Intensive management from diagnosis improves HbA1c at 12 months post-diagnosis: results from a prospective cohort study in children with newly diagnosed type 1 diabetes.

AIMS: To examine the impact of intensive management of type 1 diabetes (T1D) from diagnosis on HbA1c 12 months from diagnosis. METHODS: HbA1c measured 12 months after diagnosis for 70 consecutively newly diagnosed children with T1D following implementation of an intensive management protocol was compared with 70 children consecutively diagnosed immediately pre-implementation. Intensive management involved carbohydrate counting and flexible insulin dosing from first meal with subcutaneous insulin, targeted blood glucose levels from 4-8mmol/L irrespective of time of day, avoidance of twice daily insulin regimen and promotion of continuous glucose monitoring (CGM). HbA1c, diabetes technology use and insulin regimen at 12 months post-diagnosis were compared. RESULTS: The post-intensive management implementation cohort had an improved mean HbA1c of 58.2±15.3mmol/mol vs 63.7±10.7mmol/mol at 12 months (p=0.014). The proportion of young people with diabetes meeting a target HbA1c of <53mmol/mol at 12 months improved from 11% to 40% (p=<0.001). There was a reduction of twice daily insulin regimen from 66% to 11% (p=<0.001), and increased CGM use from 57% to 76% (p=0.02). CONCLUSION: Intensive management when implemented with consistent messaging from the multi-disciplinary team resulted in clinic-wide improvements in HbA1c and the proportion meeting HbA1c targets.

Impact on diabetes control and patient-reported outcomes of a newer implantable continuous glucose monitoring system (Eversense® CGM System): a single-centre retro- and prospective observational study.

AIMS OF THE STUDY: The Eversense® CGM System is the first and only continuous glucose monitoring system (CGMS) that uses a fully subcutaneous implanted sensor. This study aimed to evaluate effectiveness, safety and patient-reported outcomes in patients using the Eversense® CGM System in a realistic clinical setting, assessed at a single Swiss diabetes centre (Luzerner Kantonsspital) with prolonged follow-up. METHODS: This was a prospective and retrospective observational study that included patients with type 1 diabetes mellitus in whom at least one Eversense® glucose sensor was implanted between 2017 and 2022. The primary endpoint was the change in HbA1c levels from the baseline (before implantation of the sensor) to 6 ± 2 and 12 ± 2 months and the last follow-up (newest available value) after implantation. The secondary outcome measures were the number of premature sensor breakdowns, adverse events related to the implantation procedure (infection, bleeding, difficulties with implantation or explantation) and patient-related outcomes (assessed with a questionnaire). RESULTS: A total of 33 patients participated in this study. The median follow-up time was 50 (IQR 22.3-58.5) months. In total, 178 sensor implantations were performed. Valid HbA1c results were available for 26 participants. Compared to the baseline values, HbA1c levels at 6 and 12 months and the last follow-up changed by -0.25%, -0.45 and -0.2 (p = 0.278, 0.308 and 0.296, respectively). We recorded 16 (9%) premature sensor breakdowns, all occurring between 2019 and 2020. Apart from one late-onset infection and four complicated sensor removals, no major complications were assessed. The results of the questionnaire showed a subjective improvement in hypoglycaemia rates, a better perception of hypoglycaemia and the impression of better diabetes management. Common issues with the device reported by the patients were technical errors (connection problems) and problems with the removal procedure. CONCLUSIONS: The use of the Eversense® CGM System resulted in changes in HbA1c of between -0.2% and -0.45%. The rate of premature sensor breakdown was low. Major complications following sensor implantation or removal were absent, apart from one case of infection and four cases of complicated removal. Patient-reported outcomes with the Eversense® CGM System showed a subjective positive impact on hypoglycaemia rates, greater confidence in managing hypoglycaemia and diabetes in general, and easy handling of the transmitter and mobile app. Technical issues must be considered but are nowadays, with the use of the newest sensor generation, very rare.

Augmenting authenticity for non-invasive in vivo detection of random blood glucose with photoacoustic spectroscopy using Kernel-based ridge regression.

