Enhancing automatic closed-loop glucose control in type 1 diabetes with an adaptive meal bolus calculator - in silico evaluation under intra- day variability

[EN] Background and Objective: Current prototypes of closed-loop systems for glucose control in type 1 diabetes mellitus, also referred to as artificial pancreas systems, require a pre-meal insulin bolus to compensate for delays in subcutaneous insulin absorption in order to avoid initial post-prandial hyperglycemia. Computing such a meal bolus is a challenging task due to the high intra-subject variability of insulin requirements. Most closed-loop systems compute this pre-meal insulin dose by a standard bolus calculation, as is commonly found in insulin pumps. However, the performance of these calculators is limited due to a lack of adaptiveness in front of dynamic changes in insulin requirements. Despite some initial attempts to include adaptation within these calculators, challenges remain. Methods: In this paper we present a new technique to automatically adapt the meal-priming bolus within an artificial pancreas. The technique consists of using a novel adaptive bolus calculator based on Case-Based Reasoning and Run-To-Run control, within a closed-loop controller. Coordination between the adaptive bolus calculator and the controller was required to achieve the desired performance. For testing purposes, the clinically validated Imperial College Artificial Pancreas controller was employed. The proposed system was evaluated against itself but without bolus adaptation. The UVa-Padova T1DM v3.2 system was used to carry out a three-month in silico study on 11 adult and 11 adolescent virtual subjects taking into account inter-and intra-subject variability of insulin requirements and uncertainty on carbohydrate intake. Results: Overall, the closed-loop controller enhanced by an adaptive bolus calculator improves glycemic control when compared to its non-adaptive counterpart. In particular, the following statistically significant improvements were found (non-adaptive vs. adaptive). Adults: mean glucose 142.2 ± 9.4 vs. 131.8 ± 4.2 mg/dl; percentage time in target [70, 180] mg/dl, 82.0 ± 7.0 vs. 89.5 ± 4.2; percentage time above target 17.7 ± 7.0 vs. 10.2 ± 4.1. Adolescents: mean glucose 158.2 ± 21.4 vs. 140.5 ± 13.0 mg/dl; percentage time in target, 65.9 ± 12.9 vs. 77.5 ± 12.2; percentage time above target, 31.7 ± 13.1 vs. 19.8 ± 10.2. Note that no increase in percentage time in hypoglycemia was observed.This project has been funded by the Welcome Trust.Herrero, P.; Bondía Company, J.; Adewuji, O.; Pesl, P.; El-Sharkawy, M.; Reddy, M.; Toumazou, C.... (2017). Enhancing automatic closed-loop glucose control in type 1 diabetes with an adaptive meal bolus calculator - in silico evaluation under intra- day variability. Computer Methods and Programs in Biomedicine. 146:125-131. https://doi.org/10.1016/j.cmpb.2017.05.010S12513114

Use of an Observational Coding System with Families of Adolescents: Psychometric Properties among Pediatric and Healthy Populations

Objective: To examine reliability and validity data for the Family Interaction Macro-coding System (FIMS) with adolescents with spina bifida (SB), adolescents with type 1 diabetes mellitus (T1DM), and healthy adolescents and their families.Methods: Sixty-eight families of children with SB, 58 families of adolescents with T1DM, and 68 families in a healthy comparison group completed family interaction tasks and self-report questionnaires. Trained coders rated family interactions using the FIMS.Results: Acceptable interrater and scale reliabilities were obtained for FIMS items and subscales. Observed FIMS parental acceptance, parental behavioral control, parental psychological control, family cohesion, and family conflict scores demonstrated convergent validity with conceptually similar self-report measures.Conclusions: Preliminary evidence supports the use of the FIMS with families of youths with SB and T1DM and healthy youths. Future research on overall family functioning may be enhanced by use of the FIMS

Reinforcement learning application in diabetes blood glucose control: A systematic review

