The effect of rising vs. falling glucose level on amperometric glucose sensor lag and accuracy in Type 1 diabetes

Extended Noninvasive Glucose Monitoring in the Interstitial Fluid Using an Epidermal Biosensing Patch

An effective, noninvasive glucose monitoring technology could be a pivotal factor for addressing the major unmet needs for managing diabetes mellitus (DM). Here, we describe a skin-worn, disposable, wireless electrochemical biosensor for extended noninvasive monitoring of glucose in the interstitial fluid (ISF). The wearable platform integrates three components: a screen-printed iontophoretic electrode system for ISF extraction by reverse iontophoresis (RI), a printed three-electrode amperometric glucose biosensor, and an electronic interface for control and wireless communication. Prolonged on-body glucose monitoring of up to 8 h, including clinical trials conducted in individuals with and without DM, demonstrated good correlation between glucose blood and ISF concentrations and the ability to monitor dynamically changing glucose levels upon food consumption, with no evidence of skin irritation or discomfort. Such successful extended operation addresses the challenges reported for the GlucoWatch platform by using a lower RI current density at shorter extraction times, along with a lower measurement frequency. Such a noninvasive skin-worn platform could address long-standing challenges with existing glucose monitoring platforms.

Evaluation of the FreeStyle® Libre Flash Glucose Monitoring System in Children and Adolescents with Type 1 Diabetes

BACKGROUND/AIMS

The FreeStyle® Libre Flash Glucose Monitoring System (FGM, Abbott) measures glucose concentrations in the interstitial fluid for up to 14 days. It has been approved for use in children aged > 4 years in January 2016. Experience in children is still limited. We evaluated the accuracy and usability of the FGM in children with type 1 diabetes mellitus (DM).

METHODS

67 children with type 1 DM (35 girls), aged 4-18 years, were included. Subjects wore a sensor on the back of their upper arm. For the first 14 days, they regularly measured capillary blood glucose (BG) with their usual BG meter (Accu-Chek® Mobile [ACM], Roche [n = 24]; Contour® Next Link [CNL], Bayer [n = 26]; OneTouch® Verio® IQ [OTV], LifeScan [n = 17]) followed by a sensor glucose (SG) scanning. SG readings were compared to BG measurements by consensus error grid (CEG) analysis; the mean difference (MD), the mean relative difference (MRD), the mean absolute difference (MAD), and the mean absolute relative difference (MARD) were calculated. After 14 days, subjects were asked to fill in a questionnaire on the usability of the FGM.

RESULTS

2,626 SG readings were paired with BG results. FGM readings were highly correlated with BG (r = 0.926, p < 0.001). 80.3% of the data pairs were in zone A (= no effect on clinical action) and 18.4% were in zone B (= altered clinical action with little or no effect on the clinical outcome) of the CEG. Overall MD was +7.5 mg/dL; MD varied with the BG meter: ACM +10.4 mg/dL, CNL +14.2 mg/dL, OTV -3.6 mg/dL (p < 0.001). Overall, MARD was 16.7%. We observed a large interindividual variability in the accuracy parameters. MD and MRD were inversely related to BMI (r = -0.261 [p < 0.05]; r = -0.266 [p < 0.05], respectively). MARD was inversely related to age (r = -0.266 [p < 0.05]). Twenty-nine patients (43.3%) reported sensor problems, mainly early detachment of the sensor. Nonetheless, the usability questionnaire indicated high levels of satisfaction.

CONCLUSIONS

Our results showed a reasonable agreement between the FGM SG readings and capillary BG measurements in children. There was, however, a large interindividual variability. The wearing of the sensor requires special attention. Further studies in children are imperative in order to document the accuracy and safety of the FGM in the paediatric population.

