Continuous Glucose Monitoring Sensors for Diabetes Management: A Review of Technologies and Applications

Efficacy of telemedicine for persons with type 1 diabetes during Covid19 lockdown

BACKGROUND

Starting March 2020 the Italian Government imposed a lockdown to limit the spread of SARS-CoV-2. During lockdown outpatient visits were limited and telemedicine (TM) was encouraged.

METHODS

We retrospectively analyzed data from continuous or flash glucose monitoring systems shared through different cloud systems during the lockdown by subjects with type 1 diabetes and compared data obtained 4 weeks before and 4 weeks after structured telephonic visit. Variables considered were mean glucose, time spent in target (70-180 mg/dl), hypoglycemia (<70 mg/dl) and hyperglycemia (>180 mg/dl), coefficient of variation, and length of sensor use.

RESULTS

During the 4 weeks following the telephonic visit there was an improvement of glycemic control, with a significant reduction of mean glucose values (161.1 before vs 156.3 mg/dl after, p = 0.001), an increase of the time spent in target (63.6 vs 66.3, p = 0.0009) and a reduction of time spent in hyperglycemia (33.4 vs 30.5, p = 0.002). No changes were observed regarding glucose variability, time spent in hypoglycemia, and length of sensor use. Similar results were observed in subjects treated with multiple daily injections or continuous subcutaneous insulin infusion.

CONCLUSIONS

A structured telephonic visit appears to be an effective way to replace or integrate routine visits in particular conditions.

Digital health and diabetes: experience from India

The digitization of healthcare and its usage in the delivery of healthcare have experienced exponential growth across the world in recent times. India's fast-growing diabetes population has been exerting immense pressure on the country's healthcare infrastructure. Various innovative and evolving technologies are converging to impact the trajectory of digital health in diabetes. The diabetes community has been adopting various technologies such as connected glucose meters, continuous glucose monitoring systems, continuous subcutaneous insulin infusion, closed-loop systems, digitalization of health data, and diabetes-related apps for the prevention and management of the condition. India has provided some excellent examples in exploiting the potential of digital transformation in revamping the diabetes ecosystem. Yet, there are still various hurdles in technology development, healthcare delivery, as well as concerns related to data privacy, digital divide, policies by the government, role of stakeholders, attitude, and absorption by healthcare professionals, and hospitals. This article provides an overview of the digital diabetes technologies currently practiced in India and recommends the need for strong technology adaptation and policy interventions for an ideal roadmap of digitalization of diabetes care in the Indian milieu.

Time in range-A1c hemoglobin relationship in continuous glucose monitoring of type 1 diabetes: a real-world study

INTRODUCTION

The availability of easily accessible continuous glucose monitoring (CGM) metrics can improve glycemic control in diabetes, and they may even become a viable alternative to hemoglobin A1c (HbA1c) laboratory tests in the next years. The REALISM-T1D study (REAl-Life glucoSe Monitoring in Type 1 Diabetes) was aimed at contributing, with real-world data, to a deeper understanding of these metrics, including the time in range (TIR)-HbA1c relationship, to facilitate their adoption by diabetologists in everyday practice.

RESEARCH DESIGN AND METHODS

70 adults affected by type 1 diabetes were monitored for 1 year by means of either flash (FGM) or real-time (rtCGM) glucose monitoring devices. Follow-up visits were performed after 90, 180 and 365 days from baseline and percentage TIR^70-180 evaluated for the 90-day time period preceding each visit. HbA1c tests were also carried out in the same occasions and measured values paired with the corresponding TIR data.

RESULTS

A monovariate linear regression analysis confirms a strong correlation between TIR and HbA1c as found in previous studies, but leveraging more homogeneous data (n=146) collected in real-life conditions. Differences were determined between FGM and rtCGM devices in Pearson's correlation (r_FGM=0.703, r_rtCGM=0.739), slope (β_1,FGM=-11.77, β_1,rtCGM=-10.74) and intercept (β_0,FGM=141.19, β_0,rtCGM=140.77) coefficients. Normality of residuals and homoscedasticity were successfully verified in both cases.