Background: Reinforcement learning (RL) is a computational approach to understanding and automating goal-directed learning and decision-making. It is designed for problems which include a learning agent interacting with its environment to achieve a goal. For example, blood glucose (BG) control in diabetes mellitus (DM), where the learning agent and its environment are the controller and the body of the patient respectively. RL algorithms could be used to design a fully closed-loop controller, providing a truly personalized insulin dosage regimen based exclusively on the patient’s own data. Objective: In this review we aim to evaluate state-of-the-art RL approaches to designing BG control algorithms in DM patients, reporting successfully implemented RL algorithms in closed-loop, insulin infusion, decision support and personalized feedback in the context of DM. Methods: An exhaustive literature search was performed using different online databases, analyzing the literature from 1990 to 2019. In a first stage, a set of selection criteria were established in order to select the most relevant papers according to the title, keywords and abstract. Research questions were established and answered in a second stage, using the information extracted from the articles selected during the preliminary selection. Results: The initial search using title, keywords, and abstracts resulted in a total of 404 articles. After removal of duplicates from the record, 347 articles remained. An independent analysis and screening of the records against our inclusion and exclusion criteria defined in Methods section resulted in removal of 296 articles, leaving 51 relevant articles. A full-text assessment was conducted on the remaining relevant articles, which resulted in 29 relevant articles that were critically analyzed. The inter-rater agreement was measured using Cohen Kappa test, and disagreements were resolved through discussion. Conclusions: The advances in health technologies and mobile devices have facilitated the implementation of RL algorithms for optimal glycemic regulation in diabetes. However, there exists few articles in the literature focused on the application of these algorithms to the BG regulation problem. Moreover, such algorithms are designed for control tasks as BG adjustment and their use have increased recently in the diabetes research area, therefore we foresee RL algorithms will be used more frequently for BG control in the coming years. Furthermore, in the literature there is a lack of focus on aspects that influence BG level such as meal intakes and physical activity (PA), which should be included in the control problem. Finally, there exists a need to perform clinical validation of the algorithms

The Effect of Aerobic Exercise Training on Cerebrovascular HSP70, HSP90, INOS and ENOS Expression in Type 1 Diabetes

The purpose of this study was to determine the effect of exercise training alone and in a model of Type 1 Diabetes Mellitus (T1DM), on Heat Shock Protein (HSP) and Nitric Oxide Synthase (NOS) expression in entorhinal adjacent large and small cerebral vessels. Thirty-two rats were randomly allocated to four groups: control sedentary (C), control exercised (CX), diabetic sedentary (D) and diabetic exercised (DX). Exercise training incorporated 5 days/week on a motorized treadmill (27m/min; 6 degree incline; 1 hour) for 10 weeks. Exercise trained groups had significantly greater Hsp70 expression than their respective non-trained groups (p\u3c0.05) and this response was not blunted in T1DM animals. The inducible NOS (iNOS) expression was greater in diabetic sedentary when compared to all other groups (p\u3c0.001). Co-localization of protein with smooth muscle cells illustrates that all HSP and NOS signal content is localized to the smooth muscle area (SMA)

Hybrid PSO-based variable translation wavelet neural network and its application to hypoglycemia detection system

To provide the detection of hypoglycemic episodes in Type 1 diabetes mellitus, hypoglycemia detection system is developed by the use of variable translation wavelet neural network (VTWNN) in this paper. A wavelet neural network with variable translation parameter is selected as a suitable classifier because of its excellent characteristics in capturing nonstationary signal analysis and nonlinear function modeling. Due to the variable translation parameters, the network becomes an adaptive network and provides better classification performance. An improved hybrid particle swarm optimization is used to train the parameters of VTWNN. Using the proposed classifier, a sensitivity of 81.40 % and a specificity of 50.91 % were achieved. The comparison results also show that the proposed detection system performs well in terms of good sensitivity and acceptable specificity. © 2012 Springer-Verlag London Limited

The effect of insulin treatment and exercise modality on skeletal muscle fiber size in streptozotocin-induced type 1 diabetic rats

