Continuous Blood Glucose Level Research Articles

Accurate blood glucose level monitoring using microwave imaging

Painless and non-invasive detection techniques are needed to replace finger-prick blood collection for people with diabetes. A first-of-its-kind, noninvasive, and continuous blood glucose level (BGL) detection method based on microwave imaging is introduced in this paper. This method avoids the complex task of frequency choice for the design of electromagnetic sensors. A radar-based microwave imaging technology combined with an improved very-deep super-resolution (VDSR-BL) method is presented to obtain high-resolution (HR) microwave images. After image super-resolution reconstruction by VDSR-BL, the peak signal-to-noise ratio and structural similarity index of HR images reach 35.4461 dB and 0.9761, respectively. Then, an ensemble learning strategy based on support vector regression and random forest algorithms is proposed to identify HR microwave images for BGL estimation. The developed detection system has been verified on the medium under tests with different glucose solutions. The final detection results obtain a root mean squared error of 0.1394 mg ml−1 and a mean absolute relative difference of 8.02%, which show good accuracy with clinical acceptance. Meanwhile, we also conducted human trials. A high correlation coefficient (R) of 0.9254 was achieved between the results of microwave imaging and invasive BGL. Together, these results show that microwave imaging offers a promising new approach for noninvasive BGL monitoring.

Continuous Glucose Monitoring System After Coronary Artery Bypass Graft Surgery: A Feasibility Study.

Blood glucose level variability has been associated with increased risk of complication in the postoperative setting of cardiovascular surgery. Although interesting for optimization of blood glucose management in this context, continuous blood glucose (CBG) devices can have a limited reliability in this context, in particular because of the use of paracetamol. The aim of this study was to evaluate the reliability of Dexcom G6®, a recently developed continuous glucose monitoring device. We performed a prospective, observational, non-randomized, single-centre study comparing Dexcom G6® CBG level monitoring with the standard methods routinely used in this context. The standard blood glucose values were paired to the time corresponding values measured with Dexcom G6®. Agreement between the two methods and potential correlation in case of paracetamol use were calculated. From May 2020 to August 2021, 36 out of 206 patients operated for isolated coronary artery bypass grafting were enrolled; 673 paired blood glucose level were analyzed. Global agreement (ρc) was 0.85 (95% C.I.: 0.84-0.86), intensive care unit agreement was 0.78 (95%C.I.: 0.74-0.82) and ward agreement was 0.91 (95%C.I.: 0.89-0.93). In the diabetic population, it was 0.87 (95%C.I.: 0.85-0.90). When paracetamol was used, the difference was 0.02 mmol/l (95%C.I.: 0.29-0.33). Dexcom G6® provides good blood glucose level accuracy in the postoperative context of cardiac surgery compared to the standard methods of measurements. The results are particularly reliable in the ward where the need for repeated capillary glucose measurements implies patient discomfort and time-consuming manipulations for the nursing staff.

A comparative observational study of carbohydrate intake and continuous blood glucose levels in relation to performance in ultramarathon

Ultra-endurance events have gained global participation, whereas the critical factors of competition results remain to be well elucidated. This study used a nutritional approach to evaluate the association of competition results with carbohydrate intake and blood glucose control during a 100-mile ultramarathon. This observational study was conducted in the 2021 LAKE BIWA 100, which covered 100 miles (169 km) and 10,500 m elevation. The course was divided into 9 segments by aid station. According to the competition results, 22 participants (18 men and 4 women) were divided into higher finishers (n = 7), lower finishers (n = 9), and non-finishers (n = 6). The participants self-recorded their overall dietary intake throughout the race. Glucose levels were monitored every 15 min by a flash glucose monitoring system. Running speed in each segment was standardized to the average of the top five finishers for each gender. Among finishers, the carbohydrate intakes were significantly higher in the higher finishers than in the lower finishers during overall segments, especially in the first half of the race (p < 0.05). There was a significant positive correlation between running speed and carbohydrate intake in the lower finishers (rho = 0.700, p = 0.036). Two-way ANOVA analysis revealed that lowering glucose levels in each segment were more frequently observed in the lower finishers compared to the higher finishers (p = 0.012). Compared to the higher finishers, the lower finishers exhibited significantly greater fluctuations (⊿highest-lowest) in glucose levels (p < 0.001). The fluctuations in glucose levels were significantly and negatively correlated with the running speed of the finishers (rho = − 0.612, p = 0.012). Faster runners consume high amounts of carbohydrates and maintain glucose levels during the 100-mile ultramarathon on the trail, especially at the beginning. Lowering and fluctuating glucose levels during the race are associated with lower running speed in endurance athletes.

