Continuous glucose monitors: current status and future developments

AI-Based Noninvasive Blood Glucose Monitoring: Scoping Review

BACKGROUND

Current blood glucose monitoring (BGM) methods are often invasive and require repetitive pricking of a finger to obtain blood samples, predisposing individuals to pain, discomfort, and infection. Noninvasive blood glucose monitoring (NIBGM) is ideal for minimizing discomfort, reducing the risk of infection, and increasing convenience.

OBJECTIVE

This review aimed to map the use cases of artificial intelligence (AI) in NIBGM.

METHODS

A systematic scoping review was conducted according to the Arksey O'Malley five-step framework. Eight electronic databases (CINAHL, Embase, PubMed, Web of Science, Scopus, The Cochrane-Central Library, ACM Digital Library, and IEEE Xplore) were searched from inception until February 8, 2023. Study selection was conducted by 2 independent reviewers, descriptive analysis was conducted, and findings were presented narratively. Study characteristics (author, country, type of publication, study design, population characteristics, mean age, types of noninvasive techniques used, and application, as well as characteristics of the BGM systems) were extracted independently and cross-checked by 2 investigators. Methodological quality appraisal was conducted using the Checklist for assessment of medical AI.

RESULTS

A total of 33 papers were included, representing studies from Asia, the United States, Europe, the Middle East, and Africa published between 2005 and 2023. Most studies used optical techniques (n=19, 58%) to estimate blood glucose levels (n=27, 82%). Others used electrochemical sensors (n=4), imaging (n=2), mixed techniques (n=2), and tissue impedance (n=1). Accuracy ranged from 35.56% to 94.23% and Clarke error grid (A+B) ranged from 86.91% to 100%. The most popular machine learning algorithm used was random forest (n=10) and the most popular deep learning model was the artificial neural network (n=6). The mean overall checklist for assessment of medical AI score on the included papers was 33.5 (SD 3.09), suggesting an average of medium quality. The studies reviewed demonstrate that some AI techniques can accurately predict glucose levels from noninvasive sources while enhancing comfort and ease of use for patients. However, the overall range of accuracy was wide due to the heterogeneity of models and input data.

CONCLUSIONS

Efforts are needed to standardize and regulate the use of AI technologies in BGM, as well as develop consensus guidelines and protocols to ensure the quality and safety of AI-assisted monitoring systems. The use of AI for NIBGM is a promising area of research that has the potential to revolutionize diabetes management.

Artificial intelligence in rheumatoid arthritis: potential applications and future implications

The widespread adoption of digital health records, coupled with the rise of advanced diagnostic testing, has resulted in an explosion of patient data, comparable in scope to genomic datasets. This vast information repository offers significant potential for improving patient outcomes and decision-making, provided one can extract meaningful insights from it. This is where artificial intelligence (AI) tools like machine learning (ML) and deep learning come into play, helping us leverage these enormous datasets to predict outcomes and make informed decisions. AI models can be trained to analyze and interpret patient data, including physician notes, laboratory testing, and imaging, to aid in the management of patients with rheumatic diseases. As one of the most common autoimmune diseases, rheumatoid arthritis (RA) has attracted considerable attention, particularly concerning the evolution of diagnostic techniques and therapeutic interventions. Our aim is to underscore those areas where AI, according to recent research, demonstrates promising potential to enhance the management of patients with RA.

Minimally invasive electrochemical continuous glucose monitoring sensors: Recent progress and perspective

Diabetes and its complications are seriously threatening the health and well-being of hundreds of millions of people. Glucose levels are essential indicators of the health conditions of diabetics. Over the past decade, concerted efforts in various fields have led to significant advances in glucose monitoring technology. In particular, the rapid development of continuous glucose monitoring (CGM) based on electrochemical sensing principles has great potential to overcome the limitations of self-monitoring blood glucose (SMBG) in continuously tracking glucose trends, evaluating diabetes treatment options, and improving the quality of life of diabetics. However, the applications of minimally invasive electrochemical CGM sensors are still limited owing to the following aspects: i) invasiveness, ii) short lifespan, iii) biocompatibility, and iv) calibration and prediction. In recent years, the performance of minimally invasive electrochemical CGM systems (CGMSs) has been significantly improved owing to breakthrough developments in new materials and key technologies. In this review, we summarize the history of commercial CGMSs, the development of sensing principles, and the research progress of minimally invasive electrochemical CGM sensors in reducing the invasiveness of implanted probes, maintaining enzyme activity, and improving the biocompatibility of the sensor interface. In addition, this review also introduces calibration algorithms and prediction algorithms applied to CGMSs and describes the application of machine learning algorithms for glucose prediction.

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.

