A Pilot Trial to Evaluate the Accuracy of a Novel Non-Invasive Glucose Meter.

The non-invasive self-monitoring of blood glucose (SMBG) has been the subject of intense investigation over recent decades. We conducted a pilot study designed to examine a novel non-invasive glucometer, the HGR GWave, utilizing radiofrequency (RF) sensing. Blood glucose levels assessed by this HGR prototype were compared to measurements performed by a hexokinase core laboratory assay during an oral glucose tolerance test (oGTT) for 5 subjects with type 2 diabetes. The HGR glucose meter readings were also compared to two Abbot Freestyle® glucose meters, which were also used for calibration. The accuracy of the results was evaluated through the calculation of relative absolute difference (RAD), specified percentage differences between 43 reference glucose measurements, and using comparator measurements. The median RAD was -4.787. We detected 79.04%, 92.99% and 97.64% of HGR readings within ±10%, ±15% and ±20% of the reference glucose measurements. The HGR readings had a high correlation with reference lab glucose measurements with R2 = 0.924 (95% CI 0.929-0.979; p < 0.0001). When compared to the Freestyle® glucose meters 94.3% and 100% of the readings were within ±5% and ±10%, with R2 = 0.975 (0.975-0.994; p < 0.0001). The HGR prototype glucose meter was found to be accurate in detecting real-time blood glucose during an oGTT in this small pilot study. A study with a broader range of blood glucose levels is needed to further assess its accuracy and its suitability for clinical use.

Effect of prandial treatment timing adjustment, based on continuous glucose monitoring, in patients with type 2 diabetes uncontrolled with once-daily basal insulin: A randomized, phase IV study.

AIMS: To evaluate the glycaemic control achieved by prandial once-daily insulin glulisine injection timing adjustment, based on a continuous glucose monitoring sensor, in comparison to once-daily insulin glulisine injection before breakfast in patients with type 2 diabetes who are uncontrolled with once-daily basal insulin glargine. MATERIALS AND METHODS: This was a 24-week open-label, randomized, controlled, multicentre trial. At the end of an 8-week period of basal insulin optimization, patients with HbA1c ≥ 7.5% and FPG < 130 mg/dL were randomized (1:1) to either arm A (no sensor) or arm B (sensor) to receive 16-week intensified prandial glulisine treatment. Patients in arm A received pre-breakfast glulisine, and patients in arm B received glulisine before the meal with the highest glucose elevation based on sensor data. The primary outcome was mean HbA1c at week 24 and secondary outcomes included rates of hypoglycaemic events and insulin dosage. RESULTS: A total of 121 patients were randomized to arm A (n = 61) or arm B (n = 60). There was no difference in mean HbA1c at week 24 between arms A and B (8.5% ± 1.2% vs 8.4% ± 1.0%; P = .66). The prandial insulin glulisine dosage for arm A and arm B was 9.3 and 10.1 units, respectively (P = .39). The frequency of hypoglycaemic events did not differ between study arms (36.1% vs 51.7%; P = .08). CONCLUSION: Using a CGM sensor to identify the meal with the highest glucose excursion and adjusting the timing of prandial insulin treatment did not show any advantage in terms of glycaemic control or safety in our patients.

Hybrid Closed-Loop Insulin Delivery in Type 1 Diabetes During Supervised Outpatient Conditions.

BACKGROUND: Efficacy and safety of the Medtronic Hybrid Closed-Loop (HCL) system were tested in subjects with type 1 diabetes in a supervised outpatient setting. METHODS: The HCL system is a prototype research platform that includes a sensor-augmented insulin pump in communication with a control algorithm housed on an Android-based cellular device. Nine subjects with type 1 diabetes (5 female, mean age 53.3 years, mean A1C 7.2%) underwent 9 studies totaling 571 hours of closed-loop control using either default or personalized parameters. The system required meal announcements with estimates of carbohydrate (CHO) intake that were based on metabolic kitchen quantification (MK), dietician estimates (D), or subject estimates (Control). Postprandial glycemia was compared for MK, D, and Control meals. RESULTS: The overall sensor glucose mean was 145 ± 43, the overall percentage time in the range 70-180 mg/dL was 80%, the overall percentage time <70 mg/dL was 0.79%. Compared to intervals of default parameter use (225 hours), intervals of personalized parameter use (346 hours), sensor glucose mean was 158 ± 49 and 137 ± 37 mg/dL (P < .001), respectively, and included more time in range (87% vs 68%) and less time below range (0.54% vs 1.18%). Most subjects underestimated the CHO content of meals, but postprandial glycemia was not significantly different between MK and matched Control meals (P = .16) or between D and matched Control meals (P = .76). There were no episodes of severe hypoglycemia. CONCLUSIONS: The HCL system was efficacious and safe during this study. Personally adapted HCL parameters were associated with more time in range and less time below range than default parameters. Accurate estimates of meal CHO did not contribute to improved postprandial glycemia.

Key elements for successful intensive insulin pump therapy in individuals with type 1 diabetes.

OBJECTIVE: Clinical trials have demonstrated that in individuals with type 1 diabetes the use of CSII pump resulted in better glucose control. Advantages of pumps therapy include many features such as the bolus calculators (wizard). These features are optional and therefore it is important to determine whether their use is associated with better glucose control. Thus, the aim of this analysis was to assess which features and parameters of insulin pump use are associated with better glucose control. METHODS: Data regarding consecutive patients with type 1 diabetes treated with an insulin pump and attending a tertiary referral for intensive glucose control was included in this analysis. The relationship between glycemic indices and treatment parameters (number of insulin units, number of glucose readings, bolus calculator use etc.) was assessed. RESULTS: A statistically significant relationship was found between glycemic indices and wizard use. Thus, individuals that used the wizard function in 50% of their boluses had an A1C, mean blood glucose values that were 0.6% (p=0.008) and 25mg/dL (p=0.000) lower respectively. CONCLUSION: The use of the bolus calculator feature was associated with better glucose control. Larger prospective clinical trials are needed in order to further validate this finding.