Continuous Glucose Monitoring Systems: A Review

Evaluation of continuous glucose monitoring and nutritional status in glycogen storage diseases

OBJECTIVES

The primary goal in managing glycogen storage disorders (GSD) is to implement dietary therapy through regular glucose monitoring while attempting to prevent complications. Self-monitoring of blood glucose is often insufficient for detecting asymptomatic hypoglycemia in patients. Therefore, Continuous glucose monitoring systems (CGMS) play a crucial role in identifying hypoglycemic episodes and providing detailed glucose profiles throughout the day. In this study, CGMS data, laboratory findings, and daily nutritional intake were examined in patients with GSDIa and GSDIII. The lack of similar studies in GSDIII patients in the literature highlights the need for further research in this field.

METHODS

The glucose profiles of 12 patients (7 GSDIa and 5 GSDIII) were analyzed over a 72 h period using CGMS. Nutritional intake, biochemical parameters, and growth parameters were also evaluated.

RESULTS

This study demonstrated that CGMS detected both hypoglycemia (<70 mg/dL) and hyperglycemia (>150 mg/dL) in GSD patients. Growth retardation was also observed in these patients. As complications of the disease, elevated levels of liver enzymes, cholesterol, triglycerides, and creatine kinase were identified, with fatty liver and hepatomegaly detected in all patients. The patients' nutritional intake is similar to the recommendations in disease-specific treatment guidelines.

CONCLUSIONS

The primary dietary treatment goal for GSD patients is to maintain normoglycemia. Patients may experience asymptomatic low glucose and/or asymptomatic hypoglycemic episodes during treatment. CGMS enables a more detailed monitoring of glucose profiles, which not only facilitates the precise adjustment of dietary therapy based on detailed results but also helps prevent complications associated with the disease.

A deep learning approach for blood glucose monitoring and hypoglycemia prediction in glycogen storage disease

Glycogen storage disease (GSD) is a group of rare inherited metabolic disorders characterized by abnormal glycogen storage and breakdown. These disorders are caused by mutations in G6PC1, which is essential for proper glucose storage and metabolism. With the advent of continuous glucose monitoring systems, development of algorithms to analyze and predict glucose levels has gained considerable attention, with the aim of preemptively managing fluctuations before they become problematic. However, there is a lack of research focusing specifically on patients with GSD. Therefore, this study aimed to forecast glucose levels in patients with GSD using state-of-the-art deep-learning (DL) algorithms. This retrospective study utilized blood glucose data from patients with GSD who were either hospitalized or managed at Yonsei University Wonju Severance Christian Hospital, Korea, between August 2020 and February 2024. In this study, three state-of-the-art DL models for time-series forecasting were employed: PatchTST, LTSF N-Linear, and TS Mixer. First, the models were used to predict the patients' Glucose levels for the next hour. Second, a binary classification task was performed to assess whether hypoglycemia could be predicted alongside direct glucose levels. Consequently, this is the first study to demonstrate the capability of forecasting glucose levels in patients with GSD using continuous glucose-monitoring data and DL models. Our model provides patients with GSD with a more accessible tool for managing glucose levels. This study has a broader effect, potentially serving as a foundation for improving the care of patients with rare diseases using DL-based solutions.

Recent progress on nanomaterial-based electrochemical sensors for glucose detection in human body fluids

In the modern age, half of the population is facing various chronic illnesses due to glucose maintenance in the body, major causes of fatality and inefficiency. The early identification of glucose plays a crucial role in medical treatment and the food industry, particularly in diabetes diagnosis. In the past few years, non-enzymatic electrochemical glucose sensors have received a lot of interest for their ability to identify glucose levels accurately. Electrochemical biosensors are developing as a propitious solution for personalized health monitoring due to their accuracy, specificity, and affordability. This review article provides an observation of a variety of non-enzymatic glucose sensor resources, such as carbon nanomaterials, noble metals gold and silver, transition metal and their oxides, and porous material composites. Moreover, basic knowledge of the reaction mechanism of enzymatic and nonenzymatic glucose sensors are outlined and recent advancements in glucose sensors applications to various human body biofluids such as sweat, tears, urine, saliva, and blood are presented. Finally, this review summarizes electrochemical sensors for glucose detection in human body fluids, the challenges they faced, and their solutions.

Evaluation of machine learning-based regression techniques for prediction of diabetes levels fluctuations

Middle-Aged and Elderly people today face a variety of health problems as a result of their modern lifestyle, which includes increased work stress, less physical activity, and altered food habits. Because of Complications arising, diabetes has become one of the most frequent, severe, and fatal illnesses around the world. Therefore, inaccurate measurements of blood glucose levels can seriously damage vital organs. Several strategies for long-term glucose prediction have been proposed in the literature. Unfortunately, these methods require the patient to identify their daily activities, which can be error-prone, such as meal intake, insulin injection, and emotional aspects. This paper suggests using continuous glucose monitoring (CGM) of 14733 patients, with three assistance factors to predict blood glucose levels independently of other parameters, hence reducing the burden on the patients. To support this an Artificial Neural Network (ANN), Binary Decision Tree (BDT), Linear Regression (LR), Boosting Regression Tree Ensemble (BSTE), Linear Regression with Stochastic Gradient Descent (LRSGD), Stepwise (SW), Support Vector Machine (SVM), and Gaussian process regression (GPR) were investigated. The result indicated that The highest classification accuracy of (92.58%) has been achieved by BDT followed by BSTE (92.04%) and GPR (88.59%). The obtained average of root means square error (MSE) was 1.64, 1.67, 1.69, mg/dL for prediction horizon (PH) respectively to GPR, BSTE, and ANN.

Sleep as a window of cardiometabolic health: The potential of digital sleep and circadian biomarkers

Digital biomarkers are quantifiable and objective indicators of a person's physiological function, behavioral state or treatment response, that can be captured using connected sensor technologies such as wearable devices and mobile apps. We envision that continuous and 24-h monitoring of the underlying physiological and behavioral processes through digital biomarkers can enhance early diagnostics, disease management, and self-care of cardiometabolic diseases. Cardiometabolic diseases, which include a combination of cardiovascular and metabolic disorders, represent an emerging global health threat. The prevention potential of cardiometabolic diseases is around 80%, indicating a promising role for interventions in the lifestyle and/or the environmental context. Disruption of sleep and circadian rhythms are increasingly recognized as risk factors for cardiometabolic disease. Digital biomarkers can be used to measure around the clock, that is, day and night, to quantify not only sleep patterns but also diurnal fluctuations of certain biomarkers and processes. In this way, digital biomarkers can support the delivery of optimal timed medical care. Night-time cardiometabolic patterns, such as blood pressure dipping, are predictive of cardiometabolic health outcomes. In addition, the sleep period provides an opportunity for digital cardiometabolic health monitoring with relatively low influence of artifacts, such as physical activity and eating. Digital biomarkers that utilize sleep as a window of health can be used during daily life to enable early diagnosis of cardiometabolic diseases, facilitate remote patient monitoring, and support self-management in people with cardiometabolic diseases. This review describes the influence of sleep and circadian rhythms on cardiometabolic disease and highlights the state-of-the-art sleep and circadian digital biomarkers which could be of benefit in the prevention of cardiometabolic disease.

