Continuous glucose monitoring: A review of the technology and clinical use

Advancements in electrochemical glucose sensors

The development of electrochemical glucose sensors with high sensitivity, specificity, and stability, enabling real-time continuous monitoring, has posed a significant challenge. However, an opportunity exists to fabricate electrochemical glucose biosensors with optimal performance through innovative device structures and surface modification materials. This paper provides a comprehensive review of recent advances in electrochemical glucose sensors. Novel classes of nanomaterials-including metal nanoparticles, carbon-based nanomaterials, and metal-organic frameworks-with excellent electronic conductivity and high specific surface areas, have increased the availability of reactive sites to improved contact with glucose molecules. Furthermore, in line with the trend in electrochemical glucose sensor development, research progress concerning their utilisation with sweat, tears, saliva, and interstitial fluid is described. To facilitate the commercialisation of these sensors, further enhancements in biocompatibility and stability are required. Finally, the characteristics of the ideal electrochemical glucose sensor are described and the developmental trends in this field are outlines.

Personalized management of hepatic glycogen storage disorders: The role of continuous glucose monitoring

OBJECTIVE

Glycogen storage disorders (GSD), inherent disorders of carbohydrate metabolism, feature hypoglycemia as a hallmark. Normoglycemia and glucose monitoring are pivotal in disease management. Conventional glucometer-based monitoring may overlook hypoglycemic trends. This study assesses glycemic control in Asian Indian GSD children using continuous glucose monitoring (CGM) and its role in facilitating dietary adjustments.

METHODS

A pre-post study enrolled molecularly confirmed GSDI, GSDIII, GSDVI, and GSDIX patients for baseline dietary compliance and CGM-based glycemic status evaluation. Hypoglycemic patients were stratified into diet-compliant and diet-noncompliant groups. Noncompliant patients received dietary reinforcement; compliant individuals underwent dietary adjustments. Repeat CGM (rCGM) was performed 6 weeks to 6 months postadjustments. Clinical and metabolic parameters were re-evaluated at 6 months.

RESULTS

Of the 20 patients assessed at baseline, 11 were diet compliant. Six among these exhibited hypoglycemia, prompting diet adjustments. Among nine noncompliant patients, eight experienced hypoglycemia and received diet reinforcement. rCGM in 10 patients (five GSDI, three GSDIII, and two GSDIXc) showed a significant reduction in hypoglycemia duration in all. An improvement in height and body mass index was observed in all GSDI and GSDIII patients. Triglyceride levels, raised at baseline in two GSDI and one GSDIII, showed a substantial decline in one GSDI patient. Hepatic transaminase levels decreased in both GSDIXc patients. Plasma lactate levels decreased in all GSDI patients.

CONCLUSION

CGM is an efficacious adjunct in the personalized management of hepatic GSD patients, in the Asian Indian population. The study also underscores the need for long-term follow-up to determine the role of glycemic management in growth, general well-being, and metabolic control in the GSD subtypes.

Management of Glucocorticoid-Induced Hyperglycemia in Cancer Patients: A Feasibility Study

Glucocorticoids are commonly used in the management of patients with hematological and solid malignancies. However, their use may be associated with impaired glycemic metabolism and increased treatment-related morbidity and mortality. This study aimed to examine the feasibility and acceptability of a nurse-led model of care (MOC) for screening and managing glucocorticoid-induced hyperglycemia (GIH) in non-diabetic patients requiring high-dose glucocorticoid (HDG) therapies, as well as patients' and health professionals' experiences with the MOC. This study was a single-site feasibility study. Patients with hematological or oncological malignancies who were >18 years of age, receiving a chemotherapy regimen including HDGs, had no prior diagnosis of diabetes or prediabetes, and were not at the end of life were considered eligible for this study. Participants were recruited from a district hospital's Cancer Centre in Australia. All consenting participants were screened for diabetes and were provided with a blood glucose meter to monitor their blood glucose levels (BGLs) four times a day on the days of glucocorticoid therapy (GT) plus one extra day following GT, for the first four cycles of their treatment, to screen for the presence of GIH. Feasibility and acceptability were assessed using rates of consent, study completion, and staff and patient surveys. Forty-eight percent (35/74) of patients approached consented to participate in the study and had screening tests for preexisting diabetes. None were diagnosed with diabetes. Six out of 35 patients withdrew, and 10/29 patients did not complete the recommended BGL monitoring. Thirteen percent (4/29) of patients developed GIH. The most common reasons for non-participation and study withdrawal were related to the self-monitoring of BGLs. While clinical stakeholders found the MOC feasible and acceptable, the results of this study suggest that alternative methods for encouraging self-monitoring of BGL and monitoring the presence of GIH during high-dose chemotherapy need to be explored to address issues associated with adherence and sustainability.

Health beyond biology: the extended health hypothesis and technology

There are ethical dilemmas faced by clinicians when responding to using unregistered medical devices, such as innovative internet technologies for managing type 1 diabetes mellitus. This chronic disease significantly impacts patients' health, requiring intensive daily activities like blood glucose monitoring, insulin injections, and specific dietary recommendations. Recent technological advances, including continuous glucose monitors and insulin pumps, have been shown to improve glycemic control. Di-it Yourself Artificial Pancreas Systems are emerging open-source automated delivery methods initiated by the diabetes community, although they are not clinically evaluated and present a liability challenge for healthcare providers. To use them or not? Should parents and healthcare providers use such technology that helps, but is not proven?Having all of that in mind, we argue that the World Health Organization's (WHO) definition of health is outdated, advocating for the "Extended Health Hypothesis". This hypothesis claims that health extends beyond traditional biological boundaries to include essential functional structures like diabetes-related technology, making technology a part of a patient's health. This view aligns with the "Extended Mind Hypothesis," suggesting that health should include elements beyond organic material if they are vital to a patient's functions.In the commentary, we highlight that both naturalist and normative conceptions of health support the extended health hypothesis, emphasizing that human health is not confined to organic material. This perspective raises critical questions about whether devices like insulin pumps and continuous glucose monitors are integral to a patient's health and whether their malfunction constitutes a form of disease. Devices are considered integral to health, there is no ethical dilemma in using unregistered medical devices for managing type 1 diabetes. Finally, we call for reevaluating the definitions of health and patients, particularly for children with type 1 diabetes using advanced technologies. It asserts that the optimal use of such devices represents a new form of health, creating a health-device symbiosis that should be evaluated with the child's best interests in mind.

Continuous Biosensor Based on Particle Motion: How Does the Concentration Measurement Precision Depend on Time Scale?

Continuous biosensors measure concentration-time profiles of biomolecular substances in order to allow for comparisons of measurement data over long periods of time. To make meaningful comparisons of time-dependent data, it is essential to understand how measurement imprecision depends on the time interval between two evaluation points, as the applicable imprecision determines the significance of measured concentration differences. Here, we define a set of measurement imprecisions that relate to different sources of variation and different time scales, ranging from minutes to weeks, and study these using statistical analyses of measurement data. The methodology is exemplified for Biosensing by Particle Motion (BPM), a continuous, affinity-based sensing technology with single-particle and single-molecule resolution. The studied BPM sensor measures specific small molecules (glycoalkaloids) in an industrial food matrix (potato fruit juice). Measurements were performed over several months at two different locations, on nearly 50 sensor cartridges with in total more than 1000 fluid injections. Statistical analyses of the measured signals and concentrations show that the relative residuals are normally distributed, allowing extraction and comparisons of the proposed imprecision parameters. The results indicate that sensor noise is the most important source of variation followed by sample pretreatment. Variations caused by fluidic transport, changes of the sensor during use (drift), and variations due to different sensor cartridges and cartridge replacements appear to be small. The imprecision due to sensor noise is recorded over few-minute time scales and is attributed to stochastic fluctuations of the single-molecule measurement principle, false-positive signals in the signal processing, and nonspecific interactions. The developed methodology elucidates both time-dependent and time-independent factors in the measurement imprecision, providing essential knowledge for interpreting concentration-time profiles as well as for further development of continuous biosensing technologies.

