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Recent Advances in Continuous Glucose Monitoring Sensors

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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Biosensors".

Deadline for manuscript submissions: closed (20 April 2023) | Viewed by 27862

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Dr. Martina Vettoretti

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Guest Editor

Department of Information Engineering, University of Padova, Via G. Gradenigo 6B, 35131 Padova PD, Italy
Interests: signal processing and modeling techniques for the analysis of glucose sensor data; strategies for type 1 diabetes insulin therapy optimization; statistical learning; machine-learning techniques applied to clinical predictive model development
Special Issues, Collections and Topics in MDPI journals

Dr. Andrea Facchinetti

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Guest Editor

Department of Information Engineering, University of Padova, Padova, Italy
Interests: Signal processing and classification of biomedical signals; algorithms and software to improve both performance and usability of continuous glucose monitoring (CGM) sensors; statistical methods and machine learning techniques to analyze big data in medicine

Prof. Dr. Giovanni Sparacino

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Guest Editor

Department of Information Engineering, University of Padova, Padova, Italy
Interests: sensors and algorithms for continuous glucose monitoring; deconvolution and parameter estimation techniques for the study of physiological systems; linear and nonlinear biological time-series analysis; measurement and processing of biomedical signals (EEG, event-related potentials, local field potentials, fNIRS, etc.) for clinical research and applications
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In the last 20 years, we have experienced a revolution of glucose monitoring with the introduction of continuous glucose monitoring (CGM) sensors that can measure interstitial glucose concentration almost continuously for several days or weeks. CGM sensors can really be a game changer in the therapy of diabetes (especially type 1 diabetes), because the rich information they provide can be used to improve both patient and clinician decision-making with positive effects on glycemic control.

Most CGM devices currently on the market are based on minimally invasive electrochemical sensors. Implantable fluorescence sensors have also recently been developed and brought into the market. Other technologies have been investigated for noninvasive monitoring of glucose concentration in various biological fluids (e.g., interstitial fluid, tears, and saliva).

Although the first CGM sensors suffered from poor accuracy and required frequent calibration with capillary glucose measurements, great improvements were recently achieved for both sensing technologies and processing algorithms, with resulting improvements in sensor accuracy. Nevertheless, the accuracy of CGM sensors can still be problematic in some situations, such as immediately after sensor insertion, in proximity of the sensor end-of-life, and during rapid glucose changes. Moreover, the estimation of glucose trends and the generation of predictive alerts remains challenging because of the presence of noise on the CGM trace.

In this Special Issue, we seek original papers and review papers on the recent advances in CGM sensors, including:

New CGM technologies (e.g., new sensing technologies);
New algorithms for improving the analytical performance of CGM sensors (e.g., calibration and filtering algorithms);
New algorithms to enhance the output of CGM devices (e.g., new algorithms for trend estimation and alert generation).

Dr. Martina Vettoretti
Dr. Andrea Facchinetti
Prof. Dr. Giovanni Sparacino
Guest Editors

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Keywords

continuous glucose monitoring
wearable glucose sensors
minimally invasive glucose sensors
implantable glucose sensors
noninvasive glucose sensors
calibration algorithms
denoising
glucose prediction

Published Papers (7 papers)

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Research

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7 pages, 1442 KiB

Open AccessArticle

A Pilot Trial to Evaluate the Accuracy of a Novel Non-Invasive Glucose Meter

by Yair Schwarz, Noa Konvalina and Amir Tirosh

Sensors 2021, 21(20), 6704; https://doi.org/10.3390/s21206704 - 09 Oct 2021

Cited by 3 | Viewed by 3219

Abstract

The non-invasive self-monitoring of blood glucose (SMBG) has been the subject of intense investigation over recent decades. We conducted a pilot study designed to examine a novel non-invasive glucometer, the HGR GWave, utilizing radiofrequency (RF) sensing. Blood glucose levels assessed by this HGR prototype were compared to measurements performed by a hexokinase core laboratory assay during an oral glucose tolerance test (oGTT) for 5 subjects with type 2 diabetes. The HGR glucose meter readings were also compared to two Abbot Freestyle^® glucose meters, which were also used for calibration. The accuracy of the results was evaluated through the calculation of relative absolute difference (RAD), specified percentage differences between 43 reference glucose measurements, and using comparator measurements. The median RAD was −4.787. We detected 79.04%, 92.99% and 97.64% of HGR readings within ±10%, ±15% and ±20% of the reference glucose measurements. The HGR readings had a high correlation with reference lab glucose measurements with R² = 0.924 (95% CI 0.929–0.979; p < 0.0001). When compared to the Freestyle^® glucose meters 94.3% and 100% of the readings were within ±5% and ±10%, with R² = 0.975 (0.975–0.994; p < 0.0001). The HGR prototype glucose meter was found to be accurate in detecting real-time blood glucose during an oGTT in this small pilot study. A study with a broader range of blood glucose levels is needed to further assess its accuracy and its suitability for clinical use. Full article

