Continuous Glucose Monitoring System Research Articles

Consensus Considerations and Good Practice Points for Use of Continuous Glucose Monitoring Systems in Hospital Settings.

Continuous glucose monitoring (CGM) systems provide frequent glucose measurements in interstitial fluid and have been used widely in ambulatory settings for diabetes management. During the coronavirus disease 2019 (COVID-19) pandemic, regulators in the U.S. and Canada temporarily allowed for CGM systems to be used in hospitals with the aim of reducing health care professional COVID-19 exposure and limiting use of personal protective equipment. As such, studies on hospital CGM system use have been possible. With improved sensor accuracy, there is increased interest in CGM usage for diabetes management in hospitals. Laboratorians and health care professionals must determine how to integrate CGM usage into practice. The aim of this consensus guidance document is to provide an update on the application of CGM systems in hospital, with insights and opinions from laboratory medicine, endocrinology, and nursing.

The Relation of Diabetes Complications to a New Interpretation of Glycaemic Variability from Continuous Glucose Monitoring in People with Type 1 Diabetes.

Microvascular and macrovascular complications in type 1 diabetes (T1D) may be linked to endothelial stress due to glycaemic variability. Continuous glucose monitoring systems (CGMs) provide new opportunities to quantify this variability, utilising the amplitude of glucose change summated over time. The aim of this study was to examine whether this determination of glucose variability (GV) is associated with microvascular clinical sequelae. Continuous glucose monitoring values were downloaded for 89 type 1 diabetes mellitus (T1D) individuals for up to 18 months from 2021 to 2023. Data for patient demographics was also taken from the patient record which included Sex, Date of Birth, and Date of Diagnosis. The recorded laboratory glycated haemoglobin (HbA1c) test results were also recorded. The glucose management index (GMI) was calculated from average glucose readings for 18 months using the formula GMI (%) = (0.82-(Average glucose/100)). This was then adjusted to give GMI (mmol/mol) = 10.929 * (GMI (%) - 2.15). Average Glucose Fluctuation (AGF) was calculated by adding up the total absolute change value between all recorded results over 18 months and dividing by the number of results minus one. The % Above Critical Threshold (ACT) was calculated by summing the total number of occurrences for each result value. A cumulative 95% limit was then applied to identify the glucose value that only 5% of results exceeded in the overall population. Using this value, we estimated the percentage of total tests that were above the Critical Threshold (ACT). Results for the 89 individuals (44 men and 45 women) were analysed over 18months. The mean age of participants was 43 years and the mean duration of diabetes was 18years. A total of 3.22 million readings were analysed, giving an average of 10.3mmol/L blood glucose. Those with the largest change in glucose from reading to reading, summated over time, showed the greatest change in eGFR of 3.12ml/min/1.73 m2 (p = 0.007). People with a higher proportion of glucose readings > 18mmol/L showed a fall in eGFR of 2.8ml/min/1.73 m2 (p = 0.009) and experienced higher rates of sight-threatening retinopathy (44% of these individuals) (p = 0.01) as did 39% of individuals in the highest tertile of glucose levels (p = 0.008). Those individuals with T1D in the highest tertile of reading-to-reading glucose change showed the greatest change in eGFR. Those with a higher proportion of glucose readings > 18mmol/L also showed a fall in eGFR and experienced higher rates of sight-threatening retinopathy, as did people with higher mean glucose. Discussions with T1D individuals could reflect on how the percentage recorded glucose above a critical level and degree of change in glucose are important in avoiding future tissue complications.