CONCLUSIONS

Regression lines for two patient groups monitored through FGM and rtCGM devices, respectively, while confirming a linear relationship between TIR and A1c hemoglobin (A1C) in good accordance with previous studies, also show a statistically significant difference in the regression intercept, thus suggesting the need for different models tailored to device characteristics. The predictive power of A1C as a TIR estimator also deserves further investigations.

Factors Associated with Adherence to Self-Monitoring of Blood Glucose Among Young People with Type 1 Diabetes in China: A Cross-Sectional Study

BACKGROUND

Self-monitoring of blood glucose (SMBG) plays a crucial role in the maintenance of glycemic control in young people with type 1 diabetes mellitus (T1DM), but most of them do not perform SMBG as recommended. Few studies comprehensively explored factors that correlate with adherence to SMBG among this population on the basis of a framework. Hence, the aims of this study were to describe adherence to SMBG among young people with T1DM in China and explore its associating factors according to the Self and Family Management (SFM) framework.

METHODS

A cross-sectional study was conducted on young people with T1DM in Hunan Province of China from July to August 2020. Based on the SFM framework, self-reported questionnaires were organized for the collection of data on adherence to SMBG, socio-demographic and clinical factors, resources, health care system, and personal factors. Factors associated with adherence to SMBG were determined through multivariate logistic regression analysis.

RESULTS

A total of 165 young people were invited, of which 122 (73.9%) completed the questionnaires. The mean age was 12.41 years (SD = 3.18), and the proportion of young people who adhered to SMBG was 53.3%. Multivariate logistic regression analysis revealed that children aged 8-12 years (OR = 0.188, P = 0.002), from two-parent families (OR = 0.232, P = 0.019), and with better personal factors (eg, with more information of SMBG, OR = 1.072, P = 0.020; lower diabetes-related worry, OR = 0.917, P = 0.031; higher level of pain during SMBG, OR = 1.852, P = 0.001), had better adherence to SMBG.

CONCLUSION

Nearly half of the young people with T1DM were not adherent to SMBG in China. Clinicians need to pay more attention to adolescents from single-parent families with regard to their adherence to SMBG. Providing management strategies of SMBG, including delivering SMBG-related information, decreasing diabetes-related worry, and relieving pain related to SMBG, may improve adherence.

Smartphone-Based Data Collection in Ophthalmology

Due to their widespread use among the population and their wide range of functions and sensors, smartphones are suitable for data collection for medical purposes. App-supported input masks, patient diaries, and patient information systems, mobile access to the patient file as well as telemedical services will continue to find their way into our field of expertise in the future. In addition, the use of smartphone sensors (GPS and motion sensors, touch display, microphone) and coupling possibilities with biosensors (for example with Continuous Glucose Monitoring [CGM] systems), advanced camera technology, the possibility of regular and appointment independent checking of the visual system (visual acuity/contrast vision) as well as real-time data transfer offer interesting possibilities for patient treatment and clinical research. The present review deals with the current status and future perspectives of smartphone-based data collection and possible applications in ophthalmology.

Glycemic Status Assessment by the Latest Glucose Monitoring Technologies

The advanced and performing technologies of glucose monitoring systems provide a large amount of glucose data that needs to be properly read and interpreted by the diabetology team in order to make therapeutic decisions as close as possible to the patient's metabolic needs. For this purpose, new parameters have been developed, to allow a more integrated reading and interpretation of data by clinical professionals. The new challenge for the diabetes community consists of promoting an integrated and homogeneous reading, as well as interpretation of glucose monitoring data also by the patient himself. The purpose of this review is to offer an overview of the glycemic status assessment, opened by the current data management provided by latest glucose monitoring technologies. Furthermore, the applicability and personalization of the different glycemic monitoring devices used in specific insulin-treated diabetes mellitus patient populations will be evaluated.

Glycemic profile of women with normoglycemia and gestational diabetes mellitus during early pregnancy using continuous glucose monitoring system

AIM

We studied women between 8 and 20 weeks of gestation with the aim of evaluating and comparing those having normoglycemia and GDM according to WHO 2013 criteria.

METHODS

In this cross-sectional study (2017-2019), eligible pregnant women underwent a 75-g OGTT, followed by placement of a CGMS.

