BACKGROUND The safety and effectiveness of automated glycemic management have not been tested in multiday studies under unrestricted outpatient conditions. METHODS In two random-order, crossover studies with similar but distinct designs, we compared glycemic control with a wearable, bihormonal, automated, “bionic” pancreas (bionic-pancreas period) with glycemic control with an insulin pump (control period) for 5 days in 20 adults and 32 adolescents with type 1 diabetes mellitus. The automatically adaptive algorithm of the bionic pancreas received data from a continuous glucose monitor to control subcutaneous delivery of insulin and glucagon. RESULTS Among the adults, the mean plasma glucose level over the 5-day bionic-pancreas period was 138 mg per deciliter (7.7 mmol per liter), and the mean percentage of time with a low glucose level (<70 mg per deciliter [3.9 mmol per liter]) was 4.8%. After 1 day of automatic adaptation by the bionic pancreas, the mean (±SD) glucose level on continuous monitoring was lower than the mean level during the control period (133±13 vs. 159±30 mg per deciliter [7.4±0.7 vs. 8.8±1.7 mmol per liter], P<0.001) and the percentage of time with a low glucose reading was lower (4.1% vs. 7.3%, P = 0.01). Among the adolescents, the mean plasma glucose level was also lower during the bionic-pancreas period than during the control period (138±18 vs. 157±27 mg per deciliter [7.7±1.0 vs. 8.7±1.5 mmol per liter], P = 0.004), but the percentage of time with a low plasma glucose reading was similar during the two periods (6.1% and 7.6%, respectively; P = 0.23). The mean frequency of interventions for hypoglycemia among the adolescents was lower during the bionic-pancreas period than during the control period (one per 1.6 days vs. one per 0.8 days, P<0.001). CONCLUSIONS As compared with an insulin pump, a wearable, automated, bihormonal, bionic pancreas improved mean glycemic levels, with less frequent hypoglycemic episodes, among both adults and adolescents with type 1 diabetes mellitus. (Funded by the National Institute of Diabetes and Digestive and Kidney Diseases and others; ClinicalTrials.gov numbers, NCT01762059 and NCT01833988.)

The effectiveness and safety of continuous glucose monitors (CGMs) is dependent on their accuracy and reliability. The objective of this study was to compare 3 CGMs in adult and pediatric subjects with type 1 diabetes under closed-loop blood-glucose (BG) control. Twenty-four subjects (12 adults) with type 1 diabetes each participated in one 48-hour closed-loop BG control experiment. Venous plasma glucose (PG) measurements obtained every 15 minutes (4657 values) were paired in time with corresponding CGM glucose (CGMG) measurements obtained from 3 CGMs (FreeStyle Navigator, Abbott Diabetes Care; G4 Platinum, Dexcom; Enlite, Medtronic) worn simultaneously by each subject. The Navigator and G4 Platinum (G4) had the best overall accuracy, with an aggregate mean absolute relative difference (MARD) of all paired points of 12.3 ± 12.1% and 10.8 ± 9.9%, respectively. Both had lower MARDs of all paired points than Enlite (17.9 ± 15.8%, P < .005). Very large errors (MARD > 50%) were less common with the G4 (0.5%) than with the Enlite (4.3%, P = .0001) while the number of very large errors with the Navigator (1.4%) was intermediate between the G4 and Enlite (P = .1 and P = .06, respectively). The average MARD for experiments in adolescent subjects were lower than in adult subjects for the Navigator and G4, while there was no difference for Enlite. All 3 devices had similar reliability. A comprehensive head-to-head-to-head comparison of 3 CGMs revealed marked differences in both accuracy and precision. The Navigator and G4 were found to outperform the Enlite in these areas.

A single control algorithm, initialized only with subject weight, can quickly adapt to regulate glycemia in patients with TID and highly variable insulin requirements.

OBJECTIVEWe studied the association between glycemic variability (GV) reflecting hypoglycemic stress and cardiovascular autonomic function in subjects with type 1 diabetes.RESEARCH DESIGN AND METHODSForty-four type 1 diabetic patients (mean age 34 ± 13 years, 40% male, 86% Caucasian, mean diabetes duration 13 ± 6 years, mean hemoglobin A1c [HbA1c] 8.0 ± 1.2% [64 ± 5 mmol/mol]) without cardiovascular disease, dyslipidemia, or hypertension participated in this pilot study. Indices of GV reflective of hypoglycemic stress (low blood glucose index [LBGI] and area under the curve [AUC] for hypoglycemia) were computed using data obtained during 5-day continuous glucose monitoring. Cardiovascular autonomic neuropathy (CAN) was assessed using standardized cardiovascular reflex testing and measures of heart rate variability (HRV), which were analyzed as time and frequency domain measures.RESULTSBoth LBGI and AUC hypoglycemia had a significant negative association with the low-frequency power of HRV (r = −0.47, P = 0.002; r = −0.43, P = 0.005, respectively) and with the high-frequency power of HRV (r = −0.37, P = 0.018; r = −0.38, P = 0.015, respectively). These inverse associations persisted after adjusting for HbA1c, although they were attenuated in multivariable analysis after adjustment for age, diabetes duration, and several other covariates.CONCLUSIONSIncreased GV promoting hypoglycemic stress was associated with reduced HRV independent of glycemic control as assessed by HbA1c. These pilot data suggest that glucose variability may contribute to cardiovascular autonomic dysfunction among adults with type 1 diabetes.

Background: The physiologic delay in glucose diffusion from the blood to the interstitial fluid and instrumental factors contribute to the delay between changes in plasma glucose (PG) and measurements made by continuous glucose monitors (CGMs). This study compared the duration of this delay for three CGMs. Methods: A total of 24 healthy adolescent and adult subjects with type 1 diabetes wore three CGM devices simultaneously for 48 hours: Dexcom G4 Platinum, Abbott Navigator, and Medtronic Enlite. The time delay between PG and CGM-estimated plasma glucose (CGMG) was estimated by comparing time-shifted CGMG with reference PG taken every 15 minutes. Results: The delay estimated by our approach was larger for the Navigator than for the G4 Platinum in adolescents (7.7 ± 1.1 versus 5.6 ± 0.9 min, P = .0396) and adults (10.9 ± 1.1 versus 8.1 ± 0.7 min, P = .0107). The delay was nominally longer for the Navigator than for the Enlite in both the adolescent (7.7 ± 1.1 versus 4.3 ± 1.0 min, P = .0728) and adult (10.9 ± 1.1 versus 8.3 ± 0.9 min, P = .111) populations, but these differences were not statistically significant. There was no difference in the delay between G4 Platinum and Enlite. Adolescents had shorter delays than adults for all three devices. There was a significant correlation between longer delay and increasing age for the G4 Platinum and Navigator. Conclusions: There are differences in the estimated PG to CGMG time delays between CGM devices in the same subjects. The delay between PG and CGMG is smaller for adolescents than for adults. The PG–to–CGMG time delay is influenced by both instrument and host factors.