Continuous Glucose Monitoring: A Review of Recent Studies Demonstrating Improved Glycemic Outcomes

The Digital Revolution in Medicine: Applications in Cardio-Oncology

Purpose of review

A critical evaluation of contemporary literature regarding the role of big data, artificial intelligence, and digital technologies in precision cardio-oncology care and survivorship, emphasizing innovative and groundbreaking endeavors.

Recent findings

Artificial intelligence (AI) algorithm models can automate the risk assessment process and augment current subjective clinical decision tools. AI, particularly machine learning (ML), can identify medically significant patterns in large data sets. Machine learning in cardio-oncology care has great potential in screening, diagnosis, monitoring, and managing cancer therapy-related cardiovascular complications. To this end, large-scale imaging data and clinical information are being leveraged in training efficient AI algorithms that may lead to effective clinical tools for caring for this vulnerable population. Telemedicine may benefit cardio-oncology patients by enhancing healthcare delivery through lowering costs, improving quality, and personalizing care. Similarly, the utilization of wearable biosensors and mobile health technology for remote monitoring holds the potential to improve cardio-oncology outcomes through early intervention and deeper clinical insight. Investigations are ongoing regarding the application of digital health tools such as telemedicine and remote monitoring devices in enhancing the functional status and recovery of cancer patients, particularly those with limited access to centralized services, by increasing physical activity levels and providing access to rehabilitation services.

Summary

In recent years, advances in cancer survival have increased the prevalence of patients experiencing cancer therapy-related cardiovascular complications. Traditional cardio-oncology risk categorization largely relies on basic clinical features and physician assessment, necessitating advancements in machine learning to create objective prediction models using diverse data sources. Healthcare disparities may be perpetuated through AI algorithms in digital health technologies. In turn, this may have a detrimental effect on minority populations by limiting resource allocation. Several AI-powered innovative health tools could be leveraged to bridge the digital divide and improve access to equitable care.

Continuous glucose monitoring in adults with short bowel syndrome receiving overnight infusions of home parenteral nutrition

BACKGROUND/OBJECTIVES

Consumers of home parenteral nutrition (HPN) are susceptible to dysglycemia. The aim was to characterize 24-h glucose profiles of HPN consumers using continuous glucose monitors (CGM) and to identify factors that influence glucose.

SUBJECTS/METHODS

Glucose profiles of 20 adults with short bowel syndrome (SBS) without diabetes were assessed using the Freestyle Libre Pro CGM. Measures included mean 24-h glucose, coefficient of variation (%), % time in range (TIR 70-140 mg/dL), among others. HPN parameters and lifestyle behaviors were obtained from self-reports and validated surveys. Linear mixed-effects models were used to test associations with glycemic measures adjusted for age, sex, and BMI. Significance was considered at P < 0.05.

RESULTS

Participants (77% female, age = 52 years, BMI = 21.4 kg/m², 95% white) had a 24-h mean and CV for glucose of 94.69 (8.96) mg/dL and 20.27%, respectively, and a mean TIR of 87.73%. Among non-daily HPN-dependent patients, the mean glucose and TIR were higher on days receiving HPN. Tapering HPN was associated with -6.882 (95% confidence interval = -12.436, -1.329) % lower CV, and higher HPN dextrose content per gram was associated with 0.039 (95% confidence interval = 0.008, 0.07) % higher CV. Smoking, more depressive symptoms, and higher insomnia severity showed associations with glucose levels and variability.

CONCLUSIONS

Metabolically stable HPN adult consumers have 24-h glucose measures comparable to healthy adults yet are notable for more time spent below range. The glucose profiles are influenced by HPN parameters such as tapering and dextrose and behaviors including smoking, depressive symptoms, and insomnia.

RNA-based diagnostic innovations: A new frontier in diabetes diagnosis and management

Background/Objective: Diabetes mellitus (DM) remains a major global health challenge due to its chronic nature and associated complications. Traditional diagnostic approaches, though effective, often lack the sensitivity required for early-stage detection. Recent advancements in molecular biology have identified RNA molecules, particularly non-coding RNAs such as microRNAs (miRNAs), long non-coding RNAs (lncRNAs), and circular RNAs (circRNAs), as promising biomarkers for diabetes. This review aims to explore the role of RNA-based biomarkers in the diagnosis, prognosis, and management of diabetes, highlighting their potential to revolutionize diabetes care.Method: A comprehensive literature review was conducted using electronic databases including PubMed, Scopus, and Web of Science. Articles published up to 2024 were screened and analyzed to extract relevant findings related to RNA-based diagnostics in diabetes. Emphasis was placed on studies demonstrating clinical utility, mechanistic insights, and translational potential of RNA molecules.Results: Numerous RNA species, particularly miRNAs such as miR-375, miR-29, and lncRNAs like H19 and MEG3, exhibit altered expression patterns in diabetic patients. These molecules are involved in key regulatory pathways of glucose metabolism, insulin resistance, and β-cell function. Circulating RNAs are detectable in various biofluids, enabling non-invasive diagnostic approaches. Emerging technologies, including RNA sequencing and liquid biopsy platforms, have enhanced the sensitivity and specificity of RNA detection, fostering the development of novel diagnostic tools and personalized therapeutic strategies.Conclusion: RNA-based biomarkers hold significant promise in advancing early detection, risk stratification, and therapeutic monitoring in diabetes care. Despite current challenges such as standardization and clinical validation, the integration of RNA diagnostics into routine clinical practice could transform diabetes management, paving the way for precision medicine approaches. Further research and multi-center trials are essential to validate these biomarkers and facilitate their regulatory approval and clinical implementation.

Toward Detection of Nocturnal Hypoglycemia in People With Diabetes Using Consumer-Grade Smartwatches and a Machine Learning Approach

BACKGROUND

Nocturnal hypoglycemia poses significant risks to individuals with insulin-treated diabetes, impacting health and quality of life. Although continuous glucose monitoring (CGM) systems reduce these risks, their poor accuracy at low glucose levels, high cost, and availability limit their use. This study examined physiological biomarkers associated with nocturnal hypoglycemia and evaluated the use of machine learning (ML) to detect hypoglycemia during nighttime sleep using data from consumer-grade smartwatches.

METHODS

This study analyzed 351 nights of 36 adults with insulin-treated diabetes. Participants wore two smartwatches alongside CGM systems. Linear mixed-effects models compared sleep and vital signs between nights with and without hypoglycemia during early and late sleep. A ML model was trained to detect hypoglycemia solely using smartwatch data.

RESULTS

Sixty-six nights with spontaneous hypoglycemia were recorded. Hypoglycemic nights showed increased wake periods, heart rate, stress levels, and activity during early sleep, with weaker effects during late sleep. In nights when hypoglycemia occurred during early sleep, the ML model performed comparable or better than prior studies with an area under the receiver operator curve of 0.78 for level 1 and 0.83 for level 2 hypoglycemia, with sensitivity of 0.78 and 0.89, specificity of 0.64 for both, negative predictive value of 0.94 and 0.99, and positive predictive value of 0.25 and 0.13 for level 1 and level 2 hypoglycemia, respectively.

CONCLUSIONS

Consumer-grade smartwatches demonstrate promise for detecting nocturnal hypoglycemia, particularly during early sleep. Refining models to reduce false alarms could enhance their clinical utility as low-cost, accessible tools to complement CGM.

Continuous Glucose Monitoring in Hospitalized Adults With Diabetic Ketoacidosis: A Prospective Open-Label Pilot Study

BACKGROUND

Continuous glucose monitoring (CGM) devices are increasingly used in critical and non-critical care hospital units. The efficacy of CGM in assessing glucose control in adults with diabetic ketoacidosis (DKA) is unknown.

METHODS

This single-center pilot study compared glycemic control by real-time CGM (Dexcom G6), capillary point-of-care (POC), and basic metabolic panel (BMP) during intravenous (IV) insulin treatment and after the resolution of DKA. We compared the mean absolute relative difference (MARD), median absolute relative difference (ARD) glucose values, and Diabetes Technology Society (DTS) Error Grid analyses.

RESULTS

We recruited 52 patients (49 ± 19 years, admission glucose: 503 ± 239.4 mg/dL) with type 1 diabetes (n = 24) and type 2 diabetes (n = 28). Compared with POC testing, the MARD was 17.4% ± 13.2%, and the median ARD was 14.2% (interquartile range [IQR]: 6.4, 28) during the initial IV insulin period and 19.8% ± 18.7% and 14.3% (7, 26.2) after DKA resolution. The DTS Error Grid analysis showed that 100% of values during the IV insulin treatment and 95% after the DKA resolution were in zones A+B. Compared with BMP glucose values, the MARD and median ARD were 18.5% ± 19.1% and 12.2% (5.4, 23.8) during the IV insulin treatment and 22.5% ± 24.7% and 15.1% (6.6, 27.6) after DKA resolution.

CONCLUSION

This is the first report on the use of real-time CGM in adults with DKA. Our study indicates that CGM technology is a reliable tool for hospital use during acute insulin treatment and after the resolution of DKA. Future multicentre randomized studies are needed to determine the benefits of real-time CGM in facilitating diabetes care in hospitalized patients with hyperglycemic crises.

Limitations in Achieving Glycemic Targets From CGM Data and Persistence of Severe Hypoglycemia in Adults With Type 1 Diabetes Regardless of Insulin Delivery Method

OBJECTIVE

We captured continuous glucose monitoring (CGM) metrics from a large online survey of adults with type 1 diabetes to determine how glycemic outcomes varied by insulin delivery form.