Evaluating the precision and reliability of real-time continuous glucose monitoring systems in ambulatory settings: a systematic review

Background

Continuous glucose monitoring (CGM) with minimally invasive devices plays a key role in the assessment of daily diabetes management by detecting and alerting to potentially dangerous trends in glucose levels, improving quality of life, and treatment adherence. However, there is still uncertainty as to whether CGMs are accurate enough to replace self-monitoring of blood glucose, especially in detecting episodes of hypoglycemia.

Objectives

Evaluate clinical, numerical accuracy, sensitivity, and specificity of the CGM devices commercially available when compared to the reference standard of arterial or venous blood glucose.

Data sources and methods

We searched the Cochrane Library, PubMed, EMBASE, and LILACS databases. The quality was assessed with the Quality Assessment Diagnostic Accuracy Studies (QUADAS-2) tool. Clinical and numerical accuracy data were extracted. Sensitivity and specificity were calculated using Review Manager software. Heterogeneity was assessed by visual examination of forest plot and summary receiver operating characteristic curves.

Results

Twenty-two studies with a total of 2294 patients were included. The average mean absolute relative difference for overall diagnostic accuracy was 9.4%. None of the devices evaluated with ISO 15197:2013 criteria achieved values ⩾95% of measurements in the stipulated ranges in hypoglycemia (±15 mg/dL), but two devices did achieve it in hyperglycemia (±15%; Dexcom G6 and G7). Most of the devices evaluated with consensus error grids reached values above 99% in zones A and B only in overall accuracy and hyperglycemia. For hypoglycemia, the average sensitivity was 85.7% and specificity 95.33%, and for hyperglycemia was 97.45% and 96% respectively.

Conclusion

Currently available CGM devices have adequate accuracy for euglycemia and hyperglycemia; however, it is still inadequate for hypoglycemia, although it has improved over time.

Trial registration

Prospero registration ID CRD42023399767.

Riemannian manifold-based geometric clustering of continuous glucose monitoring to improve personalized diabetes management

BACKGROUND

Continuous Glucose Monitoring (CGM) provides a detailed representation of glucose fluctuations in individuals, offering a rich dataset for understanding glycemic control in diabetes management. This study explores the potential of Riemannian manifold-based geometric clustering to analyze and interpret CGM data for individuals with Type 1 Diabetes (T1D) and healthy controls (HC), aiming to enhance diabetes management and treatment personalization.

METHODS

We utilized CGM data from publicly accessible datasets, covering both T1D individuals on insulin and HC. Data were segmented into daily intervals, from which 27 distinct glycemic features were extracted. Uniform Manifold Approximation and Projection (UMAP) was then applied to reduce dimensionality and visualize the data, with model performance validated through correlation analysis between Silhouette Score (SS) against HC cluster and HbA1c levels.

RESULTS

UMAP effectively distinguished between T1D on daily insulin and HC groups, with data points clustering according to glycemic profiles. Moderate inverse correlations were observed between SS against HC cluster and HbA1c levels, supporting the clinical relevance of the UMAP-derived metric.

CONCLUSIONS

This study demonstrates the utility of UMAP in enhancing the analysis of CGM data for diabetes management. We revealed distinct clustering of glycemic profiles between healthy individuals and diabetics on daily insulin indicating that in most instances insulin does not restore a normal glycemic phenotype. In addition, the SS quantifies day by day the degree of this continued dysglycemia and therefore potentially offers a novel approach for personalized diabetes care.

IoT-enabled biosensors for real-time monitoring and early detection of chronic diseases

PURPOSE

The main objective of this study is to examine and highlight the substantial impact of integrating Internet of Things (IoT) technology and biosensors in the healthcare sector, focusing on their potential to drive substantial advancements and improvements in healthcare. Emphasis is placed on tackling the global challenge posed by chronic diseases by proposing an all-encompassing healthcare system that facilitates real-time monitoring, early detection, and remote management of these conditions. Chronic diseases, distinguished by their prolonged duration and gradual progression, have emerged as a marked challenge for healthcare systems worldwide. This paper seeks to illustrate how biosensors, with the capability to identify specific biomarkers, can play a pivotal role in delivering personalized patient care, enhancing outcomes, and mitigating healthcare expenses.

METHODS

This review was conducted using a systematic and comprehensive approach to analyze the integration of Internet of Things (IoT) technology with biosensors for real-time monitoring and early detection of chronic diseases. Relevant literature was sourced from reputable databases, including IEEE Xplore, PubMed, and Elsevier's ScienceDirect, focusing on studies published between 2014 and 2024. Keywords such as "IoT in healthcare," "biosensors for chronic diseases," and "real-time monitoring systems" guided the selection process. This review included original research articles, review papers, and case studies, which were critically analyzed to assess current advancements, challenges, and future directions in this interdisciplinary field. The findings were synthesized to provide an in-depth understanding of how IoT-enabled biosensors are transforming healthcare, particularly in chronic disease management.

RESULTS

This research explores the integration of IoT and biosensors for real-time monitoring of chronic diseases. The combination offers personalized healthcare, early detection, and cost reduction. Applications include remote patient monitoring, cardiac health, glucose management, and elderly care. Despite challenges, ongoing advancements promise to optimize accuracy, efficiency, and ethical soundness, ushering in a patient-centric healthcare era.

CONCLUSION

The integration of IoT-enabled biosensors approach to addressing global challenges posed by chronic diseases. This study highlights the potential of this convergence in healthcare by facilitating real-time monitoring, early detection, and personalized care. By surpassing limitations of traditional monitoring systems, IoT-enabled biosensors provide continuous insights into patients' health, enabling proactive interventions. Their applications are demonstrated in diverse domains, including remote monitoring, cardiac health, glucose management, and elderly care, showcasing their role in advancing precision medicine and improving patient outcomes. Despite technical hurdles, ongoing advancements in miniaturization, edge computing, and AI-driven analytics aim to enhance accuracy, efficiency, and ethical practices, paving the way for a proactive and patient-centric healthcare era.

Metabolic impact of residual C-peptide secretion in type 1 diabetes mellitus

Objective

This study aimed to evaluate the association of detectable C-peptide levels with various continuous glucose monitoring (CGM) metrics and diabetes complications in patients with type 1 diabetes mellitus (T1DM).

Subjects and methods

Retrospective, descriptive study of 112 patients with T1DM undergoing intensive insulin therapy, categorized according to fasting C-peptide level into undetectable (<0.05 ng/mL) and detectable (≥0.05 ng/mL) groups.

Results

The patients' median age at diagnosis was 22 (12-34) years and the median T1DM duration was 18.5 (12-29) years. Patients with detectable versus undetectable C-peptide levels were older (27.5 [16.5-38.5] versus 17.5 [9.8-28.8] years, respectively, p = 0.002) and had shorter disease duration (14 [9-24] versus 20 [14-32] years, respectively, p = 0.004). After adjustment for covariates (sex, disease duration, body mass index, and use of continuous subcutaneous insulin infusion), detectable C-peptide level was associated with lower time above range (TAR; aβ -11.03, p = 0.002), glucose management indicator (GMI, aβ -0.55, p = 0.024), and average glucose (aβ -14.48, p = 0.045) and HbA1c (aβ -0.41, p = 0.035) levels. Patients with detectable versus those with undetectable C-peptide level had significantly higher time in range (TIR) before (β = 7.13, p = 0.044) and after (aβ = 11.42, p = 0.001) adjustments. Detectable C-peptide level was not associated with lower time below range (TBR), coefficient of variation (CV), or prevalence of chronic microvascular and macrovascular complications.