Photoacoustic Spectroscopy (PAS) is a potential method for the noninvasive detection of blood glucose. However random blood glucose testing can help to diagnose diabetes at an early stage and is crucial for managing and preventing complications with diabetes. In order to improve the diagnosis, control, and treatment of Diabetes Mellitus, an appropriate approach of noninvasive random blood glucose is required for glucose monitoring. A polynomial kernel-based ridge regression is proposed in this paper to detect random blood glucose accurately using PAS. Additionally, we explored the impact of the biological parameter BMI on the regulation of blood glucose, as it serves as the primary source of energy for the body's cells. The kernel function plays a pivotal role in kernel ridge regression as it enables the algorithm to capture intricate non-linear associations between input and output variables. Using a Pulsed Laser source with a wavelength of 905 nm, a noninvasive portable device has been developed to collect the Photoacoustic (PA) signal from a finger. A collection of 105 individual random blood glucose samples was obtained and their accuracy was assessed using three metrics: Root Mean Square Error (RMSE), Mean Absolute Difference (MAD), and Mean Absolute Relative Difference (MARD). The respective values for these metrics were found to be 10.94 (mg/dl), 10.15 (mg/dl), and 8.86%. The performance of the readings was evaluated through Clarke Error Grid Analysis and Bland Altman Plot, demonstrating that the obtained readings outperformed the previously reported state-of-the-art approaches. To conclude the proposed IoT-based PAS random blood glucose monitoring system using kernel-based ridge regression is reported for the first time with more accuracy.

Long-term assessment of the NHS hybrid closed-loop real-world study on glycaemic outcomes, time-in-range, and quality of life in children and young people with type 1 diabetes.

Hybrid closed-loop (HCL) systems seamlessly interface continuous glucose monitoring (CGM) with insulin pumps, employing specialised algorithms and user-initiated automated insulin delivery. This study aimed to assess the efficacy of HCLs at 12 months post-initiation on glycated haemoglobin (HbA1c), time-in-range (TIR), hypoglycaemia frequency, and quality of life measures among children and young people (CYP) with type 1 diabetes mellitus (T1DM) and their caregivers in a real-world setting. Conducted between August 1, 2021, and December 10, 2022, the prospective recruitment took place in eight paediatric diabetes centres across England under the National Health Service England's (NHSE) HCL pilot real-world study. A cohort of 251 CYP (58% males, mean age 12.3 years) with T1DM participated (89% white, 3% Asian, 4% black, 3% mixed ethnicity, and 1% other). The study utilised three HCL systems: (1) Tandem Control-IQ AP system, which uses the Tandem t:slim X2 insulin pump (Tandem Diabetes Care, San Diego, CA, USA) with the Dexcom G6® CGM (Dexcom, San Diego, CA, USA) sensor; (2) Medtronic MiniMed™ 780G with the Guardian 4 sensor (Medtronic, Northridge, CA, USA); and (3) the CamAPS FX (CamDiab, Cambridge, UK) with the Ypsomed insulin pump (Ypsomed Ltd, Escrick, UK) and Dexcom G6® CGM.All systems were fully funded by the NHS. Results demonstrated significant improvements in HbA1c (average reduction at 12 months 7 mmol/mol; P < 0.001), time-in-range (TIR) (average increase 13.4%; P < 0.001), hypoglycaemia frequency (50% reduction), hypoglycaemia fear, and quality of sleep (P < 0.001) among CYP over a 12-month period of HCL usage. Additionally, parents and carers experienced improvements in hypoglycaemia fear and quality of sleep after 6 and 12 months of use. In addition to the improvements in glycaemic management, these findings underscore the positive impact of HCL systems on both the well-being of CYP with T1DM and the individuals caring for them.

Internet of Things enabled open source assisted real-time blood glucose monitoring framework.

