A new-generation continuous glucose monitoring system: improved accuracy and reliability compared with a previous-generation system

A Critical Discussion of Alert Evaluations in the Context of Continuous Glucose Monitoring System Performance

Many continuous glucose monitoring (CGM) systems provide functionality which alerts users of potentially unwanted glycemic conditions. These alerts can include glucose threshold alerts to call the user's attention to hypoglycemia or hyperglycemia, predictive alerts warning about impeding hypoglycemia or hyperglycemia, and rate-of-change alerts. A recent review identified 129 articles about CGM performance studies, of which approximately 25% contained alert evaluations. In some studies, real alerts were assessed; however, most of these studies retrospectively determined the timing of CGM alerts because not all CGM systems record alerts which necessitates manual documentation. In contrast to assessment of real alerts, retrospective determination allows assessment of a variety of alert settings for all three types of glycemic condition alerts. Based on the literature and the Clinical and Laboratory Standards Institute's POCT05 guideline, two common approaches to threshold alert evaluation were identified, one value-based and one episode-based approach. In this review, a critical discussion of the two approaches, including a post hoc analysis of clinical study data, indicates that the episode-based approach should be preferred over the value-based approach. For predictive alerts, fewer results were found in the literature, and retrospective determination of CGM alert timing is complicated by the prediction algorithms being proprietary information. Rate-of-change alert evaluations were not reported in the identified literature, and POCT05 does not contain recommendations for assessment. A possible approach is discussed including post hoc analysis of clinical study data. To conclude, CGM systems should record alerts, and the episode-based approach to alert evaluation should be preferred.

Clinical Performance Evaluation of Continuous Glucose Monitoring Systems: A Scoping Review and Recommendations for Reporting

The use of different approaches for design and results presentation of studies for the clinical performance evaluation of continuous glucose monitoring (CGM) systems has long been recognized as a major challenge in comparing their results. However, a comprehensive characterization of the variability in study designs is currently unavailable. This article presents a scoping review of clinical CGM performance evaluations published between 2002 and 2022. Specifically, this review quantifies the prevalence of numerous options associated with various aspects of study design, including subject population, comparator (reference) method selection, testing procedures, and statistical accuracy evaluation. We found that there is a large variability in nearly all of those aspects and, in particular, in the characteristics of the comparator measurements. Furthermore, these characteristics as well as other crucial aspects of study design are often not reported in sufficient detail to allow an informed interpretation of study results. We therefore provide recommendations for reporting the general study design, CGM system use, comparator measurement approach, testing procedures, and data analysis/statistical performance evaluation. Additionally, this review aims to serve as a foundation for the development of a standardized CGM performance evaluation procedure, thereby supporting the goals and objectives of the Working Group on CGM established by the Scientific Division of the International Federation of Clinical Chemistry and Laboratory Medicine.

Dynamics of glucose concentration during the initiation of ketogenic diet treatment in children with refractory epilepsy: Results of continuous glucose monitoring

OBJECTIVE

The ketogenic diet (KD) is a diet low in carbohydrates and rich in fats which has long been used to treat refractory epilepsy. The metabolic changes related to the KD may increase the risk of hypoglycemia, especially during the first days. The study focused on the impact of KD initiation on glycemia in non-diabetic patients with refractory epilepsy.

METHODS

The subjects were 10 pediatric patients (6 boys, mean age 6.1 ± 2.4 years), treated for intractable epilepsy. Blinded continuous glucose monitoring system (CGM) Dexcom G4 was used. Patients started on their regular diet in the first 36 hours of monitoring, followed by an increase in lipids intake and a gradual reduction of carbohydrates (relations 1:1; 2:1; 3:1; 3.5:1). We analyzed changes in glycemia during fat: nonfat ratio changes using a generalized linear model.

RESULTS

The mean monitored time per person was 6 days, 10 hours and 44 minutes. The mean ± SD glycemia for the regular diet was 4.84 ± 0.20 mmol/L, for the carbohydrates/fat ratio of 1:1 it was 4.03 ± 0.16, for the ratio of 2:1 it was 3.57 ± 0.10, for the ratio 3:1 it was 3.39 ± 0.13 and for the final ratio of 3.5:1 it was 2.79 ± 0.06 mmol/L (P < 0.001). The portions of time spent in glycemia ≤3.5 mmol/L (≤2.5 mmol/L respectively) were: on the normal diet 0.88% (0.31%) of the monitored period, during 1:1 KD ratio 1.92% (0.95%), during 2:1 ratio 3.18% (1.02%), and during 3:1 and 3.5:1 ratios 13.64% (2.36%) of the monitored time (P < 0.05).

SIGNIFICANCE

Continuous glucose monitoring system shows the dynamic of glucose concentration in ketogenic diet treatment initiation. It may be a useful tool to control the effects of this diet on glucose metabolism, especially in hypoglycemia detection.

Noninvasive Glucose Sensing In Vivo

Blood glucose monitoring is an essential aspect of disease management for individuals with diabetes. Unfortunately, traditional methods require collecting a blood sample and thus are invasive and inconvenient. Recent developments in minimally invasive continuous glucose monitors have provided a more convenient alternative for people with diabetes to track their glucose levels 24/7. Despite this progress, many challenges remain to establish a noninvasive monitoring technique that works accurately and reliably in the wild. This review encompasses the current advancements in noninvasive glucose sensing technology in vivo, delves into the common challenges faced by these systems, and offers an insightful outlook on existing and future solutions.

Analysis of "Multicenter Evaluation Study Comparing a New Factory-Calibrated Real-Time Continuous Glucose Monitoring System to Existing Flash Glucose Monitoring System"

In an article in the Journal of Diabetes Science and Technology, Ji et al. report on the accuracy of a new factory calibrated continuous glucose monitoring system, the AiDEX CGM (Microtech Medical Company, Ltd., Hangzhou, China). This is the first report from a new manufacturer of a highly accurate factory calibrated CGM. The authors report that the accuracy of the AiDEX CGM is comparable to previous results from Abbott Diabetes Care and Dexcom. However, the study protocol was significantly different from previous studies. This study is the first of numerous studies likely from other manufacturers of CGM technology. It raises the question of how to evaluate sensor performance in the coming era of mass adoption of CGM and increased use of automated insulin delivery systems.

Catalytic Biosensors Operating under Quasi-Equilibrium Conditions for Mitigating the Changes in Substrate Diffusion

Despite the success of continuous glucose measuring systems operating through the skin for about 14 days, long-term implantable biosensors are facing challenges caused by the foreign-body reaction. We present a conceptually new strategy using catalytic enzyme-based biosensors based on a measuring sequence leading to minimum disturbance of the substrate equilibrium concentration by controlling the sensor between "on" and "off" state combined with short potentiometric data acquisition. It is required that the enzyme activity can be completely switched off and no parasitic side reactions allow substrate turnover. This is achieved by using an O2 -independent FAD-dependent glucose dehydrogenase embedded within a crosslinked redox polymer. A short measuring interval allows the glucose concentration equilibrium to be restored quickly which enables the biosensor to operate under quasi-equilibrium conditions.

Recent progress and perspectives of continuous in vivo testing device

Devices for continuous in-vivo testing (CIVT) can detect target substances in real time, thus providing a valuable window into a patient's condition, their response to therapeutics, metabolic activities, and neurotransmitter transmission in the brain. Therefore, CIVT devices have received increased attention because they are expected to greatly assist disease diagnosis and treatment and research on human pathogenesis. However, CIVT has been achieved for only a few markers, and it remains challenging to detect many key markers. Therefore, it is important to summarize the key technologies and methodologies of CIVT, and to examine the direction of future development of CIVT. We review recent progress in the development of CIVT devices, with consideration of the structure of these devices, principles governing continuous detection, and nanomaterials used for electrode modification. This detailed and comprehensive review of CIVT devices serves three purposes: (1) to summarize the advantages and disadvantages of existing devices, (2) to provide a reference for development of CIVT equipment to detect additional important markers, and (3) to discuss future prospects with emphasis on problems that must be overcome for further development of CIVT equipment. This review aims to promote progress in research on CIVT devices and contribute to future innovation in personalized medical treatments.