(This article belongs to the Special Issue Recent Advances in Continuous Glucose Monitoring Sensors)

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14 pages, 1637 KiB

Open AccessArticle

Benefits of a Switch from Intermittently Scanned Continuous Glucose Monitoring (isCGM) to Real-Time (rt) CGM in Diabetes Type 1 Suboptimal Controlled Patients in Real-Life: A One-Year Prospective Study ^§

by Yannis Préau, Sébastien Galie, Pauline Schaepelynck, Martine Armand and Denis Raccah

Sensors 2021, 21(18), 6131; https://doi.org/10.3390/s21186131 - 13 Sep 2021

Cited by 10 | Viewed by 2067

Abstract

The switch from intermittently scanned continuous glucose monitoring (isCGM) to real-time (rt) CGM could improve glycemic management in suboptimal controlled type 1 diabetes patients, but long-term study is lacking. We evaluated retrospectively the ambulatory glucose profile (AGP) in such patients after switching from Free Style Libre 1 (FSL1) to Dexcom G4 (DG4) biosensors over 1 year. Patients (n = 21, 43 ± 15 years, BMI 25 ± 5, HbA1c 8.1 ± 1.0%) had severe hypoglycemia and/or HbA1c ≥ 8%. AGP metrics (time-in-range (TIR) 70–180 mg/dL, time-below-range (TBR) <70 mg/dL or <54 mg/dL, glucose coefficient of variation (%CV), time-above-range (TAR) >180 mg/dL or >250 mg/dL, glucose management indicator (GMI), average glucose) were collected the last 3 months of FSL1 use (M0) and of DG4 for 3, 6 (M6) and 12 (M12) months of use. Values were means ± standard deviation or medians [Q1;Q3]. At M12 versus M0, the higher TIR (50 ± 17 vs. 45 ± 16, p = 0.036), and lower TBR < 70 mg/dL (2.5 [1.6;5.5] vs. 7.0 [4.5;12.5], p = 0.0007), TBR < 54 mg/dL (0.7 [0.4;0.8] vs. 2.3 [0.8;7.0], p = 0.007) and %CV (39 ± 5 vs. 45 ± 8, p = 0.0009), evidenced a long-term effectiveness of the switch. Compared to M6, TBR < 70 mg/dL decreased, %CV remained stable, while the improvement on hyperglycemia exposure decreased (higher GMI, TAR and average glucose). This switch was a relevant therapeutic option, though a loss of benefit on hyperglycemia stressed the need for optimized management of threshold alarms. Nevertheless, few patients attained the recommended values for AGP metrics, and the reasons why some patients are “responders” vs. “non-responders” warrant to be investigated. Full article

(This article belongs to the Special Issue Recent Advances in Continuous Glucose Monitoring Sensors)

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17 pages, 643 KiB

Open AccessArticle

Probabilistic Model of Transition between Categories of Glucose Profiles in Patients with Type 1 Diabetes Using a Compositional Data Analysis Approach

by Lyvia Biagi, Arthur Bertachi, Marga Giménez, Ignacio Conget, Jorge Bondia, Josep Antoni Martín-Fernández and Josep Vehí

