Estimating plasma glucose from interstitial glucose: the issue of calibration algorithms in commercial continuous glucose monitoring devices

Recent advances in the precision control strategy of artificial pancreas

The scientific diagnosis and treatment of patients with diabetes require frequent blood glucose testing and insulin delivery to normoglycemia. Therefore, an artificial pancreas with a continuous blood glucose (BG) monitoring function is an urgent research target in the medical industry. The problem of closed-loop algorithmic control of the BG with a time delay is a key and difficult issue that needs to be overcome in the development of an artificial pancreas. Firstly, the composition, structure, and control characteristics of the artificial pancreas are introduced. Subsequently, the research progress of artificial pancreas control algorithms is reviewed, and the characteristics, advantages, and disadvantages of proportional-integral-differential control, model predictive control, and artificial intelligence control are compared and analyzed to determine whether they are suitable for the practical application of the artificial pancreas. Additionally, key advancements in areas such as blood glucose data monitoring, adaptive models, wearable devices, and fully automated artificial pancreas systems are also reviewed. Finally, this review highlights that meal prediction, control safety, integration, streamlining the optimization of control algorithms, constant temperature preservation of insulin, and dual-hormone artificial pancreas are issues that require further attention in the future.

Practical considerations for continuous glucose monitoring in elite athletes with type 1 diabetes mellitus: A narrative review

Type 1 diabetes mellitus (T1DM) refers to a metabolic condition where a lack of insulin impairs the usual homeostatic mechanisms to control blood glucose levels. Historically, participation in competitive sport has posed a challenge for those with T1DM, where the dynamic changes in blood glucose during exercise can result in dangerously high (hyperglycaemia) or low blood glucoses (hypoglycaemia) levels. Over the last decade, research and technological development has enhanced the methods of monitoring and managing blood glucose levels, thus reducing the chances of experiencing hyper- or hypoglycaemia during exercise. The introduction of continuous glucose monitoring (CGM) systems means that glucose can be monitored conveniently, without the need for frequent fingerpick glucose checks. CGM devices include a fine sensor inserted under the skin, measuring levels of glucose in the interstitial fluid. Readings can be synchronized to a reader or mobile phone app as often as every 1-5 min. Use of CGM devices is associated with lower HbA1c and a reduction in hypoglycaemic events, promoting overall health and athletic performance. However, there are limitations to CGM, which must be considered when being used by an athlete with T1DM. These limitations can be addressed by individualized education plans, using protective equipment to prevent sensor dislodgement, as well as further research aiming to: (i) account for disparities between CGM and true blood glucose levels during vigorous exercise; (ii) investigate the effects of temperature and altitude on CGM accuracy, and (iii) explore of the sociological impact of CGM use amongst sportspeople without diabetes on those with T1DM.

GluGAN: Generating Personalized Glucose Time Series Using Generative Adversarial Networks

Time series data generated by continuous glucose monitoring sensors offer unparalleled opportunities for developing data-driven approaches, especially deep learning-based models, in diabetes management. Although these approaches have achieved state-of-the-art performance in various fields such as glucose prediction in type 1 diabetes (T1D), challenges remain in the acquisition of large-scale individual data for personalized modeling due to the elevated cost of clinical trials and data privacy regulations. In this work, we introduce GluGAN, a framework specifically designed for generating personalized glucose time series based on generative adversarial networks (GANs). Employing recurrent neural network (RNN) modules, the proposed framework uses a combination of unsupervised and supervised training to learn temporal dynamics in latent spaces. Aiming to assess the quality of synthetic data, we apply clinical metrics, distance scores, and discriminative and predictive scores computed by post-hoc RNNs in evaluation. Across three clinical datasets with 47 T1D subjects (including one publicly available and two proprietary datasets), GluGAN achieved better performance for all the considered metrics when compared with four baseline GAN models. The performance of data augmentation is evaluated by three machine learning-based glucose predictors. Using the training sets augmented by GluGAN significantly reduced the root mean square error for the predictors over 30 and 60-minute horizons. The results suggest that GluGAN is an effective method in generating high-quality synthetic glucose time series and has the potential to be used for evaluating the effectiveness of automated insulin delivery algorithms and as a digital twin to substitute for pre-clinical trials.

Within-Person and Between-Sensor Variability in Continuous Glucose Monitoring Metrics

BACKGROUND

The within-person and between-sensor variability of metrics from different interstitial continuous glucose monitoring (CGM) sensors in adults with type 2 diabetes not taking insulin is unclear.

METHODS

Secondary analysis of data from 172 participants from the Hyperglycemic Profiles in Obstructive Sleep Apnea randomized clinical trial. Participants simultaneously wore Dexcom G4 and Abbott Libre Pro CGM sensors for up to 2 weeks at baseline and again at the 3-month follow-up visit.

RESULTS

At baseline (up to 2 weeks of CGM), mean glucose for both the Abbott and Dexcom sensors was approximately 150 mg/dL (8.3 mmol/L) and time in range (70180 mg/dL [3.910.0 mmol/L]) was just below 80. When comparing the same sensor at 2 different time points (two 2-week periods, 3 months apart), the within-person coefficient of variation (CVw) in mean glucose was 17.4 (Abbott) and 14.2 (Dexcom). CVw for percent time in range: 20.1 (Abbott) and 18.6 (Dexcom). At baseline, the Pearson correlation of mean glucose from the 2 sensors worn simultaneously was r 0.86, root mean squared error (RMSE), 13 mg/dL (0.7 mmol/L); for time in range, r 0.88, RMSE, 8 percentage points.

CONCLUSIONS

Substantial variation was observed within sensors over time and across 2 different sensors worn simultaneously on the same individuals. Clinicians should be aware of this variability when using CGM technology to make clinical decisions.ClinicalTrials.gov Identifier: NCT02454153.