Conclusions

Persistent C-peptide secretion in patients with T1DM was associated with significantly better metabolic control reflected by different glucose metrics, namely, TIR, TAR, GMI, and HbA1c.

Management of diabetes mellitus patients with sickle cell anemia: Challenges and therapeutic approaches

The coexistence of diabetes mellitus (DM) and sickle cell anemia (SCA) poses significant challenges in clinical management due to the complex interactions and overlapping complications associated with both conditions. Managing diabetes in individuals with SCA requires a comprehensive approach that addresses the unique physiological and pathological aspects of both diseases. This paper reviews the challenges encountered in the management of DM in patients with SCA and explores therapeutic strategies and approaches to optimize patient care. Challenges in the management of DM in individuals with SCA stem from several factors, including the impact of hemoglobin variants on glycemic control assessment, increased susceptibility to infections, altered immune response, and complications associated with both diseases. Moreover, the coexistence of SCA and DM heightens the susceptibility to infections due to compromised immune function, emphasizing the need for vigilant preventive measures, including vaccinations and close monitoring for infectious complications. Close collaboration among healthcare providers specializing in diabetes, hematology, and other relevant fields is crucial for developing comprehensive care plans. Individualized treatment strategies that balance glycemic control, pain management, and preventive care are essential to mitigate complications and optimize the overall health outcomes of patients with both DM and SCA. In conclusion, managing diabetes in the context of SCA necessitates a nuanced and patient-centered approach. By addressing the challenges and employing tailored therapeutic strategies, healthcare providers can improve the quality of life and health outcomes for individuals affected by both conditions.

Blood glucose monitoring devices for type 1 diabetes: a journey from the food and drug administration approval to market availability

Blood glucose monitoring constitutes a pivotal element in the clinical management of Type 1 diabetes (T1D), a globally escalating metabolic disorder. Continuous glucose monitoring (CGM) devices have demonstrated efficacy in optimizing glycemic control, mitigating adverse health outcomes, and augmenting the overall quality of life for individuals afflicted with T1D. Recent progress in the field encompasses the refinement of electrochemical sensors, which enhances the effectiveness of blood glucose monitoring. This progress empowers patients to assume greater control over their health, alleviating the burdens associated with their condition, and contributing to the overall alleviation of the healthcare system. The introduction of novel medical devices, whether derived from existing prototypes or originating as innovative creations, necessitates adherence to a rigorous approval process regulated by the Food and Drug Administration (FDA). Diverse device classifications, stratified by their associated risks, dictate distinct approval pathways, each characterized by varying timelines. This review underscores recent advancements in blood glucose monitoring devices primarily based on electrochemical sensors and elucidates their regulatory journey towards FDA approval. The advent of innovative, non-invasive blood glucose monitoring devices holds promise for maintaining stringent glycemic control, thereby preventing T1D-associated comorbidities, and extending the life expectancy of affected individuals.

Wireless Battery-free and Fully Implantable Organ Interfaces

Advances in soft materials, miniaturized electronics, sensors, stimulators, radios, and battery-free power supplies are resulting in a new generation of fully implantable organ interfaces that leverage volumetric reduction and soft mechanics by eliminating electrochemical power storage. This device class offers the ability to provide high-fidelity readouts of physiological processes, enables stimulation, and allows control over organs to realize new therapeutic and diagnostic paradigms. Driven by seamless integration with connected infrastructure, these devices enable personalized digital medicine. Key to advances are carefully designed material, electrophysical, electrochemical, and electromagnetic systems that form implantables with mechanical properties closely matched to the target organ to deliver functionality that supports high-fidelity sensors and stimulators. The elimination of electrochemical power supplies enables control over device operation, anywhere from acute, to lifetimes matching the target subject with physical dimensions that supports imperceptible operation. This review provides a comprehensive overview of the basic building blocks of battery-free organ interfaces and related topics such as implantation, delivery, sterilization, and user acceptance. State of the art examples categorized by organ system and an outlook of interconnection and advanced strategies for computation leveraging the consistent power influx to elevate functionality of this device class over current battery-powered strategies is highlighted.

Recent Development of Implantable Chemical Sensors Utilizing Flexible and Biodegradable Materials for Biomedical Applications

Implantable chemical sensors built with flexible and biodegradable materials exhibit immense potential for seamless integration with biological systems by matching the mechanical properties of soft tissues and eliminating device retraction procedures. Compared with conventional hospital-based blood tests, implantable chemical sensors have the capability to achieve real-time monitoring with high accuracy of important biomarkers such as metabolites, neurotransmitters, and proteins, offering valuable insights for clinical applications. These innovative sensors could provide essential information for preventive diagnosis and effective intervention. To date, despite extensive research on flexible and bioresorbable materials for implantable electronics, the development of chemical sensors has faced several challenges related to materials and device design, resulting in only a limited number of successful accomplishments. This review highlights recent advancements in implantable chemical sensors based on flexible and biodegradable materials, encompassing their sensing strategies, materials strategies, and geometric configurations. The following discussions focus on demonstrated detection of various objects including ions, small molecules, and a few examples of macromolecules using flexible and/or bioresorbable implantable chemical sensors. Finally, we will present current challenges and explore potential future directions.

High-accuracy noninvasive continuous glucose monitoring using OCT angiography-purified blood scattering signals in human skin

The accuracy of noninvasive continuous glucose monitoring (CGM) through near-infrared scattering is challenged by mixed scattering signals from different compartments, where glucose has a positive correlation with a blood scattering coefficient but a negative correlation with a tissue scattering coefficient. In this study, we developed a high-accuracy noninvasive CGM based on OCT angiography (OCTA)-purified blood scattering signals. The blood optical scattering coefficient (BOC) was initially extracted from the depth attenuation of backscattered light in OCT and then purified by eliminating the scattering signals from the surrounding tissues under the guidance of a 3D OCTA vascular map in human skin. The purified BOC was used to estimate the optical blood glucose concentration (BGC) through a linear calibration. The optical and reference BGC measurements were highly correlated (R = 0.94) without apparent time delay. The mean absolute relative difference was 6.09%. All optical BGC measurements were within the clinically acceptable Zones A + B, with 96.69% falling in Zone A on Parke's error grids. The blood glucose response during OGTT was mapped with a high spatiotemporal resolution of the single vessel and 5 seconds. This noninvasive OCTA-based CGM shows promising accuracy for clinical use. Future research will involve larger sample sizes and diabetic participants to confirm these preliminary findings.

How online peer support affects management efficacy and mitigates difficulties of parents caring for children with type 1 diabetes

The proper management of pediatric type 1 diabetes depends substantially on the parents of affected children. Parental psychological traits specifically related to diabetes caregiving influence the quality of their care and management of the disease in their children. Parents often use Facebook groups as sources of peer support and information about diabetes care. The present study aimed to examine the influence of devices used in diabetes management, ascertain the impact of Facebook peer-support groups on parental perceptions of problems and probe parental self-efficacy. 318 parents of children with type 1 diabetes completed an online questionnaire on demographics, diabetes-related data, and diabetes care-related psychological characteristics. Data analysis revealed three variables that determined competence in diabetes management: the utility of information and suggestions offered in the Facebook groups on diabetes; the form of insulin administration and membership in Facebook groups. Our results underpin that peer-support groups on social media platforms can serve as sources of the necessary information, support, and feedback on diabetes management competence for parents of children living with type 1 diabetes, they may thus facilitate parental diabetes management capabilities.

