Prevalence of nocturnal hypoglycemia in pediatric type 1 diabetes: a pilot study using continuous glucose monitoring

Brain Regulation of Cardiac Function during Hypoglycemia

Hypoglycemia occurs frequently in people with type 1 and type 2 diabetes. Hypoglycemia activates the counter-regulatory response. Besides peripheral glucose sensors located in the pancreas, mouth, gastrointestinal tract, portal vein, and carotid body, many brain regions also contain glucose-sensing neurons that detect this fall in glucose. The autonomic nervous system innervates the heart, and during hypoglycemia, can cause many changes. Clinical and animal studies have revealed changes in electrocardiograms during hypoglycemia. Cardiac repolarization defects (QTc prolongation) occur during moderate levels of hypoglycemia. When hypoglycemia is severe, it can be fatal. Cardiac arrhythmias are thought to be the major mediator of sudden death due to severe hypoglycemia. Both the sympathetic and parasympathetic nervous systems of the brain have been implicated in regulating these arrhythmias. Besides cardiac arrhythmias, hypoglycemia can have profound changes in the heart and most of these changes are exacerbated in the setting of diabetes. A better understanding of how the brain regulates cardiac changes during hypoglycemia will allow for better therapeutic intervention to prevent cardiovascular death associated with hypoglycemia in people with diabetes. The aim of this paper is to provide a narrative review of what is known in the field regarding how the brain regulates the heart during hypoglycemia.

Data-based modeling for hypoglycemia prediction: Importance, trends, and implications for clinical practice

Background and objective

Hypoglycemia is a key barrier to achieving optimal glycemic control in people with diabetes, which has been proven to cause a set of deleterious outcomes, such as impaired cognition, increased cardiovascular disease, and mortality. Hypoglycemia prediction has come to play a role in diabetes management as big data analysis and machine learning (ML) approaches have become increasingly prevalent in recent years. As a result, a review is needed to summarize the existing prediction algorithms and models to guide better clinical practice in hypoglycemia prevention.

Materials and methods

PubMed, EMBASE, and the Cochrane Library were searched for relevant studies published between 1 January 2015 and 8 December 2022. Five hypoglycemia prediction aspects were covered: real-time hypoglycemia, mild and severe hypoglycemia, nocturnal hypoglycemia, inpatient hypoglycemia, and other hypoglycemia (postprandial, exercise-related).

Results

From the 5,042 records retrieved, we included 79 studies in our analysis. Two major categories of prediction models are identified by an overview of the chosen studies: simple or logistic regression models based on clinical data and data-based ML models (continuous glucose monitoring data is most commonly used). Models utilizing clinical data have identified a variety of risk factors that can lead to hypoglycemic events. Data-driven models based on various techniques such as neural networks, autoregressive, ensemble learning, supervised learning, and mathematical formulas have also revealed suggestive features in cases of hypoglycemia prediction.

Conclusion

In this study, we looked deep into the currently established hypoglycemia prediction models and identified hypoglycemia risk factors from various perspectives, which may provide readers with a better understanding of future trends in this topic.

A Pilot randomized trial to examine effects of a hybrid closed-loop insulin delivery system on neurodevelopmental and cognitive outcomes in adolescents with type 1 diabetes

