The Virtual DCCT #3: Relationship of HbA1c and CGM Metrics with Cardiovascular Outcomes.

Using a multistep machine-learning approach, the aim is to create virtual continuous glucose monitoring (CGM) traces from glycemic data collected in the Diabetes Control and Complications Trial (DCCT) to assess the relationship between CGM metrics and DCCT cardiovascular (CV) outcomes in people with type 1 diabetes. Utilizing the virtual CGM traces created for each DCCT participant, as previously published, discrete Cox proportional hazard models were used to calculate hazard ratios (HRs) for the association between glycemic metrics (hemoglobin A1c [HbA1c] and virtual CGM) and 3 separate DCCT CV outcome definitions: (1) all DCCT-recorded events; (2) a restricted set of "hard" CV end points; and (3) a restricted set of major CV and major peripheral vascular events. Mean HbA1c and CGM metrics reflective of hyperglycemia were consistently higher, and time-in-range (70-180 mg/dL) and time-in-tight-range (70-140 mg/dL) were consistently lower, in DCCT participants who experienced a CV outcome versus those who did not.

Metabolic Models, in Silico Trials, and Algorithms.

Artificial pancreas (AP) systems, also called automated insulin delivery systems, have improved the time in range of glucose levels, reduced the daily burden of the user for glucose regulation, and improved their quality of life. Several commercially available AP systems operate in hybrid closed-loop mode that requires manual information from the user for meals and exercise. This article summarizes the progress on mathematical models of glucose-insulin dynamics, continuous glucose monitoring systems, and insulin pumps that form the building blocks of AP systems, the shift from animal studies to in silico clinical trials that accelerated the rate of progress in AP technologies and the efforts for developing the next-generation AP systems, and the fully automated AP that eliminates manual inputs and mitigates the effects of disturbances to glucose homeostasis-meals, physical activities, acute stress, and variations in sleep characteristics.

Registration and Real-Life Studies on Automated Insulin Delivery Systems.

The Diabetes Control and Complications Trial (DCCT) clearly documented long-term beneficial effects on both micro- and macrovascular complications associated with type 1 diabetes (T1D) by using intensive insulin therapy (IIT) via multiple daily injections or insulin pumps more than 30 years ago. IIT, both during the DCCT and with translation into clinical practice, has been demonstrated to increase the risk of severe hypoglycemia and weight gain. Automated insulin delivery (AID) systems have become the standard of care in T1D management in the developed countries.

Registration and Real-Life Studies on Automated Insulin Delivery Systems.

Introduction: The Diabetes Control and Complications Trial (DCCT) clearly documented long-term beneficial effects on both micro- and macro-vascular complications associated with type 1 diabetes (T1D) by using intensive insulin therapy (IIT) via multiple daily injections (MDIs) or insulin pumps more than 30 year ago. IIT, both during the DCCT and with translation into clinical practice, has been demonstrated to increase the risk of severe hypoglycemia and weight gain. Automated insulin delivery (AID) systems have become the standard of care in T1D management in the developed countries.

Metabolic Models, in Silico Trials, and Algorithms.

Artificial pancreas (AP) systems, also called automated insulin delivery systems, have improved the time in range of glucose levels, reduced the daily burden of the user for glucose regulation, and improved their quality of life. Several commercially available AP systems operate in hybrid closed-loop mode that requires manual information from the user for meals and exercise. This article summarizes the progress on mathematical models of glucose-insulin dynamics, continuous glucose monitoring systems, and insulin pumps that form the building blocks of AP systems, the shift from animal studies to in silico clinical trials that accelerated the rate of progress in AP technologies and the efforts for developing the next-generation AP systems, and the fully automated AP that eliminates manual inputs and mitigates the effects of disturbances to glucose homeostasis-meals, physical activities, acute stress, and variations in sleep characteristics.

Human-machine co-adaptation to automated insulin delivery: a randomised clinical trial using digital twin technology.

Most automated insulin delivery (AID) algorithms do not adapt to the changing physiology of their users, and none provide interactive means for user adaptation to the actions of AID. This randomised clinical trial tested human-machine co-adaptation to AID using new 'digital twin' replay simulation technology. Seventy-two individuals with T1D completed the 6-month study.

The Association of Time-in-Range and Time-in-Tight-Range with Retinopathy Progression in the Virtual Diabetes Control and Complications Trial Continuous Glucose Monitoring Dataset.