RESULTS

Women (n = 96, 58 with normoglycemia and 38 with GDM) were enrolled at 14.0 ± 3.2 weeks of gestation. Mean preprandial, 1-h and 2-h postprandial and peak glucose values were significantly higher in women with GDM. Peak glucose value was achieved 60.0 ± 12.3 and 64.3 ± 11.6 min after meal in the normoglycemia and GDM group, respectively. 24-h mean glucose (5.8 ± 0.6 vs. 5.3 ± 0.4 mmol/L), mean daytime glucose (6.0 ± 0.6 vs. 5.5 ± 0.4 mmol/L) and mean nocturnal glucose (5.4 ± 0.7 vs. 5.0 0 ± 0.5 mmol/L) were significantly higher in women with GDM. Total time spent in range was significantly lower in the GDM group compared to the normoglycemia group (92.1 vs. 98.2%).

CONCLUSIONS

This study highlights differences in glycemic patterns between women with normoglycemia and GDM in the context of a South Asian population where burden of GDM is high but good quality data in early pregnancy are limited.

A single-blind, randomised, crossover study to reduce hypoglycaemia risk during postprandial exercise with closed-loop insulin delivery in adults with type 1 diabetes: announced (with or without bolus reduction) vs unannounced exercise strategies

AIMS/HYPOTHESIS

For individuals living with type 1 diabetes, closed-loop insulin delivery improves glycaemic control. Nonetheless, maintenance of glycaemic control during exercise while a prandial insulin bolus remains active is a challenge even to closed-loop systems. We investigated the effect of exercise announcement on the efficacy of a closed-loop system, to reduce hypoglycaemia during postprandial exercise.

METHODS

A single-blind randomised, crossover open-label trial was carried out to compare three strategies applied to a closed-loop system at mealtime in preparation for exercise taken 90 min after eating at a research testing centre: (1) announced exercise to the closed-loop system (increases target glucose levels) in addition to a 33% reduction in meal bolus (A-RB); (2) announced exercise to the closed-loop system and a full meal bolus (A-FB); (3) unannounced exercise and a full meal bolus (U-FB). Participants performed 60 min of exercise at 60% [Formula: see text] 90 min after eating breakfast. The investigators were not blinded to the interventions. However, the participants were blinded to the sensor glucose readings and to the insulin infusion rates throughout the intervention visits.

RESULTS

The trial was completed by 37 adults with type 1 diabetes, all using insulin pumps: mean±SD, 40.0 ± 15.0 years of age, HbA_1c 57.1 ± 10.8 mmol/mol (7.3 ± 1.0%). Reported results were based on plasma glucose values. During exercise and the following 1 h recovery period, time spent in hypoglycaemia (<3.9 mmol/l; primary outcome) was reduced with A-RB (mean ± SD; 2.0 ± 6.2%) and A-FB (7.0 ± 12.6%) vs U-FB (13.0 ± 19.0%; p < 0.0001 and p = 0.005, respectively). During exercise, A-RB had the least drop in plasma glucose levels: A-RB -0.3 ± 2.8 mmol/l, A-FB -2.6 ± 2.9 mmol/l vs U-FB -2.4 ± 2.7 mmol/l (p < 0.0001 and p = 0.5, respectively). Comparison of A-RB vs U-FB revealed a decrease in the time spent in target (3.9-10 mmol/l) by 12.7% (p = 0.05) and an increase in the time spent in hyperglycaemia (>10 mmol/l) by 21% (p = 0.001). No side effects were reported during the applied strategies.

CONCLUSIONS/INTERPRETATION

Combining postprandial exercise announcement, which increases closed-loop system glucose target levels, with a 33% meal bolus reduction significantly reduced time spent in hypoglycaemia compared with the other two strategies, yet at the expense of more time spent in hyperglycaemia.

TRIAL REGISTRATION

ClinicalTrials.gov NCT0285530 FUNDING: JDRF (2-SRA-2016-210-A-N), the Canadian Institutes of Health Research (354024) and the Fondation J.-A. DeSève chair held by RR-L.

