Advances in Continuous Glucose Monitoring and Integrated Devices for Management of Diabetes with Insulin-Based Therapy: Improvement in Glycemic Control

Comparison of Real-Time and Intermittently-Scanned Continuous Glucose Monitoring for Glycemic Control in Type 1 Diabetes Mellitus: Nationwide Cohort Study

BACKGRUOUND

This study compares the association between real-time continuous glucose monitoring (rtCGM) and intermittently- scanned CGM (isCGM) and glycemic control in individuals with type 1 diabetes mellitus (T1DM) in a real-world setting.

METHODS

Using data from the Korean National Health Insurance Service Cohort, individuals with T1DM managed by intensive insulin therapy were followed at 3-month intervals for 2 years after the initiation of CGM. The glycosylated hemoglobin (HbA1c) levels and coefficients of variation (CVs) of rtCGM and isCGM users were compared using independent two-sample t-test and a linear mixed model.

RESULTS

The analyses considered 7,786 individuals (5,875 adults aged ≥19 years and 1,911 children and adolescents aged <19 years). Overall, a significant reduction in HbA1c level was observed after 3 months of CGM, and the effect was sustained for 2 years. The mean HbA1c level at baseline was higher in rtCGM users than in isCGM users (8.9%±2.7% vs. 8.6%±2.2%, P<0.001). However, from 3 to 24 months, rtCGM users had lower HbA1c levels than isCGM users at every time point (7.1%±1.2% vs. 7.5%±1.3% at 24 months, P<0.001 for all time points). In both adults and children, the greater reduction in HbA1c with rtCGM remained significant after adjusting for the baseline characteristics of the users. The CV also showed greater decrease with rtCGM than with isCGM.

CONCLUSION

In this large nationwide cohort study, the use of rtCGM was associated with a greater improvement in glycemic control, including HbA1c reduction, than the use of isCGM in both adults and children with T1DM.

Harnessing Machine Learning, a Subset of Artificial Intelligence, for Early Detection and Diagnosis of Type 1 Diabetes: A Systematic Review

Type 1 diabetes (T1D) is an autoimmune condition characterized by the destruction of insulin-producing pancreatic beta cells, leading to lifelong insulin dependence and significant complications. Early detection of T1D is essential to delay disease onset and improve outcomes. Recent advancements in artificial intelligence (AI) and machine learning (ML) have provided powerful tools for predicting and diagnosing T1D. This systematic review evaluates the current landscape of AI/ML-based approaches for early T1D detection. A comprehensive search across PubMed, EMBASE, Science Direct, and Scopus identified 1447 studies, of which 10 met the inclusion criteria for narrative synthesis after screening and full-text review. The studies utilized diverse ML models, including logistic regression, support vector machines, random forests, and artificial neural networks. The datasets encompassed clinical parameters, genetic risk markers, continuous glucose monitoring (CGM) data, and proteomic and metabolomic biomarkers. The included studies involved a total of 49,172 participants and employed case-control, retrospective cohort, and prospective cohort designs. Models integrating multimodal data achieved the highest predictive accuracy, with area under the curve (AUC) values reaching up to 0.993 in sex-specific models. CGM data and plasma biomarkers, such as CXCL10 and IL-1RA, also emerged as valuable tools for identifying at-risk individuals. While the results highlight the potential of AI/ML in revolutionizing T1D risk stratification and diagnosis, challenges remain. Data heterogeneity and limited model generalizability present barriers to widespread implementation. Future research should prioritize the development of universal frameworks and real-world validation to enhance the reliability and clinical integration of these tools. Ultimately, AI/ML technologies hold transformative potential for clinical practice by enabling earlier diagnosis, guiding targeted interventions, and improving long-term patient outcomes. These advancements could support clinicians in making more informed, timely decisions, thus reducing diagnostic delays and paving the way for personalized prevention strategies in both pediatric and adult populations.