RESEARCH DESIGN AND METHODS

Adults with type 1 diabetes from the T1D Exchange Registry/online communities completed the survey and contributed retrospective CGM data for up to 1 year. Self-reported glycemic outcomes and CGM measures were described overall and by insulin delivery method.

RESULTS

The 926 participants completed the survey and provided CGM data. Mean ± SD age was 41.9 ± 15.7 years, and 50.8% reported using automated insulin delivery (AID). While AID users spent more time in range, 27.9% did not achieve time in range targets, 15.5% reported severe hypoglycemic events (SHEs), and 16.0% had CGM-detected level 2 hypoglycemic events.

CONCLUSIONS

Despite use of diabetes technologies, many individuals are unable to achieve glycemic targets and experience severe hypoglycemia, highlighting the need for novel treatments.

Unsupervised Retrospective Detection of Pressure Induced Failures in Continuous Glucose Monitoring Sensors for T1D Management

Continuous Glucose Monitoring sensors (CGMs) have revolutionized type 1 diabetes (T1D) management. In particular, in several cases, the retrospective analysis of CGM recordings allows clinicians to review and adjust patients' therapy. However, in this set-up, the artifacts that are often present in CGM data could lead to incorrect therapeutic actions. To mitigate this risk, we investigate how to detect one of the most common of these artifacts, the so-called pressure induced sensor attenuations, by means of anomaly detection algorithms. Specifically, these methods belong to the class of unsupervised techniques, which is particularly appealing since it does not require a labeled dataset, hardly available in practice. After having designed five features to highlight the anomalous state of the sensor, 8 different methods (e.g. Isolation Forest and Histogram-based Outlier Score) are assessed both in silico using the UVa/Padova Type 1 Diabetes Simulator and on real data of 36 subjects monitored for about 10 days. In the in silico scenario, the best results are achieved with Isolation Forest, which recognized the 74% of the failures generating on average only 2 false alerts during the whole monitoring time. In real data, Isolation Forest is confirmed to be effective in the detection of failures, achieving a recall of 55% and generating 3 false alarms in 10 days. By allowing to detect more than 50% of the artifacts while discarding only a few portions of correct data in several days of monitoring, the proposed approach could effectively improve the quality of CGM data used by clinicians to retrospectively evaluate and adjust T1D therapy.

Glucometrics and Patient-Reported Outcomes in Individuals With Type 1 Diabetes Mellitus: Insights From the Correlation of Time in Range (CorrelaTIR) Study in Real-World Settings

Background This study aimed to measure the association between time in range (TIR) and other continuous glucose monitoring (CGM)-derived glucometrics, quality of life (QoL), healthcare resource use (HCRU), and costs in persons with type 1 diabetes mellitus (T1DM) in routine clinical practice in Spain. Methods This observational, cross-sectional, multicentre study evaluated persons with T1DM who received insulin via multiple daily injections. The study collected data on the participants (demographic and clinical), the use of the CGM devices, patient-reported outcomes (PROs) for general and diabetes-related QoL, treatment satisfaction, work productivity and activity impairment, HCRU, and costs. Data were analysed descriptively. The Spearman correlation coefficient was used to measure the association between glucometrics and PROs, HCRU and costs. Results Participants (N=114) had a mean age (standard deviation) of 44.53 (14.39) years, were 50.88% men, and 53.51% had glycated haemoglobin ≤7%. A higher TIR was significantly associated with better diabetes-related QoL but not with general QoL. HCRU and PRO scores for treatment satisfaction and work productivity and activity impairment showed no correlation with TIR. Higher TIR correlated with a lower number of emergency room visits. Conclusion Good glycaemic control (high TIR) is favourably associated with some aspects of diabetes-related QoL.

Novel Glucose Metric "Latest Spike Time" Correlated with Weight Loss at Six Months in People with Obesity Using the Signos System

Introduction: The rise of digital health applications utilizing continuous glucose monitoring (CGM) allows for novel assessments of glucose management and weight changes in people without diabetes. The Signos System incorporates a digital health app paired with a CGM to provide information and prompts aimed to help people without diabetes to manage weight. Objectives: The primary objective of this study was to determine whether the average timing of the latest chronological glucose excursion ("spike") was correlated with amount of weight loss. Methods: This was a retrospective analysis of prospectively obtained glucose and weight data from people without diabetes who enrolled in the Signos System from November 2021 to August 2023. Participants were provided CGMs as well as encouraged to use the Signos app with personalized advice and logging capabilities for weight, food, physical activity, heart rate, sleep, and activities. "Latest spike time" (LST) was retrospectively derived from CGM data and compared with weight changes at 6 months. Results: Nine hundred and twenty-six subjects met the inclusion criteria including sufficient days wearing a CGM and a weight log within 15 days of 6 months from their first weight log. There was a strong correlation between an earlier spike time and increased weight loss. The top quintile of subjects, with an average LST before 5:41 PM, lost over three times as much weight as the bottom quintile of users, with LST after 8:40 PM; this separation was predictable within 1 month of data. Conclusion: In a large population of obese people without diabetes, continuous glucose data, specifically a novel metric "LST," was highly correlated with percentage of total body weight loss at 6 months. This research suggests that for people attempting weight loss, review and alteration of behaviors relating to later glucose excursions may be of specific benefit.

Single-Walled Carbon Nanotubes as Optical Transducers for Nanobiosensors In Vivo

Semiconducting single-walled carbon nanotubes (SWCNTs) may serve as signal transducers for nanobiosensors. Recent studies have developed innovative methods of engineering molecularly specific sensors, while others have devised methods of deploying such sensors within live animals and plants. These advances may potentiate the use of implantable, noninvasive biosensors for continuous drug, disease, and contaminant monitoring based on the optical properties of single-walled carbon nanotubes (SWCNTs). Such tools have substantial potential to improve disease diagnostics, prognosis, drug safety, therapeutic response, and patient compliance. Outside of clinical applications, such sensors also have substantial potential in environmental monitoring or as research tools in the lab. However, substantial work remains to be done to realize these goals through further advances in materials science and engineering. Here, we review the current landscape of quantitative SWCNT-based optical biosensors that have been deployed in living plants and animals. Specifically, we focused this review on methods that have been developed to deploy SWCNT-based sensors in vivo as well as analytes that have been detected by SWCNTs in vivo. Finally, we evaluated potential future directions to take advantage of the promise outlined here toward field-deployable or implantable use in patients.

Mixed-effects neural network modelling to predict longitudinal trends in fasting plasma glucose

BACKGROUND

Accurate fasting plasma glucose (FPG) trend prediction is important for management and treatment of patients with type 2 diabetes mellitus (T2DM), a globally prevalent chronic disease. (Generalised) linear mixed-effects (LME) models and machine learning (ML) are commonly used to analyse longitudinal data; however, the former is insufficient for dealing with complex, nonlinear data, whereas with the latter, random effects are ignored. The aim of this study was to develop LME, back propagation neural network (BPNN), and mixed-effects NN models that combine the 2 to predict FPG levels.

METHODS

Monitoring data from 779 patients with T2DM from a multicentre, prospective study from the shared platform Figshare repository were divided 80/20 into training/test sets. The first 10 important features were modelled via random forest (RF) screening. First, an LME model was built to model interindividual differences, analyse the factors affecting FPG levels, compare the AIC and BIC values to screen the optimal model, and predict FPG levels. Second, multiple BPNN models were constructed via different variable sets to screen the optimal BPNN. Finally, an LME/BPNN combined model, named LMENN, was constructed via stacking integration. A 10-fold cross-validation cycle was performed using the training set to build the model and evaluate its performance, and then the final model was evaluated on the test set.

RESULTS

The top 10 variables screened by RF were HOMA-β, HbA1c, HOMA-IR, urinary sugar, insulin, BMI, waist circumference, weight, age, and group. The best-fitting random-intercept mixed-effects (lm22) model showed that each patient's baseline glucose levels influenced subsequent glucose measurements, but the trend over time was consistent. The LMENN model combines the strengths of LME and BPNN and accounts for random effects. The RMSE of the LMENN model ranges were 0.447-0.471 (training set), 0.525-0.552 (validation set), and 0.511-0.565 (test set). It improves the prediction performance of the single LME and BPNN models and shows some advantages in predicting FPG levels.

CONCLUSIONS

The LMENN model built by integrating LME and BPNN has several potential applications in analysing longitudinal FPG monitoring data. This study provides new ideas and methods for further research in the field of blood glucose prediction.

Novel Detection and Progression Markers for Diabetes Based on Continuous Glucose Monitoring Data Dynamics

CONTEXT

Static measures of continuous glucose monitoring (CGM) data, such as time spent in specific glucose ranges (70-180 mg/dL or 70-140 mg/dL), do not fully capture the dynamic nature of blood glucose, particularly the subtle gradual deterioration of glycemic control over time in individuals with early-stage type 1 diabetes.

OBJECTIVE

Develop a diabetes diagnostic tool based on 2 markers of CGM dynamics: CGM entropy rate (ER) and Poincaré plot (PP) ellipse area (S).

METHODS

A total of 5754 daily CGM profiles from 843 individuals with type 1, type 2 diabetes, or healthy individuals with or without islet autoantibody status were used to compute 2 individual dynamic markers: ER (in bits per transition; BPT) of daily probability matrices describing CGM transitions between 8 glycemic states, and the area S (mg2/dL2) of individual CGM PP ellipses using standard PP descriptors. The Youden index was used to determine "optimal" cut-points for ER and S for health vs diabetes (case 1); type 1 vs type 2 (case 2); and low vs high type 1 immunological risk (case 3). The markers' discriminative power was assessed through the area under the receiver operating characteristics curves (AUC).