An analytical approach to determine the optimal duration of continuous glucose monitoring data required to reliably estimate time in hypoglycemia

Diabetes is a chronic metabolic disease that causes blood glucose (BG) concentration to make dangerous excursions outside its physiological range. Measuring the fraction of time spent by BG outside this range, and, specifically, the time-below-range (TBR), is a clinically common way to quantify the effectiveness of therapies. TBR is estimated from data recorded by continuous glucose monitoring (CGM) sensors, but the duration of CGM recording guaranteeing a reliable indicator is under debate in the literature. Here we framed the problem as random variable estimation problem and studied the convergence of the estimator, deriving a formula that links the TBR estimation error variance with the CGM recording length. Validation is performed on CGM data of 148 subjects with type-1-diabetes. First, we show the ability of the formula to predict the uncertainty of the TBR estimate in a single patient, using patient-specific parameters; then, we prove its applicability on population data, without the need of parameters individualization. The approach can be straightforwardly extended to other similar metrics, such as time-in-range and time-above-range, widely adopted by clinicians. This strengthens its potential utility in diabetes research, e.g., in the design of those clinical trials where minimal CGM monitoring duration is crucial in cost-effectiveness terms.

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.

A New Approach to Determining Liquid Concentration Using Multiband Annular Ring Microwave Sensor and Polarity Correlator

This article presents a new approach to determining liquid concentration using a new microwave sensor and polarity correlator. The sensor design incorporates an annular ring resonator having inside three parallel lines, a trapezoid ground plane and a co-planar waveguide (CPW) tapered feeder, which altogether achieve multiple frequency bands. Multiple bands of interest are obtained at the lower end of the microwave spectrum, i.e., from 1–6 GHz, as this region is widely accepted in analyzing various liquid samples. The sensor size is 71 × 40 × 1.6 mm3 with material selection based on an economically available FR4 substrate. The sensor is realized and experimentally validated for its sensitivity by utilizing in-lab prepared aqueous solution samples. Further, liquid concentration is determined by adopting a polarity correlator, which is applied to the sensor’s responses obtained at different values.

Association Between Continuous Glucose Monitoring-Derived Time in Range, Other Core Metrics, and Albuminuria in Type 2 Diabetes

Background: As the use of continuous glucose monitoring (CGM) has increased, time in range (TIR) and other core CGM metrics are now emerging as the core metrics for clinical targets and assessing diabetic complications, beyond HbA1c. This study investigated the association between the CGM-derived TIR, hyperglycemia, hypoglycemia metrics, and albuminuria. Methods: A total of 866 subjects with type 2 diabetes who underwent 3 or 6 days of CGM and had urinary albumin-to-creatinine ratio (ACR) measurements were retrospectively reviewed. CGM metrics were defined according to the most recent international consensus. Albuminuria was defined as one or more of the ACR measurements being >30 mg/g. Results: The overall prevalence of albuminuria was 36.6%. The prevalence of albuminuria was lower in subjects who achieved the target of TIR 70-180 mg/dL, time above range (TAR) >180 mg/dL, and TAR >250 mg/dL, as recommended by international consensus (P < 0.001). Multiple logistic regression analysis revealed that the odds ratio of having albuminuria was 0.94 (95% confidence interval: 0.88-0.99, P for trend = 0.04) per 10% increase in TIR of 70-180 mg/dL, after adjusting for multiple factors, including glycemic variability. The results were similar for hyperglycemia metrics (TAR >250 mg/dL and TAR >180 mg/dL). Conclusions: TIR 70-180 mg/dL and hyperglycemia metrics are strongly associated with albuminuria in type 2 diabetes.

Is HbA1c an ideal biomarker of well-controlled diabetes?

HbA1c is a biomarker with a central role in the diagnosis and follow-up of patients with diabetes, although not a perfect one. Common comorbidities encountered in patients with diabetes mellitus, such as renal insufficiency, high output states (iron deficiency anaemia, haemolytic anaemia, haemoglobinopathies and pregnancy) and intake of specific drugs could compromise the sensitivity and specificity of the biomarker. COVID-19 pandemic poses a pressing challenge for the diabetic population, since maintaining optimal blood glucose control is key to reduce morbidity and mortality rates. Alternative methods for diabetes management, such as fructosamine, glycosylated albumin and device-based continuous glucose monitoring, are discussed.