Unlocking Real-Time Data Access in Diabetes Management: Toward an Interoperability Model

BACKGROUND

In today's data-driven era, openness promotes transparency and accessibility, particularly in health initiatives like the European Health Data Space. Diabetes management relies on real-time data from medical devices, such as continuous glucose monitors (CGMs), insulin pumps, and hybrid closed-loop systems. These devices provide critical insights for treatment adjustments, making real-time data access essential.

METHODS

This article explores real-time data access for third-party applications, focusing on primary (treatment) and secondary (research) use. We examine how application programming interfaces (APIs) enable secure data retrieval and assess the impact of terms of service and copyright law on patient-driven innovation in open-source communities. Our research evaluates diabetes medical devices and software solutions in Norway, assessing their real-time data access and API functionalities. In addition, we analyze legal frameworks governing these technologies, focusing on challenges faced by open-source solutions. Based on our findings, we propose an interoperability model to improve data accessibility while ensuring security and transparency.

RESULTS

Findings reveal seven diabetes devices and nine regulated software solutions, with only one offering a publicly accessible API. This emphasizes a significant gap in real-time data access. Comparisons between vendor-specific and open-source software expose interoperability and accessibility challenges. While Do-It-Yourself (DIY) solutions foster innovation, they face technical and legal barriers.

CONCLUSION

Real-time diabetes management presents security, transparency, and access challenges. Regulatory decisions are needed to implement an interoperability model. The lack of real-time data access highlights the necessity of publicly accessible APIs that prioritize transparency, accessibility, and patient-driven innovation-marking a shift from today's constrained diabetes management landscape.

Abuse-deterrent wearable device with potential for extended delivery of opioid drugs

Purpose

Unethical attempts to misuse and overdose opioids have led to strict prescription limits, necessitating frequent hospital visits and prescriptions for long-term severe pain management. Therefore, this study aimed to develop a prototype wearable device that facilitates the extended delivery of opioid drugs while incorporating abuse-deterrent functionality, referred to as the abuse deterrent device (ADD).

Methods

The ADD was designed and fabricated using 3D-printed components, including reservoirs for the drug and contaminant, as well as an actuator. In vitro tests were conducted using a skin-mimicking layer and phosphate-buffered saline (PBS) to evaluate the drug release profile and the effectiveness of the ADD abuse-deterrent mechanism.

Results

Under simulated skin attachment, ADD demonstrated sustained drug release with the potential to persist for up to 200 days. Upon detachment from the skin mimic, the mechanical components of the ADD facilitated immediate exposure of the contaminant to the drug and effectively halted further drug exposure throughout-diffusion.

Conclusion

Wearable ADD provides a secure and practical solution for the long-term treatment of high-risk medications such as opioids, enhances patient convenience, and addresses important public health concerns.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13534-025-00459-7.

Glycemia Risk Index is Associated With Risk of Albuminuria Among Individuals With Type 1 Diabetes

BACKGROUND

The glycemia risk index (GRI) is a novel composite continuous glucose monitoring (CGM) metric composed of hypoglycemia and hyperglycemia components and is weighted toward extremes. This study aimed to investigate the association between GRI and the risk of albuminuria in type 1 diabetes.

METHODS

The 90-day CGM tracings of 330 individuals with type 1 diabetes were included in the analysis. Glycemia risk index was divided into five risk zones (A-E), and hypoglycemia and hyperglycemia components were divided into quintiles. Albuminuria was defined as a spot urine albumin-to-creatinine ratio ≥30 mg/g. Associations of albuminuria with GRI and its hypoglycemia and hyperglycemia components were estimated.

RESULTS

Mean GRI and glycated hemoglobin (HbA1c) were 40.9 ± 21.3 and 7.3 ± 1.0%, respectively, and the overall prevalence of albuminuria was 17.6%. Prevalence of albuminuria differed significantly by GRI zone (P = .023). In logistic regression analysis, the adjusted odds ratio (OR) of albuminuria per increase in the GRI zone was 1.70 (95% confidence interval [CI]: 1.19-2.41) after adjusting for various factors affecting albuminuria. The association remained significant after adjusting for achievement of the recommended target of time in range (70-180 mg/dL; >70%) or HbA1c (<7%). The hyperglycemia component of GRI was also associated with albuminuria, and the association remained significant even after adjusting for HbA1c level itself (adjusted OR 1.44, 95% CI: 1.05-1.98).