α-MnO2 with a cryptomelane structure for the non-enzymatic glucose electrooxidation in a neutral medium

A high demand for non-enzymatic glucose sensors and continuous glucose monitoring systems encourages the development of stable and active catalysts for the enzymeless anodic oxidation of glucose in the neutral medium. In regard to this reaction, we analyze the prospects of alpha manganese dioxide with a cryptomelane structure synthesized via an aqueous chemical route. To provide various specific surface area, the hydrated α-MnO2 oxide is annealed in a wide temperature range from 60 to 700 °C. Furthermore, we investigate the influence of the oxide annealing temperature on the amount of crystal water, chemical composition, morphology, electrochemical recharging behavior, electrocatalytic activity and stability in the glucose oxidation carried out in an isotonic NaCl-based phosphate-buffered saline (pH 7.40). To solidly determine potentials of the irreversible cathodic dissolution of α-MnO2 as well as O2/Cl2 evolution on α-MnO2, a rotating ring-disk electrode is applied. For the most active α-MnO2 sample with the 100 μg cm−2geo loading the glucose oxidation current of ca. 40 μA cm−2geo after 1 h at +0.8 V (SCE) for 7 mM of glucose is achieved. The linear concentration dependence in a physiological glucose range (1–30 mM) with the sensitivity of ca. 17.8 μA mM−0.5 cm−2geo at 25 °C is observed. Finally, glucose selectivity of α-MnO2 in relation to fructose, galactose, xylose, ribose, urea, lactic acid, acetone, ascorbic acid and paracetamol is revealed.

Corrigendum: Accuracy of a novel real-time continuous glucose monitoring system: a prospective self-controlled study in thirty hospitalized patients with type 2 diabetes

Corrigendum: Accuracy of a novel real-time continuous glucose monitoring system: a prospective self-controlled study in thirty hospitalized patients with type 2 diabetes

Accuracy of continuous glucose monitoring systems in intensive care unit patients: a scoping review.

Glycemic control poses a challenge in intensive care unit (ICU) patients and dysglycemia is associated with poor outcomes. Continuous glucose monitoring (CGM) has been successfully implemented in the type 1 diabetes out-patient setting and renewed interest has been directed into the transition of CGM into the ICU. This scoping review aimed to provide an overview of CGM accuracy in ICU patients to inform future research and CGM implementation. We systematically searched PubMed and EMBASE between 5th of December 2023 and 21st of May 2024 and reported findings in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guideline for scoping reviews (PRISMA-ScR). We assessed studies reporting the accuracy of CGM in the ICU and report study characteristics and accuracy outcomes. We identified 2133 studies, of which 96 were included. Most studies were observational (91.7%), conducted in adult patients (74%), in mixed ICUs (47.9%), from 2014 and onward, and assessed subcutaneous CGM systems (80%) using arterial blood samples as reference test (40.6%). Half of the studies (56.3%) mention the use of a prespecified reference test protocol. The mean absolute relative difference (MARD) ranged from 6.6 to 30.5% for all subcutaneous CGM studies. For newer factory calibrated CGM, MARD ranged from 9.7 to 20.6%. MARD for intravenous CGM was 5-14.2% and 6.4-13% for intraarterial CGM. In this scoping review of CGM accuracy in the ICU, we found great diversity in accuracy reporting. Accuracy varied depending on CGM and comparator, and may be better for intravascular CGM and potentially lower during hypoglycemia.

Icodec ONWARDS: A review of the first once-weekly diabetes treatment for nurse practitioners and physician assistants.

Diabetes management is challenged by the complexity of treatment regimens and the need for frequent injections, affecting patient adherence and quality of life. Insulin icodec, a once-weekly basal insulin analog, represents a significant innovation, potentially simplifying diabetes care and improving outcomes. This review aims to evaluate the safety, efficacy, and clinical implications of insulin icodec for individuals with type 1 and type 2 diabetes, highlighting its potential to affect current treatment paradigms. A review was conducted comparing once-weekly insulin icodec with daily basal insulin analogs using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines to ensure transparent reporting of systematic reviews. A search was performed in the following databases: PubMed, Google Scholar, Embase, and ClinicalTrials.gov, focusing on efficacy and safety outcomes. Insulin icodec has demonstrated effective glycemic management and a safety profile comparable to daily basal insulins. Its extended half-life and steady-state glucose-lowering effect have the potential to reduce the burden of daily injections and improve patient adherence. The introduction of once-weekly insulin icodec represents an advancement in diabetes care. For front-line clinicians, this innovation aligns with the need for more straightforward medication regimens. Coupled with continuous glucose monitoring systems, it enables a more personalized and efficient approach to diabetes management, with the potential to improve patient satisfaction and clinical outcomes. This underscores the impact of integrating such advancements into practice, highlighting the role of nurse practitioners and physician assistants in adopting these innovations to optimize patient care.

