Continuous Glucose Monitoring In Subjects Research Articles

Continuous glucose monitoring in subjects undergoing bariatric surgery: Diurnal and nocturnal glycemic patterns

Background and aimsAlthough the use of Continuous Glucose Monitoring (CGM) is rapidly extending, little evidence is currently available on daily glycemic excursions after different bariatric procedures. We assessed glycemic patterns after sleeve gastrectomy (SG) and roux-en-Y gastric bypass (RYGB) using CGM. Methods and resultsCross-sectional study in subjects who had undergone RYGB (n = 22) or SG (n = 29) since at least 1 year, without pre-surgery or in current diabetes (T2DM) remission. All subjects underwent 7 day-CGM (Dexcom G4 PLATINUM), which provides glucose variability (GV), number and time spent in hypoglycemia, hypoglycemia patterns (postprandial, nocturnal or mixed). All indexes of GV were higher after RYGB than after SG (p < 0.001). Twenty-eight (55%) subjects experienced hypoglycemia. The number of events was higher after RYGB than SG (p = 0.017) while it did not differ in subjects with or without pre-surgery T2DM (p = 0.129). Overall, 9 (32%) subjects presented hypoglycemia exclusively during the postprandial period, 8 (29%) an exclusively nocturnal pattern and 11 (39%) a mixed pattern. The nocturnal pattern was more frequent after SG than RYGB (53.8% vs 6.7%, p = 0.036) while no difference was observed in subjects with or without pre-surgery T2DM (p = 0.697). Hypoglycemia symptoms were more frequent in subjects with postprandial than in those with nocturnal pattern (77.8% vs 12.5%, p = 0.015). ConclusionsRYGB is characterized by a greater GV and a higher number of hypoglycemia events mostly post-prandial and symptomatic, while SG is associated with nocturnal and often asymptomatic hypoglycemia. These findings suggest that post-bariatric hypoglycemia is a more complex, not exclusively, postprandial phenomenon.

2202-PUB: Time in Hypoglycemia Recorded by Continuous Glucose Monitoring in Subjects with Type 1 Diabetes in a Single Center in Costa Rica

Glycemic variability and frequent hypoglycemic episodes are some of the most difficult issues in the management of type 1 diabetes (T1D). We performed a retrospective analysis that included 27 subjects from 13 to 54 years of age diagnosed with T1D, using a multiple daily insulin injection treatment and under continuous glucose monitoring with a Medtronic iPro2 device for six days. We divided the sample in two groups: A) those with 10% or less of the time in hypoglycemia and B) those with more than 10% of time in hypoglycemia. Their median age was 27.8 years, 48.1% were males, with a median duration of disease of 13 years and a median of A1C of 7.8%. The time in range (blood glucose from 70 to 180 mg/dL) was 44% in both groups. The group of patients with more than 10% of the time in hypoglycemia had a lower A1C (7.5 vs. 7.9%, p=0.034) along with a higher number of hypoglycemic episodes in the night time (0 vs. 3, p&amp;lt;0.001), as well as increased episodes during the entire day (2 vs. 12.0 p&amp;lt;0.001). Patients who spent more time in range also showed lower levels of A1C, although all of them had more than 10% of time in hypoglycemia. In conclusion, adequate A1C levels do not assure adequate control of disease given the increased time in hypoglycemia observed in this set of patients. Figure 1. Correlation Between Mean A1c and Time in Range in Patients with T1D A1C, Glycated Hemoglobin; SD, Standard Deviation. Disclosure J. Calvo: None.

