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.

Real-Life Use of Automated Insulin Delivery in Individuals With Type 2 Diabetes.

Background: The objective of this work is to document performance of automated insulin delivery (AID) during real-life use in type 2 diabetes (T2D).

Methods: A retrospective analysis was performed of continuous glucose monitoring and insulin delivery data from 796 individuals with T2D, who transitioned from 1-month predictive low-glucose suspend (PLGS) use to 3-month AID use, in real-life settings. Primary outcome was change of time in range (TIR = 70-180 mg/dL) from PLGS to AID.

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.

Adaptive Biobehavioral Control: A Pilot Analysis of Human-Machine Coadaptation in Type 1 Diabetes.

While it is well recognized that an automated insulin delivery (AID) algorithm should adapt to changes in physiology, it is less understood that the individual would also have to adapt to the AID system. The adaptive biobehavioral control (ABC) method presented here attempts to compensate for this deficiency by including AID into an information cloud-based ecosystem. The Web Information Tool (WIT) implements the ABC concept via the following: (1) a Physiological Adaptation Module (PAM) that tracks metabolic changes and adapts AID parameters accordingly and (2) a Behavioral Adaptation Module (BAM) that provides information feedback.