Type 1 diabetes (T1D) is associated with lower scores on tests of cognitive and neuropsychological function and alterations in brain structure and function in children. This proof-of-concept pilot study (ClinicalTrials.gov Identifier NCT03428932) examined whether MRI-derived indices of brain development and function and standardized IQ scores in adolescents with T1D could be improved with better diabetes control using a hybrid closed-loop insulin delivery system. Eligibility criteria for participation in the study included age between 14 and 17 years and a diagnosis of T1D before 8 years of age. Randomization to either a hybrid closed-loop or standard diabetes care group was performed after pre-qualification, consent, enrollment, and collection of medical background information. Of 46 participants assessed for eligibility, 44 met criteria and were randomized. Two randomized participants failed to complete baseline assessments and were excluded from final analyses. Participant data were collected across five academic medical centers in the United States. Research staff scoring the cognitive assessments as well as those processing imaging data were blinded to group status though participants and their families were not. Forty-two adolescents, 21 per group, underwent cognitive assessment and multi-modal brain imaging before and after the six month study duration. HbA1c and sensor glucose downloads were obtained quarterly. Primary outcomes included metrics of gray matter (total and regional volumes, cortical surface area and thickness), white matter volume, and fractional anisotropy. Estimated power to detect the predicted treatment effect was 0.83 with two-tailed, α = 0.05. Adolescents in the hybrid closed-loop group showed significantly greater improvement in several primary outcomes indicative of neurotypical development during adolescence compared to the standard care group including cortical surface area, regional gray volumes, and fractional anisotropy. The two groups were not significantly different on total gray and white matter volumes or cortical thickness. The hybrid closed loop group also showed higher Perceptual Reasoning Index IQ scores and functional brain activity more indicative of neurotypical development relative to the standard care group (both secondary outcomes). No adverse effects associated with study participation were observed. These results suggest that alterations to the developing brain in T1D might be preventable or reversible with rigorous glucose control. Long term research in this area is needed.

Prevalence of nocturnal hypoglycemia in free-living conditions in adults with type 1 diabetes: What is the impact of daily physical activity?

OBJECTIVE

Studies investigating strategies to limit the risk of nocturnal hypoglycemia associated with physical activity (PA) are scarce and have been conducted in standardized, controlled conditions in people with type 1 diabetes (T1D). This study sought to investigate the effect of daily PA level on nocturnal glucose management in free-living conditions while taking into consideration reported mitigation strategies to limit the risk of nocturnal hyoglycemia in people with T1D.

METHODS

Data from 25 adults (10 males, 15 females, HbA_1c: 7.6 ± 0.8%), 20-60 years old, living with T1D, were collected. One week of continuous glucose monitoring and PA (assessed using an accelerometer) were collected in free-living conditions. Nocturnal glucose values (midnight-6:00 am) following an active day "ACT" and a less active day "L-ACT" were analyzed to assess the time spent within the different glycemic target zones (<3.9 mmol/L; 3.9 - 10.0 mmol/L and >10.0 mmol/L) between conditions. Self-reported data about mitigation strategies applied to reduce the risk of nocturnal hypoglycemia was also analyzed.

RESULTS

Only 44% of participants reported applying a carbohydrate- or insulin-based strategy to limit the risk of nocturnal hypoglycemia on ACT day. Nocturnal hypoglycemia occurrences were comparable on ACT night versus on L-ACT night. Additional post-meal carbohydrate intake was higher on evenings following ACT (27.7 ± 15.6 g, ACT vs. 19.5 ± 11.0 g, L-ACT; P=0.045), but was frequently associated with an insulin bolus (70% of participants). Nocturnal hypoglycemia the night following ACT occurred mostly in people who administrated an additional insulin bolus before midnight (3 out of 5 participants with nocturnal hypoglycemia).

CONCLUSIONS

Although people with T1D seem to be aware of the increased risk of nocturnal hypoglycemia associated with PA, the risk associated with additional insulin boluses may not be as clear. Most participants did not report using compensation strategies to reduce the risk of PA related late-onset hypoglycemia which may be because they did not consider habitual PA as something requiring treatment adjustments.

Nocturnal hypoglycemia in type 1 and type 2 diabetes: an update on prevalence, prevention, pathophysiology and patient awareness

INTRODUCTION

Despite considerable progress in diabetes treatment, prevalence of nocturnal hypoglycemia in type 1 diabetes mellitus (T1DM) and advanced insulin treated type 2 diabetes mellitus (T2DM) remains high.

AREAS COVERED

The present manuscript describes the prevalence of night-time hypoglycemia as reported in observational and randomized controlled trials. Factors that affect the risk of hypoglycemia are highlighted. The authors also describe impaired awareness of hypoglycemia and available preventive methods.

EXPERT OPINION

Prevention of nocturnal hypoglycemia includes behavioral, dietary and pharmacologic interventions. The most recent development with the lowest rate of hypoglycemia is sensor-augmented pumps with predictive low glucose suspend technology. These pumps combine continuous subcutaneous insulin infusion with continuous glucose monitoring and use various algorithms to predict and stop hypoglycemia before it develops.