The Relationship between A1C and Hypoglycemia in the HypoDE Study

There is an inverse relationship between A1C and hypoglycemia which may be impacted by use of continuous glucose monitoring (CGM). Using data from the HypoDE clinical trial, we compared usual care to real-time CGM in subjects with type 1 diabetes using MDI and impaired hypoglycemia awareness or recent severe hypoglycemia. Data from 149 subjects (75 CGM/intervention and 74 SMBG/control) were analyzed. The percentages of sensor glucose (SG) values ≤70 mg/dL (≤3.9 mmol/L) (“%≤70”) and of those indicating clinically significant hypoglycemia (≤54 mg/dL [≤3.0 mmol/L]) (“%≤54”) during the blinded 4-week run-in and the final 4 weeks of the 26-week study were calculated for each subject and compared to corresponding baseline and 26-week A1C values. At baseline, the percent ≤70 and the percent ≤54 were similar for the two groups and there was a trend toward more hypoglycemia for subjects with lower A1C values (Figure A). At 26 weeks (Figure B), there were significant (p&amp;lt;0.01) between-group differences in favor of CGM at both thresholds, and the association between decreasing A1C and increasing exposure to SG values ≤54 mg/dL was attenuated. These data suggest patients at highest risk of hypoglycemia with access to CGM alerts and alarms take appropriate and timely measures to nearly eliminate exposure to clinically meaningful hypoglycemia. CGM allows safe intensification of MDI therapy and achievement of near-normal A1C levels in hypoglycemia-prone individuals. Disclosure N. Oliver: Advisory Panel; Self; Dexcom, Inc., Roche Diabetes Care Health and Digital Solutions, Medtronic. Research Support; Self; Dexcom, Inc., Roche Diabetes Care Health and Digital Solutions. Other Relationship; Self; Novo Nordisk A/S, Takeda Development Centre Europe Ltd. G. Freckmann: Speaker's Bureau; Self; Ascensia Diabetes Care. Research Support; Self; Ascensia Diabetes Care. Speaker's Bureau; Self; Roche Diabetes Care Health and Digital Solutions. Advisory Panel; Self; Roche Diabetes Care Health and Digital Solutions. Research Support; Self; Roche Diabetes Care Health and Digital Solutions. Advisory Panel; Self; Abbott, Novo Nordisk Inc.. Consultant; Self; Sensile Medical AG. Speaker's Bureau; Self; Ypsomed AG. M. Gimenez: None. M. Reddy: Advisory Panel; Self; Roche Diabetes Care Health and Digital Solutions. Other Relationship; Self; Dexcom, Inc. N. Hermanns: Speaker's Bureau; Self; Berlin-Chemie AG. Advisory Panel; Self; Abbott. Research Support; Self; Abbott. Speaker's Bureau; Self; Abbott. Research Support; Self; Berlin-Chemie AG. Advisory Panel; Self; Eli Lilly and Company. Speaker's Bureau; Self; Eli Lilly and Company. Advisory Panel; Self; Roche Diabetes Care Health and Digital Solutions. Research Support; Self; Ypsomed AG, Dexcom, Inc.. Speaker's Bureau; Self; Novo Nordisk Inc. L. Heinemann: Stock/Shareholder; Self; Profil Institute for Metabolic Research, ProSciento. Consultant; Self; Roche Diabetes Care Health and Digital Solutions.

Glycemic Variability Percentage: A Novel Method for Assessing Glycemic Variability from Continuous Glucose Monitor Data.

Background: High levels of glycemic variability are still observed in most patients with diabetes with severe insulin deficiency. Glycemic variability may be an important risk factor for acute and chronic complications. Despite its clinical importance, there is no consensus on the optimum method for characterizing glycemic variability.Method: We developed a simple new metric, the glycemic variability percentage (GVP), to assess glycemic variability by analyzing the length of the continuous glucose monitoring (CGM) temporal trace normalized to the duration under evaluation. The GVP is similar to other recently proposed glycemic variability metrics, the distance traveled, and the mean absolute glucose (MAG) change. We compared results from distance traveled, MAG, GVP, standard deviation (SD), and coefficient of variation (CV) applied to simulated CGM traces accentuating the difference between amplitude and frequency of oscillations. The GVP metric was also applied to data from clinical studies for the Dexcom G4 Platinum CGM in subjects without diabetes, with type 2 diabetes, and with type 1 diabetes (adults, adolescents, and children).Results: In contrast to other metrics, such as CV and SD, the distance traveled, MAG, and GVP all captured both the amplitude and frequency of glucose oscillations. The GVP metric was also able to differentiate between diabetic and nondiabetic subjects and between subjects with diabetes with low, moderate, and high glycemic variability based on interquartile analysis.Conclusion: A new metric for the assessment of glycemic variability has been shown to capture glycemic variability due to fluctuations in both the amplitude and frequency of glucose given by CGM data.