Blood Glucose Regulation in Patients with Type 1 Diabetes by Robust Optimal Safety Critical Control

Introduction: Diabetes disease is a group of metabolic diseases in which a person has high blood sugar, either because the pancreas does not produce enough insulin, or because cells do not respond to the insulin that is produced. Designing an automated system for regulating blood glucose in patients with diabetes is a solution that researchers have been paying close attention to in recent years. Therefore, safety is the minimum requirement for safety-critical systems such as the artificial pancreas. The present study introduces a safe, robust, performance-guaranteed optimal controller that can safely regulate blood glucose in the disturbance.Material and Methods: In this section, first, regulate blood glucose levels in simulation studies is evaluated. For this purpose, a dynamic model is used. The model includes a virtual patient, an insulin pump, and a continuous blood glucose level sensor. The virtual patient model represents the dynamics of insulin-glucose, carbohydrate-glucose, and exercise-glucose.Results: The need to not reset the controller parameters for patients in each category is one of the suggested controller's benefits. However, the PID controller needs to reset the parameters for each group of patients, the predictive control method requires the estimated model of the patient, and its performance is different on different days because the insulin-glucose dynamics for an individual changes day by day.Conclusion: Taking into account different sensitivities of body tissue to insulin, the results of evaluating the controller for two different groups of patients have shown that the controller is resistant to day-to-day changes in patients who may experience changes in insulin sensitivity, even with stress or medication and will not lose its optimal function. Based on the simulation results, the proposed controller can reduce the external disturbances' effect, whose amplitude is to a good extent within the body's physiological range.

Blood Glucose Level Monitoring Using an FMCW Millimeter-Wave Radar Sensor

In this article, a novel sensing approach is presented for glucose level monitoring where a robust low-power millimeter(mm)-wave radar system is used to differentiate between blood samples of disparate glucose concentrations in the range 0.5 to 3.5 mg/mL. The proposed radar sensing mechanism shows greater capabilities for remote detection of blood glucose inside test tubes through detecting minute changes in their dielectric properties. In particular, the reflected mm-waves that represent unique signatures for the internal synthesis and composition of the tested blood samples, are collected from the multi-channels of the radar and analyzed using signal processing techniques to identify different glucose concentrations and correlate them to the reflected mm-wave readings. The mm-wave spectrum is chosen for glucose sensing in this study after a set of preliminary experiments that investigated the dielectric permittivity behavior of glucose-loaded solutions across different frequency bands. In this regard, a newly-developed commercial coaxial probe kit (DAK-TL) is used to characterize the electromagnetic properties of glucose-loaded samples in a broad range of frequencies from 300 MHz to 67 GHz using two different 50 Ω open-coaxial probes. This would help to determine the portion of the frequency spectrum that is more sensitive to slight variations in glucose concentrations as indicated by the amount of change in the dielectric constant and loss tangent parameters due to the different concentrations under test. The mm-wave frequency range 50 to 67 GHz has shown to be promising for acquiring both high sensitivity and sufficient penetration depth for the most interaction between the glucose molecules and electromagnetic waves. The processed results have indicated the reliability of using mm-wave radars in identifying changes in blood glucose levels while monitoring trends among those variations. Particularly, blood samples of higher glucose concentrations are correlated with reflected mm-wave signals of greater energy. The proposed system could likely be adapted in the future as a portable non-invasive continuous blood glucose level monitoring for daily use by diabetics.

Dynamic properties of glucose complexity during the course of critical illness: a pilot study.