Technological Ecological Momentary Assessment Tools to Study Type 1 Diabetes in Youth: Viewpoint of Methodologies

Type 1 diabetes (T1D) is one of the most common chronic childhood diseases, and its prevalence is rapidly increasing. The management of glucose in T1D is challenging, as youth must consider a myriad of factors when making diabetes care decisions. This task often leads to significant hyperglycemia, hypoglycemia, and glucose variability throughout the day, which have been associated with short- and long-term medical complications. At present, most of what is known about each of these complications and the health behaviors that may lead to them have been uncovered in the clinical setting or in laboratory-based research. However, the tools often used in these settings are limited in their ability to capture the dynamic behaviors, feelings, and physiological changes associated with T1D that fluctuate from moment to moment throughout the day. A better understanding of T1D in daily life could potentially aid in the development of interventions to improve diabetes care and mitigate the negative medical consequences associated with it. Therefore, there is a need to measure repeated, real-time, and real-world features of this disease in youth. This approach is known as ecological momentary assessment (EMA), and it has considerable advantages to in-lab research. Thus, this viewpoint aims to describe EMA tools that have been used to collect data in the daily lives of youth with T1D and discuss studies that explored the nuances of T1D in daily life using these methods. This viewpoint focuses on the following EMA methods: continuous glucose monitoring, actigraphy, ambulatory blood pressure monitoring, personal digital assistants, smartphones, and phone-based systems. The viewpoint also discusses the benefits of using EMA methods to collect important data that might not otherwise be collected in the laboratory and the limitations of each tool, future directions of the field, and possible clinical implications for their use.

Assessment of postprandial hyperglycemia and circadian fluctuation of glucose concentrations in diabetic dogs using a flash glucose monitoring system

BACKGROUND

Postprandial hyperglycemia (PPH) and circadian glucose concentration fluctuations recorded in the home environment of dogs with naturally occurring diabetes mellitus (DM) have not been reported.

OBJECTIVES

To determine if a flash glucose monitoring system (FGMS; FreeStyle Libre) can detect PPH and circadian fluctuations in glucose concentrations in dogs with variably controlled DM.

ANIMALS

Fourteen client-owned dogs with DM.

METHODS

Prospective observational study. Interstitial glucose (IG) concentrations measured by the FGMS during a 13-day study period were analyzed.

RESULTS

A total of 17, 446 FGMS IG concentrations were analyzed. For all dogs analyzed together, median IG concentration measured within 30 (288 mg/dL), 60 (286 mg/dL), 90 (285 mg/dL), and 120 (285 mg/dL) minutes of meals was each significantly higher than the median IG concentration at all other times (260 mg/dL, 259 mg/dL, 258 mg/dL, and 257 mg/dL, respectively; range, 40-500 mg/dL; P < .001 for each). Median night-time IG concentration measured from all dogs on 3,547 samples recorded between 1:00 am and 6:00 am (268 mg/dL; range, 40-500 mg/dL) was significantly higher than median IG measured on 13, 899 samples at all other time points (259 mg/dL; range, 40-500 mg/dL; P < .001).

CONCLUSIONS AND CLINICAL IMPORTANCE

The FGMS can be used for future studies of PPH and circadian fluctuations of glucose concentrations in dogs with DM in their home environment.

A Novel CGM Metric-Gradient and Combining Mean Sensor Glucose Enable to Improve the Prediction of Nocturnal Hypoglycemic Events in Patients with Diabetes

