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

Tailoring the Padova Type 2 Diabetes Simulator for Treatment Guidance in Target Populations

OBJECTIVE

The Padova type 2 diabetes (T2D) simulator (T2DS) has been recently proposed to optimize T2D treatments including novel long-acting insulins. It consists of a physiological model and an in silico population describing glucose dynamics, derived from early-stage T2D subjects studied with sophisticated tracer-based experimental techniques. This limits T2DS domain of validity to this specific sub-population. Conversely, running simulations in insulin-naïve or advanced T2D subjects, would be more valuable. However, it is rarely possible or cost-effective to run complex experiments in such populations. Therefore, we propose a method for tuning the T2DS to any desired T2D sub-population using published clinical data. As case study, we extended the T2DS to insulin-naïve T2D subjects, who need to start insulin therapy to compensate the reduced insulin function.

METHODS

T2DS model was identified based on literature data of the target population. The estimated parameters were used to generate a virtual cohort of insulin-naïve T2D subjects (inC1). A model of basal insulin degludec (IDeg) was also incorporated into the T2DS to enable basal insulin therapy. The resulting tailored T2DS was assessed by simulating IDeg therapy initiation and comparing simulated vs. clinical trial outcomes. For further validation, this procedure was reiterated to generate a new cohort of insulin-naïve T2D (inC2) assuming inC1 as target population.

RESULTS

No statistically significant differences were found when comparing fasting plasma glucose and IDeg dose, neither in clinical data vs. inC1, nor inC1 vs. inC2.

CONCLUSIONS

The tuned T2DS allowed reproducing the main findings of clinical studies in insulin-naïve T2D subjects.

SIGNIFICANCE

The proposed methodology makes the Padova T2DS usable for supporting treatment guidance in target T2D populations.

Simulation-driven optimization of insulin therapy profiles in a commercial hybrid closed-loop system

BACKGROUND

Automated insulin delivery (AID) has represented a breakthrough in managing type 1 diabetes (T1D), showing safe and effective glucose control extensively across the board. However, metabolic variability still poses a challenge to commercial hybrid closed-loop (HCL) solutions, whose performance depends on customizable insulin therapy profiles. In this work, we propose an Identification-Replay-Optimization (IRO) approach to optimize gradually and safely such profiles for the Control-IQ AID algorithm.

METHODS

Closed-loop data are generated using the full adult cohort of the UVA/Padova T1D simulation platform in diverse glycemic scenarios. For each subject, daily records are processed and used to estimate a personalized model of the underlying insulin-glucose dynamics. Every two weeks, all identified models are integrated into an optimization procedure where daily basal and bolus profiles are adjusted so as to minimize the risks for hypo- and hyperglycemia. The proposed strategy is tested under different scenarios of metabolic and behavioral variability in order to evaluate the efficacy and convergence of the proposed strategy. Finally, glycemic metrics between cycles are compared using paired t-tests with p<0.05 as the significance threshold.

RESULTS

Simulations reveal that the proposed IRO approach was able to improve glucose control over time by safely mitigating the risks for both hypo- and hyperglycemia. Furthermore, smaller changes were recommended at each cycle, indicating convergence when simulation conditions were maintained.

CONCLUSIONS

The use of reliable simulation-driven tools capable of accurately reproducing field-collected data and predicting changes can substantially shorten the process of optimizing insulin therapy, adjusting it to metabolic changes and leading to improved glucose control.

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. Priming was achieved by detecting large glycemic disturbances, such as meals, and delivering a fraction of the patient's total daily insulin (TDI) modulated by the disturbance's likelihood (bolus priming system [BPS]). In the anticipatory module, glycemic disturbance profiles were generated from historical data using clustering to group days with similar behaviors; the probability of each cluster is then evaluated at every controller step and informs the MS-MPC framework to anticipate each profile. We tested four configurations: MPC, MPC + BPS, MS-MPC, and MS-MPC + BPS in simulation to contrast the effect of each controller module.

RESULTS

Postprandial time in range was highest for MS-MPC + BPS: 60.73 ± 25.39%, but improved with each module: MPC + BPS: 56.95±25.83 and MS-MPC: 54.83 ± 26.00%, compared with MPC: 51.79 ± 26.12%. Exposure to hypoglycemia was maintained for all controllers (time below 70 mg/dL <0.5%), and improvement came primarily from a reduction in postprandial time above range (MS-MPC + BPS: 39.10 ± 25.32%, MPC + BPS: 42.99 ± 25.81%, MS-MPC: 45.09 ± 25.96%, MPC: 48.18 ± 26.09%).

CONCLUSIONS

The BPS and anticipatory disturbance profiles improved blood glucose control and were most efficient when combined.

Low-Order Nonlinear Animal Model of Glucose Dynamics for a Bihormonal Intraperitoneal Artificial Pancreas

OBJECTIVE

The design of an Artificial Pancreas to regulate blood glucose levels requires reliable control methods. Model Predictive Control has emerged as a promising approach for glycemia control. However, model-based control methods require computationally simple and identifiable mathematical models that represent glucose dynamics accurately, which is challenging due to the complexity of glucose homeostasis.