In-silico Assessment of Preventive Hypotreatment Efficacy and Development of a Continuous Glucose Monitoring Based Algorithm to Prevent/Mitigate Hypoglycemia in Type 1 Diabetes

In Type 1 diabetes (T1D) standard treatment, the mitigation of hypoglycemia is achieved by the assumption of small amounts of carbohydrates (CHO), called hypotreatments (HTs), as soon as hypoglycemia is revealed. However, since CHO takes time to reach the blood stream, hypoglycemia cannot be totally avoided. Our purpose is to evaluate in-silico the effectiveness of preventive HTs and to propose a new real-time algorithm for the mitigation/avoidance of hypoglycemia, based on continuous glucose monitoring (CGM) sensor data. To such a purpose, the algorithm exploits the "dynamic risk" non linear-function that, by combining CGM value and trend, allows predicting the forthcoming hypoglycemic event. The algorithm is tested in an ideal noise-free environment on 100 virtual subjects (VSs) generated by the UVA/Padova T1D simulator and undergoing a single-meal experiment, with induced post-meal hypoglycemia. Compared to a reference HT rule, which suggest to assume HTs when hypoglycemia is detected, the algorithm reduces, on median [25^th - 75^th percentiles], both the time spent in hypoglycemia (from 36 [29 - 43] min to 10 [0 - 20] min) and the post-treatment rebound (from 136 [121 - 148] mg/dl to 114 [98 - 130] mg/dl). In conclusion, the proposed real-time algorithm efficiently generates preventive HTs that allow to almost totally avoid hypoglycemia. Future work will concern to modify the algorithm for detecting in advance the severity of the hypoglycemic episode -since performance are influenced on the hypoglycemic episode aggressiveness level- and to assess algorithm in a more challenging environment, including CGM measurement error.

Retrospective Continuous-Time Blood Glucose Estimation in Free Living Conditions with a Non-Invasive Multisensor Device

Even if still at an early stage of development, non-invasive continuous glucose monitoring (NI-CGM) sensors represent a promising technology for optimizing diabetes therapy. Recent studies showed that the Multisensor provides useful information about glucose dynamics with a mean absolute relative difference (MARD) of 35.4% in a fully prospective setting. Here we propose a method that, exploiting the same Multisensor measurements, but in a retrospective setting, achieves a much better accuracy. Data acquired by the Multisensor during a long-term study are retrospectively processed following a two-step procedure. First, the raw data are transformed to a blood glucose (BG) estimate by a multiple linear regression model. Then, an enhancing module is applied in cascade to the regression model to improve the accuracy of the glucose estimation by retrofitting available BG references through a time-varying linear model. MARD between the retrospectively reconstructed BG time-series and reference values is 20%. Here, 94% of values fall in zone A or B of the Clarke Error Grid. The proposed algorithm achieved a level of accuracy that could make this device a potential complementary tool for diabetes management and also for guiding prediabetic or nondiabetic users through life-style changes.

Classification of Postprandial Glycemic Status with Application to Insulin Dosing in Type 1 Diabetes-An In Silico Proof-of-Concept

In the daily management of type 1 diabetes (T1D), determining the correct insulin dose to be injected at meal-time is fundamental to achieve optimal glycemic control. Wearable sensors, such as continuous glucose monitoring (CGM) devices, are instrumental to achieve this purpose. In this paper, we show how CGM data, together with commonly recorded inputs (carbohydrate intake and bolus insulin), can be used to develop an algorithm that allows classifying, at meal-time, the post-prandial glycemic status (i.e., blood glucose concentration being too low, too high, or within target range). Such an outcome can then be used to improve the efficacy of insulin therapy by reducing or increasing the corresponding meal bolus dose. A state-of-the-art T1D simulation environment, including intraday variability and a behavioral model, was used to generate a rich in silico dataset corresponding to 100 subjects over a two-month scenario. Then, an extreme gradient-boosted tree (XGB) algorithm was employed to classify the post-prandial glycemic status. Finally, we demonstrate how the XGB algorithm outcome can be exploited to improve glycemic control in T1D through real-time adjustment of the meal insulin bolus. The proposed XGB algorithm obtained good accuracy at classifying post-prandial glycemic status (AUROC = 0.84 [0.78, 0.87]). Consequently, when used to adjust, in real-time, meal insulin boluses obtained with a bolus calculator, the proposed approach improves glycemic control when compared to the baseline bolus calculator. In particular, percentage time in target [70, 180] mg/dL was improved from 61.98 (±13.89) to 67.00 (±11.54; p < 0.01) without increasing hypoglycemia.

Optimal Insulin Bolus Dosing in Type 1 Diabetes Management: Neural Network Approach Exploiting CGM Sensor Information

Type 1 diabetes (TID) therapy is based on multiple daily injections of exogenous insulin. The so-called insulin bolus calculators facilitate insulin dose calculation to the patients by implementing a standard formula SF which, besides some patient-related parameters, also considers the current value of blood glucose concentration (BG), normally measured by the patient through a fingerprick device. The recent approval by the U.S. Food and Drug Administration to use the measurements collected by wearable continuous glucose monitoring (CGM) sensors for insulin dosing of fers new perspectives. Indeed, CGM sensors provide real-time information on both glucose concentration and rate of change, currently not considered in the SF. The purpose of this work is to preliminary investigate the possibility of using neural networks (NN)s for the calculation of meal insulin bolus dose exploiting CGM-based information. Using the UVa/Padova TID Simulator, we generated data of 100 subjects in 9-h, single-meal, noise-free scenarios. In particular, for each subject we analyzed different meal conditions in terms of carbohydrate intakes, preprandial BG and glucose rate-of -change. Then, a fully-connected feedforward NN was trained, with the aim of estimating the insulin bolus needed to obtain the best glycemic outcomes according to the blood glucose risk index (BGRI). Preliminary results show that by using the NN to calculate insulin doses lower BGRI values are obtained, on average, compared to the SF. These results encourage further development of the approach and its assessment in more challenging scenarios.