Flexible Enzymatic Glucose Electrochemical Sensor Based on Polystyrene-Gold Electrodes

Metabolic disorders such as the highly prevalent disease diabetes require constant monitoring. The health status of patients is linked to glucose levels in blood, which are typically measured invasively, but can also be correlated to other body fluids such as sweat. Aiming at a reliable glucose biosensor, an enzymatic sensing layer was fabricated on flexible polystyrene foil, for which a versatile nanoimprinting process for microfluidics was presented. For the sensing layer, a gold electrode was modified with a cysteine layer and glutaraldehyde cross-linker for enzyme conformal immobilization. Chronoamperometric measurements were conducted in PBS buffered glucose solution at two potentials (0.65 V and 0.7 V) and demonstrated a linear range between 0.025 mM to 2mM and an operational range of 0.025 mM to 25 mM. The sensitivity was calculated as 1.76µA/mM/cm² and the limit of detection (LOD) was calculated as 0.055 mM at 0.7 V. An apparent Michaelis–Menten constant of 3.34 mM (0.7 V) and 0.445 mM (0.65 V) was computed. The wide operational range allows the application for point-of-care testing for a variety of body fluids. Yet, the linear range and low LOD make this biosensor especially suitable for non-invasive sweat sensing wearables.

Inaccurate Glucose Sensor Values After Hydroxyurea Administration

Objective: To assess the degree, duration, mean absolute relative difference (MARD), and error analysis of discrepant values per continuous glucose monitoring (CGM) systems after hydroxyurea (HU) administration. Research Design and Methods: Inpatient glucometer and CGM data from 16 total pancreatectomy/islet autotransplantation patients using Dexcom Professional G4 and 12 patients using Dexcom G6 were analyzed after daily dosing with HU. Timing of HU dosing and median of 9.5 days of sensor and glucometer values were assessed per patient. Results: A large positive elevation of sensor readings was identified after HU dosing. The greatest discrepancy between glucometer and sensor readings occurred 0.5-2 h after HU administration [G4 (mean 3.0 mmol/L, median 2.4 mmol/L, MARD 55%), G6 (mean 4.2 mmol/L, median 4.6 mmol/L, MARD 91%)]. The discrepancy was <1.1 mmol/L, mean (-0.5 mmol/L) and median (-0.5 mmol/L), MARD 14% (G4) and <1.1 mmol/L, mean (0.3 mmol/L) and median (0.3 mmol/L), MARD 17% (G6), by 6 h after administration. Error analysis with the G6 system found 94% of pairs in clinically acceptable range by 6-9 h after HU administration. Aspirin, also given once daily, did not result in glucose value discrepancy with the G6 system but variability was observed with the G4 system. Conclusions: There was marked elevation of sensor glucose readings compared with glucometer values [up to 13.9 mmol/L (G4), 13 mmol/L (G6)] from 0.5 to 6 h after HU administration. It is important to counsel a patient using a Dexcom CGM system and HU therapy on this finding and to advise reliance on glucometer testing for accurate glucose assessment up to 6-9 h after HU administration.

Wearable patch delivery system for artificial pancreas health diagnostic-therapeutic application: A review

The advanced stimuli-responsive approaches for on-demand drug delivery systems have received tremendous attention as they have great potential to be integrated with sensing and multi-functional electronics on a flexible and stretchable single platform (all-in-one concept) in order to develop skin-integration with close-loop sensation for personalized diagnostic and therapeutic application. The wearable patch pumps have evolved from reservoir-based to matrix patch and drug-in-adhesive (single-layer or multi-layer) type. In this review, we presented the basic requirements of an artificial pancreas, surveyed the design and technologies used in commercial patch pumps available on the market and provided general information about the latest wearable patch pump. We summarized the various advanced delivery strategies with their mechanisms that have been developed to date and representative examples. Mechanical, electrical, light, thermal, acoustic and glucose-responsive approaches on patch form have been successfully utilized in the controllable transdermal drug delivery manner. We highlighted key challenges associated with wearable transdermal delivery systems, their research direction and future development trends.

Nocturnal Hypoglycemia in Patients With Diabetes Discharged From ICUs: A Prospective Two-Center Cohort Study

OBJECTIVES

There is very limited information about glycemic control after discharge from the ICU. The aims of this study were to evaluate the prevalence of hypoglycemia in ICU survivors with type-2 diabetes and determine whether hypoglycemia is associated with cardiac arrhythmias.

DESIGN

Prospective, observational, two-center study. Participants underwent up to 5 days of simultaneous blinded continuous interstitial glucose monitoring and ambulatory 12-lead electrocardiogram monitoring immediately after ICU discharge during ward-based care. Frequency of arrhythmias, heart rate variability, and cardiac repolarization markers were compared between hypoglycemia (interstitial glucose ≤ 3.5 mmol/L) and euglycemia (5-10 mmol/L) matched for time of day.

SETTING

Mixed medical-surgical ICUs in two geographically distinct university-affiliated hospitals.

PATIENTS

Patients with type-2 diabetes who were discharged from ICU after greater than or equal to 24 hours with greater than or equal to one organ failure and were prescribed subcutaneous insulin were eligible.

MEASUREMENTS AND MAIN RESULTS

Thirty-one participants (mean ± sd, age 65 ± 13 yr, glycated hemoglobin 64 ± 22 mmol/mol) were monitored for 101 ± 32 hours post-ICU (total 3,117 hr). Hypoglycemia occurred in 12 participants (39%; 95% CI, 22-56%) and was predominantly nocturnal (40/51 hr) and asymptomatic (25/29 episodes). Participants experiencing hypoglycemia had 2.4 ± 0.7 discrete episodes lasting 45 minutes (interquartile range, 25-140 min). Glucose nadir was less than or equal to 2.2 mmol/L in 34% of episodes. The longest episode of nocturnal hypoglycemia was 585 minutes with glucose nadir less than 2.2 mmol/L. Simultaneous electrocardiogram and continuous interstitial glucose monitoring recordings were obtained during 44 hours of hypoglycemia and 991 hours of euglycemia. Hypoglycemia was associated with greater risk of bradycardia but did not affect atrial or ventricular ectopics, heart rate variability, or cardiac repolarization.

CONCLUSIONS

In ICU survivors with insulin-treated type-2 diabetes, hypoglycemia occurs frequently and is predominantly nocturnal, asymptomatic, and prolonged.

Electrochemical glucose sensors in diabetes management: an updated review (2010-2020)

While over half a century has passed since the introduction of enzyme glucose biosensors by Clark and Lyons, this important field has continued to be the focus of immense research activity. Extensive efforts during the past decade have led to major scientific and technological innovations towards tight monitoring of diabetes. Such continued progress toward advanced continuous glucose monitoring platforms, either minimal- or non-invasive, holds considerable promise for addressing the limitations of finger-prick blood testing toward tracking glucose trends over time, optimal therapeutic interventions, and improving the life of diabetes patients. However, despite these major developments, the field of glucose biosensors is still facing major challenges. The scope of this review is to present the key scientific and technological advances in electrochemical glucose biosensing over the past decade (2010-present), along with current obstacles and prospects towards the ultimate goal of highly stable and reliable real-time minimally-invasive or non-invasive glucose monitoring. After an introduction to electrochemical glucose biosensors, we highlight recent progress based on using advanced nanomaterials at the electrode-enzyme interface of three generations of glucose sensors. Subsequently, we cover recent activity and challenges towards next-generation wearable non-invasive glucose monitoring devices based on innovative sensing principles, alternative body fluids, advanced flexible materials, and novel platforms. This is followed by highlighting the latest progress in the field of minimally-invasive continuous glucose monitoring (CGM) which offers real-time information about interstitial glucose levels, by focusing on the challenges toward developing biocompatible membrane coatings to protect electrochemical glucose sensors against surface biofouling. Subsequent sections cover new analytical concepts of self-powered glucose sensors, paper-based glucose sensing and multiplexed detection of diabetes-related biomarkers. Finally, we will cover the latest advances in commercially available devices along with the upcoming future technologies.