Insulinoma Unmasked: A Continuous Glucose Monitoring-Fueled Journey

Insulinomas are rare functional neuroendocrine tumors that are usually indolent and small. Due to their rarity, there is often a delay in disease recognition and diagnosis, and small tumor size makes their localization challenging. Glucose monitoring and dietary modification with or without pharmacotherapy are crucial during diagnostics, and surgery is the only definite treatment. Continuous glucose monitoring (CGM) systems can be a valuable tool in managing insulinoma patients. We present three patients with confirmed endogenous hyperinsulinemic hypoglycemia undergoing tumor localization, medical treatment, and surgery while wearing a CGM system. By accurately depicting glucose fluctuations, CGM can help prevent hypoglycemia, decrease hypoglycemia unawareness, track hypoglycemia frequency, aid in medical therapy dose titration, and confirm a cure after surgery.

Nocturnal Hypoglycemia in the Era of Continuous Glucose Monitoring

Nocturnal hypoglycemia is a common acute complication of people with diabetes on insulin therapy. In particular, the inability to control glucose levels during sleep, the impact of external factors such as exercise, or alcohol and the influence of hormones are the main causes. Nocturnal hypoglycemia has several negative somatic, psychological, and social effects for people with diabetes, which are summarized in this article. With the advent of continuous glucose monitoring (CGM), it has been shown that the number of nocturnal hypoglycemic events was significantly underestimated when traditional blood glucose monitoring was used. The CGM can reduce the number of nocturnal hypoglycemia episodes with the help of alarms, trend arrows, and evaluation routines. In combination with CGM with an insulin pump and an algorithm, automatic glucose adjustment (AID) systems have their particular strength in nocturnal glucose regulation and the prevention of nocturnal hypoglycemia. Nevertheless, the problem of nocturnal hypoglycemia has not yet been solved completely with the technologies currently available. The CGM systems that use predictive models to warn of hypoglycemia, improved AID systems that recognize hypoglycemia patterns even better, and the increasing integration of artificial intelligence methods are promising approaches in the future to significantly minimize the risk of a side effect of insulin therapy that is burdensome for people with diabetes.

Toward Realizing the Promise of AI in Precision Health Across the Spectrum of Care

Significant progress has been made in augmenting clinical decision-making using artificial intelligence (AI) in the context of secondary and tertiary care at large academic medical centers. For such innovations to have an impact across the spectrum of care, additional challenges must be addressed, including inconsistent use of preventative care and gaps in chronic care management. The integration of additional data, including genomics and data from wearables, could prove critical in addressing these gaps, but technical, legal, and ethical challenges arise. On the technical side, approaches for integrating complex and messy data are needed. Data and design imperfections like selection bias, missing data, and confounding must be addressed. In terms of legal and ethical challenges, while AI has the potential to aid in leveraging patient data to make clinical care decisions, we also risk exacerbating existing disparities. Organizations implementing AI solutions must carefully consider how they can improve care for all and reduce inequities.

Wearable Devices Beyond Activity Trackers in Youth With Obesity: Summary of Options

Background: Current treatment protocols to prevent and treat pediatric obesity focus on prescriptive lifestyle interventions. However, treatment outcomes are modest due to poor adherence and heterogeneity in responses. Wearable technologies offer a unique solution as they provide real-time biofeedback that could improve adherence to and sustainability of lifestyle interventions. To date, all reviews on wearable devices in pediatric obesity cohorts have only explored biofeedback from physical activity trackers. Hence, we conducted a scoping review to (1) catalog other biofeedback wearable devices available in this cohort, (2) document various metrics collected from these devices, and (3) assess safety and adherence to these devices. Methods: This scoping review was conducted adhering to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews checklist. Fifteen eligible studies examined the use of biofeedback wearable devices beyond activity trackers in pediatric cohorts, with an emphasis on feasibility of these devices. Results: Included studies varied in sample sizes (15-203) and in ages 6-21 years. Wearable devices are being used to capture various metrics of multicomponent weight loss interventions to provide more insights about glycemic variability, cardiometabolic function, sleep, nutrition, and body fat percentage. High safety and adherence rates were reported among these devices. Conclusions: Available evidence suggests that wearable devices have several applications aside from activity tracking, which could modify health behaviors through real-time biofeedback. Overall, these devices appear to be safe and feasible so as to be employed in various settings in the pediatric age group to prevent and treat obesity.

Continuous glucose monitoring metrics in pregnancy with type 1 diabetes mellitus

Managing diabetes during pregnancy is challenging, given the significant risk it poses for both maternal and foetal health outcomes. While traditional methods involve capillary self-monitoring of blood glucose level monitoring and periodic HbA1c tests, the advent of continuous glucose monitoring (CGM) systems has revolutionized the approach. These devices offer a safe and reliable means of tracking glucose levels in real-time, benefiting both women with diabetes during pregnancy and the healthcare providers. Moreover, CGM systems have shown a low rate of side effects and high feasibility when used in pregnancies complicated by diabetes, especially when paired with continuous subcutaneous insulin infusion pump as hybrid closed loop device. Such a combined approach has been demonstrated to improve overall blood sugar control, lessen the occurrence of preeclampsia and neonatal hypoglycaemia, and minimize the duration of neonatal intensive care unit stays. This paper aims to offer a comprehensive evaluation of CGM metrics specifically tailored for pregnancies impacted by type 1 diabetes mellitus.

Engineering of graphene-based composites with hexagonal boron nitride and PEDOT:PSS for sensing applications

A unique nanomaterial has been developed for sweat analysis, including glucose level monitoring. Simple resusable low-cost sensors from composite materials based on graphene, hexagonal boron nitride, and conductive PEDOT:PSS (poly(3,4-ethylenedioxythiophene)polystyrene sulfonate) polymer have been developed and fabricated via 2D printing on flexible substrates. The sensors were tested as biosensors using different water-based solutions. A strong increase in the current response (several orders of magnitude) was observed for aqua vapors or glucose solution vapors. This property is associated with the sorption capacity of graphene synthesized in a volume of plasma jets and thus having many active centers on the surface. The structure and properties of graphene synthesized in a plasma are different from those of graphene created by other methods. As a result, the current response for a wearable sensor is 3-5 orders of magnitude higher for the reference blood glucose concentration range of 4-14 mM. It has been found that the most promising sensor with the highest response was fabricated based on the graphene:PEDOT:PSS composite. The graphene:h-BN:PEDOT:PSS (h-BN is hexagonal boron nitride) sensors demonstrated a longer response and the highest response after the functionalization of the sensors with a glucose oxidase enzyme. The reusable wearable graphene:PEDOT:PSS glucose sensors on a paper substrate demonstrated a current response of 10-10 to 10-5 A for an operating voltage of 0.5 V and glucose range of 4-10 mM.

Red blood cells as biomarkers and mediators in complications of diabetes mellitus: A review

Red blood cells (RBCs), traditionally recognized for their oxygen transport role, have garnered increasing attention for their significance as crucial contributors to the pathophysiology of diabetes mellitus. In this comprehensive review, we elucidate the multifaceted roles of RBCs as both biomarkers and mediators in diabetes mellitus. Amidst the intricate interplay of altered metabolic pathways and the diabetic milieu, RBCs manifest distinct alterations in their structure, function, and lifespan. The chronic exposure to hyperglycemia induces oxidative stress, leading to modifications in RBC physiology and membrane integrity. These modifications, including glycation of hemoglobin (HbA1c), establish RBCs as invaluable biomarkers for assessing glycemic control over extended periods. Moreover, RBCs serve as mediators in the progression of diabetic complications. Their involvement in vascular dysfunction, hemorheological changes, and inflammatory pathways contributes significantly to diabetic microangiopathy and associated complications. Exploring the therapeutic implications, this review addresses potential interventions targeting RBC abnormalities to ameliorate diabetic complications. In conclusion, comprehending the nuanced roles of RBCs as biomarkers and mediators in diabetes mellitus offers promising avenues for enhanced diagnostic precision, therapeutic interventions, and improved patient outcomes. This review consolidates the current understanding and emphasizes the imperative need for further research to harness the full potential of RBC-related insights in the realm of diabetes mellitus.