Regular monitoring of blood glucose levels is essential for the management of diabetes and the development of appropriate treatment protocols. The conventional blood glucose (BG) testing have an intrusive technique to prick the finger and it can be uncomfortable when it is a regular practice. Intrusive procedures, such as fingerstick testing has negatively influencing patient adherence. Diabetic patients now have an exceptional improvement in their quality of life with the development of cutting-edge sensors and healthcare technologies. intensive care unit (ICU) and pregnant women also have facing challenges including hyperglycemia and hypoglycemia. The worldwide diabetic rate has incited to develop a wearable and accurate non-invasive blood glucose monitoring system. This research developed an Internet of Things (IoT) - enabled wearable blood glucose monitoring (iGM) system to transform diabetes care and enhance the quality of life. The TTGOT-ESP32 IoT platform with a red and near-infrared (R-NIR) spectral range for blood glucose measurement has integrated into this wearable device. The primary objective of this gadget is to provide optimal comfort for the patients while delivering a smooth monitoring experience. The iGM gadget is 98.82 % accuracy when used after 10 hours of fasting and 98.04 % accuracy after 2 hours of breakfast. The primary objective points of the research were continuous monitoring, decreased risk of infection, and improved quality of life. This research contributes to the evolving field of IoT-based healthcare solutions by streaming real-time glucose values on AWS IoT Core to empower individuals with diabetes to manage their conditions effectively. The iGM Framework has a promising future with the potential to transform diabetes management and healthcare delivery.

A personalized multitasking framework for real-time prediction of blood glucose levels in type 1 diabetes patients.

Real-time prediction of blood glucose levels (BGLs) in individuals with type 1 diabetes (T1D) presents considerable challenges. Accordingly, we present a personalized multitasking framework aimed to forecast blood glucose levels in patients. The patient data was initially categorized according to gender and age and subsequently utilized as input for a modified GRU network model, creating five prediction sub-models. The model hyperparameters were optimized and tuned after introducing the decay factor and incorporating the TCN network and attention mechanism into the GRU model. This step was undertaken to improve the capability of feature extraction. The Ohio T1DM clinical dataset was used to train and evaluate the performance of the proposed model. The metrics, including Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and Clark Error Grid Analysis (EGA), were used to evaluate the performance. The results showed that the average RMSE and the MAE of the proposed model were 16.896 and 9.978 mg/dL, respectively, over the prediction horizon (PH) of 30 minutes. The average RMSE and the MAE were 28.881 and 19.347 mg/dL, respectively, over the PH of 60 min. The proposed model demonstrated excellent prediction accuracy. In addition, the EGA analysis showed that the proposed model accurately predicted 30-minute and 60-minute PH within zones A and B, demonstrating that the framework is clinically feasible. The proposed personalized multitask prediction model in this study offers robust assistance for clinical decision-making, playing a pivotal role in improving the outcomes of individuals with diabetes.

Glucometrics and device satisfaction in children and adolescents with type 1 diabetes using different treatment modalities: A multicenter real-world observational study.

AIMS: To analyze metabolic outcomes, diabetes impact and device satisfaction in children and adolescents with type 1 diabetes in Italy who used different treatment modalities for diabetes care in a real-life context. METHODS: In this multicenter, nationwide, cross-sectional study, 1464 participants were enrolled at a routine visit. The following treatment modalities were considered MDI + SMBG; MDI + CGM; Sensor Augmented Pump Therapy; predictive management of low glucose; Hybrid Closed Loop (HCL); Advanced Hybrid Closed Loop (AHCL). Health related quality of life was evaluated by the Italian version of the Diabetes Impact and Device Satisfaction Scale (DIDS) questionnaire. RESULTS: Patients treated with AID systems were more likely to have HbA1c ≤ 6.5 %, higher percentage of time with glucose levels between 70 and 180 mg/dL, lower percentage of time with glucose levels above 180 mg/dL, higher device satisfaction, and reduced impact of diabetes. All the therapeutic modalities with respect to MDI + CGM, except for MDI + SMBG, contributed to increase the device satisfaction. HCL and AHCL respect to MDI + CGM were associated with lower diabetes impact. CONCLUSION: Real-life use of automated insulin delivery systems is associated with reduced type 1 diabetes impact, increased device satisfaction, and achievement of glycemic goals.

Preserved C-peptide is common and associated with higher time in range in Chinese type 1 diabetes.