In a prior work, a virtual continuous glucose monitoring (CGM) trace was generated for each of the 1441 participants in the landmark Diabetes Control and Complications trial (DCCT). These new data allow us to compare whether time-in-tight-range (TITR) is a better predictor of diabetic microvascular complications (specifically retinopathy development or progression) than time-in-range (TIR). Discrete Cox proportional hazard models were used to calculate the hazard ratios (HRs) for the development/progression of retinopathy.

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.

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.

Biobehavioral Changes Following Transition to Automated Insulin Delivery: A Large Real-life Database Analysis.

Objective: To document glycemic and user-initiated bolus changes following transition from predictive low glucose suspend (PLGS) system to automated insulin delivery (AID) system during real-life use.

Research Design And Methods: We conducted analysis of 2,329,166 days (6,381 patient-years) of continuous glucose monitoring (CGM) and insulin therapy data for 19,354 individuals with type 1 Diabetes, during 1-month PLGS use (Basal-IQ technology) followed by 3-month AID use (Control-IQ technology). Baseline characteristics are as follows: 55.

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.

External validation of a classifier of daily continuous glucose monitoring (CGM) profiles.

As continuous glucose monitoring (CGM) sensors generate ever increasing amounts of CGM data, the need for methods to simplify the storage and analysis of this data becomes increasingly important. Lobo et al. developed a classifier of daily CGM profiles as an initial step in addressing this need.

Patient-Reported Outcomes in a Randomized Trial of Closed-Loop Control: The Pivotal International Diabetes Closed-Loop Trial.

Closed-loop control (CLC) has been shown to improve glucose time in range and other glucose metrics; however, randomized trials >3 months comparing CLC with sensor-augmented pump (SAP) therapy are limited. We recently reported glucose control outcomes from the 6-month international Diabetes Closed-Loop (iDCL) trial; we now report patient-reported outcomes (PROs) in this iDCL trial. Participants were randomized 2:1 to CLC ( = 112) versus SAP ( = 56) and completed questionnaires, including Hypoglycemia Fear Survey, Diabetes Distress Scale (DDS), Hypoglycemia Awareness, Hypoglycemia Confidence, Hyperglycemia Avoidance, and Positive Expectancies of CLC (INSPIRE) at baseline, 3, and 6 months.

One Year Real-World Use of the Control-IQ Advanced Hybrid Closed-Loop Technology.

The t:slim X2™ insulin pump with Control-IQ technology from Tandem Diabetes Care is an advanced hybrid closed-loop system that was first commercialized in the United States in January 2020. Longitudinal glycemic outcomes associated with real-world use of this system have yet to be reported. A retrospective analysis of Control-IQ technology users who uploaded data to Tandem's t:connect web application as of February 11, 2021 was performed.

The Digital/Virtual Diabetes Clinic: The Future Is Now-Recommendations from an International Panel on Diabetes Digital Technologies Introduction.

The increasing prevalence of diabetes, combined with a growing global shortage of health care professionals (HCP), necessitates the need to develop new approaches to diabetes care delivery to expand access to care, lessen the burden on people with diabetes, improve efficiencies, and reduce the unsustainable financial liability on health systems and payers. Use of digital diabetes technologies and telehealth protocols within a digital/virtual diabetes clinic has the potential to address these challenges. However, several issues must be resolved to move forward.

Evening and overnight closed-loop control versus 24/7 continuous closed-loop control for type 1 diabetes: a randomised crossover trial.

Background: Automated closed-loop control (CLC), known as the "artificial pancreas" is emerging as a treatment option for Type 1 Diabetes (T1D), generally superior to sensor-augmented insulin pump (SAP) treatment. It is postulated that evening-night (E-N) CLC may account for most of the benefits of 24-7 CLC; however, a direct comparison has not been done.

Methods: In this trial (NCT02679287), adults with T1D were randomised 1:1 to two groups, which followed different sequences of four 8-week sessions, resulting in two crossover designs comparing SAP vs E-N CLC and E-N CLC vs 24-7 CLC, respectively.

A Review of Continuous Glucose Monitoring-Based Composite Metrics for Glycemic Control.

We performed a literature review of composite metrics for describing the quality of glycemic control, as measured by continuous glucose monitors (CGMs). Nine composite metrics that describe CGM data were identified. They are described in detail along with their advantages and disadvantages.