Continuous Monitoring Biosensing Mediated by Single-Molecule Plasmon-Enhanced Fluorescence in Complex Matrices

Continuous detection of critical markers directly at the point of interest and in undiluted biological fluids represents the next fundamental step in biosensing. The goal of realizing such a platform is utterly challenging because it requires a reversible biosensor that enables the tracking of pico- to nanomolar molecular concentrations over long time spans in a compact device. Here we describe a sensing method based on plasmon-enhanced fluorescence capable of single-molecule detection of unlabeled analyte by employing biofunctionalized gold nanoparticles. The very strong plasmon-enhanced fluorescence signals allow for single-molecule sensing in unaltered biological media, while the use of low-affinity interactions ensures the continuous tracking of increasing and decreasing analyte concentrations with picomolar sensitivity. We demonstrate the use of a sandwich assay for a DNA cancer marker with a limit of detection of picomolar and a time response of 10 min. The enhanced single-molecule signals will allow for miniaturization into a small and cheap platform with multiplexing capability for application in point-of-care diagnostics, monitoring of industrial processes, and safe keeping of the environment.

Real World Interstitial Glucose Profiles of a Large Cohort of Physically Active Men and Women

The use of continuous glucose monitors (CGMs) in individuals living without diabetes is increasing. The purpose of this study was to profile various CGM metrics around nutritional intake, sleep and exercise in a large cohort of physically active men and women living without any known metabolic disease diagnosis to better understand the normative glycemic response to these common stimuli. A total of 12,504 physically active adults (age 40 ± 11 years, BMI 23.8 ± 3.6 kg/m2; 23% self-identified as women) wore a real-time CGM (Abbott Libre Sense Sport Glucose Biosensor, Abbott, USA) and used a smartphone application (Supersapiens Inc., Atlanta, GA, USA) to log meals, sleep and exercise activities. A total of >1 M exercise events and 274,344 meal events were analyzed. A majority of participants (85%) presented an overall (24 h) average glucose profile between 90 and 110 mg/dL, with the highest glucose levels associated with meals and exercise and the lowest glucose levels associated with sleep. Men had higher mean 24 h glucose levels than women (24 h-men: 100 ± 11 mg/dL, women: 96 ± 10 mg/dL). During exercise, the % time above >140 mg/dL was 10.3 ± 16.7%, while the % time <70 mg/dL was 11.9 ± 11.6%, with the remaining % within the so-called glycemic tight target range (70-140 mg/dL). Average glycemia was also lower for females during exercise and sleep events (p < 0.001). Overall, we see small differences in glucose trends during activity and sleep in females as compared to males and higher levels of both TAR and TBR when these active individuals are undertaking or competing in endurance exercise training and/or competitive events.

Glycemic variability and hypoglycemia before and after Roux-en-Y Gastric Bypass and Sleeve Gastrectomy - A cohort study of females without diabetes

BACKGROUND

Roux-en-Y gastric bypass (RYGB) and sleeve gastrectomy (SG) lead to lower fasting glucose concentrations, but might cause higher glycemic variability (GV) and increased risk of hypoglycemia. However, it has been sparsely studied in patients without preoperative diabetes under normal living conditions.

OBJECTIVES

To study 24-hour interstitial glucose (IG) concentrations, GV, the occurrence of hypoglycemia and dietary intake before and after laparoscopic RYGB and SG in females without diabetes.

SETTING

Outpatient bariatric units at a community and a university hospital.

METHODS

Continuous glucose monitoring and open-ended food recording over 4 days in 4 study periods: at baseline, during the preoperative low-energy diet (LED) regimen, and at 6 and 12 months postoperatively.

RESULTS

Of 47 patients included at baseline, 83%, 81%, and 79% completed the remaining 3 study periods. The mean 24-hour IG concentration was similar during the preoperative LED regimen and after surgery and significantly lower compared to baseline in both surgical groups. GV was significantly increased 6 and 12 months after surgery compared to baseline. The self-reported carbohydrate intake was positively associated with GV after surgery. IG concentrations below 3.9 mmol/L were observed in 14/25 (56%) of RYGB- and 9/12 (75%) of SG-treated patients 12 months after surgery. About 70% of patients with low IG concentrations also reported hypoglycemic symptoms.

CONCLUSIONS

The lower IG concentration in combination with the higher GV after surgery, might create a lower margin to hypoglycemia. This could help explain the increased occurrence of hypoglycemic episodes after RYGB and SG.

Advancements in pharmacotherapy options for treating diabetes ‎in children and adolescents

INTRODUCTION

This study compares diabetes management between pediatric and adult patients and identifies treatment challenges and gaps.

AREAS COVERED

We searched PubMed and Clinicaltrails.gov databases for studies published from 2001 to 2023 on diabetes management in different age groups.

EXPERT OPINION

Research shows children have lower insulin sensitivity, clearance, and β cell function than adults. The US FDA only allows insulin, metformin, and liraglutide as antidiabetic medication options for children. However, some off-label drugs, like meglitinides, sulfonylureas, and alogliptin, have demonstrated positive results in treating certain types of diabetes caused by gene mutations. It's crucial to adopt personalized and precise approaches to managing diabetes in pediatrics, which vary from those used for adult patients. New studies support the classification of type 2 diabetes into several subtypes based on age, BMI, glycemia, homeostasis model estimates, varying insulin resistance, ‎different rates of complications‎, and islet ‎autoantibodies. With this insight, prevention, treatment, and precision medicine of diabetes might be changed. More research is necessary to assess the safety and efficacy of different antidiabetic drugs and improve diabetes treatment for children and adolescents.

Neural Networks With Gated Recurrent Units Reduce Glucose Forecasting Error Due to Changes in Sensor Location

BACKGROUND

Continuous glucose monitors (CGMs) have become important tools for providing estimates of glucose to patients with diabetes. Recently, neural networks (NNs) have become a common method for forecasting glucose values using data from CGMs. One method of forecasting glucose values is a time-delay feedforward (FF) NN, but a change in the CGM location on a participant can increase forecast error in a FF NN.

METHODS

In response, we examined a NN with gated recurrent units (GRUs) as a method of reducing forecast error due to changes in sensor location.

RESULTS

We observed that for 13 participants with type 2 diabetes wearing blinded CGMs on both arms for 12 weeks (FreeStyle Libre Pro-Abbott), GRU NNs did not produce significantly different errors in glucose prediction due to sensor location changes (P < .05).

CONCLUSION

We observe that GRU NNs can mitigate error in glucose prediction due to differences in CGM location.

Wearing the Lab: Advances and Challenges in Skin-Interfaced Systems for Continuous Biochemical Sensing

Continuous, on-demand, and, most importantly, contextual data regarding individual biomarker concentrations exemplify the holy grail for personalized health and performance monitoring. This is well-illustrated for continuous glucose monitoring, which has drastically improved outcomes and quality of life for diabetic patients over the past 2 decades. Recent advances in wearable biosensing technologies (biorecognition elements, transduction mechanisms, materials, and integration schemes) have begun to make monitoring of other clinically relevant analytes a reality via minimally invasive skin-interfaced devices. However, several challenges concerning sensitivity, specificity, calibration, sensor longevity, and overall device lifetime must be addressed before these systems can be made commercially viable. In this chapter, a logical framework for developing a wearable skin-interfaced device for a desired application is proposed with careful consideration of the feasibility of monitoring certain analytes in sweat and interstitial fluid and the current development of the tools available to do so. Specifically, we focus on recent advancements in the engineering of biorecognition elements, the development of more robust signal transduction mechanisms, and novel integration schemes that allow for continuous quantitative analysis. Furthermore, we highlight the most compelling and promising prospects in the field of wearable biosensing and the challenges that remain in translating these technologies into useful products for disease management and for optimizing human performance.

Effectiveness, safety and costs of the FreeStyle Libre glucose monitoring system for children and adolescents with type 1 diabetes in Spain: a prospective, uncontrolled, pre-post study

OBJECTIVES

This study aimed to evaluate the effectiveness, safety and costs of FreeStyle Libre (FSL) glucose monitoring system for children and adolescents with type 1 diabetes mellitus (T1DM) in Spain.

DESIGN

Prospective, multicentre pre-post study.

SETTING

Thirteen Spanish public hospitals recruited patients from January 2019 to March 2020, with a 12-month follow-up.

PARTICIPANTS

156 patients were included.