Objective: The aim of this study is to determine the residual C-peptide level and to explore the clinical significance of preserved C-peptide secretion in glycemic control in Chinese individuals with type 1 diabetes (T1D). Research design and methods: A total of 534 participants with T1D were enrolled and divided into two groups, low-C-peptide group (fasting C-peptide ≤10 pmol/L) and preserved-C-peptide group (fasting C-peptide >10 pmol/L), and clinical factors were compared between the two groups. In 174 participants who were followed, factors associated with C-peptide loss were also identified by Cox regression. In addition, glucose metrics derived from intermittently scanned continuous glucose monitoring were compared between individuals with low C-peptide and those with preserved C-peptide in 178 participants. Results: The lack of preserved C-peptide was associated with longer diabetes duration, glutamic acid decarboxylase autoantibody, and higher daily insulin doses, after adjustment {OR, 1.10 [interquartile range (IQR), 1.06-1.14]; OR, 0.46 (IQR, 0.27-0.77); OR, 1.04 (IQR, 1.02-1.06)}. In the longitudinal analysis, the percentages of individuals with preserved C-peptide were 71.4%, 56.8%, 71.7%, 62.5%, and 22.2% over 5 years of follow-up. Preserved C-peptide was also associated with higher time in range after adjustment of diabetes duration [62.4 (IQR, 47.3-76.6) vs. 50.3 (IQR, 36.2-63.0) %, adjusted P = 0.003]. Conclusions: Our results indicate that a high proportion of Chinese patients with T1D had preserved C-peptide secretion. Meanwhile, residual C-peptide was associated with favorable glycemic control, suggesting the importance of research on adjunctive therapy to maintain ß-cell function in T1D.

An estimate of the need for continuous glucose monitoring in type 2 diabetes with intensive insulin treatment in secondary healthcare.

AIM: To estimate the proportion of persons with type 2 diabetes (T2DM) receiving intensive insulin treatment in the secondary healthcare who could be candidates for continuous glucose monitoring (CGM), based on different HbA1c criteria. For comparison, the results are also presented as proportion of persons with type 1 diabetes (T1DM) in the same region. PATIENTS AND METHODS: In the Central Denmark Region, we identified all persons with T1DM (n = 6179) and T2DM (n = 4315) who had a minimum of one contact to a diabetes outpatient clinic from September 2021 to September 2022. Insulin regimen and HbA1c measured after a minimum of 2 months with a stable insulin regimen were retrieved from the healthcare administrative electronic platform used in the region. RESULTS: The numbers of persons with T1DM and T2DM with HbA1c meeting the criteria were 5145 and 3090, respectively. The fraction of T2DM with basal-bolus insulin was 35.3%, and the fraction with basal-bolus insulin and HbA1c >53 (7%) mmol/mol or >58 (7.5%) mmol/mol was 20.5% and 16.6%, respectively. These proportions correspond to 19.4%, 14.4% and 11.7% of the persons with T1DM in the same geographical area. CONCLUSION: The proportion of persons with T2DM in secondary healthcare undergoing intensive insulin treatment who could be candidates for CGM corresponded to only a minor fraction of persons with T1DM in the same region, irrespective of any HbA1c criteria applied.

Identification of individuals with diabetes who are eligible for continuous glucose monitoring forecasting.

BACKGROUND AND OBJECTIVES: Predicting glucose levels in individuals with diabetes offers potential improvements in glucose control. However, not all patients exhibit predictable glucose dynamics, which may lead to ineffective treatment strategies. We sought to investigate the efficacy of a 7-day blinded screening test in identifying diabetes patients suitable for glucose forecasting. METHODS: Participants with type 1 diabetes (T1D) were stratified into high and low initial error groups based on screening results (eligible and non-eligible). Long-term glucose predictions (30/60 min lead time) were evaluated among 334 individuals who underwent continuous glucose monitoring (CGM) over a total of 64,460,560 min. RESULTS: A strong correlation was observed between screening accuracy and long-term mean absolute relative difference (MARD) (0.661-0.736; p < 0.001), suggesting significant predictability between screening and long-term errors. Group analysis revealed a notable reduction in predictions falling within zone D of the Clark Error Grid by a factor of three and in zone C by a factor of two. CONCLUSIONS: The identification of eligible patients for glucose prediction through screening represents a practical and effective strategy. Implementation of this approach could lead to a decrease in adverse glucose predictions.

Diabetes Technology in People with Type 2 Diabetes: Novel Indications.