The Associations of Mean Glucose and Time in Range from Continuous Glucose Monitoring with HbA1c in Adults with Type 2 Diabetes

Associations of mean glucose and time in range (70-180 mg/dL) from continuous glucose monitoring (CGM) with HbA1c in adults with type 2 diabetes are not well characterized. We conducted a secondary analysis of 186 participants from the Hyperglycemic Profiles in Obstructive Sleep Apnea (HYPNOS) trial. Participants simultaneously wore Dexcom G4 and Abbott Libre Pro CGM sensors up to 4 weeks. Mean HbA1c was 7.7% (SD, 1.3). There were strong negative Pearson's correlations of HbA1c with CGM time in range (-0.79, Abbott; -0.81, Dexcom) and strong positive correlations with CGM mean glucose (Dexcom, 0.84; Abbott, 0.82). However, there were large differences in CGM mean glucose (±20 mg/dL) and time in range (±14%) at any given HbA1c value. Mean glucose and HbA1c are strongly correlated in type 2 diabetes patients not taking insulin but discordance is evident at the individual level. Clinicians should expect discordance and use HbA1c and CGM in a complementary manner. ClinicalTrials.gov Identifier: NCT02454153.

Au@bovine serum albumin nanoparticle-based acid-resistant nanozyme quartz crystal microbalance sensing of urine glucose

A robust, efficient and sensitive quartz crystal microbalance (QCM) for glucose detection has been constructed using Au@bovine serum albumin (Au@BSA) nanoparticles as an active layer. The nanoparticles serve as tandem nanozymes and their stability over natural enzymes enable the sensor to show a wider linear dynamic range between 0.05 and 15 mM, a higher acid-resistance (pH 2.0-8.0) and heat-resistance (35-60 °C) than conventional glucose oxidase (GOx)-based sensors. The sensor has been further applied to measure glucose content in artificial urine directly without dilution, where the recovery of 99.6-105.2% and the relative standard deviations (RSDs) below 0.88% confirm a good reproducibility for the measurement results. In addition, the developed Au@BSA QCM sensor can retain 95% of its initial activity after 40 days of storage. Overall, the Au@BSA sensor shows better comprehensive performance than the commercial sensor strips for urine glucose analysis and provides a promising approach in a more precise and robust manner.

Three short postmeal walks as an alternate therapy to continuous walking for women with gestational diabetes

The purpose of this study was to determine whether postmeal walking (breaking up exercise into short bouts after meals) is an effective and feasible alternate to continuous walking for the management of gestational diabetes. Forty-one women with gestational diabetes were randomised between wk 28-30 gestation to either standard-care (30-min continuous exercise) or standard-care with postmeal walking (10-min of walking after breakfast, lunch and dinner). Continuous glucose and activity monitors were worn to measure glycaemic control and adherence during three-days of standard-care (baseline) followed by three-days of postmeal or continuous walking. A linear mixed model analysed the changes from baseline between postmeal and continuous walking, as an average of the three-day periods. Thirty-two women (postmeal walking n=17: control n=15, 33±5 y, body mass index 25±4 kg.m2) completed the trial. Postprandial and overnight glucose concentrations were similar between postmeal walking and control, both interventions improved from baseline. There was no difference in adherence between groups, however postmeal walking completed more minutes of prescribed physical activity across baseline and intervention days compared to the continuous walking standard-care group. Preliminary findings from this proof-of-concept study suggest postmeal walking could be a promising alternative to, and work interchangeably with, traditional advice to perform continuous moderate-intensity physical activity, in women with gestational diabetes. Novelty bullets -Three ten-minute postmeal walks may be comparable to thirty minutes continuous walking for glucose control in women with gestational diabetes - Accumulating activity in short bouts after meals is a feasible alternate to continuous exercise for women with gestational diabetes.

Hypoglycemia Detection Using Hand Tremors: A Home Study in Patients with Type 1 Diabetes (Preprint)

BACKGROUND

Diabetes affects millions of people worldwide and is steadily increasing. A serious condition associated with diabetes is low glucose levels (hypoglycemia). Monitoring blood glucose is usually performed by invasive methods or intrusive devices, and these devices are currently not available to all patients with diabetes. Hand tremor is a significant symptom of hypoglycemia, as nerves and muscles are powered by blood sugar. However, to our knowledge, no validated tools or algorithms exist to monitor and detect hypoglycemic events via hand tremors.

OBJECTIVE

In this paper, we propose a noninvasive method to detect hypoglycemic events based on hand tremors using accelerometer data.

METHODS

We analyzed triaxial accelerometer data from a smart watch recorded from 33 patients with type 1 diabetes for 1 month. Time and frequency domain features were extracted from acceleration signals to explore different machine learning models to classify and differentiate between hypoglycemic and nonhypoglycemic states.

RESULTS

The mean duration of the hypoglycemic state was 27.31 (SD 5.15) minutes per day for each patient. On average, patients had 1.06 (SD 0.77) hypoglycemic events per day. The ensemble learning model based on random forest, support vector machines, and k-nearest neighbors had the best performance, with a precision of 81.5% and a recall of 78.6%. The results were validated using continuous glucose monitor readings as ground truth.

CONCLUSIONS

Our results indicate that the proposed approach can be a potential tool to detect hypoglycemia and can serve as a proactive, nonintrusive alert mechanism for hypoglycemic events.

Glycaemia dynamics in gestational diabetes mellitus

Pregnant women may develop gestational diabetes mellitus (GDM), a disease of pregnancy characterised by maternal and fetal hyperglycaemia with hazardous consequences to the mother, the fetus, and the newborn. Maternal hyperglycaemia in GDM results in fetoplacental endothelial dysfunction. GDM-harmful effects result from chronic and short periods of hyperglycaemia. Thus, it is determinant to keep glycaemia within physiological ranges avoiding short but repetitive periods of hyper or hypoglycaemia. The variation of glycaemia over time is defined as 'glycaemia dynamics'. The latter concept regards with a variety of mechanisms and environmental conditions leading to blood glucose handling. In this review we summarized the different metrics for glycaemia dynamics derived from quantitative, plane distribution, amplitude, score values, variability estimation, and time series analysis. The potential application of the derived metrics from self-monitoring of blood glucose (SMBG) and continuous glucose monitoring (CGM) in the potential alterations of pregnancy outcome in GDM are discussed.