New insights from continuous glucose monitoring into the route to diabetes

AIM

Type 2 diabetes mellitus (T2DM) is preceded by a period of impaired glucoregulation. We investigated if continuous glucose monitoring system (CGMS) (1) could improve our capacity to predict the development of T2DM in subjects at risk. (2) Find out if impaired fasting glucose/impaired glucose tolerance differentiation through CGMS would also elucidate differences in clinical phenotypes.

MATERIAL AND METHODS

Observational study of 209 hypertensive patients, aged 18 to 85 years who wore at entry a CGMS. Two CGMS metrics, percent of time under the 100 mg/dL glycaemic threshold (TU100) (impaired fasting glucose surrogate phenotype) and area above the 140 mg/dL glycemic threshold (AO140) (impaired glucose tolerance surrogate phenotype) were measured. The median follow-up was 32 months (6-72 mo), and there were 17 new cases of T2DM.

RESULTS

In a multivariate Cox proportional hazard survival analysis including the conventional prediabetes-defining criteria and the 2 CGMS-derived variables, only TU100 and HbA_1c were significant and independent variables in predicting T2DM development. An increase in 0.1 in TU100 resulted in a 0.69 (95% CI, 0.54-0.88; P < .01) odds ratio of developing T2DM. With cut-off points of 0.5 for TU100 and 5.7% for HbA_1c , the test "TU < 0.5 and HbA_1c  > 5.7%" had a sensitivity of 0.81 (SD, 0.10), a specificity of 0.83 (SD, 0.03), and a likelihood ratio of 4.82 (SD, 1.03) for T2DM development.

CONCLUSIONS

Continuous glucose monitoring system allows for a better T2DM risk-development categorization than fasting glucose and HbA_1c in a high-risk population. Continuous glucose monitoring system-derived phenotyping reveals clinical differences, not disclosed by conventional fasting plasma glucose/HbA_1c categorization. These differences may correlate with distinct pathophysiological mechanisms.

Human Subcutaneous Tissue Response to Glucose Sensors: Macrophages Accumulation Impact on Sensor Accuracy

BACKGROUND

Subcutaneous (s.c.) glucose sensors have become a key component in type 1 diabetes management. However, their usability is limited by the impact of foreign body response (FBR) on their duration, reliability, and accuracy. Our study gives the first description of human acute and subacute s.c. response to glucose sensors, showing the changes observed in the sensor surface, the inflammatory cells involved in the FBR and their relationship with sensor performance.

METHODS

Twelve obese patients (seven type 2 diabetes) underwent two abdominal biopsies comprising the surrounding area where they had worn two glucose sensors: the first one inserted 7 days before and the second one 24 h before biopsy procedure. Samples were processed and studied to describe tissue changes by two independent pathologists (blind regarding sensor duration). Macrophages quantification was studied by immunohistochemistry methods in the area surrounding the sensor (CD68, CD163). Sensor surface changes were studied by scanning electron microscopy. Seven-day continuous glucose monitoring records were considered inaccurate when mean absolute relative difference was higher than 10%.

RESULTS

Pathologists were able to correctly classify all the biopsies regarding sensor duration. Acute response (24 h) was characterized by the presence of neutrophils while macrophages were the main cell involved in subacute inflammation. The number of macrophages around the insertion hole was higher for less accurate sensors compared with those performing more accurately (32.6 ± 14 vs. 10.6 ± 1 cells/0.01 mm²; P < 0.05).

CONCLUSION

The accumulation of macrophages at the sensor-tissue interface is related with decrease in accuracy of the glucose measure.

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.

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.

Diabetes and Prediabetes Classification Using Glycemic Variability Indices From Continuous Glucose Monitoring Data

BACKGROUND

Tens of glycemic variability (GV) indices are available in the literature to characterize the dynamic properties of glucose concentration profiles from continuous glucose monitoring (CGM) sensors. However, how to exploit the plethora of GV indices for classifying subjects is still controversial. For instance, the basic problem of using GV indices to automatically determine if the subject is healthy rather than affected by impaired glucose tolerance (IGT) or type 2 diabetes (T2D), is still unaddressed. Here, we analyzed the feasibility of using CGM-based GV indices to distinguish healthy from IGT&T2D and IGT from T2D subjects by means of a machine-learning approach.

METHODS

The data set consists of 102 subjects belonging to three different classes: 34 healthy, 39 IGT, and 29 T2D subjects. Each subject was monitored for a few days by a CGM sensor that produced a glucose profile from which we extracted 25 GV indices. We used a two-step binary logistic regression model to classify subjects. The first step distinguishes healthy subjects from IGT&T2D, the second step classifies subjects into either IGT or T2D.

RESULTS

Healthy subjects are distinguished from subjects with diabetes (IGT&T2D) with 91.4% accuracy. Subjects are further subdivided into IGT or T2D classes with 79.5% accuracy. Globally, the classification into the three classes shows 86.6% accuracy.