PRIMARY AND SECONDARY OUTCOME MEASURES

Primary: glycated haemoglobin (HbA1c) change. Secondary: severe hypoglycaemic events (self-reported and clinical records), quality of life, diabetes treatment knowledge, treatment satisfaction, adverse events, adherence, sensor usage time and scans. Healthcare resource utilisation was assessed for cost analysis from the National Health System perspective, incorporating direct healthcare costs. Data analysis used mixed regression models with repeated measures. The intervention's total cost was estimated by multiplying health resource usage with unit costs.

RESULTS

In the whole sample, HbA1c increased significantly (0.32%; 95% CI 0.10% to 0.55%). In the subgroup with baseline HbA1c≥7.5% (n=88), there was a significant reduction at 3 months (-0.46%; 95% CI -0.69% to -0.23%), 6 months (-0.49%; 95% CI -0.73% to -0.25%) and 12 months (-0.43%; 95% CI -0.68% to -0.19%). Well-controlled patients had a significant 12-month worsening (0.32%; 95% CI 0.18% to 0.47%). Self-reported severe hypoglycaemia significantly decreased compared with the previous year for the whole sample (-0.37; 95% CI -0.62 to -0.11). Quality of life and diabetes treatment knowledge showed no significant differences, but satisfaction increased. Adolescents had lower sensor usage time and scans than children. Reduction in HbA1c was significantly associated with device adherence. No serious adverse effects were observed. Data suggest that use of FSL could reduce healthcare resource use (strips and lancets) and costs related to productivity loss.

CONCLUSIONS

The use of FSL in young patients with T1DM was associated with a significant reduction in severe hypoglycaemia, and improved HbA1c levels were seen in patients with poor baseline control. Findings suggest cost savings and productivity gains for caregivers. Causal evidence is limited due to the study design. Further research is needed to confirm results and assess risks, especially for patients with lower baseline HbA1c.

The Impact of Sex, Body Mass Index, Age, Exercise Type and Exercise Duration on Interstitial Glucose Levels during Exercise

The impact of age, sex and body mass index on interstitial glucose levels as measured via continuous glucose monitoring (CGM) during exercise in the healthy population is largely unexplored. We conducted a multivariable generalized estimating equation (GEE) analysis on CGM data (Dexcom G6, 10 days) collected from 119 healthy exercising individuals using CGM with the following specified covariates: age; sex; BMI; exercise type and duration. Females had lower postexercise glycemia as compared with males (92 ± 18 vs. 100 ± 20 mg/dL, p = 0.04) and a greater change in glycemia during exercise from pre- to postexercise (p = 0.001) or from pre-exercise to glucose nadir during exercise (p = 0.009). Younger individuals (i.e., <20 yrs) had higher glucose during exercise as compared with all other age groups (all p < 0.05) and less CGM data in the hypoglycemic range (<70 mg/dL) as compared with those aged 20-39 yrs (p < 0.05). Those who were underweight, based on body mass index (BMI: <18.5 kg/m2), had higher pre-exercise glycemia than the healthy BMI group (104 ± 20 vs. 97 ± 17 mg/dL, p = 0.02) but similar glucose levels after exercise. Resistance exercise was associated with less of a drop in glycemia as compared with aerobic or mixed forms of exercise (p = 0.008) and resulted in a lower percent of time in the hypoglycemic (p = 0.04) or hyperglycemic (glucose > 140 mg/dL) (p = 0.03) ranges. In summary, various factors such as age, sex and exercise type appear to have subtle but potentially important influence on CGM measurements during exercise in healthy individuals.

Impact of 8-hour time-limited eating on sleep in adolescents with obesity

STUDY OBJECTIVES

The relationship between time-limited eating (TLE) and sleep quality is a topic of growing interest in the field of chronobiology. Data in adult cohorts shows that TLE may improve sleep quality, but this has not been evaluated in adolescents. The aim of this secondary analysis was to (1) examine the impact of 8-hour TLE on sleep parameters in youth with obesity and (2) explore if there was any association between sleep patterns and glycemic profiles.

METHODS

Adolescents with obesity were randomized into one of three groups: 8-hour TLE (participants self-selected their eating window) + real-time continuous glucose monitor, 8-hour TLE + blinded continuous glucose monitor, or a prolonged eating window. In the primary analysis, it was found that participants in the real-time continuous glucose monitor group + 8-hour TLE group did not access their continuous glucose monitor data and thus for this analysis the two TLE groups were combined and only completers who had available Pittsburgh Sleep Quality Index (PSQI) data at all three time points were included. Participants completed the PSQI at baseline, week 4, and week 12. Mixed-effects generalized linear regression models were utilized to examine the change in PSQI score and assess association between glycemic variability and PSQI total score overtime by intervention arm.

RESULTS

The median PSQI total score for the TLE groups (n = 27) was 6 at week 0 (interquartile range = 5 to 10) and 5 at week 12 (interquartile range = 2 to 7). There was no significant difference in the change in total PSQI score or sleep latency between TLE and control over the study period (P > .05). There was no association between PSQI score and change in weight or glycemic profile between groups (all P values > 0.05).

CONCLUSIONS

These results suggest that in adolescents with obesity, an 8-hour TLE approach did not negatively impact sleep quality or efficiency when compared to a prolonged eating window. The potential effects of TLE on sleep should be further investigated in larger randomized trials.

CITATION

Jayakumr A, Gillett ES, Wee CP, Kim A, Vidmar AP. Impact of 8-hour time-limited eating on sleep in adolescents with obesity. J Clin Sleep Med. 2023;19(11):1941-1949.

Management of Glucose-Lowering Therapy in Older Adults with Type 2 Diabetes: Challenges and Opportunities

The population of older adults (≥65 years) with type 2 diabetes mellitus (T2DM) is diverse, encompassing individuals with varying functional capabilities, living arrangements, concomitant medical conditions, and life expectancies. Hence, their categorization into different patient profiles (ie, good health, intermediate health, poor health) may aid in clinical decision-making when establishing glycemic goals and pharmacological treatment strategies. Further granularity in assessing each patient profile through interdisciplinary collaboration may also add precision to therapeutic and monitoring decisions. In this review, we discuss with a multidisciplinary approach how to deliver the best benefit from advanced diabetes therapies and technologies to older adults with T2DM according to each patient profile. There remain however several areas that deserve further research in older adults with T2DM, including the efficacy and safety of continuous glucose monitoring and automated insulin delivery systems, the switch to once-weekly insulin, the effectiveness of multidisciplinary care models, and the use of supported telemedicine and remote blood glucose monitoring in the oldest-old (≥85 years) who particularly require the assistance of others.

A Markov Model of Gap Occurrence in Continuous Glucose Monitoring Data for Realistic in Silico Clinical Trials

BACKGROUND AND OBJECTIVE

Continuous glucose monitoring (CGM) sensors measure interstitial glucose concentration every 1-5 min for days or weeks. New CGM-based diabetes therapies are often tested in in silico clinical trials (ISCTs) using diabetes simulators. Accurate models of CGM sensor inaccuracies and failures could help improve the realism of ISCTs. However, the modeling of CGM failures has not yet been fully addressed in the literature. This work aims to develop a mathematical model of CGM gaps, i.e., occasional portions of missing data generated by temporary sensor errors (e.g., excessive noise or artifacts).

METHODS

Two datasets containing CGM traces collected in 167 adults and 205 children, respectively, using the Dexcom G6 sensor (Dexcom Inc., San Diego, CA) were used. Four Markov models, of increasing complexity, were designed to describe three main characteristics: number of gaps for each sensor, gap distribution in the monitoring days, and gap duration. Each model was identified on a portion of each dataset (training set). The remaining portion of each dataset (real test set) was used to evaluate model performance through a Monte Carlo simulation approach. Each model was used to generate 100 simulated test sets with the same size as the real test set. The distributions of gap characteristics on the simulated test sets were compared with those observed on the real test set, using the two-sample Kolmogorov-Smirnov test and the Jensen-Shannon divergence.

RESULTS

A six-state Markov model, having two states to describe normal sensor operation and four states to describe gap occurrence, achieved the best results. For this model, the Kolmogorov-Smirnov test found no significant differences between the distribution of simulated and real gap characteristics. Moreover, this model obtained significantly lower Jensen-Shannon divergence values than the other models.

CONCLUSIONS

A Markov model describing CGM gaps was developed and validated on two real datasets. The model describes well the number of gaps for each sensor, the gap distribution over monitoring days, and the gap durations. Such a model can be integrated into existing diabetes simulators to realistically simulate CGM gaps in ISCTs and thus enable the development of more effective and robust diabetes management strategies.