RESULTS

Optimal cutoff points were determined for ER and S for each of the 3 cases. ER and S discriminated case 1 with AUC = 0.98 (95% CI, 0.97-0.99) and AUC = 0.99 (95% CI, 0.99-1.00), respectively (cutoffs ERcase1 = 0.76 BPT, Scase1 = 1993.91 mg2/dL2), case 2 with AUC = 0.81 (95% CI, 0.77-0.84) and AUC = 0.76 (95% CI, 0.72-0.81), respectively (ERcase2 = 1.00 BPT, Scase2 = 5112.98 mg2/dL2), and case 3 with AUC = 0.72 (95% CI, 0.58-0.86), and AUC = 0.66 (95% CI, 0.47-0.86), respectively (ERcase3 = 0.52 BPT, Scase3 = 923.65 mg2/dL2).

CONCLUSION

CGM dynamics markers can be an alternative to fasting plasma glucose or glucose tolerance testing to identify individuals at higher immunological risk of progressing to type 1 diabetes.

The effect of telehealth on clinical outcomes in patients with hypertension and diabetes: A meta-analysis of 106,261 patients

INTRODUCTION

Telemedicine, propelled by recent technological advancements, has transformed healthcare delivery, notably benefiting patients with chronic non-communicable diseases (NCDs) such as systemic arterial hypertension and diabetes mellitus. This meta-analysis of randomized clinical trials aimed to assess the efficacy of telehealth-based interventions on disease control rates and clinical parameters among NCD patients, including systolic and diastolic blood pressure (SBP and DBP), fasting blood glucose (FBG), and glycated hemoglobin (HbA1c) levels.

METHODS

We conducted searches in PubMed, Scopus, Web of Science, and the Cochrane Database for interventional studies that compared tele-monitoring with usual care in patients with hypertension and type 2 diabetes mellitus. Odds ratios with 95% confidence intervals (CIs) were computed.

RESULTS

Our meta-analysis included 75 studies, encompassing a total of 106,261 patients, with 50,074 (47.12%) receiving usual care and 56,187 (52.88%) receiving tele-monitoring care. The telemedicine group was associated with a statistically significant reduction in SBP (mean difference (MD) -4.927 mmHg; 95% CI -6.193 to -3.660; p < 0.001; I² = 90%), DBP (MD -2.019 mmHg; 95% CI -2.679 to -1.359; p < 0.001; I² = 54%), FBG (MD -0.405 mmol/L; 95% CI -0.597 to -0.213; p < 0.001; I² = 32%), and HbA1c (MD -0.418%; 95% CI -0.525 to -0.312; p < 0.001; I² = 76%).

CONCLUSIONS

Our meta-analysis shows that telehealth technologies notably enhance blood pressure and blood glucose control. This supports integrating telemedicine into clinical protocols as a valuable complementary tool for managing hypertension and diabetes mellitus comprehensively.

A combined diabetes and continuous glucose monitoring education program for adults with type 2 diabetes

Objective

The lack of descriptions for education programs in studies evaluating the efficacy of continuous glucose monitoring (CGM) compared to blood glucose monitoring (BGM) for individuals with T2DM makes it difficult to compare results across trials. This study aimed to develop and evaluate a new education program for adults with insulin-treated T2DM and HbA1c ≥58 mmol/mol (7.5 %) initiating CGM.

Methods

A 3-h education program was created to provide information on diabetes self-management and CGM or BGM based on international guidelines and a pre-evaluation based on user needs assessment. Questionnaires were used to post-evaluate participant-rated benefits from the program.

Results

Seven individuals attended a user needs assessment of the program and 96 participated in the final education program (61.5 % men, mean age 61 (59.5;63) years, mean diabetes duration 18.2 (16.9;19.5) years, and median HbA1c 69 (63-78)mmol/mol (8.5 (7.9-9.3)%). Benefit from this program was rated good/very good by 95.5 % with no statistically significant difference between glucose monitoring groups.

Conclusions

This study presents a new well-received education program for T2DM for both the CGM and BGM group.

Innovation

The description of the development process and the education provided for both glucose monitoring groups may be useful for CGM initiation in clinics and trials.

Clinical research framework proposal for ketogenic metabolic therapy in glioblastoma

Glioblastoma (GBM) is the most aggressive primary brain tumor in adults, with a universally lethal prognosis despite maximal standard therapies. Here, we present a consensus treatment protocol based on the metabolic requirements of GBM cells for the two major fermentable fuels: glucose and glutamine. Glucose is a source of carbon and ATP synthesis for tumor growth through glycolysis, while glutamine provides nitrogen, carbon, and ATP synthesis through glutaminolysis. As no tumor can grow without anabolic substrates or energy, the simultaneous targeting of glycolysis and glutaminolysis is expected to reduce the proliferation of most if not all GBM cells. Ketogenic metabolic therapy (KMT) leverages diet-drug combinations that inhibit glycolysis, glutaminolysis, and growth signaling while shifting energy metabolism to therapeutic ketosis. The glucose-ketone index (GKI) is a standardized biomarker for assessing biological compliance, ideally via real-time monitoring. KMT aims to increase substrate competition and normalize the tumor microenvironment through GKI-adjusted ketogenic diets, calorie restriction, and fasting, while also targeting glycolytic and glutaminolytic flux using specific metabolic inhibitors. Non-fermentable fuels, such as ketone bodies, fatty acids, or lactate, are comparatively less efficient in supporting the long-term bioenergetic and biosynthetic demands of cancer cell proliferation. The proposed strategy may be implemented as a synergistic metabolic priming baseline in GBM as well as other tumors driven by glycolysis and glutaminolysis, regardless of their residual mitochondrial function. Suggested best practices are provided to guide future KMT research in metabolic oncology, offering a shared, evidence-driven framework for observational and interventional studies.

Effect of islet alone or islets after kidney transplantation on quality of life in type 1 diabetes: A systematic review

BACKGROUND

Pancreatic islet transplantation for type 1 diabetes mellitus (T1DM) is efficacious in supressing severe hypoglycaemic episodes (SHE) and restoring glycaemic regulation, which are both pivotal in increasing health-related quality of life (HRQoL). Therefore, a systematic assessment of reports detailing HRQoL outcomes is warranted to better understand the benefits of islet transplantation. To this end, we performed a systematic review of the literature to assess the impact of islet transplantation on HRQoL in individuals with T1DM, whether as a standalone procedure (ITA) or following renal transplantation (IAK).

METHOD

All studies providing a quantitative assessment of HRQoL following ITA or IAK were included. Selected studies had to meet the following criteria: they had to (i) involve adult recipients of islet grafts for T1DM, (ii) use either generic or disease-specific QoL assessment tools, (iii) provide a comparative analysis of QoL metrics between the pre- and post-transplantation state or between the post-transplantation state and other pre-transplant patients or the general population.

RESULTS

Seven studies that met the inclusion criteria provided data on 205 subjects. In the included studies, HRQoL was measured using both generic instruments, such as the 36-item Short Form Health Survey (SF-36) and the Health Status Questionnaire (HSQ) 2.0, and disease-specific instruments, such as the Diabetes Distress Scale (DDS), the Diabetes Quality of Life Questionnaire, and the Hypoglycaemia Fear Survey (HFS). These instruments cover physical, mental, social, or functional health dimensions. We found that pancreatic islet transplantation was associated with improvements in all HRQoL dimensions compared with the pre-transplant baseline.

CONCLUSIONS

Our systematic review demonstrates that islet transplantation significantly enhances quality of life in individuals with T1DM who are experiencing SHE. To our knowledge, this is the most extensive systematic review conducted to date, evaluating the impact of islet transplantation on HRQoL.

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

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

Continuous Glucose Monitoring Among Patients with Type 1 Diabetes in Rwanda (CAPT1D) Phase I: Feasibility Study

BACKGROUND

The development of minimally invasive continuous glucose monitoring systems (CGMs) has transformed diabetes management. CGMs have shown clinical significance by improving time in the euglycemic range, decreasing rates of hypoglycemia, and improving HbA1c. In Rwanda, CGMs are currently not in routine use, and no clinical studies of CGM use were identified in the literature.

OBJECTIVE

To determine impact and feasibility of real-time CGM use among people living with T1D in Rwanda, through assessment of sensor usage, time in range, rates of hypo-and-hyperglycemia, HbA1c and rates of diabetes-related hospitalizations over time.

METHODS

The Continuous Glucose Monitoring Among Patients with Type 1 Diabetes in Rwanda (CAPT1D) study is a single-arm prospective observational study conducted at the Rwandan Diabetes Association (RDA) clinic in Kigali, Rwanda, aiming to assess the impact and feasibility of CGM use in Rwanda. A cohort of 50 participants diagnosed with T1D were enrolled. Participants were at least 21 years old, undergoing multiple daily insulin therapy, and not currently pregnant. Phase I of the study was conducted over 12 months, using the Dexcom G6 CGM. Phase II and Phase III extended CGM use for an additional 6 months respectively, using the next generation, Dexcom G7 CGM. Here we report the quantitative results of the Phase I study.

RESULTS

Participants used the sensor for >80% of the time throughout the study period. A significant increase in time in range was observed within 3 months, and sustained over 12 months. HbA1c decreased significantly in 3 months and stayed lower throughout the 12-month period. Mean HbA1c levels decreased by 2.8% at 6 months (p<0.01) and 3.2% at 12 months (p<0.01) A total of 12 diabetes-related hospitalizations were reported during the study period. No cases of DKA or episodes of severe hypoglycemia occurred.