Monitoring saliva compositions for non-invasive detection of diabetes using a colorimetric-based multiple sensor

The increasing population of diabetic patients, especially in developing countries, has posed a serious risk to the health sector, so that the lack of timely diagnosis and treatment process of diabetes can lead to threatening complications for the human lifestyle. Here, a multiple sensor was fabricated on a paper substrate for rapid detection and controlling the progress of the diabetes disease. The proposed sensor utilized the sensing ability of porphyrazines, pH-sensitive dyes and silver nanoparticles in order to detect the differences in saliva composition of diabetic and non-diabetic patients. A unique color map (sensor response) was obtained for each studied group, which can be monitored by a scanner. Moreover, a good correlation was observed between the colorimetric response resulting from the analysis of salivary composition and the fasting blood glucose (FBG) value measured by standard laboratory instruments. It was also possible to classify participants into two groups, including patients caused by diabetes and those were non-diabetic persons with a total accuracy of 88.9%. Statistical evaluations show that the multiple sensor can be employed as an effective and non-invasive device for continuous monitoring of diabetes, substantially in the elderly.

The role of continuous glucose monitoring in physical activity and nutrition management: perspectives on present and possible uses

PURPOSE

Continuous glucose monitoring (CGM) is on the rise as the prevalence of obesity and diabetes increases. This review aimed to explore the use of CGM and its potential novel applications in physical activity and nutrition management.

METHODS

We searched PubMed, Web of Science, and Wiley Online Library databases using the keywords 'continuous glucose monitor,' 'nutrition,' 'physical activity,' and 'numerical modeling.'

RESULTS

Continuous blood glucose measurement is useful for individuals with obesity and diabetes. Long-term blood glucose data allow for personalized planning of nutritional composition, meal timing, and physical activity type and intensity, as well as help prevent hypoglycemia and hyperglycemia. Thus, understanding the limitations of CGM is important for its effective use.

CONCLUSION

CGM systems are being increasingly used to monitor and identify appropriate blood glucose controlling interventions. Blood glucose level is influenced by various factors such as nutrient composition, meal timing, physical activity, circadian rhythm, and cortisol levels. Numerical modeling can be used to analyze the complex relationship between stress, sleep, nutrition, and physical activity, which affect blood glucose levels. In future, blood glucose, sleep, and stress data will be integrated to predict appropriate lifestyle levels for blood glucose management. This integrated approach improves glucose control and overall wellbeing, potentially reducing societal costs.

Flux-Type versus Concentration-Type Sensors in Transdermal Measurements

New transdermal biosensors measure analytes that diffuse from the bloodstream through the skin, making it important to reduce the system response time and understand measurement output. While highly customized models have been created for specific sensors, a generalized model for transdermal sensor systems is lacking. Here, a simple one-dimensional diffusion model was used to characterize the measurement system and classify biosensors as either flux types or concentration types. Results showed that flux-type sensors have significantly faster response times than concentration sensors. Furthermore, flux sensors do not measure concentration, but rather have an output measurement that is proportional to skin permeability. These findings should lead to an improved understanding of transdermal measurements and their relation to blood analyte concentration. In the realm of alcohol research, where the majority of commercially available sensors are flux types, our work advocates toward moving away from transdermal alcohol concentration as a metric, and instead suggests embracing transdermal alcohol flux as a more suitable alternative.

Stillbirth: prevention and supportive bereavement care

Around half of the two million stillbirths occurring worldwide each year are preventable. This review compiles the most up-to-date evidence to inform stillbirth prevention. Many general maternal health interventions also reduce the risk of stillbirth, for example, antenatal care attendance. This review focuses on specific aspects of care: glucose metabolism, targeted aspirin prophylaxis, clotting and immune disorders, sleep positions, fetal movement monitoring, and preconception and interconception health. In the past few years, covid-19 infection during pregnancy has emerged as a risk factor for stillbirth, particularly among women who were not vaccinated. Alongside prevention, efforts to address stillbirth must include provision of high quality, supportive, and compassionate bereavement care to improve parents' wellbeing. A growing body of evidence suggests beneficial effects for parents who received supportive care and were offered choices such as mode of birth and the option to see and hold their baby. Staff need support to be able to care for parents effectively, yet, studies consistently highlight the scarcity of specific bereavement care training for healthcare providers. Action is urgently needed and is possible. Action must be taken with the evidence available now, in healthcare settings with high or low resources, to reduce stillbirths and improve training and care.

Best Time of Day for Strength and Endurance Training to Improve Health and Performance? A Systematic Review with Meta-analysis

BACKGROUND

Current recommendations for physical exercise include information about the frequency, intensity, type, and duration of exercise. However, to date, there are no recommendations on what time of day one should exercise. The aim was to perform a systematic review with meta-analysis to investigate if the time of day of exercise training in intervention studies influences the degree of improvements in physical performance or health-related outcomes.

METHODS

The databases EMBASE, PubMed, Cochrane Library, and SPORTDiscus were searched from inception to January 2023. Eligibility criteria were that the studies conducted structured endurance and/or strength training with a minimum of two exercise sessions per week for at least 2 weeks and compared exercise training between at least two different times of the day using a randomized crossover or parallel group design.

RESULTS

From 14,125 screened articles, 26 articles were included in the systematic review of which seven were also included in the meta-analyses. Both the qualitative synthesis and the quantitative synthesis (i.e., meta-analysis) provide little evidence for or against the hypothesis that training at a specific time of day leads to more improvements in performance-related or health-related outcomes compared to other times. There was some evidence that there is a benefit when training and testing occur at the same time of day, mainly for performance-related outcomes. Overall, the risk of bias in most studies was high.

CONCLUSIONS

The current state of research provides evidence neither for nor against a specific time of the day being more beneficial, but provides evidence for larger effects when there is congruency between training and testing times. This review provides recommendations to improve the design and execution of future studies on this topic.

REGISTRATION

PROSPERO (CRD42021246468).

The Use of Continuous Glucose Monitors in Sport: Possible Applications and Considerations

This review discusses the potential value of tracking interstitial glucose with continuous glucose monitors (CGMs) in athletes, highlighting possible applications and important considerations in the collection and interpretation of interstitial glucose data. CGMs are sensors that provide real time, longitudinal tracking of interstitial glucose with a range of commercial monitors currently available. Recent advancements in CGM technology have led to the development of athlete-specific devices targeting glucose monitoring in sport. Although largely untested, the capacity of CGMs to capture the duration, magnitude, and frequency of interstitial glucose fluctuations every 1-15 min may present a unique opportunity to monitor fueling adequacy around competitive events and training sessions, with applications for applied research and sports nutrition practice. Indeed, manufacturers of athlete-specific devices market these products as a "fueling gauge," enabling athletes to "push their limits longer and get bigger gains." However, as glucose homeostasis is a complex phenomenon, extensive research is required to ascertain whether systemic glucose availability (estimated by CGM-derived interstitial glucose) has any meaning in relation to the intended purposes in sport. Whether CGMs will provide reliable and accurate information and enhance sports nutrition knowledge and practice is currently untested. Caveats around the use of CGMs include technical issues (dislodging of sensors during periods of surveillance, loss of data due to synchronization issues), practical issues (potential bans on their use in some sporting scenarios, expense), and challenges to the underpinning principles of data interpretation, which highlight the role of sports nutrition professionals to provide context and interpretation.