CONCLUSIONS

Glycemia risk index is significantly associated with albuminuria in individuals with type 1 diabetes.

Innovations in Diabetes Management for Pregnant Women: Artificial Intelligence and the Internet of Medical Things

Pregnancies impacted by diabetes face the compounded challenge of strict glycemic control with mounting insulin resistance as the pregnancy progresses. New technological advances, including artificial intelligence (AI) and the Internet of Medical Things (IoMT), are revolutionizing health care delivery by providing innovative solutions for diabetes care during pregnancy. Together, AI and the IoMT are a multibillion-dollar industry that integrates advanced medical devices and sensors into a connected network that enables continuous monitoring of glucose levels. AI-driven clinical decision support systems (CDSSs) can predict glucose trends and provide tailored evidence-based treatments with real-time adjustments as insulin resistance changes with placental growth. Additionally, mobile health (mHealth) applications facilitate patient education and self-management through real-time tracking of diet, physical activity, and glucose levels. Remote monitoring capabilities are particularly beneficial for pregnant persons with diabetes as they extend quality care to underserved populations and reduce the need for frequent in-person visits. This high-resolution monitoring allows physicians and patients access to an unprecedented wealth of data to make more informed decisions based on real-time data, reducing complications for both the mother and fetus. These technologies can potentially improve maternal and fetal outcomes by enabling timely, individualized interventions based on personalized health data. While AI and IoMT offer significant promise in enhancing diabetes care for improved maternal and fetal outcomes, their implementation must address challenges such as data security, cost-effectiveness, and preserving the essential patient-provider relationship. KEY POINTS: · The IoMT expands how patients interact with their health care.. · AI has widespread application in the care of pregnancies complicated by diabetes.. · A need for validation and black-box methodologies challenges the application of AI-based tools.. · As research in AI grows, considerations for data privacy and ethical dilemmas will be required..

Continuous Glucose Monitoring-New Diagnostic Tool in Complex Pathophysiological Disorder of Glucose Metabolism in Children and Adolescents with Obesity

Obesity is one of the leading causes of chronic diseases, and its prevalence is still rising in children and adolescent populations. Chronic cardiovascular complications result in metabolic syndrome (MS) and type 2 diabetes mellitus. Key factors in the development of MS are insulin resistance and low-grade inflammation. The disorder of glucose and insulin metabolism has not been fully elucidated so far, and an oral glucose tolerance test (OGTT) has been the only tool used to look into the complex metabolism disorder in children and adolescents with obesity. Continuous glucose monitoring (CGM) has become commercially available for over two decades and is primarily used to manage type 1 diabetes mellitus in pediatric populations. This review aims to present the current knowledge about the use of CGM in children and adolescent populations with obesity. CGM systems have the potential to serve as valuable tools in everyday clinical practices, not only in the better diagnosis of chronic complications associated with obesity, but CGM can also assist in interventions to make better adjustments to nutritional and therapeutic approaches based on real-time glucose monitoring data. Despite these promising benefits, further research is needed to fully understand the role of CGM in metabolic disorders in pediatric populations with obesity, which will additionally strengthen the importance of CGM systems in everyday clinical practices.

Clinical Trial Protocol for Porcine Islet Xenotransplantation in South Korea

BACKGRUOUND

Islet transplantation holds promise for treating selected type 1 diabetes mellitus patients, yet the scarcity of human donor organs impedes widespread adoption. Porcine islets, deemed a viable alternative, recently demonstrated successful longterm survival without zoonotic risks in a clinically relevant pig-to-non-human primate islet transplantation model. This success prompted the development of a clinical trial protocol for porcine islet xenotransplantation in humans.