CONCLUSIONS

Significant and meaningful improvements in key glycemic indices indicate the potential feasibility and impact of CGM among people living with T1D in Rwanda. Future studies could be designed to include pre- and post-intervention analysis to determine the effectiveness in terms of complications and costs.

CLINICALTRIAL

Continuous Glucose Monitoring: A Transformative Approach to the Detection of Prediabetes

Prediabetes, as an intermediary stage between normal glucose homeostasis and overt diabetes, affects an estimated 720 million individuals worldwide, highlighting the urgent need for proactive intervention strategies. Continuous glucose monitoring (CGM) emerges as a transformative tool, offering unprecedented insights into glycemic dynamics and facilitating tailored therapeutic interventions. This perspective scores the clinical significance of even slightly elevated fasting blood glucose levels and the critical role of early intervention. CGM technology provides real-time, continuous data on glucose concentrations, surpassing the constraints of conventional monitoring methods. Both retrospectively analyzed and real-time CGM systems offer valuable tools for glycemic management, each with unique strengths. The integration of CGM into routine care can detect early indicators of type 2 diabetes, inform the development of personalized intervention strategies, and foster patient engagement and empowerment. Despite challenges such as cost and the need for effective utilization through training and education, CGM's potential to revolutionize prediabetes management is evident. Future research should focus on refining CGM algorithms, exploring personalized intervention strategies, and leveraging wearable technology and artificial intelligence advancements to optimize glycemic control and patient well-being.

AI-Based Noninvasive Blood Glucose Monitoring: Scoping Review

BACKGROUND

Current blood glucose monitoring (BGM) methods are often invasive and require repetitive pricking of a finger to obtain blood samples, predisposing individuals to pain, discomfort, and infection. Noninvasive blood glucose monitoring (NIBGM) is ideal for minimizing discomfort, reducing the risk of infection, and increasing convenience.

OBJECTIVE

This review aimed to map the use cases of artificial intelligence (AI) in NIBGM.

METHODS

A systematic scoping review was conducted according to the Arksey O'Malley five-step framework. Eight electronic databases (CINAHL, Embase, PubMed, Web of Science, Scopus, The Cochrane-Central Library, ACM Digital Library, and IEEE Xplore) were searched from inception until February 8, 2023. Study selection was conducted by 2 independent reviewers, descriptive analysis was conducted, and findings were presented narratively. Study characteristics (author, country, type of publication, study design, population characteristics, mean age, types of noninvasive techniques used, and application, as well as characteristics of the BGM systems) were extracted independently and cross-checked by 2 investigators. Methodological quality appraisal was conducted using the Checklist for assessment of medical AI.

RESULTS

A total of 33 papers were included, representing studies from Asia, the United States, Europe, the Middle East, and Africa published between 2005 and 2023. Most studies used optical techniques (n=19, 58%) to estimate blood glucose levels (n=27, 82%). Others used electrochemical sensors (n=4), imaging (n=2), mixed techniques (n=2), and tissue impedance (n=1). Accuracy ranged from 35.56% to 94.23% and Clarke error grid (A+B) ranged from 86.91% to 100%. The most popular machine learning algorithm used was random forest (n=10) and the most popular deep learning model was the artificial neural network (n=6). The mean overall checklist for assessment of medical AI score on the included papers was 33.5 (SD 3.09), suggesting an average of medium quality. The studies reviewed demonstrate that some AI techniques can accurately predict glucose levels from noninvasive sources while enhancing comfort and ease of use for patients. However, the overall range of accuracy was wide due to the heterogeneity of models and input data.

CONCLUSIONS

Efforts are needed to standardize and regulate the use of AI technologies in BGM, as well as develop consensus guidelines and protocols to ensure the quality and safety of AI-assisted monitoring systems. The use of AI for NIBGM is a promising area of research that has the potential to revolutionize diabetes management.

Continuous Glucose Monitoring Profiles and Health Outcomes After Dapagliflozin Plus Saxagliptin vs Insulin Glargine

CONTEXT

Glycemic variability and hypoglycemia during diabetes treatment may impact therapeutic effectiveness and safety, even when glycated hemoglobin (HbA1c) reduction is comparable between therapies.

OBJECTIVE

We employed masked continuous glucose monitoring (CGM) during a randomized trial of dapagliflozin plus saxagliptin (DAPA + SAXA) vs insulin glargine (INS) to compare glucose variability and patient-reported outcomes (PROs).

DESIGN

24-week substudy of a randomized, open-label, 2-arm, parallel-group, phase 3b study.

SETTING

Multicenter study (112 centers in 11 countries).

PATIENTS

283 adults with type 2 diabetes (T2D) inadequately controlled with metformin ± sulfonylurea.

INTERVENTIONS

DAPA + SAXA vs INS.

MAIN OUTCOME MEASURES

Changes in CGM profiles, HbA1c, and PROs.

RESULTS

Changes from baseline in HbA1c with DAPA + SAXA were similar to those observed with INS, with mean difference [95% confidence interval] between decreases of -0.12% [-0.37 to 0.12%], P = .33. CGM analytics were more favorable for DAPA + SAXA, including greater percent time in range (> 3.9 and ≤ 10 mmol/L; 34.3 ± 1.9 vs 28.5 ± 1.9%, P = .033), lower percent time with nocturnal hypoglycemia (area under the curve ≤ 3.9 mmol/L; 0.6 ± 0.5 vs 2.7 ± 0.5%, P = .007), and smaller mean amplitude of glycemic excursions (-0.7 ± 0.1 vs -0.3 ± 0.1 mmol/L, P = .017). Improvements in CGM were associated with greater satisfaction, better body weight image, less weight interference, and improved mental and emotional well-being.

CONCLUSION

DAPA + SAXA and INS were equally effective in reducing HbA1c at 24 weeks, but people with T2D treated with DAPA + SAXA achieved greater time in range, greater reductions in glycemic excursions and variability, less time with hypoglycemia, and improved patient-reported health outcomes.

Impact of Telemedicine Versus In-Person Pediatric Outpatient Type 1 Diabetes Visits on Immediate Glycemic Control: Retrospective Chart Review

BACKGROUND

Children and adolescents with type 1 diabetes require frequent outpatient evaluation to assess glucose trends, modify insulin doses, and screen for comorbidities. Continuous glucose monitoring (CGM) provides a detailed glycemic control assessment. Telemedicine has been increasingly used since the COVID-19 pandemic.

OBJECTIVE

To investigate CGM profile parameter improvement immediately following pediatric outpatient diabetes visits and determine if visit modality impacted these metrics, completion of screening laboratory tests, or diabetic emergency occurrence.

METHODS

A dual-center retrospective review of medical records assessed the CGM metrics time in range and glucose management indicator for pediatric outpatient diabetes visits during 2021. Baseline values were compared with those at 2 and 4 weeks post visit. Rates of completion of screening laboratory tests and diabetic emergencies following visits were determined.

RESULTS

A total of 269 outpatient visits (41.2% telemedicine) were included. Mean time in range increased by 1.63% and 1.35% at 2 and 4 weeks post visit (P=.003 and .01, respectively). Mean glucose management indicator decreased by 0.07% and 0.06% at 2 and 4 weeks post visit (P=.003 and .02, respectively). These improvements in time in range and glucose management indicator were seen across both telemedicine visits and in-person visits without a significant difference. However, patients seen in person were 2.69 times more likely to complete screening laboratory tests (P=.03). Diabetic emergencies occurred too infrequently to analyze.

CONCLUSIONS

Our findings demonstrate an immediate improvement in CGM metrics following outpatient visits, regardless of modality. While statistically significant, the magnitude of these changes was small; hence, multiple visits over time would be required to achieve clinically relevant improvement. However, completion of screening laboratory tests was found to be more likely after visits occurring in person. Therefore, we suggest a hybrid approach that allows patient convenience with telemedicine but also incorporates periodic in-person assessment.

Continuous glycemic monitoring in managing diabetes in adult patients with wolfram syndrome

AIMS

In this study we evaluated the use of Continuous Glucose Monitoring system in adults with insulin-dependent diabetes in the course of Wolfram syndrome (WFS) in comparison to patients with type 1 diabetes (T1D).

METHODS

Individuals with WFS (N = 10) used continuous glucose monitoring for 14 days and were compared with 30 patients with T1D matched using propensity score for age and diabetes duration. Glycemic variability was calculated with Glyculator 3.0.

RESULTS

We revealed significant differences in glycemic indices between adults with Wolfram syndrome-related diabetes and matched comparison group. Patients with Wolfram syndrome presented lower mean glucose in 24-h and nighttime records [24h: 141.1 ± 30.4mg/dl (N = 10) vs 164.9 ± 31.3mg/dl (N = 30), p = 0.0427; nighttime: 136.7 ± 39.6mg/dl vs 166.2 ± 32.1mg/dl (N = 30), p = 0.0442]. Moreover, they showed lower standard deviation of sensor glucose over all periods [24h: 50.3 ± 9.2mg/dl (N = 10) vs 67.7 ± 18.7 mg/dl (N = 30), p = 0.0075; daytime: 50.8 ± 8.7mg/dl (N = 10) vs 67.4 ± 18.0mg/dl (N = 30), p = 0.0082; nighttime: 45.1 ± 14.9mg/dl (N = 10) vs 65.8 ± 23.2mg/dl (n = 30), p = 0.0119] and coefficient of variation at night [33.3 ± 5.8% (N = 10) vs 40.5 ± 8.8% (N = 30), p = 0.0210]. Additionally, WFS patients displayed lower time in high-range hyperglycemia (> 250mg/dl) across all parts of day [24h: 4.6 ± 3.8% (N = 10) vs 13.4 ± 10.5% (N = 30), p = 0.0004; daytime: 4.7 ± 3.9% (N = 10) vs 13.8 ± 11.2% (N = 30), p = 0.0005; nighttime: 4.2 ± 5.5% (N = 10) vs 12.1 ± 10.3% (N = 30), p = 0.0272].