Experimental and probabilistic model validation of ultrasonic MEMS transceiver for blood glucose sensing

In contrast to traditional laboratory glucose monitoring, recent developments have focused on blood glucose self-monitoring and providing patients with a self-monitoring device. This paper proposes a system based on ultrasound principles for quantifying glucose levels in blood by conducting an in-vitro experiment with goat blood before human blood. The ultrasonic transceiver is powered by a frequency generator that operates at 40 kHz and 1.6 V, and variations in glucose level affect the ultrasonic transceiver readings. The RVM probabilistic model is used to determine the variation in glucose levels in a blood sample. Blood glucose levels are measured simultaneously using a commercial glucose metre for confirmation. The experimental data values proposed are highly correlated with commercial glucose metre readings. The proposed ultrasonic MEMS-based blood glucometer measures a glucose level of [Formula: see text] mg/dl. In the near future, the miniature version of the experimental model may be useful to human society.

Continuous Glucose Monitoring in Primary Care: Understanding and Supporting Clinicians' Use to Enhance Diabetes Care

PURPOSE

Diabetes affects approximately 34 million Americans and many do not achieve glycemic targets. Continuous glucose monitoring (CGM) is associated with improved health outcomes for patients with diabetes. Most adults with diabetes receive care for their diabetes in primary care practices, where uptake of CGM is unclear.

METHODS

We used a cross-sectional web-based survey to assess CGM prescribing behaviors and resource needs among primary care clinicians across the United States. We used descriptive statistics and multivariable regression to identify characteristics associated with prescribing behaviors, openness to prescribing CGM, and to understand resources needed to support use of CGM in primary care.

RESULTS

Clinicians located more than 40 miles from the nearest endocrinologist's office were more likely to have prescribed CGM and reported greater likelihood to prescribe CGM in the future than those located within 10 miles of an endocrinologist. Clinicians who served more Medicare patients reported favorable attitudes toward future prescribing and higher confidence using CGM to manage diabetes than clinicians with lower Medicare patient volume. The most-needed resources to support CGM use in primary care were consultation on insurance issues and CGM training.

CONCLUSIONS

Primary care clinicians are interested in using CGM for patients with diabetes, but many lack the resources to implement use of this diabetes technology. Use of CGM can be supported with education in the form of workshops and consultation on insurance issues targeted toward residents, recent graduates, and practices without a nearby endocrinologist. Continued expansion of Medicare and Medicaid coverage for CGM can also support CGM use in primary care.

A Prospective Study on Continuous Glucose Monitoring in Glycogen Storage Disease Type Ia: Toward Glycemic Targets

CONTEXT

Although previous research has shown the benefit of continuous glucose monitoring (CGM) for hepatic glycogen storage diseases (GSDs), current lack of prospectively collected CGM metrics and glycemic targets for CGM-derived outcomes in the hepatic GSD population limits its use.

OBJECTIVE

To assess CGM metrics for glycemic variation and glycemic control in adult patients with GSDIa as compared to matched healthy volunteers.

DESIGN

Prospective CGM data were collected during the ENGLUPRO GSDIa trial (NCT04311307) in which a Dexcom G6 device was used. Ten adult patients with GSDIa and 10 age-, sex- and body mass index-matched healthy volunteers were enrolled. Capillary blood glucose was concurrently measured during 2 standardized 2-hour time intervals. Descriptive [eg, glycemic variability (GV), time below range, time in range (TIR), time above range (TAR)] and advanced (ie, first- and second-order derivatives, Fourier analysis) CGM outcomes were calculated. For each descriptive CGM outcome measure, 95% CIs were computed in patients with GSDIa and healthy volunteers, respectively.

RESULTS

CGM overestimation was higher under preprandial and level 1 hypoglycemia (ie, capillary glucose values ≥ 3.0 mmol/L and < 3.9 mmol/L) conditions. GV and TAR were higher while TIR was lower in patients with GSDIa compared to healthy volunteers (P < 0.05). Three patients with GSDIa showed descriptive CGM outcomes outside the calculated 95% CI in GSDIa patients. Advanced CGM analysis revealed a distinct pattern (ie, first- and second-order derivatives and glucose curve amplitude) in each of these 3 patients within the patients group.

CONCLUSIONS

This is the first study to prospectively compare CGM outcomes between adult patients with GSDIa and matched healthy volunteers. The generation of a set of CGM metrics will provide guidance in using and interpreting CGM data in GSDIa and will be useful for the definition of glycemic targets for CGM in patients with GSDIa. Future studies should investigate the prognostic value of CGM outcomes and their major determinants in patients with GSDIa.

Smart Nanodevices for Point-of-Care Applications

Background::

While significant strides have been made to avoid mortality during the treatment of chronic diseases, it is still one of the biggest health-care challenges that have a profound effect on humanity. The development of specific, sensitive, accurate, quick, low-cost, and easy-to-use diagnostic tools is therefore still in urgent demand. Nanodiagnostics is defined as the application of nanotechnology to medical diagnostics that can offer many unique opportunities for more successful and efficient diagnosis and treatment for infectious diseases.

Methods::

In this review we provide an overview of infectious disease using nanodiagnostics platforms based on nanoparticles, nanodevices for point-of-care (POC) applications.

Results::

Current state-of-the-art and most promising nanodiagnostics POC technologies, including miniaturized diagnostic tools, nanorobotics and drug delivery systems have been fully examined for the diagnosis of diseases. It also addresses the drawbacks, problems and potential developments of nanodiagnostics in POC applications for chronic diseases.

Conclusions::

While progress is gaining momentum in this field and many researchers have dedicated their time in developing new smart nanodevices for POC applications for various chronic diseases, the ultimate aim of achieving longterm, reliable and continuous patient monitoring has not yet been achieved. Moreover, the applicability of the manufactured nanodevices to rural patients for on-site diagnosis, cost, and usability are the crucial aspects that require more research, improvements, and potential testing stations. Therefore, more research is needed to develop the demonstrated smart nanodevices and upgrade their applicability to hospitals away from the laboratories.

Spectral Methods for Response Enhancement of Microwave Resonant Sensors in Continuous Non-Invasive Blood Glucose Monitoring

In this paper, the performance of three recent algorithms for the frequency-response enhancement of microwave resonant sensors are compared. The first one, a single-step algorithm, is based on a couple of direct-inverse Fourier transforms, giving a densely sampled response as a result. The second algorithm exploits an iterative procedure to progressively restricts the frequency response. The final one is based on the super-resolution MUSIC algorithm. The comparison is carried out through a Monte Carlo analysis. In particular, synthetic signals are firstly exploited to mimic the frequency response of a resonant microwave sensor. Then, experimental data collected from water-glucose solutions are adopted as validation test for potential applications in noninvasive blood-glucose monitoring.

Continuous Glucose Monitoring System Based on Percutaneous Microneedle Array

A continuous blood glucose monitoring system (CGMS) which include a microneedle-array blood glucose sensor, a circuit module, and a transmission module placed in a wearable device is developed in this research. When in use, the wearable device is attached to the human body with the microneedle array inserted under the skin for continuous blood glucose sensing, and the measured signals are transmitted wirelessly to a mobile phone or computer for analysis. The purpose of this study is to replace the conventionally used method of puncture for blood collection and test strips are used to measure the blood glucose signals. The microneedle sensor of this CGMS uses a 1 mm length needle in a 3 mm × 3 mm microneedle array for percutaneous minimally invasive blood glucose measurement. This size of microneedle does not cause bleeding damage to the body when used. The microneedle sensor is placed under the skin and their solutions are discussed. The blood glucose sensor measured the in vitro simulant fluid with a glucose concentration range of 50~400 mg/dL. In addition, a micro-transfer method is developed to accurately deposit the enzyme onto the tip of the microneedle, after which cyclic voltammetry (CV) is used to measure the glucose simulation solution to verify whether the difference in the amount of enzyme on each microneedle is less than 10%. Finally, various experiments and analyses are carried out to reduce the size of the device, test effective durability (approximately 7 days), and the feasibility of minimally invasive CGMS is evaluated by tests on two persons.