Glycemic Monitoring and Management in Advanced Chronic Kidney Disease

Glucose and insulin metabolism in patients with diabetes are profoundly altered by advanced chronic kidney disease (CKD). Risk of hypoglycemia is increased by failure of kidney gluconeogenesis, impaired insulin clearance by the kidney, defective insulin degradation due to uremia, increased erythrocyte glucose uptake during hemodialysis, impaired counterregulatory hormone responses (cortisol, growth hormone), nutritional deprivation, and variability of exposure to oral antihyperglycemic agents and exogenous insulin. Patients with end-stage kidney disease frequently experience wide glycemic excursions, with common occurrences of both hypoglycemia and hyperglycemia. Assessment of glycemia by glycated hemoglobin (HbA1c) is hampered by a variety of CKD-associated conditions that can bias the measure either to the low or high range. Alternative glycemic biomarkers, such as glycated albumin or fructosamine, are not fully validated. Therefore, HbA1c remains the preferred glycemic biomarker despite its limitations. Based on observational data for associations with mortality and risks of hypoglycemia with intensive glycemic control regimens in advanced CKD, an HbA1c range of 7% to 8% appears to be the most favorable. Emerging data on the use of continuous glucose monitoring in this population suggest promise for more precise monitoring and treatment adjustments to permit fine-tuning of glycemic management in patients with diabetes and advanced CKD.

Tissue Response to Subcutaneous Infusion Catheter

Insulin infusion pump, continuous glucose monitoring (CGM), and insulin infusion set (IIS) have been developed to be increasingly feasible for people with type 1 diabetes (T1D). Several recently approved CGMs are transitioning from 7-day to 10-day wear time without the need for fingerprick recalibration. Nevertheless, studies and improvements on IIS, a critical part of insulin pump therapy, have been limited. In particular, the recommended wear time of IIS is still 2-3 days, which can hardly match the current duration of CGM for potential closed-loop system development. It is generally believed that both the inserted catheter and the subsequent infused insulin drug could induce particular subcutaneous tissue response and skin-related complications at the infusion site. In certain cases, poor glycaemic control, increased risk of hypoglycemia, and serious cosmetic impact on people with diabetes were observed. Skin complication has also been attributed as an important factor resulting users to discontinue insulin pump therapy. This article provides the rare systematic review of IIS induced subcutaneous tissue responses and skin complications, including the impacts from the inserted catheters, the subcutaneous infused insulin, and the adhesive or tape used to immobilize the catheter. The FDA's recommendation for the frequency of IIS change was further discussed. Future studies on this topic are required to further understand the IIS-related problems, and future strategies could be developed accordingly to significantly reduce the incidence of these problems, extend the wear time, and increase the acceptance of insulin pump based therapy.

Benefits and Limitations of MARD as a Performance Parameter for Continuous Glucose Monitoring in the Interstitial Space

High-quality performance of medical devices for glucose monitoring is important for a safe and efficient usage of this diagnostic option by patients with diabetes. The mean absolute relative difference (MARD) parameter is used most often to characterize the measurement performance of systems for continuous glucose monitoring (CGM). Calculation of this parameter is relatively easy and comparison of the MARD numbers between different CGM systems appears to be straightforward on the first glance. However, a closer look reveals that a number of complex aspects make interpretation of the MARD numbers provided by the manufacturer for their CGM systems difficult. In this review, these aspects are discussed and considerations are made for a systematic and appropriate evaluation of the MARD in clinical trials. The MARD should not be used as the sole parameter to characterize CGM systems, especially when it comes to nonadjunctive usage of such systems.

Advances in Type 1 Diabetes Technology Over the Last Decade

The past 10 years have witnessed rapid advances in the technology used to treat patients with type 1 diabetes (T1D). While the disease burden is still high, these advances have contributed to improvements in both glycaemic control and quality of life for many of those affected. New technologies allow for individualisation of care, as patients are able to work with their providers to determine which systems best fit their lifestyle and needs. In addition, thanks to improved glucose monitoring technologies, patients can now simultaneously improve glycaemic control and reduce hypoglycaemia, thereby mitigating risk for acute and chronic complications. Technological advances in T1D care are rapidly moving us toward increasingly automated devices, which offer the promise of reduced disease burden. In this article, we review advances in glucose monitoring, insulin and glucagon delivery, and the applications and algorithms seeking to integrate novel technologies.

Continuous Glucose Monitoring Sensors for Diabetes Management: A Review of Technologies and Applications

By providing blood glucose (BG) concentration measurements in an almost continuous-time fashion for several consecutive days, wearable minimally-invasive continuous glucose monitoring (CGM) sensors are revolutionizing diabetes management, and are becoming an increasingly adopted technology especially for diabetic individuals requiring insulin administrations. Indeed, by providing glucose real-time insights of BG dynamics and trend, and being equipped with visual and acoustic alarms for hypo- and hyperglycemia, CGM devices have been proved to improve safety and effectiveness of diabetes therapy, reduce hypoglycemia incidence and duration, and decrease glycemic variability. Furthermore, the real-time availability of BG values has been stimulating the realization of new tools to provide patients with decision support to improve insulin dosage tuning and infusion. The aim of this paper is to offer an overview of current literature and future possible developments regarding CGM technologies and applications. In particular, first, we outline the technological evolution of CGM devices through the last 20 years. Then, we discuss about the current use of CGM sensors from patients affected by diabetes, and, we report some works proving the beneficial impact provided by the adoption of CGM. Finally, we review some recent advanced applications for diabetes treatment based on CGM sensors.

Design and Prestudy Assessment of a Dashboard for Presenting Self-Collected Health Data of Patients With Diabetes to Clinicians: Iterative Approach and Qualitative Case Study

Background

Introducing self-collected health data from patients with diabetes into consultation can be beneficial for both patients and clinicians. Such an initiative can allow patients to be more proactive in their disease management and clinicians to provide more tailored medical services. Optimally, electronic health record systems (EHRs) should be able to receive self-collected health data in a standard representation of medical data such as Fast Healthcare Interoperability Resources (FHIR), from patients systems like mobile health apps and display the data directly to their users—the clinicians. However, although Norwegian EHRs are working on implementing FHIR, no solution or graphical interface is available today to display self-collected health data.

Objective

The objective of this study was to design and assess a dashboard for displaying relevant self-collected health data from patients with diabetes to clinicians.

Methods

The design relied on an iterative participatory process involving workshops with patients, clinicians, and researchers to define which information should be available and how it should be displayed. The assessment is based on a case study, presenting an instance of the dashboard populated with data collected from one patient with diabetes type 1 (in-house researcher) face-to-face by 14 clinicians. We performed a qualitative analysis based on usability, functionality, and expectation by using responses to questionnaires that were distributed to the 14 clinicians at the end of the workshops and collected before the participants left. The qualitative assessment was guided by the Standards for Reporting Qualitative Research.

Results

We created a dashboard permitting clinicians to assess the reliability of self-collected health data, list all collected data including medical calculations, and highlight medical situations that need to be investigated to improve the situation of the patients. The dashboard uses a combination of tables, graphs, and other visual representations to display the relevant information. Clinicians think that this type of solution will be useful during consultations every day, especially for patients living in remote areas or those who are technologically interested.