CONCLUSIONS

Even with a basic classification strategy, CGM-based GV indices show good accuracy in classifying healthy and subjects with diabetes. The classification into IGT or T2D seems, not surprisingly, more critical, but results encourage further investigation of the present research.

Exploring the Frequency Domain of Continuous Glucose Monitoring Signals to Improve Characterization of Glucose Variability and of Diabetic Profiles

BACKGROUND

Continuous glucose monitoring (CGM) devices measure interstitial glucose concentrations (normally every 5 minutes), allowing observation of glucose variability (GV) patterns during the whole day. This information could be used to improve prescription of treatments and of insulin dosages for people suffering diabetes. Previous efforts have been focused on proposing indices of GV either in time or glucose domains, while the frequency domain has been explored only partially. The aim of this work is to explore the CGM signal in the frequency domain to understand if new indexes or features could be identified and contribute to a better characterization of glucose variability.

METHODS

The direct fast Fourier transform (FFT) and the Welch method were used to analyze CGM signals from three different profiles: people at risk of developing type 2 diabetes (P@R), T2D patients, and type 1 diabetes (T1D) patients.

RESULTS

The results suggests that features extracted from the FFT (ie, the localization and power of the maximum peak of the power spectrum and the bandwidth at 3 dB) are able to provide a characterization for all the three populations under study compared with the Welch approach.

CONCLUSIONS

Such preliminary results can represent a good insight for futures investigations with the possibility of building and using new indexes of glucose variability based on the frequency features.

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.

Parental evaluation of a telemonitoring service for children with Type 1 Diabetes

Introduction In the past years, we developed a telemonitoring service for young patients affected by Type 1 Diabetes. The service provides data to the clinical staff and offers an important tool to the parents, that are able to oversee in real time their children. The aim of this work was to analyze the parents' perceived usefulness of the service. Methods The service was tested by the parents of 31 children enrolled in a seven-day clinical trial during a summer camp. To study the parents' perception we proposed and analyzed two questionnaires. A baseline questionnaire focused on the daily management and implications of their children's diabetes, while a post-study one measured the perceived benefits of telemonitoring. Questionnaires also included free text comment spaces. Results Analysis of the baseline questionnaires underlined the parents' suffering and fatigue: 51% of total responses showed a negative tendency and the mean value of the perceived quality of life was 64.13 in a 0-100 scale. In the post-study questionnaires about half of the parents believed in a possible improvement adopting telemonitoring. Moreover, the foreseen improvement in quality of life was significant, increasing from 64.13 to 78.39 ( p-value = 0.0001). The analysis of free text comments highlighted an improvement in mood, and parents' commitment was also proved by their willingness to pay for the service (median = 200 euro/year). Discussion A high number of parents appreciated the telemonitoring service and were confident that it could improve communication with physicians as well as the family's own peace of mind.

Biosensors for the Diagnosis of Celiac Disease: Current Status and Future Perspectives

Celiac disease (CD) is an autoimmune enteropathy initiated and sustained by the ingestion of gluten in genetically susceptible individuals. It is caused by a dysregulated immune response toward both dietary antigens, the gluten proteins of wheat, rye, and barley, and autoantigens, the enzyme tissue transglutaminase (TG2). The small intestine is the target organ. Although routine immunochemical protocols for a laboratory diagnosis of CD are available, faster, easier-to-use, and cheaper analytical devices for CD diagnosis are currently unavailable. This review focuses on biosensors, consisting of a physicochemical transducer and a bioreceptor, as promising analytical tools for diagnosis of CD and other diseases. Examples of recently developed biosensors as well as expectations for future lines of research and development in this field are presented.

Patient decision-making of CGM sensor driven insulin therapies in type 1 diabetes: In silico assessment

In type 1 diabetes (T1D) therapy, continuous glucose monitoring (CGM) sensors, which provide glucose concentration in the subcutis every 1-5 min for 7 consecutive days, should allow in principle a more efficient insulin dosing than that based on the conventional 3-4 self-monitoring of blood glucose (SMBG) measurements per day. However, CGM, at variance with SMBG, is still not approved for insulin dosing in T1D management because regulatory agencies, e.g. FDA, are looking for more factual evidence on its safety. An in silico assessment of SMBG- vs CGM-driven insulin therapy can be a first step. Here we present a simulation model of T1D patient decision-making obtained by interconnecting models of glucose-insulin dynamics, SMBG and CGM measurement errors, carbohydrates-counting errors, insulin boluses time variability and forgetfulness, and subcutaneous insulin pump delivery. Inter- and intra- patient variability of model parameters are considered. The T1D patient decision-making model allows to run realistic multi-day simulations scenarios in a population of virtual subjects. We present the first results of simulations run in 20 virtual subjects over a 7-day period, which demonstrates that additional information brought by CGM (trend and hypo/hyperglycemic warnings) with respect to SMBG produces a statistically significant increment (about of 9%) of time spent by the patient in the euglycemic range (70-180 mg/dl).