PURPOSE OF REVIEW: Diabetes technology has been continuously evolving. Current versions of continuous glucose monitors (CGM) use minimally invasive designs, monitor glucose values with high accuracy, and can be used to guide insulin dosing. Extensive evidence supports the use of diabetes technology for monitoring and insulin administration in people with type 1 diabetes. However, there is emerging evidence for people with type 2 diabetes. In this review, we present the different technological devices used to monitor glucose and deliver insulin and the evidence supporting their use in people with type 2 diabetes. RECENT FINDINGS: The use of CGMs in people with type 2 diabetes treated with insulin or non-insulin therapies has been associated with improvements in glycemic control and time spent in hypoglycemia. Smart insulin pens and smart connected devices are options to track compliance and guide insulin delivery in people who do not require insulin pump therapy. Mechanical patch pumps can be used to reduce the burden of multiple daily insulin injections. Automated insulin delivery algorithms improve glycemic control without an increase in hypoglycemia. The use of technology in the management of type 2 diabetes generates glycemic data previously inaccessible, reduces barriers for insulin initiation, improves glycemic control, tracks adherence to therapy, and improves user satisfaction.

The Health Economics of Automated Insulin Delivery Systems and the Potential Use of Time in Range in Diabetes Modeling: A Narrative Review.

Intensive therapy with exogenous insulin is the treatment of choice for individuals living with type 1 diabetes (T1D) and some with type 2 diabetes, alongside regular glucose monitoring. The development of systems allowing (semi-)automated insulin delivery (AID), by connecting glucose sensors with insulin pumps and algorithms, has revolutionized insulin therapy. Indeed, AID systems have demonstrated a proven impact on overall glucose control, as indicated by effects on glycated hemoglobin (HbA1c), risk of severe hypoglycemia, and quality of life measures. An alternative endpoint for glucose control that has arisen from the use of sensor-based continuous glucose monitoring is the time in range (TIR) measure, which offers an indication of overall glucose control, while adding information on the quality of control with regard to blood glucose level stability. A review of literature on the health-economic value of AID systems was conducted, with a focus placed on the growing place of TIR as an endpoint in studies involving AID systems. Results showed that the majority of economic evaluations of AID systems focused on individuals with T1D and found AID systems to be cost-effective. Most studies incorporated HbA1c, rather than TIR, as a clinical endpoint to determine treatment effects on glucose control and subsequent quality-adjusted life year (QALY) gains. Likely reasons for the choice of HbA1c as the chosen endpoint is the use of this metric in most validated and established economic models, as well as the limited publicly available evidence on appropriate methodologies for TIR data incorporation within conventional economic evaluations. Future studies could include the novel TIR metric in health-economic evaluations as an additional measure of treatment effects and subsequent QALY gains, to facilitate a holistic representation of the impact of AID systems on glycemic control. This would provide decision makers with robust evidence to inform future recommendations for health care interventions.

Continuous glucose monitoring and advanced glycation endproducts for prediction of clinical outcomes and development of cystic fibrosis-related diabetes in adults with CF.