Conclusions

Displaying self-collected health data during consultations is not enough for clinicians; the data reliability has to be assured and the relevant information needs to be extracted and displayed along with the data to ease the introduction during a medical encounter. The prestudy assessment showed that the system provides relevant information to meet clinicians’ need and that clinicians were eager to start using it during consultations. The system has been under testing in a medical trial since November 2018, and the first results of its assessment in a real-life situation are expected in the beginning of next year (2020).

Novel Single-Site Device for Conjoined Glucose Sensing and Insulin Infusion: Performance Evaluation in Diabetes Patients During Home-Use

OBJECTIVE

This study evaluated a novel diabetes treatment device that combines commercially available continuous glucose monitoring and insulin infusion technology in such a way as to perform insulin delivery and glucose sensing through a single skin insertion site (single-port device).

METHODS

Ten type 1 diabetes patients used the device for up to six days in their home/work environment for open-loop insulin delivery and glucose sensing. On an additional day, the device was used in combination with an algorithm to perform automated closed-loop glucose control under hospital settings. To assess the performance of the device, capillary blood glucose concentrations were frequently determined and a continuous glucose sensor was additionally worn by the patients.

RESULTS

The average mean absolute relative deviation from blood glucose concentrations obtained for the sensor of the device was low (median, 13.0%; interquartile range, 10.5-16.7%; n = 10) and did not differ from that of the additionally worn glucose sensor (versus 13.9%; 11.9-15.3%; P = 0.922). Furthermore, insulin delivery with the single-port device was reliable and safe during home use and, when performed in combination with the control algorithm, was adequate to achieve and maintain near normoglycemia.

CONCLUSION

Our data show the feasibility of open- and closed-loop glucose control in diabetes patients using a device that combines insulin delivery and glucose sensing at a single tissue site.

SIGNIFICANCE

The reduction in device size and invasiveness achieved by this design may largely increase patient convenience and enhance acceptance of diabetes treatment with continuous glucose monitoring and insulin delivery technology.

Molecular and Materials Engineering for Delivery of Peptide Drugs to Treat Type 2 Diabetes

Type 2 diabetes is exploding globally. Despite numerous treatment options, nearly half of type 2 diabetics are unsuccessful at properly managing the disease, primarily due to a lack of patient compliance, driven by adverse side effects as well as complicated and frequent dosing schedules. Improving the delivery of type 2 diabetes drugs has the potential to increase patient compliance and thus, greatly enhance health outcomes and quality of life. This review focuses on molecular and materials engineering strategies that have been implemented to improve the delivery of peptide drugs to treat type 2 diabetes. Peptide drugs benefit from high potency and specificity but suffer from instability and short half-lives that limit their utility as therapeutics and pose a significant delivery challenge. Several approaches have been developed to improve the availability and efficacy of antidiabetic peptides and proteins in vivo. These methods are reviewed herein and include devices, which sustain the release of peptides in long term, and molecular engineering strategies, which prolong circulation time and slow the release of therapeutic peptides. By optimizing the delivery of these peptides and proteins using these approaches, long-term glucose control can be achieved in type 2 diabetes patients.

Development of a Single-Site Device for Conjoined Glucose Sensing and Insulin Delivery in Type-1 Diabetes Patients

OBJECTIVE

Diabetes patients are increasingly using a continuous glucose sensor to monitor blood glucose and an insulin pump connected to an infusion cannula to administer insulin. Applying these devices requires two separate insertion sites, one for the sensor and one for the cannula. Integrating sensor with cannula to perform glucose sensing and insulin infusion through a single insertion site would significantly simplify and improve diabetes treatment by reducing the overall system size and the number of necessary needle pricks. Presently, several research groups are pursuing the development of combined glucose sensing and insulin infusion devices, termed single-port devices, by integrating sensing and infusion technologies created from scratch.

METHODS

Instead of creating the device from scratch, we utilized already existing technologies and introduced three design concepts of integrating commercial glucose sensors and infusion cannulas. We prototyped and evaluated each concept according to design simplicity, ease of insertion, and sensing accuracy.

RESULTS

We found that the best single-port device is the one in which a Dexcom sensor is housed inside a Medtronic cannula so that its glucose sensitive part protrudes from the cannula tip. The low degree of component modification required to arrive at this configuration allowed us to test the efficiency and safety of the device in humans.

CONCLUSION

Results from these studies indicate the feasibility of combining commercial glucose sensing and insulin delivery technologies to realize a functional single-port device.

SIGNIFICANCE

Our development approach may be generally useful to provide patients with innovative medical devices faster and at reduced costs.

Automated closed-loop control of diabetes: the artificial pancreas

The incidence of Diabetes Mellitus is on the rise worldwide, which exerts enormous health toll on the population and enormous pressure on the healthcare systems. Now, almost hundred years after the discovery of insulin in 1921, the optimization problem of diabetes is well formulated as maintenance of strict glycemic control without increasing the risk for hypoglycemia. External insulin administration is mandatory for people with type 1 diabetes; various medications, as well as basal and prandial insulin, are included in the daily treatment of type 2 diabetes. This review follows the development of the Diabetes Technology field which, since the 1970s, progressed remarkably through continuous subcutaneous insulin infusion (CSII), mathematical models and computer simulation of the human metabolic system, real-time continuous glucose monitoring (CGM), and control algorithms driving closed-loop control systems known as the "artificial pancreas" (AP). All of these developments included significant engineering advances and substantial bioelectronics progress in the sensing of blood glucose levels, insulin delivery, and control design. The key technologies that enabled contemporary AP systems are CSII and CGM, both of which became available and sufficiently portable in the beginning of this century. This powered the quest for wearable home-use AP, which is now under way with prototypes tested in outpatient studies during the past 6 years. Pivotal trials of new AP technologies are ongoing, and the first hybrid closed-loop system has been approved by the FDA for clinical use. Thus, the closed-loop AP is well on its way to become the digital-age treatment of diabetes.

Biopsychosocial Factors Associated With Satisfaction and Sustained Use of Artificial Pancreas Technology and Its Components: a Call to the Technology Field

PURPOSE OF REVIEW

Summarize biopsychosocial factors associated with using continuous glucose monitors (CGMs), insulin pumps, and artificial pancreas (AP) systems and provide a "call to the field" about their importance to technology uptake and maintained use.

RECENT FINDINGS

Insulin pumps and CGMs are becoming standard of care for individuals with type 1 diabetes (T1D). AP systems combining a CGM, insulin pump, and automated dosing algorithm are available for commercial use. Despite improved glycemic control with AP system use, numerous barriers exist which may limit their benefit. Studies on components of AP systems (pumps, CGMs) are limited and demonstrate mixed results of their impact on fear of hypoglycemia, adherence, quality of life, depression and anxiety, and diabetes distress. Studies examining biopsychological factors associated specifically with sustained use of AP systems are also sparse. Biological, psychological and social impacts of AP systems have been understudied and the information they provide has not been capitalized upon.

Consistency of Continuous Ambulatory Interstitial Glucose Monitoring Sensors

AIMS

The abdominal region is the most common location for continuous glucose monitor (CGM) sensor insertion. However, a paucity of post-marketing data is available to demonstrate intra-individual consistency of CGM readings at different abdominal insertion sites.

METHODS

Healthy adults (fasting glucose (FG) < 5.5 mmol/L; BMI < 30 kg/m²) were recruited and a CGM sensor was placed on each side of the abdomen. Postprandial and continuous 48-h interstitial glucose levels were analyzed.