Assessment of Glycemic Response to Model Breakfasts Varying in Glycemic Index (GI) in 5-7-Year-Old School Children

Reduced Glycemic Index (GI) of breakfast has been linked to improved cognitive performance in both children and adult populations across the morning. However, few studies have profiled the post-prandial glycemic response (PPGR) in younger children. The aim of this study was to assess PPGR to breakfast interventions differing in GI in healthy children aged 5-7 years. Eleven subjects completed an open-label, randomized, cross-over trial, receiving three equicaloric test beverages (260 kcal) consisting of 125 mL semi-skimmed milk and 50 g sugar (either glucose, sucrose, or isomaltulose). On a fourth occasion, the sucrose beverage was delivered as intermittent supply. PPGR was measured over 180 min using Continuous Glucose Monitoring (CGM). The incremental area under the curve (3h-iAUC) was highest for the glucose beverage, followed by intermittent sucrose (-21%, p = 0.288), sucrose (-27%, p = 0.139), and isomaltulose (-48%, p = 0.018). The isomaltulose beverage induced the smallest Cmax (7.8 mmol/L vs. >9.2 mmol/L for others) and the longest duration with moderate glucose level, between baseline value and 7.8 mmol/L (150 vs. <115 min for others). These results confirm that substituting mid-high GI sugars (e.g., sucrose and glucose) with low GI sugars (e.g., isomaltulose) during breakfast are a viable strategy for sustained energy release and glycemic response during the morning even in younger children.

Continuous Glucose Monitoring for the Detection of Hypoglycemia in Patients With Diabetes of the Exocrine Pancreas

BACKGROUND

Detailed evaluations of hypoglycemia and associated indices based on continuous glucose monitoring (CGM) are limited in patients with diabetes of the exocrine pancreas. Our study sought to evaluate the frequency and pattern of hypoglycemic events and to investigate hypoglycemia-specific indices in this population.

METHODS

This was a cross-sectional study comprising 83 participants with diabetes of the exocrine pancreas. CGM and self-monitoring of blood glucose (SMBG) were performed on all participants for a minimum period of 72 hours. The frequency and pattern of hypoglycemic events, as well as hypoglycemia-related indices, were evaluated.

RESULTS

Hypoglycemia was detected in 90.4% of patients using CGM and 38.5% of patients using SMBG. Nocturnal hypoglycemic events were more frequent (1.9 episodes/patient) and prolonged (142 minutes) compared with day-time events (1.1 episodes/patient; 82.8 minutes, P < 0.05). The mean low blood glucose index was 2.1, and glycemic risk assessment diabetes equation hypoglycemia was 9.1%. The mean time spent below (TSB) <70 mg/dL was 9.2%, and TSB <54 mg/dL was 3.7%. The mean area under curve (AUC) <70 mg/dL was 1.7 ± 2.5 mg/dL/hour and AUC <54 mg/dL was 0.6 ± 1.3 mg/dL/hour. All of the CGM-derived hypoglycemic indices were significantly more deranged at night compared with during the day (P < 0.05).

CONCLUSION

Patients with diabetes of the exocrine pancreas have a high frequency of hypoglycemic episodes that are predominantly nocturnal. CGM is superior to SMBG in the detection of nocturnal and asymptomatic hypoglycemic episodes. CGM-derived hypoglycemic indices are beneficial in estimating the risk of hypoglycemia.

Evaluation of a flash glucose monitoring system in nondiabetic dogs with rapidly changing blood glucose concentrations

Background

A flash glucose monitoring system (FGMS; FreeStyle Libre) is useful for monitoring hypoglycemic dogs with diabetes.

Objective

To assess the utility of this FGMS in dogs with induced hypoglycemia and rapid fluctuations in blood glucose (BG) concentrations.

Animals

Twenty‐four apparently healthy research (n = 10) and teaching (n = 14) dogs.

Methods

Prospective, observational study performed in tandem with a teaching laboratory. Regular insulin was administered to dogs and resulting hypoglycemia was corrected. Before insulin administration and every 10 minutes over a 90‐minute period, serial measurements of interstitial glucose (IG) with FGMS and BG with a portable blood glucose meter (PBGM) and clinical chemistry analyzer concentrations were made. Portable blood glucose meter and FGMS readings were compared to that of the clinical chemistry analyzer. Analytical and clinical accuracy were assessed using ISO 15197:2013 criteria, including Parkes error grid analysis.

Results

The proportions of readings in the low BG range (BG <100 mg/dL) for which the test method measurement was within ±15 mg/dL of the reference BG for the PBGM and FGMS were 81.7% (161/197) and 39.1% (72/184), respectively. The proportions of readings for the PBGM and FGMS, which were not likely to affect clinical outcome according to Parkes error grid analysis, were 97.9% (233/238) and 80.1% (177/221), respectively.

Conclusions and Clinical Importance

In this model, there was limited agreement between the FGMS and reference standard BG measurements. The FGMS (measuring IG concentrations) was compared to peripheral BG concentrations, not brain‐tissue glucose concentrations, and failed to reliably detect hypoglycemia.

Hypoglycaemia detection and prediction techniques: A systematic review on the latest developments

The main objective of diabetes control is to correct hyperglycaemia while avoiding hypoglycaemia, especially in insulin-treated patients. Fear of hypoglycaemia is a hurdle to effective correction of hyperglycaemia because it promotes under-dosing of insulin. Strategies to minimise hypoglycaemia include education and training for improved hypoglycaemia awareness and the development of technologies to allow their early detection and thus minimise their occurrence. Patients with impaired hypoglycaemia awareness would benefit the most from these technologies. The purpose of this systematic review is to review currently available or in-development technologies that support detection of hypoglycaemia or hypoglycaemia risk, and identify gaps in the research. Nanomaterial use in sensors is a promising strategy to increase the accuracy of continuous glucose monitoring devices for low glucose values. Hypoglycaemia is associated with changes on vital signs, so electrocardiogram and encephalogram could also be used to detect hypoglycaemia. Accuracy improvements through multivariable measures can make already marketed galvanic skin response devices a good noninvasive alternative. Breath volatile organic compounds can be detected by dogs and devices and alert patients at hypoglycaemia onset, while near-infrared spectroscopy can also be used as a hypoglycaemia alarms. Finally, one of the main directions of research are deep learning algorithms to analyse continuous glucose monitoring data and provide earlier and more accurate prediction of hypoglycaemia. Current developments for early identification of hypoglycaemia risk combine improvements of available 'needle-type' enzymatic glucose sensors and noninvasive alternatives. Patient usability will be essential to demonstrate to allow their implementation for daily use in diabetes management.