Nocturnal hypoglycemia is a serious complication of insulin-treated diabetes, and it is often asymptomatic. A novel CGM metric-gradient was proposed in this paper, and a method of combining mean sensor glucose (MSG) and gradient was presented for the prediction of nocturnal hypoglycemia. For this purpose, the data from continuous glucose monitoring (CGM) encompassing 1,921 patients with diabetes were analyzed, and a total of 302 nocturnal hypoglycemic events were recorded. The MSG and gradient values were calculated, respectively, and then combined as a new metric (i.e., MSG+gradient). In addition, the prediction was conducted by four algorithms, namely, logistic regression, support vector machine, random forest, and long short-term memory. The results revealed that the gradient of CGM showed a downward trend before hypoglycemic events happened. Additionally, the results indicated that the specificity and sensitivity based on the proposed method were better than the conventional metrics of low blood glucose index (LBGI), coefficient of variation (CV), mean absolute glucose (MAG), lability index (LI), etc., and the complex metrics of MSG+LBGI, MSG+CV, MSG+MAG, and MSG+LI, etc. Specifically, the specificity and sensitivity were greater than 96.07% and 96.03% at the prediction horizon of 15 minutes and greater than 87.79% and 90.07% at the prediction horizon of 30 minutes when the proposed method was adopted to predict nocturnal hypoglycemic events in the aforementioned four algorithms. Therefore, the proposed method of combining MSG and gradient may enable to improve the prediction of nocturnal hypoglycemic events. Future studies are warranted to confirm the validity of this metric.

An Owner-Independent Investigation of Diabetes Alert Dog Performance

Objective: To quantify Diabetes Alert Dog (DAD) performance by using owner-independent measures.

Research Design and Methods: Eight owners of accredited DADs used a FreeStyle Libre Flash Glucose Monitoring System (FGMS). Concurrent Closed Circuit Television (CCTV) footage was collected for between 5 and 14 days in each owner's home or workplace. The footage was blind-coded for dogs' alerting behaviors. The sensitivity, False Positive Rate and Positive Predictive Values (PPV) of dogs' alerts to out-of-range (OOR) episodes were calculated. Ratings for 11 attributes describing participant's lifestyle and compliance (taken from each dog's instructor) and the percentage of DAD alerts responded to by the owner as per training protocol (taken from CCTV footage) were assessed for association with dog performance.

Results: Dogs alerted more often when their owners' glucose levels were outside vs. inside target range (hypoglycaemic 2.80-fold, p = 0.001; hyperglycaemic 2.29-fold, p = 0.005). Sensitivity to hypoglycaemic episodes ranged from 33.3 to 91.7%, the mean was 55.9%. Mean PPV for OOR episodes was 69.7%. Sensitivity and PPV were associated with aspects of the dog and owner's behavior, and the owner's adherence to training protocol.

Conclusions: Owner-independent methods support that some dogs alert to hypo- and hyperglycaemic events accurately, but performance varies between dogs. We find that DAD performance is affected by traits and behaviors of both the dog and owner. Combined with existing research showing the perceived psychosocial value and reduced critical health care needs of DAD users, this study supports the value of a DAD as part of a diabetes care plan. It also highlights the importance of ongoing training and continued monitoring to ensure optimal performance.

Optimization of insulin regimen and glucose outcomes with short-term real-time continuous glucose monitoring (RT-CGM) in type 1 diabetic children with sub-optimal glucose control on multiple daily injections: The pediatric DIACCOR study

BACKGROUND

The impact of 7-day real-time continuous glucose monitoring (RT-CGM) on type 1 diabetes (T1D) management remains unknown in youths with suboptimal control by multiple daily injections (MDI). The DIACCOR Study aimed to describe treatment decisions and glucose outcomes after a short-term RT-CGM sequence in real-life conditions.

METHODS

This French multicenter longitudinal observational study included T1D youths with HbA1c>7.5% or a history of severe hypoglycemia (SH) or recurrent documented hypoglycemia. A sensor was inserted at the study-inclusion visit, and one of three predefined treatment changes was proposed by the investigator within 7-15 days: INT=MDI intensification, CSII=switch to continuous insulin infusion, or ER=educational reinforcement with no change in insulin regimen and a 4-month follow-up visit (M4) was scheduled.

RESULTS

A total of 229 children (12.2±3.5 years old) were recruited by 74 pediatricians; 12.8% had a history of SH, 22.2% had recurrent hypoglycemia. Baseline HbA1c was 8.7±1.5% (>7.5% in 82.8%). Overall, 139 (79.4%), 19 (10.9%), and 17 patients (9.7%) were, respectively, included in the INT, CSII, and ER subgroups. At M4, the global incidence of SH and recurrent hypoglycemia dropped (3.4% vs. 12.8% and 6.0% vs. 22.2%, respectively) as well as the incidence of ketoacidosis (2.1% vs. 8.1%) or ketosis (6.9% vs. 11.4%). The HbA1c decrease was significant overall and in the INT subgroup (adjusted difference -0.29%, P=0.009). The satisfaction rate was≥93.0% among children.

