Sleep and the response to hypoglycaemia

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.

Association between Low Energy Availability (LEA) and Impaired Sleep Quality in Young Rugby Players

Low energy availability (LEA) has been associated with several physiological consequences, but its impact on sleep has not been sufficiently investigated, especially in the context of young athletes. This study examined the potential association between energy availability (EA) status and objective sleep quality in 42 male rugby players (mean age: 16.2 ± 0.8 years) during a 7-day follow-up with fixed sleep schedules in the midst of an intensive training phase. Participants' energy intake was weighed and recorded. Exercise expenditure was estimated using accelerometry. Portable polysomnography devices captured sleep on the last night of the follow-up. Mean EA was 29.3 ± 9.14 kcal·kg FFM-1·day-1, with 47.6% of athletes presenting LEA, 35.7% Reduced Energy Availability (REA), and 16.7% Optimal Energy Availability (OEA). Lower sleep efficiency (SE) and N3 stage proportion, along with higher wake after sleep onset (WASO), were found in participants with LEA compared to those with OEA (p = 0.04, p = 0.03 and p = 0.005, respectively, with large effect sizes). Segmented regression models of the EA-sleep outcomes (SE, sleep onset latency [SOL]), WASO and N3) relationships displayed two separate linear regions and produced a best fit with a breakpoint between 21-33 kcal·kg FFM-1·day-1. Below these thresholds, sleep quality declines considerably. It is imperative for athletic administrators, nutritionists, and coaches to conscientiously consider the potential impact of LEA on young athletes' sleep, especially during periods of heavy training.

Continuous Glucose Monitoring in Endurance Athletes: Interpretation and Relevance of Measurements for Improving Performance and Health

Blood glucose regulation has been studied for well over a century as it is intimately related to metabolic health. Research in glucose transport and uptake has also been substantial within the field of exercise physiology as glucose delivery to the working muscles affects exercise capacity and athletic achievements. However, although exceptions exist, less focus has been on blood glucose as a parameter to optimize training and competition outcomes in athletes with normal glucose control. During the last years, measuring glucose has gained popularity within the sports community and successful endurance athletes have been seen with skin-mounted sensors for continuous glucose monitoring (CGM). The technique offers real-time recording of glucose concentrations in the interstitium, which is assumed to be equivalent to concentrations in the blood. Although continuous measurements of a parameter that is intimately connected to metabolism and health can seem appealing, there is no current consensus on how to interpret measurements within this context. Well-defined approaches to use glucose monitoring to improve endurance athletes' performance and health are lacking. In several studies, blood glucose regulation in endurance athletes has been shown to differ from that in healthy controls. Furthermore, endurance athletes regularly perform demanding training sessions and can be exposed to high or low energy and/or carbohydrate availability, which can affect blood glucose levels and regulation. In this current opinion, we aim to discuss blood glucose regulation in endurance athletes and highlight the existing research on glucose monitoring for performance and health in this population.

Still Excited, but Less Aroused-The Effects of Nutritional Ketosis on Epinephrine Response and Hypothalamic Orexin Neuron Activation Following Recurrent Hypoglycemia in Diabetic Rats

Hypoglycemia-associated autonomic failure (HAAF) is a serious, life-threatening complication of intensive insulin therapy, particularly in people with type 1 diabetes. The ketogenic diet is reported to beneficially affect glycemic control in people with type 1 diabetes, however its effects on the neurohormonal counterregulatory response to recurrent hypoglycemia and HAAF development are understudied. In this study we used Sprague Dawley rats to establish a HAAF model under non-diabetic and streptozotocin (STZ)-induced diabetic conditions and determined how nutritional ketosis affected the neurohormonal counterregulation and the activity of energy-sensing orexin (OX) neurons. We found that antecedent hypoglycemia diminished the sympathoexcitatory epinephrine response to subsequent hypoglycemia in chow-fed non-diabetic rats, but this did not occur in STZ-diabetic animals. In all cases a ketogenic diet preserved the epinephrine response. Contrary to expectations, STZ-diabetic keto-fed rats showed reduced OX activity in the recurrent hypoglycemia group, which did not occur in any other group. It is possible that the reduced activation of OX neurons is an adaptation aimed at energy conservation accompanied by diminished arousal and exploratory behaviour. Our data suggests that while a ketogenic diet has beneficial effects on glycemia, and epinephrine response, the reduced activation of OX neurons could be detrimental and warrants further investigation.

Scientific advisory on nocturnal hypoglycemia in insulin-treated patients with diabetes: Recommendations from Indian experts

INTRODUCTION

Insulin is one of the commonly prescribed glucose lowering agents in diabetes. Hypoglycemia is the most common complication, and severe hypoglycemia is the most serious complication of insulin therapy. Almost half of all severe hypoglycemia episodes (HEs) occur at night. However, patients are often unaware of their nocturnal hypoglycaemia (NH) risk. Additionally, both healthcare professionals and patients find it difficult to manage NH. The purpose of this expert group meeting is to improve NH awareness and provide guidance for the physicians to recognize and manage NH.