Practical implementation, education and interpretation guidelines for continuous glucose monitoring: A French position statement

The use by diabetes patients of real-time continuous interstitial glucose monitoring (CGM) or the FreeStyle Libre^® (FSL) flash glucose monitoring (FGM) system is becoming widespread and has changed diabetic practice. The working group bringing together a number of French experts has proposed the present practical consensus. Training of professionals and patient education are crucial for the success of CGM. Also, institutional recommendations must pay particular attention to the indications for and reimbursement of CGM devices in populations at risk of hypoglycaemia. The rules of good practice for CGM are the precursors of those that need to be enacted, given the oncoming emergence of artificial pancreas devices. It is necessary to have software combining user-friendliness, multiplatform usage and average glucose profile (AGP) presentation, while integrating glucose and insulin data as well as events. Expression of CGM data must strive for standardization that facilitates patient phenotyping and their follow-up, while integrating indicators of variability. The introduction of CGM involves a transformation of treatment support, rendering it longer and more complex as it also includes specific educational and technical dimensions. This complexity must be taken into account in discussions of organization of diabetes care.

Using meta-differential evolution to enhance a calculation of a continuous blood glucose level

We developed a new model of glucose dynamics. The model calculates blood glucose level as a function of transcapillary glucose transport. In previous studies, we validated the model with animal experiments. We used analytical method to determine model parameters. In this study, we validate the model with subjects with type 1 diabetes. In addition, we combine the analytic method with meta-differential evolution. To validate the model with human patients, we obtained a data set of type 1 diabetes study that was coordinated by Jaeb Center for Health Research. We calculated a continuous blood glucose level from continuously measured interstitial fluid glucose level. We used 6 different scenarios to ensure robust validation of the calculation. Over 96% of calculated blood glucose levels fit A+B zones of the Clarke Error Grid. No data set required any correction of model parameters during the time course of measuring. We successfully verified the possibility of calculating a continuous blood glucose level of subjects with type 1 diabetes. This study signals a successful transition of our research from an animal experiment to a human patient. Researchers can test our model with their data on-line at https://diabetes.zcu.cz.

Nonadjunctive Use of Continuous Glucose Monitoring for Diabetes Treatment Decisions

While self-monitoring of blood glucose (SMBG) is the current standard used by people with diabetes to manage glucose levels, recent improvements in accuracy of continuous glucose monitoring (CGM) technology are making it very likely that diabetes-related treatment decisions will soon be made based on CGM values alone. Nonadjunctive use of CGM will lead to a paradigm shift in how patients manage their glucose levels and will require substantial changes in how care providers educate their patients, monitor their progress, and provide feedback to help them manage their diabetes. The approval to use CGM nonadjunctively is also a critical step in the pathway toward FDA approval of an artificial pancreas system, which is further expected to transform diabetes care for people with type 1 diabetes. In this article, we discuss how nonadjunctive CGM is expected to soon replace routine SMBG and how this new usage scenario is expected to transform health outcomes and patient care.

Time Delay of CGM Sensors: Relevance, Causes, and Countermeasures

BACKGROUND

Continuous glucose monitoring (CGM) is a powerful tool to support the optimization of glucose control of patients with diabetes. However, CGM systems measure glucose in interstitial fluid but not in blood. Rapid changes in one compartment are not accompanied by similar changes in the other, but follow with some delay. Such time delays hamper detection of, for example, hypoglycemic events. Our aim is to discuss the causes and extent of time delays and approaches to compensate for these.

METHODS

CGM data were obtained in a clinical study with 37 patients with a prototype glucose sensor. The study was divided into 5 phases over 2 years. In all, 8 patients participated in 2 phases separated by 8 months. A total number of 108 CGM data sets including raw signals were used for data analysis and were processed by statistical methods to obtain estimates of the time delay.

RESULTS

Overall mean (SD) time delay of the raw signals with respect to blood glucose was 9.5 (3.7) min, median was 9 min (interquartile range 4 min). Analysis of time delays observed in the same patients separated by 8 months suggests a patient dependent delay. No significant correlation was observed between delay and anamnestic or anthropometric data. The use of a prediction algorithm reduced the delay by 4 minutes on average.

CONCLUSIONS

Prediction algorithms should be used to provide real-time CGM readings more consistent with simultaneous measurements by SMBG. Patient specificity may play an important role in improving prediction quality.