Skin Patchable Sensor Surveillance for Continuous Glucose Monitoring

Diabetes mellitus is a physiological and metabolic disorder affecting millions of people worldwide, associated with global morbidity, mortality, and financial expenses. Long-term complications can be avoided by frequent, continuous self-monitoring of blood glucose. Therefore, this review summarizes the current state-of-art glycemic control regimes involving measurement approaches and basic concepts. Following an introduction to the significance of continuous glucose sensing, we have tracked the evolution of glucose monitoring devices from minimally invasive to non-invasive methods to present an overview of the spectrum of continuous glucose monitoring (CGM) technologies. The conveniences, accuracy, and cost-effectiveness of the real-time CGM systems (rt-CGMs) are the factors considered for discussion. Transdermal biosensing and drug delivery routes have recently emerged as an innovative approach to substitute hypodermal needles. This work reviews skin-patchable glucose monitoring sensors for the first time, providing specifics of all the major findings in the past 6 years. Skin patch sensors and their progressive form, i.e., microneedle (MN) array sensory and delivery systems, are elaborated, covering self-powered, enzymatic, and non-enzymatic devices. The critical aspects reviewed are material design and assembly techniques focusing on flexibility, sensitivity, selectivity, biocompatibility, and user-end comfort. The review highlights the advantages of patchable MNs' multi-sensor technology designed to maintain precise blood glucose levels and administer diabetes drugs or insulin through a "sense and act" feedback loop. Subsequently, the limitations and potential challenges encountered from the MN array as rt-CGMs are listed. Furthermore, the current statuses of working prototype glucose-responsive "closed-loop" insulin delivery systems are discussed. Finally, the expected future developments and outlooks in clinical applications are discussed.

Impact of Custom Features of Do-it-yourself Artificial Pancreas Systems (DIYAPS) on Glycemic Outcomes of People with Type 1 Diabetes

One of the benefits of Do-it-yourself Artificial Pancreas Systems (DIYAPS) over commercially available systems is the high degree of customization possible through various features developed by the community. This paper investigates the impact of thirteen commonly used custom features on the glycemic outcomes of users with type 1 diabetes. Significant differences were observed in the group using the Automated Microbolus, Autotune (automatic), and the Superbolus feature. As many of the features aim to improve not only glycemic outcomes but also reduce the burden of managing diabetes on the user, future studies should investigate the impact of these features on the quality of life of their users.Clinical Relevance-This paper expands the existing knowledge on the DIYAPS for people with type 1 diabetes which have been gaining popularity among the patient population in recent years.

Noninvasive Glucose Monitor Using Dielectric Spectroscopy

OBJECTIVE

The Alertgy noninvasive continuous glucose monitor (ANICGM) is a novel wristband device that reports glucose levels without entailing skin puncture. This study evaluated the performance of the ANICGM compared to a Food and Drug Administration-approved glucose meter in patients with type 2 diabetes.

METHODS

The ANICGM device measures changes in the electromagnetic field generated by its sensor to produce a dielectric spectrum. The data contained within this spectrum are used in tandem with machine learning algorithms to estimate blood glucose levels. Values from the ANICGM were collected, sent to the Alertgy lab, formatted, and compared with fingerstick blood glucose levels, which were measured using the Accuchek Inform II glucometer. Fifteen patients completed three 120-minute sessions. The mean absolute relative difference (MARD) was calculated.

RESULTS

MARD values were compared between study days 2 and 3. The MARD for day 2 was 18.5% (95% CI, 12.8-42.2%), and the MARD for day 3 was 15.3% (95% CI, 12.3-18.4%). The difference in the MARD between days 2 and 3 was not statistically significant (P = .210).

CONCLUSION

The resulting MARDs suggest that further investigation into the use of dielectric spectroscopy for glucose monitoring should be explored.

A retrospective in-depth analysis of continuous glucose monitoring datasets for patients with hepatic glycogen storage disease: Recommended outcome parameters for glucose management

Continuous glucose monitoring (CGM) systems have great potential for real-time assessment of glycemic variation in patients with hepatic glycogen storage disease (GSD). However, detailed descriptions and in-depth analysis of CGM data from hepatic GSD patients during interventions are scarce. This is a retrospective in-depth analysis of CGM parameters, acquired in a continuous, real-time fashion describing glucose management in 15 individual GSD patients. CGM subsets are obtained both in-hospital and at home, upon nocturnal dietary intervention (n = 1), starch loads (n = 11) and treatment of GSD Ib patients with empagliflozin (n = 3). Descriptive CGM parameters, and parameters reflecting glycemic variation and glycemic control are considered useful CGM outcome parameters. Furthermore, the combination of first and second order derivatives, cumulative sum and Fourier analysis identified both subtle and sudden changes in glucose management; hence, aiding assessment of dietary and medical interventions. CGM data interpolation for nocturnal intervals reduced confounding by physical activity and diet. Based on these analyses, we conclude that in-depth CGM analysis can be a powerful tool to assess glucose management and optimize treatment in individual hepatic GSD patients.

Electrochemical Detection of Glucose Molecules Using Laser-Induced Graphene Sensors: A Review

This paper deals with recent progress in the use of laser-induced graphene sensors for the electrochemical detection of glucose molecules. The exponential increase in the exploitation of the laser induction technique to generate porous graphene from polymeric and other naturally occurring materials has provided a podium for researchers to fabricate flexible sensors with high dynamicity. These sensors have been employed largely for electrochemical applications due to their distinct advantages like high customization in their structural dimensions, enhanced characteristics and easy roll-to-roll production. These laser-induced graphene (LIG)-based sensors have been employed for a wide range of sensorial applications, including detection of ions at varying concentrations. Among the many pivotal electrochemical uses in the biomedical sector, the use of these prototypes to monitor the concentration of glucose molecules is constantly increasing due to the essentiality of the presence of these molecules at specific concentrations in the human body. This paper shows a categorical classification of the various uses of these sensors based on the type of materials involved in the fabrication of sensors. The first category constitutes examples where the electrodes have been functionalized with various forms of copper and other types of metallic nanomaterials. The second category includes other miscellaneous forms where the use of both pure and composite forms of LIG-based sensors has been shown. Finally, the paper concludes with some of the possible measures that can be taken to enhance the use of this technique to generate optimized sensing prototypes for a wider range of applications.