Cyclodextrin Host-Guest Recognition in Glucose-Monitoring Sensors

Diabetes mellitus is a prevalent chronic health condition that has caused millions of deaths worldwide. Monitoring blood glucose levels is crucial in diabetes management, aiding in clinical decision making and reducing the incidence of hypoglycemic episodes, thereby decreasing morbidity and mortality rates. Despite advancements in glucose monitoring (GM), the development of noninvasive, rapid, accurate, sensitive, selective, and stable systems for continuous monitoring remains a challenge. Addressing these challenges is critical to improving the clinical utility of GM technologies in diabetes management. In this concept, cyclodextrins (CDs) can be instrumental in the development of GM systems due to their high supramolecular recognition capabilities based on the host-guest interaction. The introduction of CDs into GM systems not only impacts the sensitivity, selectivity, and detection limit of the monitoring process but also improves biocompatibility and stability. These findings motivated the current review to provide a comprehensive summary of CD-based blood glucose sensors and their chemistry of glucose detection, efficiency, and accuracy. We categorize CD-based sensors into four groups based on their modification strategies, including CD-modified boronic acid, CD-modified mediators, CD-modified nanoparticles, and CD-modified functionalized polymers. These findings shed light on the potential of CD-based sensors as a promising tool for continuous GM in diabetes mellitus management.

Patient-Reported Usefulness and Challenges in Using Hypoglycemia-Informing Features of Continuous Glucose Monitors to Manage Hypoglycemia

PURPOSE

The purpose of this study series, which involves a questionnaire survey and qualitative interviews, was to (a) evaluate patient-reported usefulness of continuous glucose monitor (CGM) hypoglycemia-informing features and (b) identify challenges in using these features (ie, CGM glucose numbers, trend arrows, trend graphs, and hypoglycemia alarms) during hypoglycemia in adults with type 1 diabetes (T1DM).

METHODS

A cross-sectional questionnaire survey study was conducted with adults who have T1DM and were using CGMs to assess the perceived usefulness of hypoglycemia-informing features. A semistructured interview study with T1DM CGM-using adults and inductive thematic analysis were subsequently performed to identify challenges in using CGM hypoglycemia-informing features to manage hypoglycemia.

RESULTS

In the survey study (N = 252), the CGM glucose numbers, trend arrows, trend graphs, and hypoglycemia alarms were found to be very useful by 79%, 70%, 43%, and 64% of participants, respectively. Several challenges in using these features to manage hypoglycemia were identified in the qualitative study (N = 23): (1) hypoglycemia information not fully reliable,; (2) unpredictability of future blood glucose levels, (3) lack of awareness about how information can be used, and (4) disruptions associated with information.

CONCLUSIONS

Although the majority of T1DM adults found their CGMs' hypoglycemia-informing features helpful, challenges in optimally using these features persisted. Targeted knowledge and behavioral interventions could improve CGM use to reduce hypoglycemia.

Insulin detection in diabetes mellitus: challenges and new prospects

Tremendous progress has been made towards achieving tight glycaemic control in individuals with diabetes mellitus through the use of frequent or continuous glucose measurements. However, in patients who require insulin, accurate dosing must consider multiple factors that affect insulin sensitivity and modulate insulin bolus needs. Accordingly, an urgent need exists for frequent and real-time insulin measurements to closely track the dynamic blood concentration of insulin during insulin therapy and guide optimal insulin dosing. Nevertheless, traditional centralized insulin testing cannot offer timely measurements, which are essential to achieving this goal. This Perspective discusses the advances and challenges in moving insulin assays from traditional laboratory-based assays to frequent and continuous measurements in decentralized (point-of-care and home) settings. Technologies that hold promise for insulin testing using disposable test strips, mobile systems and wearable real-time insulin-sensing devices are discussed. We also consider future prospects for continuous insulin monitoring and for fully integrated multisensor-guided closed-loop artificial pancreas systems.

ELSAH (electronic smart patch system for wireless monitoring of molecular biomarkers for healthcare and wellbeing): definition of possible use cases

The ELSAH (electronic smart patch system for wireless monitoring of molecular biomarkers for healthcare and wellbeing) project has received funding from EU's Horizon 2020 research and innovation program (grant agreement no. 825549). Its aim is to develop a wearable smart patch-based microneedle sensor system that can simultaneously measure several biomarkers in users' dermal interstitial fluid. This system could have several use cases based on continuous glucose and lactate monitoring: early detection of (pre-) diabetes mellitus, increasing physical performance through optimal carbohydrate intake, achieving a healthier lifestyle through behavioral changes based on the interpretation of glucose data, performance diagnostics (lactate threshold test), control of optimal training intensities corresponding with certain lactate levels, or warning of diseases/health threats, such as the metabolic syndrome or sepsis associated with increased lactate levels. The ELSAH patch system has a high potential of increasing health and wellbeing in users.

Insight into continuous glucose monitoring: from medical basics to commercialized devices

According to the latest statistics, more than 537 million people around the world struggle with diabetes and its adverse consequences. As well as acute risks of hypo- or hyper- glycemia, long-term vascular complications may occur, including coronary heart disease or stroke, as well as diabetic nephropathy leading to end-stage disease, neuropathy or retinopathy. Therefore, there is an urgent need to improve diabetes management to reduce the risk of complications but also to improve patient's quality life. The impact of continuous glucose monitoring (CGM) is well recognized, in this regard. The current review aims at introducing the basic principles of glucose sensing, including electrochemical and optical detection, summarizing CGM technology, its requirements, advantages, and disadvantages. The role of CGM systems in the clinical diagnostics/personal testing, difficulties in their utilization, and recommendations are also discussed. In the end, challenges and prospects in future CGM systems are discussed and non-invasive, wearable glucose biosensors are introduced. Though the scope of this review is CGMs and provides information about medical issues and analytical principles, consideration of broader use will be critical in future if the right systems are to be selected for effective diabetes management.

The Clinical Impact of Flash Glucose Monitoring-a Digital Health App and Smartwatch Technology in Patients With Type 2 Diabetes: Scoping Review

BACKGROUND

Type 2 diabetes has a growing prevalence and confers significant cost burden to the health care system, raising the urgent need for cost-effective and easily accessible solutions. The management of type 2 diabetes requires significant commitment from the patient, caregivers, and the treating team to optimize clinical outcomes and prevent complications. Technology and its implications for the management of type 2 diabetes is a nascent area of research. The impact of some of the more recent technological innovations in this space, such as continuous glucose monitoring, flash glucose monitoring, web-based applications, as well as smartphone- and smart watch-based interactive apps has received limited attention in the research literature.

OBJECTIVE

This scoping review aims to explore the literature available on type 2 diabetes, flash glucose monitoring, and digital health technology to improve diabetic clinical outcomes and inform future research in this area.

METHODS

A scoping review was undertaken by searching Ovid MEDLINE and CINAHL databases. A second search using all identified keywords and index terms was performed on Ovid MEDLINE (January 1966 to July 2021), EMBASE (January 1980 to July 2021), Cochrane Central Register of Controlled Trials (CENTRAL; the Cochrane Library, latest issue), CINAHL (from 1982), IEEE Xplore, ACM Digital Libraries, and Web of Science databases.

RESULTS

There were very few studies that have explored the use of mobile health and flash glucose monitoring in type 2 diabetes. These studies have explored somewhat disparate and limited areas of research, and there is a distinct lack of methodological rigor in this area of research. The 3 studies that met the inclusion criteria have addressed aspects of the proposed research question.

CONCLUSIONS

This scoping review has highlighted the lack of research in this area, raising the opportunity for further research in this area, focusing on the clinical impact and feasibility of the use of multiple technologies, including flash glucose monitoring in the management of patients with type 2 diabetes.

Reliability of Inpatient CGM: Comparison to Standard of Care

BACKGROUND

Optimal inpatient glycemic management targets a blood glucose (BG) of 140-180 mg/dL and is an important safety measure for hospitalized patients with hyperglycemia. Traditional barriers to appropriate insulin administration include incorrect timing of prandial insulin administration, failure to administer basal insulin to persons with insulin deficiency/type 1 diabetes mellitus (DM), and inaccurate insulin dosing or timing resulting in hypoglycemia. Given the ongoing rapid assimilation of technology to manage our patients with DM, we investigated the use of continuous glucose monitoring (CGM) in the inpatient setting as a potential solution to traditional barriers to optimal hyperglycemia management for inpatient care. In this study, we evaluated the efficacy of use of inpatient CGM for insulin dosing in comparison with current standard of care and whether CGM could aid in minimizing hypoglycemic events.