Severe Type 1 Diabetes Mellitus (T1DM) is known to have several negative effects on skeletal muscle mass, a condition known as diabetic myopathy. One of these effects is the atrophy of the glycolytic muscle fibers. However, the role of intensity of insulin treatment and exercise modality in attenuating this loss in fiber cross-sectional area (CSA) specifically has yet to be determined. The purpose of this investigation was to determine the effects of 12 weeks of differing intensity of insulin therapy and exercise modality on the CSA of plantaris muscle fibers, identified by myosin heavy chain (MHC) of STZ-induced T1DM rats. Rats were divided into control sedentary (CS), T1DM-sedentary (DCT - conventional versus DIT- intensive insulin therapy, blood glucose 9-15mmol/l and 7-9mmol/l, respectively) and T1DM-exercised (DRE- resistance, DHE-endurance, DCE-combined training, blood glucose 9-15mmol/l) groups. Exercises consisted of repeated weighted ladder climbs (50%, 75%, 90%, and 100% pre-determined max. carrying capacity, 1.1 m, 80°) or treadmill running (27 m/min, 6% grade, 1hr) 5 days/week for 12 weeks. DCE animals performed resistance and endurance exercise on alternating days. Following 12 weeks, diabetic sedentary (DCT, DIT) and diabetic exercised (DRE, DHE, DCE) groups did not differ in body weight (BW) or plantaris whole-muscle weight, except where DIT BW was significantly greater than DRE (P0.05) or aerobic exercise (DHE) in some instances (all fibers expressing fast MHC’s except MHCIIx/IIb and MHCiib – P0.05). Surprisingly, DIT exhibited significantly larger MHCIIa and fewer hybrid fibers than any other group. MHCI fibers were unaffected by either diabetes or exercise, however, exercised animals displayed changes in percent fiber composition, including a significant proportion of hybrid fibers, that appeared to indicate a shift in fiber type particularly from MHCIIx to MHCIIa. Resistance exercise, whether alone (DRE) or in conjunction with aerobic training (DCE) resulted in significantly larger MHCIIx and MHCIIb than CS. Treadmill running (DHE) resulted in less hypertrophy of fibers expressing fast MHC than the other exercised groups. These findings indicate that conventional insulin therapy is sufficient to maintain muscle mass and fiber CSA, while intensive insulin therapy and exercise with conventional insulin therapy induce differential changes in fiber percent composition and fiber CSA increases

Trajectories of Health-Related Quality of Life and HbA1c Values of Children and Adolescents With Diabetes Mellitus Type 1 Over 6 Months: A Longitudinal Observational Study

Introduction: To achieve optimized blood glucose concentrations (assessed by HbA1c) and high health-related quality of life (HRQL), children and adolescents with diabetes mellitus type 1 (T1DM) must follow strict disease management strategies. This study aims to investigate HRQL of children and adolescents with T1DM and its association with HbA1c values over the course of 6 months. Methods: Patients aged 7-17 years (n = 203) with T1DM provided HRQL data on a monthly basis. HRQL was measured using the Kids-CAT, a computer-adaptive test (CAT) comprising five generic HRQL domains. HbA1c concentrations were assessed at baseline, at 3 and 6 months. We explored the trajectory of HRQL at the domain level using linear mixed effects models. Further, we investigated the association between HRQL and HbA1c concentrations over time using path analysis models. Results: Children and adolescents with T1DM reported high scores across all HRQL domains over time. However, those with an HbA1c concentrations of \u3e 9.0% reported significantly lower scores in physical well-being and parent relations compared with those with an HbA1c concentration of \u3c 7.5%. Path analysis models revealed a minimal temporal relationship between HbA1c and HRQL, with a small negative impact of HbA1c on physical well-being, psychological well-being and parent relations. Conclusion: Although observed HRQL of young patients with T1DM was comparable to age-related German-speaking reference population over the course of 6 months, those with an HbA1c concentration \u3e 9.0% reported lower scores in selected HRQL domains. Thus, special attention should be drawn to HRQL of children and adolescents with higher HbA1c concentrations. The minimal relationship between HbA1c and HRQL indicates that the two therapy goals, i.e., achievement and maintenance of glycemic targets and high HRQL, should be considered and evaluated independently in clinical routine. Trial Registration: DRKS00006326 (German Clinical Trial Register), date of registration: August 1st, 2014