HypoDE: Research Design and Methods of a Randomized Controlled Study Evaluating the Impact of Real-Time CGM Usage on the Frequency of CGM Glucose Values

Systems for continuous glucose monitoring (CGM) have been available for a number of years, and numerous clinical studies have been performed with them. Interestingly, in many of these studies patients with an increased risk of hypoglycemic events were excluded. In addition, in most studies subjects were using a pump for insulin delivery. Therefore our knowledge about the benefit of CGM in patients employing multiple daily injections (MDI) of insulin is limited, especially when it comes to a reduction in the risk of low glucose events in high-risk individuals. We are planning to run a 26-week randomized controlled study in Germany (HypoDE, Hypoglycemia in Deutschland) that is focused on evaluating if such a reduction can be observed in patients on MDI with an increased risk of low glucose events. In all, 160 patients will participate in the study, randomized into the intervention group and control group. Ideally one would study if the frequency of severe hypoglycemic events is different between both groups. However, this would require such a large sample size and study duration, so for pragmatic reasons we will use low glucose levels <55 mg/dl (measured by CGM) for at least 20 minutes as a risk marker for severe hypoglycemic events. The results from the HypoDE study shall help determine the advantage of using CGM in subjects with type 1 diabetes with an increased risk of low glucose events treated with MDI.

Unreliable oral glucose tolerance test and haemoglobin A1C in beta thalassaemia major – a case for continuous glucose monitoring?

Unreliable oral glucose tolerance test and haemoglobin A1C in beta thalassaemia major – a case for continuous glucose monitoring?

Professional Continuous Glucose Monitoring in Subjects with Type 1 Diabetes: Retrospective Hypoglycemia Detection

An important task in diabetes management is detection of hypoglycemia. Professional continuous glucose monitoring (CGM), which produces a glucose reading every 5 min, is a powerful tool for retrospective identification of unrecognized hypoglycemia. Unfortunately, CGM devices tend to be inaccurate, especially in the hypoglycemic range, which limits their applicability for hypoglycemia detection. The objective of this study was to develop an automated pattern recognition algorithm to detect hypoglycemic events in retrospective, professional CGM. Continuous glucose monitoring and plasma glucose (PG) readings were obtained from 17 data sets of 10 type 1 diabetes patients undergoing insulin-induced hypoglycemia. The CGM readings were automatically classified into a hypoglycemic group and a nonhypoglycemic group on the basis of different features from CGM readings and insulin injection. The classification was evaluated by comparing the automated classification with PG using sample-based and event-based sensitivity and specificity measures. With an event-based sensitivity of 100%, the algorithm produced only one false hypoglycemia detection. The sample-based sensitivity and specificity levels were 78% and 96%, respectively. The automated pattern recognition algorithm provides a new approach for detecting unrecognized hypoglycemic events in professional CGM data. The tool may assist physicians and diabetologists in conducting a more thorough evaluation of the diabetes patient's glycemic control and in initiating necessary measures for improving glycemic control.

Exenatide Improves Glycemic Variability Assessed by Continuous Glucose Monitoring in Subjects with Type 2 Diabetes

Daily glycemic fluctuation leads to development of long-term complications. The aim of our pilot study was to determine if exenatide reduces glycemic variability, assessed with a continuous glucose monitoring (CGM) system, compared with glimepiride. We enrolled six consecutive subjects with type 2 diabetes, for whom exenatide was suggested as second-line treatment, and six control subjects, for whom glimepiride was suggested as second-line treatment. CGM was performed at baseline and after 16 weeks of treatment. As measures of glycemic variability we calculated the total daily mean glucose (MG), SD, and mean amplitude of glycemic excursions (MAGE). Exenatide significantly reduced MG, SD, and MAGE, whereas glimepiride did not. Fasting glucose and glycated hemoglobin were lowered in both groups, even if the reduction was not significant. Exenatide can reduce glycemic variability compared with glimepiride, providing additional beneficial effects in controlling glucose homeostasis.