Online Calibration of Glucose Sensors From the Measured Current by a Time-Varying Calibration Function and Bayesian Priors

GOAL

Minimally invasive continuous glucose monitoring (CGM) sensors measure in the subcutis a current signal, which is converted into interstitial glucose (IG) concentration by a calibration process periodically updated using fingerstick blood glucose (BG) references. Though important in diabetes management, CGM sensors still suffer from accuracy problems. Here, we propose a new online calibration method improving accuracy of CGM glucose profiles with respect to manufacturer calibration.

METHOD

The proposed method fits CGM current signal against the BG references collected twice a day for calibration purposes, by a time-varying calibration function whose parameters are identified in a Bayesian framework using a priori second-order statistical knowledge. The distortion introduced by BG-to-IG kinetics is compensated before parameter identification via nonparametric deconvolution.

RESULTS

The method was tested on a database where 108 CGM signals were collected for 7 days by the Dexcom G4 Platinum sensor. Results show the new method drives to a statistically significant accuracy improvement as measured by three commonly used metrics: mean absolute relative difference reduced from 12.73% to 11.47%; percentage of accurate glucose estimates increased from 82.00% to 89.19%; and percentage of values falling in the "A" zone of the Clark error grid increased from 82.22% to 88.86%.

CONCLUSION

The new calibration method significantly improves CGM glucose profiles accuracy with respect to manufacturer calibration.

SIGNIFICANCE

The proposed algorithm provides a real-time improvement of CGM accuracy, which can be crucial in several CGM-based applications, including the artificial pancreas, thus providing a potential great impact in the diabetes technology research community.

Developmental changes in the roles of patients and families in type 1 diabetes management

Developmentally-tailored diabetes self-care education and support are integral parts of contemporary multidisciplinary T1D care. The patient with T1D must have the support of the family and the diabetes team to maintain the rigors of diabetes management, but the specific roles of patients and families with regard to daily diabetes tasks change considerably throughout the developmental span of early childhood, middle childhood/school-age years, and adolescence. This review provides a framework of key normative developmental issues for each of these developmental stages. Within this context, ideal family diabetes management is reviewed within each developmental stage and anticipated challenges that can arise during these stages and that can adversely impact diabetes management are presented. This paper also summarizes empirical evidence for specific intervention and care strategies to support optimal diabetes management across these stages in order to maximize opportunities for a successful transfer of diabetes management tasks from parents to maturing youth. Finally, the review provides an emphasis on approaches to promote family teamwork and adolescent diabetes self-care adherence as well as opportunities to use novel technology platforms as a means to support optimal diabetes management.

Biosensors containing acetylcholinesterase and butyrylcholinesterase as recognition tools for detection of various compounds

Acetylcholinesterase (AChE) and butyrylcholinesterase (BChE) are enzymes expressed in the human body under physiological conditions. AChE is an important part of the cholinergic nerves where it hydrolyses neurotransmitter acetylcholine. Both cholinesterases are sensitive to inhibitors acting as neurotoxic compounds. In analytical applications, the enzymes can serve as a biorecognition element in biosensors as well as simple disposable sensors (dipsticks) and be used for assaying the neurotoxic compounds. In the present review, the mechanism of AChE and BChE inhibition by disparate compounds is explained and methods for assaying the enzymes activity are shown. Optical, electrochemical, and piezoelectric biosensors are described. Attention is also given to the application of sol-gel techniques and quantum dots in the biosensors’ construction. Examples of the biosensors are provided and the pros and cons are discussed.

Designing an artificial pancreas architecture: the AP@home experience

The latest achievements in sensor technologies for blood glucose level monitoring, pump miniaturization for insulin delivery, and the availability of portable computing devices are paving the way toward the artificial pancreas as a treatment for diabetes patients. This device encompasses a controller unit that oversees the administration of insulin micro-boluses and continuously drives the pump based on blood glucose readings acquired in real time. In order to foster the research on the artificial pancreas and prepare for its adoption as a therapy, the European Union in 2010 funded the AP@home project, following a series of efforts already ongoing in the USA. This paper, authored by members of the AP@home consortium, reports on the technical issues concerning the design and implementation of an architecture supporting the exploitation of an artificial pancreas platform. First a PC-based platform was developed by the authors to prove the effectiveness and reliability of the algorithms responsible for insulin administration. A mobile-based one was then adopted to improve the comfort for the patients. Both platforms were tested on real patients, and a description of the goals, the achievements, and the major shortcomings that emerged during those trials is also reported in the paper.