METHODS

In this work, a simple model is deduced to estimate blood glucose concentration in subjects with Type 1 Diabetes Mellitus. Novel features in the model are power-law kinetics for intraperitoneal insulin absorption and a separate glucagon sensitivity state. Profile likelihood and a method based on singular value decomposition of the sensitivity matrix are carried out to assess parameter identifiability and guide a model reduction for improving the identification of parameters.

RESULTS

A reduced model with 10 parameters is obtained and calibrated, showing good fit to experimental data from pigs where insulin and glucagon boluses were delivered in the intraperitoneal cavity.

CONCLUSION

A simple model with power-law kinetics can accurately represent glucose dynamics submitted to intraperitoneal insulin and glucagon injections.

IMPORTANCE

The parameters of the reduced model were not found to lack of local practical or structural identifiability.

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.a. artificial pancreas) based on the Model Predictive Control (MPC) methodology to adjust insulin infusion while automatically rejecting uninformed meals.

METHODS

The proposed advanced hybrid closed-loop system relies on the integration of three key elements: (i) an adaptive personalized MPC control law that modulates the control strength depending on recent past control actions, glucose measurements, and its derivative, (ii) an automatic Bolus Priming System (BPS) that commands additional insulin injections safely upon the detection of enabling metabolic conditions (e.g., an unacknowledged meal), and (iii) a new hyperglycemia mitigation system to avoid prevailing hyperglycemia. The benefits of the proposed system are demonstrated through simulations and tests using the most up-to-date Type 1 UVA/Padova simulator as preclinical stage prior to in vivo clinical tests. We used a legacy algorithm (USS Virginia), currently used in clinical care, as a benchmark controller.

RESULTS

Overall, the proposed control strategy enhanced by an automatic BPS improves glycemic control when compared with an available system. When a large meal is not announced (80g CHO), the proposed controller outperformed the legacy controller in time-in-target-range TIR (postprandial and overnight) and time-in-tight-range TTR (overall, postprandial, and overnight).

CONCLUSION

The integration of a novel BPS into an advanced control system allowed to automatically reject unannounced meals. Exhaustive simulation studies indicated the safety and feasibility of the proposed controller to be deployed in human clinical trials.

Diabetes Technology Meeting 2020

Diabetes Technology Society hosted its annual Diabetes Technology Meeting on November 12 to November 14, 2020. This meeting brought together speakers to cover various perspectives about the field of diabetes technology. The meeting topics included artificial intelligence, digital health, telemedicine, glucose monitoring, regulatory trends, metrics for expressing glycemia, pharmaceuticals, automated insulin delivery systems, novel insulins, metrics for diabetes monitoring, and discriminatory aspects of diabetes technology. A live demonstration was presented.

"Fighting spirit": specific personality traits as one of the key factors for sport championship in type 1 diabetes mellitus

There is ample evidence that sport is a protective factor against a number of health risks, across all ages, in the general population. An in-depth understanding of energy metabolism has reasonably entailed exercise as a cornerstone in the lifestyle of almost all subjects with type 1 diabetes mellitus (T1DM). Nevertheless, individuals with T1DM often fail in accomplishing exercise guidelines and they are often less active than their peer without diabetes. Two major obstacles are feared: management of blood glucose control and hypoglycemia. Nowadays, strategies, including glucose monitoring technology and insulin pump therapy, have significantly contributed to the participation in regular physical activity, and even in competitive sports, for T1DM people. The case report presents an analysis of specific personality traits and psychological parameters of 20 years old female Polish multiple champion in weightlifting with excellent T1DM control.

Artificial Intelligence in Decision Support Systems for Type 1 Diabetes

Type 1 diabetes (T1D) is a chronic health condition resulting from pancreatic beta cell dysfunction and insulin depletion. While automated insulin delivery systems are now available, many people choose to manage insulin delivery manually through insulin pumps or through multiple daily injections. Frequent insulin titrations are needed to adequately manage glucose, however, provider adjustments are typically made every several months. Recent automated decision support systems incorporate artificial intelligence algorithms to deliver personalized recommendations regarding insulin doses and daily behaviors. This paper presents a comprehensive review of computational and artificial intelligence-based decision support systems to manage T1D. Articles were obtained from PubMed, IEEE Xplore, and ScienceDirect databases. No time period restrictions were imposed on the search. After removing off-topic articles and duplicates, 562 articles were left to review. Of those articles, we identified 61 articles for comprehensive review based on algorithm evaluation using real-world human data, in silico trials, or clinical studies. We grouped decision support systems into general categories of (1) those which recommend adjustments to insulin and (2) those which predict and help avoid hypoglycemia. We review the artificial intelligence methods used for each type of decision support system, and discuss the performance and potential applications of these systems.