METHODS

A single-center, open-label clinical trial initiated by the sponsor will assess the safety and efficacy of porcine islet transplantation for diabetes patients at Gachon Hospital. The protocol received approval from the Gachon Hospital Institutional Review Board (IRB) and the Korean Ministry of Food and Drug Safety (MFDS) under the Investigational New Drug (IND) process. Two diabetic patients, experiencing inadequate glycemic control despite intensive insulin treatment and frequent hypoglycemic unawareness, will be enrolled. Participants and their family members will engage in deliberation before xenotransplantation during the screening period. Each patient will receive islets isolated from designated pathogen-free pigs. Immunosuppressants and systemic infection prophylaxis will follow the program schedule. The primary endpoint is to confirm the safety of porcine islets in patients, and the secondary endpoint is to assess whether porcine islets can reduce insulin dose and the frequency of hypoglycemic unawareness.

CONCLUSION

A clinical trial protocol adhering to global consensus guidelines for porcine islet xenotransplantation is presented, facilitating streamlined implementation of comparable human trials worldwide.

Smart solutions in hypertension diagnosis and management: a deep dive into artificial intelligence and modern wearables for blood pressure monitoring

Hypertension, a widespread cardiovascular issue, presents a major global health challenge. Traditional diagnosis and treatment methods involve periodic blood pressure monitoring and prescribing antihypertensive drugs. Smart technology integration in healthcare offers promising results in optimizing the diagnosis and treatment of various conditions. We investigate its role in improving hypertension diagnosis and treatment effectiveness using machine learning algorithms for early and accurate detection. Intelligent models trained on diverse datasets (encompassing physiological parameters, lifestyle factors, and genetic information) to detect subtle hypertension risk patterns. Adaptive algorithms analyze patient-specific data, optimizing treatment plans based on medication responses and lifestyle habits. This personalized approach ensures effective, minimally invasive interventions tailored to each patient. Wearables and smart sensors provide real-time health insights for proactive treatment adjustments and early complication detection.

Effectiveness of a hybrid closed-loop system for children and adolescents with type 1 diabetes during physical exercise: A cross-sectional study in real life

OBJECTIVE

The aim of the study was to describe how physical exercise affects metabolic control, insulin requirements and carbohydrate intake in children who use hybrid closed-loop systems.

METHODS

Cross-sectional study design. The sample included 21 children and adolescents diagnosed with type 1 diabetes. During the study, participants were monitored for a period of 7 days to gather comprehensive data on these factors.

RESULTS

Nine participants (42.9%) had switched to exercise mode to raise the target glucose temporarily to 150 mg/dL. The HbA1c values ranged from 5.5% to 7.9% (median, 6.5%; IQR, 0.75). The percentage of time within the target range of 70-180 mg/dL was similar; however, there was an increased duration of hyperglycaemia and more autocorrections on exercise days. The time spent in severe hyperglycaemia (>250 mg/dL) increased by 2.7% in exercise compared to non-exercise days (P = .02). It is worth noting that hypoglycaemic episodes did not increase during the exercise days compared with non-exercise days.

CONCLUSION

The hybrid closed-loop system was effective and safe in children and adolescents with type 1 diabetes during the performance of competitive sports in real life.

Continuous glucose monitoring with structured education in adults with type 2 diabetes managed by multiple daily insulin injections: a multicentre randomised controlled trial

AIMS/HYPOTHESIS

The aim of this study was to compare the effectiveness of stand-alone intermittently scanned continuous glucose monitoring (isCGM) with or without a structured education programme and blood glucose monitoring (BGM) in adults with type 2 diabetes on multiple daily insulin injections (MDI).