Use of Continuous Glucose Monitoring in Subjects With Type 1 Diabetes on Multiple Daily Injections Versus Continuous Subcutaneous Insulin Infusion Therapy

OBJECTIVETo compare use of continuous glucose monitoring in subjects with type 1 diabetes on multiple daily injection (MDI) therapy versus continuous subcutaneous insulin infusion (CSII) therapy for 6 months.RESEARCH DESIGN AND METHODSSixty type 1 diabetic adults with similar baseline characteristics, using either MDI (n = 30) or CSII (n = 30) therapy, were enrolled in this 6-month prospective study. Subjects were instructed to wear the DexCom SevenPLUS continuous glucose monitor at all times throughout the study. All subjects were initially blinded from the continuous glucose monitoring (CGM) glucose data. After 4 weeks of blinded CGM use, the CGM was unblinded, making glucose data available to the patient. The CGM remained in the unblinded state for the remainder of the study (20 weeks). Clinic visits occurred every 4 weeks, at which time A1C values were collected and CGM data were downloaded.RESULTSMean baseline (± SD) A1C was 7.61 (± 0.76) and 7.63 (± 0.68) for CSII and MDI, respectively (P > 0.05). Without any significant therapy change, A1C decrease at 12 weeks was similar in both groups (P = 0.03). When compared with the blinded phase, unblinded use of CGM was associated with similar but significant reductions in glycemic control and variability parameters. In addition, both therapy groups had similar changes in mean glucose and glucose variability indexes at 3 and 6 months (ITT analysis, P > 0.05). Predefined per protocol analysis (sensor use at least 6 days/week) showed greater improvement in time spent in target range glycemia, 3.9–10.0 mmol/L (70–180 mg/dL), in the CSII group.CONCLUSIONSWe conclude that CGM provides similar benefits in glucose control for patients using MDI or CSII therapy.

Continuous Glucose Monitoring in Subjects with Type 1 Diabetes: Improvement in Accuracy by Correcting for Background Current

A cause of suboptimal accuracy in amperometric glucose sensors is the presence of a background current (current produced in the absence of glucose) that is not accounted for. We hypothesized that a mathematical correction for the estimated background current of a commercially available sensor would lead to greater accuracy compared to a situation in which we assumed the background current to be zero. We also tested whether increasing the frequency of sensor calibration would improve sensor accuracy. This report includes analysis of 20 sensor datasets from seven human subjects with type 1 diabetes. Data were divided into a training set for algorithm development and a validation set on which the algorithm was tested. A range of potential background currents was tested. Use of the background current correction of 4 nA led to a substantial improvement in accuracy (improvement of absolute relative difference or absolute difference of 3.5-5.5 units). An increase in calibration frequency led to a modest accuracy improvement, with an optimum at every 4 h. Compared to no correction, a correction for the estimated background current of a commercially available glucose sensor led to greater accuracy and better detection of hypoglycemia and hyperglycemia. The accuracy-optimizing scheme presented here can be implemented in real time.