Methods to control the blood glucose (BG) levels of patients in intensive care units (ICU) improve the outcomes. The development of continuous BG levels monitoring devices has also permitted to optimize these processes. Recently it was shown that a complexity loss of the BG signal is linked to poor clinical outcomes. Thus, it becomes essential to decipher this relation to design efficient BG level control methods. In previous studies the BG signal complexity was calculated as a single index for the whole ICU stay. Although, these approaches did not grasp the potential variability of the BG signal complexity. Therefore, we setup this pilot study using a continuous monitoring of central venous BG levels in ten critically ill patients (EIRUS platform, Maquet Critical CARE AB, Solna, Sweden). Data were processed and the complexity was assessed by the detrended fluctuation analysis and multiscale entropy (MSE) methods. Finally, recordings were split into 24h overlapping intervals and a MSE analysis was applied to each of them. The MSE analysis on time intervals revealed an entropy variation and allowed periodic BG signal complexity assessments. To highlight differences of MSE between each time interval we calculated the MSE complexity index defined as the area under the curve. This new approach could pave the way to future studies exploring new strategies aimed at restoring blood glucose complexity during the ICU stay.

Continuous blood glucose level prediction of Type 1 Diabetes based on Artificial Neural Network

Recent technological advancements in diabetes technologies, such as Continuous Glucose Monitoring (CGM) systems, provide reliable sources to blood glucose data. Following its development, a new challenging area in the field of artificial intelligence has been opened and an accurate prediction method of blood glucose levels has been targeted by scientific researchers. This article proposes a new method based on Artificial Neural Networks (ANN) for blood glucose level prediction of Type 1 Diabetes (T1D) using only CGM data as inputs. To show the efficiency of our method and to validate our ANN, real CGM data of 13 patients were investigated. The accuracy of the strategy is discussed based on some statistical criteria such as the Root Mean Square Error (RMSE) and the Mean Absolute Percentage Error (MAPE). The obtained averages of RMSE are 6.43 mg/dL, 7.45 mg/dL, 8.13 mg/dL and 9.03 mg/dL for Prediction Horizon (PH) respectively 15 min, 30 min, 45 min and 60 min and the average of MAPE was 3.87% for PH = 15 min, knowing that the smaller is the RMSE and MAPE, the more accurate is the prediction. Experimental results show that the proposed ANN is accurate, adaptive, and very encouraging for a clinical implementation. Furthermore, while other studies have only focused on the prediction accuracy of blood glucose, this work aims to improve the quality of life of T1D patients by using only CGM data as inputs and by limiting human intervention.

T Cell-Mediated Beta Cell Destruction: Autoimmunity and Alloimmunity in the Context of Type 1 Diabetes.

Type 1 diabetes (T1D) results from destruction of pancreatic beta cells by T cells of the immune system. Despite improvements in insulin analogs and continuous blood glucose level monitoring, there is no cure for T1D, and some individuals develop life-threatening complications. Pancreas and islet transplantation have been attractive therapeutic approaches; however, transplants containing insulin-producing cells are vulnerable to both recurrent autoimmunity and conventional allograft rejection. Current immune suppression treatments subdue the immune system, but not without complications. Ideally a successful approach would target only the destructive immune cells and leave the remaining immune system intact to fight foreign pathogens. This review discusses the autoimmune diabetes disease process, diabetic complications that warrant a transplant, and alloimmunity. First, we describe the current understanding of autoimmune destruction of beta cells including the roles of CD4 and CD8 T cells and several possibilities for antigen-specific tolerance induction. Second, we outline diabetic complications necessitating beta cell replacement. Third, we discuss transplant recognition, potential sources for beta cell replacement, and tolerance-promoting therapies under development. We hypothesize that a better understanding of autoreactive T cell targets during disease pathogenesis and alloimmunity following transplant destruction could enhance attempts to re-establish tolerance to beta cells.

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

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

Effect of treatment guidance using a retrospective continuous glucose monitoring system on glycaemic control in outpatients with type 2 diabetes mellitus: A randomized controlled trial.

