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Artificial Pancreas or Novel Beta-Cell Replacement Therapies: a Race for Optimal Glycemic Control?

ABSTRACT: PURPOSE OF REVIEW:New treatment strategies are needed for patients with type 1 diabetes (T1D). Closed loop insulin delivery and beta-cell replacement therapy are promising new strategies. This review aims to give an insight in the most relevant literature on this topic and to compare the two radically different treatment modalities. RECENT FINDINGS:Multiple clinical studies have been performed with closed loop insulin delivery devices and have shown an improvement in overall glycemic control and time spent in hypoglycemia. Beta-cell transplantation has been shown to normalize or greatly improve glycemic control in T1D, but the donor organ shortage and the necessity to use immunosuppressive agents are major drawbacks. Donor organ shortage may be solved by the utilization of stem cell-derived beta cells, which has shown great promise in animal models and are now tested in clinical studies. Immunosuppression may be avoided by encapsulation. Closed loop insulin delivery devices are promising treatment strategies and are likely to be used in clinical practice in the short term. But this approach will always suffer from delays in glucose measurement and insulin action preventing it from normalizing glycemic control. In the long term, stem cell-derived beta cell transplantation may be able to achieve this, but wide implementation in clinical practice is still far away.

SUBMITTER: Nijhoff MF

PROVIDER: S-EPMC6153567 | biostudies-other | 2018 Sep

REPOSITORIES: biostudies-other

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Artificial Pancreas or Novel Beta-Cell Replacement Therapies: a Race for Optimal Glycemic Control?

Nijhoff Michiel F MF de Koning Eelco J P EJP

Current diabetes reports 20180924 11

<h4>Purpose of review</h4>New treatment strategies are needed for patients with type 1 diabetes (T1D). Closed loop insulin delivery and beta-cell replacement therapy are promising new strategies. This review aims to give an insight in the most relevant literature on this topic and to compare the two radically different treatment modalities.<h4>Recent findings</h4>Multiple clinical studies have been performed with closed loop insulin delivery devices and have shown an improvement in overall glyce ...[more]

PMID: 30250968

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Project description:BackgroundThe safety and effectiveness of automated glycemic management have not been tested in multiday studies under unrestricted outpatient conditions.MethodsIn two random-order, crossover studies with similar but distinct designs, we compared glycemic control with a wearable, bihormonal, automated, "bionic" pancreas (bionic-pancreas period) with glycemic control with an insulin pump (control period) for 5 days in 20 adults and 32 adolescents with type 1 diabetes mellitus. The automatically adaptive algorithm of the bionic pancreas received data from a continuous glucose monitor to control subcutaneous delivery of insulin and glucagon.ResultsAmong the adults, the mean plasma glucose level over the 5-day bionic-pancreas period was 138 mg per deciliter (7.7 mmol per liter), and the mean percentage of time with a low glucose level (<70 mg per deciliter [3.9 mmol per liter]) was 4.8%. After 1 day of automatic adaptation by the bionic pancreas, the mean (±SD) glucose level on continuous monitoring was lower than the mean level during the control period (133±13 vs. 159±30 mg per deciliter [7.4±0.7 vs. 8.8±1.7 mmol per liter], P<0.001) and the percentage of time with a low glucose reading was lower (4.1% vs. 7.3%, P=0.01). Among the adolescents, the mean plasma glucose level was also lower during the bionic-pancreas period than during the control period (138±18 vs. 157±27 mg per deciliter [7.7±1.0 vs. 8.7±1.5 mmol per liter], P=0.004), but the percentage of time with a low plasma glucose reading was similar during the two periods (6.1% and 7.6%, respectively; P=0.23). The mean frequency of interventions for hypoglycemia among the adolescents was lower during the bionic-pancreas period than during the control period (one per 1.6 days vs. one per 0.8 days, P<0.001).ConclusionsAs compared with an insulin pump, a wearable, automated, bihormonal, bionic pancreas improved mean glycemic levels, with less frequent hypoglycemic episodes, among both adults and adolescents with type 1 diabetes mellitus. (Funded by the National Institute of Diabetes and Digestive and Kidney Diseases and others; ClinicalTrials.gov numbers, NCT01762059 and NCT01833988.).

