Abstract

Aim:

Glucagon is recommended to treat severe hypoglycemia in nonhospital environments, when a patient with type 1 diabetes mellitus (T1DM) is unconscious and unable to eat or drink. However, the actual possession rate of glucagon in Japan has not been investigated. Subjects and

Methods:

We recruited 208 T1DM patients older than 15 years of age. The patients were treated at 16 hospitals and clinics in different regions of Japan. Answers were obtained using a self-administered questionnaire about the possession, the experience of usage, and the preference to possess glucagon after reading what is glucagon and when it is used. A stepwise logistic regression analysis was performed to assess the influence of various factors on the possession of glucagon.

Results:

The possession rate of glucagon was 15.9%, and the rate of those who had experience of using glucagon to treat severe hypoglycemia was 6.0%. The rate of preference to possess glucagon at home after reading the description of glucagon was 39.0%. The possession of glucagon was significantly associated with results of the Glucagon Knowledge Test (odds ratio=24.1; 95% confidence interval, 3.2-183.3; P=0.002) and the history of severe hypoglycemia within 1 year (odds ratio=4.8; 95% confidence interval, 2.0-12.0; P=0.001).

Conclusions:

Glucagon as a measure to treat severe hypoglycemia was underutilized among T1DM patients in Japan.

Abstract

Objective:

Knowledge of the accuracy of continuous glucose monitoring (CGM) devices is important for its use as a management tool for individuals with diabetes and for its use to assess outcomes in clinical studies. Using data from several inpatient studies, we compared the accuracy of two sensors, the Medtronic Enlite™ using MiniMed Paradigm(®) Veo™ calibration and the Sof-Sensor(®) glucose sensor using Guardian(®) REAL-Time CGM calibration (all from Medtronic Diabetes, Northridge, CA). Subjects and

Methods:

Nocturnal data were analyzed from eight inpatient studies in which both CGM and reference glucose measurements were available. The analyses included 1,666 CGM-reference paired glucose values for the Enlite in 54 participants over 69 nights and 3,627 paired values for the Sof-Sensor in 66 participants over 91 nights.

Results:

The Enlite sensor tended to report glucose levels lower than the reference over the entire range of glucose values, whereas the Sof-Sensor values tended to be higher than reference values in the hypoglycemic range and lower than reference values in the hyperglycemic range. The overall median sensor-reference difference was -15 mg/dL for the Enlite and -1 mg/dL for the Sof-Sensor (P<0.001). The median relative absolute difference was 15% for the Enlite versus 12% for the Sof-Sensor (P=0.06); 66% of Enlite values and 73% of Sof-Sensor values met International Organization for Standardization criteria.

Conclusions:

We found that the Enlite tended to be biased low over the entire glucose range, whereas the Sof-Sensor showed the more typical sensor pattern of being biased high in the hypoglycemic range and biased low in the hyperglycemic range.

Abstract

Background and Aims:

The aim of this study was to compare blood glucose and plasma insulin profiles after bolus insulin infusion by a patch pump (PP) versus a conventional pump (CP), directly after placement and after Day 3 of use. Patients and

Methods:

Twenty patients with type 1 diabetes came in for two blocks of visits: one block of two visits while wearing the OmniPod(®) (Insulet Corp., Bedford, MA) insulin pump (PP) and one block of two visits while wearing the Medtronic Diabetes (Northridge, CA) Paradigm(®) pump (CP). Patients administered an identical mealtime insulin bolus of at least 6 IU.

Results:

For PP, maximum glucose levels were 28.7% lower on Day 3 (P=0.020), when maximum insulin levels were 30.3% higher (P=0.002). For CP, maximum glucose levels were 26.5% lower on Day 3 (P=0.015), when maximum insulin levels were 46.4% higher (P=0.003). Glucose levels (mean [interquartile range]) were significantly lower on Day 3 for PP (168.2 [145.8] mg/dL vs. 139.4 [77.8] mg/dL; P=0.013), but not significantly so for CP (159.0 [66.1] mg/dL vs. 139.5 [57.9] mg/dL; P=0.084). Mean insulin levels were significantly higher on Day 3 for CP (195 [120] pmol/L vs. 230 [90] pmol/L; P=0.01), but not significantly so for PP (178 [106] pmol/L vs. 194 [120] pmol/L; P=0.099). There were no significant differences between the two catheter lengths.