The Performance of Continuous Glucose Monitoring During the Intraoperative Period: A Scoping Review.

Introduction: Perioperative dysglycemia is associated with negative surgical outcomes, including increased risk of infections and longer hospital stays. Continuous glucose monitoring (CGM) provides real-time glucose data, potentially improving glycemic control during surgery. However, the performance of CGM in the intraoperative environment has not been well established. This scoping review aimed to evaluate the performance of CGM systems during the intraoperative period, focusing on their technical reliability, accuracy, adverse device effects, and efficacy. Inclusion criteria: Studies that assessed intraoperative CGM performance, focusing on technical reliability, accuracy, adverse effects, or efficacy, were included. No restrictions were placed on the study design, surgical type, participant demographics, or publication date. Methods: A comprehensive literature search was performed using PubMed, EMBASE, and the Cochrane Library, covering publications up to 12 June 2024. Two independent reviewers screened and selected the studies for inclusion based on predefined eligibility criteria. Data extraction focused on the study characteristics, CGM performance, and outcomes. Results: Twenty-two studies were included, the majority of which were prospective cohort studies. CGM systems demonstrated a high technical reliability, with sensor survival rates above 80%. However, the accuracy varied, with some studies reporting mean or median absolute relative differences of over 15%. The adverse effects were minimal and mainly involved minor skin irritation. One randomized trial found no significant difference between CGM and point-of-care glucose monitoring for glycemic control. Conclusions: Although CGM has the potential to improve intraoperative glycemic management, its accuracy remains inconsistent. Future research should explore newer CGM technologies and assess their impact on surgical outcomes.

Relationship between hemoglobin glycation index and risk of hypoglycemia in type 2 diabetes with time-in-range in target

BACKGROUND In patients with type 2 diabetes mellitus (T2DM), the risk of hypoglycemia also occurs in at a time-in-range (TIR) of > 70%. The hemoglobin glycation index (HGI) is considered the best single factor for predicting hypoglycemia, and offers new perspectives for the individualized treatment of patients with well-controlled blood glucose levels that are easily ignored in clinical settings. AIM To investigate the relationship between HGI and hypoglycemia and the implications of HGI on hypoglycemia in T2DM with TIR > 70%. METHODS All participants underwent a 7-days continuous glucose monitoring (CGM) using a retrospective CGM system. We obtained glycemic variability indices using the CGM system. We defined HGI as laboratory hemoglobin A1c minus the glucose management indicator. Patients were categorized into low HGI (HGI < 0.5) and high HGI groups (HGI ≥ 0.5) according to HGI median (0.5). Logistic regression and receiver operating characteristic curve analyses were used to determine the risk factors for hypoglycemia. RESULTS We included 129 subjects with T2DM (54.84 ± 12.56 years, 46% male) in the study. Median TIR score was 90%. The high HGI group exhibited lower TIR and greater time below range with higher hemoglobin A1c than the low HGI group; this suggests more glycemic excursions and an increased incidence of hypoglycemia in the high HGI group. Multivariate analyses revealed that mean blood glucose, standard deviation of blood glucose and HGI were independent risk factors for hypoglycemia. Receiver operating characteristic curve analysis indicated that the HGI was the best predictor of hypoglycemia. In addition, the optimal cut-off points for HGI, mean blood glucose, and standard deviation of blood glucose in predicting hypoglycemia were 0.5%, 7.2 mmol/L and 1.4 mmol/L respectively. CONCLUSION High HGI was significantly associated with greater glycemic excursions and increased hypoglycemia in patients with TIR > 70%. Our findings indicate that HGI is a reliable predictor of hypoglycemia in this population.

Development of a Theoretical Continuous Glucose Monitoring Module for Pharmacy Students: Preparing Pharmacists for the Future.