Continuous glucose monitoring in patients with type 2 diabetes on hemodialysis

Diabetic kidney disease is the leading cause of end-stage kidney disease in high-income countries. The strict control of glycemic oscillations is the principal therapeutic target, but this could be hard to achieve in uremic patients due to their unpredictable insulin sensitivity. Currently, the evaluation of the glycemic profile relies on serum markers (glycated hemoglobin HbA1c, glycated albumin, and fructosamine), capillary glucose blood control (self-monitoring of blood glucose), and interstitial glucose control (continue glucose monitoring). We conducted a systematic review of published articles on continue glucose monitoring in hemodialysis patients with type 2 diabetes, which included 12 major articles. Four studies found significant fluctuations in glucose levels during hemodialysis sessions. All studies reported a higher mean amplitude of glucose variations on the hemodialysis day. Three studies agreed that continue glucose monitoring is better than glycated hemoglobin in detecting these abnormalities. Moreover, continue glucose monitoring was more accurate and perceived as easier to use by patients and their caregivers. In patients with type 2 diabetes on hemodialysis, glucose levels show different variation patterns than the patients on hemodialysis without diabetes. Considering manageability, accuracy, and cost-effectiveness, continue glucose monitoring could be the ideal diagnostic tool for the patient with diabetes on hemodialysis.

Fiber Optic Sensors: A Review for Glucose Measurement

Diabetes mellitus is a chronic metabolic disorder, being globally one of the most deadly diseases. This disease requires continually monitoring of the body's glucose levels. There are different types of sensors for measuring glucose, most of them invasive to the patient. Fiber optic sensors have been proven to have advantages compared to conventional sensors and they have great potential for various applications, especially in the biomedical area. Compared to other sensors, they are smaller, easy to handle, mostly non-invasive, thus leading to a lower risk of infection, high precision, well correlated and inexpensive. The objective of this review article is to compare different types of fiber optic sensors made with different experimental techniques applied to biomedicine, especially for glucose sensing. Observations are made on the way of elaboration, as well as the advantages and disadvantages that each one could have in real applications.

Can Type 2 Diabetes Sufferers Actually Estimate Serum Glucose Level From Interstitial Fluid Glucose Level: A Diabetes Patient's Experience

Introduction:

The general assumption is that blood glucose (BG) and interstitial fluid glucose (IntFG) are practically the same. We aimed to determine whether the typical patient with type 2 diabetes can use IntFG to estimate BG.

Description:

The study was conducted on an 83-year-old white male with type 2 diabetes. One hundred pairs of IntFG and BG observations mg/dL (n = 50 simultaneous; n = 50 with 15-minute lag) were made over a 10-day period. We used paired t tests, correlation coefficients, and linear regression to predict relationships between IntFG and BG.

Results:

There were significant (P < .0001) mean differences between IntFG and BG (simultaneous: 53.8 mg/dL; 15-minute time lag: 46.4 mg/dL). There were significant (P < .0001) positive correlations between IntFG and BG (simultaneous: r = 0.641; 15-minute time lag: r = 0.712). Linear regression revealed that increased IntFG was significantly (P < .0001) associated with declines in mean predicted BG.

Conclusion:

The typical type 2 diabetes patient cannot use IntFG level to estimate BG.

Surface Enhanced Raman Spectroscopy Based Biosensor with a Microneedle Array for Minimally Invasive In Vivo Glucose Measurements

To monitor blood glucose levels reliably, diabetic patients usually have to undergo frequent fingerstick tests to draw out fresh blood, which is painful and inconvenient with the potential risk of cross contamination especially when the lancet is reused or not properly sterilized. This work reports a novel surface-enhanced Raman spectroscopy (SERS) sensor for the in situ intradermal detection of glucose based on a low-cost poly(methyl methacrylate) microneedle (PMMA MN) array. After incorporating 1-decanethiol (1-DT) onto the silver-coated array surface, the sensor was calibrated in the range of 0-20 mM in skin phantoms then tested for the in vivo quantification of glucose in a mouse model of streptozocin (STZ)-induced type I diabetes. The results showed that the functional poly(methyl methacrylate) microneedle (F-PMMA MN) array was able to directly measure glucose in the interstitial fluid (ISF) in a few minutes and retain its structural integrity without swelling. The Clarke error grid analysis of measured data indicated that 93% of the data points lie in zones A and B. Moreover, the MN array exhibited minimal invasiveness to the skin as the skin recovered well without any noticeable adverse reaction in 10 min after measurements. With further improvement and proper validation, this polymeric MN array-based SERS biosensor has the potential to be used in painless glucose monitoring of diabetic patients in the future.

Design of a Real-time Self-adjusting Calibration Algorithm to Improve the Accuracy of Continuous Blood Glucose Monitoring

The aim of this study is to establish a real-time self-adjusting calibration algorithm to compensate for signal drift and sensitivity attenuation of subcutaneous implantable glucose sensors. A real-time self-adjusting in vivo calibration method was designed based on the one-point calibration model. The current signal was compensated in real-time and the sensitivity was calibrated regularly. The least squares method was used to fit the initial parameters of the model, and then, the in vivo monitored current data was calibrated. Comparing with the mean absolute relative difference (MARD) of the blood glucose concentration by the traditional one-point calibration model (22.85 ± 5.76%), the MARD of the blood glucose concentration calibrated by the real-time self-adjusting in vivo calibration method was 6.28 ± 2.31%. The accuracy of the dynamic blood glucose monitoring was effectively improved. This calibration algorithm could compensate the signal drift in real time and correct sensitivity regularly to improve the accuracy of dynamic glucose monitoring, thus significantly enhancing diabetic self-management.

Glucose Concentration Measurement in Human Blood Plasma Solutions with Microwave Sensors

Three microwave sensors are used to track the glucose level of different human blood plasma solutions. In this paper, the sensors are evaluated as glucose trackers in a context close to real human blood. Different plasma solutions sets were prepared from a human blood sample at several added glucose concentrations up to 10 wt%, adding also ascorbic acid and lactic acid at different concentrations. The experimental results for the different sensors/solutions combinations are presented in this work. The sensors show good performance and linearity as glucose level retrievers, although the sensitivities change as the rest of components vary. Different sensor behaviors depending upon the concentrations of glucose and other components are identified and characterized. The results obtained in terms of sensitivity are coherent with previous works, highlighting the contribution of glucose to the dielectric losses of the solution. The results are also consistent with the frequency evolution of the electromagnetic signature of glucose found in the literature, and are helpful for selecting frequency bands for sensing purposes and envisioning future approaches to the challenging measurement in real biological contexts. Discussion of the implications of the results and guidelines for further research and development of more accurate sensors is offered.

