Non-invasive Glucose Monitoring System Research Articles

Smart Contact Lens for Colorimetric Visualization of Glucose Levels in the Body Fluid.

Frequent blood glucose monitoring is a crucial routine for diabetic patients. Traditional invasive methods can cause discomfort and pain and even pose a risk of infection. As a result, researchers have been exploring noninvasive techniques. However, a limited number of products have been developed for the market due to their high cost. In this study, we developed a low-cost, highly accessible, and noninvasive contact lens-based glucose monitoring system. We functionalized the surface of the contact lens with boronic acid, which has a strong but reversible binding affinity to glucose. To achieve facile conjugation of boronic acid, we utilized a functional coating layer called poly(tannic acid). The functionalized contact lens binds to glucose in body fluids (e.g., tear) and releases it when soaked in an enzymatic cocktail, allowing for the glucose level to be quantified through a colorimetric assay. Importantly, the transparency and oxygen permeability of the contact lens, which are crucial for practical use, were maintained after functionalization, and the lenses showed high biocompatibility. Based on the analysis of colorimetric data generated by the smartphone application and ultraviolet-visible (UV-vis) spectra, we believe that this contact lens has a high potential to be used as a smart diagnostic tool for monitoring and managing blood glucose levels.

Using Artificial Intelligence to Improve the Accuracy of a Wrist-Worn, Noninvasive Glucose Monitor: A Pilot Study.

Self-monitoring of glucose is important to the successful management of diabetes; however, existing monitoring methods require a degree of invasive measurement which can be unpleasant for users. This study investigates the accuracy of a noninvasive glucose monitoring system that analyses spectral variations in microwave signals. An open-label, pilot design study was conducted with four cohorts (N = 5/cohort). In each session, a dial-resonating sensor (DRS) attached to the wrist automatically collected data every 60 seconds, with a novel artificial intelligence (AI) model converting signal resonance output to a glucose prediction. Plasma glucose was measured in venous blood samples every 5 minutes for Cohorts 1 to 3 and every 10 minutes for Cohort 4. Accuracy was evaluated by calculating the mean absolute relative difference (MARD) between the DRS and plasma glucose values. Accurate plasma glucose predictions were obtained across all four cohorts using a random sampling procedure applied to the full four-cohort data set, with an average MARD of 10.3%. A statistical analysis demonstrates the quality of these predictions, with a surveillance error grid (SEG) plot indicating no data pairs falling into the high-risk zones. These findings show that MARD values approaching accuracies comparable to current commercial alternatives can be obtained from a multiparticipant pilot study with the application of AI. Microwave biosensors and AI models show promise for improving the accuracy and convenience of glucose monitoring systems for people with diabetes.

Non-invasive glucometer monitoring system through optical based near-infrared sensor method

ABSTRACT Diabetes is a fast-developing medical issue that causes most renal and cardiac illnesses. Thus, diabetes management requires regular glucose monitoring. One potential technology is non-invasive glucometer monitoring. This work aims to develop a user-friendly near-infrared sensor-based non-invasive glucose monitoring system, correlating sensor output voltage variations with glucose levels, to provide accurate and convenient glucose monitoring for diabetes management. The objective is to validate the system’s accuracy against existing fingerpick methods and analyze its performance across different age groups and food intake conditions through experimental testing and Clarke grid analysis. In our research, we propose a near-infrared sensor-based non-invasive-type glucose monitoring technique which is a user-friendly system. The experimental setup and prototype system are designed and implemented for measuring the variation of glucose level with respect to a sensor output voltage. Using Beer Lambert’s law, the established results correlated the absorbance property of light with the sample concentration level. Demonstration of testing for different aged people was done under various food intake conditions. The obtained results are tabulated and validated with the existing fingerpick method and achieved an accuracy of 97.8%. Also, Clarke grid analysis has been done and depicted the pattern obtained.

