Persistently high glucose levels in young children with type 1 diabetes

Young Children with Type 1 Diabetes: Recent Advances in Behavioral Research

PURPOSE OF REVIEW

This review provides a recent update of behavioral research pertinent to young children with T1D and addresses current priorities and future directions.

RECENT FINDINGS

Rates of type 1 diabetes (T1D) in young children (ages 1-7) are continuing to rise. Since 2014, changes to diabetes care and management have impacted young children and reinforced the need for increased attention and interventions to support diabetes management, especially in caregivers who are primarily responsible for their young child's diabetes management. T1D is associated with unique physiologic challenges in young children, with constant management demands elevating parental diabetes-related stress and fear of hypoglycemia. Diabetes technology use has significantly increased in young children, contributing to improvements in glycemic levels and parent and child psychosocial functioning. Yet despite the positive outcomes demonstrated in select clinical behavioral interventions, research with this young child age group remains limited in scope and quantity.

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.

An Analysis of Self-Reported Barriers to Type 1 Diabetes Care in a Pediatric Population in British Columbia, Canada

OBJECTIVES

Our aim in this study was to identify patient-level barriers to attending pediatric type 1 diabetes mellitus (T1DM) clinic and to better understand the demographic and clinical characteristics of these reporting barriers.

METHODS

Patients were recruited from pediatric T1DM clinics throughout British Columbia. Barriers to attending clinic were identified through a survey. Demographic and clinical characteristics of patients who reported difficulty attending clinic appointments were compared with those who did not.

RESULTS

Of the 197 study participants, 31% reported difficulty attending appointments. Commonly reported barriers were distance to clinic and missing work. Younger child age and residing in northern regions increased the odds of reporting a barrier, whereas residing on Vancouver Island decreased odds of reporting a barrier. There were no differences in glycated hemoglobin levels between the 2 groups.

CONCLUSIONS

Approximately 1 in 3 patients identified challenges in attending T1DM appointments in British Columbia. Further research is needed to determine whether similar challenges exist in other provinces.

Time spent outside of target glucose range for young children with type 1 diabetes: a continuous glucose monitor study

AIM

To assess the associations between demographic and clinical characteristics and sensor glucose metrics in young children with type 1 diabetes, using masked, continuous glucose monitoring data from children aged 2 to < 8 years.

RESEARCH DESIGN AND METHODS

The analysis included 143 children across 14 sites in the USA, enrolled in a separate clinical trial. Eligibility criteria were: age 2 to <8 years; type 1 diabetes duration ≥3 months; no continuous glucose monitoring use for past 30 days; and HbA_1c concentration 53 to <86 mmol/mol (7.0 to <10.0%). All participants wore masked continuous glucose monitors up to 14 days.

RESULTS

On average, participants spent the majority (13 h) of the day in hyperglycaemia (>10.0 mmol/l) and a median of ~1 h/day in hypoglycaemia (<3.9 mmol/l). Participants with minority race/ethnicity and higher parent education levels spent more time in target range, 3.9-10.0 mmol/l, and less time in hyperglycaemia. More time in hypoglycaemia was associated with minority race/ethnicity and younger age at diagnosis. Continuous glucose monitoring metrics were similar in pump and injection users.

CONCLUSIONS

Given that both hypo- and hyperglycaemia negatively impact neurocognitive development, strategies to increase time in target glucose range for young children are needed.

In Silico Trials of an Open-Source Android-Based Artificial Pancreas: A New Paradigm to Test Safety and Efficacy of Do-It-Yourself Systems

Objective: Safety data on Do-It-Yourself Artificial Pancreas Systems are missing. The most widespread in Europe is the AndroidAPS implementation of the OpenAPS algorithm. We used the UVA/Padova Type 1 Diabetes Simulator to in silico test safety and efficacy of this algorithm in different scenarios. Methods: We tested five configurations of the AndroidAPS algorithm differing in aggressiveness and patient's interaction with the system. All configurations were tested with insulin sensitivity variation of ±30%. The most promising configurations were tested in real-life scenarios: over- and underestimated bolus by 50%, bolus delivered 15 min before meal, and late bolus delivered 15 min after meal. Continuous Glucose Monitoring (CGM) time in ranges (TIRs) metrics were used to assess the glycemic control. Results: In silico testing showed that open-source closed-loop system AndroidAPS works effectively and safely. The best results were reached if AndroidAPS algorithm worked with microboluses and when half of calculated bolus was issued (mean glycemia 131 mg/dL, SD 27 mg/dL, TIR 91%, time between 54 and 70 mg/dL <1%, and low blood glucose index even <1). The meal bolus over- and underestimation as well as late bolus did not affect the TIR and, importantly, the time between 54 and 70 mg/dL. Conclusion: In silico testing proved that AndroidAPS implementation of the OpenAPS algorithm is safe and effective, and it showed a great potential to be tested in prospective home setting study.