RESULTS

There was no significant difference in the 3-h postprandial glucose (PPG) level derived from the left versus right CGM, which remained non-significant after adjusting for waist circumference or FG. Among the glucose levels recorded over 48-h, values on the left site were greater in 3.6% of the data points (p < 0.05). After adjusting for waist circumference, only 0.5% of the glucose values remained significantly greater on the left (p < 0.05). When adjusted for FG, similar results were observed. For both PPG and 48-h readings, the mean absolute relative difference was not significant between the two abdominal sites.

CONCLUSIONS

CGM-derived glucose measures were highly consistent between the left and right abdomen during both the postprandial and post-absorptive periods.

Prediction of Adverse Glycemic Events From Continuous Glucose Monitoring Signal

The most important objective of any diabetes therapy is to maintain the blood glucose concentration within the euglycemic range, avoiding or at least mitigating critical hypo/hyperglycemic episodes. Modern continuous glucose monitoring (CGM) devices bear the promise of providing the patients with an increased and timely awareness of glycemic conditions as these get dangerously near to hypo/hyperglycemia. The challenge is to detect, with reasonable advance, the patterns leading to risky situations, allowing the patient to make therapeutic decisions on the basis of future (predicted) glucose concentration levels. We underline that a technically sound performance comparison of the approaches proposed in recent years has yet to be done, thus it is unclear which one is preferred. The aim of this study is to fill this gap by carrying out a comparative analysis among the most common methods for glucose event prediction. Both regression and classification algorithms have been implemented and analyzed, including static and dynamic training approaches. The dataset consists of 89 CGM time series measured in diabetic subjects for 7 subsequent days. Performance metrics, specifically defined to assess and compare the event-prediction capabilities of the methods, have been introduced and analyzed. Our numerical results show that a static training approach exhibits better performance, in particular when regression methods are considered. However, classifiers show some improvement when trained for a specific event category, such as hyperglycemia, achieving performance comparable to the regressors, with the advantage of predicting the events sooner.

Glycaemic variability-based classification of impaired glucose tolerance vs. type 2 diabetes using continuous glucose monitoring data

Many glycaemic variability (GV) indices extracted from continuous glucose monitoring systems data have been proposed for the characterisation of various aspects of glucose concentration profile dynamics in both healthy and non-healthy individuals. However, the inter-index correlations have made it difficult to reach a consensus regarding the best applications or a subset of indices for clinical scenarios, such as distinguishing subjects according to diabetes progression stage. Recently, a logistic regression-based method was used to address the basic problem of differentiating between healthy subjects and those affected by impaired glucose tolerance (IGT) or type 2 diabetes (T2D) in a pool of 25 GV-based indices. Whereas healthy subjects were classified accurately, the distinction between patients with IGT and T2D remained critical. In the present work, by using a dataset of CGM time-series collected in 62 subjects, we developed a polynomial-kernel support vector machine-based approach and demonstrated the ability to distinguish between subjects affected by IGT and T2D based on a pool of 37 GV indices complemented by four basic parameters-age, sex, BMI, and waist circumference-with an accuracy of 87.1%.

Calibration of Minimally Invasive Continuous Glucose Monitoring Sensors: State-of-The-Art and Current Perspectives

Minimally invasive continuous glucose monitoring (CGM) sensors are wearable medical devices that provide real-time measurement of subcutaneous glucose concentration. This can be of great help in the daily management of diabetes. Most of the commercially available CGM devices have a wire-based sensor, usually placed in the subcutaneous tissue, which measures a "raw" current signal via a glucose-oxidase electrochemical reaction. This electrical signal needs to be translated in real-time to glucose concentration through a calibration process. For such a scope, the first commercialized CGM sensors implemented simple linear regression techniques to fit reference glucose concentration measurements periodically collected by fingerprick. On the one hand, these simple linear techniques required several calibrations per day, with the consequent patient's discomfort. On the other, only a limited accuracy was achieved. This stimulated researchers to propose, over the last decade, more sophisticated algorithms to calibrate CGM sensors, resorting to suitable signal processing, modelling, and machine-learning techniques. This review paper will first contextualize and describe the calibration problem and its implementation in the first generation of CGM sensors, and then present the most recently-proposed calibration algorithms, with a perspective on how these new techniques can influence future CGM products in terms of accuracy improvement and calibration reduction.

Performance of a new real-time continuous glucose monitoring system: A multicenter pilot study

AIMS/INTRODUCTION

The present study aimed to investigate the performance of a new real-time continuous glucose monitoring system.

MATERIALS AND METHODS

Interstitial glucose levels were monitored for 7 days in 63 patients with type 1 or type 2 diabetes using the Medtrum A6 TouchCare® CGM System. Venous blood was collected on a randomized day of the wear period. Plasma glucose levels were measured as reference values.

RESULTS

Among 1,678 paired sensor-reference values, 90.5% (95% confidence interval 89.1-91.9%) were within ±20%/20 mg/dL of the reference values, with a mean absolute relative difference of 9.1 ± 8.7% (95% CI: 8.9-9.2%). The percentages of paired sensor-reference values falling within zone A and B of the Clarke error grid analysis (EGA) and the type 1 diabetes consensus EGA were 99.1 and 99.8%. Continuous EGA showed that the percentages of accurate readings, benign errors, and erroneous readings were 89.9, 6.3 and 3.8%, respectively. Surveillance EGA showed that 90.6, 9.2, and 0.2% of sensor-reference values with no, slight and lower moderate risk, respectively. The mean absolute relative difference was 16.6, and 96.0% of the sensor values fell within zones A and B of the consensus EGA for hypoglycemia. More than 85% of sensor values were within ±20%/20 mg/dL of reference values, the mean absolute relative difference was <11, and >99.5% of the sensor values fell in zones A and B of the consensus EGA.

CONCLUSIONS

The Medtrum real-time continuous glucose monitoring system was numerically and clinically accurate over a large glucose range across 7 days of wear.

FreeStyle Libre and Dexcom G4 Platinum sensors: Accuracy comparisons during two weeks of home use and use during experimentally induced glucose excursions

BACKGROUND AND AIMS

This study compared the accuracy of the FreeStyle Libre (Abbott, Alameda, CA) and Dexcom G4 Platinum (DG4P, Dexcom, San Diego, CA) CGM sensors.

METHODS AND RESULTS

Twenty-two adults with type 1 diabetes wore the two sensors simultaneously for 2 weeks. Libre was used according to manufacturer-specified lifetime (MSL); DG4P was used 7 days beyond MSL. At a clinical research center (CRC), subjects were randomized to receive the same breakfast with standard insulin bolus (standard) or a delayed and increased (delayed & increased) bolus to induce large glucose swings during weeks 1 and 2; venous glucose was checked every 5-15 min for 6 h. Subjects performed ≥4 reference fingersticks/day at home. Accuracy was assessed by differences in mean absolute relative difference (%MARD) in glucose levels compared with fingerstick test (home use) and YSI reference (CRC). During home-stay the Libre MARD was 13.7 ± 3.6% and the DG4P MARD 12.9 ± 2.5% (difference not significant [NS]). With both systems MARD increased during hypoglycaemia and decreased during hyperglycaemia, without significant difference between sensors. In the euglycaemic range MARD was smaller with DG4P [12.0 ± 2.4% vs 14.0 ± 3.6%, p = 0.026]. MARD increased in both sensors following delayed & increased vs. standard bolus (Libre: 14.9 ± 5.5% vs. 10.9 ± 4.1%, p = 0.008; DG4P: 18.1 ± 8.1% vs. 13.1 ± 4.6%, p = 0.026); between-sensor differences were not significant (p = 0.062). Libre was more accurate during moderate and rapid glucose changes.

CONCLUSIONS

DG4P and Libre performed similarly up to 7 days beyond DG4P MSL. Both sensors performed less well during hypoglycaemia but Libre was more accurate during glucose swings.

TRIAL REGISTRATION

The study was registered in ClinicalTrials.gov (NCT02734745) April 12, 2016.