Learned prediction of cholesterol and glucose using ARIMA and LSTM models – A comparison

Painless remedies and diagnosis have become the primary objective in medical sciences as diseases shoot up. This research addresses the pressing need for cost-effective, non-invasive, and real-time monitoring solutions for glucose and cholesterol levels to reduce mortality rates associated with diabetes and cardiovascular diseases. Diabetes and cardiovascular diseases are significant global health challenges, affecting millions of individuals, particularly in low to middle-income countries. The inconvenience and discomfort of traditional blood tests have often resulted in delayed testing and monitoring, making it imperative to develop alternative methods for early disease detection and continuous monitoring. The primary objective of this research is to create a non-invasive glucose and cholesterol monitoring and forecasting system that leverages Artificial Intelligence and Cloud computing. The specific focus is comparing the prediction efficiency of two models: the Auto-Regressive Integrated Moving Average (ARIMA) model and the Long Short-Term Memory (LSTM) model. The research methodology involves the development of a hardware prototype for the non-invasive measurement of glucose and cholesterol, employing Near Infra-Red (NIR) sensors. Data on glucose and cholesterol levels are collected and stored from patients over a period of one month. The collected data is used to train both the ARIMA and LSTM models. The models are evaluated using Root Mean Square Error (RMSE), and their prediction accuracy is compared. The study findings reveal that the ARIMA model surpasses the LSTM model in predicting glucose and cholesterol levels. This is supported by a lower RMSE of the ARIMA model (about 71.7 % less for Glucose and 50.3 % less for Cholesterol) compared to the LSTM model, indicating higher prediction accuracy.Additionally, the hardware prototype facilitates non-invasive measurement and forecasting, offering a promising solution for improved early diagnosis and monitoring. The research highlights the potential of ARIMA and LSTM models in healthcare and diagnostic applications. The superior performance of the ARIMA model in predicting glucose and cholesterol levels positions it as a valuable tool for real-time applications. This study provides a unified solution for non-invasive measurement and forecasting, aiming to enhance early diagnosis and monitoring of diabetes and cardiovascular risk.

Invasive and Non-Invasive Glucose Monitoring Systems: A Review and Comparative Study

Invasive and Non-Invasive Glucose Monitoring Systems: A Review and Comparative Study

A novel effective immobilization of glucose oxidase on Ni0.25Zn0.25Cu0.25Co0.25La0.06Fe1.94O4 – Chitosan nanocomposite as an enzymatic glucose biosensor

An effectual enzymatic glucose biosensor has drawn significant attention in the natural world due to its continuous glucose monitoring systems on human beings. A need for accurate and dependable glucose biosensors is needed and has notably augmented the keen interest to synthesize new non-invasive glucose monitoring systems in the recent phase. A novel Ni0.25Zn0.25Cu0.25Co0.25La0.06Fe1.94O4 nanocomposite has been synthesized via the combustion method to develop an appreciable glucose biosensor. The glucose biosensor was fabricated by immobilization of glucose oxidase (GOx) onto chitosan (CH)-Ni0.25Zn0.25Cu0.25Co0.25La0.06Fe1.94O4 heterojunction nanocomposite on FTO glass substrate. The performance of the as-prepared enzymatic glucose biosensor was estimated using cyclic voltammetry (CV) and electrochemical impedance spectroscopy (EIS). The electrochemical studies revealed an enhanced diffusion of molecules on the electrode surface, superior charge transfer rate, high sensitivity, and fast response time. The Ni0.25Zn0.25Cu0.25Co0.25La0.06Fe1.94O4-CH bi-junction conjoining with GOx exhibits a higher sensitivity of 52.76 µAmM-1cm-2 in a comprehensive undeviating range. The catalytic properties of the electrode in the H2O2 solution were studied using cyclic voltammetry, which showed a good linear response with an increase in scan rate and peak current resulting in enriched electrostatic interaction. In addition, the fabricated biosensor with a low Michaelis-Menten constant contributes a better affinity of the electrode surface towards glucose oxide.

Glycemia control using remote technologies

Diabetes mellitus is a chronic metabolic disease with a rapidly increasing prevalence. Glycemic control in diabetes mellitus remains the key to improving the effectiveness of therapy, reducing the risk of hypoglycemia, preventing microvascular complications, and reducing the long-term risk of macrovascular complications. However, regular glycemic control is only a part of this process, since an equally important step is the timely and correct interpretation of the data obtained, as well as the decision on further therapeutic tactics. Technological advances are providing tools to help diabetic patients reach their glycemic targets and facilitate ongoing monitoring of blood glucose levels. Currently, there is the possibility of remote monitoring of glycemia, transmission of data to medical professionals and caregivers: blood glucose meters with wireless glucose reporting, continuous glucose monitors, flash glucose monitors, and non-invasive glucose monitoring systems. Large scientific studies have proven the effectiveness and prospects of telemedicine technologies in the treatment of diabetes. Today, the digitalization of healthcare is actively developing from telemedicine and remote interaction with patients to new digital approaches to diagnostics and information exchange. Thus, in the Russian Federation, within the framework of the digital transformation strategy of the Healthcare industry until 2024 and for the planned period until 2030, the Personal Medical Assistants project is being implemented, aimed at creating technologies for dynamic remote monitoring of patients using platforms of centralized diagnostic services based on a unified state information system in the field of healthcare.