METHOD

The panel of experts in an e-board deliberated extensively upon the available literature and guidelines on hypoglycemia and NH discussed the consensus on definition, detection, reporting, monitoring, treatment, and optimization of therapy in NH.

RESULT

& Conclusion: Though there are many guidelines on the management of HEs in patients with diabetes, very few touch the topic of NH. This scientific advisory on management of NH in insulin treated patients with diabetes is formulated to address this gap in understanding regarding management of NH. The experts provide recommendations for the nocturnal window, defining NH based on blood glucose values, recognition, prevention and management of NH.

Basal Insulin Reduces Glucose Variability and Hypoglycaemia Compared to Premixed Insulin in Type 2 Diabetes Patients: A Study Based on Continuous Glucose Monitoring Systems

Aims

To examine the glycaemic variability and safety of basal and premixed insulin by using continuous glucose monitoring (CGM) systems.

Methods

393 patients with type 2 diabetes mellitus (T2DM) treated with basal or premixed insulin for more than 3 months were enrolled. Patients were classified into a basal insulin group or premixed insulin group according to their insulin regimens. CGMs were used for 72 h with their previous hypoglycaemic regimen unchanged. The following glycaemic parameters were calculated for each 24 h using CGM data.

Results

Despite similar HbA1c and fasting C-peptide concentrations, glycaemic variability (GV), including the mean amplitude of glycaemic excursion (MAGE), standard deviation (SD) and coefficient of variation (CV), and the time below range (TBR) were significantly lower in the basal insulin group than these in the premixed insulin group. Night-time hypoglycaemia was lower in the basal insulin group than that in the premixed insulin group (p<0.01). Among participants with haemoglobin A1c (HbA1c) < 7%, the GV and TBR were higher in the premixed insulin group than that in the basal insulin group.

Conclusion

Compared with basal insulin, the patients who use premixed insulin had higher GV, smaller TIR and an increased incidence of hypoglycaemia. For patients who use premixed insulin and with HbA1c < 7%, more attention needs to be given to hypoglycaemic events and asymptomatic hypoglycaemia.

Clinical Trial Registration

ClinicalTrials.gov, identifier NCT03566472.

Persistent Postgastrectomy Hypoglycemia Unawareness in Patients With Gastric Cancer Unveiled by a Prospective Study

Objective

Late dumping syndrome is a common postgastrectomy complication characterized by reactive hypoglycemia. This study aimed to explore the glycemic trend in patients who underwent gastrectomy for gastric cancer and clarify its changes over time in association with postgastrectomy symptoms.

Summary Background Data

Changes over time in glycemic trend in association with postgastrectomy symptoms have not been evaluated.

Methods

We conducted a prospective study of 71 patients who underwent curative gastrectomy for gastric cancer between November 2017 and April 2020. The patients underwent continuous glucose monitoring twice-at 1- and 12-month postgastrectomy-and were assessed using the Post-Gastrectomy Syndrome Assessment Scale 37-item questionnaire (PGSAS-37) at 1-, 6-, and 12-month postgastrectomy.

Results

Our results revealed that hypoglycemia (<70 mg/dL), especially nocturnal hypoglycemia (00:00-06:00), frequently occurred even at 12-month postgastrectomy. Hypoglycemia improved in total gastrectomy patients but remained unchanged in distal gastrectomy patients, which was still high in both groups at 12-month postgastrectomy. Glycemic variability (SD of the glycemic trend) was exacerbated in both gastrectomy groups. However, the PGSAS-37 symptom scores remained unchanged, and the living status and quality of life tended to improve. Hypoglycemia unawareness, including postprandial hypoglycemia without symptoms and nocturnal hypoglycemia, was evident even 12-month postgastrectomy.

Conclusions

Persistent postgastrectomy hypoglycemia unawareness, including late dumping syndrome without symptoms and nocturnal hypoglycemia, should be recognized as an important issue in postgastrectomy syndrome. Therefore, meticulous long-term evaluation for glycemic trends and care of patients is required.

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).

Clustering of Hypoglycemia Events in Patients With Hyperinsulinism: Extension of the Digital Phenotype Through Retrospective Data Analysis

Background

Hyperinsulinism (HI) due to excess and dysregulated insulin secretion is the most common cause of severe and recurrent hypoglycemia in childhood. High cerebral glucose use in the early hours results in a high risk of hypoglycemia in people with diabetes and carries a significant risk of brain injury. Prevention of hypoglycemia is the cornerstone of the management of HI, but the risk of hypoglycemia at night or the timing of hypoglycemia in children with HI has not been studied; thus, the digital phenotype remains incomplete and management suboptimal.

