Body mass index changes in youth in the first year after type 1 diabetes diagnosis.

OBJECTIVES: To describe changes in weight and body mass index (BMI) during the first year following diagnosis of type 1 diabetes (T1D) and associations with demographic and clinical characteristics. STUDY DESIGN: The Pediatric Diabetes Consortium includes 7 US centers with prospective longitudinal data from initial T1D diagnosis. This analysis includes 530 youth with diabetes duration of ≥1 year and measures of BMI at 3 and 12 months after diagnosis. BMI trajectory of participants and relationships between the change in BMI z-score from baseline (3 months) to 12 months with demographic characteristics, hemoglobin A1c at baseline, and insulin delivery mode at baseline were evaluated. RESULTS: As a group, BMI z-scores increased sharply from diagnosis for 1-3 months but remained relatively stable from +0.51 at 3 months to +0.48 at 12 months. Children aged 2-<5 years experienced a significant positive change in BMI z-score between 3 and 12 months, and there was a similar trend among girls that did not reach statistical significance. No significant differences were found for race, socioeconomic status, or insulin delivery mode. CONCLUSIONS: These data suggest that increased BMI during the first year of treatment of most youth with T1D reflects regain of weight lost before diagnosis. There is, however, a propensity toward additional weight gain in younger children and girls.

The Impact of Accelerometer and Heart Rate Data on Hypoglycemia Mitigation in Type 1 Diabetes.

Aerobic exercise can lower blood glucose levels and alter insulin sensitivity both during and several hours after exercise, creating challenges for a closed-loop artificial pancreas. Predictive low glucose suspend (PLGS) algorithms are a first step toward an artificial pancreas, but few of these have been successfully applied to exercise. This study incorporates physical activity measurements from a combined accelerometer/heart rate monitor (HRM) to improve the performance of an existing PLGS algorithm at mitigating exercise-associated hypoglycemia in participants with type 1 diabetes. In all, 22 subjects with type 1 diabetes on insulin pump therapy were provided a combined accelerometer/HRM and (if not already using one) a continuous glucose monitor (CGM), then instructed to go about their everyday lives while wearing the devices. After the monitoring period, each subject's insulin pump, CGM, and accelerometer/HRM were downloaded and the data were used to augment an existing PLGS algorithm to incorporate activity. Using a computer simulator, the accelerometer-augmented algorithm was compared to the HRM-augmented algorithm to determine which was most effective at mitigating hypoglycemia. Mean length of monitoring was 4.9 days. Across all subjects, 11 061 CGM readings were recorded during the monitoring period. In the simulator analysis, the PLGS algorithm reduced hypoglycemia by 62%, compared to 71% and 74% reductions for the HRM-augmented and accelerometer-augmented algorithms, respectively; combined accelerometer and HRM augmentation provided a 76% reduction. In a simulated setting, the accelerometer-augmented pump suspension algorithm decreases the incidence of exercise-related hypoglycemia by a meaningful amount compared to the PLGS algorithm alone. Results also failed to justify the additional user burden of a HRM.

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.

Severe hypoglycemia and diabetic ketoacidosis in adults with type 1 diabetes: results from the T1D Exchange clinic registry.

CONTEXT: Few studies have assessed factors associated with severe hypoglycemia (SH) and diabetic ketoacidosis (DKA) in adults with type 1 diabetes (T1D). OBJECTIVE: Our objective was to determine frequency of and factors associated with the occurrence of SH and DKA in adults with T1D. DESIGN AND SETTING: We conducted a cross-sectional analysis from the T1D Exchange clinic registry at 70 U.S. endocrinology centers. PATIENTS: Analysis included 7012 participants in the T1D Exchange clinic registry aged 26 to 93 years old with T1D for ≥2 years. RESULTS: Higher frequencies of SH and DKA were associated with lower socioeconomic status (P < .001). SH was strongly associated with diabetes duration (P < .001), with 18.6% of those with diabetes ≥40 years having an event in the past 12 months. SH frequency was lowest in those with hemoglobin A1c (HbA1c) levels of 7.0% (53 mmol/mol) to 7.5% (58 mmol/mol), being higher in those with HbA1c levels <7.0% (<53 mmol/mol) or >7.5% (>58 mmol/mol). DKA frequency increased with higher HbA1c levels (P < .001), with 21.0% of those with HbA1c ≥10.0% (≥86 mmol/mol) having an event in the past 12 months. CONCLUSIONS: SH and DKA are more common in those with lower socioeconomic status. DKA, most common in those with HbA1c ≥10.0% (≥86 mmol/mol), should be largely preventable. In contrast, SH, most frequent with diabetes ≥40 years duration, cannot be abolished given the limitation of current therapies. To reduce SH in adults with longstanding diabetes, consideration should be given to modifying HbA1c goals, particularly in patients with very low HbA1c levels.