METHODS

In this 24 week randomised open-label multicentre trial, adults with type 2 diabetes on intensive insulin therapy with HbA1c levels of 58-108 mmol/mol (7.5-12.0%) were randomly assigned in a 1:1:1 ratio to isCGM with a structured education programme on adjusting insulin dose and timing according to graphical patterns in CGM (intervention group), isCGM with conventional education (control group 1) or BGM with conventional education (control group 2). Block randomisation was conducted by an independent statistician. Due to the nature of the intervention, blinding of participants and investigators was not possible. The primary outcome was change in HbA1c from baseline at 24 weeks, assessed using ANCOVA with the baseline value as a covariate.

RESULTS

A total of 159 individuals were randomised (n=53 for each group); 148 were included in the full analysis set, with 52 in the intervention group, 49 in control group 1 and 47 in control group 2. The mean (± SD) HbA1c level at baseline was 68.19±10.94 mmol/mol (8.39±1.00%). The least squares mean change (± SEM) from baseline HbA1c at 24 weeks was -10.96±1.35 mmol/mol (-1.00±0.12%) in the intervention group, -6.87±1.39 mmol/mol (-0.63±0.13%) in control group 1 (p=0.0367 vs intervention group) and -6.32±1.42 mmol/mol (-0.58±0.13%) in control group 2 (p=0.0193 vs intervention group). Adverse events occurred in 28.85% (15/52) of individuals in the intervention group, 26.42% (14/53) in control group 1 and 48.08% (25/52) in control group 2.

CONCLUSIONS/INTERPRETATION

Stand-alone isCGM offers a greater reduction in HbA1c in adults with type 2 diabetes on MDI when education on the interpretation of graphical patterns in CGM is provided.

TRIAL REGISTRATION

ClinicalTrials.gov NCT04926623.

FUNDING

This study was supported by Daewoong Pharmaceutical Co., Ltd.

Continuous glucose monitoring in pregnant women with pregestational type 2 diabetes: a narrative review

Background: Type 2 diabetes (T2DM) in pregnancy is associated with poor perinatal outcomes; however, there are limited data on outcomes of continuous glucose monitoring (CGM) use in this population. Objective: We reviewed the literature on studies reporting CGM outcomes in pregnant women with T2DM. We aimed to synthesise in a narrative review, the effects of CGM on glycaemic and perinatal outcomes as well as current research gaps. Results: Of 34 articles screened, three reported CGM outcomes in T2DM pregnancies compared to self-monitoring of blood glucose (SMBG). Other feasibility and mixed population studies were also reviewed. CGM in T2DM has good feasibility, acceptability, and improved glycaemic control beyond SMBG. There were limited data to draw conclusions on its effect on maternal and fetal outcomes. Conclusion: Further studies of perinatal outcomes in pregnant women with T2DM are required to determine the impact of improved glycaemia with CGM.

Recent advances in the precision control strategy of artificial pancreas

The scientific diagnosis and treatment of patients with diabetes require frequent blood glucose testing and insulin delivery to normoglycemia. Therefore, an artificial pancreas with a continuous blood glucose (BG) monitoring function is an urgent research target in the medical industry. The problem of closed-loop algorithmic control of the BG with a time delay is a key and difficult issue that needs to be overcome in the development of an artificial pancreas. Firstly, the composition, structure, and control characteristics of the artificial pancreas are introduced. Subsequently, the research progress of artificial pancreas control algorithms is reviewed, and the characteristics, advantages, and disadvantages of proportional-integral-differential control, model predictive control, and artificial intelligence control are compared and analyzed to determine whether they are suitable for the practical application of the artificial pancreas. Additionally, key advancements in areas such as blood glucose data monitoring, adaptive models, wearable devices, and fully automated artificial pancreas systems are also reviewed. Finally, this review highlights that meal prediction, control safety, integration, streamlining the optimization of control algorithms, constant temperature preservation of insulin, and dual-hormone artificial pancreas are issues that require further attention in the future.