Objective

This study aims to quantify the timing of hypoglycemia in patients with HI to describe glycemic variability and to extend the digital phenotype. This will facilitate future work using computational modeling to enable behavior change and reduce exposure of patients with HI to injurious hypoglycemic events.

Methods

Patients underwent continuous glucose monitoring (CGM) with a Dexcom G4 or G6 CGM device as part of their clinical assessment for either HI (N=23) or idiopathic ketotic hypoglycemia (IKH; N=24). The CGM data were analyzed for temporal trends. Hypoglycemia was defined as glucose levels <3.5 mmol/L.

Results

A total of 449 hypoglycemic events totaling 15,610 minutes were captured over 237 days from 47 patients (29 males; mean age 70 months, SD 53). The mean length of hypoglycemic events was 35 minutes. There was a clear tendency for hypoglycemia in the early hours (3-7 AM), particularly for patients with HI older than 10 months who experienced hypoglycemia 7.6% (1480/19,370 minutes) of time in this period compared with 2.6% (2405/92,840 minutes) of time outside this period (P<.001). This tendency was less pronounced in patients with HI who were younger than 10 months, patients with a negative genetic test result, and patients with IKH. Despite real-time CGM, there were 42 hypoglycemic events from 13 separate patients with HI lasting >30 minutes.

Conclusions

This is the first study to have taken the first step in extending the digital phenotype of HI by describing the glycemic trends and identifying the timing of hypoglycemia measured by CGM. We have identified the early hours as a time of high hypoglycemia risk for patients with HI and demonstrated that simple provision of CGM data to patients is not sufficient to eliminate hypoglycemia. Future work in HI should concentrate on the early hours as a period of high risk for hypoglycemia and must target personalized hypoglycemia predictions. Focus must move to the human-computer interaction as an aspect of the digital phenotype that is susceptible to change rather than simple mathematical modeling to produce small improvements in hypoglycemia prediction accuracy.

Hypoglycaemia and Cardiovascular Disease Risk in Patients with Diabetes

Hypoglycaemia represents an important side effect of insulin therapy and insulin secretagogues. It can occur in both type 1 and type 2 diabetes mellitus patients. Also, some associations between hypoglycaemia and cardiovascular (CV) risk have been reported. Several mechanisms may be involved, including the sympathoadrenal system, hypokalaemia, endothelial dysfunction, coagulation, platelets, inflammation, atherothrombosis and impaired autonomic cardiac reflexes. This narrative review discusses the associations of hypoglycaemia with CV diseases, including coronary heart disease (CHD), cardiac arrhythmias, stroke, carotid disease and peripheral artery disease (PAD), as well as with dementia. Severe hypoglycaemia has been related to CHD, CV and all-cause mortality. Furthermore, there is evidence supporting an association between hypoglycaemia and cardiac arrhythmias, potentially predisposing to sudden death. The data linking hypoglycaemia with stroke, carotid disease and PAD is limited. Several factors may affect the hypoglycaemia-CV relationships, such as the definition of hypoglycaemia, patient characteristics, co-morbidities (including chronic kidney disease) and antidiabetic drug therapy. However, the association between hypoglycaemia and dementia is bilateral. Both the disorders are more common in the elderly; thus, glycaemic goals should be carefully selected in older patients. Further research is needed to elucidate the impact of hypoglycaemia on CV disease.

Sleep and type 1 diabetes in children and adolescents: Proposed theoretical model and clinical implications

Youth with type 1 diabetes mellitus (T1D) experience more sleep disturbances and shorter sleep durations compared to their healthy peers. Researchers have now uncovered the negative mental health and physical health outcomes associated with poor sleep in youth with T1D. The field of T1D sleep research currently operates under the broad notion that sleep behaviors impact treatment adherence, which ultimately lead to worse long-term health outcomes. This model however does not explain how behavior influences T1D management and sleep outcomes on a day-to-day basis, leading to difficulties in providing tailored treatment recommendations. In this review, we present a theoretical framework that describes the recursive cycle between sleep behaviors, T1D outcomes, and symptoms of negative affect/stress over a 24-hour period. This model is guided by the sleep literature, showing a clear relationship between poor sleep and negative affect, and the T1D literature demonstrating a link between poor sleep and disease management for youth with T1D. Further, emerging literature indicates a need for additional parent sleep assessment considering that T1D management and fear of hypoglycemia negatively impact parent sleep behaviors. Recommendations are provided to move the field toward effective intervention studies and new areas of research to evaluate and modify the proposed model.