METHODS

This study evaluated the use of Abbott professional (blinded) Freestyle Libre CGMs in participants treated with basal bolus insulin administered with subcutaneous insulin (basal bolus therapy [BBT]: n = 20) or on intravenous insulin (IVI) infusions (n =16) compared with standard point of care (POC) BG measurements. All participants on IVI were admitted with a diagnosis of diabetic ketoacidosis (DKA). The CGM data was not available in real time. Sensors were removed at the time of discharge and data uploaded to Libre View. Continuous BG data were aggregated for each subject and matched to POC BG or lab chemistry values within five minutes. The POC BG results were assessed for comparability (CGM vs standard BG testing). Data were further analyzed for clinical decision-making for correction insulin.

RESULTS

The overall mean absolute relative difference including both IVI and BBT groups was 22.3% (SD, 9.0), with a median of 20.0%. By group, the IVI arm mean was 19.6% (SD, 9.4), with a median of 16.0%; for BBT, the arm mean was 24.6% (SD, 8.1), with a median 23.4%. Using the Wilcoxon two-sample test, the means were not different (P = .10), whereas the medians were (P = .015). The CGM consistently reported lower glucose values than POC BG in the majority of paired values (BBT arm mean difference = 44.8 mg/dL, IVI mean difference = 19.7 mg/dL). Glucose results were in agreement for the group 83% of the time with Bland-Altman Plot of Difference versus the mean of all glucometric data. Analysis of correction dose insulin using either CGM or POC BG values resulted in a negligible difference in calculated insulin dose recommended in those receiving subcutaneous insulin. Corrective doses were based on weight and insulin sensitivity (type 1 vs type 2 DM). Participants initially on IVI were included in a data set of BBT once IVI therapy ceased and basal bolus insulin regimen was started. The data of all basal bolus therapy participants with 1142 paired values of CGM versus POC glucose were used. The dosing difference was less for CGM than POC BG in the majority of paired values, and there was an absolute difference in dose of insulin of only 1.34 units. In the IVI group with 300 paired values of CGM versus POC glucose, there was an absolute difference in dose of insulin of only 0.74 units. About a third of the patients studied in the BBT arm experienced a hypoglycemic event with POC BG <70 mg/dL. If used in real time, CGM would have identified a hypoglycemic event for our patients on average 3 hours and 34 minutes before it was detected by standard POC BG. Two participants incurred severe nocturnal hypoglycemia during the study with POC BG <54 mg/dL with hypoglycemia detected on CGM up to 3 hours and 42 minutes before POC testing.

CONCLUSIONS

These results suggest that the use of inpatient CGM arrives at similar correction insulin dosing. The routine use of CGM for inpatients would consistently underestimate the BG compared with POC BG and could aid in minimizing and predicting hypoglycemia in the hospital setting. Our data support that the model of adoption of real-time inpatient CGM technology is anticipated to have significant impact in the clinical setting in efforts to maintain adequate glycemic control targeting BG 140-180 mg/dL while minimizing the frequency of hypoglycemic events.

Accuracy of Flash Glucose Monitoring in Hemodialysis Patients With and Without Diabetes Mellitus

AIMS

Glucose and insulin metabolism are altered in hemodialysis patients, and diabetes management is difficult in these patients. We aimed to validate flash glucose monitoring (FGM) in hemodialysis patients with and without diabetes mellitus as an attractive option for glucose monitoring not requiring regular self-punctures.

METHODS

We measured interstitial glucose using a FreeStyle Libre device in eight hemodialysis patients with and seven without diabetes mellitus over 14 days and compared the results to simultaneously performed self-monitoring of capillary blood glucose (SMBG).

RESULTS

In 720 paired measurements, mean flash glucose values were significantly lower than self-measured capillary values (6.17±2.52 vs. 7.15±2.41 mmol/L, p=1.3 E-86). Overall, the mean absolute relative difference was 17.4%, and the mean absolute difference was 1.20 mmol/L. The systematic error was significantly larger in patients without vs. with diabetes (- 1.17 vs. - 0.82 mmol/L) and on dialysis vs. interdialytic days (-1.09 vs. -0.90 mmol/L). Compared to venous blood glucose (72 paired measurements), the systematic error of FGM was even larger (5.89±2.44 mmol/L vs. 7.78±7.25 mmol/L, p=3.74E-22). Several strategies to reduce the systematic error were evaluated, including the addition of +1.0 mmol/L as a correction term to all FGM values, which significantly improved accuracy.

CONCLUSIONS

FGM systematically underestimates blood glucose in hemodialysis patients but, taking this systematic error into account, the system may be useful for glucose monitoring in hemodialysis patients with or without diabetes.

Correlation Between Hemoglobin Glycation Index Measured by Continuous Glucose Monitoring With Complications in Type 1 Diabetes

OBJECTIVE

HbA1C is the "gold standard" parameter to evaluate glycemic control in diabetes; however, its correlation with mean glucose is not always perfect. The objective of this study was to correlate continuous glucose monitoring (CGM)-derived hemoglobin glycation index (HGI) with microvascular complications.

METHODS

We conducted a cross-sectional study including permanent users of CGM with type 1 diabetes mellitus or latent autoimmune diabetes of the adult. HGI was estimated, and presence of microvascular complications was compared in subgroups with high or low HGI. A logistic regression analysis to assess the contribution of high HGI to chronic kidney disease (CKD) was performed.

RESULTS

In total, 52 participants who were aged 39.7 ± 14.7 years, with 73.1% women and 15.5 years (IQR, 7.5-29 years) since diagnosis, were included; 32.7% recorded diabetic retinopathy, 25% CKD, and 19.2% neuropathy. The median HbA1C was 7.6% (60 mmol/mol) and glucose management indicator (GMI) 7.0% (53 mmol/mol). The average HGI was 0.55% ± 0.66%. The measured HbA1C was higher in the group with high HGI (8.1% [65 mmol/mol] vs 6.9% [52 mmol/mol]; P < .001), whereas GMI (7.0% [53 mmol/mol] vs 7.0% [53 mmol/mol]; P = .495) and mean glucose were similar in both groups (153 mg/dL vs 153 mg/dL; P = .564). In the high HGI group, higher occurrence of CKD (P = .016) and neuropathy were observed (P = .025). High HGI was associated with increased risk of CKD (odds ratio [OR]: 5.05; 95% CI: 1.02-24.8; P = .04) after adjusting for time since diagnosis (OR: 1.09; 95% CI: 1.02-1.16; P = .008).

CONCLUSION

High HGI measured by CGM may be a useful marker for increased risk of microvascular diabetic complications.

Advancing Digital Solutions to Overcome Longstanding Barriers in Asthma and COPD Management

Maintenance therapy delivered via inhaler is central to asthma and chronic obstructive pulmonary disease (COPD) management. Poor adherence to inhaled medication and errors in inhalation technique have long represented major barriers to the optimal management of these chronic conditions. Technological innovations may provide a means of overcoming these barriers. This narrative review examines ongoing advances in digital technologies relevant to asthma and COPD with the potential to inform clinical decision-making and improve patient care. Digital inhaler devices linked to mobile apps can help bring about changes in patients' behaviors and attitudes towards disease management, particularly when they build in elements of interactivity and gamification. They can also support ongoing technique education, empowering patients and helping providers maximize the value of consultations and develop effective action plans informed by insights into the patient's inhaler use patterns and their respiratory health. When combined with innovative techniques such as machine learning, digital devices have the potential to predict exacerbations and prompt pre-emptive intervention. Finally, digital devices may support an advanced precision medicine approach to respiratory disease management and help support shared decision-making. Further work is needed to increase uptake of digital devices and integrate their use into care pathways before their full potential in personalized asthma and COPD management can be realized.