Model of glucose sensor error components: identification and assessment for new Dexcom G4 generation devices

It is clinically well-established that minimally invasive subcutaneous continuous glucose monitoring (CGM) sensors can significantly improve diabetes treatment. However, CGM readings are still not as reliable as those provided by standard fingerprick blood glucose (BG) meters. In addition to unavoidable random measurement noise, other components of sensor error are distortions due to the blood-to-interstitial glucose kinetics and systematic under-/overestimations associated with the sensor calibration process. A quantitative assessment of these components, and the ability to simulate them with precision, is of paramount importance in the design of CGM-based applications, e.g., the artificial pancreas (AP), and in their in silico testing. In the present paper, we identify and assess a model of sensor error of for two sensors, i.e., the G4 Platinum (G4P) and the advanced G4 for artificial pancreas studies (G4AP), both belonging to the recently presented "fourth" generation of Dexcom CGM sensors but different in their data processing. Results are also compared with those obtained by a sensor belonging to the previous, "third," generation by the same manufacturer, the SEVEN Plus (7P). For each sensor, the error model is derived from 12-h CGM recordings of two sensors used simultaneously and BG samples collected in parallel every 15 ± 5 min. Thanks to technological innovations, G4P outperforms 7P, with average mean absolute relative difference (MARD) of 11.1 versus 14.2%, respectively, and lowering of about 30% the error of each component. Thanks to the more sophisticated data processing algorithms, G4AP resulted more reliable than G4P, with a MARD of 10.0%, and a further decrease to 20% of the error due to blood-to-interstitial glucose kinetics.

Advances in management of type 1 diabetes mellitus

Treatment of type 1 diabetes mellitus has always posed a challenge to balance hyperglycemia control with hypoglycemia episodes. The quest for newer therapies is continuing and this review attempts to outline the recent developments. The insulin molecule itself has got moulded into different analogues by minor changes in its structure to ensure well controlled delivery, stable half-lives and lesser side effects. Insulin delivery systems have also consistently undergone advances from subcutaneous injections to continuous infusion to trials of inhalational delivery. Continuous glucose monitoring systems are also becoming more accurate and user friendly. Smartphones have also made their entry into therapy of diabetes by integrating blood glucose levels and food intake with calculated adequate insulin required. Artificial pancreas has enabled to a certain extent to close the loop between blood glucose level and insulin delivery with devices armed with meal and exercise announcements, dual hormone delivery and pramlintide infusion. Islet, pancreas-kidney and stem cells transplants are also being attempted though complete success is still a far way off. Incorporating insulin gene and secretary apparatus is another ambitious leap to achieve insulin independence though the search for the ideal vector and target cell is still continuing. Finally to stand up to the statement, prevention is better than cure, immunological methods are being investigated to be used as vaccine to prevent the onset of diabetes mellitus.

Glucose variability indices in type 1 diabetes: parsimonious set of indices revealed by sparse principal component analysis

BACKGROUND

Continuous glucose monitoring (CGM) time-series are often analyzed, retrospectively, to investigate glucose variability (GV), a risk factor for the development of complications in type 1 diabetes (T1D). In the literature, several tens of different indices for GV quantification have been proposed, but many of them carry very similar information. The aim of this article is to select a relatively small subset of GV indices from a wider pool of metrics, to obtain a parsimonious but still comprehensive description of GV in T1D datasets.

MATERIALS AND METHODS

A pool of 25 GV indices was evaluated on two CGM time-series datasets of 17 and 16 T1D subjects, respectively, collected during the European Union Seventh Framework Programme project "Diadvisor" (2008-2012) in two different clinical research centers using the Dexcom(®) (San Diego, CA) SEVEN(®) Plus. After the indices were centered and scaled, the Sparse Principal Component Analysis (SPCA) technique was used to determine a reduced set of metrics that allows preserving a high percentage of the variance of the whole original set. In order to assess whether or not the selected subset of GV indices is dataset-dependent, the analysis was applied to both datasets, as well as to the one obtained by merging them.

RESULTS

SPCA revealed that a subset of up to 10 different GV indices can be sufficient to preserve more than the 60% of the variance originally explained by all the 25 variables. It is remarkable that four of these GV indices (i.e., Index of Glycemic Control, percentage of Glycemic Risk Assessment Diabetes Equation score due to euglycemia, percentage Coefficient of Variation, and Low Blood Glucose Index) were selected for all the considered T1D datasets.

CONCLUSIONS

The SPCA methodology appears a suitable candidate to identify, among the large number of literature GV indices, subsets that allow obtaining a parsimonious, but still comprehensive, description of GV.

Closed-loop system in the management of diabetes: past, present, and future

Intensive insulin therapy (IIT) has been shown to reduce micro- and macrovascular complications in patients with type 1 diabetes mellitus (T1DM). However, IIT is associated with a significant increase in severe hypoglycemic events, resulting in increased morbidity and mortality. Optimization of glycemic control without hypoglycemia (especially nocturnal) should be the next major goal for subjects on insulin treatment. The use of insulin pumps along with continuous glucose monitors (CGMs) has made it easier but requires significant resources and patient education. Research is ongoing to close the loop by integrating the pump and the CGM using different algorithms. The currently available closed-loop system is the threshold suspend. Steps needed to achieve a near-perfect closed-loop are (1) a control-to-range system that will reduce the incidence and/or severity of hyper- and/or hypoglycemia by adjusting the insulin dose and (2) a control-to-target system, a fully automated or hybrid system that sets target glucose levels to individual needs and maintains glucose levels throughout the day using insulin (unihormonal) alone or with other hormones such as glucagon or possibly pramlintide (bihormonal). Future research is also focusing on better insulin delivery devices (pumps), more accurate CGMs, better predictive algorithms, and ultra-rapid-acting insulin analogs to make the closed-loop system as physiological as possible.