Hypoglycaemia Remains the Key Obstacle to Optimal Glycaemic Control - Continuous Glucose Monitoring is the Solution

We queried PubMed and other internet databases to identify studies, meta-analyses, review articles and other data sources regarding hypoglycaemia incidence/costs/impacts and continuous glucose monitoring (CGM) use. Our analysis of the evidence showed that hypoglycaemia remains a significant health concern and a primary obstacle to optimal adherence to prescribed diabetes treatment. In addition to its adverse clinical consequences, hypoglycaemia negatively impacts quality of life and places additional financial burdens on patients, patient families, employers and healthcare payers. Clinical trials have shown that the use of CGM can reduce the incidence and duration of hypoglycaemic episodes. This article reviews relevant CGM studies, discusses the prevalence and clinical/financial implications of hypoglycaemia, and explores the strengths and limitations of current CGM systems in minimising the burden of hypoglycaemia.

Sleep in Teens With Type 1 Diabetes: Perspectives From Adolescents and Their Caregivers

PURPOSE

The purpose of this study is to identify barriers, facilitators, and consequences of obtaining sufficient sleep in adolescents with type 1 diabetes.

METHODS

Semistructured interviews were conducted with 25 adolescents (52% female, mean age = 15.6 years) and 25 caregivers. Interviews were transcribed and coded using Atlas.ti. A thematic analytic approach was used to identify and organize significant patterns of meaning (themes) and interpret themes across the data.

RESULTS

Several barriers were identified, with the most common being the use of electronics before bed and sleep disturbances related to diabetes management. Caregivers described strategies for helping adolescents achieve sufficient sleep, such as enforcing bedtimes and limiting distractions, but many adolescents could not identify facilitators of sleep. Weekday/weekend discrepancies in sleep timing were commonly disclosed.

CONCLUSIONS

This study is the first to examine the perceptions of barriers and facilitators to obtaining sufficient sleep in adolescents with T1D and their caregivers. Results have the potential to inform providers' recommendations regarding sleep, including possible interventions to promote sleep in this high-risk population.

Effect of Insulin Analogs on Frequency of Non-Severe Hypoglycemia in Patients with Type 1 Diabetes Prone to Severe Hypoglycemia: Much Higher Rates Detected by Continuous Glucose Monitoring than by Self-Monitoring of Blood Glucose-The HypoAna Trial

BACKGROUND

Hypoglycemia is an increasingly important endpoint in clinical diabetes trials. The assessment of hypoglycemia should therefore be as complete as possible. Blinded continuous glucose monitoring (CGM) provides an improved opportunity to capture asymptomatic and nocturnal events. Here we report results from the HypoAna trial comparing all-analog-insulin therapy (aspart/detemir) with all-human-insulin therapy (neutral protamine Hagedorn/regular) on non-severe hypoglycemia (symptomatic and asymptomatic hypoglycemia) as assessed by blinded CGM and compared with data obtained by self-monitoring of blood glucose (SMBG) in patients with type 1 diabetes and recurrent severe hypoglycemia.

METHODS

Fifty-three patients completed a substudy of 4 × 3 days of blinded CGM. CGM traces were reviewed for hypoglycemic events lasting 15 min or longer.

RESULTS

At the threshold ≤3.9 mmol/L, the per-protocol analysis demonstrated a 40% rate reduction (95% confidence interval [CI] 20%-60%; P = 0.002) in nocturnal non-severe hypoglycemia during analog treatment, mainly due to a 40% rate reduction (95% CI 0%-70%; P = 0.03) of nocturnal asymptomatic hypoglycemia. Similar nonsignificant trends were seen at the glucose threshold ≤3.0 mmol/L. Overall CGM-detected that nocturnal asymptomatic hypoglycemia ≤3.9 mmol/L was ∼17 times more frequent than SMBG-detected episodes (52 vs. 3 events/patient-year). This translates into a time needed to treat one patient with insulin analogs to prevent one episode that is 34 times shorter using CGM data than SMBG data (1.4 vs. 47 weeks).

CONCLUSIONS

Capturing hypoglycemic events by the conventional method of SMBG in patients with impaired awareness reveals only a limited number of events. Blinded CGM can provide more complete data, particularly in terms of asymptomatic and nocturnal events.

Comparing effects of insulin analogues and human insulin on nocturnal glycaemia in hypoglycaemia-prone people with Type 1 diabetes

AIMS

To assess the difference between analogue and human insulin with regard to nocturnal glucose profiles and risk of hypoglycaemia in people with recurrent severe hypoglycaemia.

METHODS

A total of 72 people [46 men, mean ± sd age 54 ± 12 years, mean ± sd HbA_1c 65 ± 12 mmol/mol (8.1 ± 1.1%), mean ± sd duration of diabetes 30 ± 14 years], who participated in a 2-year randomized, crossover trial of basal-bolus therapy with insulin detemir/insulin aspart or human NPH insulin/human regular insulin (the HypoAna trial) were studied for 2 nights during each treatment. Venous blood was drawn hourly during sleep. Primary endpoints were nocturnal glucose profiles and occurrence of hypoglycaemia (blood glucose ≤ 3.9 mmol/l).