Design and Experimental Validation of a Noninvasive Glucose Monitoring System Using RF Antenna-Based Biosensor

This article presents an end-to-end microwave-based system to detect the glucose level in aqueous solutions through a noninvasive scheme. A microwave signal is generated and transmitted through the sample under test (i.e., glucose–water solution). The received signal is then conditioned using a low-noise amplifier (LNA), a bandpass filter (BPF), and an RF detector. The change in the dc output voltage on the receiver side is used as a new way to detect the glucose level. The proposed glucose sensor is implemented using two RF microstrip patch antennas that resonate at 5.7 GHz and are fabricated using an FR-4 substrate. The design specifications of the sensing antennas are thoroughly studied and presented. The system is verified experimentally using glucose–water testing samples. The experimental results confirm the correlation between the glucose concentration and the dc output voltage for a concentration range of 0–5000 mg/dL. The effect of the transmitted power level on the system performance is also investigated. Finally, the proposed system is compared with the state-of-the-art systems reported in the literature.

Potential of User Experience in non-invasive Glucose Monitoring system

Abstract: User experience is about the experiences people have with the designed world, this research focuses on giving a better experience for diabetic patients who constantly have to prick themselves to check their blood glucose levels. With the help of new and emerging technology, near-infrared spectroscopy (NIR) is a promising option that can be incorporated with a better design to provide usability, accessibility, and pleasure for diabetic patients

A 186μW Photoplethysmography-Based Noninvasive Glucose Sensing SoC

Recent trends in research and the market show high demand for frequent monitoring of glucose to estimate the blood sugar level non-invasively which can replace the conventional finger-prick glucometer for daily use. This paper presents a high precision near-infrared Photoplethysmography (PPG) based noninvasive glucose monitoring System on Chip (SoC). The proposed system implements a fully differential Analog Frontend (AFE) with nonlinear medium Gaussian support-vector-regression (NMG-SVR) for glucose estimation. The AFE design incorporates chopping which enables the reduction of the integrated input-referred current noise to 9.4pArms thus achieving a dynamic range of 115dB. The glucose prediction processor (GPP) removes noise from the PPG signal, extracts ten unique features, and estimates the blood glucose level using a trained customized NMG-SVR model that minimizes the hardware cost by 25%. The extracted features are carefully designed and implemented to ensure inter-feature dependency, which helps to reduce the overall area by more than 40%. Moreover, GPP is implemented using power and clock gating techniques to minimize both static and dynamic power consumption. The proposed SoC is realized with <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$0.18~\mu \text{m}$ </tex-math></inline-formula> CMOS technology and occupies an area of 6 mm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> . It dissipates a power of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$186~\mu \text{W}$ </tex-math></inline-formula> and achieves a mean absolute relative difference (mARD) of 6.9% verified on 200 subjects.

Recent advancements in noninvasive glucose monitoring and closed-loop management systems for diabetes.

Diabetes can cause many complications, and has become one of the most common diseases that may lead to death. Currently, the number of diabetics continues to increase year by year. Thus, it is very important and highly desirable to monitor, control and cure diabetes. However, such management usually require long-term blood glucose monitoring and regular medication to reduce the risk of many complications. In order to provide patients with more effective and more convenient treatments, a portable, miniaturized, intelligent, painless and automatic closed-loop system is highly required and many related research studies have recently been reported. It is the right time and important to provide a summary in this field. Here, this review covers important parts of a closed-loop management system for diabetes, including the principle of electrochemical sensing of glucose, recent progress of noninvasive glucose monitoring technology, and its applications in wearable glucose sensors. Moreover, the latest advancements in an insulin delivery system and a diabetic closed-loop management system are also presented in detail. Finally, challenges and perspectives for an artificial pancreas are also presented. We believe that with the innovative glucose monitoring technology and the optimization of the drug delivery system, the closed-loop management system for diabetes will make much progress in the near future.

Evaluation of a Continuous Blood Glucose Monitor: A Novel and Non-Invasive Wearable Using Bioimpedance Technology.