Enabling the Internet of Mobile Crowdsourcing Health Things: A Mobile Fog Computing, Blockchain and IoT Based Continuous Glucose Monitoring System for Diabetes Mellitus Research and Care

Diabetes patients suffer from abnormal blood glucose levels, which can cause diverse health disorders that affect their kidneys, heart and vision. Due to these conditions, diabetes patients have traditionally checked blood glucose levels through Self-Monitoring of Blood Glucose (SMBG) techniques, like pricking their fingers multiple times per day. Such techniques involve a number of drawbacks that can be solved by using a device called Continuous Glucose Monitor (CGM), which can measure blood glucose levels continuously throughout the day without having to prick the patient when carrying out every measurement. This article details the design and implementation of a system that enhances commercial CGMs by adding Internet of Things (IoT) capabilities to them that allow for monitoring patients remotely and, thus, warning them about potentially dangerous situations. The proposed system makes use of smartphones to collect blood glucose values from CGMs and then sends them either to a remote cloud or to distributed fog computing nodes. Moreover, in order to exchange reliable, trustworthy and cybersecure data with medical scientists, doctors and caretakers, the system includes the deployment of a decentralized storage system that receives, processes and stores the collected data. Furthermore, in order to motivate users to add new data to the system, an incentive system based on a digital cryptocurrency named GlucoCoin was devised. Such a system makes use of a blockchain that is able to execute smart contracts in order to automate CGM sensor purchases or to reward the users that contribute to the system by providing their own data. Thanks to all the previously mentioned technologies, the proposed system enables patient data crowdsourcing and the development of novel mobile health (mHealth) applications for diagnosing, monitoring, studying and taking public health actions that can help to advance in the control of the disease and raise global awareness on the increasing prevalence of diabetes.

Novel ambulatory glucose-sensing technology improves hypoglycemia detection and patient monitoring adherence in children and adolescents with type 1 diabetes

Purpose

Glucose monitoring [GM] is a mainstay of diabetes control and management. Improving glycemic control is essential to prevent microvascular complications. However, adherence to GM can be a challenge in children and adolescents. Detecting hypoglycemia is essential for its prevention and treatment. We aim to study the impact of the flash ambulatory glucose monitoring in detecting hypoglycemia and enhancing adherence in children and adolescents with type 1 diabetes.

Methods

The study is prospective involving 3 hospital visits. Children and adolescents with diabetes were enrolled in the study which involved a period on conventional glucose self-monitoring [glucometers] followed by a similar period of monitoring using the flash glucose monitoring device (FreeStyle Libre). Frequency of GM, duration and frequency of hypoglycemia were compared on conventional and the flash monitoring.

Results

75 subjects were studied. Age mean (range) was 11.9 years (2-19). Significant difference was seen in hypoglycemia detection between both testing devices. 68 (94%) and 65 (90%) patients detected nocturnal and diurnal hypoglycemia respectively on Flash monitoring compared to 12 (16.6%) and 30 (41%) on glucometer testing (p < 0.00). Mean (range) duration of hypoglycemia was 95 min (15-330). Statistically-significant difference was found between the frequency of GM on glucometer testing compared with Flash monitoring (2.87 and 11.6/day) (p < 0.001).

Conclusions

Flash monitoring is a useful tool to improve adherence to GM and detecting hypoglycemia [diurnal and nocturnal] in children and adolescents with type 1 diabetes.

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.

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.

Combined Interval Training and Post-exercise Nutrition in Type 2 Diabetes: A Randomized Control Trial

Background: High-intensity interval training (HIIT) can improve several aspects of cardiometabolic health. Previous studies have suggested that adaptations to exercise training can be augmented with post-exercise milk or protein consumption, but whether this nutritional strategy can impact the cardiometabolic adaptations to HIIT in type 2 diabetes is unknown.

Objective: To determine if the addition of a post-exercise milk or protein beverage to a high-intensity interval training (HIIT) intervention improves cardiometabolic health in individuals with type 2 diabetes.

Design: In a proof-of-concept, double-blind clinical trial 53 adults with uncomplicated type 2 diabetes were randomized to one of three nutritional beverages (500 mL skim-milk, macronutrient control, or flavored water placebo) consumed after exercise (3 days/week) during a 12 week low-volume HIIT intervention. HIIT involved 10 X 1-min high-intensity intervals separated by 1-min low-intensity recovery periods. Two sessions per week were cardio-based (at ~90% of heart rate max) and one session involved resistance-based exercises (at RPE of 5–6; CR-10 scale) in the same interval pattern. Continuous glucose monitoring (CGM), glycosylated hemoglobin (HbA_1c), body composition (dual-energy X-ray absorptiometry), cardiorespiratory fitness (V˙O2peak), blood pressure, and endothelial function (%FMD) were measured before and after the intervention.

Results: There were significant main effects of time (all p < 0.05) but no difference between groups (Interaction: all p > 0.71) for CGM 24-h mean glucose (−0.5 ± 1.1 mmol/L), HbA_1c (−0.2 ± 0.4%), percent body fat (−0.8 ± 1.6%), and lean mass (+1.1 ± 2.8 kg). Similarly, V˙O2peak (+2.5 ± 1.6 mL/kg/min) and %FMD (+1.4 ± 1.9%) were increased, and mean arterial blood pressure reduced (−6 ± 7 mmHg), after 12 weeks of HIIT (all p < 0.01) with no difference between beverage groups (Interaction: all p > 0.11).

Conclusion: High-intensity interval training is a potent stimulus for improving several important metabolic and cardiovascular risk factors in type 2 diabetes. The benefits of HIIT are not augmented by the addition of post-exercise protein.