CONCLUSIONS

Adult patients with Wolfram syndrome show lower mean blood glucose, less extreme hyperglycemia, and lower glycemic variability in comparison to patients with type 1 diabetes.

Systematic review of disparities in continuous glucose monitoring and insulin pump utilization in the United States: Key themes and evidentiary gaps

AIM

This study aims to provide a comprehensive overview of real-world evidence pertaining to disparities in the utilization of continuous glucose monitors (CGMs)/insulin pumps to highlight potential evidentiary gaps and discern emerging themes from the literature.

MATERIALS AND METHODS

A systematic review of published manuscripts and abstracts was conducted from: MEDLINE, EMBASE, Nursing and Allied Health, Web of Science and CINHAL. Attributes related to patients, outcomes, interventions (CGMs/pumps/both) and study type were captured. In addition, factors associated with disparities in device utilization were examined.

RESULTS

Thirty-six studies were included in the final analysis; the studies predominantly focused on people living with type 1 diabetes. Only two studies included individuals with type 2 diabetes. Almost two-thirds of the studies reported outcomes associated with disparities (e.g. glycated haemoglobin, diabetic ketoacidosis, resource utilization). Most studies highlighted disparities across race, ethnicity and insurance type. Evidentiary gaps were identified, particularly in the evidence for people with type 2 diabetes, the continuation of CGM/pump use and limited studies addressing disparities among Native Americans/American Indians.

CONCLUSION

This study reveals critical disparities in diabetes technology use across race, ethnicity and insurance type, particularly among people with type 1 diabetes. Evidentiary gaps assessing disparities in diabetes technology use persist, particularly concerning people with type 2 diabetes, Native American/American Indian and LGBTQ+ populations, and in outcomes related to continuation of use. Social and digital determinants of health, such as income, transportation, residential location and technological literacy, are crucial to achieving equitable access. Future research should focus on the patient journey to identify opportunities for equitable access to diabetes technology as its use grows.

Elevated urinary albumin predicts increased time in range after initiation of SGLT2 inhibitors in individuals with type 1 diabetes on sensor-augmented pump therapy

Aims

We aimed to investigate potential predictors of effectiveness of SGLT2 inhibitors (SGLT2i) in individuals with type 1 diabetes (T1D) on sensor-augmented pump (SAP) therapy.

Methods

We included individuals with T1D receiving SAP therapy at our hospital who were newly initiated on SGLT2i between 2019 and 2020 and were followed for at least 1 year. Data on BMI, blood tests, and continuous glucose monitoring (CGM) were compared before and 12 months after initiation of SGLT2i. Predictors of incremental increases in time in range (ΔTIR) were explored using a multiple regression analysis. Cutoff values for the predictors were determined using an ROC curve analysis.

Results

A total of 17 individuals (females, 70.6%; median age, 44.0 years) were included, excluding three individuals who discontinued SGLT2i due to side effects. During follow-up, their median BMI decreased significantly (P = 0.013), while no significant change was seen in their total daily dose of insulin, basal-to-total insulin ratio. Again, their HbA1c, TIR, and time above range (TAR) improved significantly (P = 0.004, P = 0.003, and P = 0.003, respectively), while their time below range (TBR) showed no significant change. The predictor of increased ΔTIR was high urinary albumin-to-creatinine ratio (UACR) at baseline (P = 0.026) only, with the cutoff value determined to be 28.0 mg/g Cr or higher (AUC = 0.82, P = 0.003).

Conclusions

It may be suggested that individuals with T1D on SAP therapy and having near-microalbuminuria or higher could be expected to show significant improvement in TIR.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13340-024-00743-4.

A comprehensive review on electromagnetic wave based non-invasive glucose monitoring in microwave frequencies

Diabetes is a chronic disease that affects millions of humans worldwide. This review article provides an analysis of the recent advancements in non-invasive blood glucose monitoring, detailing methods and techniques, with a special focus on Electromagnetic wave microwave glucose sensors. While optical, thermal, and electromagnetic techniques have been discussed, the primary emphasis is focussed on microwave frequency sensors due to their distinct advantages. Microwave sensors exhibit rapid response times, require minimal user intervention, and hold potential for continuous monitoring, renders them extremely potential for real-world applications. Additionally, their reduced susceptibility to physiological interferences further enhances their appeal. This review critically assesses the performance of microwave glucose sensors by considering factors such as accuracy, sensitivity, specificity, and user comfort. Moreover, it sheds light on the challenges and upcoming directions in the growth of microwave sensors, including the need for reduction and integration with wearable platforms. By concentrating on microwave sensors within the broader context of non-invasive glucose monitoring, this article aims to offer significant enlightenment that may drive further innovation in diabetes care.

A review of biopolymer-based hydrogels and IoT integration for enhanced diabetes diagnosis, management, and treatment

The prevalence of diabetes has been increasing globally, necessitating innovative approaches beyond conventional blood sugar monitoring and insulin control. Diabetes is associated with complex health complications, including cardiovascular diseases. Continuous Glucose Monitoring (CGM) devices, though automated, have limitations such as irreversibility and interference with bodily fluids. Hydrogel technologies provide non-invasive alternatives to traditional methods, addressing the limitations of current approaches. This review explores hydrogels as macromolecular biopolymeric materials capable of absorbing and retaining a substantial amount of water within their structure. Due to their high-water absorption properties, these macromolecules are utilized as coating materials for wound care and diabetes management. The study emphasizes the need for early diagnosis and monitoring, especially during the COVID-19 pandemic, where heightened attention to diabetic patients is crucial. Additionally, the article examines the role of the Internet of Things (IoT) and machine learning-based systems in enhancing diabetes management effectiveness. By leveraging these technologies, there is potential to revolutionize diabetes care, providing more personalized and proactive solutions. This review explores cutting-edge hydrogel-based systems as a promising avenue for diabetes diagnosis, management, and treatment, highlighting key biopolymers and technological integrations.

Evaluating the Impact of Continuous Glucose Monitoring on Erectile Dysfunction in Type 1 Diabetes: A Focus on Reducing Glucose Variability and Inflammation

Type 1 diabetes (T1D) severely impairs metabolic control and can lead to erectile dysfunction (ED) through hyperglycemia-induced vascular damage, autonomic neuropathy, and psychological distress. This review examines the role of continuous glucose monitoring (CGM) in ameliorating ED by addressing glucose variability and inflammation. A comprehensive analysis of studies and clinical trials was conducted to evaluate the impact of CGM on metabolic control, inflammatory responses, and vascular health in patients with T1D. Evidence suggests that CGM systems significantly stabilize blood glucose levels and reduce hyper- and hypoglycemic episodes that contribute to endothelial dysfunction and ED. CGM's real-time feedback helps patients optimize metabolic control, improve vascular health, and reduce inflammation. CGM has the potential to redefine ED management in patients with T1D by improving glycemic control and reducing the physiological stressors that cause ED, potentially improving quality of life and sexual health. Further research is warranted to explore the specific benefits of CGM for ED management.

Analytical Performance of the FreeStyle Libre 2 Glucose Sensor in Healthy Male Adults

Continuous Glucose Monitoring (CGM) not only can be used for glycemic control in chronic diseases (e.g., diabetes), but is increasingly being utilized by individuals and athletes to monitor fluctuations in training and everyday life. However, it is not clear how accurately CGM reflects plasma glucose concentration in a healthy population in the absence of chronic diseases. In an oral glucose tolerance test (OGTT) with forty-four healthy male subjects (25.5 ± 4.5 years), the interstitial fluid glucose (ISFG) concentration obtained by a CGM sensor was compared against finger-prick capillary plasma glucose (CPG) concentration at fasting baseline (T0) and 30 (T30), 60 (T60), 90 (T90), and 120 (T120) min post OGTT to investigate differences in measurement accuracy. The overall mean absolute relative difference (MARD) was 12.9% (95%-CI: 11.8-14.0%). Approximately 100% of the ISFG values were within zones A and B in the Consensus Error Grid, indicating clinical accuracy. A paired t-test revealed statistically significant differences between CPG and ISFG at all time points (T0: 97.3 mg/dL vs. 89.7 mg/dL, T30: 159.9 mg/dL vs. 144.3 mg/dL, T60: 134.8 mg/dL vs. 126.2 mg/dL, T90: 113.7 mg/dL vs. 99.3 mg/dL, and T120: 91.8 mg/dL vs. 82.6 mg/dL; p < 0.001) with medium to large effect sizes (d = 0.57-1.02) and with ISFG systematically under-reporting the reference system CPG. CGM sensors provide a convenient and reliable method for monitoring blood glucose in the everyday lives of healthy adults. Nonetheless, their use in clinical settings wherein implications are drawn from CGM readings should be handled carefully.