To enhance the digital competencies of pharmacy students, the Institute of Clinical Pharmacy and Pharmacotherapy at Heinrich-Heine University Duesseldorf developed and evaluated a theoretical module on digital health and data analysis. This innovative module integrated a continuous glucose-monitoring (CGM) wearable device into teaching, providing students with in-depth practical experience and a 2.5 h seminar on digital health and CGM systems. Students' knowledge of CGM and self-assessment of their CGM competencies were assessed in a pre-post manner. Additionally, students' satisfaction with the module and their perceptions of the future integration of digital health training and the role of wearables in pharmacy practice were also assessed after the module. A total of 39 final-year pharmacy students completed the module conducted in June 2024 as part of the clinical pharmacy seminar. In total, 32 students completed the pre- and post-knowledge tests and self-assessment questionnaires. Both the knowledge and the students' self-assessment of CGM-related skills after the module increased significantly (p < 0.05). Students expanded their knowledge regarding digital health solutions, in particular the CGM systems, and increased their self-reported competence in CGM-related skills. With this module, an important foundation was laid, as this is the first theoretical module including the essentials of CGM digital health tools for pharmacy students in Germany.

7252 Difference between interstitial and blood glucose during glucose loading is associated with the degree of insulin resistance in subjects with prediabetes and type 2 diabetes

Abstract Disclosure: D. Kim: None. S. Ryang: None. M. Im: None. J. Kim: None. Y. Kim: None. Y. kim: None. J. ha: None. J. Kim: None. S. Kim: None. Background and Aims: This study investigated the main factors responsible for the differences between blood glucose (BG) and interstitial fluid glucose (ISF) during glucose loading. Methods: A total of 204 Korean subjects without history of type 2 diabetes were enrolled. The subjects were categorized into three groups according to diabetes criteria by American Diabetes Association; NGT (normal glucose tolerance, n=36), PreDM (prediabetes, n=130) and T2D (undiagnosed type 2 diabetes, n=38). For glucose monitoring, a CGM (continuous glucose monitoring) system (Dexcom G6, Dexcom, San Diego, USA) was attached for 10 days. 75g oral glucose tolerance test (OGTT) was performed during applying CGM with stabilization (after wear of 3 days). Blood sample collected at t=0, 30, 60, 90 and 120 min for glucose and insulin concentration. Two parameters [insulin sensitivity (mIS) and beta-cell function (mBCF)] were derived novel mathematical model. Results: Among NGT, PreDM and T2D, there are heterogeneity in the difference and pattern between BG and ISF in this study. We identified the probability density function of BG - ISF across glucose tolerance, there is a trend that the means of BG - ISF with G60, 90, and 120 move to the right, as glucose tolerance status changes NGT to PreDM and DM. BG - ISF is significantly associated with insulin resistance (R=0.32, p&amp;lt;0.001), not diminished beta cell function. Conclusions: Because individuals vary in their degree of insulin resistance as a primary pathophysiology of type 2 diabetes, the caution should be needed in interpreting the glycemic response of CGM after a glucose load. Presentation: 6/3/2024

Efficacy and safety of henagliflozin combined with continuous subcutaneous insulin infusion in the treatment of Chinese inpatients with type 2 diabetes mellitus based on a continuous glucose monitoring system: protocol of a multicentre, open-label, inpatient, randomised, controlled trial

IntroductionThe role of sodium-glucose cotransporter 2 inhibitors (SGLT2is) in diabetes treatment is expanding; however, few studies have investigated the efficacy and safety of combining SGLT2is with insulin pump therapy. Notably,...

Tecnología aplicada a la diabetes

Diabetes technology refers to devices and applications that people with diabetes use to help manage their disease. There are two main categories: blood glucose monitoring systems (including continuous glucose monitoring (CGM) systems) and insulin delivery devices (including insulin pumps and smart insulin pens). CGM devices measure interstitial blood glucose continuously or intermittently (flash measurements), showing alerts and trends that help predict glucose behavior. The data are represented by the ambulatory glucose profile, which provides measurements and a summary graph to aid in its interpretation. Insulin pumps or continuous subcutaneous insulin infusion systems are devices that infuse rapid-acting insulin continuously with the objective of maintaining glucose levels in range. Among these, hybrid closed loop systems are capable of adjusting insulin infusion according to CGM blood glucose figures. Smart insulin pens are linked to smartphone apps and have features that help patients manage their diabetes (dose reminder, bolus calculator, active insulin monitoring).