Real-World Continuous Glucose Monitoring Data from a Population with Type 1 Diabetes in South Korea: Nationwide Single-System Analysis

Background: We used continuous glucose monitoring (CGM) data to investigate glycemic outcomes in a real-world population with type 1 diabetes (T1D) from South Korea, where the widespread use of CGM and the nationwide education program began almost simultaneously. Methods: Data from Dexcom G6 users with T1D in South Korea were collected between January 2019 and January 2023. Users were included if they provided at least 90 days of glucose data and used CGM at least 70% of the days in the investigational period. The relationship between CGM utilization and glycemic metrics, including the percentage of time in range (TIR), time below range (TBR), and time above range (TAR), was assessed. The study was approved by the Institutional Review Board of Samsung Medical Center (SMC 2023-05-030). Results: A total of 2288 users were included. Mean age was 41.5 years (57% female), with average uploads of 428 days. Mean TIR was 62.4% ± 18.5%, mean TBR <70 mg/dL was 2.6% ± 2.8%, mean TAR >180 mg/dL was 35.0% ± 19.3%, mean glucose was 168.1 ± 35.8 mg/dL, mean glucose management indicator was 7.2% ± 0.9%, and mean coefficient of variation was 36.7% ± 6.0%. Users with higher CGM utilization had higher TIR (67.8% vs. 52.7%), and lower TBR <70 mg/dL (2.3% vs. 4.7%) and TAR >180 mg/dL (30.0% vs. 42.6%) than those with low CGM utilization (P < 0.001 for all). Users whose data were shared with others had higher TIR than those who did not (63.3% vs. 60.8%, P = 0.001). Conclusions: In this South Korean population, higher CGM utilization was associated with a favorably higher mean TIR, which was close to the internationally recommended target. Using its remote data-sharing feature showed beneficial impact on TIR.

Accuracy and Safety of the 15-Day CareSens Air Continuous Glucose Monitoring System

Background: We evaluated the accuracy and safety of the CareSens Air, a novel real-time continuous glucose monitoring system (CGMS), during 15 days of use in adults with diabetes. Methods: Adults with either type 1 diabetes or type 2 diabetes requiring intensive insulin therapy participated at four sites in South Korea. All participants wore the sensor for 15 days. Participants were scheduled for four 8-h clinic sessions on Day 1, 5 ± 1, 10 ± 1, and 15. Accuracy was evaluated based on the proportion of continuous glucose monitoring (CGM) values within 15% of YSI values ≥100 mg/dL or within 15 mg/dL of YSI values <100 mg/dL (%15/15), along with the %20/20, %30/30, and %40/40 agreement rates. The mean absolute relative difference (MARD) between the CGM and YSI values was calculated. Results: Data from 83 participants (83 sensors, 10,029 CGM-YSI matched pairs) were analyzed. The overall MARD was 10.42%, and the overall %15/15, %20/20, %30/30, and %40/40 accuracy were 78.55%, 89.04%, 96.47%, and 98.87%, respectively. The consensus error grid analysis showed that 99.92% of CGM values fell into Zone A or B (Zone A: 89.83%, Zone B: 10.09%). The %20/20 accuracy of CGMS was 88.11% on Day 1, 90.11% on Day 3-5, 92.09% on Day 8-10, and 85.63% on Day 15. No serious adverse events were reported. Conclusions: The CareSens Air demonstrated accurate performance across the wide glycemic range and was well tolerated during the 15-day sensor use period.

Digital Health in Diabetes and Cardiovascular Disease

BACKGROUND

Digital health technologies are rapidly evolving and transforming the care of diabetes and cardiovascular disease (CVD).

PURPOSE OF THE REVIEW

In this review, we discuss emerging approaches incorporating digital health technologies to improve patient outcomes through a more continuous, accessible, proactive, and patient-centered approach. We discuss various mechanisms of potential benefit ranging from early detection to enhanced physiologic monitoring over time to helping shape important management decisions and engaging patients in their care. Furthermore, we discuss the potential for better individualization of management, which is particularly important in diseases with heterogeneous and complex manifestations, such as diabetes and cardiovascular disease. This narrative review explores ways to leverage digital health technology to better extend the reach of clinicians beyond the physical hospital and clinic spaces to address disparities in the diagnosis, treatment, and prevention of diabetes and cardiovascular disease.