Sensors 2021, 21(11), 3593; https://doi.org/10.3390/s21113593 - 21 May 2021

Cited by 3 | Viewed by 2139

Abstract

The time spent in glucose ranges is a common metric in type 1 diabetes (T1D). As the time in one day is finite and limited, Compositional Data (CoDa) analysis is appropriate to deal with times spent in different glucose ranges in one day. This work proposes a CoDa approach applied to glucose profiles obtained from six T1D patients using continuous glucose monitor (CGM). Glucose profiles of 24-h and 6-h duration were categorized according to the relative interpretation of time spent in different glucose ranges, with the objective of presenting a probabilistic model of prediction of category of the next 6-h period based on the category of the previous 24-h period. A discriminant model for determining the category of the 24-h periods was obtained, achieving an average above 94% of correct classification. A probabilistic model of transition between the category of the past 24-h of glucose to the category of the future 6-h period was obtained. Results show that the approach based on CoDa is suitable for the categorization of glucose profiles giving rise to a new analysis tool. This tool could be very helpful for patients, to anticipate the occurrence of potential adverse events or undesirable variability and for physicians to assess patients’ outcomes and then tailor their therapies. Full article

(This article belongs to the Special Issue Recent Advances in Continuous Glucose Monitoring Sensors)

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26 pages, 3781 KiB

Open AccessArticle

Glucose Prediction under Variable-Length Time-Stamped Daily Events: A Seasonal Stochastic Local Modeling Framework

by Eslam Montaser, José-Luis Díez and Jorge Bondia

Sensors 2021, 21(9), 3188; https://doi.org/10.3390/s21093188 - 04 May 2021

Cited by 4 | Viewed by 1692

Abstract

Accurate glucose prediction along a long-enough time horizon is a key component for technology to improve type 1 diabetes treatment. Subjects with diabetes might benefit from supervision and control systems that accurately predict risks and trigger corrective actions early enough with improved mitigation. However, large intra-patient variability poses big challenges to glucose prediction. In previous works by the authors, clustering and local modeling techniques with seasonal stochastic models proved to be efficient, allowing for good glucose prediction accuracy for long prediction horizons. Continuous glucose monitoring (CGM) data were partitioned into fixed-length postprandial time subseries and clustered with Fuzzy C-Means to collect similar behaviors, enforcing seasonality at each cluster after subseries concatenation. Then, seasonal stochastic models were identified for each cluster and local predictions were integrated into a global prediction. However, free-living conditions do not support the fixed-length partition of CGM data since daily events duration is variable. In this work, a new algorithm is provided to overcome this constraint, allowing better coping with patient’s variability under variable-length time-stamped daily events in supervision and control applications. Besides predicted glucose, two real-time indices are additionally provided—a crispness index, indicating good representation of current glucose behavior by a single model, and a normality index, allowing for the detection of an abnormal glucose behavior (unusual according to registered historical data). The framework is tested in a proof-of-concept in silico study with ten patients over four month training data and two independent two month validation datasets, with and without abnormal behaviors, from the distribution version of the UVA/Padova simulator extended with diverse sources of intra-patient variability. Full article

(This article belongs to the Special Issue Recent Advances in Continuous Glucose Monitoring Sensors)

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21 pages, 649 KiB

Open AccessArticle

Forecasting of Glucose Levels and Hypoglycemic Events: Head-to-Head Comparison of Linear and Nonlinear Data-Driven Algorithms Based on Continuous Glucose Monitoring Data Only

by Francesco Prendin, Simone Del Favero, Martina Vettoretti, Giovanni Sparacino and Andrea Facchinetti

Sensors 2021, 21(5), 1647; https://doi.org/10.3390/s21051647 - 27 Feb 2021

Cited by 27 | Viewed by 3939

Abstract

In type 1 diabetes management, the availability of algorithms capable of accurately forecasting future blood glucose (BG) concentrations and hypoglycemic episodes could enable proactive therapeutic actions, e.g., the consumption of carbohydrates to mitigate, or even avoid, an impending critical event. The only input of this kind of algorithm is often continuous glucose monitoring (CGM) sensor data, because other signals (such as injected insulin, ingested carbs, and physical activity) are frequently unavailable. Several predictive algorithms fed by CGM data only have been proposed in the literature, but they were assessed using datasets originated by different experimental protocols, making a comparison of their relative merits difficult. The aim of the present work was to perform a head-to-head comparison of thirty different linear and nonlinear predictive algorithms using the same dataset, given by 124 CGM traces collected over 10 days with the newest Dexcom G6 sensor available on the market and considering a 30-min prediction horizon. We considered the state-of-the art methods, investigating, in particular, linear black-box methods (autoregressive; autoregressive moving-average; and autoregressive integrated moving-average, ARIMA) and nonlinear machine-learning methods (support vector regression, SVR; regression random forest; feed-forward neural network, fNN; and long short-term memory neural network). For each method, the prediction accuracy and hypoglycemia detection capabilities were assessed using either population or individualized model parameters. As far as prediction accuracy is concerned, the results show that the best linear algorithm (individualized ARIMA) provides accuracy comparable to that of the best nonlinear algorithm (individualized fNN), with root mean square errors of 22.15 and 21.52 mg/dL, respectively. As far as hypoglycemia detection is concerned, the best linear algorithm (individualized ARIMA) provided precision = 64%, recall = 82%, and one false alarm/day, comparable to the best nonlinear technique (population SVR): precision = 63%, recall = 69%, and 0.5 false alarms/day. In general, the head-to-head comparison of the thirty algorithms fed by CGM data only made using a wide dataset shows that individualized linear models are more effective than population ones, while no significant advantages seem to emerge when employing nonlinear methodologies. Full article