Technology to Reduce Hypoglycemia

Hypoglycemia is a major barrier toward achieving glycemic targets and is associated with significant morbidity (both psychological and physical) and mortality. This article reviews technological strategies, from simple to more advanced technologies, which may help prevent or mitigate exposure to hypoglycemia. More efficient insulin delivery systems, bolus advisor calculators, data downloads providing information on glucose trends, continuous glucose monitoring with alarms warning of hypoglycemia, predictive algorithms, and finally closed loop insulin delivery systems are reviewed. The building blocks to correct use and interpretation of this range of available technology require patient education and appropriate patient selection.

CGM-measured glucose values have a strong correlation with C-peptide, HbA1c and IDAAC, but do poorly in predicting C-peptide levels in the two years following onset of diabetes

AIMS/HYPOTHESIS

The aim of this work was to assess the association between continuous glucose monitoring (CGM) data, HbA1c, insulin-dose-adjusted HbA1c (IDAA1c) and C-peptide responses during the first 2 years following diagnosis of type 1 diabetes.

METHODS

A secondary analysis was conducted of data collected from a randomised trial assessing the effect of intensive management initiated within 1 week of diagnosis of type 1 diabetes, in which mixed-meal tolerance tests were performed at baseline and at eight additional time points through 24 months. CGM data were collected at each visit.

RESULTS

Among 67 study participants (mean age [± SD] 13.3 ± 5.7 years), HbA1c was inversely correlated with C-peptide at each time point (p < 0.001), as were changes in each measure between time points (p < 0.001). However, C-peptide at one visit did not predict the change in HbA1c at the next visit and vice versa. Higher C-peptide levels correlated with increased proportion of CGM glucose values between 3.9 and 7.8 mmol/l and lower CV (p = 0.001 and p = 0.02, respectively) but not with CGM glucose levels <3.9 mmol/l. Virtually all participants with IDAA1c < 9 retained substantial insulin secretion but when evaluated together with CGM, time in the range of 3.9-7.8 mmol/l and CV did not provide additional value in predicting C-peptide levels.

CONCLUSIONS/INTERPRETATION

In the first 2 years after diagnosis of type 1 diabetes, higher C-peptide levels are associated with increased sensor glucose levels in the target range and with lower glucose variability but not hypoglycaemia. CGM metrics do not provide added value over the IDAA1c in predicting C-peptide levels.

Factors affecting the success of glucagon delivered during an automated closed-loop system in type 1 diabetes

BACKGROUND

In bi-hormonal closed-loop systems for treatment of diabetes, glucagon sometimes fails to prevent hypoglycemia. We evaluated glucagon responses during several closed-loop studies to determine factors, such as gain factors, responsible for glucagon success and failure.

METHODS

We extracted data from four closed-loop studies, examining blood glucose excursions over the 50min after each glucagon dose and defining hypoglycemic failure as glucose values<60 mg/dl. Secondly, we evaluated hyperglycemic excursions within the same period, where glucose was>180 mg/dl. We evaluated several factors for association with rates of hypoglycemic failure or hyperglycemic excursion. These factors included age, weight, HbA1c, duration of diabetes, gender, automation of glucagon delivery, glucagon dose, proportional and derivative errors (PE and DE), insulin on board (IOB), night vs. day delivery, and point sensor accuracy.

RESULTS

We analyzed a total of 251 glucagon deliveries during 59 closed-loop experiments performed on 48 subjects. Glucagon successfully maintained glucose within target (60-180 mg/dl) in 195 (78%) of instances with 40 (16%) hypoglycemic failures and 16 (6%) hyperglycemic excursions. A multivariate logistic regression model identified PE (p<0.001), DE (p<0.001), and IOB (p<0.001) as significant determinants of success in terms of avoiding hypoglycemia. Using a model of glucagon absorption and action, simulations suggested that the success rate for glucagon would be improved by giving an additional 0.8μg/kg.