Feasibility and precision of subcutaneous continuous glucose monitoring in patients undergoing CABG surgery

OBJECTIVES

To evaluate if subcutaneous continuous glucose monitoring (sCGM) is feasible in cardiac surgery and if reliable glucose values are reported under hypothermic extracorporeal circulation.

DESIGN

Feasibility trial.

SETTING

University hospital.

PARTICIPANTS

Ten consecutive patients undergoing coronary artery bypass grafting.

INTERVENTIONS

Prior to surgery, during hypothermic extracorporeal bypass, and 48 hours postoperatively, arterial blood glucose samples were compared with sCGM every 30 minutes. Statistical analysis utilized Clarke's error grid and Bland-Altman plot.

MEASUREMENTS AND MAIN RESULTS

Three hundred fifty-one pairs of glucose measurements were recorded including 59 during hypothermic extracorporeal circulation. Agreement between these measurements was acceptable, with a regression line slope of 0.88 and an offset of 17.4 (p = 0.87). Error grid analysis indicated a safe margin of 99.1% within zone A (no clinical action needed) or zone B (values would not lead to inappropriate treatment). Only 0.9% were plotted in zone D (potentially dangerous failure). Measurements during hypothermic extracorporeal circulation were comparable. Correlation coefficient was 0.760. The offset regression line was more pronounced (50.9) with a flatter slope (0.640). Within the error grid all plot values were in zone A or B.

CONCLUSIONS

sCGM compared with arterial blood gas glucose monitoring under hypothermic extracorporeal circulation appears to be feasible and reliable.

Adaptive control of artificial pancreas systems - a review

Artificial pancreas (AP) systems offer an important improvement in regulating blood glucose concentration for patients with type 1 diabetes, compared to current approaches. AP consists of sensors, control algorithms and an insulin pump. Different AP control algorithms such as proportional-integral-derivative, model-predictive control, adaptive control, and fuzzy logic control have been investigated in simulation and clinical studies in the past three decades. The variability over time and complexity of the dynamics of blood glucose concentration, unsteady disturbances such as meals, time-varying delays on measurements and insulin infusion, and noisy data from sensors create a challenging system to AP. Adaptive control is a powerful control technique that can deal with such challenges. In this paper, a review of adaptive control techniques for blood glucose regulation with an AP system is presented. The investigations and advances in technology produced impressive results, but there is still a need for a reliable AP system that is both commercially viable and appealing to patients with type 1 diabetes.

Bringing closed-loop home: recent advances in closed-loop insulin delivery

PURPOSE OF REVIEW

To highlight the recent advances in closed-loop research, the development and progress towards utilizing closed loop outside of the clinical research setting and at patients' homes.

RECENT FINDINGS

In spite of the modern insulin therapy in type 1 diabetes, hypoglycaemia is still a major limiting factor. This often leads to suboptimal glycaemic control and risk of diabetes complications. Closed loop has been shown to improve glycaemic control whilst avoiding hypoglycaemia. Incremental progress has been made in this field, from the use of automated systems and bihormonal closed-loop systems in clinical research facility settings under close supervision to the use of ambulatory closed-loop prototype at patients' homes in free-living conditions. Different population of patients with type 1 diabetes and control algorithm approaches have been studied, assessing the efficacy and safety. Transitional and home studies present different challenges at achieving better glycaemic outcome with closed loop. Improved glucose sensor reliability may accelerate the clinical use and faster insulin analogues increase the clinical utility.

SUMMARY

Results and experience with closed-loop insulin delivery have been encouraging, leading the way for future improvements and development in the field, to make closed loop suitable for use in clinical practice.

Design of the Glucose Rate Increase Detector: A Meal Detection Module for the Health Monitoring System

The Glucose Rate Increase Detector (GRID), a module of the Health Monitoring System (HMS), has been designed to operate in parallel to the glucose controller to detect meal events and safely trigger a meal bolus. The GRID algorithm was tuned on clinical data with 40-70 g CHO meals and tested on simulation data with 50-100 g CHO meals. Active closed- and open-loop protocols were executed in silico with various treatments, including automatic boluses based on a 75 g CHO meal and boluses based on simulated user input of meal size. An optional function was used to reduce the recommended bolus using recent insulin and glucose history. For closed-loop control of a 3-meal scenario (50, 75, and 100 g CHO), the GRID improved median time in the 80-180 mg/dL range by 17% and in the >180 range by 14% over unannounced meals, using an automatic bolus for a 75 g CHO meal at detection. Under open-loop control of a 75 g CHO meal, the GRID shifted the median glucose peak down by 73 mg/dL and earlier by 120 min and reduced the time >180 mg/dL by 57% over a missed-meal bolus scenario, using a full meal bolus at detection. The GRID improved closed-loop control in the presence of large meals, without increasing late postprandial hypoglycemia. Users of basal-bolus therapy could also benefit from GRID as a safety alert for missed meal corrections.

Evaluation of stochastic adjustment for glucose sensor bias during closed-loop insulin delivery

BACKGROUND

In outpatient studies of closed-loop insulin delivery systems, it is not typically practical to obtain blood glucose measurements for an outcome measure. Using a continuous glucose monitoring (CGM) device as both part of the intervention and as the outcome in a clinical trial can give a biased estimate of the treatment effect. A stochastic adjustment has been proposed to correct this problem.