Continuous Glucose Monitoring in Subjects After Simultaneous Pancreas-Kidney and Kidney-Alone Transplantation

Simultaneous pancreas-kidney (SPK) transplantation is an important replacement therapy for individuals with diabetes and end-stage renal disease. Kidney-alone (KA) transplantation is associated with a high incidence of post-transplant diabetes. This was a cross-sectional study. We studied 48-h glucose concentrations in eight subjects with type 1 diabetes mellitus after SPK transplantation, six subjects post-KA transplantation, and nine healthy controls using the CGMS (Medtronic Minimed, Northridge, CA) continuous glucose monitoring system. The 48-h mean glucose concentration was 101 +/- 7 mg/dL in the SPK subjects, 105 +/- 12 mg/dL in the KA subjects, and 99 +/- 7 mg/dL in the healthy controls. The glycemic excursions were higher in the KA group compared to the SPK cohort and healthy controls (P < 0.0001). No differences in the incidence of hypoglycemia were detected among the three groups. Significant postprandial hyperglycemia was uncovered in four of the six KA subjects. SPK transplantation is very effective at normalizing glycemic excursions. Unsuspected hyperglycemia was identified in the KA group. The CGMS was a useful ambulatory tool to study glucose profiles in the post-transplant period and may help uncover hyperglycemia undetected by routine laboratory testing.

Responses to Continuous Glucose Monitoring in Subjects with Type 1 Diabetes Using Continuous Subcutaneous Insulin Infusion or Multiple Daily Injections

We compared changes in response to unmasking of continuous glucose monitoring (CGM) in subjects with type 1 diabetes who use multiple daily injections (MDI) or continuous subcutaneous insulin infusion (CSII). Use of real-time CGM (DexCom [San Diego, CA] SEVEN was studied in 38 subjects using CSII and 26 using MDI. CGM output was masked during Week 1 and unmasked during Weeks 2 and 3. We evaluated changes in 16 criteria for quality of glycemic control and eight criteria for glycemic variability. All 24 criteria showed highly statistically significant improvement when considered simultaneously (P < 0.000001). For subjects using CSII, 18 of 24 criteria improved significantly (nominal P < 0.05); for subjects using MDI, 16 of 24 criteria improved significantly (P < 0.05). Twelve of the comparisons remained significant (P < 0.05) after applying the overconservative Bonferroni correction for multiple comparisons. The percentage of glucose values within the range 80-140 mg/dL increased by 19% and 17% relative to their control values (Week 1) for subjects using MDI and CSII, respectively. Mean glucose, overall SD (SD(T)), SD between daily means (SD(dm)), mean amplitude of glycemic excursion (MAGE), and mean of daily differences (MODD) improved significantly. Responses to CGM display were not significantly different between the MDI and CSII subject groups for any of the 24 criteria considered individually or in groups of eight, 16, or 24. CGM has similar effectiveness in subjects with type 1 diabetes using either CSII or MDI.

Restoring Euglycemia in the Basal State Using Continuous Glucose Monitoring in Subjects with Type 1 Diabetes Mellitus

Our objective was to use continuous glucose monitoring to derive the optimal basal insulin infusion rates in adults with type 1 diabetes and using continuous subcutaneous insulin infusion (CSII) pumps. In an effort to mimic euglycemia during the basal state, we used a standard protocol to adjust basal insulin infusion rates in 16 subjects with type 1 diabetes mellitus who were using CSII pumps. All subjects wore Continuous Glucose Monitoring System sensors (CGMS), Medtronic Minimed, Northridge, CA) in order to obtain around-the-clock tracings of their glucose measurements. Subjects were asked to skip meals periodically in order to optimize basal insulin infusion rates, defined as the basal infusion rates that maintained glucose levels in the range of 65-120 mg/dL during the fasting state or between meals. In order to demonstrate improved glycemic control, with blunting of glucose excursion, we compared the baseline CGMS area under the curve (AUC) to the AUC obtained after optimizing the basal insulin dosages. We analyzed the curves by determining the AUC for glucose excursions above 110 mg/dL. The AUC for glucose excursions above 110 mg/dL was significantly smaller after optimization (19 +/- 13 mg/dL.day) compared to the baseline AUC (50 +/- 31 mg/dL.day) (P < 0.001). Using both a standard protocol for initial basal insulin infusion rates and CGMS curves to optimize basal infusion rates, one can improve glycemia in subjects with type 1 diabetes using CSII.