The Virtual DCCT: Adding Continuous Glucose Monitoring to a Landmark Clinical Trial for Prediction of Microvascular Complications.

Using a multistep machine-learning procedure, add virtual continuous glucose monitoring (CGM) traces to the original sparse data of the landmark Diabetes Control and Complications Trial (DCCT). Assess the association of CGM metrics with the microvascular complications of type 1 diabetes observed during the DCCT and establish time-in-range (TIR) as a viable marker of glycemic control. Utilizing the DCCT glycated hemoglobin data obtained every 1 or 3 months plus quarterly 7-point blood glucose (BG) profiles in a multistep procedure: (i) utilized archival BG traces to model interday BG variability and estimate glycated hemoglobin; (ii) trained across the DCCT BG profiles and associated each profile with an archival BG trace; and (iii) used previously identified CGM "motifs" to associate a CGM trace to a BG trace, for each DCCT participant.

Evaluation of an Automated Priming Bolus for Improving Prandial Glucose Control in Full Closed Loop Delivery.

Automated insulin delivery (AID) is widely available to people with type 1 diabetes (T1D), providing superior glycemic control versus traditional methods. The next generation of AID devices focus on minimizing user/device interactions, especially around meals ("full closed loop," [FCL]). Our goal was to assess the postprandial glycemic impact of the bolus priming system (BPS), an algorithm delivering fixed insulin doses based on the likelihood of a meal having occurred, in conjunction with UVA's latest AID.

Post Prandial Glucose Turnover Differs Between Adolescents & Adults with Type 1 Diabetes: Triple Tracer Mixed Meal Study.

Context: Insulin sensitivity (SI) varies with age in Type 1 diabetes (T1D).

Objective: To compare postprandial glucose turnover and insulin sensitivity between adolescents and adults with T1D.

Design: Cross-sectional comparison.

Use of diabetes technology in children.

Children with type 1 diabetes and their caregivers face numerous challenges navigating the unpredictability of this complex disease. Although the burden of managing diabetes remains significant, new technology has eased some of the load and allowed children with type 1 diabetes to achieve tighter glycaemic management without fear of excess hypoglycaemia. Continuous glucose monitor use alone improves outcomes and is considered standard of care for paediatric type 1 diabetes management.

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.

Integration of a Safety Module to Prevent Rebound Hypoglycemia in Closed-Loop Artificial Pancreas Systems.

Background: With automated insulin delivery (AID) systems becoming widely adopted in the management of type 1 diabetes, we have seen an increase in occurrences of rebound hypoglycemia or generated hypoglycemia induced by the controller's response to rapid glucose rises following rescue carbohydrates. Furthermore, as AID systems aim to enable complete automation of prandial control, algorithms are designed to react even more strongly to glycemic rises. This work introduces a rebound hypoglycemia prevention layer (HypoSafe) that can be easily integrated into any AID system.

Artificial Intelligence and Machine Learning for Improving Glycemic Control in Diabetes: Best Practices, Pitfalls, and Opportunities.

Objective: Artificial intelligence and machine learning are transforming many fields including medicine. In diabetes, robust biosensing technologies and automated insulin delivery therapies have created a substantial opportunity to improve health. While the number of manuscripts addressing the topic of applying machine learning to diabetes has grown in recent years, there has been a lack of consistency in the methods, metrics, and data used to train and evaluate these algorithms.

Therapy Settings Associated with Optimal Outcomes for t:slim X2 with Control-IQ Technology in Real-World Clinical Care.

To determine insulin dosing parameters that are associated with and predict optimal outcomes for people using t:slim X2 with Control-IQ technology (CIQ). Retrospective deidentified data from CIQ users were analyzed to determine the effect of Correction Factor, Carbohydrate-to-Insulin (C:I) Ratio, and basal rate settings (standardized by total daily insulin [TDI]) on glycemic control. We performed an associative analysis followed by linear regressions to determine the relative importance of the settings and confounding variables (e.

Assessment of Meal Anticipation for Improving Fully Automated Insulin Delivery in Adults With Type 1 Diabetes.

Objective: Meals are a consistent challenge to glycemic control in type 1 diabetes (T1D). Our objective was to assess the glycemic impact of meal anticipation within a fully automated insulin delivery (AID) system among adults with T1D.

Research Design And Methods: We report the results of a randomized crossover clinical trial comparing three modalities of AID systems: hybrid closed loop (HCL), full closed loop (FCL), and full closed loop with meal anticipation (FCL+).

Predicting Immunological Risk for Stage 1 and Stage 2 Diabetes Using a 1-Week CGM Home Test, Nocturnal Glucose Increments, and Standardized Liquid Mixed Meal Breakfasts, with Classification Enhanced by Machine Learning.

Predicting the risk for type 1 diabetes (T1D) is a significant challenge. We use a 1-week continuous glucose monitoring (CGM) home test to characterize differences in glycemia in at-risk healthy individuals based on autoantibody presence and develop a machine-learning technology for CGM-based islet autoantibody classification. Sixty healthy relatives of people with T1D with mean ± standard deviation age of 23.