Combination of Nitric Oxide Release and Surface Texture for Mitigating the Foreign Body Response

The tissue response to polyurethane (PU)-coated implants employing active and/or passive FBR mitigation techniques was evaluated over a 28 day study in a diabetic swine model. Active FBR mitigation was achieved through the sustained release of nitric oxide (NO) from a mesoporous silica nanoparticle-doped PU coating. Passive FBR mitigation was achieved through the application of a foam- or fiber-based topcoat. These topcoats were designed to possess topographical features known to promote tissue integration with foam-coated implants having pore sizes of approximately 50 μm and fiber-coated implants consisting of fiber diameters of less than 1 μm. Nitric oxide-release profiles were minimally impacted by the presence of either topcoat. Inflammatory cell density and collagen density at the implant-tissue interface were assessed at 7, 14, 21, and 28 days following implantation. Nitric oxide-releasing implants had significantly lower inflammatory cell density and collagen density than non-NO-releasing controls. The presence of a topcoat did not significantly impact inflammatory cell density, though top-coated textured implants resulted in significantly lower collagen density, irrespective of NO release. Overall, coatings that combine NO release with surface texture demonstrated the greatest potential for tissue-based biomedical device applications.

Biosensing platform on ferrite magnetic nanoparticles: Synthesis, functionalization, mechanism and applications

Ferrite magnetic nanoparticles (FMNPs) are gaining popularity to design biosensors for high-performance clinical diagnosis. The fusion of information shows that FMNPs based biosensors require well-tuned FMNPs as detection probes to produce large and specific biological signals with minimal non-specific binding. Nevertheless, there is a noticeable lacuna of information to solve the issues related to suitable synthesis route, particle size reduction, functionalization, sensitivity towards targeted intercellular biological tiny particles, and lower signal-to-noise ratio. Therefore it allows exploring unique characteristics of FMNPs to design a suitable sensing device for intracellular measurements and diseases detection. This review focuses on the extensively used synthesis routes, their advantages and limitations, crystalline structure, functionalization, along with recent applications of FMNPs in biosensors, taking into consideration their analytical figures of merit and range of linearity. This work also addresses the current progress, key factors for sensitivity, selectivity and productivity improvement along with the challenges, future trends and perspectives of FMNPs based biosensors.

Adoption of Digital Health Technologies in the Practice of Behavioral Health: Qualitative Case Study of Glucose Monitoring Technology

Background

Evaluation of patients with serious mental illness (SMI) relies largely on patient or caregiver self-reported symptoms. New digital technologies are being developed to better quantify the longitudinal symptomology of patients with SMI and facilitate disease management. However, as these new technologies become more widely available, psychiatrists may be uncertain about how to integrate them into daily practice. To better understand how digital tools might be integrated into the treatment of patients with SMI, this study examines a case study of a successful technology adoption by physicians: endocrinologists’ adoption of digital glucometers.

Objective

This study aims to understand the key facilitators of and barriers to clinician and patient adoption of digital glucose monitoring technologies to identify lessons that may be applicable across other chronic diseases, including SMIs.

Methods

We conducted focus groups with practicing endocrinologists from 2 large metropolitan areas using a semistructured discussion guide designed to elicit perspectives of and experiences with technology adoption. The thematic analysis identified barriers to and facilitators of integrating digital glucometers into clinical practice. Participants also provided recommendations for integrating digital health technologies into clinical practice more broadly.

Results

A total of 10 endocrinologists were enrolled: 60% (6/10) male; a mean of 18.4 years in practice (SD 5.6); and 80% (8/10) working in a group practice setting. Participants stated that digital glucometers represented a significant change in the treatment paradigm for diabetes care and facilitated more effective care delivery and patient engagement. Barriers to the adoption of digital glucometers included lack of coverage, provider reimbursement, and data management support, as well as patient heterogeneity. Participant recommendations to increase the use of digital health technologies included expanding reimbursement for clinician time, streamlining data management processes, and customizing the technologies to patient needs.

Conclusions

Digital glucose monitoring technologies have facilitated more effective, individualized care delivery and have improved patient engagement and health outcomes. However, key challenges faced by the endocrinologists included lack of reimbursement for clinician time and nonstandardized data management across devices. Key recommendations that may be relevant for other diseases include improved data analytics to quickly and accurately synthesize data for patient care management, streamlined software, and standardized metrics.

Theoretical Simulation of the Near-Field Probe for Non-Invasive Measurements on Planar Layers with Biological Characteristics

The article presents the design of the near-field probe, which is a combined emitter (a combination of a symmetric dipole and an annular frame). The design of the probe allows forming a prolonged zone of the near-field. This effect can be used for in-depth penetration of the field in media with high absorption, without loss of information. Particular attention in this article is given to a detailed study of the interaction of the field created by this probe on plane-layered biological media. A theoretical analysis of the interaction of the electromagnetic field was carried out in a wide frequency band with a model plane-layer biological medium containing blood vessels of shallow depth using the proposed probe design. Conclusions are drawn about the depth of penetration of a useful signal into different media-analogs of biological tissue. This study is necessary to consider the possibility of using this probe for non-invasive measurements of blood glucose concentration. The studies were carried out using numerical simulation in the CST (Computer Simulation Technology) Microwave Studio environment. All biological tissues were simulated over a wide frequency range from 10 MHz to 10 GHz.

Paper-Based In-Situ Gold Nanoparticle Synthesis for Colorimetric, Non-Enzymatic Glucose Level Determination

Due to its properties, paper represents an alternative to perform point-of-care tests for colorimetric determination of glucose levels, providing simple, rapid, and inexpensive means of diagnosis. In this work, we report the development of a novel, rapid, disposable, inexpensive, enzyme-free, and colorimetric paper-based assay for glucose level determination. This sensing strategy is based on the synthesis of gold nanoparticles (AuNPs) by reduction of a gold salt precursor, in which glucose acts simultaneously as reducing and capping agent. This leads to a direct measurement of glucose without any enzymes or depending on the detection of intermediate products as in conventional enzymatic colorimetric methods. Firstly, we modelled the synthesis reaction of AuNPs to determine the optical, morphological, and kinetic properties and their manipulation for glucose sensing, by determining the influence of each of the reaction precursors towards the produced AuNPs, providing a guide for the manipulation of nucleation and growth. The adaptation of this synthesis into the developed paper platform was tested and calibrated using different standard solutions with physiological concentrations of glucose. The response of the colorimetric signals obtained with this paper-based platform showed a linear behavior until 20 mM, required for glycemic control in diabetes, using the Red × Value/Grey feature combination as a calibration metric, to describe the variations in color intensity and hue in the spot test zone. The colorimetric sensor revealed a detection limit of 0.65 mM, depending on calibration metric and sensitivity of 0.013 AU/mM for a linear sensitivity range from 1.25 to 20 mM, with high specificity for the determination of glucose in complex standards with other common reducing interferents and human serum.

Electrochemical glucose sensors in diabetes management: an updated review (2010-2020)

While over half a century has passed since the introduction of enzyme glucose biosensors by Clark and Lyons, this important field has continued to be the focus of immense research activity. Extensive efforts during the past decade have led to major scientific and technological innovations towards tight monitoring of diabetes. Such continued progress toward advanced continuous glucose monitoring platforms, either minimal- or non-invasive, holds considerable promise for addressing the limitations of finger-prick blood testing toward tracking glucose trends over time, optimal therapeutic interventions, and improving the life of diabetes patients. However, despite these major developments, the field of glucose biosensors is still facing major challenges. The scope of this review is to present the key scientific and technological advances in electrochemical glucose biosensing over the past decade (2010-present), along with current obstacles and prospects towards the ultimate goal of highly stable and reliable real-time minimally-invasive or non-invasive glucose monitoring. After an introduction to electrochemical glucose biosensors, we highlight recent progress based on using advanced nanomaterials at the electrode-enzyme interface of three generations of glucose sensors. Subsequently, we cover recent activity and challenges towards next-generation wearable non-invasive glucose monitoring devices based on innovative sensing principles, alternative body fluids, advanced flexible materials, and novel platforms. This is followed by highlighting the latest progress in the field of minimally-invasive continuous glucose monitoring (CGM) which offers real-time information about interstitial glucose levels, by focusing on the challenges toward developing biocompatible membrane coatings to protect electrochemical glucose sensors against surface biofouling. Subsequent sections cover new analytical concepts of self-powered glucose sensors, paper-based glucose sensing and multiplexed detection of diabetes-related biomarkers. Finally, we will cover the latest advances in commercially available devices along with the upcoming future technologies.