DDLA: a double deep latent autoencoder for diabetic retinopathy diagnose based on continuous glucose sensors.

The current diagnosis of diabetic retinopathy is based on fundus images and clinical experience. However, considering the ineffectiveness and non-portability of medical devices, we aimed to develop a diagnostic model for diabetic retinopathy based on glucose series data from the wearable continuous glucose monitoring system. Therefore, this study developed a novel method, i.e., double deep latent autoencoder, for exploring glycemic variability influence from multi-day glucose data for diabetic retinopathy. Specifically, the model proposed in this research could encode continuous glucose sensor data with non-continuous and variable length via the integration of a data reorganization module and a novel encoding module with fragmented-missing-wise objective function. Additionally, the model implements a double deep autoencoder, which integrated convolutional neural network, long short-term memory, to jointly capturing the inter-day and intra-day glucose latent features from glucose series. The effectiveness of the proposed model is evaluated through a cross-validation method to clinical datasets of 765 type 2 diabetes patients. The proposed method achieves the highest accuracy value (0.89), precision value (0.88), and F1 score (0.73). The results suggest that our model can be used to remotely diagnose and screen for diabetic retinopathy by learning potential features of glucose series data collected by wearable continuous glucose monitoring systems.

EFFECTIVENESS OF CONTINUOUS GLUCOSE MONITORING IN UNCONTROLLED TYPE-2 DIABETICS IN TERTIARY HEALTHCARE CENTRE

Long term diabetic complications can be prevented or reduced by achieving good metabolic control, reflected by HbA1c &lt;6.5–7.0%.1-3 Glucose excursions might also contribute to the development of diabetic complications.4,5 Fear of hypoglycemia limits the ability to reach strict glycemic control, because it is usually accompanied by reluctance of the patient to intensify insulin therapy. Indeed, the goal of intensive therapy is to normalize HbA1c and control fasting and postprandial glycemia, while concurrently limiting the number and severity of hypoglycemic events. To reach tight glycemic control, frequent self- monitoring of blood glucose (SMBG) must be performed.6 SMBG devices provide the patient with accurate but discreet blood glucose levels. They do not provide trend information nor do they reflect glycemic fluctuations, which is possible by using continuous glucose monitoring (CGM) systems. Thus, implementation of strict glycemic control may be facilitated by a CGM device. This manuscript critically reviews the proposed benefits and indications of CGM and the current evidence of CGM on health outcomes in diabetic patients.

Various Approaches and Future Prospective for Therapies for Diabetes: A Review

The global prevalence of diabetes mellitus disease marked by increased blood glucose levels, has reached epidemic proportions. This review provides a comprehensive assessment of the current landscape of diabetes therapies and outlines future prospects for improving the management and treatment of this condition. The current scenario highlights a multifaceted approach to diabetes management, with a focus on lifestyle interventions, oral antidiabetic drugs, insulin therapy, and emerging technologies like continuous glucose monitoring and closed-loop systems. While these approaches have significantly improved the lives of people with diabetes, several challenges persist. These include medication adherence, hypoglycemia risk, and the financial burden of diabetes care. Innovative treatments that improve glucose regulation, improve cardiovascular health, and help patients lose weight—like SGLT-2 inhibitors and GLP-1 receptor agonists—have showed promise in treating these issues. The future prospects for diabetes therapies are exciting, with ongoing research into cell-based therapies, including pancreatic islet transplantation and stem cell-derived beta cells. Personalized medicine approaches, driven by genetics and artificial intelligence, hold the potential to optimize treatment regimens and predict individual responses to various therapies. Furthermore, advancements in telemedicine and digital health technologies will enhance patient self-management and remote monitoring.