Current status of continuous glucose monitoring among Korean children and adolescents with type 1 diabetes mellitus

Type 1 diabetes mellitus (T1DM) requires life-long insulin therapy because of diminished insulin-secretion capability. Glycemic control and glucose monitoring are important to prevent T1DM complications. Continuous glucose monitoring (CGM) measures glucose level, every one to five minutes, in the interstitial fluid from a subcutaneous sensor and facilitates better glycemic control, reduces hypoglycemia, and is safely used in the pediatric population. CGM can be categorized as retrospective, real-time, or intermittently scanned CGM, and all forms are available in Korea. The CGM device has 3 components: sensor, transmitter, and monitor/receiver. Key metrics of CGM include days of CGM application, percentage of time with CGM, mean glucose, glucose management indicator, glycemic variability, and use of Ambulatory Glucose Profile for CGM reports. CGM sensors and transmitters have been partly reimbursed by the Korean National Health Insurance Service (NHIS) since 2019, and 1,434 T1DM patients (male, 40.8%; age <20 years, 52.4%) in Korea were prescribed CGM as of December 2019. In Korea, the number of CGM users will increase due to reimbursement for CGM sensors and transmitters by the NHIS. Successful CGM use requires long-term policies to establish diabetes education and financial assistance. Clinicians should become well-acquainted with interpretation of CGM data and information updates to facilitate integration of CGM data into clinical practice among pediatric T1DM patients.

Efficacy and safety of evogliptin treatment in patients with type 2 diabetes: A multicentre, active-controlled, randomized, double-blind study with open-label extension (the EVERGREEN study)

AIM

To investigate the efficacy and safety of evogliptin compared with linagliptin in patients with type 2 diabetes.

MATERIALS AND METHODS

In this 12-week, multicentre, randomized, double-blind, active-controlled, and 12-week open-label extension study, a total of 207 patients with type 2 diabetes who had HbA1c levels of 7.0%-10.0% were randomized 1:1 to receive evogliptin 5 mg (n = 102) or linagliptin 5 mg (n = 105) daily for 12 weeks. The primary efficacy endpoint was the change from baseline HbA1c at week 12. The secondary endpoint was the change in the mean amplitude of glycaemic excursion (MAGE) assessed by continuous glucose monitoring. In the extension study conducted during the following 12 weeks, evogliptin 5 mg daily was administered to both groups: evogliptin/evogliptin group (n = 95) and linagliptin/evogliptin group (n = 92).

RESULTS

After 12 weeks of treatment, the mean change in HbA1c in the evogliptin group and in the linagliptin group was -0.85% and -0.75%, respectively. The between-group difference was -0.10% (95% CI: -0.32 to 0.11), showing non-inferiority based on a non-inferiority margin of 0.4%. The change in MAGE was -24.6 mg/dL in the evogliptin group and -16.7 mg/dL in the linagliptin group. These values were significantly lower than the baseline values in both groups. However, they did not differ significantly between the two groups. In the evogliptin/evogliptin group at week 24, HbA1c decreased by -0.94%, with HbA1c values of <7.0% in 80.2% of the patients. The incidence and types of adverse events were comparable between the two groups for 24 weeks.

CONCLUSION

In this study, the glucose-lowering efficacy of evogliptin was non-inferior to linagliptin. It was maintained at week 24 with a 0.94% reduction in HbA1c. Evogliptin therapy improved glycaemic variability without causing any serious adverse events in patients with type 2 diabetes.

Fit-for-Purpose Biometric Monitoring Technologies: Leveraging the Laboratory Biomarker Experience

Biometric monitoring technologies (BioMeTs) are becoming increasingly common to aid data collection in clinical trials and practice. The state of BioMeTs, and associated digitally measured biomarkers, is highly reminiscent of the field of laboratory biomarkers 2 decades ago. In this review, we have summarized and leveraged historical perspectives, and lessons learned from laboratory biomarkers as they apply to BioMeTs. Both categories share common features, including goals and roles in biomedical research, definitions, and many elements of the biomarker qualification framework. They can also be classified based on the underlying technology, each with distinct features and performance characteristics, which require bench and human experimentation testing phases. In contrast to laboratory biomarkers, digitally measured biomarkers require prospective data collection for purposes of analytical validation in human subjects, lack well‐established and widely accepted performance characteristics, require human factor testing, and, for many applications, access to raw (sample‐level) data. Novel methods to handle large volumes of data, as well as security and data rights requirements add to the complexity of this emerging field. Our review highlights the need for a common framework with appropriate vocabulary and standardized approaches to evaluate digitally measured biomarkers, including defining performance characteristics and acceptance criteria. Additionally, the need for human factor testing drives early patient engagement during technology development. Finally, use of BioMeTs requires a relatively high degree of technology literacy among both study participants and healthcare professionals. Transparency of data generation and the need for novel analytical and statistical tools creates opportunities for precompetitive collaborations.