Glucose Variability Based on Continuous Glucose Monitoring Assessment Is Associated with Postoperative Complications after Cardiovascular Surgery

PURPOSE

This purpose of this prospective study was to use a continuous glucose monitoring (CGM) system to evaluate the suitability of our institution's glucose management protocol after cardiovascular surgery and to clarify the impact of glycemic variability on postoperative complications.

METHODS

In all, 76 patients who underwent elective cardiovascular surgery and were monitored perioperatively using a CGM system were evaluated. Postoperative glucose management consisted of continuous intravenous insulin infusion (CIII) in the intensive care unit, and subcutaneous insulin injections (SQII) after oral food intake started. CIII and subcutaneous injections were initiated when blood glucose level exceeded 150 mg/dL. CGM data were used to analyze perioperative glycemic variability and association with postoperative complications.

RESULTS

Target glucose levels (71-180 mg/dL) were achieved during 97.1 ± 5.5% and 86.4 ± 19.0% of the continuous insulin infusion and subcutaneous injection periods, respectively. Major postoperative complications were surgical site infections, found in 6.6% of total patients, and atrial fibrillation, found in 44% of patients with off-pump coronary artery bypass grafting. High glycemic variability during SQII was associated with increased risk for both complications.

CONCLUSION

Data analysis revealed that our glucose management protocol during CIII was adequate. However, the management protocol during SQII required improvement.

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.

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.

Factory-Calibrated Continuous Glucose Sensors: The Science Behind the Technology

The use of commercially available continuous glucose monitors for diabetes management requires sensor calibrations, which until recently are exclusively performed by the patient. A new development is the implementation of factory calibration for subcutaneous glucose sensors, which eliminates the need for user calibrations and the associated blood glucose tests. Factory calibration means that the calibration process is part of the sensor manufacturing process and performed under controlled laboratory conditions. The ability to move from a user calibration to factory calibration is based on several technical requirements related to sensor stability and the robustness of the sensor manufacturing process. The main advantages of factory calibration over the conventional user calibration are: (a) more convenience for the user, since no more fingersticks are required for calibration and (b) elimination of use errors related to the execution of the calibration process, which can lead to sensor inaccuracies. The FreeStyle Libre^™ and FreeStyle Libre Pro^™ flash continuous glucose monitoring systems are the first commercially available sensor systems using factory-calibrated sensors. For these sensor systems, no user calibrations are required throughout the sensor wear duration.

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.

The Lymphatic System: Integral Roles in Immunity

The lymphatic vasculature is not considered a formal part of the immune system, but it is critical to immunity. One of its major roles is in the coordination of the trafficking of antigen and immune cells. However, other roles in immunity are emerging. Lymphatic endothelial cells, for example, directly present antigen or express factors that greatly influence the local environment. We cover these topics herein and discuss how other properties of the lymphatic vasculature, such as mechanisms of lymphatic contraction (which immunologists traditionally do not take into account), are nonetheless integral in the immune system. Much is yet unknown, and this nascent subject is ripe for exploration. We argue that to consider the impact of lymphatic biology in any given immunological interaction is a key step toward integrating immunology with organ physiology and ultimately many complex pathologies.

Accuracy of Continuous Glucose Monitoring in Patients After Total Pancreatectomy with Islet Autotransplantation

BACKGROUND

Among postsurgical and critically ill patients, malglycemia is associated with increased complications. Continuous glucose monitoring (CGM) in the inpatient population may enhance glycemic control. CGM reliability may be compromised by postsurgical complications such as edema or vascular changes. We utilized Clarke Error Grid (CEG) and Surveillance Error Grid (SEG) analysis to evaluate CGM performance after total pancreatectomy with islet autotransplantation.

MATERIALS AND METHODS

This subanalysis evaluated Medtronic Enlite 2 CGM values against YSI serum glucose in seven post-transplant patients (86% female; 38.6 ± 9.4 years) on artificial pancreas for 72 h at transition from intravenous to subcutaneous insulin. Sensor recalibration occurred for absolute relative difference (ARD) ≥20% x2, ≥30% x1, or by investigator discretion based on trend.

RESULTS

Sensor analysis showed mean absolute relative difference (MARD) of 11.0% ± 11.5%. The sensors were recalibrated 8.3 times/day; active sensor was switched 1.4 times/day. Calibration factor was 7.692 ± 3.786 mg/nA·dL (target = 1.5-20 mg/nA·dL). CEG analysis showed 86.1% of pairs in Zone A (clinically accurate zone) and 99.4% of pairs in Zones A + B (low risk of error). SEG analysis of hypoglycemia/hyperglycemia risk showed 92.22% of pairs in the "no risk" zone, 5.96% of pairs in the "slight lower" risk zone, 1.01% of pairs in the "slight higher" risk zone, and only 0.81% of pairs in the "moderate lower" risk zone.

CONCLUSIONS

Overall performance of the Medtronic Enlite 2 CGM in the post-transplant population was reasonably good with "no risk" or "slight lower" risk by SEG analysis and high CGM-YSI agreement by CEG analysis; however, frequent recalibrations were required in this intensive care population.

From Two to One Per Day Calibration of Dexcom G4 Platinum by a Time-Varying Day-Specific Bayesian Prior

BACKGROUND

In the DexCom G4 Platinum (DG4P) continuous glucose monitoring (CGM) sensor, the raw current signal generated by glucose-oxidase is transformed to glucose concentration by a calibration function whose parameters are periodically updated by matching self-monitoring of blood glucose references, usually twice a day, to compensate for sensor variability in time. The aim of this work is to reduce DG4P calibration frequency to once a day by a recently proposed Bayesian calibration algorithm, which employs a time-varying calibration function and suitable day-specific priors.

METHODS

The database consists of 57 CGM signals that are collected by the DG4P for 7 days. The Bayesian calibration algorithm is used to calibrate the raw current signal following two different schedules, that is, two and one calibration per day. Calibrated glycemic profiles are compared with those originally acquired by the manufacturer, on days 1, 4, and 7, where frequent blood glucose references were available, by using standard metrics, that is, mean absolute relative difference (MARD), percentage of accurate glucose estimates, and percentage of data in the A-zone of Clarke Error Grid.

RESULTS

The one per day Bayesian calibration algorithm has accuracy similar to that of two per day (11.8% vs. 11.7% MARD, respectively), and it is statistically better (P-value of 0.0411) than the manufacturer calibration algorithm, which requires two calibrations per day (13.1% MARD).