CONCLUSION

We are at the early stages of the shift to digital medicine, which holds substantial promise not only to improve patient outcomes but also to lower the costs of care. The review concludes by recognizing the challenges and limitations that need to be addressed for optimal implementation and impact. We present recommendations on how to navigate these challenges as well as goals and opportunities in utilizing digital health technology in the management of diabetes and prevention of adverse cardiovascular outcomes.

Real-time continuous glucose monitoring vs. self-monitoring of blood glucose: cost-utility in South Korean type 2 diabetes patients on intensive insulin

AIMS

This study investigated the cost-utility of real-time continuous glucose monitoring (rt-CGM) versus self-monitoring of blood glucose (SMBG) in people with type 2 diabetes (T2D) receiving intensive insulin therapy in South Korea.

METHODS

The IQVIA Core Diabetes Model (CDM v9.5) was used, with clinical effectiveness data obtained from a large-scale real world study. Costs were obtained from South Korean sources and inflated to 2022 South Korean Won (KRW). A South Korean payer perspective was adopted over a lifetime horizon, with future costs and effects discounted at 4.5% per annum. Baseline characteristics included a mean baseline HbA1c level of 8.6% (71 mmol/mol), and a mean age of 64.4 years. A willingness-to-pay (WTP) threshold of KRW 46.0 million was used.

RESULTS

Rt-CGM led to an increase of 0.683 quality-adjusted life years (QALYs) versus SMBG (7.526 QALYs for rt-CGM versus 6.843 QALYs for SMBG). An increase in costs of KRW 16.4 million (from KRW 90.4 million to KRW 106.8 million) was associated with rt-CGM. The incremental cost-utility ratio was KRW 24.0 million per QALY gained, significantly lower than the KRW 46 million threshold.

CONCLUSIONS

For individuals with T2D managed by intensive insulin therapy in South Korea, rt-CGM is cost-effective relative to SMBG.

Comparison of Glycemia Risk Index with Time in Range for Assessing Glycemic Quality

Background: The glycemia risk index (GRI) is a novel composite continuous glucose monitoring (CGM) metric that gives greater weight to hypoglycemia than to hyperglycemia and to extreme hypo/hyperglycemia over less extreme hypo/hyperglycemia. This study aimed at validating the effectiveness of GRI and at comparing it with time in range (TIR) in assessing glycemic quality in clinical practice. Methods: A total of 524 ninety-day CGM tracings of 194 insulin-treated adults with diabetes were included in the analysis. GRI was assessed according to standard metrics in ambulatory glucose profiles. Both cross-sectional and longitudinal analyses were performed to compare the GRI and TIR. Results: The GRI was strongly correlated not only with TIR (r = -0.974), but also with the coefficient of variation (r = 0.683). To identify whether the GRI differed by hypoglycemia even with a similar TIR, CGM tracings were grouped according to TIR (50% to <60%, 60% to <70%, 70% to <80%, and ≥80%). In each TIR group, the GRI increased as time below range (TBR)<70 mg/dL increased (P < 0.001 for all TIR groups). In longitudinal analysis, as TBR<70 mg/dL improved, the GRI improved significantly (P = 0.003) whereas TIR did not (P = 0.704). Both GRI and TIR improved as time above range (TAR)>180 mg/dL improved (P < 0.001 for both). The longitudinal change was easily identifiable on a GRI grid. Conclusions: The GRI is a useful tool for assessing glycemic quality in clinical practice and reflects hypoglycemia better than does TIR.