MATERIALS AND METHODS

We performed Monte Carlo simulations to assess the performance of the stochastic adjustment in various scenarios where the CGM device was used passively and when it was used to inform insulin delivery. The resulting bias for using CGM to estimate the percentage of glucose values inside a target range was compared with and without the proposed stochastic adjustment.

RESULTS

CGM bias for estimating the percentage of glucose values 70-180 mg/dL ranged from -6% to +4% in the various scenarios studied. In some circumstances, stochastic adjustment did indeed reduce this CGM bias. However, in other circumstances, stochastic adjustment made the bias worse. Stochastic adjustment tended to underestimate the true percentage of glucose values in range for most, but not all, scenarios considered in these simulations.

CONCLUSIONS

Stochastic adjustment is not a general solution to the problem of CGM bias. The proposed adjustment relies on an implicit assumption that usually does not hold. The appropriate level of adjustment depends on how efficacious the closed-loop system is, which is not typically known in practice.

Physical activity measured by physical activity monitoring system correlates with glucose trends reconstructed from continuous glucose monitoring

BACKGROUND

In type 1 diabetes mellitus (T1DM), physical activity (PA) lowers the risk of cardiovascular complications but hinders the achievement of optimal glycemic control, transiently boosting insulin action and increasing hypoglycemia risk. Quantitative investigation of relationships between PA-related signals and glucose dynamics, tracked using, for example, continuous glucose monitoring (CGM) sensors, have been barely explored.

SUBJECTS AND METHODS

In the clinic, 20 control and 19 T1DM subjects were studied for 4 consecutive days. They underwent low-intensity PA sessions daily. PA was tracked by the PA monitoring system (PAMS), a system comprising accelerometers and inclinometers. Variations on glucose dynamics were tracked estimating first- and second-order time derivatives of glucose concentration from CGM via Bayesian smoothing. Short-time effects of PA on glucose dynamics were quantified through the partial correlation function in the interval (0, 60 min) after starting PA.

RESULTS

Correlation of PA with glucose time derivatives is evident. In T1DM, the negative correlation with the first-order glucose time derivative is maximal (absolute value) after 15 min of PA, whereas the positive correlation is maximal after 40-45 min. The negative correlation between the second-order time derivative and PA is maximal after 5 min, whereas the positive correlation is maximal after 35-40 min. Control subjects provided similar results but with positive and negative correlation peaks anticipated of 5 min.

CONCLUSIONS

Quantitative information on correlation between mild PA and short-term glucose dynamics was obtained. This represents a preliminary important step toward incorporation of PA information in more realistic physiological models of the glucose-insulin system usable in T1DM simulators, in development of closed-loop artificial pancreas control algorithms, and in CGM-based prediction algorithms for generation of hypoglycemic alerts.

Modeling the glucose sensor error

Continuous glucose monitoring (CGM) sensors are portable devices, employed in the treatment of diabetes, able to measure glucose concentration in the interstitium almost continuously for several days. However, CGM sensors are not as accurate as standard blood glucose (BG) meters. Studies comparing CGM versus BG demonstrated that CGM is affected by distortion due to diffusion processes and by time-varying systematic under/overestimations due to calibrations and sensor drifts. In addition, measurement noise is also present in CGM data. A reliable model of the different components of CGM inaccuracy with respect to BG (briefly, "sensor error") is important in several applications, e.g., design of optimal digital filters for denoising of CGM data, real-time glucose prediction, insulin dosing, and artificial pancreas control algorithms. The aim of this paper is to propose an approach to describe CGM sensor error by exploiting n multiple simultaneous CGM recordings. The model of sensor error description includes a model of blood-to-interstitial glucose diffusion process, a linear time-varying model to account for calibration and sensor drift-in-time, and an autoregressive model to describe the additive measurement noise. Model orders and parameters are identified from the n simultaneous CGM sensor recordings and BG references. While the model is applicable to any CGM sensor, here, it is used on a database of 36 datasets of type 1 diabetic adults in which n = 4 Dexcom SEVEN Plus CGM time series and frequent BG references were available simultaneously. Results demonstrates that multiple simultaneous sensor data and proper modeling allow dissecting the sensor error into its different components, distinguishing those related to physiology from those related to technology.

P4 medicine: how systems medicine will transform the healthcare sector and society

Ten years ago, the proposition that healthcare is evolving from reactive disease care to care that is predictive, preventive, personalized and participatory was regarded as highly speculative. Today, the core elements of that vision are widely accepted and have been articulated in a series of recent reports by the US Institute of Medicine. Systems approaches to biology and medicine are now beginning to provide patients, consumers and physicians with personalized information about each individual's unique health experience of both health and disease at the molecular, cellular and organ levels. This information will make disease care radically more cost effective by personalizing care to each person's unique biology and by treating the causes rather than the symptoms of disease. It will also provide the basis for concrete action by consumers to improve their health as they observe the impact of lifestyle decisions. Working together in digitally powered familial and affinity networks, consumers will be able to reduce the incidence of the complex chronic diseases that currently account for 75% of disease-care costs in the USA.