RESULTS

During insulin analogue treatment, the mean nocturnal plasma glucose level was significantly higher than during treatment with human insulin (10.6 vs 8.1 mmol/l). The fasting plasma glucose level was similar between the treatments. Nocturnal hypoglycaemia was registered during 41/101 nights (41%) in the human insulin arm and 19/117 nights (16%) in the insulin analogue arm, corresponding to a hazard ratio of 0.26 (95% CI 0.14 to 0.45; P < 0.0001) with insulin analogue.

CONCLUSIONS

Treatment with insulin analogue reduces the occurrence of nocturnal hypoglycaemia assessed by nocturnal glucose profiles in people with Type 1 diabetes prone to severe hypoglycaemia. Nocturnal glucose profiles provide a more comprehensive assessment of clinical benefit of insulin regimens as compared to conventional recording of hypoglycaemia.

Are Changes in Heart Rate Variability During Hypoglycemia Confounded by the Presence of Cardiovascular Autonomic Neuropathy in Patients with Diabetes?

BACKGROUND

We have recently shown how the combination of information from continuous glucose monitor (CGM) and heart rate variability (HRV) measurements can be used to construct an algorithm for prediction of hypoglycemia in both bedbound and active patients with type 1 diabetes (T1D). Questions remain on how cardiovascular autonomic neuropathy (CAN) influences these measurable changes. This study aims to examine if changes in HRV during hypoglycemia are confounded by the presence of CAN.

METHODS

Twenty-one adults (hereof 13 men) with T1D prone to hypoglycemia were recruited and monitored with CGM and a Holter device while they performed normal daily activities. CAN was diagnosed using two cardiac reflex tests: (1) deep breathing and (2) orthostatic hypotension and end organ symptoms. Early CAN was defined as the presence of one abnormal reflex test and severe CAN was defined as two abnormal tests or one abnormal test combined with symptoms of autonomic neuropathy.

RESULTS

A total of 19 hypoglycemic and corresponding euglycemia periods were identified among the 21 patients. Eleven patients were diagnosed with CAN, hereof five with severe CAN. The HRV parameter low frequency (LF) was significantly (P = 0.029) reduced during hypoglycemia. Changes in LF during hypoglycemia were not different between patients with and without CAN, -0.64 versus -0.48 log(LF) (P = 0.74).

CONCLUSIONS

Our data show that there are measurable changes in HRV during hypoglycemia, and present in both patients with and without CAN.

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.

Managing hypoglycaemia

Intensive glycaemic control reduces the diabetic microvascular disease burden but iatrogenic hypoglycaemia is a major barrier preventing tight glycaemic control because of the limitations of subcutaneous insulin preparations and insulin secretagogues. Severe hypoglycaemia is uncommon early in the disease as robust physiological defences, particularly glucagon and adrenaline release, limit falls in blood glucose whilst associated autonomic symptoms drive patients to take action by ingesting oral carbohydrate. With increasing diabetes duration, glucagon release is progressively impaired and sympatho-adrenal responses are activated at lower glucose levels. Repeated hypoglycaemic episodes contribute to impaired defences, increasing the risk of severe hypoglycaemia in a vicious downward spiral. Managing hypoglycaemia requires a systematic clinical approach with structured insulin self-management training and support of experienced diabetes educators. Judicious use of technologies includes insulin analogues, insulin pump therapy, continuous glucose monitoring, and in a few cases islet cell transplantation. Some individuals require specialist psychological support.

Incidence, characteristics and impact of hypoglycaemia in patients receiving intensified treatment for inadequately controlled type 2 diabetes mellitus

AIMS

With the intensification of antidiabetic treatment, there is an increasing risk of hypoglycaemia. We aimed to determine incidence, characteristics and outcomes.

METHODS

Prospective, observational, multicenter cohort study. The included 3810 patients with type 2 diabetes had their treatment intensified at baseline.

RESULTS

The incidence of hypoglycaemia was 11.4% per year with 4.2 ± 4.4 episodes per patient. Hypoglycaemia was more frequent in patients with high blood glucose variability. Predictors were heart failure (odds ratio: 1.66; 95% confidence interval: 1.20-2.29) and insulin use (odds ratio: 4.03; 95% confidence interval: 3.05-5.33), with dipeptidyl peptidase-4 inhibitors being associated with reduced risk (odds ratio: 0.69; 95% confidence interval: 0.53-0.89). Macrovascular events were more frequent among patients reporting severe episodes of hypoglycaemia (odds ratio: 3.39; 95% confidence interval: 1.32-8.73). Microvascular events were more frequent in patients with non-severe episodes (odds ratio: 1.92; 95% confidence interval: 1.49-2.49).