The challenges of achieving postprandial glucose control using closed-loop systems in patients with type 1 diabetes

For patients with type 1 diabetes, closed-loop delivery systems (CLS) combining an insulin pump, a glucose sensor and a dosing algorithm allowing a dynamic hormonal infusion have been shown to improve glucose control when compared with conventional therapy. Yet, reducing glucose excursion and simplification of prandial insulin doses remain a challenge. The objective of this literature review is to examine current meal-time strategies in the context of automated delivery systems in adults and children with type 1 diabetes. Current challenges and considerations for post-meal glucose control will also be discussed. Despite promising results with meal detection, the fully automated CLS has yet failed to provide comparable glucose control to CLS with carbohydrate-matched bolus in the post-meal period. The latter strategy has been efficient in controlling post-meal glucose using different algorithms and in various settings, but at the cost of a meal carbohydrate counting burden for patients. Further improvements in meal detection algorithms or simplified meal-priming boluses may represent interesting avenues. The greatest challenges remain in regards to the pharmacokinetic and dynamic profiles of available rapid insulins as well as sensor accuracy and lag-time. New and upcoming faster acting insulins could provide important benefits. Multi-hormone CLS (eg, dual-hormone combining insulin with glucagon or pramlintide) and adjunctive therapy (eg, GLP-1 and SGLT2 inhibitors) also represent promising options. Meal glucose control with the artificial pancreas remains an important challenge for which the optimal strategy is still to be determined.

Toward Calibration-Free Continuous Glucose Monitoring Sensors: Bayesian Calibration Approach Applied to Next-Generation Dexcom Technology

BACKGROUND

Continuous glucose monitoring (CGM) sensors need to be calibrated twice/day by using self-monitoring of blood glucose (SMBG) samples. Recently, to reduce the calibration frequency, we developed an online calibration algorithm based on a multiple-day model of sensor time variability and Bayesian parameter estimation. When applied to Dexcom G4 Platinum (DG4P) sensor data, the algorithm allowed the frequency of calibrations to be reduced to one-every-four-days without significant worsening of sensor accuracy. The aim of this study is to assess the same methodology on raw CGM data acquired by a next-generation Dexcom CGM sensor prototype and compare the results with that obtained on DG4P.

METHODS

The database consists of 55 diabetic subjects monitored for 10 days by a next-generation Dexcom CGM sensor prototype. The new calibration algorithm is assessed, retrospectively, by simulating an online procedure using progressively fewer SMBG samples until zero. Accuracy is evaluated with mean absolute relative differences (MARD) between blood glucose versus CGM values.

RESULTS

The one-per-day and one-every-two-days calibration scenarios in the next-generation CGM data have an accuracy of 8.5% MARD (vs. 11.59% of DG4P) and 8.4% MARD (vs. 11.63% of DG4P), respectively. Accuracy slightly worsens to 9.2% (vs. 11.62% of DG4P) when calibrations are reduced to one-every-four-days. The calibration-free scenario results in 9.3% MARD (vs. 12.97% of DG4P).

CONCLUSIONS

In next-generation Dexcom CGM sensor data, the use of an online calibration algorithm based on a multiple-day model of sensor time variability and Bayesian parameter estimation aids in the shift toward a calibration-free scenario with even better results than those obtained in present-generation sensors.

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.

Venous, Arterialized-Venous, or Capillary Glucose Reference Measurements for the Accuracy Assessment of a Continuous Glucose Monitoring System

BACKGROUND

Different reference methods are used for the accuracy assessment of continuous glucose monitoring (CGM) systems. The effect of using venous, arterialized-venous, or capillary reference measurements on CGM accuracy is unclear.

METHODS

We evaluated 21 individuals with type 1 diabetes using a capillary calibrated CGM system. Venous or arterialized-venous reference glucose samples were taken every 15 min at two separate visits and assessed per YSI 2300 STAT Plus. Arterialization was achieved by heated-hand technique. Capillary samples were collected hourly during the venous reference visit. The investigation sequence (venous or arterialized-venous) was randomized. Effectiveness of arterialization was measured by comparing free venous oxygen pressure (PO2) of both visit days. Primary endpoint was the median absolute relative difference (ARD).

RESULTS

Median ARD using arterialized-venous reference samples was not different from venous samples (point estimated difference 0.52%, P = 0.181). When comparing the three reference methods, median ARD was also not different over the full glycemic range (venous 9.0% [n = 681], arterialized-venous 8.3% [n = 684], and capillary 8.1% [n = 205], P = 0.216), nor over the separate glucose ranges. Arterialization was successful (PO2 venous 5.4 kPa vs. arterialized-venous 8.9 kPa, P < 0.001). Arterialized-venous glucose was significantly higher than venous glucose and numerically higher than capillary glucose (arterialized-venous 142 mg/dL vs. venous 129 mg/dL [P < 0.001] and vs. capillary 134 mg/dL [P = 0.231]). Inconvenience related to arterialization included transient mild edema and redness of the hand in 4 out of 21 (19%) patients.

CONCLUSIONS

The use of venous, arterialized-venous, or capillary reference measurements did not significantly impact CGM accuracy. Venous reference seems preferable due to its ease of operation.

Wearable Continuous Glucose Monitoring Sensors: A Revolution in Diabetes Treatment

Worldwide, the number of people affected by diabetes is rapidly increasing due to aging populations and sedentary lifestyles, with the prospect of exceeding 500 million cases in 2030, resulting in one of the most challenging socio-health emergencies of the third millennium. Daily management of diabetes by patients relies on the capability of correctly measuring glucose concentration levels in the blood by using suitable sensors. In recent years, glucose monitoring has been revolutionized by the development of Continuous Glucose Monitoring (CGM) sensors, wearable non/minimally-invasive devices that measure glucose concentration by exploiting different physical principles, e.g., glucose-oxidase, fluorescence, or skin dielectric properties, and provide real-time measurements every 1–5 min. CGM opened new challenges in different disciplines, e.g., medicine, physics, electronics, chemistry, ergonomics, data/signal processing, and software development to mention but a few. This paper first makes an overview of wearable CGM sensor technologies, covering both commercial devices and research prototypes. Then, the role of CGM in the actual evolution of decision support systems for diabetes therapy is discussed. Finally, the paper presents new possible horizons for wearable CGM sensor applications and perspectives in terms of big data analytics for personalized and proactive medicine.

Characterization of carrier erythrocytes for biosensing applications

Erythrocyte abundance, mobility, and carrying capacity make them attractive as a platform for blood analyte sensing as well as for drug delivery. Sensor-loaded erythrocytes, dubbed erythrosensors, could be reinfused into the bloodstream, excited noninvasively through the skin, and used to provide measurement of analyte levels in the bloodstream. Several techniques to load erythrocytes, thus creating carrier erythrocytes, exist. However, their cellular characteristics remain largely unstudied. Changes in cellular characteristics lead to removal from the bloodstream. We hypothesize that erythrosensors need to maintain native erythrocytes’ (NEs) characteristics to serve as a long-term sensing platform. Here, we investigate two loading techniques and the properties of the resulting erythrosensors. For loading, hypotonic dilution requires a hypotonic solution while electroporation relies on electrical pulses to perforate the erythrocyte membrane. We analyze the resulting erythrosensor signal, size, morphology, and hemoglobin content. Although the resulting erythrosensors exhibit morphological changes, their size was comparable with NEs. The hypotonic dilution technique was found to load erythrosensors much more efficiently than electroporation, and the sensors were loaded throughout the volume of the erythrosensors. Finally, both techniques resulted in significant loss of hemoglobin. This study points to the need for continued development of loading techniques that better preserve NE characteristics.