The association of continuous glucose monitoring with glycemic parameters in patients with uncontrolled type 2 diabetes: A prospective observational study

BACKGROUND

Uncontrolled glycemic parameters in type 2 diabetes mellitus (T2DM) are a major concern. The present study aimed to evaluate the effectiveness of continuous glucose monitoring (CGM) on glycemic control in type 2 diabetics on insulin therapy.

MATERIALS AND METHODS

This prospective observational study was done in the Outpatient Department of General Medicine from January 1, 2021 till December 31, 2021 on patients with confirmed T2DM and on insulin therapy. Patients underwent detailed history and physical examination. The CGM device was inserted to record blood glucose levels throughout the day and night for monitoring. Parameters like glycosylated hemoglobin (HbA1c), fasting blood sugar (FBS), post-paradial blood sugar (PPBS), and lipid profile parameters [cholesterol, triglyceride (TG), and low-density lipoprotein (LDL)] were compared at baseline and after a follow-up of 3 months. P-value < 0.05 was used to indicate significant difference.

RESULTS

Of 107 patients screened, 100 were included in the study and seven were excluded. The mean age of the patients was 60.6 ± 11.1 years. Fifty-six (56%) of the patients were males, and 44 (44%) were females. The mean body mass index (BMI) was 22.9 ± 2.4 kg/m2. Compared to baseline values, after 3 months of CGM, there was significantly decreased HbA1c (9.41 ± 0.83 vs 9.87 ± 1.16 g%, P < 0.001), FBS (194.640 ± 22.4587 vs 205.10 ± 35.7758 mg/dl, P = 0.002), PPBS (271.160 ± 29.1235 vs 299.180 ± 42.3798, P < 0.001), cholesterol (184.470 ± 28.5192 vs 198.430 ± 38.8367 mg/dl, P < 0.001), LDL (102.410 ± 22.8973 vs 112.040 ± 30.8859, P < 0.001), and TG (140.890 ± 18.0979 vs 146.730 ± 20.8665 mg/dl, P < 0.001).

CONCLUSION

There was a significant improvement in the glycemic parameters and lipid profile parameters with the adoption of CGM. Overall, CGM is a novel method for practical use for management of patients with T2DM.

Consequences of Data Loss on Clinical Decision-Making in Continuous Glucose Monitoring: Retrospective Cohort Study

BACKGROUND

The impact of missing data on individual continuous glucose monitoring (CGM) data is unknown but can influence clinical decision-making for patients.

OBJECTIVE

We aimed to investigate the consequences of data loss on glucose metrics in individual patient recordings from continuous glucose monitors and assess its implications on clinical decision-making.

METHODS

The CGM data were collected from patients with type 1 and 2 diabetes using the FreeStyle Libre sensor (Abbott Diabetes Care). We selected 7-28 days of 24 hours of continuous data without any missing values from each individual patient. To mimic real-world data loss, missing data ranging from 5% to 50% were introduced into the data set. From this modified data set, clinical metrics including time below range (TBR), TBR level 2 (TBR2), and other common glucose metrics were calculated in the data sets with and that without data loss. Recordings in which glucose metrics deviated relevantly due to data loss, as determined by clinical experts, were defined as expert panel boundary error (εEPB). These errors were expressed as a percentage of the total number of recordings. The errors for the recordings with glucose management indicator <53 mmol/mol were investigated.

RESULTS

A total of 84 patients contributed to 798 recordings over 28 days. With 5%-50% data loss for 7-28 days recordings, the εEPB varied from 0 out of 798 (0.0%) to 147 out of 736 (20.0%) for TBR and 0 out of 612 (0.0%) to 22 out of 408 (5.4%) recordings for TBR2. In the case of 14-day recordings, TBR and TBR2 episodes completely disappeared due to 30% data loss in 2 out of 786 (0.3%) and 32 out of 522 (6.1%) of the cases, respectively. However, the initial values of the disappeared TBR and TBR2 were relatively small (<0.1%). In the recordings with glucose management indicator <53 mmol/mol the εEPB was 9.6% for 14 days with 30% data loss.

CONCLUSIONS

With a maximum of 30% data loss in 14-day CGM recordings, there is minimal impact of missing data on the clinical interpretation of various glucose metrics.

TRIAL REGISTRATION

ClinicalTrials.gov NCT05584293; https://clinicaltrials.gov/study/NCT05584293.

Advancements in Insulin Pumps: A Comprehensive Exploration of Insulin Pump Systems, Technologies, and Future Directions

Insulin pumps have transformed the way diabetes is managed by providing a more accurate and individualized method of delivering insulin, in contrast to conventional injection routines. This research explores the progression of insulin pumps, following their advancement from initial ideas to advanced contemporary systems. The report proceeds to categorize insulin pumps according to their delivery systems, specifically differentiating between conventional, patch, and implantable pumps. Every category is thoroughly examined, emphasizing its unique characteristics and capabilities. A comparative examination of commercially available pumps is provided to enhance informed decision making. This section provides a thorough analysis of important specifications among various brands and models. Considered factors include basal rate and bolus dosage capabilities, reservoir size, user interface, and compatibility with other diabetes care tools, such as continuous glucose monitoring (CGM) devices and so on. This review seeks to empower healthcare professionals and patients with the essential information to improve diabetes treatment via individualized pump therapy options. It provides a complete assessment of the development, categorization, and full specification comparisons of insulin pumps.

An automatic deep reinforcement learning bolus calculator for automated insulin delivery systems

In hybrid automatic insulin delivery (HAID) systems, meal disturbance is compensated by feedforward control, which requires the announcement of the meal by the patient with type 1 diabetes (DM1) to achieve the desired glycemic control performance. The calculation of insulin bolus in the HAID system is based on the amount of carbohydrates (CHO) in the meal and patient-specific parameters, i.e. carbohydrate-to-insulin ratio (CR) and insulin sensitivity-related correction factor (CF). The estimation of CHO in a meal is prone to errors and is burdensome for patients. This study proposes a fully automatic insulin delivery (FAID) system that eliminates patient intervention by compensating for unannounced meals. This study exploits the deep reinforcement learning (DRL) algorithm to calculate insulin bolus for unannounced meals without utilizing the information on CHO content. The DRL bolus calculator is integrated with a closed-loop controller and a meal detector (both previously developed by our group) to implement the FAID system. An adult cohort of 68 virtual patients based on the modified UVa/Padova simulator was used for in-silico trials. The percentage of the overall duration spent in the target range of 70-180 mg/dL was 71.2 % and 76.2 % , < 70 mg/dL was 0.9 % and 0.1 % , and > 180 mg/dL was 26.7 % and 21.1 % , respectively, for the FAID system and HAID system utilizing a standard bolus calculator (SBC) including CHO misestimation. The proposed algorithm can be exploited to realize FAID systems in the future.

Evaluation of a pharmacist-led personal continuous glucose monitor workflow to improve glycemic management in an internal medicine clinic

BACKGROUND

The use of personal continuous glucose monitors (CGMs) in patients with diabetes has increased substantially and is expected to continue to increase as CGMs become more affordable and insurance plans improve coverage. The utilization of CGMs has improved diabetes management and reduced hypoglycemic events.

OBJECTIVES

To create pharmacist-led personal CGM workflow and evaluate its impact on glycemic management in patients with diabetes.

PRACTICE DESCRIPTION

The study took place at an Internal Medicine Clinic. The practice providers include 2 medical doctors, 5 physician assistants, 2 nurse practitioners, and 1 clinical pharmacist.

PRACTICE INNOVATION

To create and implement a sustainable pharmacy led CGM workflow for enhanced CGM use within an internal medicine clinic.

EVALUATION METHODS

This was a prospective, investigator-initiated pilot study conducted at an Atrium Health Internal Medicine clinic over 28 weeks. In this pilot, 42 patients were qualifying candidates with diabetes and personal CGM use. In addition, 30 patients were followed until study completion and included into final analysis.

RESULTS

The average baseline A1c was reduced from 8.3% to 7.1% over a 3- to 6-month period. The pharmacist-led CGM workflow revealed a statistically significant reduction in A1c from baseline by an average of 1.2% (95% CI -0.6 to -1.8, P = 0.0006). On average, patients were enrolled for 19.9 weeks and had an average of 5 visits during this time. During the study duration, 100 medications changes were implemented under the existing clinical pharmacist practitioner agreement between the pharmacists and the provider. Overall, 58 Current Procedural Terminology 95251 codes were billed yielding $7052.00 in billed CGM services for the clinic. This project generated 40.6 provider relative value units.

CONCLUSION

The utilization of a pharmacist-led personal CGM workflow can improve diabetes outcomes.

Type 1 diabetes-related distress: Current implications in care

Type 1 diabetes (T1D) is a complex chronic disease associated with major health and economic consequences, also involving important issues in the psychosocial sphere. In this regard, T1D-related distress, defined as the emotional burden of living with T1D, has emerged as a specific entity related to the disease. Diabetes distress (DD) is an overlooked but prevalent condition in people living with T1D, and has significant implications in both glycemic control and mental health in this population. Although overlapping symptoms may be found between DD and mental health disorders, specific approaches should be performed for the diagnosis of this problem. In recent years, different DD-targeted interventions have been postulated, including behavioral and psychosocial strategies. Moreover, new technologies in this field may be helpful to address DD in people living with T1D. In this article, we summarize the current knowledge on T1D-related distress, and we also discuss the current approaches and future perspectives in its management.