The Benefits Of Continuous Glucose Monitoring In Pregnancy

Previous studies have consistently demonstrated the positive effects of continuous glucose monitoring (CGM) on glycemic outcomes and complications of diabetes in people with type 1 diabetes. Guidelines now consider CGM to be an essential and cost-effective device for managing type 1 diabetes. As a result, insurance coverage for it is available. Evidence supporting CGM continues to grow and expand to broader populations, such as pregnant people with type 1 diabetes, people with type 2 diabetes treated only with basal insulin therapy, and even type 2 diabetes that does not require insulin treatment. However, despite the significant risk of hyperglycemia in pregnancy, which leads to complications in more than half of affected newborns, CGM indications and insurance coverage for those patients are unresolved. In this review article, we discuss the latest evidence for using CGM to offer glycemic control and reduce perinatal complications, along with its cost-effectiveness in pregestational type 1 and type 2 diabetes and gestational diabetes mellitus. In addition, we discuss future prospects for CGM coverage and indications based on this evidence.

Glycemic Outcomes During Early Use of the MiniMed™ 780G Advanced Hybrid Closed-Loop System with Guardian™ 4 Sensor

Background: Safety and significant improvement in overall glycated hemoglobin (A1C) and percentage of time spent in (TIR), below (TBR), and above (TAR) glucose range were demonstrated in the pivotal trial of adolescents and adults using the MiniMed™ advanced hybrid closed-loop (AHCL) system with the adjunctive, calibration-required Guardian™ Sensor 3. The present study evaluated early outcomes of continued access study (CAS) participants who transitioned from the pivotal trial investigational system to the approved MiniMed™ 780G system with the non-adjunctive, calibration-free Guardian™ 4 Sensor (MM780G+G4S). Study data were presented alongside those of real-world MM780G+G4S users from Europe, the Middle East, and Africa. Methods: The CAS participants (N = 109, aged 7-17 years and N = 67, aged >17 years) used the MM780G+G4S for 3 months and data of real-world MM780G+G4S system users (N = 10,204 aged ≤15 years and N = 26,099 aged >15 years) were uploaded from September 22, 2021 to December 02, 2022. At least 10 days of real-world continuous glucose monitoring (CGM) data were required for analyses. Glycemic metrics, delivered insulin and system use/interactions underwent descriptive analyses. Results: Time in AHCL and CGM use were >90% for all groups. AHCL exits averaged 0.1/day and there were few blood glucose measurements (BGMs) (0.8/day-1.0/day). Adults in both cohorts met most consensus recommendations for glycemic targets. Pediatric groups met recommendations for %TIR and %TBR, although not those for mean glucose variability and %TAR, possibly due to low use of recommended glucose target (100 mg/dL) and active insulin time (2 h) settings (28.4% in the CAS cohort and 9.4% in the real-world cohort). The CAS pediatric and adult A1C were 7.2% ± 0.7% and 6.8% ± 0.7%, respectively, and there were no serious adverse events. Conclusions: Early clinical use of the MM780G+G4S was safe and involved minimal BGMs and AHCL exits. Consistent with real-world pediatric and adult use, outcomes were associated with achievement of recommended glycemic targets. Clinical Trial Registration number: NCT03959423.

APSec1.0: Innovative Security Protocol Design with Formal Security Analysis for the Artificial Pancreas System

The Medical Internet-of-Things (MIoT) has developed revolutionary ways of delivering medical care to patients. An example system, showing increasing demand, is the artificial pancreas system that offers convenience and reliable support care to patients with Type 1 Diabetes. Despite the apparent benefits, the system cannot escape potential cyber threats that may worsen a patient's condition. The security risks need immediate attention to ensure the privacy of the patient and preserve safe functionality. Motivated by this, we proposed a security protocol for the APS environment wherein support to essential security requirements is guaranteed, the security context negotiation is resource-friendly, and the protocol is resilient to emergencies. Accordingly, the security requirements and correctness of the design protocol were formally verified using BAN logic and AVISPA, and proved its feasibility through the emulation of APS in a controlled environment using commercial off-the-shelf devices. Moreover, the results of our performance analysis indicate that the proposed protocol is more efficient than the other existing works and standards.