CONCLUSION

In a real-life setting, a 1-week RT-CGM can promote treatment optimization in youths with uncontrolled T1D resulting mostly in less acute events. CGM acceptance may improve with new-generation sensors.

Sleep in Caregivers of Children With Type 1 Diabetes

Purpose

The purpose of this study was to explore caregivers’ descriptions of their experience of nighttime sleep.

Design and Methods

Caregivers (N = 22) of children 10 to 18 years of age with type 1 diabetes (T1D) were recruited for this descriptive study. Anonymous questionnaires contained demographic information and both open- and closed-ended questions that focused on caregiving as it related to sleep. Open-ended questions were reviewed to help understand the effect of nocturnal caregiving activities on parental sleep.

Results

The sample of caregivers were all female and had a mean age of 43 years; 96% graduated high school, 68% were married or partnered, and 100% were white. Children had been diagnosed with T1D for a mean of 5 years, with a mean age of 12.2 years. Caregivers reported short sleep duration (mean, 5.8 hours). Over half of the participants reported they required ≥7 hours of sleep to feel their best, 64% indicated trouble sleeping at night, and 86% reported that caregiving interfered with their nighttime sleep, while 54% responded that sleep was “very important.” Content analysis of the open-ended questions revealed 2 themes: (1) anxiety about the child’s blood glucose levels and (2) nighttime disruptions.

Conclusions

Caregivers are frequently sleep deprived and worry about their child’s nighttime glucose. Caregiving duties, anxiety, and sleep fragmentation may contribute to their poor sleep.

Reliability of Trained Dogs to Alert to Hypoglycemia in Patients With Type 1 Diabetes

BACKGROUND

We examined the reliability of trained dogs to alert to hypoglycemia in individuals with type 1 diabetes.

METHODS

Patients with type 1 diabetes who currently used diabetes alert dogs participated in this exploratory study. Subjects reported satisfaction, perceived dog glucose sensing ability and reasons for obtaining a trained dog. Reliability of dog alerts was assessed using capillary blood glucose (CBG) and blinded continuous glucose monitoring (CGM) as comparators in 8 subjects (age 4-48). Hypoglycemia was defined as CBG or CGM <70 mg/dL.

RESULTS

Dog users were very satisfied (8.9/10 on a Likert-type scale) and largely confident (7.9/10) in their dog's ability to detect hypoglycemia. Detection of hypoglycemia was the primary reason for obtaining a trained dog. During hypoglycemia, spontaneous dog alerts occurred at a rate 3.2 (2.0-5.2, 95% CI) times higher than during euglycemia (70-179 mg/dL). Dogs provided timely alerts in 36% (sensitivity) of all hypoglycemia events (n = 45). Due to inappropriate alerts, the PPV of a dog alert for hypoglycemia was 12%. When there was concurrence of a hypoglycemic event between the dog alert and CGM (n = 30), CGM would have alerted prior to the dog in 73% of events (median 22-minute difference).

CONCLUSIONS

This is the first study evaluating reliability of trained dogs to alert to hypoglycemia under real-life conditions. Trained dogs often alert a human companion to otherwise unknown hypoglycemia; however due to high false-positive rate, a dog alert alone is unlikely to be helpful in differentiating hypo-/hyper-/euglycemia. CGM often detects hypoglycemia before a trained dog by a clinically significant margin.