Continuous glucose monitoring assessment in patients suffering from anorexia nervosa reveals chronic prolonged mild hypoglycemia all over the nycthemeron

OBJECTIVE

Anorexia nervosa (AN) is an eating disorder characterised by voluntary dietary restriction leading to severe undernutrition. Hypoglycaemia is mostly described through severe case reports and is always evaluated by fasting or post-meal blood glucose, showing nothing about hypoglycaemia's length or duration. The interest of continuous interstitial glucose monitoring (CGM), largely used in diabetes mellitus, has never been evaluated in AN patients.

METHOD

Glycaemia cycles in AN patients were assessed using CGM over 5 days and then analysed according to food intake.

RESULTS

Mean glycaemia was within normal range. 91% of the patients presented with at least one episode with glycaemia under 70 mg/dl. Within the 24 h, the percentage of time spent with a glycaemia under 70 mg/dl was of 20.82 ± 3.90% with a maximum of 52%. We found 2.52 ± 0.33 hypoglycaemia events per 24 h, including 21.11 ± 3.76% at night. CGM parameters correlated with cortisol and IGF1 plasma levels. Comparison with estimated carbohydrate intakes discriminated concordant and non-concordant estimations depending on patient.

CONCLUSIONS

AN patients display chronic prolonged mild hypoglycaemia all over the nycthemeron despite normal fasting glycaemia. Associated adaptive increased counter-regulatory hormones might protect AN patients from deeper hypoglycaemia. CGM allowed testing food intake self-estimation reliability of AN patients and could be a very useful biofeedback tool.

Comparison of continuous glucose monitoring to reference standard oral glucose tolerance test for the detection of dysglycemia in cystic Fibrosis: A systematic review

Aims

Increasing evidence for benefit of early detection of cystic fibrosis related diabetes (CFRD) coupled with limitations of current diagnostic investigations has led to interest and utilisation of continuous glucose monitoring (CGM). We conducted a systematic review to assess current evidence on CGM compared to reference standard oral glucose tolerance test for the detection of dysglycemia in people with cystic fibrosis without confirmed diabetes.

Methods

MEDLINE, Embase, CENTRAL, Evidence-Based Medicine Reviews, grey literature and six relevant journals were searched for studies published after year 2000. Studies reporting contemporaneous CGM metrics and oral glucose tolerance test results were included. Outcomes on oral glucose tolerance tests were categorised into a) normal, b) abnormal (indeterminate and impaired) or c) diabetic as defined by American Diabetes Association criteria. CGM outcomes were defined as hyperglycemia (≥1 peak sensor glucose ≥ 200 mg/dL), dysglycemia (≥1 peak sensor glucose ≥ 140-199 mg/dL) or normoglycemia (all sensor glucose peaks < 140 mg/dL). CGM hyperglycemia in people with normal or abnormal glucose tolerances was used to define an arbitrary CGM-diagnosis of diabetes. The Quality Assessment of Diagnostic Accuracy Studies tool was used to assess risk of bias. Primary outcome was relative risk of an arbitrary CGM-diagnosis of diabetes compared to the oral glucose tolerance test.

Results

We identified 1277 publications, of which 19 studies were eligible comprising total of 416 individuals with contemporaneous CGM and oral glucose tolerance test results. Relative risk of an arbitrary CGM-diagnosis of diabetes compared to oral glucose tolerance test was 2.92. Studies analysed were highly heterogenous, prone to bias and inadequately assessed longitudinal associations between CGM and relevant disease-specific sequela.

Conclusions

A single reading > 200 mg/dL on CGM is not appropriate for the diagnosis of CFRD. Prospective studies correlating CGM metrics to disease-specific outcomes are needed to determine appropriate cut-points.

Assessment of Seasonal Stochastic Local Models for Glucose Prediction without Meal Size Information under Free-Living Conditions

Accurate blood glucose (BG) forecasting is key in diabetes management, as it allows preventive actions to mitigate harmful hypoglycemic/hyperglycemic episodes. Considering the encouraging results obtained by seasonal stochastic models in proof-of-concept studies, this work assesses the methodology in two datasets (open-loop and closed-loop) recorded in free-living conditions. First, similar postprandial glycemic profiles are grouped together with fuzzy C-means clustering. Then, a seasonal stochastic model is identified for each cluster. Finally, real-time BG forecasting is performed by weighting each model’s prediction. The proposed methodology (named C-SARIMA) is compared to other linear and nonlinear black-box methods: autoregressive integrated moving average (ARIMA), its variant with input (ARIMAX), a feed-forward neural network (NN), and its modified version (NN-X) fed by BG, insulin, and carbohydrates (timing and dosing) information for several prediction horizons (PHs). In the open-loop dataset, C-SARIMA grants a median root-mean-squared error (RMSE) of 20.13 mg/dL (PH = 30) and 27.23 mg/dL (PH = 45), not significantly different from ARIMA and NN. Over a longer PH, C-SARIMA achieves an RMSE = 31.96 mg/dL (PH = 60) and RMSE = 33.91 mg/dL (PH = 75), significantly outperforming the ARIMA and NN, without significant differences from the ARIMAX for PH ≥ 45 and the NN-X for PH ≥ 60. Similar results hold on the closed-loop dataset: for PH = 30 and 45 min, the C-SARIMA achieves an RMSE = 21.63 mg/dL and RMSE = 29.67 mg/dL, not significantly different from the ARIMA and NN. On longer PH, the C-SARIMA outperforms the ARIMA for PH > 45 and the NN for PH > 60 without significant differences from the ARIMAX for PH ≥ 45. Although using less input information, the C-SARIMA achieves similar performance to other prediction methods such as the ARIMAX and NN-X and outperforming the CGM-only approaches on PH > 45min.

Examining Sensor Agreement in Neural Network Blood Glucose Prediction

Successful measurements of interstitial glucose are a key component in providing effective care for patients with diabetes. Recently, there has been significant interest in using neural networks to forecast future glucose values from interstitial measurements collected by continuous glucose monitors (CGMs). While prediction accuracy continues to improve, in this work we investigated the effect of physiological sensor location on neural network blood glucose forecasting. We used clinical data from patients with Type 2 Diabetes who wore blinded FreeStyle Libre Pro CGMs (Abbott) on both their right and left arms continuously for 12 weeks. We trained patient-specific prediction algorithms to test the effect of sensor location on neural network forecasting (N = 13, Female = 6, Male = 7). In 10 of our 13 patients, we found at least one significant (P < .05) increase in forecasting error in algorithms which were tested with data taken from a different location than data which was used for training. These reported results were independent from other noticeable physiological differences between subjects (eg, height, age, weight, blood pressure) and independent from overall variance in the data. From these results we observe that CGM location can play a consequential role in neural network glucose prediction.

Continuous biomarker monitoring with single molecule resolution by measuring free particle motion

There is a need for sensing technologies that can continuously monitor concentration levels of critical biomolecules in applications such as patient care, fundamental biological research, biotechnology and food industry, as well as the environment. However, it is fundamentally difficult to develop measurement technologies that are not only sensitive and specific, but also allow monitoring over a broad concentration range and over long timespans. Here we describe a continuous biomolecular sensing methodology based on the free diffusion of biofunctionalized particles hovering over a sensor surface. The method records digital events due to single-molecule interactions and enables biomarker monitoring at picomolar to micromolar concentrations without consuming any reagents. We demonstrate the affinity-based sensing methodology for DNA-based sandwich and competition assays, and for an antibody-based cortisol assay. Additionally, the sensor can be dried, facilitating storage over weeks while maintaining its sensitivity. We foresee that this will enable the development of continuous monitoring sensors for applications in fundamental research, for studies on organs on a chip, for the monitoring of patients in critical care, and for the monitoring of industrial processes and bioreactors as well as ecological systems.

A Review on Newer Interventions for the Prevention of Diabetic Foot Disease

Diabetic foot disease (DFD), which includes ulcers on the foot, infections, and gangrene of the foot, is one of the leading causes of disability worldwide. About half of diabetic foot disease (DFD) patients have a recurrence in less than a year. To alleviate the burden of DFD globally, it is essential to give long-term medication to reduce the likelihood of recurrence. The effectiveness of telemedicine, wearable technologies, and sensors in DFD prevention is discussed in this review. Offloading footwear helps to cure and prevent ulcerated diabetic foot by distributing physical stress away from bony prominences. Sensors and wearables can record the temperatures of the foot, blood pressure (BP), and blood sugar levels and estimate lipid profile. These technologies have offered a practical means of reaching individuals in rural areas with a heightened risk of developing DFD. There is less need for in-person consultations with this strategy. This methodology is simple to operate and lessens reliance on patients. The benefits of adopting these remote monitoring approaches have been demonstrated in some studies with DFD-at-risk individuals. It is required to do more analysis to ascertain the effectiveness and value of incorporating different remote monitoring systems as part of an all-encompassing strategy to prevent DFD.