(This article belongs to the Special Issue Recent Advances in Continuous Glucose Monitoring Sensors)

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Review

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19 pages, 6853 KiB

Open AccessReview

A Review of Non-Invasive Optical Systems for Continuous Blood Glucose Monitoring

by Bushra Alsunaidi, Murad Althobaiti, Mahbubunnabi Tamal, Waleed Albaker and Ibraheem Al-Naib

Sensors 2021, 21(20), 6820; https://doi.org/10.3390/s21206820 - 14 Oct 2021

Cited by 38 | Viewed by 12419

Abstract

The prevalence of diabetes is increasing globally. More than 690 million cases of diabetes are expected worldwide by 2045. Continuous blood glucose monitoring is essential to control the disease and avoid long-term complications. Diabetics suffer on a daily basis with the traditional glucose monitors currently in use, which are invasive, painful, and cost-intensive. Therefore, the demand for non-invasive, painless, economical, and reliable approaches to monitor glucose levels is increasing. Since the last decades, many glucose sensing technologies have been developed. Researchers and scientists have been working on the enhancement of these technologies to achieve better results. This paper provides an updated review of some of the pioneering non-invasive optical techniques for monitoring blood glucose levels that have been proposed in the last six years, including a summary of state-of-the-art error analysis and validation techniques. Full article

(This article belongs to the Special Issue Recent Advances in Continuous Glucose Monitoring Sensors)

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Other

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11 pages, 2264 KiB

Open AccessCase Report

Continuous Glucose Monitoring: A Possible Aid for Detecting Hypoglycemic Events during Insulin Tolerance Tests

by Soo Yeun Sim and Moon Bae Ahn

Sensors 2023, 23(15), 6892; https://doi.org/10.3390/s23156892 - 03 Aug 2023

Viewed by 904

Abstract

The combined pituitary function test evaluates the anterior pituitary gland, while the insulin tolerance test evaluates growth hormone deficiencies. However, successful stimulation requires achieving an appropriate level of hypoglycemia. Close medical supervision for glucose monitoring is required during hypoglycemia induction and the test is often very tedious. In addition, a capillary blood sugar test (BST) and serum glucose levels may differ greatly. An alternative approach may be utilizing a continuous glucose-monitoring (CGM) system. We provide three cases in which CGM was successfully used alongside a standard BST and serum glucose levels during the combined pituitary function test to better detect and induce hypoglycemia. Three participants who were diagnosed with multiple pituitary hormone deficiencies during childhood were re-evaluated in adulthood; a Dexcom G6 CGM was used. The CGM sensor glucose and BST levels were simultaneously assessed for glycemic changes and when adequate hypoglycemia was reached during the combined pituitary function test. The CGM sensor glucose, BST, and serum glucose levels showed similar glucose trends in all three patients. A Bland–Altman analysis revealed that the CGM underestimated the BST values by approximately 9.68 mg/dL, and a Wilcoxon signed-rank test showed that the CGM and BST measurements significantly differed during the stimulation test (p = 0.003). Nevertheless, in all three cases, the CGM sensor mimicked the glycemic variability changes in the BST reading and assisted in monitoring appropriate hypoglycemia nadir. Thus, CGM can be used as a safe aid for clinicians to use during insulin tolerance tests where critical hypoglycemia is induced. Full article

(This article belongs to the Special Issue Recent Advances in Continuous Glucose Monitoring Sensors)

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