Multi-Scale Simulation Modeling for Prevention and Public Health Management of Diabetes in Pregnancy and Sequelae

Diabetes in pregnancy (DIP) is an increasing public health priority in the Australian Capital Territory, particularly due to its impact on risk for developing Type 2 diabetes. While earlier diagnostic screening results in greater capacity for early detection and treatment, such benefits must be balanced with the greater demands this imposes on public health services. To address such planning challenges, a multi-scale hybrid simulation model of DIP was built to explore the interaction of risk factors and capture the dynamics underlying the development of DIP. The impact of interventions on health outcomes at the physiological, health service and population level is measured. Of particular central significance in the model is a compartmental model representing the underlying physiological regulation of glycemic status based on beta-cell dynamics and insulin resistance. The model also simulated the dynamics of continuous BMI evolution, glycemic status change during pregnancy and diabetes classification driven by the individual-level physiological model. We further modeled public health service pathways providing diagnosis and care for DIP to explore the optimization of resource use during service delivery. The model was extensively calibrated against empirical data.Comment: 10 pages, SBP-BRiMS 201

Association of a homozygous GCK missense mutation with mild diabetes

Background: Homozygous inactivating GCK mutations have been repeatedly reported to cause severe hyperglycemia, presenting as permanent neonatal diabetes mellitus (PNDM). Conversely, only two cases of GCK homozygous mutations causing mild hyperglycemia have been so far described. We here report a novel GCK mutation (c.1116G>C, p.E372D), in a family with one homozygous member showing mild hyperglycemia. Methods: GCK mutational screening was carried out by Sanger sequencing. Computational analyses to investigate pathogenicity and molecular dynamics (MD) were performed for GCK-E372D and for previously described homozygous mutations associated with mild (n = 2) or severe (n = 1) hyperglycemia, used as references. Results: Of four mildly hyperglycemic family-members, three were heterozygous and one, diagnosed in the adulthood, was homozygous for GCK-E372D. Two nondiabetic family members carried no mutations. Fasting glucose (p = 0.016) and HbA1c (p = 0.035) correlated with the number of mutated alleles (0–2). In-silico predicted pathogenicity was not correlated with the four mutations’ severity. At MD, GCK-E372D conferred protein structure flexibility intermediate between mild and severe GCK mutations. Conclusions: We present the third case of homozygous GCK mutations associated with mild hyperglycemia, rather than PNDM. Our in-silico analyses support previous evidences suggesting that protein stability plays a role in determining clinical severity of GCK mutations

Projection of Diabetes Population Size and Associated Economic Burden through 2030 in Iran : Evidence from Micro-Simulation Markov Model and Bayesian Meta-Analysis

Acknowledgments The authors would like to thank kindly all advisors and colleagues, for their valuable technical support. We would like to thank you Ms Laura Marie Dysart for editing the paper.Peer reviewedPublisher PD

Linear parameter-varying model to design control laws for an artificial pancreas

The contribution of this work is the generation of a control-oriented model for insulin-glucose dynamic regulation in type 1 diabetes mellitus (T1DM). The novelty of this model is that it includes the time-varying nature, and the inter-patient variability of the glucose-control problem. In addition, the model is well suited for well-known and standard controller synthesis procedures. The outcome is an average linear parameter-varying (LPV) model that captures the dynamics from the insulin delivery input to the glucose concentration output constructed based on the UVA/Padova metabolic simulator. Finally, a system-oriented reinterpretation of the classical ad-hoc 1800 rule is applied to adapt the model's gain. The effectiveness of this approach is quantified both in open- and closed-loop. The first one by computing the root mean square error (RMSE) between the glucose deviation predicted by the proposed model and the UVA/Padova one. The second measure is determined by using the ν-gap as a metric to determine distance, in terms of closed-loop performance, between both models. For comparison purposes, both open- (RMSE) and closed-loop (ν-gap metric) quality indicators are also computed for other control-oriented models previously presented. This model allows the design of LPV controllers in a straightforward way, considering its affine dependence on the time-varying parameter, which can be computed in real-time. Illustrative simulations are included. In addition, the presented modeling strategy was employed in the design of an artificial pancreas (AP) control law that successfully withstood rigorous testing using the UVA/Padova simulator, and that was subsequently deployed in a clinical trial campaign where five adults remained in closed-loop for 36 h. This was the first ever fully closed-loop clinical AP trial in Argentina, and the modeling strategy presented here is considered instrumental in resulting in a very successful clinical outcome.Fil: Colmegna, Patricio Hernán. Universidad Nacional de Quilmes. Departamento de Ciencia y Tecnología; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Sánchez Peña, Ricardo S.. Instituto Tecnológico de Buenos Aires; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Gondhalekar, R.. Harvard University; Estados Unido

A nonparametric approach for model individualization in an artificial pancreas

The identification of patient-tailored linear time invariant glucose-insulin models is investigated for type 1 diabetic patients, that are characterized by a substantial inter-subject variability. The individualized linear models are identified by considering a novel kernel-based nonparametric approach and are compared with a linear time invariant average model in terms of prediction performance by means of the coefficient of determination, fit, positive and negative max errors, and root mean squared error. Model identification and validation are based on in-silico data collected from the adult virtual population of the UVA/Padova simulator. The data generation involves a protocol designed to produce a sufficient input excitation without compromising patient safety, compatible also with real life scenarios. The identified models are exploited to synthesize an individualized Model Predictive Controller (MPC) for each patient, which is used in an Artificial Pancreas to maintain the blood glucose concentration within an euglycemic range. The MPC used in several clinical studies, synthesized on the basis of a non-individualized average linear time invariant model, is also considered as reference. The closed-loop control performance is evaluated in an in-silico study on the adult virtual population of the UVA/Padova simulator in a perturbed scenario, in which the MPC is blind to random variations of insulin sensitivity in each virtual patient. © 2015, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved

Fluid Vesicles with Viscous Membranes in Shear Flow

The effect of membrane viscosity on the dynamics of vesicles in shear flow is studied. We present a new simulation technique, which combines three-dimensional multi-particle collision dynamics for the solvent with a dynamically-triangulated membrane model. Vesicles are found to transit from steady tank-treading to unsteady tumbling motion with increasing membrane viscosity. Depending on the reduced volume and membrane viscosity, shear can induce both discocyte-to-prolate and prolate-to-discocyte transformations. This dynamical behavior can be understood from a simplified model.Comment: 4 pages, 4 figure

A SYSTEM SIMULATION MODEL FOR TYPE 2 DIABETES IN THE SASKATOON HEALTH REGION

Diabetes mellitus is a prevailing chronic disease in Canada and around the world. The population with diabetes is rapidly increasing. The growth of diabetes prevalence places a heavy burden on the health care system and causes a significant loss to society. The prevention and treatment of diabetes is a complex problem that involves many parties such as government, health care system, communities and patients. It is very difficult to design cost-effective interventions that will suit all parties to slow the prevalence of diabetes. The diabetes-related burden, such as diabetes prevalence and incidence, in the Saskatoon Health Region were investigated by using the System Dynamics approach. A System Dynamics model was built based on the current diabetes situation of the Saskatoon Health Region. The model is able to simulate the entire disease progress as a dynamic system and to catch the interactions and feedbacks between factors. According to the simulation result, the diabetes prevalence and the number of incident cases will continue to increase in the next four decades. The model is able to help the decision-makers to observe the future impacts of current interventions and find bottlenecks, as well as key uncertainties. The model has the capability to answer many “what-if” and “why” questions. The decision-makers can use the model as a useful tool to evaluate different interventions by performing different experimental scenarios