In Vitro Evaluation of Miniaturized Amperometric Enzyme Sensor Based on the Direct Electron Transfer Principle for Continuous Glucose Monitoring

BACKGROUND

The bacterial derived flavin adenine dinucleotide (FAD)-dependent glucose dehydrogenase (FADGDH) is the most promising enzyme for the third-generation principle-based enzyme sensor for continuous glucose monitoring (CGM). Due to the ability of the enzyme to transfer electrons directly to the electrode, recognized as direct electron transfer (DET)-type FADGDH, although no investigation has been reported about DET-type FADGDH employed on a miniaturized integrated electrode.

METHODS

The miniaturized integrated electrode was formed by sputtering gold (Au) onto a flexible film with 0.1 mm in thickness and divided into 3 parts. After an insulation layer was laminated, 3 openings for a working electrode, a counter electrode and a reference electrode were formed by dry etching. A reagent mix containing 1.2 × 10^-4 Unit of DET-type FADGDH and carbon particles was deposited. The long-term stability of sensor was evaluated by continuous operation, and its performance was also evaluated in the presence of acetaminophen and the change in oxygen partial pressure (pO₂) level.

RESULTS

The amperometric response of the sensor showed a linear response to glucose concentration up to 500 mg/dL without significant change of the response over an 11-day continuous measurement. Moreover, the effect of acetaminophen and pO₂ on the response were negligible.

CONCLUSIONS

These results indicate the superb potential of the DET-type FADGDH-based sensor with the combination of a miniaturized integrated electrode. Thus, the described miniaturized DET-type glucose sensor for CGM will be a promising tool for effective glycemic control. This will be further investigated using an in vivo study.

Patient-Tailored Decision Support System Improves Short- and Long-Term Glycemic Control in Type 2 Diabetes

BACKGROUND

The increasing prevalence of type 2 diabetes mellitus (T2D) and specialist shortage has caused a healthcare gap that can be bridged by a decision support system (DSS). We investigated whether a diabetes DSS can improve long- and/or short-term glycemic control.

METHODS

This is a retrospective observational cohort study of the Diabetiva program, which offered a patient-tailored DSS using Karlsburger Diabetes-Management System (KADIS) once a year. Glycemic control was analyzed at baseline and after 12 months in 452 individuals with T2D. Time in range (TIR; glucose 3.9-10 mmol/L) and Q-Score, a composite metric developed for analysis of continuous glucose profiles, were short-term and HbA1c long-term measures of glycemic control. Glucose variability (GV) was also measured.

RESULTS

At baseline, one-third of patients had good short- and long-term glycemic control. Q-Score identified insufficient short-term glycemic control in 17.9% of patients with HbA1c <6.5%, mainly due to hypoglycemia. GV and hyperglycemia were responsible in patients with HbA1c >7.5% and >8%, respectively. Application of DSS at baseline improved short- and long-term glycemic control, as shown by the reduced Q-Score, GV, and HbA1c after 12 months. Multiple regression demonstrated that the total effect on GV resulted from the single effects of all influential parameters.

CONCLUSIONS

DSS can improve short- and long-term glycemic control in individuals with T2D without increasing hypoglycemia. The Q-Score allows identification of individuals with insufficient glycemic control. An effective strategy for therapy optimization could be the selection of individuals with T2D most at need using the Q-Score, followed by offering patient-tailored DSS.

Machine Learning Models for Inpatient Glucose Prediction

PURPOSE OF REVIEW

Glucose management in the hospital is difficult due to non-static factors such as antihyperglycemic and steroid doses, renal function, infection, surgical status, and diet. Given these complex and dynamic factors, machine learning approaches can be leveraged for prediction of glucose trends in the hospital to mitigate and prevent suboptimal hypoglycemic and hyperglycemic outcomes. Our aim was to review the clinical evidence for the role of machine learning-based models in predicting hospitalized patients' glucose trajectory.

RECENT FINDINGS

The published literature on machine learning algorithms has varied in terms of population studied, outcomes of interest, and validation methods. There have been tools developed that utilize data from both continuous glucose monitors and large electronic health records (EHRs). With increasing sample sizes, inclusion of a greater number of predictor variables, and use of more advanced machine learning algorithms, there has been a trend in recent years towards increasing predictive accuracy for glycemic outcomes in the hospital setting. While current models predicting glucose trajectory offer promising results, they have not been tested prospectively in the clinical setting. Accurate machine learning algorithms have been developed and validated for prediction of hypoglycemia and hyperglycemia in the hospital. Further work is needed in implementation/integration of machine learning models into EHR systems, with prospective studies to evaluate effectiveness and safety of such clinical decision support on glycemic and other clinical outcomes.

Families' Experiences of Continuous Glucose Monitoring in the Management of Congenital Hyperinsulinism: A Thematic Analysis

Background and Aims

In patients with congenital hyperinsulinism (CHI), recurrent hypoglycaemia can lead to longstanding neurological impairments. At present, glycaemic monitoring is with intermittent fingerprick blood glucose testing but this lacks utility to identify patterns and misses hypoglycaemic episodes between tests. Although continuous glucose monitoring (CGM) is well established in type 1 diabetes, its use has only been described in small studies in patients with CHI. In such studies, medical perspectives have been provided without fully considering the views of families using CGM. In this qualitative study, we aimed to explore families' experiences of using CGM in order to inform future clinical strategies for the management of CHI.

Methods

Ten patients with CHI in a specialist centre used CGM for twelve weeks. All were invited to participate. Semi-structured interviews were conducted with nine families in whom patient ages ranged between two and seventeen years. Transcripts of the audio-recorded interviews were analysed using an inductive thematic analysis method.

Results

Analysis revealed five core themes: CGM's function as an educational tool; behavioural changes; positive experiences; negative experiences; and design improvements. Close monitoring and retrospective analysis of glucose trends allowed for enhanced understanding of factors that influenced glucose levels at various times of the day. Parents noted more hypoglycaemic episodes than previously encountered through fingerprick tests; this new knowledge prompted modification of daily routines to prevent and improve the management of hypoglycaemia. CGM use was viewed favourably as offering parental reassurance, reduced fingerprick tests and predictive warnings. However, families also reported unfavourable aspects of alarms and questionable accuracy at low glucose levels. Adolescents were frustrated by the short proximity range for data transmission resulting in the need to always carry a separate receiver. Overall, families were positive about the use of CGM but expected application to be tailored to their child's medical condition.

Conclusions

Patients and families with CHI using CGM noticed trends in glucose levels which motivated behavioural changes to reduce hypoglycaemia with advantages outweighing disadvantages. They expected CHI-specific modifications to enhance utility. Future design of CGM should incorporate end users' opinions and experiences for optimal glycaemic monitoring of CHI.

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.

Glucose Oxidase, an Enzyme "Ferrari": Its Structure, Function, Production and Properties in the Light of Various Industrial and Biotechnological Applications

Glucose oxidase (GOx) is an important oxidoreductase enzyme with many important roles in biological processes. It is considered an "ideal enzyme" and is often called an oxidase "Ferrari" because of its fast mechanism of action, high stability and specificity. Glucose oxidase catalyzes the oxidation of β-d-glucose to d-glucono-δ-lactone and hydrogen peroxide in the presence of molecular oxygen. d-glucono-δ-lactone is sequentially hydrolyzed by lactonase to d-gluconic acid, and the resulting hydrogen peroxide is hydrolyzed by catalase to oxygen and water. GOx is presently known to be produced only by fungi and insects. The current main industrial producers of glucose oxidase are Aspergillus and Penicillium. An important property of GOx is its antimicrobial effect against various pathogens and its use in many industrial and medical areas. The aim of this review is to summarize the structure, function, production strains and biophysical and biochemical properties of GOx in light of its various industrial, biotechnological and medical applications.