CONCLUSION

We conclude that glucagon fails to prevent hypoglycemia when it is given at a low glucose threshold and when glucose is falling steeply. We also confirm that high IOB significantly increases the risk for glucagon failures. Tuning of glucagon subsystem parameters may help reduce this risk.

Can glucose be monitored accurately at the site of subcutaneous insulin delivery?

Because insulin promotes glucose uptake into adipocytes, it has been assumed that during measurement of glucose at the site of insulin delivery, the local glucose level would be much lower than systemic glucose. However, recent investigations challenge this notion. What explanations could account for a reduced local effect of insulin in the subcutaneous space? One explanation is that, in humans, the effect of insulin on adipocytes appears to be small. Another is that insulin monomers and dimers (from hexamer disassociation) might be absorbed into the circulation before they can increase glucose uptake locally. In addition, negative cooperativity of insulin action (a lower than expected effect of very high insulin concentrations)may play a contributing role. Other factors to be considered include dilution of interstitial fluid by the insulin vehicle and the possibility that some of the local decline in glucose might be due to the systemic effect of insulin. With regard to future research, redundant sensing units might be able to quantify the effects of proximity, leading to a compensatory algorithm. In summary, when measured at the site of insulin delivery, the decline in subcutaneous glucose level appears to be minimal, though the literature base is not large. Findings thus far support (1) the development of integrated devices that monitor glucose and deliver insulin and (2) the use of such devices to investigate the relationship between subcutaneous delivery of insulin and its local effects on glucose. A reduction in the number of percutaneous devices needed to manage diabetes would be welcome.

Interstitium versus Blood Equilibrium in Glucose Concentration and its Impact on Subcutaneous Continuous Glucose Monitoring Systems

The relationship between both interstitial and blood glucose remains a debated topic, on which there is still no consensus. The experimental evidence suggests that blood and interstitial fluid glucose levels are correlated by a kinetic equilibrium, which as a consequence has a time and magnitude gradient in glucose concentration between blood and interstitium. Furthermore, this equilibrium can be perturbed by several physiological effects (such as foreign body response, wound-healing effect, etc.), with a consequent reduction of interstitial fluid glucose versus blood glucose correlation. In the present study, the impact of operating in the interstitium on continuous glucose monitoring systems (CGMs) will be discussed in depth, both for the application of CGMs in the management of diabetes and in other critical areas, such as tight glycaemic control in critically ill patients.

Feasibility of Factory Calibration for Subcutaneous Glucose Sensors in Subjects With Diabetes

BACKGROUND

Continuous glucose monitoring using subcutaneously inserted sensors currently requires blood glucose tests for sensor calibration. Alternatively, sensors precalibrated during the manufacturing process may eliminate the need for fingerstick calibrations. In this study we evaluated the feasibility of sensor factory calibration in subjects with diabetes.

METHODS

A total of 33 subjects with diabetes were asked to wear 4 sensors in parallel, 2 on the arm and 2 on the abdomen. Sensors from a lot with low in vitro sensitivity coefficient of variation were used in the study. Based on frequent capillary blood glucose measurements, the average glucose sensitivity of each sensor was determined over a 5-day wear time. The in vivo sensitivities were analyzed for inter- and intrasubject variation. Mean absolute relative difference (MARD) calculation and consensus error grid analysis (EGA) were performed using a single calibration factor for all sensors, to simulate factory calibration and compared against conventional finger-stick calibration.

RESULTS

The sensitivity coefficient of variation between sensors increased from 2.9% in vitro to 6.0% in vivo. No difference in sensor response between subjects (P = .069) as well as between insertion sites (arm and abdomen) was detected (P = .104). Applying one calibration factor to all sensors in the study resulted in an MARD of 13.4%, and 83.5% of the values fell in consensus EGA zone A. Multiple fingerstick calibration resulted in an MARD of 12.7% and 84.1% in zone A.

CONCLUSIONS

Feasibility of factory calibration was demonstrated in subjects with diabetes using sensors based on "wired enzyme" technology, resulting in accuracy metrics similar to sensors calibrated with capillary blood glucose.