Introducing a very low carbohydrate diet for a child with type 1 diabetes

Type 1 diabetes mellitus is a serious autoimmune disease for which no cure is available. The treatment includes insulin therapy, carbohydrate counting, eating healthy foods, exercising regularly, and maintaining a healthy weight. The goal is to keep blood glucose levels close to normal most of the time to delay or prevent complications. Despite the increase in the use of insulin pumps and continuous glucose monitors in recent years, the management of type 1 diabetes remains suboptimal in terms of glycaemic control and normal glycated haemoglobin (HbA1c) level. This article discusses the case of a child with type 1 diabetes who was successfully treated with a very low-carbohydrate diet, resulting in normal levels of HbA1c and normal blood glucose 95% of the time in a range of 70-180 mg/dL (4.0 mmol/L-10 mmol/L). Therefore, further studies are needed to verify how a very low carbohydrate diet impacts child development.

Optimizing the use of continuous glucose monitoring in young children with type 1 diabetes with an adaptive study design and multiple randomizations

Parents of young children with type 1 diabetes (T1D) experience unique, developmental challenges in managing their child's T1D, resulting in psychosocial distress. Only a small portion of young children reach glucose goals and adherence to diabetes devices that help improve T1D management have historically been low in this population. The purpose of this study is to test four interventions that couple developmentally tailored behavioral supports with education to optimize use of diabetes devices, improve glucose control, and reduce psychosocial distress for parents of young children with T1D. The study team designed four behavioral interventions, two aimed at improving glucose control and two aimed at optimizing use of diabetes devices. The goal of this paper is to describe the behavioral interventions developed for this study, including the results of a pilot test, and describe the methods and analysis plan to test this intervention strategy with ninety participants in a large-scale, randomized trial using a sequential multiple assignment randomization trial (SMART) design. A SMART design will permit a clinically relevant evaluation of the intervention strategy, as it allows multiple randomizations based on individualized assessments throughout the study instead of a fixed intervention dose seen in most traditional randomized controlled trials.

Individual glucose responses to prolonged moderate intensity aerobic exercise in adolescents with type 1 diabetes: The higher they start, the harder they fall

OBJECTIVE

To evaluate the pattern of change in blood glucose concentrations and hypoglycemia risk in response to prolonged aerobic exercise in adolescents with type 1 diabetes (T1D) that had a wide range in pre-exercise blood glucose concentrations.

METHODS

Individual blood glucose responses to prolonged (~60 minutes) moderate-intensity exercise were profiled in 120 youth with T1D.

RESULTS

The mean pre-exercise blood glucose concentration was 178 ± 66 mg/dL, ranging from 69 to 396 mg/dL, while the mean change in glucose during exercise was -76 ± 55 mg/dL (mean ± SD), ranging from +83 to -257 mg/dL. Only 4 of 120 youth (3%) had stable glucose levels during exercise (ie, ± ≤10 mg/dL), while 4 (3%) had a rise in glucose >10 mg/dL, and the remaining (93%) had a clinically significant drop (ie, >10 mg/dL). A total of 53 youth (44%) developed hypoglycemia (≤70 mg/dL) during exercise. The change in glucose was negatively correlated with the pre-exercise glucose concentration (R² = 0.44, P < 0.001), and tended to be greater in those on multiple daily insulin injections (MDI) vs continuous subcutaneous insulin infusion (CSII) (-98 ± 15 vs -65 ± 7 mg/dL, P = 0.05). No other collected variables appeared to predict the change in glucose including age, weight, height, body mass index, disease duration, daily insulin dose, HbA_1c , or sex.

CONCLUSION

Youth with T1D have variable glycemic responses to prolonged aerobic exercise, but this variability is partially explained by their pre-exercise blood glucose levels. When no implementation strategies are in place to limit the drop in glycemia, the incidence of exercise-associated hypoglycemia is ~44% and having a high pre-exercise blood glucose concentration is only marginally protective.

Continued improvement of metabolic control in Swedish pediatric diabetes care

BACKGROUND

To prospectively investigate if the grand mean HbA1c and the differences in mean HbA1c between centers in Sweden could be reduced, thereby improving care delivered by pediatric diabetes teams.