Insertable Biosensors: Combining Implanted Sensing Materials with Wearable Monitors

Insertable biosensor systems are medical diagnostic devices with two primary components: an implantable biosensor within the body and a wearable monitor that can remotely interrogate the biosensor from outside the body. Because the biosensor does not require a physical connection to the electronic monitor, insertable biosensor systems promise improved patient comfort, reduced inflammation and infection risk, and extended operational lifetimes relative to established percutaneous biosensor systems. However, the lack of physical connection also presents technical challenges that have necessitated new innovations in developing sensing chemistries, transduction methods, and communication modalities. In this review, we discuss the key developments that have made insertables a promising option for longitudinal biometric monitoring and highlight the essential needs and existing development challenges to realizing the next generation of insertables for extended-use diagnostic and prognostic devices.

Parent, child, and adolescent lived experience using the insulin-only iLet Bionic Pancreas

OBJECTIVE

Automated insulin delivery (AID) systems show great promise for improving glycemic outcomes and reducing disease burden for youth with type 1 diabetes (T1D). The current study examined youth and parent perspectives after using the insulin-only iLet Bionic Pancreas (BP) during the 13-week pivotal trial.

METHODS

Parents and youth participated in focus group interviews, with questions assessing participants' experiences in a variety of settings and were grounded in the Unified Theory of Acceptance and Use of Technology. Qualitative analysis was completed by 3 authors using a hybrid thematic analysis approach.

RESULTS

Qualitative analysis of focus groups revealed a total of 19 sub-themes falling into 5 major themes (Diabetes Burden, Freedom and Flexibility, Daily Routine, Managing Glucose Levels, and User Experience). Participants' overall experience was positive, with decreased burden and improved freedom and flexibility. Some participants reported challenges in learning to trust the system, adjusting to the user interface, and the system learning their body.

CONCLUSION

This study adds to the growing literature on patient perspectives on using AID systems and was among the first to assess caregiver and youth experiences with the BP system over an extended period (13 weeks). Patient feedback on physical experiences with the device and experiences trusting the device to manage glucose should inform future development of technologies as well as approaches to education for patients and their families.

Pancreas after kidney transplantation, is it the time to overcome the stigma?

BACKGROUND

Pancreas transplantation is the most effective treatment to improve quality of life and overcome complications in patients with end-stage renal disease and diabetes mellitus. One of the main approaches for concurrent renal disease and diabetes mellitus which has been underutilized during the past decade is a pancreas transplant after kidney transplantation. Our study aimed to quantify outcomes following pancreas after kidney transplants (PAKs) in the United States from 2001 to 2020 with an emphasis on graft and patient survival.

METHODS AND MATERIALS

A retrospective registry analysis was performed by accessing the OPTN/UNOS database for PAKs that were performed in the United States from January 2001 to April 2020. The study population was divided into two subgroups: patients receiving a pancreas transplant between 2001 and 2010 and those receiving a pancreas transplant between 2011 and 2020.

RESULTS

The study examined a total number of 3706 PAK recipients; patients who received a PAK from January 2001 through December 2010 (n = 2892) and those who received a PAK from January 2011 to April 2020 (n = 814). The selection process of transplant recipients did not drastically change throughout the 2001-2010 and 2011-2020 periods. Length of stay at the hospital after the transplantation improved significantly in the 2011-2020 group relative to the 2001-2010 group (8.48 vs. 10.08 days, mean, p < 0.01). Additionally, more transplantation with 4-6 human leukocyte antigen mismatch occurred in the 2011-2020 group than in the 2001-2010 group (80.6% vs. 71.4%, p < 0.01). The pancreas preservation time of 13.35 h in the 2001-2010 group decreased significantly to 11.17 h in the 2011-2020 group (p < 0.001). The mean donor's amylase and lipase also decreased significantly in the 2011-2020 cohort. Significant graft survival improvement was observed in the 2011-2020 group compared to the 2001-2010 group after a long-term follow-up (p < 0.001). The mean Calculated Pancreas Donor Risk Index was 1.08 for the 2001-2010 group and 0.99 for the 2011-2020 group with a significant difference (p < 0.001).

CONCLUSION

The beneficial results and improved outcomes observed in PAK patients demonstrate the effectiveness of the operation for individuals in need of a pancreas transplant. PAKs can prove to be a meaningful solution to overcome long waiting times, decrease the donor-recipient imbalance, expand the donor pool, and overcome the current underutilization in order to improve the short- and long-term quality of life in the groups of interest.

Clinically relevant stratification of patients with type 2 diabetes by using continuous glucose monitoring data

AIM

The wealth of data generated by continuous glucose monitoring (CGM) provides new opportunities for revealing heterogeneities in patients with type 2 diabetes mellitus (T2DM). We aimed to develop a method using CGM data to discover T2DM subtypes and investigate their relationship with clinical phenotypes and microvascular complications.

METHODS

The data from 3119 patients with T2DM who wore blinded CGM at an academic medical centre was collected, and a glucose symbolic pattern (GSP) metric was created that combined knowledge-based temporal abstraction with numerical vectorization. The k-means clustering was applied to GSP to obtain subgroups of patients with T2DM. Clinical characteristics and the presence of diabetic retinopathy and albuminuria were compared among the subgroups. The findings were validated in an independent population comprising 773 patients with T2DM.

RESULTS

By using GSP, four subgroups were identified with distinct features in CGM profiles and parameters. Moreover, the clustered subgroups differed significantly in clinical phenotypes, including indices of pancreatic β-cell function and insulin resistance (all p < .001). After adjusting for confounders, group C (the most insulin resistant) had a significantly higher risk of albuminuria (odds ratio = 1.24, 95% confidence interval: 1.03-1.39) relative to group D, which had the best glucose control. These findings were confirmed in the validation set.

CONCLUSION

Subtyping patients with T2DM using CGM data may help identify high-risk patients for microvascular complications and provide insights into the underlying pathophysiology. This method may help refine clinically meaningful stratification of patients with T2DM and inform personalized diabetes care.

Accuracy of an Off-Label Transmitter and Data Manager Paired With an Intermittent Scanned Continuous Glucose Monitor in Adults With Type 1 Diabetes

BACKGROUND

This work evaluates the accuracy and agreement between the FreeStyle Libre sensor (FSL) and an off-label converted real-time continuous glucose monitor (c-rtCGM) device consisting of the MiaoMiao transmitter and the xDrip+ application which can be coupled to the FSL.

METHODS

Four weeks of glucose data were collected from 21 participants with type 1 diabetes using the c-rtCGM and FSL: two weeks with a single initial calibration (uncalibrated) and two weeks with a daily calibration (calibrated). Accuracy and agreement evaluation included mean absolute relative difference (MARD), the %20/20 rule, Bland-Altman plots, and the Consensus Error Grid analysis.

RESULTS

Values reported by the c-rtCGM system compared with the FSL resulted in an overall MARD of 12.06% and 84.71% of the results falling within Consensus Error Grid Zone A when the device is calibrated. For uncalibrated devices, an overall MARD of 17.49% was obtained. Decreased accuracy was shown in the hypoglycemic range and for rates of change greater than 2 mg/dL/min. The between-device bias also incremented with increasing glucose values.

CONCLUSION

Measurements recorded by the c-rtCGM were found to be accurate when compared with FSL data only when performing daily c-rtCGM device calibrations. High drops in accuracy and agreement between devices occurred when the c-rtCGM was not calibrated.

Integrated modeling of labile and glycated hemoglobin with glucose for enhanced diabetes detection and short-term monitoring

Metabolic biomarkers, particularly glycated hemoglobin and fasting plasma glucose, are pivotal in the diagnosis and control of diabetes mellitus. Despite their importance, they exhibit limitations in assessing short-term glucose variations. In this study, we propose labile hemoglobin as an additional biomarker, providing insightful perspectives into these fluctuations. By utilizing datasets from 40,652 retrospective general participants and conducting glucose tolerance tests on 60 prospective pediatric subjects, we explored the relationship between plasma glucose and labile hemoglobin. A mathematical model was developed to encapsulate short-term glucose kinetics in the pediatric group. Applying dimensionality reduction techniques, we successfully identified participant subclusters, facilitating the differentiation between diabetic and non-diabetic individuals. Intriguingly, by integrating labile hemoglobin measurements with plasma glucose values, we were able to predict the likelihood of diabetes in pediatric subjects, underscoring the potential of labile hemoglobin as a significant glycemic biomarker for diabetes research.

Total Pancreatectomy in a Patient Treated with a Sensor-augmented Pump Showing No Evidence of Hyperglycemia or Ketoacidosis without Any Insulin Administration

Total pancreatectomy results in complete loss of insulin and glucagon. Sensor-augmented pumps (SAPs) allow fine-tuning of the basal insulin rate, which helps avoid both hypo- and hyperglycemic events. We herein report a case of total pancreatectomy treated with a SAP with no evidence of ketoacidosis without any insulin administration during a certain period of time. Furthermore, we observed a sudden drop in blood glucose levels without insulin, which may have been due to glucose effectiveness. Our case is valuable in arguing the concept of glucose effectiveness in the absence of insulin and glucagon.

Glucose sensor with predictive alarm for hypoglycaemia: Improved glycaemic control in adolescents with type 1 diabetes

AIM

Hypoglycaemic events are linked to microvascular and macrovascular complications in people with type 1 diabetes. We aimed to evaluate the efficacy of glucose sensor [real-time continuous glucose monitoring (RT-CGM)] with predictive alarm (PA) in reducing the time spent below the range (%TBR <70 mg/dl) in a group of adolescents with type 1 diabetes (AwD).