Continuous Glucose Monitoring: Impact on Hypoglycemia

The necessity of strict glycemic control is unquestionable. However, hypoglycemia remains a major limiting factor in achieving satisfactory glucose control, and evidence is mounting to show that hypoglycemia is not benign. Over the past decade, evidence has consistently shown that real-time continuous glucose monitoring improves glycemic control in terms of lowering glycated hemoglobin levels. However, real-time continuous glucose monitoring has not met the expectations of the diabetes community with regard to hypoglycemia prevention. The earlier trials did not demonstrate any effect on either mild or severe hypoglycemia and the effect of real-time continuous glucose monitoring on nocturnal hypoglycemia was often not reported. However, trials specifically designed to reduce hypoglycemia in patients with a high hypoglycemia risk have demonstrated a reduction in hypoglycemia, suggesting that real-time continuous glucose monitoring can prevent hypoglycemia when it is specifically used for that purpose. Moreover, the newest generation of diabetes technology currently available commercially, namely sensor-augmented pump therapy with a (predictive) low glucose suspend feature, has provided more convincing evidence for hypoglycemia prevention. This article provides an overview of the hypoglycemia outcomes of randomized controlled trials that investigate the effect of real-time continuous glucose monitoring alone or sensor-augmented pump therapy with a (predictive) low glucose suspend feature. Furthermore, several possible explanations are provided why trials have not shown a reduction in severe hypoglycemia. In addition, existing evidence is presented of real-time continuous glucose monitoring in patients with impaired awareness of hypoglycemia who have the highest risk of severe hypoglycemia.

Comparative effects of vildagliptin and sitagliptin determined by continuous glucose monitoring in patients with type 2 diabetes mellitus

The dipeptidyl peptidase-4 inhibitors vildagliptin and sitagliptin are effective in treating patients with type 2 diabetes mellitus. Patients receiving standard doses of sitagliptin plus insulin may require increased doses of sitagliptin or switching to vildagliptin to improve blood glucose control. This study compared the effects of increasing sitagliptin and switching to vildagliptin in type 2 diabetes patients receiving standard doses of sitagliptin plus insulin. This prospective, randomized, parallel-group comparison trial enrolled 33 type 2 diabetes patients receiving 50 mg sitagliptin once daily plus insulin. Seventeen patients were randomized to 50 mg vildagliptin twice daily, and 16 to 100 mg sitagliptin once daily, and evaluated by continuous glucose monitoring at baseline and after 8 weeks. The primary end-point was the change in mean amplitude of glycemic excursions (MAGE). MAGE decreased from baseline in both the vildagliptin (-13.4 ± 35.7 mg/dL) and sitagliptin (-8.4 ± 24.3 mg/dL) groups, but neither within- nor between-group changes were statistically significant. Similarly, the areas under the curve for blood glucose levels ≥180 mg/dL and <70 mg/dL tended to improve in both groups, but these differences were not statistically significant. In contrast, HbA1c was significantly reduced only in the vildagliptin group, from 7.1 ± 0.6% at baseline to 6.8 ± 0.6% at 8 weeks (p=0.006). Increasing sitagliptin dose and switching to vildagliptin had limited effects in improving MAGE in type 2 diabetic patients treated with standard doses of sitagliptin.

EEG power and glucose fluctuations are coupled during sleep in young adults with type 1 diabetes

OBJECTIVE

To determine the coupling between brain activity and glucose variations during sleep in young adults with type 1 diabetes mellitus (T1DM).

METHODS

27 participants, age 18-30, wore a continuous glucose monitoring system (CGMS) and underwent in-laboratory overnight polysomnography (PSG). Quantitative electroencephalogram (qEEG) metrics were determined from the PSG and included Delta, Theta, Alpha, Sigma, Beta and Gamma Band power at 5-min intervals. Wavelet Coherence Analysis was employed to determine the time varying and frequency specific coupling between glucose and EEG Band power. ANOVA was used to compare differences across fluctuation speeds and EEG bands.

RESULTS

There was a high degree of time varying and frequency specific coupling between glucose variations and EEG power in all EEG Bands during sleep. The average number of intervals of statistically significant coherence was highest for fluctuations periods between 10 and 30min in all Bands (p<0.0001 for each). Mean significant coherence was negatively correlated with hemoglobin A1c, a marker of glycemic control.

CONCLUSIONS

The relationship between glucose and EEG power during sleep is time varying and frequency dependent in young adults with T1DM.

SIGNIFICANCE

Understanding the time varying mutual relationship between glucose changes and brain activity during sleep may have implications for disease management in T1DM.

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.

Hypoglycemia in Type 2 Diabetes--More Common Than You Think: A Continuous Glucose Monitoring Study

BACKGROUND

Hypoglycemia is often the limiting factor for intensive glucose control in diabetes management, however its actual prevalence in type 2 diabetes (T2DM) is not well documented.