Towards sensor-based calving detection in the rangelands: a systematic review of credible behavioral and physiological indicators

Calving is a critical point in both a cow and calf’s life, when both become more susceptible to disease and risk of death. Ideally, this period is carefully monitored. In extensive grazing systems, however, it is often not economically or physically possible for producers to continuously monitor animals, and thus, calving frequently goes undetected. The development of sensor systems, particularly in these environments, could provide significant benefits to the industry by increasing the quantity and quality of individual animal monitoring. In the time surrounding calving, cows undergo a series of behavioral and physiological changes, which can potentially be detected using sensing technologies. Before developing a sensor-based approach, it is worthwhile considering these behavioral and physiological changes, such that the appropriate technologies can be designed and developed. A systematic literature review was conducted to identify changes in the dam’s behavioral and physiological states in response to a calving event. Articles (n = 104) consisting of 111 independent experiments were assessed following an intensive search of electronic databases. Commonly reported indicators of parturition (n = 38) were identified, and temporal trend graphs were generated for 13 of these changes. The results compare trends in behavioral and physiological changes across a variety of animal-related factors and identifies several reliable indicators of parturition for detection with sensors, namely calf grooming behavior, changes in rumination duration, and lying bouts. This synthesis of literature suggests that variability exists between individuals and thus, combining several calving indicators may result in a more broadly applicable and accurate detection of parturition.

Applying Nanomaterials to Modern Biomedical Electrochemical Detection of Metabolites, Electrolytes, and Pathogens

Personal biosensors and bioelectronics have been demonstrated for use in out-of-clinic biomedical devices. Such modern devices have the potential to transform traditional clinical analysis into a new approach, allowing patients or users to screen their own health or warning of diseases. Researchers aim to explore the opportunities of easy-to-wear and easy-to-carry sensors that would empower users to detect biomarkers, electrolytes, or pathogens at home in a rapid and easy way. This mobility would open the door for early diagnosis and personalized healthcare management to a wide audience. In this review, we focus on the recent progress made in modern electrochemical sensors, which holds promising potential to support point-of-care technologies. Key original research articles covered in this review are mainly experimental reports published from 2018 to 2020. Strategies for the detection of metabolites, ions, and viruses are updated in this article. The relevant challenges and opportunities of applying nanomaterials to support the fabrication of new electrochemical biosensors are also discussed. Finally, perspectives regarding potential benefits and current challenges of the technology are included. The growing area of personal biosensors is expected to push their application closer to a new phase of biomedical advancement.

Advanced Diabetes Management Using Artificial Intelligence and Continuous Glucose Monitoring Sensors

Wearable continuous glucose monitoring (CGM) sensors are revolutionizing the treatment of type 1 diabetes (T1D). These sensors provide in real-time, every 1-5 min, the current blood glucose concentration and its rate-of-change, two key pieces of information for improving the determination of exogenous insulin administration and the prediction of forthcoming adverse events, such as hypo-/hyper-glycemia. The current research in diabetes technology is putting considerable effort into developing decision support systems for patient use, which automatically analyze the patient's data collected by CGM sensors and other portable devices, as well as providing personalized recommendations about therapy adjustments to patients. Due to the large amount of data collected by patients with T1D and their variety, artificial intelligence (AI) techniques are increasingly being adopted in these decision support systems. In this paper, we review the state-of-the-art methodologies using AI and CGM sensors for decision support in advanced T1D management, including techniques for personalized insulin bolus calculation, adaptive tuning of bolus calculator parameters and glucose prediction.

