Search

There are multiple information sources available to assist families in learning about rapidly advancing diabetes technologies as care options for their children. This study explored where and from whom families of young children with type 1 diabetes get information about diabetes technologies and the valence (positive vs. negative) of that information. Semi-structured interviews were conducted with parents (86% mothers) of 79 youth

Objective: To provide a snapshot of the profile of adults and youth with type 1 diabetes (T1D) in the United States and assessment of longitudinal changes in T1D management and clinical outcomes in the T1D Exchange registry. Research Design and Methods: Data on diabetes management and outcomes from 22,697 registry participants (age 1–93 years) were collected between 2016 and 2018 and compared with data collected in 2010–2012 for 25,529 registry participants. Results: Mean HbA1c in 2016–2018 increased from 65 mmol/mol at the age of 5 years to 78 mmol/mol between ages 15 and 18, with a decrease to 64 mmol/mol by age 28 and 58–63 mmol/mol beyond age 30. The American Diabetes Association (ADA) HbA1c goal of 10-fold in children

Enthusiasm for the benefits of sodium-glucose cotransporter 2 inhibitors (SGLT2i) as an adjunctive treatment in type 1 diabetes (T1D) has been offset by the possible increased risk of diabetic ketoacidosis (DKA). Since pump-treated T1D patients are susceptible to DKA due to infusion site problems, this study was undertaken to assess how treatment with SGLT2i affects patterns of early metabolic decompensation following suspension of basal insulin.Ten T1D participants (age 19-35 years, duration 10 ± 8 years, A1c 7.4% ± 0.8%) underwent overnight pump suspension studies before and after treatment with canagliflozin (CANA). On both nights, basal insulin was suspended at 3 AM and plasma glucose (PG), β-hydroxybutyrate (BHB), free fatty acids (FFA), plasma insulin (PI), and glucagon were measured. Studies were terminated 6 h after suspension or if PG rose to350 mg/dL or BHB2.5 mmol/L.PI levels at the start of suspension were reduced by 30% after CANA treatment (44 ± 11 uU/mL vs. 31 ± 10 uU/mL, P 0.01), but baseline PG, BHB, FFA, and glucagon levels were not significantly different. During the suspension, PG rose from 104 ± 10 to 301 ± 21 mg/dL before treatment, but only from 109 ± 8 to 195 ± 14 mg/dL after treatment (P = 0.002 vs. pretreatment values). On the other hand, CANA treatment did not significantly affect the magnitude of increases in FFA, BHB, and glucagon levels during the suspension study.These data indicate that SGLT2i do not accelerate the rate of ketogenesis following the interruption of basal insulin infusion in T1D. Rather, the failure of patients to promptly recognize early metabolic decompensation relates to the much more gradual rise in PG levels.

Background: Managing type 1 diabetes (T1D) in young children presents challenges to families and caregivers. Pump therapy may reduce challenges and benefit glycemic control. However, pump use is not universal; parent-reported reasons for lack of uptake are not well described. Methods: Parents of children

Sensor-augmented pump therapy (SAPT) integrates real-time continuous glucose monitoring (RT-CGM) with continuous subcutaneous insulin infusion (CSII) and offers an alternative to multiple daily injections (MDI). Previous studies provide evidence that SAPT may improve clinical outcomes among people with type 1 diabetes. Sensor-Augmented Pump Therapy for A1c Reduction (STAR) 3 is a multicenter randomized controlled trial comparing the efficacy of SAPT to that of MDI in subjects with type 1 diabetes.Subjects were randomized to either continue with MDI or transition to SAPT for 1 year. Subjects in the MDI cohort were allowed to transition to SAPT for 6 months after completion of the study. SAPT subjects who completed the study were also allowed to continue for 6 months. The primary end point was the difference between treatment groups in change in hemoglobin A1c (HbA1c) percentage from baseline to 1 year of treatment. Secondary end points included percentage of subjects with HbA1cor =7% and without severe hypoglycemia, as well as area under the curve of time spent in normal glycemic ranges. Tertiary end points include percentage of subjects with HbA1cor =7%, key safety end points, user satisfaction, and responses on standardized assessments.A total of 495 subjects were enrolled, and the baseline characteristics similar between the SAPT and MDI groups. Study completion is anticipated in June 2010.Results of this randomized controlled trial should help establish whether an integrated RT-CGM and CSII system benefits patients with type 1 diabetes more than MDI.

The optimal number/timing of calibrations entered into the CGMS (Medtronic MiniMed, Northridge, CA) continuous glucose monitoring system have not been previously described.Fifty subjects with Type 1 diabetes mellitus (10-18 years old) were hospitalized in a clinical research center for approximately 24 h on two separate days. CGMS and OneTouch Ultra meter (LifeScan, Milpitas, CA) data were obtained. The CGMS was retrospectively recalibrated using the Ultra data varying the number and timing of calibrations. Resulting CGMS values were compared against laboratory reference values.There was a modest improvement in accuracy with increasing number of calibrations. The median relative absolute deviation (RAD) was 14%, 15%, 13%, and 13% when using three, four, five, and seven calibration values, respectively (P0.001). Corresponding percentages of CGMS-reference pairs meeting the International Organisation for Standardisation criteria were 66%, 67%, 71%, and 72% (P0.001). Nighttime accuracy improved when daytime calibrations (pre-lunch and pre-dinner) were removed leaving only two calibrations at 9 p.m. and 6 a.m. (median difference, -2 vs. -9 mg/dL, P0.001; median RAD, 12% vs. 15%, P = 0.001). Accuracy was better on visits where the average absolute rate of glucose change at the times of calibration was lower. On visits with average absolute rates0.5, 0.5 to1.0, 1.0 to1.5, andor=1.5 mg/dL/min, median RAD values were 13% versus 14% versus 17% versus 19%, respectively (P = 0.05).Although accuracy is slightly improved with more calibrations, the timing of the calibrations appears more important. Modifying the algorithm to put less weight on daytime calibrations for nighttime values and calibrating during times of relative glucose stability may have greater impact on accuracy.