CONCLUSION

Case-by-case evaluation of patients as well as appropriate selection of antidiabetic pharmacotherapy and blood glucose treatment goals could maximize the benefits while reducing the risks of antidiabetic treatment.

Subjective sleep impairment in adults with type 1 or type 2 diabetes: Results from Diabetes MILES--The Netherlands

AIMS

Despite growing recognition of the impact of sleep on diabetes, a clear profile of people with diabetes regarding subjective sleep impairment has yet to be established. This study examines: (1) subjective sleep characteristics in adults with type 1 and type 2 diabetes; (2) the relationship of poor subjective sleep quality with glycaemic control, self-care and daytime functioning; (3) possible risk markers for poor sleep quality.

METHODS

In a cross-sectional study, Dutch adults with type 1 (n=267) or type 2 diabetes (n=361) completed an online survey, including the Pittsburgh Sleep Quality Index (PSQI), socio-demographic, clinical, self-care and psychological measures.

RESULTS

Poor sleep quality (PSQI-score >5) was reported by 31% of adults with type 1 and 42% of adults with type 2 diabetes. Participants with good and poor sleep quality did not differ in self-reported HbA1c or the frequency of meeting lifestyle recommendations. Poor sleep quality was related to a higher self-care burden and higher levels of daytime sleepiness, fatigue, depressive and anxiety symptoms, and diabetes-specific distress. In multivariable logistic regression analyses examining risk markers, poor sleep quality was associated with depressive symptoms in adults with type 1 (OR=1.39, 95% CI 1.25-1.54) and type 2 diabetes (OR=1.31, 1.16-1.47), and with being female in those with type 2 diabetes (OR=2.72, 1.42-5.20).

CONCLUSIONS

Poor subjective sleep quality is prevalent both in adults with type 1 and type 2 diabetes, and is related to poor daytime functioning and higher self-care burden. The temporal relation with depression and merits of therapy should be explored.

Remote glucose monitoring in camp setting reduces the risk of prolonged nocturnal hypoglycemia

OBJECTIVE

This study tested the feasibility and effectiveness of remote continuous glucose monitoring (CGM) in a diabetes camp setting.

SUBJECTS AND METHODS

Twenty campers (7-21 years old) with type 1 diabetes were enrolled at each of three camp sessions lasting 5-6 days. On alternating nights, 10 campers were randomized to usual wear of a Dexcom (San Diego, CA) G4™ PLATINUM CGM system, and 10 were randomized to remote monitoring with the Dexcom G4 PLATINUM communicating with the Diabetes Assistant, a cell phone platform, to allow wireless transmission of CGM values. Up to 15 individual graphs and sensor values could be displayed on a single remote monitor or portable tablet. An alarm was triggered for values <70 mg/dL, and treatment was given for meter-confirmed hypoglycemia. The primary end point was to decrease the duration of hypoglycemic episodes <50 mg/dL.

RESULTS

There were 320 nights of CGM data and 197 hypoglycemic events. Of the remote monitoring alarms, 79% were true (meter reading of <70 mg/dL). With remote monitoring, 100% of alarms were responded to, whereas without remote monitoring only 54% of alarms were responded to. The median duration of hypoglycemic events <70 mg/dL was 35 min without remote monitoring and 30 min with remote monitoring (P=0.078). Remote monitoring significantly decreased prolonged hypoglycemic events, eliminating all events <50 mg/dL lasting longer than 30 min as well as all events <70 mg/dL lasting more than 2 h.

CONCLUSIONS

Remote monitoring is feasible at diabetes camps and effective in reducing the risk of prolonged nocturnal hypoglycemia. This technology will facilitate forthcoming studies to evaluate the efficacy of automated closed-loop systems in the camp setting.

Association between excessive daytime sleepiness and severe hypoglycemia in people with type 2 diabetes: the Edinburgh Type 2 Diabetes Study

OBJECTIVE

Sleep-disordered breathing and sleepiness cause metabolic, cognitive, and behavioral disturbance. Sleep-disordered breathing is common in type 2 diabetes, a condition that requires adherence to complex dietary, behavioral, and drug treatment regimens. Hypoglycemia is an important side effect of treatment, causing physical and psychological harm and limiting ability to achieve optimal glycemic control. We hypothesized that sleep disorder might increase the risk of hypoglycemia through effects on self-management and glucose regulation.

RESEARCH DESIGN AND METHODS

People with type 2 diabetes (n = 898) completed questionnaires to assess sleep-disordered breathing, daytime sleepiness, and occurrence of severe hypoglycemia.

RESULTS

Subjects who scored highly on the Epworth Sleepiness Scale were significantly more likely to have suffered from severe hypoglycemia. This was a significant predictor of severe hypoglycemia in regression analysis including the variables age, sex, duration of diabetes, HbA1c, BMI, and treatment type.