Efficacy of Intermittently Scanned Continuous Glucose Monitoring in the Prevention of Recurrent Severe Hypoglycemia

Background: People with diabetes experiencing hypoglycemia are at increased risk of recurrence because of attenuated autonomic warning. We assessed the efficacy of intermittently scanned continuous glucose monitoring (isCGM; FreeStyle Libre™, Abbott) compared with usual-care self-monitoring of blood glucose (SMBG) in reducing this risk in type 1 and insulin-treated type 2 diabetes. Methods: Insulin-treated adults with diabetes and an episode of clinically significant biochemical hypoglycemia (blood glucose [BG] <3.0 mM) or symptomatic hypoglycemia and BG <4.0 mM were randomized to 6 months of isCGM (intensive group) or SMBG (control group) against a background of usual care. The primary outcome was hypoglycemia requiring second-party assistance for recovery. Prespecified secondary outcomes included other hypoglycemic episodes (self-reported, and BG <3.0, 3.0-3.9, <4.0 mM) and change in HbA_1c at 24 weeks. Results: Of 59 participants (mean age 53.6 years, 44.1% males, median HbA_1c 61.8 mmol/mol or 7.8%), 30 were allocated to isCGM and 29 to SMBG. The incidence of severe hypoglycemia was not significantly different between the two groups (incident rate ratio [95% confidence interval]: 1.49 [0.46-5.56], P = 0.47). The incidence of other recorded hypoglycemic episodes in the intervention group was double that in the control group (P < 0.001). There was no difference in the change in HbA_1c between the two groups (P = 0.74). There were seven serious adverse events and none was considered related to the intervention. Conclusions: Although isCGM is safe, it does not appear to have a role in preventing recurrent severe hypoglycemia in at-risk individuals with diabetes.

Acute glycemic variability on admission predicts the prognosis in hospitalized patients with coronary artery disease: a meta-analysis

PURPOSE

Increased glycemic variability has been related with poor prognosis in patients with coronary artery disease (CAD). However, whether diabetic status or subtype of CAD could affect the association remains unknown. We performed a meta-analysis to systematically evaluate the association between the mean amplitude of glycemic excursions (MAGE) on continuous glucose monitoring and the incidence of major adverse cardiovascular events (MACEs) in CAD patients.

METHODS

Relevant prospective cohort studies were identified through search of PubMed, Embase, WanFang, and CNKI databases. A random-effect model was used to pool the results. Subgroup analyses were performed to evaluate the influences of the predefined study characteristics on the outcome.

RESULTS

Eleven cohort studies with 2666 hospitalized patients with acute coronary syndrome (ACS) or stable CAD for percutaneous coronary intervention were included. Pooled results showed that higher MAGE at admission was associated with higher incidence of MACEs during follow-up (adjusted relative risk [RR]: 1.84, p < 0.001; I² = 12%). Stratified analyses showed that the association between higher MAGE and higher risk of MACEs in CAD patients were consistent in patients with or without diabetes, and in those with ACS or stable CAD (p for subgroup difference both >0.05). Significant publication bias was detected (p = 0.041). Trim-and-fill analysis retrieved three studies to generate symmetrical funnel plots. Meta-analysis that incorporated these studies showed similar results (RR: 1.80, p < 0.001).

CONCLUSIONS

Increased glycemic variability may be associated with poor prognosis in CAD patients regardless of the diabetic status and the subtype of CAD.

Development of an Error Model for a Factory-Calibrated Continuous Glucose Monitoring Sensor with 10-Day Lifetime

Factory-calibrated continuous glucose monitoring (FC-CGM) sensors are new devices used in type 1 diabetes (T1D) therapy to measure the glucose concentration almost continuously for 10-14 days without requiring any in vivo calibration. Understanding and modelling CGM errors is important when designing new tools for T1D therapy. Available literature CGM error models are not suitable to describe the FC-CGM sensor error, since their domain of validity is limited to 12-h time windows, i.e., the time between two consecutive in vivo calibrations. The aim of this paper is to develop a model of the error of FC-CGM sensors. The dataset used contains 79 FC-CGM traces collected by the Dexcom G6 sensor. The model is designed to dissect the error into its three main components: effect of plasma-interstitium kinetics, calibration error, and random measurement noise. The main novelties are the model extension to cover the entire sensor lifetime and the use of a new single-step identification procedure. The final error model, which combines a first-order linear dynamic model to describe plasma-interstitium kinetics, a second-order polynomial model to describe calibration error, and an autoregressive model to describe measurement noise, proved to be suitable to describe FC-CGM sensor errors, in particular improving the estimation of the physiological time-delay.