Project description:Diabetes mellitus and the associated complications represent a global burden on human health and economics. Cardiovascular diseases are the leading cause of death in diabetic patients, who have a 2-5 times higher risk of developing heart failure than age-matched non-diabetic patients, independent of other comorbidities. Diabetic cardiomyopathy is defined as the presence of abnormal cardiac structure and performance in the absence of other cardiac risk factors, such coronary artery disease, hypertension, and significant valvular disease. Hyperglycemia, hyperinsulinemia, and insulin resistance mediate the pathological remodeling of the heart, characterized by left ventricle concentric hypertrophy and perivascular and interstitial fibrosis leading to diastolic dysfunction. A change in the metabolic status, impaired calcium homeostasis and energy production, increased inflammation and oxidative stress, as well as an accumulation of advanced glycation end products are among the mechanisms implicated in the pathogenesis of diabetic cardiomyopathy. Despite a growing interest in the pathophysiology of diabetic cardiomyopathy, there are no specific guidelines for diagnosing patients or structuring a treatment strategy in clinical practice. Anti-hyperglycemic drugs are crucial in the management of diabetes by effectively reducing microvascular complications, preventing renal failure, retinopathy, and nerve damage. Interestingly, several drugs currently in use can improve cardiac health beyond their ability to control glycemia. GLP-1 receptor agonists and sodium-glucose co-transporter 2 inhibitors have been shown to have a beneficial effect on the cardiovascular system through a direct effect on myocardium, beyond their ability to lower blood glucose levels. In recent years, great improvements have been made toward the possibility of modulating the expression of specific cardiac genes or non-coding RNAs in vivo for therapeutic purpose, opening up the possibility to regulate the expression of key players in the development/progression of diabetic cardiomyopathy. This review summarizes the pathogenesis of diabetic cardiomyopathy, with particular focus on structural and molecular abnormalities occurring during its progression, as well as both current and potential future therapies.

Project description:ObjectiveOur aim is to analyze the identifiability of three commonly used control-oriented models for glucose control in patients with type 1 diabetes (T1D).MethodsStructural 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. For practical identifiability and model identification, continuous glucose monitor (CGM), insulin pump, and meal records were selected from a set of patients (n = 5) on continuous subcutaneous insulin infusion (CSII) that underwent a clinical trial in an outpatient setting. The performance of the identified models was analyzed by means of the root mean square (RMS) criterion.ResultsA reliable set of identifiable parameters was found for every studied model after analyzing the possible identifiability issues of the original parameter sets. According to an importance factor ([Formula: see text]), it was shown that insulin sensitivity is not the most influential parameter from the dynamical point of view, that is, is not the parameter impacting the outputs the most of the three models, contrary to what is assumed in the literature. For the test data, the models demonstrated similar performance with most RMS values around 20 mg/dl (min: 15.64 mg/dl, max: 51.32 mg/dl). However, MMC failed to identify the model for patient 4. Also, considering the three models, the MMC model showed the higher parameter variability when reidentified every 6 hours.ConclusionThis study shows that both structural and practical identifiability analysis need to be considered prior to the model identification/individualization in patients with T1D. It was shown that all the studied models are able to represent the CGM data, yet their usefulness in a hypothetical artificial pancreas could be a matter of debate. In spite that the three models do not capture all the dynamics and metabolic effects as a maximal model (ie, our FDA-accepted UVa/Padova simulator), SOGMM and ICING appear to be more appealing than MMC regarding both the performance and parameter variability after reidentification. Although the model predictions of ICING are comparable to the ones of the SOGMM model, the large parameter set makes the model prone to overfitting if all parameters are identified. Moreover, the existence of a high nonlinear function like [Formula: see text] prevents the use of tools from the linear systems theory.

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Artificial Pancreas or Novel Beta-Cell Replacement Therapies: a Race for Optimal Glycemic Control?

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Artificial Pancreas or Novel Beta-Cell Replacement Therapies: a Race for Optimal Glycemic Control?

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