Forecasting glucose values for patients with type 1 diabetes using heart rate data

Background:Type 1 Diabetes Mellitus (T1DM) is a chronic metabolic disease affecting millions of people worldwide. T1DM requires patients to continuously monitor their blood glucose levels. Due to pancreatic dysfunctions, patients use insulin injections to correct glucose values by synthetic insulin. Continuous Glucose Monitoring (CGM) is a system which includes an algorithm allowing to measure (and in some cases to predict) glucose levels at a frequent sampling time. This enable implementing advanced devices, including automated insulin pump delivery. Nevertheless, CGM still presents some limitations, including (i) the delay (time lag) in detecting change in glucose levels compared to the traditional blood glucose measurement, and (ii) the lack of a sufficient and acceptable time to accurately predict glucose values. Methods:We propose a framework based on a Gated Recurrent Unit (GRU) model to forecast both short- and long-term glucose values using heart rate (HR) and interstitial glucose (IG) values. The framework acquires HR and IG data and predicts glucose values with higher precision compared to state-of-the-art models. For training and testing the proposed framework, we used the OhioT1DM Dataset, which includes physiological data such as HR and IG values collected over an 8-week observation period. Additionally, we validated our framework using two other glucose datasets to ensure its generalizability across different HR and IG sampling frequencies. The proposed framework can be used to optimize the CGM system by incorporating patient HR measurements, thereby improving the prediction of short- and long-term glucose levels and reducing risks associated with conditions like hypoglycemia. Results:Experimental tests were conducted using HR and IG data from the OhioT1DM Dataset, as well as from two additional T1DM patient datasets. We analyzed 6 patients from Ohio dataset while we validated the algorithm on 23 patients coming from two different university hospitals (6 from the University of Catanzaro medical hospital and 17 gathered from a validated study at IRCCS San Matteo Hospital in Pavia) for a total number of 29 patients. Our framework demonstrates an improvement in forecasting IG values in terms of RMSE and MAE for different choice of prediction horizons (PH). In the case of a PH of 5, 10, 20, 30, and 60 min, we reach an RMSE of 5.0, 9.38, 15.27, 20.48, and 34.16 respectively. The framework is freely available as an open-source, with an example dataset on a GitHub repository (see https://github.com/rafgia/attention_to_glycemia). Conclusion:Our framework offers a promising solution for improving glucose level prediction and management in T1DM patients. By leveraging a GRU model and incorporating HR and IG values, we achieve more precise glucose level forecasting compared to state-of-the-art models. This approach not only enhances the accuracy of glucose predictions but also mitigates the risks associated with hypoglycemia.

First experiences of using the Dexcom ONE CGM system: results from a national survey on the perception of early adopters

Introduction: The availability of increasingly accurate and high-performing Continuous Glucose Monitoring (CGM) systems today allows for the adoption of selection criteria based not only on clinical profiles but also on the preferences of people with diabetes. Methods: The “Dexcom ONE Experience” study aimed to investigate the initial user experiences with the Dexcom ONE CGM system in the real lives of people with diabetes undergoing insulin therapy, specifically early adopters (i.e., patients who first in Italy adopted Dexcom ONE CGM who have used the device for at least 30 days). Empirical evidence was collected, focusing primarily on usability, satisfaction, and impact on quality of life (QoL) of the system, through an online survey. All survey participants were insulin-treated patients, as indicated by CGM device recommendations. Results: Analysis was conducted on 300 completed surveys. 93% of respondents consider the device useful for diabetes management; 91% find it helps in more effectively managing therapy, while 88% report an improvement in health. 86% find it easy to learn how to use, and 93% plan to continue using it in the coming months. 74% believe the system to be highly reliable. The most recognized and utilized functions are data visibility on smartphones/receivers and glycaemic trend visibility. 70% of respondents express being “very or extremely satisfied” with the Dexcom ONE device experience (scoring 4/5), and 90% note a “positive or extremely positive” impact on their QoL from using the device. Conclusions: In conclusion, usability and satisfaction levels have proven to be high among early adopters of the Dexcom ONE CGM system.