METHODOLOGY

A total of 108 patients with T2DM wore a continuous glucose monitoring system (CGMS) for 5 days. Rates and patterns of hypoglycemia and glycemic variability (GV) were calculated. Patient and medication factors were correlated with rates, timing, and severity of hypoglycemia.

RESULTS

Of the patients, 49.1% had at least 1 hypoglycemic episode (mean 1.74 episodes/patient/ 5 days of CGMS) and 75% of those patients experienced at least 1 asymptomatic hypoglycemic episode. There was no significant difference in the frequency of daytime versus nocturnal hypoglycemia. Hypoglycemia was more frequent in individuals on insulin (alone or in combination) (P = .02) and those on oral hypoglycemic agents (P < .001) compared to noninsulin secretagogues. CGMS analysis resulted in treatment modifications in 64% of the patients. T2DM patients on insulin exhibited higher glycemic variability (GV) scores (2.3 ± 0.6) as compared to those on oral medications (1.8 ± 0.7, P = .017).

CONCLUSIONS

CGMS can provide rich data that show glucose excursions in diabetes patients throughout the day. Consequently, unwarranted onset of hypo- and hyperglycemic events can be detected, intervened, and prevented by using CGMS. Hypoglycemia was frequently unrecognized by the patients in this study (75%), which increases their potential risk of significant adverse events. Incorporation of CGMS into the routine management of T2DM would increase the detection and self-awareness of hypoglycemia resulting in safer and potentially better overall control.

Detection and treatment efficacy of hypoglycemic events in the everyday life of children younger than 7 yr

BACKGROUND

Mild hypoglycemia is commonly observed in children treated for type 1 diabetes mellitus (T1DM). Hypoglycemia disturbs cognition and learning.

OBJECTIVE

To describe how and to what extent hypoglycemia in young children with T1DM is detected in everyday life. To learn how parents and caregivers treat hypoglycemia and to evaluate how efficient this treatment is.

METHODS

Twenty-three children [12 girls, mean age: 4.5 yr, mean HbA1c: 59 mmol/mol (7.5%)], 17 of whom were treated with an insulin pump, underwent blinded continuous glucose monitoring (CGM). Data on symptoms and treatment of hypoglycemia were collected in a logbook. Plasma glucose values were collected through self-monitoring of blood glucose and entered in the logbook, and glucometer memories were uploaded. Data were collected during 1 wk in autumn and 1 wk in spring.

RESULTS

Only 32% of all hypoglycemic events were detected despite plasma glucose being checked 10 times per day. Most hypoglycemic events were asymptomatic (90% overall and 98% of those occurring at night). Untreated hypoglycemic events were associated with a relapse into hypoglycemia within 3 h in the majority of events. Compared to treatment of hypoglycemia events with a defined dose of simple carbohydrates, treatment with a mixed meal resulted in a significantly higher glucose value 1 and 2 h after the hypoglycemia.

CONCLUSION

For optimum treatment, children younger than 7 yr with T1DM need better strategies and support for detecting hypoglycemia with real-time CGM. Hypoglycemia should be treated with a defined dose of carbohydrates rather than a mixed meal.

Type 1 diabetes

Over the past decade, knowledge of the pathogenesis and natural history of type 1 diabetes has grown substantially, particularly with regard to disease prediction and heterogeneity, pancreatic pathology, and epidemiology. Technological improvements in insulin pumps and continuous glucose monitors help patients with type 1 diabetes manage the challenge of lifelong insulin administration. Agents that show promise for averting debilitating disease-associated complications have also been identified. However, despite broad organisational, intellectual, and fiscal investments, no means for preventing or curing type 1 diabetes exists, and, globally, the quality of diabetes management remains uneven. This Seminar discusses current progress in epidemiology, pathology, diagnosis, and treatment of type 1 diabetes, and prospects for an improved future for individuals with this disease.