Frequent blood glucose level (BGL) monitoring is essential for effective diabetes management. Poor compliance is common due to the painful finger pricking or subcutaneous lancet implantation required from existing technologies. There are currently no commercially available non-invasive devices that can effectively measure BGL. In this real-world study, a prototype non-invasive continuous glucose monitoring system (NI-CGM) developed as a wearable ring was used to collect bioimpedance data. The aim was to develop a mathematical model that could use these bioimpedance data to estimate BGL in real time. The prototype NI-CGM was worn by 14 adult participants with type 2 diabetes for 14 days in an observational clinical study. Bioimpedance data were collected alongside paired BGL measurements taken with a Food and Drug Administration (FDA)-approved self-monitoring blood glucose (SMBG) meter and an FDA-approved CGM. The SMBG meter data were used to improve CGM accuracy, and CGM data to develop the mathematical model. A gradient boosted model was developed using a randomized 80-20 training-test split of data. The estimated BGL from the model had a Mean Absolute Relative Difference (MARD) of 17.9%, with the Parkes error grid (PEG) analysis showing 99% of values in clinically acceptable zones A and B. This study demonstrated the reliability of the prototype NI-CGM at collecting bioimpedance data in a real-world scenario. These data were used to train a model that could successfully estimate BGL with a promising MARD and clinically relevant PEG result. These results will enable continued development of the prototype NI-CGM as a wearable ring.

Non-invasive continuous-time glucose monitoring system using a chipless printable sensor based on split ring microwave resonators

This paper reports a highly sensitive, non-invasive sensor for real-time glucose monitoring from interstitial fluid. The structure is comprised of a chip-less tag sensor which may be taped over the patient’s skin and a reader, that can be embedded in a smartwatch. The tag sensor is energized through the established electromagnetic coupling between the tag and the reader and its frequency response is reflected on the spectrum of the reader in the same manner. The tag sensor consumes zero power as there is no requirement for any active readout or communication circuitry on the tag side. When measuring changes in glucose concentrations within saline replicating interstitial fluid, the sensor was able to detect glucose with an accuracy of ~ 1 mM/l over a physiological range of glucose concentrations with 38 kHz of the resonance frequency shift. This high sensitivity is attained as a result of the proposed new design and extended field concentration on the tag. The impact of some of the possible interferences on the response of the sensor’s performance was also investigated. Variations in electrolyte concentrations within the test samples have a negligible effect on the response of the sensor unless these variations are supra-physiologically large.

Glucose oxidase-based biosensor for glucose detection from biological fluids

PurposeThe present study aims to summarize different non-invasive techniques for continuous glucose monitoring (CGM) in diabetic patients using glucose-oxidase biosensors. In diabetic patients, the self-monitoring of blood glucose (BG) levels through minimally invasive techniques provides a quick method of measuring their BG concentration, unlike conventional laboratory measurements. The drawbacks of minimally invasive techniques include physical pain, anxiety and reduced patient compliance. To overcome these limitations, researchers shifted their attention towards the development of a pain-free and non-invasive glucose monitoring system, which showed encouraging results.Design/methodology/approachThis study reviews the development of minimally and non-invasive method for continuous glucose level monitoring in diabetic or hyperglycemic patients. Specifically, glucose monitoring using non-invasive techniques, such as spectroscopy-based methods, polarimetry, fluorescence, electromagnetic variations, transdermal extraction-based methods and using body fluids, has been discussed. The various strategies adopted for improving the overall specificity and performance of biosensors are discussed.FindingsIn conclusion, the technology of glucose oxidase-based biosensors for glucose level monitoring is becoming a strong competitor, probably because of high specificity and selectivity, low cost and increased patient compliance. Many industries currently working in this field include Google, Novartis and Microsoft, which demonstrates the significance and strong market potential of self-monitored glucose-oxidase-based biosensors in the near future.Originality/valueThis review paper summarizes comprehensive strategies for continuous glucose monitoring (CGM) in diabetic patients using non-invasive glucose-oxidase biosensors. Non-invasive techniques received significant research interest because of high sensitivity and better patient compliance, unlike invasive ones. Although the results from these innovative devices require frequent calibration against direct BG data, they might be a preferable candidate for future CGM. However, the challenges associated with designing accurate level sensors to biomonitor BG data easily and painlessly needs to be addressed.