Wearable non-invasive epidermal glucose sensors: A review

The growing recent interest in wearable and mobile technologies has led to increased research efforts toward development of non-invasive glucose monitoring platforms. Continuous glucose monitoring addresses the limitations of finger-stick blood testing and provides the opportunity for optimal therapeutic interventions. This article reviews recent advances and challenges toward the development of non-invasive epidermal electrochemical glucose sensing systems. Recent reports claim success in glucose monitoring in human subjects using skin-worn electrochemical sensors. Such epidermal electrochemical biosensors obviate the disadvantages of minimally-invasive subcutaneous glucose biosensors and offer promise for improved glycemic control. The ability of such systems to monitor glucose non-invasively offers an attractive route toward advancing the management of diabetes and achieving improved glycemic control. However, realizing the potential diagnostic impact of these new epidermal sensing strategies would require extensive efforts toward addressing key technological challenges and establishing a reliable correlation to gold standard blood glucose meters.

Accuracy of a Fourth-Generation Subcutaneous Continuous Glucose Sensor

BACKGROUND

This study evaluated the accuracy and performance of a fourth-generation subcutaneous glucose sensor (Guardian^™ Sensor 3) in the abdomen and arm.

METHODS

Eighty-eight subjects (14-75 years of age, mean ± standard deviation [SD] of 42.0 ± 19.1 years) with type 1 or type 2 diabetes participated in the study. Subjects wore two sensors in the abdomen that were paired with either a MiniMed^™ 640G insulin pump, or an iPhone^® or iPod^® touch^® running a glucose monitoring mobile application (Guardian Connect system) and a third sensor in the arm, which was connected to a glucose sensor recorder (GSR). Subjects were also asked to undergo in-clinic visits of 12-14 h on study days 1, 3, and 7 for frequent blood glucose sample testing using a Yellow Springs Instrument (YSI) reference.

RESULTS

The overall mean absolute relative difference (MARD ± SD) between abdomen sensor glucose (SG) and YSI reference values was 9.6% ± 9.0% and 9.4% ± 9.8% for the MiniMed 640G insulin pump and Guardian Connect system, respectively; and 8.7% ± 8.0% between arm SG and YSI reference values. The percentage of SG values within 20% agreement of the YSI reference value (for YSI >80 mg/dL) was 90.7% with the MiniMed 640G insulin pump, 91.8% with the Guardian Connect system, and 93.1% for GSR-connected arm sensors. Mean functional sensor life, when calibrating 3-4 times/day, was 145.9 ± 39.3 h for sensors paired with the MiniMed 640G insulin pump, 146.1 ± 41.6 h for sensors paired with the Guardian Connect system, and 147.6 ± 40.4 h for sensors connected to the GSR. Responses to survey questions regarding sensor comfort and ease of use were favorable.

CONCLUSIONS

The Guardian Sensor 3 glucose sensor, whether located in abdomen or the arm, provided accurate glucose readings when compared with the YSI reference and demonstrated functional life commensurate with the intended 7-day use. ClinicalTrials.gov : NCT02246582.

A Model of Self-Monitoring Blood Glucose Measurement Error

BACKGROUND

A reliable model of the probability density function (PDF) of self-monitoring of blood glucose (SMBG) measurement error would be important for several applications in diabetes, like testing in silico insulin therapies. In the literature, the PDF of SMBG error is usually described by a Gaussian function, whose symmetry and simplicity are unable to properly describe the variability of experimental data. Here, we propose a new methodology to derive more realistic models of SMBG error PDF.

METHODS

The blood glucose range is divided into zones where error (absolute or relative) presents a constant standard deviation (SD). In each zone, a suitable PDF model is fitted by maximum-likelihood to experimental data. Model validation is performed by goodness-of-fit tests. The method is tested on two databases collected by the One Touch Ultra 2 (OTU2; Lifescan Inc, Milpitas, CA) and the Bayer Contour Next USB (BCN; Bayer HealthCare LLC, Diabetes Care, Whippany, NJ). In both cases, skew-normal and exponential models are used to describe the distribution of errors and outliers, respectively.

RESULTS

Two zones were identified: zone 1 with constant SD absolute error; zone 2 with constant SD relative error. Goodness-of-fit tests confirmed that identified PDF models are valid and superior to Gaussian models used so far in the literature.

CONCLUSIONS

The proposed methodology allows to derive realistic models of SMBG error PDF. These models can be used in several investigations of present interest in the scientific community, for example, to perform in silico clinical trials to compare SMBG-based with nonadjunctive CGM-based insulin treatments.

Modeling the Error of the Medtronic Paradigm Veo Enlite Glucose Sensor

Continuous glucose monitors (CGMs) are prone to inaccuracy due to time lags, sensor drift, calibration errors, and measurement noise. The aim of this study is to derive the model of the error of the second generation Medtronic Paradigm Veo Enlite (ENL) sensor and compare it with the Dexcom SEVEN PLUS (7P), G4 PLATINUM (G4P), and advanced G4 for Artificial Pancreas studies (G4AP) systems. An enhanced methodology to a previously employed technique was utilized to dissect the sensor error into several components. The dataset used included 37 inpatient sessions in 10 subjects with type 1 diabetes (T1D), in which CGMs were worn in parallel and blood glucose (BG) samples were analyzed every 15 ± 5 min Calibration error and sensor drift of the ENL sensor was best described by a linear relationship related to the gain and offset. The mean time lag estimated by the model is 9.4 ± 6.5 min. The overall average mean absolute relative difference (MARD) of the ENL sensor was 11.68 ± 5.07% Calibration error had the highest contribution to total error in the ENL sensor. This was also reported in the 7P, G4P, and G4AP. The model of the ENL sensor error will be useful to test the in silico performance of CGM-based applications, i.e., the artificial pancreas, employing this kind of sensor.

Future of Automated Insulin Delivery Systems

Advances in continuous glucose monitoring (CGM) have brought on a paradigm shift in the management of type 1 diabetes. These advances have enabled the automation of insulin delivery, where an algorithm determines the insulin delivery rate in response to the CGM values. There are multiple automated insulin delivery (AID) systems in development. A system that automates basal insulin delivery has already received Food and Drug Administration approval, and more systems are likely to follow. As the field of AID matures, future systems may incorporate additional hormones and/or multiple inputs, such as activity level. All AID systems are impacted by CGM accuracy and future CGM devices must be shown to be sufficiently accurate to be safely incorporated into AID. In this article, we summarize recent achievements in AID development, with a special emphasis on CGM sensor performance, and discuss the future of AID systems from the point of view of their input-output characteristics, form factor, and adaptability.

Reduction of Blood Glucose Measurements to Calibrate Subcutaneous Glucose Sensors: A Bayesian Multiday Framework

OBJECTIVE

In most continuous glucose monitoring (CGM) devices used for diabetes management, the electrical signal measured by the sensor is transformed to glucose concentration by a calibration function whose parameters are estimated using self-monitoring of blood glucose (SMBG) samples. The calibration function is usually a linear model approximating the nonlinear relationship between electrical signal and glucose concentration in certain time intervals. Thus, CGM devices require frequent calibrations, usually twice a day. The aim here is to develop a new method able to reduce the frequency of calibrations.

METHODS

The algorithm is based on a multiple-day model of sensor time-variability with second-order statistical priors on its unknown parameters. In an online setting, these parameters are numerically determined by the Bayesian estimation exploiting SMBG sparsely collected by the patient. The method is assessed retrospectively on 108 CGM signals acquired for 7 days by the Dexcom G4 Platinum sensor, testing progressively less-calibration scenarios.

RESULTS

Despite the reduction of calibration frequency (on average from 2/day to 0.25/day), the method shows a statistically significant accuracy improvement compared to manufacturer calibration, e.g., mean absolute relative difference when compared to a laboratory reference decreases from 12.83% to 11.62% (p-value of 0.006).

CONCLUSION

The methodology maintains (sometimes improves) CGM sensor accuracy compared to that of the original manufacturer, while reducing the frequency of calibrations.