CONCLUSIONS

Daytime sleepiness may be a novel risk factor for hypoglycemia.

Cardiac implications of hypoglycaemia in patients with diabetes - a systematic review

BACKGROUND

Hypoglycaemia has been associated with increased cardiovascular (CV) risk and mortality in a number of recent multicentre trials, but the mechanistic links driving this association remain ill defined. This review aims to summarize the available data on how hypoglycaemia may affect CV risk in patients with diabetes.

METHODS

This was a systematic review of available mechanistic and clinical studies on the relationship between hypoglycaemia and cardiovascular risk. Study outcomes were compiled from relevant articles, and factors contributing to hypoglycaemia-mediated CVD and its complications are discussed.

RESULTS

Six recent comprehensive clinical trials have reinforced the critical importance of understanding the link between hypoglycaemia and the CV system. In addition, 88 studies have indicated that hypoglycaemia mechanistically contributes to CV risk by increasing thrombotic tendency, causing abnormal cardiac repolarization, inducing inflammation, and contributing to the development of atherosclerosis. These hypoglycaemia-associated risk factors are conducive to events such as unstable angina, non-fatal and fatal myocardial infarction, sudden death, and stroke in patients with diabetes.

CONCLUSIONS

Emerging data suggest that there is an impact of hypoglycaemia on CV function and mechanistic link is multifactorial. Further research will be needed to ascertain the full impact of hypoglycaemia on the CV system and its complications.

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

BACKGROUND

Sleep plays an important role in health, and poor sleep is associated with negative impacts on diabetes management, but few studies have objectively evaluated sleep in adults with type 1 diabetes mellitus (T1DM). Nocturnal glycemia and sleep characteristics in T1DM were evaluated using body-worn sensors in real-world conditions.

METHODS

Analyses were performed on data collected by the Diabetes Management Integrated Technology Research Initiative pilot study of 17 T1DM subjects: 10 male, 7 female; age 19-61 years; T1DM duration 14.9 ± 11.0 years; hemoglobin A1c (HbA1c) 7.3% ± 1.3% (mean ± standard deviation). Each subject was equipped with a continuous glucose monitor and a wireless sleep monitor (WSM) for four nights. Sleep stages [rapid eye movement (REM), light, and deep sleep] were continuously recorded by the WSM. Nocturnal glycemia (mg/dl) was evaluated as hypoglycemia (<50 mg/dl), low (50-69 mg/dl), euglycemia (70-120 mg/dl), high (121-250 mg/dl), and hyperglycemia (>250 mg/dl) and by several indices of glycemic variability. Glycemia was analyzed within each sleep stage.

RESULTS

Subjects slept 358 ± 48 min per night, with 85 ± 27 min in REM sleep, 207 ± 42 min in light sleep, and 66 ± 30 min in deep sleep (mean ± standard deviation). Increased time in deep sleep was associated with lower HbA1c (R2 = 0.42; F = 9.37; p < .01). Nocturnal glycemia varied widely between and within subjects. Glycemia during REM sleep was hypoglycemia 5.5% ± 18.1%, low 6.6% ± 18.5%, euglycemia 44.6% ± 39.5%, high 37.9% ± 39.7%, and hyperglycemia 5.5% ± 21.2%; glycemia during light sleep was hypoglycemia 4.8% ± 12.4%, low 7.3% ± 12.9%, euglycemia 42.1% ± 33.7%, high 39.2% ± 34.6%, and hyperglycemia 6.5% ± 20.5%; and glycemia during deep sleep was hypoglycemia 0.5% ± 2.2%, low 5.8% ± 14.3%, euglycemia 48.0% ± 37.5%, high 39.5% ± 37.6%, and hyperglycemia 6.2% ± 19.5%. Significantly less time was spent in the hypoglycemic range during deep sleep compared with light sleep (p = .02).

CONCLUSIONS

Increased time in deep sleep was associated with lower HbA1c, and less hypoglycemia occurred in deep sleep in T1DM, though this must be further evaluated in larger subsequent studies. Furthermore, the consumer-grade WSM device was useful for objectively studying sleep in a real-world setting.

Hypoglycemia: The neglected complication

Hypoglycemia is an important complication of glucose-lowering therapy in patients with diabetes mellitus. Attempts made at intensive glycemic control invariably increases the risk of hypoglycemia. A six-fold increase in deaths due to diabetes has been attributed to patients experiencing severe hypoglycemia in comparison to those not experiencing severe hypoglycemia Repeated episodes of hypoglycemia can lead to impairment of the counter-regulatory system with the potential for development of hypoglycemia unawareness. The short- and long-term complications of diabetes related hypoglycemia include precipitation of acute cerebrovascular disease, myocardial infarction, neurocognitive dysfunction, retinal cell death and loss of vision in addition to health-related quality of life issues pertaining to sleep, driving, employment, recreational activities involving exercise and travel. There is an urgent need to examine the clinical spectrum and burden of hypoglycemia so that adequate control measures can be implemented against this neglected life-threatening complication. Early recognition of hypoglycemia risk factors, self-monitoring of blood glucose, selection of appropriate treatment regimens with minimal or no risk of hypoglycemia and appropriate educational programs for healthcare professionals and patients with diabetes are the major ways forward to maintain good glycemic control, minimize the risk of hypoglycemia and thereby prevent long-term complications.