Estimating Breakfast Characteristics Using Continuous Glucose Monitoring and Machine Learning in Adults With or at Risk of Type 2 Diabetes

Background: Continuous glucose monitoring (CGM) systems allow detailed assessment of postprandial glucose responses (PPGR), offering new insights into food choices’ impact on dysglycemia. However, current approaches to analyze PPGR using a CGM require manual meal logging, limiting the scalability of CGM-driven applications like personalized nutrition and at-home diabetes risk assessment. Objective: We propose a machine learning (ML) framework to automatically identify and characterize breakfast-related PPGRs from CGM profiles in adults at risk of or living with noninsulin-treated type 2 diabetes (T2D). Methods: Our PPGR estimation framework uses a random forest ML algorithm trained on 15 adults without diabetes who wore a CGM for up to four weeks. The algorithm performance was evaluated on a held-out subset of the participants’ CGM data as well as on an external validation data set of 36 individuals at risk for or with noninsulin-treated T2D. Results: Our algorithm’s estimations of breakfast PPGRs displayed no statistically significant differences to annotated PPGRs, in terms of incremental area under the curve and glucose rise ( P &gt; .05 for both data sets), while a small difference in prebreakfast glucose was found in the nondiabetes data set ( P = .005) but not in the validation T2D data set ( P = .18). Conclusions: We designed an ML framework to automatically estimate the timing of meal events from CGM data in individuals without diabetes and in individuals at risk or with T2D. This could provide a more scalable approach for analyzing postprandial glycemia, increasing the feasibility of CGM-based precision nutrition and diabetes risk assessment applications.

Improved Glycemic Control during a One-Week Adventure Camp in Adolescents with Type 1 Diabetes-The DIACAMP Study.

Adolescence remains a crucial age associated with diabetes distress in individuals living with type 1 diabetes (T1D). The Austrian organization "Diabär" regularly hosts a one-week adventure camp for adolescents (12-18 years) living with T1D. The camp focuses on "fun activities" without a structured educational protocol in order to minimize diabetes distress and increase diabetes management skills. In contrast to educational camps, training is kept to a minimum. However, attendees analyze the glycemic data of the previous day with their medical supervisor once daily during the camp. All subjects used a standardized real-time continuous glucose monitoring (CGM) system (DexcomG7) throughout the whole study. Glycemic metrics were prospectively analyzed during three periods: week 1 = home phase, week 2 = adventure camp, and week 3 = after the camp. Safety (time below range 1 [TBR1], 69-54 mg/dL, and time below range 2 [TBR2], <54 mg/dL) and efficacy (time in range [TIR], 70-180 mg/dL) were assessed by comparing the CGM data during weeks 1-3. The CGM data of 14 participants were analyzed. The TIR was higher during the camp week versus week 1 (70.4 ± 11.1% vs. 53.1 ± 20.2%; p = 0.001). The TBR1 significantly increased during camp compared to week 1 (2.5 ±1.7% vs. 1.3 ± 1.2%; p = 0.009), whereas the TBR2 did not differ. No serious adverse events occurred. This adventure camp without a main focus on education showed feasibility and safety in adolescents with T1D.

Insulin Delivery Technology for Treatment of Infants with Neonatal Diabetes Mellitus: A Systematic Review.

Neonatal diabetes mellitus is a rare disorder of glucose metabolism with onset within the first 6months of life. The initial treatment is based on insulin infusion. The technologies for diabetes treatment can be very helpful, even if guidelines are still lacking. The current study aimed to provide a comprehensive review of the literature about the safety and efficacy of insulin treatment with technology for diabetes to support clinicians in the management of infants with neonatal diabetes mellitus. A total of 22 papers were included, most of them case reports or case series. The first infants with neonatal diabetes mellitus treated with insulin pumps were described nearly two decades ago. Over the years, continuous glucose monitoring systems were added to treat these individuals, allowing for a better customization of insulin administration. Insulin was diluted in some cases to further minimize the doses. Improvement in technology for diabetes prompted clinicians to use new devices and algorithms for insulin delivery in infants with neonatal diabetes as well. These systems are safe and effective, may shorten hospital stay, and help clinicians weaning insulin during the remission phase in the transient forms or switching from insulin to sulfonylurea when suggested by the molecular diagnosis. New technologies for insulin delivery in infants with neonatal diabetes can be used safely and closed-loop algorithms can work properly in these situations, optimizing blood glucose control.