METHODS

We used an 18-month quality improvement collaborative (QIC) together with the Swedish pediatric diabetes quality registry (SWEDIABKIDS). The first program (IQ-1), started in April 2011 and the second (IQ-2) in April 2012; together they encompassed 70% of Swedish children and adolescents with diabetes.

RESULTS

The proportion of patients in IQ-1 with a mean HbA1c <7.4% (57 mmol/mol) increased from 26.4% before start to 35.9% at 36 months (P < .001), and from 30.2% to 37.2% (P < .001) for IQ-2. Mean HbA1c decreased in both participating and non-participating (NP) centers in Sweden, thereby indicating an improvement by a spatial spill over effect in NP centers. The grand mean HbA1c decreased by 0.45% (4.9 mmol/mol) during 36 months; at the end of 2014 it was 7.43% (57.7 mmol/mol) (P < .001). A linear regression model with the difference in HbA1c before start and second follow-up as dependent variable showed that QIC participation significantly decreased mean HbA1c both for IQ-1 and IQ-2. The proportion of patients with high HbA1c values (>8.7%, 72 mmol/mol) decreased significantly in both QICs, while it increased in the NP group.

CONCLUSIONS

The grand mean HbA1c has decreased significantly in Sweden from 2010 to 2014, and QICs have contributed significantly to this decrease. There seems to be a spatial spill-over effect in NP centers.

The role of glycemia in insulin resistance in youth with type 1 and type 2 diabetes

BACKGROUND

Hyperglycemia has traditionally been considered a major contributor to insulin resistance (IR) in type 1 diabetes (T1D), yet studies examining the relationship between HbA1c and IR are conflicting. Glucose measures captured by continuous glucose monitoring (CGM) (eg, peak glucose, standard deviation, hypoglycemia) in youth have not been explored as predictors of insulin sensitivity (IS).

OBJECTIVE

Assess the relationship between IS and glycemia in youth with T1D and type 2 diabetes (T2D).

METHODS

Sedentary 12-19 year olds with diabetes had peripheral IS measured by hyperinsulinemic-euglycemic clamp. HbA1c and 3 days of CGM data were also collected. Spearman correlation coefficients were calculated to examine the association between variables.

RESULTS

Participants included 100 youth with T1D [46% male, median body mass index (BMI) 74 percentile, HbA1c 8.5%] and 42 with T2D (26% male, BMI 99 percentile, HbA1c 6.9%). Nineteen with T1D and 13 with T2D also wore CGM. In T2D youth, higher HbA1c, average sensor glucose, area under the CGM curve, and metabolic syndrome characteristics correlated with lower IS. In T1D youth, higher BMI percentile, waist circumference, triglycerides, and LDL cholesterol, but not HbA1c, correlated with lower IS. Moreover, higher CGM overnight means glucose correlated with greater IS, and CGM hypoglycemia correlated with lower IS.

CONCLUSIONS

Markers of metabolic syndrome and hyperglycemia predicted decreased IS in T2D youth. Paradoxically, hypoglycemia predicted decreased IS in T1D youth and hyperglycemia, particularly overnight, predicted improved IS. These preliminary results imply different mechanisms underlying IR in T1D vs T2D and suggest a role for non-insulin therapies in T1D to improve IR.

Achievement of metabolic control among children and adolescents with type 1 diabetes in Spain

AIMS

To assess metabolic control in a paediatric T1D population in Spain and analyse the rate of severe acute decompensations and chronic complications.

METHODS

Data from patients treated at eight paediatric diabetes units with experienced diabetes teams between June and December 2014 were analysed in an observational prospective study. Variables included: age, sex, diabetes duration, number of follow-up visits/year, anthropometrical data, insulin treatment modalities, mean annual HbA1c and the prevalence of acute and chronic complications. SPSS statistics 21.0 was used.

RESULTS

A total of 853 patients (49.7% female) with a mean age of 12.1 ± 3.7 years were included. Anthropometric data were normal. Mean diabetes duration was 8 ± 3.4 years. Mean outpatient follow-up was 4.7 ± 0.04 visits/year. Twenty-five per cent were on continuous subcutaneous insulin infusion (CSII). Mean HbA1c was 7.3 ± 1% (56 ± 8 mmol/mol) and 66.6% had HbA1c < 7.5% (58 mmol/mol). HbA1c value correlated negatively with age at onset and positively with years of diabetes, number of visits/year and current age (F = 7.06; p = 0.01). Patients on CSII (n = 213) were younger, attended the outpatient clinic more frequently, were diagnosed earlier, had better metabolic control and had presented more severe hypoglycaemic episodes the previous year. The rate of severe decompensation (episodes/100 patients/year) was ketoacidosis 1.5 and severe hypoglycaemia 4.5. The prevalence of chronic complications was very low.