Nocturnal continuous glucose monitoring: accuracy and reliability of hypoglycemia detection in patients with type 1 diabetes at high risk of severe hypoglycemia

BACKGROUND

A reliable method to detect biochemical nocturnal hypoglycemia is highly needed, especially in patients with recurrent severe hypoglycemia. We evaluated reliability of nocturnal continuous glucose monitoring (CGM) in patients with type 1 diabetes at high risk of severe hypoglycemia.

PATIENTS AND METHODS

Seventy-two type 1 diabetes patients with recurrent severe hypoglycemia (two or more events within the last year) participated for 4 nights in blinded CGM recordings (Guardian(®) REAL-Time CGMS and Sof-Sensor(®); Medtronic MiniMed, Northridge, CA). Blood was drawn hourly from 23:00 to 07:00 h for plasma glucose (PG) measurements (gold standard).

RESULTS

Valid data were obtained in 217 nights. The sensitivity of CGM was 65% (95% confidence interval, 53-77%) below 4 mmol/L, 40% (24-56%) below 3 mmol/L, and 17% (0-47%) below 2.2 mmol/L. PG and CGM readings correlated in the total measurement range (Spearman's ρ=0.82; P<0.001). In the normo- and hyperglycemic ranges CGM underestimated PG by 1.1 mmol/L (0.9-1.2 mmol/L) (P<0.001); in contrast, in the hypoglycemic range (PG<4 mmol/L) CGM overestimated PG levels by 1.0 mmol/L (P<0.001). The mean absolute relative differences in the hypo- (≤3.9 mmol/L), normo- (4-9.9 mmol/L), and hyperglycemic (≥10 mmol/L) ranges were 45% (37-53%), 23% (22-25%), and 20% (19-21%), respectively. Continuous glucose error grid analysis indicated a clinical accuracy of 56%, 99%, and 93% in the hypo-, normo-, and hyperglycemic ranges, respectively.

CONCLUSIONS

The accuracy in the hypoglycemic range of nocturnal CGM data using Sof-Sensor is suboptimal in type 1 diabetes patients at high risk of severe hypoglycemia. To ensure clinical useful sensitivity in detection of nocturnal hypoglycemic episodes, an alarm threshold should not be lower than 4 mmol/L.

Switching to biphasic insulin aspart 30/50/70 from biphasic human insulin 30/50 in patients with type 2 diabetes in normal clinical practice: observational study results

OBJECTIVE

To investigate the safety and efficacy of switching to biphasic insulin aspart (BIAsp) 30, 50 or 70 in patients with type 2 diabetes previously treated with biphasic human insulin (BHI) 30/50 (with or without oral glucose-lowering drugs) in routine clinical practice.

METHODS

This was a 26-week, prospective, observational study conducted in Belgium and Luxembourg. Data were collected at baseline before patients switched and at 12 and 26 weeks after starting BIAsp 30, 50 or 70. Safety endpoints were incidence and rate of hypoglycemia (major, minor, nocturnal), adverse events and body-weight changes. Efficacy assessments included HbA(1c) and 7-point self-measured plasma glucose (PG) profiles. Changes from baseline were analyzed using paired t-tests.

RESULTS

Of 592 patients analyzed, 72% switched to twice-daily BIAsp and 20% to three-times daily BIAsp. Upon switching, 27% of patients received intensified treatment (i.e., more daily doses than with their previous BHI). At all three data-collection points, approximately two-thirds of patients were taking BIAsp 30 and approximately one-third were taking BIAsp 50; very few patients took BIAsp 70. Mean total daily insulin dose increased significantly from baseline (51.2 U) to 26 weeks (54.3 U) and mean time of intake before meals changed from 17 minutes for BHI to ∼3 minutes with BIAsp. Incidence of hypoglycemia did not change during the study (baseline: 30.7%, week 26: 29.2%). HbA(1c) improved significantly from baseline (7.9 %) to weeks 12 and 26 (7.6% and 7.5%, respectively; p < 0.001). Mean PG profiles also showed significant improvements. As this is an observational study, some limitations should be considered such as the absence of a control group and a possible bias of increased medical attention.

CONCLUSIONS

Patients with long-standing type 2 diabetes can switch safely from BHI to BIAsp therapy, even if they receive intensified treatment, and they have no problems changing the timing of their insulin injections.