CGM Data Analysis 2.0: Functional Data Pattern Recognition and Artificial Intelligence Applications.

New methods of continuous glucose monitoring (CGM) data analysis are emerging that are valuable for interpreting CGM patterns and underlying metabolic physiology. These new methods use functional data analysis and artificial intelligence (AI), including machine learning (ML). Compared to traditional metrics for evaluating CGM tracing results (CGM Data Analysis 1.

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.