CONCLUSIONS

Our data describe the good compliance of paediatric T1D patients treated at eight paediatric units in Spain following international standards of metabolic control.

An alternative sensor-based method for glucose monitoring in children and young people with diabetes

OBJECTIVE

To determine accuracy, safety and acceptability of the FreeStyle Libre Flash Glucose Monitoring System in the paediatric population.

DESIGN, SETTING AND PATIENTS

Eighty-nine study participants, aged 4-17 years, with type 1 diabetes were enrolled across 9 diabetes centres in the UK. A factory calibrated sensor was inserted on the back of the upper arm and used for up to 14 days. Sensor glucose measurements were compared with capillary blood glucose (BG) measurements. Sensor results were masked to participants.

RESULTS

Clinical accuracy of sensor results versus BG results was demonstrated, with 83.8% of results in zone A and 99.4% of results in zones A and B of the consensus error grid. Overall mean absolute relative difference (MARD) was 13.9%. Sensor accuracy was unaffected by patient factors such as age, body weight, sex, method of insulin administration or time of use (day vs night). Participants were in the target glucose range (3.9-10.0 mmol/L) ∼50% of the time (mean 12.1 hours/day), with an average of 2.2 hours/day and 9.5 hours/day in hypoglycaemia and hyperglycaemia, respectively. Sensor application, wear/use of the device and comparison to self-monitoring of blood glucose were rated favourably by most participants/caregivers (84.3-100%). Five device related adverse events were reported across a range of participant ages.

CONCLUSIONS

Accuracy, safety and user acceptability of the FreeStyle Libre System were demonstrated for the paediatric population. Accuracy of the system was unaffected by subject characteristics, making it suitable for a broad range of children and young people with diabetes.

TRIAL REGISTRATION NUMBER

NCT02388815.

Bolus Calculator Settings in Well-Controlled Prepubertal Children Using Insulin Pumps Are Characterized by Low Insulin to Carbohydrate Ratios and Short Duration of Insulin Action Time

BACKGROUND

The "500 rule" has been used extensively to find the insulin to carbohydrate ratio (ICR) for carbohydrate counting (CC). Duration of insulin action (DIA) is often recommended to be set to 4 hours. Data are lacking on validating these routines in young children.

METHODS

ICR was calculated by dividing carbohydrate grams by insulin units. Insulin sensitivity factor (ISF) was defined by the 100 rule (100 divided by total daily insulin dose [TDD]). DIA was set to 3 hours. ICR, ISF, and DIA were adjusted continuously. Data for this retrospective analysis were taken from pump downloads at a routine visit. ICR and ISF were recalculated to rules (ICR/ISF multiplied by TDD).

RESULTS

A total of 21 prepubertal children aged 7.0 ± 2.3 (mean ± SD), range 2-10 years, with diabetes duration 3.0 ± 1.9, range 0.5-7.7 years, used the pump bolus calculator for CC. HbA1c IFCC (NGSP) was 53 ± 6 mmol/mol (7.0 ± 0.5%). None had experienced severe hypoglycemia (unconsciousness/seizures) since diabetes diagnosis. TDD was 0.7 ± 0.1 U/kg/24 h (range 0.5-1.0), and the percentage basal insulin 38 ± 11%. Median breakfast rule was 211 (Q, quartiles 162;310), and for other meals 434 (Q 301;496). Median ISF rule was 113 (Q 100;128) in the morning, and 120 (Q 104;134) during the rest of the day. DIA was 2.6 ± 0.5 h (range 2-3) and target BG 5.3 ± 0.4 mmol/l (range 5.0-6.0).

CONCLUSIONS

Prepubertal children seem to need more bolus insulin for meals than calculated from the 500 rule, especially at breakfast, but less insulin for corrections than calculated from the 100 rule. Two to 3 hours seems to be the appropriate range for DIA in this age group.

Cognition and Type 1 Diabetes in Children and Adolescents

IN BRIEF In children and adolescents with type 1 diabetes, exposure to glycemic extremes (severe hypoglycemia, chronic hyperglycemia, and diabetic ketoacidosis) overlaps with the time period of most active brain and cognitive development, leading to concerns that these children are at risk for cognitive side effects. This article summarizes the existing literature examining the impact of glycemic extremes on cognitive function and brain structure in youth with type 1 diabetes and points out areas for future research.