Introductions: Cystic fibrosis-related diabetes (CFRD) is associated with pulmonary decline, compromised nutritional status, and earlier mortality. Onset is often insidious, so screening for early detection of glycemic abnormalities is important. Continuous glucose monitoring (CGM) has been validated in people with CF and has been shown to detect early glycemic variability otherwise missed on 2-hour oral glucose tolerance testing (OGTT). We previously reported that CGM measures of hyperglycemia and glycemic variability are superior to hemoglobin A1c (HbA1c) in distinguishing those with and without CFRD. However, little is known about the long-term predictive value of CGM measures of glycemia for both the development of CFRD and their effect on key clinical outcomes such as weight maintenance and pulmonary function. In addition, there have been no studies investigating advanced glycation endproducts (AGE) assessed by skin autofluorescence in people with CF. Methods: In this prospective observational study, CGM and HbA1c were measured at 2 to 3 time points 3 months apart in 77 adults with CF. Participants who did not have CFRD at the time of enrollment underwent OGTT at the baseline visit, and all participants had AGE readings at baseline. Follow up data including anthropometric measures, pulmonary function and CFRD status were collected by review of medical records 1- and 2-years after the baseline visits. We applied multivariable linear regression models correlating glycemic measures to change in key clinical outcomes (weight, BMI, FEV1) accounting for age, gender and elexacaftor/tezacaftor/ivacaftor (ETI) use. We also conducted logistic regression analyses comparing baseline glycemic data to development of CFRD during the 2-year follow up period. Results: Of the 77 participants, 25 had pre-existing CFRD at the time of enrollment, and six participants were diagnosed with CFRD by the OGTT performed at the baseline visit. When adjusting for age, gender, and ETI use, multiple CGM measures correlated with weight and BMI decline after one year but not after two years. CGM and HbA1c at baseline did not predict decline in FEV1 (p>0.05 for all). In the 46 participants without a diagnosis of CFRD at baseline, two participants were diagnosed with CFRD over the following two years, but CGM measures at baseline did not predict progression to CFRD. Baseline AGE values were higher in individuals with CFRD and correlated with multiple measures of dysglycemia (HbA1c, AG, SD, CV, TIR, % time >140, >180, >250) as well as weight. AGE values also correlated with FEV1 decline at year 1 and weight decline at year 1 and year 2. Conclusions: Several key CGM measures of hyperglycemia and glycemic variability were predictive of future decline in weight and BMI over one year in this population of adults with CF with and without CFRD. None of the baseline glycemic variables predicted progression to CFRD over 2 years. To our knowledge, this is the first report correlating AGE levels with key clinical and glycemic measures in CF. Limitations of these analyses include the small number of participants who developed CFRD (n=2) during the follow up period and the initiation of ETI by many participants, affecting their trajectory in weight and pulmonary function. These results provide additional data supporting the potential role for CGM in identifying clinically significant dysglycemia in CF. Future studies are needed to investigate CGM as a diagnostic and screening tool for CFRD and to understand the implications of AGE measures in this patient population.

A concept for human use of real-time and remote monitoring of diabetic subjects using intermittent scanned continuous glucose measurement.

BACKGROUND: Flash glucose monitoring systems like the FreeStyle Libre (FSL) sensor have gained popularity for monitoring glucose levels in people with diabetes mellitus. This sensor can be paired with an off-label converted real-time continuous glucose monitor (c-rtCGM) plus an ad hoc computer/smartphone interface for remote real-time monitoring of diabetic subjects, allowing for trend analysis and alarm generation. OBJECTIVES: This work evaluates the accuracy and agreement between the FSL sensor and the developed c-rtCGM system. As real-time monitoring is the main feature, the system's connectivity was assessed at 5-min intervals during the trials. METHODS: One week of glucose data were collected from 16 type 1 diabetic rats using the FSL sensor and the c-rtCGM. Baseline blood samples were taken the first day before inducing type 1 diabetes with streptozotocin. Once confirmed diabetic rats, FSL and c-rtCGM, were implanted, and to improve data matching between the two monitoring devices, the c-rtCGM was calibrated to the FSL glucometer readings. A factorial design 2 × 3^3 and a second-order regression was used to find the base values of the linear model transformation of the raw data obtained from the sensor. Accuracy, agreement, and connectivity were assessed by median absolute relative difference (Median ARD), range averaging times, Parkes consensus error grid analysis (EGA), and Bland-Altman analysis with a non-parametric approach. RESULTS: Compared to the FSL sensor, the c-rtCGM had an overall Median ARD of 6.58%, with 93.06% of results in zone A when calibration was not carried out. When calibration frequency changed from every 50 h to 1 h, the overall Median ARD improved from 6.68% to 2.41%, respectively. The connectivity evaluation showed that 95% of data was successfully received every 5 min by the computer interface. CONCLUSIONS AND CLINICAL IMPORTANCE: The results demonstrate the feasibility and reliability of real-time and remote subjects with diabetes monitoring using the developed c-rtCGM system. Performing calibrations relative to the FSL readings increases the accuracy of the data displayed at the interface.

Estimating Glycemia From HbA1c and CGM: Analysis of Accuracy and Sources of Discrepancy.