ObjectivesTo assess the effect of treatment guidance based on data from a continuous glucose monitoring (CGM) device on glycaemic control, and patient satisfaction, in patients with type 2 diabetes mellitus (T2DM).MethodsPatients with poorly-controlled T2DM treated with insulin were randomly assigned to the intervention or nonintervention group. Continuous blood-glucose levels were recorded for 4–5 days using a CGM device on three separate occasions during the 8-month study period. The intervention group received treatment guidance based on the CGM data; the nonintervention group received advice based on blood glucose and glycosylated haemoglobin (HbA1c) levels.ResultsA total of 34 patients were enrolled in the study. The mean ± SD baseline HbA1c was 8.2 ± 1.2% in the intervention group and 8.2 ± 0.9% in the nonintervention group. At the study end, there was no significant difference in the change from baseline of HbA1c between the two groups. There was also no significant difference in the change from baseline in the Diabetes Treatment Satisfaction Questionnaire score between the two groups.ConclusionsThe present study did not demonstrate that treatment guidance using retrospective CGM data was effective for improving glycaemic control and therapeutic satisfaction in Japanese patients with T2DM.

Simulation tool for the prediction of compound dependence of CW-photoacoustic-based sensor using dual optical excitation

Towards the development of a sensor dedicated to noninvasive and continuous blood glucose level monitoring, we developed a protocol called optical power balance shift (OPBS) based on a continuous-wave photoacoustic technique using a dual wavelength excitation sequence. In order to optimize the sensor response, we showed, using an analytical model, that the OPBS response depends exclusively on the absorption coefficients at the two wavelengths. Then, using a standard near-infrared spectrophotometer, we measured the absorption coefficient of water as well as the fractional absorption coefficient relative to glucose and albumin concentrations and temperature. We then plotted the OPBS-based sensor response for the three aforementioned parameters covering all the possible combinations of the two wavelengths from 1300 to 2500nm. Despite overall similar tendencies, the responses exhibit highs and lows at optical pairs specific to each compound, which allow potential improvement of measurement specificity to one compound in particular by choosing the appropriate pair of optical wavelengths. The good agreement between the model and experimental results for the 1610/1382-nm combination validates the approach.

Simulation Tool for the Prediction of Compound Dependence of CW-Photoacoustic-based Sensor using Dual Differential Optical Excitation

Abstract Towards the development of a sensor dedicated to noninvasive and continuous blood glucose level monitoring, we developed a protocol called optical power balance shift based on a continuous-wave photoacoustic technique using a dual differential wavelength excitation sequence. In order to optimize the sensor response, we showed using an analytical model that the OPBS response depends exclusively on the absorption coefficients at the two wavelengths. Then, using a standard near-infrared spectrophotometer, we measured the dependence of relative absorption coefficients on glucose and albumin concentrations and temperature, and built a simulation tool covering all the possible combinations of the two wavelengths from 1300 to 2500 nm. The model was validated by good agreement when compared with experimental results for the 1382/1610 nm combination.

Analysis on blood glucose excursions in well-controlled patients with type 2 diabetes mellitus

Objective To probe glycemic excursions in type 2 diabetic patients whose hemoglobin A1c (HbA1c) was kept ≤6.0%. Methods Totally 36 cases with type 2 diabetes (T2DM) with HbA1c≤6.0% and 30 cases with normal glucose tolerance (NGT) from December 2005 to December 2007 in our department were subjected prospectively. Continuous glucose monitoring system (CGMS) was employed to record their continuous blood glucose level for 3 d. The blood glucose profiles including the mean blood glucose (MBG), standard differentiation (SD), mean amplitude of glycemic excursions (MAGE) and absolute means of daily differences (MODD) were analyzed. Results T2DM group had obviously postprandial hyperglycemia, in about 2 h after meal, especially after breakfast. Fifty-two hypoglycemic episodes occurred during the monitoring period in T2DM groups, of which 73.1% (38 episodes) were absence of symptomatic hypoglycemia with the lowest value of blood glucose only 2.0 mmol/L. And 20 episodes took place during the day hours, while 32 episodes observed during the night hours. Compared with NGT groups, SD, MAGE and MODD were all significantly higher in T2DM groups. MBG was significantly correlated with HbA1c in T2DM groups, but SD, MAGE, NGE and MODD were all independent of HbA1c. MAGE was independent of MODD. Conclusion The amplitude of glycemic excursions is higher in normol-controlled T2DM groups than the NGT groups. Thus ideally glycemic control is not only to make HbA1c reach standard, but also to lessen glycemic excursions and reduce hypoglycemia episodes.