SIGNIFICANCE

Reducing the need of calibrations facilitates the adoption of CGM technology both in terms of ease of use and cost, an obvious prerequisite for its use as replacement of traditional SMBG devices.

Analysis of the Accuracy and Performance of a Continuous Glucose Monitoring Sensor Prototype: An In-Silico Study Using the UVA/PADOVA Type 1 Diabetes Simulator

BACKGROUND

Computer simulation has been shown over the past decade to be a powerful tool to study the impact of medical devices characteristics on clinical outcomes. Specifically, in type 1 diabetes (T1D), computer simulation platforms have all but replaced preclinical studies and are commonly used to study the impact of measurement errors on glycemia.

METHOD

We use complex mathematical models to represent the characteristics of 3 continuous glucose monitoring systems using previously acquired data. Leveraging these models within the framework of the UVa/Padova T1D simulator, we study the impact of CGM errors in 6 simulation scenarios designed to generate a wide variety of glycemic conditions. Assessment of the simulated accuracy of each different CGM systems is performed using mean absolute relative deviation (MARD) and precision absolute relative deviation (PARD). We also quantify the capacity of each system to detect hypoglycemic events.

RESULTS

The simulated Roche CGM sensor prototype (RCGM) outperformed the 2 alternate systems (CGM-1 & CGM-2) in accuracy (MARD = 8% vs 11.4% vs 18%) and precision (PARD = 6.4% vs 9.4% vs 14.1%). These results held for all studied glucose and rate of change ranges. Moreover, it detected more than 90% of hypoglycemia, with a mean time lag less than 4 minutes (CGM-1: 86%/15 min, CGM-2: 57%/24 min).

CONCLUSION

The RCGM system model led to strong performances in these simulation studies, with higher accuracy and precision than alternate systems. Its characteristics placed it firmly as a strong candidate for CGM based therapy, and should be confirmed in large clinical studies.

Clinical Implications of Accuracy Measurements of Continuous Glucose Sensors

The accuracy of a continuous glucose monitor (CGM) now supports its use by persons with diabetes and clinicians caring for them. This article reviews measures of CGM accuracy, factors contributing to accuracy, comparative accuracy assessment, clinical implications of CGM sensor accuracy, and recent clinical trials that have demonstrated the utility of CGMs.

REPLACE-BG: A Randomized Trial Comparing Continuous Glucose Monitoring With and Without Routine Blood Glucose Monitoring in Adults With Well-Controlled Type 1 Diabetes

OBJECTIVE

To determine whether the use of continuous glucose monitoring (CGM) without confirmatory blood glucose monitoring (BGM) measurements is as safe and effective as using CGM adjunctive to BGM in adults with well-controlled type 1 diabetes (T1D).

RESEARCH DESIGN AND METHODS

A randomized noninferiority clinical trial was conducted at 14 sites in the T1D Exchange Clinic Network. Participants were ≥18 years of age (mean 44 ± 14 years), had T1D for ≥1 year (mean duration 24 ± 12 years), used an insulin pump, and had an HbA_1c ≤9.0% (≤75 mmol/mL) (mean 7.0 ± 0.7% [53 ± 7.7 mmol/mol]); prestudy, 47% were CGM users. Participants were randomly assigned 2:1 to the CGM-only (n = 149) or CGM+BGM (n = 77) group. The primary outcome was time in range (70-180 mg/dL) over the 26-week trial, with a prespecified noninferiority limit of 7.5%.

RESULTS

CGM use averaged 6.7 ± 0.5 and 6.8 ± 0.4 days/week in the CGM-only and CGM+BGM groups, respectively, over the 26-week trial. BGM tests per day (including the two required daily for CGM calibration) averaged 2.8 ± 0.9 and 5.4 ± 1.4 in the two groups, respectively (P < 0.001). Mean time in 70-180 mg/dL was 63 ± 13% at both baseline and 26 weeks in the CGM-only group and 65 ± 13% and 65 ± 11% in the CGM+BGM group (adjusted difference 0%; one-sided 95% CI -2%). No severe hypoglycemic events occurred in the CGM-only group, and one occurred in the CGM+BGM group.

CONCLUSIONS

Use of CGM without regular use of confirmatory BGM is as safe and effective as using CGM with BGM in adults with well-controlled T1D at low risk for severe hypoglycemia.

Moving Toward a Unified Platform for Insulin Delivery and Sensing of Inputs Relevant to an Artificial Pancreas

Advances in insulin pump and continuous glucose monitoring technology have primarily focused on optimizing glycemic control for people with type 1 diabetes. There remains a need to identify ways to minimize the physical burden of this technology. A unified platform with closely positioned or colocalized interstitial fluid glucose sensing and hormone delivery components is a potential solution. Present challenges to combining these components are interference of glucose sensing from proximate insulin delivery and the large discrepancy between the life span of current insulin infusion sets and glucose sensors. Addressing these concerns is of importance given that the future physical burden of this technology is likely to be even greater with the ongoing development of the artificial pancreas, potentially incorporating multiple hormone delivery, glucose sensing redundancy, and sensing of other clinically relevant nonglucose biochemical inputs.

Photonic crystals: emerging biosensors and their promise for point-of-care applications

Biosensors are extensively employed for diagnosing a broad array of diseases and disorders in clinical settings worldwide. The implementation of biosensors at the point-of-care (POC), such as at primary clinics or the bedside, faces impediments because they may require highly trained personnel, have long assay times, large sizes, and high instrumental cost. Thus, there exists a need to develop inexpensive, reliable, user-friendly, and compact biosensing systems at the POC. Biosensors incorporated with photonic crystal (PC) structures hold promise to address many of the aforementioned challenges facing the development of new POC diagnostics. Currently, PC-based biosensors have been employed for detecting a variety of biotargets, such as cells, pathogens, proteins, antibodies, and nucleic acids, with high efficiency and selectivity. In this review, we provide a broad overview of PCs by explaining their structures, fabrication techniques, and sensing principles. Furthermore, we discuss recent applications of PC-based biosensors incorporated with emerging technologies, including telemedicine, flexible and wearable sensing, smart materials and metamaterials. Finally, we discuss current challenges associated with existing biosensors, and provide an outlook for PC-based biosensors and their promise at the POC.

Recruitment Into a Pediatric Continuous Glucose Monitoring RCT

BACKGROUND

The purpose was to identify patient/family characteristics and recruitment process characteristics associated with the decision to participate in a 2-year continuous glucose monitoring (CGM) RCT for youth with type 1 diabetes and their families.

METHOD

Study staff approached patients who were conditionally eligible according to medical record review or referred by a provider. We categorized families according to participation decision (agree vs decline) and timing of decision (day of approach vs later ["thinkers"]).

RESULTS

Over 18 months, we approached 456 eligible patients; 19% agreed on the day of approach, 10% agreed later, 42% declined on the day of approach, and 30% declined later. Agreers were younger ( P = .002), had shorter diabetes duration ( P = .0003), had a lower insulin dose ( P = .02), checked blood glucose levels more often ( P = .002), and were more likely to use pump therapy ( P = .009) than decliners. Patients/families were more likely to agree in fall/winter (41%) than spring/summer (19%, P < .0001). Of decliners, 50% cited no interest in CGM as the reason for nonparticipation. Among thinkers, 49% of patients who made a decision within 2 weeks of being approached agreed; only 15% of thinkers who made a decision >2 weeks after being approached agreed to participate ( P < .0001).

CONCLUSIONS

Recruitment is a critical and often challenging phase of clinical trials. Recruitment to pediatric CGM studies may be especially challenging due to youths' reluctance to use CGM. These data provide an opportunity to better understand and possibly optimize recruitment into future pediatric CGM studies and other studies of advanced diabetes technologies.