CONCLUSIONS

A Bayesian calibration algorithm employing a time-varying calibration function and suitable priors enables a reduction of the calibrations of DG4P sensor from two to one per day.

Postprandial capillary-venous glucose gradient in Type 1 diabetes: magnitude and clinical associations in a real world setting

AIMS

To determine the magnitude of the peripheral glucose gradient in patients with Type 1 diabetes in a real world setting and to explore its relationship with insulin dose and macronutrient intake.

METHODS

All patients used mealtime analogue insulin. The glucose gradient was assessed using antecubital fossa venous and finger-stick capillary samples, collected concurrently at room temperature. Baseline sampling occurred before the administration of an insulin dose and breakfast of the patient's choosing. Breakfast was consumed an average of 15 min after baseline. The macronutrient content of breakfast was documented. Sampling was repeated 1 and 2 h after baseline.

RESULTS

The mean (95% CI) plasma capillary-venous glucose gradient values for 43 patients were: pre-breakfast, 0.21 (0.08-0.34) mmol/l; 1 h after baseline, 0.87 (0.66-1.07) mmol/l; and 2 h after baseline, 0.52 (0.33-0.71) mmol/l. Glucose gradient and dietary carbohydrate intake (g/kg body weight) were positively correlated at both 1 h (P < 0.01) and 2 h after baseline (P < 0.01). No relationship was observed between this gradient and mealtime insulin dose, or the glucose concentration at either time point.

CONCLUSIONS

In patients with Type 1 diabetes, a clinically significant glucose gradient is present after the ingestion of a carbohydrate-rich meal. As postprandial capillary and venous plasma glucose concentrations are not equivalent, defining the site of sample collection is important.

The Performance and Usability of a Factory-Calibrated Flash Glucose Monitoring System

INTRODUCTION

The purpose of the study was to evaluate the performance and usability of the FreeStyle(®) Libre™ Flash glucose monitoring system (Abbott Diabetes Care, Alameda, CA) for interstitial glucose results compared with capillary blood glucose results.

MATERIALS AND METHODS

Seventy-two study participants with type 1 or type 2 diabetes were enrolled by four U.S. clinical sites. A sensor was inserted on the back of each upper arm for up to 14 days. Three factory-only calibrated sensor lots were used in the study. Sensor glucose measurements were compared with capillary blood glucose (BG) results (approximately eight per day) obtained using the BG meter built into the reader (BG reference) and with the YSI analyzer (Yellow Springs Instrument, Yellow Springs, OH) reference tests at three clinic visits (32 samples per visit). Sensor readings were masked to the participants.

RESULTS

The accuracy of the results was demonstrated against capillary BG reference values, with 86.7% of sensor results within Consensus Error Grid Zone A. The percentage of readings within Consensus Error Grid Zone A on Days 2, 7, and 14 was 88.4%, 89.2%, and 85.2%, respectively. The overall mean absolute relative difference was 11.4%. The mean lag time between sensor and YSI reference values was 4.5±4.8 min. Sensor accuracy was not affected by factors such as body mass index, age, type of diabetes, clinical site, insulin administration, or hemoglobin A1c.

CONCLUSIONS

Interstitial glucose measurements with the FreeStyle Libre system were found to be accurate compared with capillary BG reference values, with accuracy remaining stable over 14 days of wear and unaffected by patient characteristics.

Hyperglycemia-Induced Changes in Hyaluronan Contribute to Impaired Skin Wound Healing in Diabetes: Review and Perspective

Ulcers and chronic wounds are a particularly common problem in diabetics and are associated with hyperglycemia. In this targeted review, we summarize evidence suggesting that defective wound healing in diabetics is causally linked, at least in part, to hyperglycemia-induced changes in the status of hyaluronan (HA) that resides in the pericellular coat (glycocalyx) of endothelial cells of small cutaneous blood vessels. Potential mechanisms through which exposure to high glucose levels causes a loss of the glycocalyx on the endothelium and accelerates the recruitment of leukocytes, creating a proinflammatory environment, are discussed in detail. Hyperglycemia also affects other cells in the immediate perivascular area, including pericytes and smooth muscle cells, through exposure to increased cytokine levels and through glucose elevations in the interstitial fluid. Possible roles of newly recognized, cross-linked forms of HA, and interactions of a major HA receptor (CD44) with cytokine/growth factor receptors during hyperglycemia, are also discussed.

Pulse Laser Deposition Fabricating Gold Nanoclusters on a Glassy Carbon Surface for Nonenzymatic Glucose Sensing

A One-step technique for depositing gold nanoclusters (GNCs) onto the surface of a glassy carbon (GC) plate was developed by using pulse laser deposition (PLD) with appropriate process parameters. The method is simple and clean without using any templates, surfactants, or stabilizers. The experimental factors (pulse laser number and the pressure of inert gas (Ar)) that affect the morphology and structure of GNCs, and thus affect the electrocatalytic oxidation performance towards glucose were systematically investigated by means of transmission electron microscopy (TEM) and electrochemical methods (cyclic voltammograms (CV) and chronoamperometry methods). The GC electrode modified by GNCs exhibited a rapid response time (about 2 s), a broad linear range (0.1 to 20 mM), and good stability. The sensitivity was estimated to be 31.18 μA cm(-2) mM(-1) (vs. geometric area), which is higher than that of the Au bulk electrode. It has a good resistance to the common interfering species, such as ascorbic acid (AA), uric acid (UA) and 4-acetaminophen (AP). Therefore, this work has demonstrated a simple and effective sensing platform for the nonenzymatic detection of glucose, and can be used as a new material for a novel non-enzymatic glucose sensor.

CMOS image sensor-based implantable glucose sensor using glucose-responsive fluorescent hydrogel

A CMOS image sensor-based implantable glucose sensor based on an optical-sensing scheme is proposed and experimentally verified. A glucose-responsive fluorescent hydrogel is used as the mediator in the measurement scheme. The wired implantable glucose sensor was realized by integrating a CMOS image sensor, hydrogel, UV light emitting diodes, and an optical filter on a flexible polyimide substrate. Feasibility of the glucose sensor was verified by both in vitro and in vivo experiments.

