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+).

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.

Automated Insulin Delivery with SGLT2i Combination Therapy in Type 1 Diabetes.

Use of sodium-glucose cotransporter 2 inhibitors (SGLT2i) as adjunct therapy to insulin in type 1 diabetes (T1D) has been previously studied. In this study, we present data from the first free-living trial combining low-dose SGLT2i with commercial automated insulin delivery (AID) or predictive low glucose suspend (PLGS) systems. In an 8-week, randomized, controlled crossover trial, adults with T1D received 5 mg/day empagliflozin (EMPA) or no drug (NOEMPA) as adjunct to insulin therapy.

Main glucose hepatic fluxes in healthy subjects predicted from a phenomenological-based model.

Background: The liver has a unique role in blood glucose regulation in postprandial, postabsorptive, and fasting states. In the context of diabetes technology, current maximal models of glucose homeostasis lack a proper dynamical description of main glucose-related fluxes acting over and from the liver, providing a rather simplistic estimation of key quantities as endogenous glucose production and insulin and glucagon clearance.

Methods: Using a three-phase well-established phenomenological-based semi-physical modeling (PBSM) methodology, we built a detailed physiological model of hepatic glucose metabolism, including glucose utilization, endogenous glucose production through gluconeogenesis and glycogenolysis, and insulin and glucagon clearance.

Using an Online Disturbance Rejection and Anticipation System to Reduce Hyperglycemia in a Fully Closed-Loop Artificial Pancreas System.

Introduction: Hyperglycemia following meals is a recurring challenge for people with type 1 diabetes, and even the most advanced available automated systems currently require manual input of carbohydrate amounts. To progress toward fully automated systems, we present a novel control system that can automatically deliver priming boluses and/or anticipate eating behaviors to improve postprandial full closed-loop control.

Methods: A model predictive control (MPC) system was enhanced by an automated bolus system reacting to early glucose rise and/or a multistage MPC (MS-MPC) framework to anticipate historical patterns.

Advanced hybrid artificial pancreas system improves on unannounced meal response - In silico comparison to currently available system.

Background And Objective: Glycemic control, especially meal-related disturbance rejection, has proven to be a major challenge for people with type 1 diabetes. In this manuscript, we introduce a novel, personalized, advanced hybrid insulin infusion system (a.k.

Advanced Closed-Loop Control System Improves Postprandial Glycemic Control Compared With a Hybrid Closed-Loop System Following Unannounced Meal.

Objective: Meals are a major hurdle to glycemic control in type 1 diabetes (T1D). Our objective was to test a fully automated closed-loop control (CLC) system in the absence of announcement of carbohydrate ingestion among adolescents with T1D, who are known to commonly omit meal announcement.

Research Design And Methods: Eighteen adolescents with T1D (age 15.

Anticipation of Historical Exercise Patterns by a Novel Artificial Pancreas System Reduces Hypoglycemia During and After Moderate-Intensity Physical Activity in People with Type 1 Diabetes.

Physical activity is a major challenge to glycemic control for people with type 1 diabetes. Moderate-intensity exercise often leads to steep decreases in blood glucose and hypoglycemia that closed-loop control systems have so far failed to protect against, despite improving glycemic control overall. Fifteen adults with type 1 diabetes (42 ± 13.

Closed-Loop Control with Unannounced Exercise for Adults with Type 1 Diabetes using the Ensemble Model Predictive Control.

This paper presents an individualized Ensemble Model Predictive Control (EnMPC) algorithm for blood glucose (BG) stabilization and hypoglycemia prevention in people with type 1 diabetes (T1D) who exercise regularly. The EnMPC formulation can be regarded as a simplified multi-stage MPC allowing for the consideration of scenarios gathered from the patient's recent behavior. The patient's physical activity behavior is characterized by an exercise-specific input signal derived from the deconvolution of the patient's continuous glucose monitor (CGM), accounting for known inputs such as meal, and insulin pump records.

Impact of Accelerating Insulin on an Artificial Pancreas System Without Meal Announcement: An In Silico Examination.

Background: Controlling postprandial blood glucose without the benefit of an appropriately sized premeal insulin bolus has been challenging given the delays in absorption and action of subcutaneously injected insulin during conventional and artificial pancreas (AP) system diabetes treatment. We aim to understand the impact of accelerating insulin and increasing aggressiveness of the AP controller as potential solutions to address the postprandial hyperglycemia challenge posed by unannounced meals through a simulation study.

Methods: Accelerated rapid-acting insulin analogue is modeled within the UVA/Padova simulation platform by uniformly reducing its pharmacokinetic time constants (α multiplier) and used with a model predictive control, where the controller's aggressiveness depends on α.

In Silico Analysis of an Exercise-Safe Artificial Pancreas With Multistage Model Predictive Control and Insulin Safety System.

Background: Maintaining glycemic equilibrium can be challenging for people living with type 1 diabetes (T1D) as many factors (eg, length, type, duration, insulin on board, stress, and training) will impact the metabolic changes triggered by physical activity potentially leading to both hypoglycemia and hyperglycemia. Therefore, and despite the noted health benefits, many individuals with T1D do not exercise as much as their healthy peers. While technology advances have improved glucose control during and immediately after exercise, it remains one of the key limitations of artificial pancreas (AP) systems, largely because stopping insulin at the onset of exercise may not be enough to prevent impending, exercise-induced hypoglycemia.

Phenomenological-Based model of human stomach and its role in glucose metabolism.

The stomach is a segment of the gastrointestinal (GI) tract which receives food from the esophagus, mixes it, breaks it down, and then passes it on to the small intestine in smaller portions. In the stomach, the main secretory functions and digestion process begin. However, the most critical and important function of the stomach in digestive physiology is perhaps gastric motility.

Identifiability Analysis of Three Control-Oriented Models for Use in Artificial Pancreas Systems.

Objective: Our aim is to analyze the identifiability of three commonly used control-oriented models for glucose control in patients with type 1 diabetes (T1D).

Methods: Structural and practical identifiability analysis were performed on three published control-oriented models for glucose control in patients with type 1 diabetes (T1D): the subcutaneous oral glucose minimal model (SOGMM), the intensive control insulin-nutrition-glucose (ICING) model, and the minimal model control-oriented (MMC). Structural identifiability was addressed with a combination of the generating series (GS) approach and identifiability tableaus whereas practical identifiability was studied by means of (1) global ranking of parameters via sensitivity analysis together with the Latin hypercube sampling method (LHS) and (2) collinearity analysis among parameters.