OBJECTIVE: To examine the accuracy of different periods of continuous glucose monitoring (CGM), hemoglobin A1c (HbA1c), and their combination for estimating mean glycemia over 90 days (AG90). RESEARCH DESIGN AND METHODS: We retrospectively studied 985 CGM periods of 90 days with <10% missing data from 315 adults (86% of whom had type 1 diabetes) with paired HbA1c measurements. The impact of mean red blood cell age as a proxy for nonglycemic effects on HbA1c was estimated using published theoretical models and in comparison with empirical data. Given the lack of a gold standard measurement for AG90, we applied correction methods to generate a reference (eAG90) that we used to assess accuracy for HbA1c and CGM. RESULTS: Using 14 days of CGM at the end of the 90-day period resulted in a mean absolute error (95th percentile) of 14 (34) mg/dL when compared with eAG90. Nonglycemic effects on HbA1c led to a mean absolute error for average glucose calculated from HbA1c of 12 (29) mg/dL. Combining 14 days of CGM with HbA1c reduced the error to 10 (26) mg/dL. Mismatches between CGM and HbA1c >40 mg/dL occurred more than 5% of the time. CONCLUSIONS: The accuracy of estimates of eAG90 from limited periods of CGM can be improved by averaging with an HbA1c-based estimate or extending the monitoring period beyond ∼26 days. Large mismatches between eAG90 estimated from CGM and HbA1c are not unusual and may persist due to stable nonglycemic factors.

Patient reported outcomes (PROs) and user experiences of young children with type 1 diabetes using t:slim X2 insulin pump with control-IQ technology.

OBJECTIVE: Examine patient-reported outcomes (PROs) after the use of t:slim X2 insulin pump with Control-IQ technology (CIQ) in young children with type 1 diabetes. METHODS: Children with type 1 diabetes, ages 2 to < 6 years (n = 102), were randomly assigned 2:1 to either CIQ or standard care (SC) with pump or multiple daily injections (MDI) plus continuous glucose monitoring (CGM) for 13 weeks. Both groups were offered to use CIQ for an additional 13 weeks after the randomized control trial's (RCT) completion. Guardians completed PRO questionnaires at baseline, 13-, and 26-weeks examining hypoglycemia concerns, quality of life, parenting stress, and sleep. At 26 weeks, 28 families participated in user-experience interviews. Repeated measures analyses compared PRO scores between systems used. RESULT: Comparing CIQ vs SC, responses on all 5 PRO surveys favored the CIQ group, showing that CIQ was superior to SC at 26 weeks (p values < 0.05). User-experience interviews indicated significant benefits in optimized glycemic control overall and nighttime control (28 of 28 families endorsed). All but 2/28 families noted substantial reduction in management burden resulting in less mental burden and all but 4 stated that they wanted their children to continue using CIQ. CONCLUSIONS: Families utilizing CIQ experienced glycemic benefits coupled with substantial benefits in PROs, documented in surveys and interviews. Families utilizing CIQ had reduced hypoglycemia concerns and parenting stress, and improved quality of life and sleep. These findings demonstrate the benefit of CIQ in young children with type 1 diabetes that goes beyond documented glycemic benefit.

Population-Specific Glucose Prediction in Diabetes Care With Transformer-Based Deep Learning on the Edge.

Leveraging continuous glucose monitoring (CGM) systems, real-time blood glucose (BG) forecasting is essential for proactive interventions, playing a crucial role in enhancing the management of type 1 diabetes (T1D) and type 2 diabetes (T2D). However, developing a model generalized to a population and subsequently embedding it within a microchip of a wearable device presents significant technical challenges. Furthermore, the domain of BG prediction in T2D remains under-explored in the literature. In light of this, we propose a population-specific BG prediction model, leveraging the capabilities of the temporal fusion Transformer (TFT) to adjust predictions based on personal demographic data. Then the trained model is embedded within a system-on-chip, integral to our low-power and low-cost customized wearable device. This device seamlessly communicates with CGM systems through Bluetooth and provides timely BG predictions using edge computing. When evaluated on two publicly available clinical datasets with a total of 124 participants with T1D or T2D, the embedded TFT model consistently demonstrated superior performance, achieving the lowest prediction errors when compared with a range of machine learning baseline methods. Executing the TFT model on our wearable device requires minimal memory and power consumption, enabling continuous decision support for more than 51 days on a single Li-Poly battery charge. These findings demonstrate the significant potential of the proposed TFT model and wearable device in enhancing the quality of life for people with diabetes and effectively addressing real-world challenges.

Cost-utility analysis of Dexcom G6 real-time continuous glucose monitoring versus FreeStyle Libre 1 intermittently scanned continuous glucose monitoring in adults with type 1 diabetes in Belgium.