Conclusions:

Postprandial glycemic excursions were lower on Day 3 of catheter wear time, but there were no differences between PPs and CPs. These findings support the proposal that catheter wear time plays an important role in insulin absorption.

Abstract

Objective:

This study assessed the accuracy and reliability of three continuous glucose monitoring (CGM) systems. Research Design and

Methods:

We studied the Animas(®) (West Chester, PA) Vibe™ with Dexcom(®) (San Diego, CA) G4™ version A sensor (G4A), the Abbott Diabetes Care (Alameda, CA) Freestyle(®) Navigator I (NAV), and the Medtronic (Northridge, CA) Paradigm(®) with Enlite™ sensor (ENL) in 20 patients with type 1 diabetes mellitus. All systems were investigated both in a clinical research center (CRC) and at home. In the CRC, patients received a meal with a delayed and increased insulin dose to induce a postprandial glucose peak and nadir. Hereafter, randomization determined which two of the three systems would be worn at home until the end of functioning, attempting use beyond manufacturer-specified lifetime. Patients performed at least five reference finger sticks per day. An analysis of variance was performed on all data points ≥15 min apart.

Results:

Overall average mean absolute relative difference (MARD) (SD) measured at the CRC was 16.5% (14.3%) for NAV and 16.4% (15.6%) for ENL, outperforming G4A at 20.5% (18.2%) (P<0.001). Overall MARD when assessed at home was 14.5% (16.7%) for NAV and 16.5 (18.8%) for G4A, outperforming ENL at 18.9% (23.6%) (P=0.006). Median time until end of functioning was similar: 10.0 (1.0) days for G4A, 8.0 (3.5) days for NAV, and 8.0 (1.5) days for ENL (P=0.119).

Conclusions:

In the CRC, G4A was less accurate than NAV and ENL sensors, which seemed comparable. However, at home, ENL was less accurate than NAV and G4A. Moreover, CGM systems often show sufficient accuracy to be used beyond manufacturer-specified lifetime.

Bergenstal et al. (Diabetes Technol Ther 2013;15:198-211) described an important approach toward standardization of reporting and analysis of continuous glucose monitoring and self-monitoring of blood glucose (SMBG) data. The ambulatory glucose profile (AGP), a composite display of glucose by time of day that superimposes data from multiple days, is perhaps the most informative and useful of the many graphical approaches to display glucose data. However, the AGP has limitations; some variations are desirable and useful. Synchronization with respect to meals, traditionally used in glucose profiles for SMBG data, can improve characterization of postprandial glucose excursions. Several other types of graphical display are available, and recently developed ones can augment the information provided by the AGP. There is a need to standardize the parameters describing glycemic variability and cross-validate the available computer programs that calculate glycemic variability. Clinical decision support software can identify and prioritize clinical problems, make recommendations for modifications of therapy, and explain its justification for those recommendations. The goal of standardization is challenging in view of the diversity of clinical situations and of computing and display platforms and software. Standardization is desirable but must be done in a manner that permits flexibility and fosters innovation.

