explain the dramatic rise in the incidence and prevalence of diabetes mellitus. Sleep has emerged as an additional lifestyle behavior, important for metabolic health and energy homeostasis. In this article, we review the current evidence surrounding the sleep-diabetes association.

Combining Fourier and lagged k-nearest neighbor imputation for biomedical time series data

Most clinical and biomedical data contain missing values. A patient's record may be split across multiple institutions, devices may fail, and sensors may not be worn at all times. While these missing values are often ignored, this can lead to bias and error when the data are mined. Further, the data are not simply missing at random. Instead the measurement of a variable such as blood glucose may depend on its prior values as well as that of other variables. These dependencies exist across time as well, but current methods have yet to incorporate these temporal relationships as well as multiple types of missingness. To address this, we propose an imputation method (FLk-NN) that incorporates time lagged correlations both within and across variables by combining two imputation methods, based on an extension to k-NN and the Fourier transform. This enables imputation of missing values even when all data at a time point is missing and when there are different types of missingness both within and across variables. In comparison to other approaches on three biological datasets (simulated and actual Type 1 diabetes datasets, and multi-modality neurological ICU monitoring) the proposed method has the highest imputation accuracy. This was true for up to half the data being missing and when consecutive missing values are a significant fraction of the overall time series length.

The impact of sleep disorders on glucose metabolism: endocrine and molecular mechanisms

Modern lifestyle has profoundly modified human sleep habits. Sleep duration has shortened over recent decades from 8 to 6.5 hours resulting in chronic sleep deprivation. Additionally, irregular sleep, shift work and travelling across time zones lead to disruption of circadian rhythms and asynchrony between the master hypothalamic clock and pacemakers in peripheral tissues. Furthermore, obstructive sleep apnea syndrome (OSA), which affects 4 - 15% of the population, is not only characterized by impaired sleep architecture but also by repetitive hemoglobin desaturations during sleep. Epidemiological studies have identified impaired sleep as an independent risk factor for all cause of-, as well as for cardiovascular, mortality/morbidity. More recently, sleep abnormalities were causally linked to impairments in glucose homeostasis, metabolic syndrome and Type 2 Diabetes Mellitus (T2DM). This review summarized current knowledge on the metabolic alterations associated with the most prevalent sleep disturbances, i.e. short sleep duration, shift work and OSA. We have focused on various endocrine and molecular mechanisms underlying the associations between inadequate sleep quality, quantity and timing with impaired glucose tolerance, insulin resistance and pancreatic β-cell dysfunction. Of these mechanisms, the role of the hypothalamic-pituitary-adrenal axis, circadian pacemakers in peripheral tissues, adipose tissue metabolism, sympathetic nervous system activation, oxidative stress and whole-body inflammation are discussed. Additionally, the impact of intermittent hypoxia and sleep fragmentation (key components of OSA) on intracellular signaling and metabolism in muscle, liver, fat and pancreas are also examined. In summary, this review provides endocrine and molecular explanations for the associations between common sleep disturbances and the pathogenesis of T2DM.

The impact of sleep disorders on glucose metabolism: endocrine and molecular mechanisms

Modern lifestyle has profoundly modified human sleep habits. Sleep duration has shortened over recent decades from 8 to 6.5 hours resulting in chronic sleep deprivation. Additionally, irregular sleep, shift work and travelling across time zones lead to disruption of circadian rhythms and asynchrony between the master hypothalamic clock and pacemakers in peripheral tissues. Furthermore, obstructive sleep apnea syndrome (OSA), which affects 4 - 15% of the population, is not only characterized by impaired sleep architecture but also by repetitive hemoglobin desaturations during sleep. Epidemiological studies have identified impaired sleep as an independent risk factor for all cause of-, as well as for cardiovascular, mortality/morbidity. More recently, sleep abnormalities were causally linked to impairments in glucose homeostasis, metabolic syndrome and Type 2 Diabetes Mellitus (T2DM). This review summarized current knowledge on the metabolic alterations associated with the most prevalent sleep disturbances, i.e. short sleep duration, shift work and OSA. We have focused on various endocrine and molecular mechanisms underlying the associations between inadequate sleep quality, quantity and timing with impaired glucose tolerance, insulin resistance and pancreatic β-cell dysfunction. Of these mechanisms, the role of the hypothalamic-pituitary-adrenal axis, circadian pacemakers in peripheral tissues, adipose tissue metabolism, sympathetic nervous system activation, oxidative stress and whole-body inflammation are discussed. Additionally, the impact of intermittent hypoxia and sleep fragmentation (key components of OSA) on intracellular signaling and metabolism in muscle, liver, fat and pancreas are also examined. In summary, this review provides endocrine and molecular explanations for the associations between common sleep disturbances and the pathogenesis of T2DM.

Metabolic and glycemic sequelae of sleep disturbances in children and adults

The prevalence of obesity in adults and children has increased greatly in the past three decades, as have metabolic sequelae, such as insulin resistance and type 2 diabetes mellitus (T2DM). Sleep disturbances are increasingly recognized as contributors to this widespread epidemic in adults, and data are emerging in children as well. The categories of sleep disturbances that contribute to obesity and its glycemic co-morbidities include the following: (1) alterations of sleep duration, chronic sleep restriction and excessive sleep; (2) alterations in sleep architecture; (3) sleep fragmentation; (4) circadian rhythm disorders and disruption (i.e., shift work); and (5) obstructive sleep apnea. This article reviews current evidence supporting the contributions that these sleep disorders play in the development of obesity, insulin resistance, and T2DM as well as possibly influences on glycemic control in type 1 diabetes, with a special focus on data in pediatric populations.