A GEE Approach to Determine Sample Size for Pre- and Post-Intervention Experiments with Dropout

Pre- and post-intervention experiments are widely used in medical and social behavioral studies, where each subject is supposed to contribute a pair of observations. In this paper we investigate sample size requirement for a scenario frequently encountered by practitioners: All enrolled subjects participate in the pre-intervention phase of study, but some of them will drop out due to various reasons, thus resulting in missing values in the post-intervention measurements. Traditional sample size calculation based on the McNemar's test could not accommodate missing data. Through the GEE approach, we derive a closed-form sample size formula that properly accounts for the impact of partial observations. We demonstrate that when there is no missing data, the proposed sample size estimate under the GEE approach is very close to that under the McNemar's test. When there is missing data, the proposed method can lead to substantial saving in sample size. Simulation studies and an example are presented.

Susceptibility of interstitial continuous glucose monitor performance to sleeping position

BACKGROUND

Developing a round-the-clock artificial pancreas requires accurate and stable continuous glucose monitoring. The most widely used continuous glucose monitors (CGMs) are percutaneous, with the sensor residing in the interstitial space. Inaccuracies in percutaneous CGM readings during periods of lying on the devices (e.g., in various sleeping positions) have been anecdotally reported but not systematically studied.

METHODS

In order to assess the impact of sleep and sleep position on CGM performance, we conducted a study in human subjects in which we measured the variability of interstitial CGM data at night as a function of sleeping position. Commercially available sensors were placed for 4 days in the abdominal subcutaneous tissue in healthy, nondiabetic volunteers (four sensors per person, two per side). Nocturnal sleeping position was determined from video recordings and correlated to sensor data.

RESULTS

We observed that, although the median of the four sensor readings was typically 70-110 mg/dl during sleep, individual sensors intermittently exhibited aberrant glucose readings (&gt;25 mg/dl away from median) and that these aberrant readings were strongly correlated with subjects lying on the sensors. We expected and observed that most of these aberrant sleep-position-related CGM readings were sudden decreases in reported glucose values, presumably due to local blood-flow decreases caused by tissue compression. Curiously, in rare cases, the aberrant CGM readings were elevated values.

CONCLUSIONS

These findings highlight limitations in our understanding of interstitial fluid physiology in the subcutaneous space and have significant implications for the utilization of sensors in the construction of an artificial pancreas.

"Turn it off!": diabetes device alarm fatigue considerations for the present and the future

Safe and widespread use of diabetes technology is constrained by alarm fatigue: when someone receives so many alarms that he or she becomes less likely to respond appropriately. Alarm fatigue and related usability issues deserve consideration at every stage of alarm system design, especially as new technologies expand the potential number and complexity of alarms. The guiding principle should be patient wellbeing, while taking into consideration the regulatory and liability issues that sometimes contribute to building excessive alarms. With examples from diabetes devices, we illustrate two complementary frameworks for alarm design: a "patient safety first" perspective and a focus on human factors. We also describe opportunities and challenges that will come with new technologies such as remote monitoring, adaptive alarms, and ever-closer integration of glucose sensing with insulin delivery.

Continuous glucose monitoring: evidence and consensus statement for clinical use

Continuous glucose monitoring (CGM) is an essential tool for modern diabetes therapy. Randomized controlled studies have provided evidence that hemoglobin A1c (HbA1c) results can be improved in patients with type 1 diabetes with elevated baseline HbA1c when using CGM frequently enough and that the frequency and duration of hypoglycemic events can be reduced in patients with satisfactory baseline HbA1c. The CGM group within the Working Group Diabetes Technology (AGDT) of the German Diabetes Association (DDG) has defined evidence-based indications for the practical use of CGM in this consensus statement related to hypoglycemia (frequent, severe, or nocturnal) or hypoglycemia unawareness, insufficient metabolic control despite use of all possible therapeutic options and patient compliance, pregnancy associated with inadequate blood glucose results, and the need for more than 10 blood glucose measurements per day. Contraindications and defined preconditions for the successful use of CGM should be considered.

Defective counterregulation and hypoglycemia unawareness in diabetes: mechanisms and emerging treatments

For people with diabetes, hypoglycemia remains the limiting factor in achieving glycemic control. This article reviews recent advances in how the brain senses and responds to hypoglycemia. Novel mechanisms by which individuals with insulin-treated diabetes develop hypoglycemia unawareness and impaired counterregulatory responses are outlined. Prevention strategies for reducing the incidence of hypoglycemia are discussed.