A Meta-Analysis of Randomized Trial Outcomes for the t:slim X2 Insulin Pump with Control-IQ Technology in Youth and Adults from Age 2 to 72.

To evaluate the effect of hybrid-closed loop Control-IQ technology (Control-IQ) in randomized controlled trials (RCTs) in subgroups based on baseline characteristics such as race/ethnicity, socioeconomic status (SES), prestudy insulin delivery modality (pump or multiple daily injections), and baseline glycemic control. Data were pooled and analyzed from 3 RCTs comparing Control-IQ to a Control group using continuous glucose monitoring in 369 participants with type 1 diabetes (T1D) from age 2 to 72 years old. Time in range 70-180 mg/dL (TIR) in the Control-IQ group ( = 256) increased from 57% ± 17% at baseline to 70% ± 11% during follow-up, and in the Control group ( = 113) was 56% ± 15% and 57% ± 14%, respectively (adjusted treatment group difference = 11.

Trial of Hybrid Closed-Loop Control in Young Children with Type 1 Diabetes.

Background: Closed-loop control systems of insulin delivery may improve glycemic outcomes in young children with type 1 diabetes. The efficacy and safety of initiating a closed-loop system virtually are unclear.

Methods: In this 13-week, multicenter trial, we randomly assigned, in a 2:1 ratio, children who were at least 2 years of age but younger than 6 years of age who had type 1 diabetes to receive treatment with a closed-loop system of insulin delivery or standard care that included either an insulin pump or multiple daily injections of insulin plus a continuous glucose monitor.

Maximizing Glycemic Benefits of Using Faster Insulin Formulations in Type 1 Diabetes: In Silico Analysis Under Open- and Closed-Loop Conditions.

Ultrarapid-acting insulin analogs that could improve or even prevent postprandial hyperglycemia are now available for both research and clinical care. However, clear glycemic benefits remain elusive, especially when combined with automated insulin delivery (AID) systems. In this work, we study two insulin formulations in silico and highlight adjustments of both open-loop and closed-loop insulin delivery therapies as a critical step to achieve clinically meaningful improvements.

Adjusting Therapy Profiles When Switching to Ultra-Rapid Lispro in an Advanced Hybrid Closed-Loop System: An in Silico Study.

Background: It has been shown that insulin acceleration by itself might not be sufficient to see clear improvements in glycemic metrics, and insulin therapy may need to be adjusted to fully leverage the extra safety margin provided by faster pharmacokinetic (PK) and pharmacodynamic (PD) profiles. The objective of this work is to explore how to perform such adjustments on a commercially available automated insulin delivery (AID) system.

Methods: Ultra-rapid lispro (URLi) is modeled within the UVA/Padova simulation platform using data from previously published clamp studies.

Consensus Recommendations for the Use of Automated Insulin Delivery Technologies in Clinical Practice.

The significant and growing global prevalence of diabetes continues to challenge people with diabetes (PwD), healthcare providers, and payers. While maintaining near-normal glucose levels has been shown to prevent or delay the progression of the long-term complications of diabetes, a significant proportion of PwD are not attaining their glycemic goals. During the past 6 years, we have seen tremendous advances in automated insulin delivery (AID) technologies.

Insulin Replacement Across the Menstrual Cycle in Women with Type 1 Diabetes: An In Silico Assessment of the Need for Ad Hoc Technology.

Women with type 1 diabetes (T1D) of fertile age may experience fluctuations in insulin needs across the menstrual cycle. When present, these fluctuations complicate glucose management and oftentimes worsen glycemic control. In this work, an in silico analysis was conducted to assess whether current technology is sufficient to handle changes in insulin needs due to the menstrual cycle in women with T1D.

Accuracy of a Factory-Calibrated Continuous Glucose Monitor in Individuals With Diabetes on Hemodialysis.

Objective: Continuous glucose monitoring (CGM) improves diabetes management, but its reliability in individuals on hemodialysis is poorly understood and potentially affected by interstitial and intravascular volume variations.

Research Design And Methods: We assessed the accuracy of a factory-calibrated CGM by using venous blood glucose measurements (vBGM) during hemodialysis sessions and self-monitoring blood glucose (SMBG) at home.

Results: Twenty participants completed the protocol.

A Glycemia Risk Index (GRI) of Hypoglycemia and Hyperglycemia for Continuous Glucose Monitoring Validated by Clinician Ratings.

Background: A composite metric for the quality of glycemia from continuous glucose monitor (CGM) tracings could be useful for assisting with basic clinical interpretation of CGM data.

Methods: We assembled a data set of 14-day CGM tracings from 225 insulin-treated adults with diabetes. Using a balanced incomplete block design, 330 clinicians who were highly experienced with CGM analysis and interpretation ranked the CGM tracings from best to worst quality of glycemia.