Various statistical methods are commonly used to assess the accuracy of near-continuous glucose sensors. The performance and reliability of these methods have not been well described.We used computer simulation to describe the behavior of several statistical measures including error grid analysis, receiver operating characteristics, correlation, and repeated measures under varying conditions. Actual data from an inpatient accuracy study conducted by the Diabetes Research in Children Network (DirecNet) were also used to demonstrate these limitations.Sensors that were made artificially inaccurate by randomly shuffling the pairings to reference values still fell in Zone A or B 78% of the time for the Clarke grid and 79% of the time for the modified grid. Area under the curve values for these shuffled pairs averaged 64% for hypoglycemia and 68% for hyperglycemia. Continuous error grid analysis resulted in 75% of shuffled pairs designated as "Accurate Readings" or "Benign Errors." Correlation analysis gave inconsistent results for sensors simulated to have identical accuracies with values ranging from 0.50 to 0.96. Simplistic repeated-measures analyses accounting for subject effects, but ignoring temporal correlation patterns substantially inflated the probability of falsely obtaining a statistically significant result. In simulations where the null hypothesis was correct, 23% of observed P values were0.05 and 12% of observed P values were0.01.Commonly used statistical methods can give overly optimistic and/or inconsistent notions of sensor accuracy if results are not placed in proper context. Novel techniques are needed to assess the accuracy of near-continuous glucose sensors.

BACKGROUND The GlucoWatch G2 Biographer (GW2B) (Cygnus, Inc., Redwood City, CA) provides near-continuous monitoring of glucose values in near real time. This device is equipped with two types of alarms to detect hypoglycemia. The hypoglycemia alarm is triggered when the current glucose measurement falls below the level set by the user. The "down alert" alarm is triggered when extrapolation of the current glucose trend anticipates hypoglycemia to occur within the next 20 min. METHODS We used data from an inpatient accuracy study to assess the performance of these alarms. During a 24-h clinical research center stay, 89 children and adolescents with Type 1 diabetes mellitus (3.5-17.7 years old) wore 174 GW2B devices and had frequent serum glucose determinations during the day and night. RESULTS Sensitivity to detect hypoglycemia (reference glucose < or = 60 mg/dL) during an insulin-induced hypoglycemia test was 24% with the hypoglycemia alarm alone and 88% when combined with the down alert alarm. Overnight sensitivity from 11 p.m. to 6 a.m. was 23% with the hypoglycemia alarm alone and 77% when combined with the down alert alarm. For 16% of hypoglycemia alarms, the reference glucose was above 70 mg/dL for 30 min before and after the time of the alarm. For the two alarm types combined, the corresponding false-positive rate increased to 62%. CONCLUSIONS The down alert alarm substantially improves the sensitivity of the GW2B to detect hypoglycemia at the price of a large increase in the false alarm rate. The utility of these alarms in the day-to-day management of children with diabetes remains to be determined.

Use of the Medtronic MiniMed Continuous Glucose Monitoring System (CGMS) in non-diabetic children has revealed many low and high sensor glucose (SG) values, suggesting that the original analytical algorithm (Solutions 2.0) might be overreading glycemic excursions. A revised algorithm (Solutions 3.0) was introduced in 2001. Our aim was to compare analyses of the same sensor profiles using both programs. Twenty-five lean, non-diabetic subjects (mean age 14 +/- 4 years) underwent continuous glucose monitoring with CGMS for up to 72 h. Sensor tracings were analyzed with both algorithms and compared. Separate analyses were performed for nocturnal readings (12-6 a.m.). Mean SG values were similar (103 +/- 24 mg/dL for version 2.0 vs. 100 +/- 14 for version 3.0), but the distribution was significantly different: 13.8% of total SG were70 mg/dL by version 2.0 versus 8.2% by version 3.0 (p0.001), and 7.7% of total SG were150 mg/dL by version 2.0 versus 4.7% by version 3.0 (p = 0.02). Of nocturnal SG values, 25.8% were70 mg/dL by version 2.0 compared with 17.9% by version 3.0, and 9.4% were150 mg/dL by version 2.0 compared with 4.0% by version 3.0. In lean non-diabetic children, Solutions 2.0 identified significantly more hypoglycemia and hyperglycemia than Solutions 3.0. Similar analyses in 40 children with type 1 diabetes revealed no significant differences. Solutions 3.0 may be a more useful algorithm for preventing over-reading of low and high SG readings in non-diabetic children, whereas both algorithms give similar results in children with diabetes.

Self-monitoring of blood glucose was described as one of the most important advancements in diabetes management since the invention of insulin in 1920. Recent advances in glucose sensor technology for measuring interstitial glucose concentrations have challenged the dominance of glucose meters in diabetes management, while raising questions about the relationships between interstitial and blood glucose levels. This article will review the differences between interstitial and blood glucose and some of the challenges in measuring interstitial glucose levels accurately.