AIMS/HYPOTHESIS: The aim of this study was to assess the long-term cost-effectiveness of Dexcom G6 real-time continuous glucose monitoring (rtCGM) with alert functionality compared with FreeStyle Libre 1 intermittently scanned continuous glucose monitoring (isCGM) without alerts in adults with type 1 diabetes in Belgium. METHODS: The IQVIA CORE Diabetes Model was used to estimate cost-effectiveness. Input data for the simulated baseline cohort were sourced from the randomised ALERTT1 trial (ClinicalTrials.gov. REGISTRATION NO: NCT03772600). The age of the participants was 42.9 ± 14.1 years (mean ± SD), and the baseline HbA1c was 57.8 ± 9.5 mmol/mol (7.4 ± 0.9%). Participants using rtCGM showed a reduction in HbA1c of 3.6 mmol/mol (0.36 percentage points) based on the 6-month mean between-group difference. In the base case, both rtCGM and isCGM were priced at €3.92/day (excluding value-added tax [VAT]) according to the Belgian reimbursement system. The analysis was performed from a Belgian healthcare payer perspective over a lifetime time horizon. Health outcomes were expressed as quality-adjusted life years. Probabilistic and one-way sensitivity analyses were used to account for parameter uncertainty. RESULTS: In the base case, rtCGM dominated isCGM, resulting in lower diabetes-related complication costs and better health outcomes. The associated main drivers favouring rtCGM were lower HbA1c, fewer severe hypoglycaemic events and reduced fear of hypoglycaemia. The results were robust under a wide range of one-way sensitivity analyses. In models where the price of rtCGM is €5.11/day (a price increase of 30.4%) or €12.34/day (a price increase of 214.8%), rtCGM was cost-neutral or reached an incremental cost-effectiveness ratio of €40,000 per quality-adjusted life year, respectively. CONCLUSIONS/INTERPRETATION: When priced similarly, Dexcom G6 rtCGM with alert functionality has both economic and clinical benefits compared with FreeStyle Libre 1 isCGM without alerts in adults with type 1 diabetes in Belgium, and appears to be a cost-effective glucose monitoring modality. Trial registration ClinicalTrials.gov NCT03772600.

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.

Comparing the glycaemic outcomes between real-time continuous glucose monitoring (rt-CGM) and intermittently scanned continuous glucose monitoring (isCGM) among adults and children with type 1 diabetes: A systematic review and meta-analysis of randomized controlled trials.

AIM: To conduct a systematic review and meta-analysis of randomized controlled trials (RCTs) comparing the effectiveness of real-time continuous glucose monitoring (rtCGM) versus intermittently scanned continuous glucose monitoring (isCGM) on key glycaemic metrics (co-primary outcomes HbA1c and time-in-range [TIR] 70-180 mg/dL, 3.9-10.0 mmol/L) among people with type 1 diabetes (T1D). METHODS: Medline, PubMed, Scopus, Web of Science and Cochrane Central Register of clinical trials were searched. Inclusion criteria were RCTs; T1D populations of any age and insulin regimen; comparing any type of rtCGM with isCGM (only the first generation had been compared to date); and reporting the glycaemic outcomes. Glycaemic outcomes were extracted post-intervention and expressed as mean differences and 95% CIs between the two comparators. Results were pooled using a random-effect meta-analysis. The risk of bias was assessed using the Cochrane RoB2 tool. The quality of evidence was assessed by the GRADE approach. RESULTS: Five RCTs met the inclusion criteria (4 parallel and 1 crossover design; 4 with CGM use <8 weeks), involving 446 participants (354 adults; 92 children and adolescents). Overall, meta-analysis showed rtCGM compared to isCGM improved absolute TIR by +7.0% (95% CI: 5.8%-8.3%, I2 = 0%, p < 0.01) accompanied by a favorable effect on time-below-range <70 mg/dL (3.9 mmol/L) - 1.7% (95%CI: -3.0% to -0.4%; p = 0.03). No differences were seen regarding HbA1c. CONCLUSIONS: This meta-analysis highlights that for people with T1D, rtCGM confers benefits over isCGM primarily related to increased TIR, with improvements in hypo- and hyperglycaemia.