MATERIALS AND METHODS

This was a crossover, monocentric and randomized study. RT-CGM was set with Alarm on Threshold (AoT) at 70 mg/dl) or PA for hypoglycaemia (20 m before threshold). Twenty AwD were enrolled and randomized to either a PA/AoT or AoT/PA treatment sequence, in a 1:1 ratio. The two groups (PA vs. AoT) were compared using two-way repeated measures ANOVA taking account of the carryover effect.

RESULTS

AwD using PA for hypoglycaemia spent less time in severe hypoglycaemia (%TBR2 <54 mg/dl; 0.32 ± 0.31 vs. 0.91 ± 0.90; p < .02) and hypoglycaemia (%TBR <70 mg/dl; 1.68 ± 1.06 vs. 2.90 ± 2.05; p < .02), with better glycaemia risk index (51.3 ± 11.0 vs. 61.5 ± 12.6; p ≤ .01).

CONCLUSION

The use of RT-CGM with PA for hypoglycaemia technology in AwD using multiple daily insulin injection treatment could significantly reduce the risk of having hypoglycaemic events resulting in an improved quality of glucose control.

CLINICAL TRIAL REGISTRATION NUMBER

NCT05574023.

Latest advances in glucose-responsive microneedle-based systems for transdermal insulin delivery

The development of a self-regulated minimally invasive system for insulin delivery can be considered as the holy grail in the field of diabetes mellitus. A delivery system capable of releasing insulin in response to blood glucose levels would significantly improve the quality of life of diabetic patients, eliminating the need for frequent finger-prick tests and providing better glycaemic control with lower risk of hypoglycaemia. In this context, the latest advances in glucose-responsive microneedle-based transdermal insulin delivery are here compiled with a thorough analysis of the delivery mechanisms and challenges lying ahead in their clinical translation. Two main groups of microneedle-based systems have been developed so far: glucose oxidase-containing and phenylboronic acid-containing systems. Both strategies in combination have also been tested and two other novel strategies are under development, namely electronic closed-loop and glucose transporter-based systems. Results from preclinical studies conducted using these different types of glucose-triggered release systems are comprehensively discussed. Altogether, this analysis from both a mechanistic and translational perspective will provide rationale and/or guidance for future trends in the research hotspot of glucose-responsive microneedle-based insulin delivery systems.

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.

Continuous glucose monitoring in people with diabetes and end-stage kidney disease-review of association studies and Evidence-Based discussion

Diabetic nephropathy is currently the leading cause of end-stage kidney disease. The present methods of assessing diabetes control, such as glycated hemoglobin or self-monitoring of blood glucose, have limitations. Over the past decade, the field of continuous glucose monitoring has been greatly improved and expanded. This review examines the use of continuous glucose monitoring in people with end-stage kidney disease treated with hemodialysis (HD), peritoneal dialysis (PD), or kidney transplantation. We assessed the use of both real-time continuous glucose monitoring and flash glucose monitoring technology in terms of hypoglycemia detection, glycemic variability, and efficacy, defined as an improvement in clinical outcomes and diabetes control. Overall, the use of continuous glucose monitoring in individuals with end-stage kidney disease may improve glycemic control and detection of hypoglycemia. However, most of the published studies were observational with no control group. Moreover, not all studies used the same assessment parameters. There are very few studies involving subjects on peritoneal dialysis. The small number of studies with limited numbers of participants, short follow-up period, and small number of manufacturers of continuous glucose monitoring systems are limitations of the review. More studies need to be performed to obtain more reliable results.

Managing Impending Nonsevere Hypoglycemia With Oral Carbohydrates in Type 1 Diabetes: The REVERSIBLE Trial

OBJECTIVE

Current guidelines recommend initiating treatment for nonsevere (NS) hypoglycemia with 15 g carbohydrates (CHO) at 15-min intervals when blood glucose (BG) reaches <70 mg/dL (3.9 mmol/L). Despite this recommendation, NS hypoglycemia management remains challenging for individuals living with type 1 diabetes (T1D). We aimed to assess the efficacy of 15 g CHO at higher BG levels.

RESEARCH DESIGN AND METHODS

A total of 29 individuals with T1D participated in an open-label crossover study. After an inpatient subcutaneous insulin-induced decrease in BG in the fasting state, 16 g CHO was administered orally at a plasma glucose (PG) of <70 (3.9), ≤80 (4.5), or ≤90 mg/dL (5.0 mmol/L). The primary outcome was time spent in hypoglycemia (<70 mg/dL) after initial CHO intake.

RESULTS

When comparing the <70 (control) with the ≤80 and ≤90 mg/dL treatment groups, 100 vs. 86 (P = 0.1201) vs. 34% (P < 0.0001) of participants reached hypoglycemia, respectively. These hypoglycemic events lasted 26.0 ± 12.6 vs. 17.9 ± 14.7 (P = 0.026) vs. 7.1 ± 11.8 min (P = 0.002), with a PG nadir of 56.57 ± 9.91 vs. 63.60 ± 7.93 (P = 0.008) vs. 73.51 ± 9.37 mg/dL (P = 0.002), respectively. In the control group, 69% of participants required more than one treatment to reach or maintain normoglycemia (≥70 mg/dL), compared with 52% in the ≤80 mg/dL group and 31% in the ≤90 mg/dL group, with no significant rebound hyperglycemia (>180 mg/dL) within the first hour.

CONCLUSIONS

For some impending NS hypoglycemia episodes, individuals with TID could benefit from CHO intake at a higher BG level.

Continuous Glucose Profiles in Healthy People With Fixed Meal Times and Under Everyday Life Conditions

BACKGROUND

The increased use of continuous glucose monitoring (CGM) and automated insulin delivery systems raises the question about therapeutic targets for glucose profiles in people with diabetes. This study aimed to assess averaged pre- and postprandial glucose profiles in people without diabetes to provide guidance for normal glucose patterns in clinical practice. For that, number and timing of meal intake were predefined.

MATERIAL AND METHODS

To assess glucose traces in 36 participants without diabetes (mean age = 23.7 ± 5.7 years), CGM was performed for up to 14 days, starting with a run-in phase (first 3 days, excluded from analysis) followed by 4 days with fixed meal times at 8:00 am, 1:00 pm, and 6:00 pm and the remaining 7 days spent under everyday life conditions. Data from two simultaneously worn CGM sensors were averaged and adjusted to capillary plasma-equivalent glucose values. Glucose data were evaluated through descriptive statistics.

RESULTS

Median glucose concentration on days with fixed meal times and under everyday life conditions was 95.0 mg/dL (91.6-99.1 mg/dL, interquartile range) and 98.1 mg/dL (93.7-100.8 mg/dL), respectively. On days with fixed meal times, mean premeal glucose was 92.8 ± 9.4 mg/dL, and mean peak postmeal glucose was 143.3 ± 23.5 mg/dL.

CONCLUSIONS

By defining the time of meal intake, a clear pattern of distinct postprandial glucose excursions in participants without diabetes could be demonstrated and analyzed. The presented glucose profiles might be helpful as an estimate for adequate clinical targets in people with diabetes.

The development and evolution of insulin pumps: from early beginnings to future prospects

Diabetes technology has proliferated extensively over the past few decades with vast ameliorations in glucose monitoring and in insulin delivery systems. From a treatment based on daily insulin injections, we have moved to increasingly advanced technologies. Despite such advancements which have allowed better glycemic control, decreased diabetes-related complications, and improved the quality of life among diabetic patients, it has left many individuals unsatisfied with the current rate of commercial artificial pancreas development, stemming the need for further research into novel technologies. Accordingly, the Juvenile Diabetes Research Foundation has marked three generations for the development of an artificial pancreas comprising historical landmarks and future prospects which aim to produce an advanced technological system that attempts to mimic the endogenous pancreas, eliminating the need for user input. This review presents a synopsis of the development and evolution of insulin pumps, starting with the earliest technologies available such as continuous subcutaneous insulin infusion and continuous glucose monitoring as separate components, to currently available integrated advanced closed-loop hybrid systems and possible future technologies. The aim of the review is to provide insight of the advantages and limitations of past and currently available insulin pumps with the hope of driving research into novel technologies that attempt to mimic endogenous pancreatic function as closely as possible.

Detection of Hypoglycemia and Hyperglycemia Using Noninvasive Wearable Sensors: Electrocardiograms and Accelerometry

BACKGROUND

Monitoring glucose excursions is important in diabetes management. This can be achieved using continuous glucose monitors (CGMs). However, CGMs are expensive and invasive. Thus, alternative low-cost noninvasive wearable sensors capable of predicting glycemic excursions could be a game changer to manage diabetes.

METHODS

In this article, we explore two noninvasive sensor modalities, electrocardiograms (ECGs) and accelerometers, collected on five healthy participants over two weeks, to predict both hypoglycemic and hyperglycemic excursions. We extract 29 features encompassing heart rate variability features from the ECG, and time- and frequency-domain features from the accelerometer. We evaluated two machine learning approaches to predict glycemic excursions: a classification model and a regression model.

RESULTS

The best model for both hypoglycemia and hyperglycemia detection was the regression model based on ECG and accelerometer data, yielding 76% sensitivity and specificity for hypoglycemia and 79% sensitivity and specificity for hyperglycemia. This had an improvement of 5% in sensitivity and specificity for both hypoglycemia and hyperglycemia when compared with using ECG data alone.

CONCLUSIONS

Electrocardiogram is a promising alternative not only to detect hypoglycemia but also to predict hyperglycemia. Supplementing ECG data with contextual information from accelerometer data can improve glucose prediction.