885-P: Evaluation of a Novel Continuous Glucose Monitoring System in Insulin-Treated Patients with Diabetes: A Pilot Study

Continuous glucose monitoring has been shown to be an effective tool in management of type 1 and type 2 diabetes. Non-invasive techniques may provide a convenient alternative to current invasive methods but their precision is lacking. We evaluated the accuracy of “LabPatch”, a non-invasive continuous glucose monitoring system that uses micropipette technology to sip interstitial fluid in comparison to YSI® (laboratory reference glucose analyzer) and 2 glucose meters (Onetouch Verio® and Freestyle Lite®) in 30 insulin-treated patients with diabetes at multiple points over 6 hours. LabPatch glucose readings significantly correlated with those of YSI (R-square=0.23, p&amp;lt;0.0001). Moreover, 85.69% of LabPatch glucose readings were within zones A and B of the Clarke Error Grid albeit B&amp;gt;A. LabPatch had an absolute relative difference (ARD) of 35.5% compared with YSI indicating wide variation in glucose readings. For glucose values between 70-180 mg/dL, LabPatch readings were within +/-15% of YSI values with a lower one-sided 95 % confidence bound of 75.25%. Estimated average glucose value (eAGV) were the closest between LabPatch and YSI than between either of the 2 glucose meters and YSI. We conclude that first-generation LabPatch Precision technology has potential to identify glucose trends in situations that require close monitoring of diabetes over shorter intervals such as gestational diabetes, pre-pregnancy planning and patients preparing for elective surgery. Additionally, LabPatch has the potential to be used for diabetes screening due to its non-invasive nature. Larger sample size and more precision of manufacturing technology may make it a valid non-invasive and wireless glucose monitor. Disclosure S. Tomah: Stock/Shareholder; Self; Amarin Corporation. A.H. Eldib: None. M. Tasabehji: None. V. Bhatia: None. H. Gardner: None. O. Hamdy: Consultant; Self; Abbott, AstraZeneca, Merck &amp; Co., Inc., Sanofi-Aventis. Research Support; Self; Novo Nordisk A/S. Stock/Shareholder; Self; Healthimation, LLC. Funding Cambridge Medical Technologies, LLC

Assessment of a Noninvasive Chronic Glucose Monitoring System in Euglycemic and Diabetic Swine (Sus scrofa).

Models of type-I diabetes are well-characterized and commonly used in the preclinical evaluation of drugs and medical devices. The diabetic minipig is an excellent example of a translational model. However, chronic glucose monitoring in this species can be challenging; frequent blood sampling can be technically difficult and poorly tolerated in conscious swine. Skin-patch continuous blood glucose monitors are FDA-approved for human use and offer a potential refinement to cageside blood collection. However, this modality has not been evaluated in pigs. In this study, young adult male STZ-induced diabetic Yucatan minipigs (n = 4) and healthy York pigs (n = 4) were implanted with a 14-d skin-patch continuous glucose monitor. Readings from continuous glucose monitors were time-matched to whole blood samples, with glucose measurements performed using point-of-care blood glucose monitors, serum chemistry or both. The aims of the study were to assess if a continuous glucose monitoring system could accurately detect glucose levels in swine, and to compare the readings to both point-of-care glucometers and serum chemistry results. We hypothesized that a continuous glucose monitoring system would accurately detect glucose levels in swine in comparison with a validated analyzer and could serve as an animal welfare refinement for studies of diabetes. We found that the continuous glucose monitor used in this study provided an adequate adjunct for clinical management in the stable diabetic pig and a minimally invasive and inexpensive option for colony maintenance of chronically diabetic swine.

94-LB: Measurement Accuracy of a Newly Developed Prototype System for Noninvasive Glucose Monitoring