Resistance to data loss from the Freestyle Libre: impact on glucose variability indices and recommendations for data analysis

Like many wearables, flash glucose monitoring relies on user compliance and is subject to missing data. As recent research is beginning to utilise glucose technologies as behaviour change tools, it is important to understand whether missing data are tolerable. Complete Freestyle Libre data files were amputed to remove 1-6 h of data both at random and over mealtimes (breakfast, lunch, and dinner). Absolute percent errors (MAPE) and intraclass correlation coefficients (ICC) were calculated to evaluate agreement and reliability. Thirty-two (91%) participants provided at least 1 complete day (24 h) of data (age: 44.8 ± 8.6 years, female: 18 (56%); mean fasting glucose: 5.0 ± 0.6 mmol/L). Mean and continuous overall net glycaemic action (CONGA) (60 min) were robust to data loss (MAPE ≤3%). Larger errors were calculated for standard deviation, coefficient of variation (CV) and mean amplitude of glycaemic excursions (MAGE) at increasing missingness (MAPE: 2%-10%, 2%-9%, and 4%-18%, respectively). ICC decreased as missing data increased, with most indicating excellent reliability (>0.9) apart from certain MAGE ICCs, which indicated good reliability (0.84-0.9). Researchers and clinicians should be aware of the potential for larger errors when reporting standard deviation, CV, and MAGE at higher rates of data loss in nondiabetic populations. But where mean and CONGA are of interest, data loss is less of a concern. Novelty: As research now utilises flash glucose monitoring as behavioural change tools in nondiabetic populations, it is important to consider the influence of missing data. Glycaemic variability indices of mean and CONGA are robust to data loss, but standard deviation, CV, and MAGE are influenced at higher rates of missingness.

Trisk 95 as a novel skin mirror for normal and diabetic systemic glucose level

Developing trustworthy, cost effective, minimally or non-invasive glucose sensing strategies is of great need for diabetic patients. In this study, we used an experimental type I diabetic mouse model to examine whether the skin would provide novel means for identifying biomarkers associated with blood glucose level. We first showed that skin glucose levels are rapidly influenced by blood glucose concentrations. We then conducted a proteomic screen of murine skin using an experimental in vivo model of type I diabetes and wild-type controls. Among the proteins that increased expression in response to high blood glucose, Trisk 95 expression was significantly induced independently of insulin signalling. A luciferase reporter assay demonstrated that the induction of Trisk 95 expression occurs at a transcriptional level and is associated with a marked elevation in the Fluo-4AM signal, suggesting a role for intracellular calcium changes in the signalling cascade. Strikingly, these changes lead concurrently to fragmentation of the mitochondria. Moreover, Trisk 95 knockout abolishes both the calcium flux and the mitochondrial phenotype changes indicating dependency of glucose flux in the skin on Trisk 95 function. The data demonstrate that the skin reacts robustly to systemic blood changes, and that Trisk 95 is a promising biomarker for a glucose monitoring assembly.

Artificial Intelligence and Digital Tools: Future of Diabetes Care

Diabetes mellitus has become a global threat, especially in the emerging economies. In the United States, there are about 24 million people with diabetes mellitus. Diabetes represents a trove of physiologic and sociologic data that are only superficially understood by the health care system. Artificial intelligence can address many problems posed by the prevalence of diabetes mellitus and the impact of diabetes on individual and societal health. We provide a brief overview of artificial intelligence and discuss case studies that illustrate how artificial intelligence can enhance diabetes care.

Is it possible to constantly and accurately monitor blood sugar levels, in people with Type 1 diabetes, with a discrete device (non-invasive or invasive)?

Real-time continuous glucose monitors using subcutaneous needle-type sensors continue to develop. The limitations of currently available systems, however, include time lag behind changes in blood glucose, the invasive nature of such systems, and in some cases, their accuracy. Non-invasive techniques have been developed, but, to date, no commercial device has been successful. A key research priority for people with Type 1 diabetes identified by the James Lind Alliance was to identify ways of monitoring blood glucose constantly and accurately using a discrete device, invasive or non-invasive. Integration of such a sensor is important in the development of a closed-loop system and the technology must be rapid, selective and acceptable for continuous use by individuals. The present review provides an update on existing continuous glucose-sensing technologies, and an overview of emergent techniques, including their accuracy and limitations.

Review of Non-invasive Glucose Sensing Techniques: Optical, Electrical and Breath Acetone

Annual deaths in the U.S. attributed to diabetes are expected to increase from 280,210 in 2015 to 385,840 in 2030. The increase in the number of people affected by diabetes has made it one of the major public health challenges around the world. Better management of diabetes has the potential to decrease yearly medical costs and deaths associated with the disease. Non-invasive methods are in high demand to take the place of the traditional finger prick method as they can facilitate continuous glucose monitoring. Research groups have been trying for decades to develop functional commercial non-invasive glucose measurement devices. The challenges associated with non-invasive glucose monitoring are the many factors that contribute to inaccurate readings. We identify and address the experimental and physiological challenges and provide recommendations to pave the way for a systematic pathway to a solution. We have reviewed and categorized non-invasive glucose measurement methods based on: (1) the intrinsic properties of glucose, (2) blood/tissue properties and (3) breath acetone analysis. This approach highlights potential critical commonalities among the challenges that act as barriers to future progress. The focus here is on the pertinent physiological aspects, remaining challenges, recent advancements and the sensors that have reached acceptable clinical accuracy.

Real-Time Noninvasive Measurement of Glucose Concentration Using a Modified Hilbert Shaped Microwave Sensor

We developed a microwave glucose sensor based on the modified first-order Hilbert curve design and measured glucose concentration in aqueous solutions by using a real-time microwave near-field electromagnetic interaction technique. We observed S₂₁ transmission parameters of the sensor at resonant frequencies depend on the glucose concentration. We could determine the glucose concentration in the 0-250 mg/dL concentration range at an operating frequency of near 6 GHz. The measured minimum detectable signal was 0.0156 dB/(mg/dL) and the measured minimum detectable concentration was 1.92 mg/dL. The simulation result for the minimum detectable signal and the minimum detectable concentration was 0.0182 dB/(mg/dL) and 1.65 mg/dL, respectively. The temperature instability of the sensor for human glycemia in situ measurement range (27-34 °C for fingers and 36-40 °C for body temperature ranges) can be improved by the integration of the temperature sensor in the microwave stripline platform and the obtained data can be corrected during signal processing. The microwave signal-temperature dependence is almost linear with the same slope for a glucose concentration range of 50-150 mg/dL. The temperature correlation coefficient is 0.05 dB/°C and 0.15 dB/°C in 27-34 °C and 36-40 °C temperature range, respectively. The presented system has a cheap, easy fabrication process and has great potential for non-invasive glucose monitoring.