Accuracy evaluation of a new real-time continuous glucose monitoring algorithm in hypoglycemia

BACKGROUND

The purpose of this study was to evaluate the performance of a new continuous glucose monitoring (CGM) calibration algorithm and to compare it with the Guardian(®) REAL-Time (RT) (Medtronic Diabetes, Northridge, CA) calibration algorithm in hypoglycemia.

SUBJECTS AND METHODS

CGM data were obtained from 10 type 1 diabetes patients undergoing insulin-induced hypoglycemia. Data were obtained in two separate sessions using the Guardian RT CGM device. Data from the same CGM sensor were calibrated by two different algorithms: the Guardian RT algorithm and a new calibration algorithm. The accuracy of the two algorithms was compared using four performance metrics.

RESULTS

The median (mean) of absolute relative deviation in the whole range of plasma glucose was 20.2% (32.1%) for the Guardian RT calibration and 17.4% (25.9%) for the new calibration algorithm. The mean (SD) sample-based sensitivity for the hypoglycemic threshold of 70 mg/dL was 31% (33%) for the Guardian RT algorithm and 70% (33%) for the new algorithm. The mean (SD) sample-based specificity at the same hypoglycemic threshold was 95% (8%) for the Guardian RT algorithm and 90% (16%) for the new calibration algorithm. The sensitivity of the event-based hypoglycemia detection for the hypoglycemic threshold of 70 mg/dL was 61% for the Guardian RT calibration and 89% for the new calibration algorithm. Application of the new calibration caused one false-positive instance for the event-based hypoglycemia detection, whereas the Guardian RT caused no false-positive instances. The overestimation of plasma glucose by CGM was corrected from 33.2 mg/dL in the Guardian RT algorithm to 21.9 mg/dL in the new calibration algorithm.

CONCLUSIONS

The results suggest that the new algorithm may reduce the inaccuracy of Guardian RT CGM system within the hypoglycemic range; however, data from a larger number of patients are required to compare the clinical reliability of the two algorithms.

An iterative learning strategy for the auto-tuning of the feedforward and feedback controller in type-1 diabetes

This paper proposes a scheme for the control of the blood glucose in subjects with type-1 diabetes mellitus based on the subcutaneous (s.c.) glucose measurement and s.c. insulin administration. The tuning of the controller is based on an iterative learning strategy that exploits the repetitiveness of the daily feeding habit of a patient. The control consists of a mixed feedback and feedforward contribution whose parameters are tuned through an iterative learning process that is based on the day-by-day automated analysis of the glucose response to the infusion of exogenous insulin. The scheme does not require any a priori information on the patient insulin/glucose response, on the meal times and on the amount of ingested carbohydrates (CHOs). Thanks to the learning mechanism the scheme is able to improve its performance over time. A specific logic is also introduced for the detection and prevention of possible hypoglycaemia events. The effectiveness of the methodology has been validated using long-term simulation studies applied to a set of nine in silico patients considering realistic uncertainties on the meal times and on the quantities of ingested CHOs.

Adaptive calibration algorithm for plasma glucose estimation in continuous glucose monitoring

Minimally or noninvasive continuous glucose monitors estimate plasma glucose from compartments alternative to blood, and may revolutionize the management of diabetes. However, the accuracy of current devices is still poor and it may partly depend on low performance of the implemented calibration algorithm. Here, a new adaptive calibration algorithm based on a population local-model-based intercompartmental glucose dynamic model is proposed. The novelty consists in the adaptation of data normalization parameters in real time to estimate and compensate patient's sensitivity variations. Adaptation is performed to minimize mean absolute relative deviation at the calibration points with a time window forgetting strategy. Four calibrations are used: preprandial and 1.5 h postprandial at two different meals. Two databases are used for validation: 1) a 9-h CGMS Gold (Medtronic, Northridge, USA) time series with paired reference glucose values from a clinical study in 17 subjects with type 1 diabetes; 2) data from 30 virtual patients (UVa simulator, Virginia, USA), where inter- and intrasubject variability of sensor's sensitivity were simulated. Results show how the adaptation of the normalization parameters improves the performance of the calibration algorithm since it counteracts sensor sensitivity variations. This improvement is more evident in one-week simulations.

LMI-Based Approaches for the Calibration of Continuous Glucose Measurement Sensors

The problem of online calibration and recalibration of continuous glucose monitoring (CGM) devices is considered. Two different parametric relations between interstitial and blood glucose are investigated and constructive algorithms to adaptively estimate the parameters within those relations are proposed. One characteristic is the explicit consideration of measurement uncertainty of the device used to collect the calibration measurements. Another feature is the automatic detection of fingerstick measurements that are not suitable to be used for calibration. Since the methods rely on the solution of linear matrix inequalities resulting in convex optimization problems, the algorithms are of moderate computational complexity and could be implemented on a CGM device. The methods were assessed on clinical data from 17 diabetic patients and the improvements with respect to the current state of the art is shown.

In vitro evaluation of fluorescence glucose biosensor response

Rapid, accurate, and minimally-invasive glucose biosensors based on Förster Resonance Energy Transfer (FRET) for glucose measurement have the potential to enhance diabetes control. However, a standard set of in vitro approaches for evaluating optical glucose biosensor response under controlled conditions would facilitate technological innovation and clinical translation. Towards this end, we have identified key characteristics and response test methods, fabricated FRET-based glucose biosensors, and characterized biosensor performance using these test methods. The biosensors were based on competitive binding between dextran and glucose to concanavalin A and incorporated long-wavelength fluorescence dye pairs. Testing characteristics included spectral response, linearity, sensitivity, limit of detection, kinetic response, reversibility, stability, precision, and accuracy. The biosensor demonstrated a fluorescence change of 45% in the presence of 400 mg/dL glucose, a mean absolute relative difference of less than 11%, a limit of detection of 25 mg/dL, a response time of 15 min, and a decay in fluorescence intensity of 72% over 30 days. The battery of tests presented here for objective, quantitative in vitro evaluation of FRET glucose biosensors performance have the potential to form the basis of future consensus standards. By implementing these test methods for a long-visible-wavelength biosensor, we were able to demonstrate strengths and weaknesses with a new level of thoroughness and rigor.