Non-invasive glucose monitoring (NIGM) may be beneficial for people with diabetes in avoiding the need for finger pricking to obtain blood samples. The aim was to assess measurement accuracy of a prototype system for NIGM in a mixed outpatient and in-clinic setting. A total of 10 subjects with type 1 diabetes participated in the study which lasted for 27 days per subject. Subjects wore a FreeStyle Libre (FL) system on the upper arm for continuous glucose monitoring, and they performed standard blood glucose (BG) monitoring with a Contour® next ONE meter and NIGM at the thenar with the prototype system. At least 6 times per day, measurement sets were performed which consisted of 1st BG meter measurement, 1st FL scan, 1st NIGM measurement, 2nd BG meter measurement, 2nd FL scan, 2nd NIGM measurement. Results from the same measurement set were averaged by device before analysis. In-clinic sessions took place on the 6th day and on the 27th day, during which rapid glucose excursions with high and low glucose values were induced. During these sessions, measurement sets were performed every 15 minutes between 0730 and 1500. Data from 24/19 days were used for calibration of the NIGM system for the first/second group of five subjects. The data from the remaining 3/8 days (including 1 in-clinic day each) were used for independent validation of the calibration. For data from validation days, mean absolute relative differences (MARD) were calculated for FL and the NIGM prototype in comparison with the BG meter values for each subject. Outpatient MARD ranged from 8.0% to 26.2% for FL (mean: 16.1%) and from 9.1% to 26.9% for the prototype (mean: 20.2%), whereas in-clinic MARD ranged from 13.7% to 45.3% for FL (mean 23.3%) and from 19.8% to 36.0% for the prototype (mean: 25.5%). Although MARD was somewhat higher for the NIGM prototype than for FL, this proof-of-concept study showed promising results for the newly developed NIGM prototype. Disclosure G. Freckmann: Advisory Panel; Self; Abbott, Novo Nordisk Pharma GmbH, Sensile Medical AG. Consultant; Self; Metronom Health Europe, Roche Diabetes Care, Sanofi. Speaker’s Bureau; Self; Ascensia Diabetes Care, Dexcom, Inc., Ypsomed AG. S. Pleus: None. P. Wintergerst: None. D. Waldenmaier: None. N.R. Jendrike: None. M. Link: None. C. Haug: None. Funding RSP Systems A/S

Multispectral photoacoustic sensing for accurate glucose monitoring using a supercontinuum laser

Accurate monitoring of glucose levels constitutes the most important parameter for diabetes management and treatment planning. In this work, we report on an in vitro glucose monitoring system based on multispectral photoacoustic sensing (MSPAS) using a cost-effective supercontinuum (SC) laser. We demonstrate for the first time, to the best of our knowledge, how the use of a broadband SC source allows the identification of distinct absorption characteristics of two major analytes (glucose and cholesterol) present in the human body in the extended near-infrared 1540–1840 nm spectral range. Employing the reported SC-based MSPAS system with a ratiometric analysis, we were able to accurately (coefficient of determination ≥0.938) measure a wide range of glucose concentration levels in vitro. We further demonstrate clinically accurate prediction of glucose concentrations over commonly encountered physiological levels inside the human body (0–400 mg/dL) with reference to a Clarke error grid analysis. These findings pave the way for devising potentially noninvasive and label-free continuous glucose monitoring systems.

Understanding of type 1 diabetes mellitus: what we know and where we go.

The incidence of type 1 diabetes mellitus (T1DM) in children and adolescents is increasing worldwide. Combined effects of genetic and environmental factors cause T1DM, which make it difficult to predict whether an individual will inherit the disease. Due to the level of self-care necessary in T1DM maintenance, it is crucial for pediatric settings to support achieving optimal glucose control, especially when adolescents are beginning to take more responsibility for their own health. Innovative insulin delivery systems, such as continuous subcutaneous insulin infusion (CSII), and noninvasive glucose monitoring systems, such as continuous glucose monitoring (CGM), allow patients with T1DM to achieve a normal and flexible lifestyle. However, there are still challenges in achieving optimal glucose control despite advanced technology in T1DM administration. In this article, disease prediction and current management of T1DM are reviewed with special emphasis on biomarkers of pancreatic β-cell stress, CSII, glucose monitoring, and several other adjunctive therapies.

Diabetes and technology in 2030: a utopian or dystopian future?

The ability of an individual living with diabetes to have human-to-human contact with their healthcare provider is not keeping pace with the number of people developing diabetes. From a futurist perspective, however, this dichotomy of diabetes care represents an opportunity for digital healthcare. The focus of technological innovation is unlikely to be the replacement of the multidisciplinary diabetes team but rather the provision of meaningful individual and family support between clinic visits and, on a larger scale, the facilitation of population health management for diabetes. We can also expect to see new therapies, including implantable drug delivery systems, automated closed-loop systems and miniaturized non-invasive glucose monitoring systems. New digital health technologies will create a 'digital diabetes ecosystem' to enhance rather than devolve care from humans. Concerns related to data privacy and ownership will inevitably rise, thus a future for diabetes care relying heavily on technology is not inevitably utopian. Nevertheless, revolutions in the development of novel sensors, accumulation of 'big data', and use of artificial intelligence will provide exciting opportunities for preventing, monitoring and treating diabetes in the near future.