This study compared glycemic variability in patients with type 2 diabetes given sitagliptin or voglibose.Seventeen type 2 diabetes patients were given sitagliptin 50 mg/day or voglibose 0.9 mg/day for 2 months and were hospitalized for a 4-day evaluation by continuous glucose monitoring (CGM). On discharge, they were crossed over to the other regimen for 2 months of treatment/4 days of evaluation. The CGM data were used to compare each parameter for glycemic variability.The average glucose levels with sitagliptin and voglibose were significantly different at 138.6 and 152.6 mg/dL for 24 h (P=0.014) and 147.2 and 160.9 mg/dL for during daytime (P=0.050), respectively. The patients' glucose levels with sitagliptin and voglibose were significantly different at 125.3 and 139.7 mg/dL before breakfast (P=0.015) and 112.7 and 131.4 mg/dL before lunch (P=0.049), respectively. The time from before meal to postprandial peak glucose levels was significantly longer after dinner with voglibose than with sitagliptin (91.5 and 122.3 min, respectively; P=0.012). All of the slopes of glucose elevation were significantly lower with voglibose after each meal, with that after breakfast, lunch, and dinner being 1.16 and 0.86 mg/dL/min (P=0.031), 0.70 and 0.45 mg/dL/min (P=0.048), and 1.06 and 0.73 mg/dL/min (P=0.028), respectively.This CGM-based pilot study revealed that sitagliptin significantly lowered 24-h and daytime mean glucose levels and glucose levels before breakfast and lunch compared with voglibose, whereas the time from before dinner to peak postprandial glucose levels was significantly longer, and the slope of postprandial elevation of glucose level was significantly lower after each meal, with voglibose compared with sitagliptin.

Quantifying body fat is currently an area of active research. Recent studies have shown that the quantity and location of fat in different compartments have varying clinical significance. This information can now be obtained from computed tomography (CT) or magnetic resonance (MR), and it can inform clinical decision making for patient management. Diabetes patients with insulin resistance and hyperinsulinemia have nonalcoholic fatty liver disease (NAFLD) ranging from steatosis to nonalcoholic steatohepatitis (NASH) and cirrhosis, typically diagnosed by liver biopsy or serum markers. There is now an emerging role of noninvasive imaging tests such as MR imaging or MR spectroscopy or elastography, which can provide quantitative information and have potential to avoid biopsy. Obese patients with diabetes are also at risk for cardiovascular disease and cancer. There is an emerging role for imaging in early detection of not only structural but also functional abnormalities of myocardium at a subclinical stage. Screening for cancer is currently recommended only for breast, colon, prostate, and cervix. Given wider availability and advances in imaging techniques such as positron emission tomography/CT (faster scans with higher resolution and less ionizing radiation) and better understanding of molecular biology and risk-stratification, more and more cancers are being detected in early stages with better clinical outcomes. Concerns related to cost, overdiagnosis, and unnecessary interventions must be addressed before population-based screening for other cancers is recommended. Based on ongoing imaging research, it is expected that it will be possible to provide more precise measurement of body fat and detect cardiovascular disease and cancers earlier in their course.

Abstract

Background:

Hypoglycemia is a potentially fatal condition. Continuous glucose monitoring (CGM) has the potential to detect hypoglycemia in real time and thereby reduce time in hypoglycemia and avoid any further decline in blood glucose level. However, CGM is inaccurate and shows a substantial number of cases in which the hypoglycemic event is not detected by the CGM. The aim of this study was to develop a pattern classification model to optimize real-time hypoglycemia detection. Materials and

Methods:

Features such as time since last insulin injection and linear regression, kurtosis, and skewness of the CGM signal in different time intervals were extracted from data of 10 male subjects experiencing 17 insulin-induced hypoglycemic events in an experimental setting. Nondiscriminative features were eliminated with SEPCOR and forward selection. The feature combinations were used in a Support Vector Machine model and the performance assessed by sample-based sensitivity and specificity and event-based sensitivity and number of false-positives.

Results:

The best model was composed by using seven features and was able to detect 17 of 17 hypoglycemic events with one false-positive compared with 12 of 17 hypoglycemic events with zero false-positives for the CGM alone. Lead-time was 14 min and 0 min for the model and the CGM alone, respectively.

Conclusions:

This optimized real-time hypoglycemia detection provides a unique approach for the diabetes patient to reduce time in hypoglycemia and learn about patterns in glucose excursions. Although these results are promising, the model needs to be validated on CGM data from patients with spontaneous hypoglycemic events.