Mathematical Modeling and Simulations of the Pathophysiology of Type-2 Diabetes Mellitus

The pathophysiology of Type 2 Diabetes Mellitus (T2DM) is modelled using a coupled system of non-linear deterministic differential equations. An attempt is made to construct to a clinically plausible mathematical model that incorporates the homeostasis associated with endocrinological regulation of glucose and glycogen levels in the human body, by the hormones, insulin and glucagon. The model variables include the concentrations of glucose in the venous blood plasma, the concentration of glycogen in the liver/tissues, the concentration of the hormone glucagon, and the concentration of insulin in the venous blood plasma. The physiological interactions between the model parameters are depicted by clinically measurable rate constants and biophysically quantifiable stoichiometric coefficients. The processes of gluconeogenesis, glycogenolysis, and pulsatile insulin secretion during type 2 diabetes are modelled using plausible auxiliary functions. Investigative computer simulations are performed to elucidate various hypothetical scenarios of glycemia, patho-physiology of T2DM and insulinoma associated hypoglycemia which results from excessive insulin production probably due to a tumor. This study has demonstrated the necessity of simultaneous monitoring of plasma glucose, glucagon, insulin, and glycogen levels in the proper assessment of the pathophysiology of type 2 diabetes and during determination of the therapeutic efficacy of anti-diabetic drugs

Association of Basal Hyperglucagonemia with Impaired Glucagon Counterregulation in Type 1 Diabetes

Glucagon counterregulation (GCR) protects against hypoglycemia, but is impaired in type 1 diabetes (T1DM). A model-based analysis of in vivo animal data predicts that the GCR defects are linked to basal hyperglucagonemia. To test this hypothesis we studied the relationship between basal glucagon (BasG) and the GCR response to hypoglycemia in 29 hyperinsulinemic clamps in T1DM patients. Glucose levels were stabilized in euglycemia and then steadily lowered to 50 mg/dL. Glucagon was measured before induction of hypoglycemia and at 10 min intervals after glucose reached levels below 70 mg/dL. GCR was assessed by CumG, the cumulative glucagon levels above basal; MaxG, the maximum glucagon response; and RIG, the relative increase in glucagon over basal. Analysis of the results was performed with our mathematical model of GCR. The model describes interactions between islet peptides and glucose, reproduces the normal GCR axis and its impairment in diabetes. It was used to identify a control mechanism consistent with the observed link between BasG and GCR. Analysis of the clinical data showed that higher BasG was associated with lower GCR response. In particular, CumG and RIG correlated negatively with BasG (r = −0.46, p = 0.012 and r = −0.74, p < 0.0001 respectively) and MaxG increased linearly with BasG at a rate less than unity (p < 0.001). Consistent with these results was a model of GCR in which the secretion of glucagon has two components. The first is under (auto) feedback control and drives a pulsatile GCR and the second is feedback independent (basal secretion) and its increase suppresses the GCR. Our simulations showed that this model explains the observed relationships between BasG and GCR during a three-fold simulated increase in BasG. Our findings support the hypothesis that basal hyperglucagonemia contributes to the GCR impairment in T1DM and show that the predictive power of our GCR animal model applies to human pathophysiology in T1DM

A Quantitative Systems Pharmacology Kidney Model of Diabetes Associated Renal Hyperfiltration and the Effects of SGLT Inhibitors

The early stage of diabetes mellitus is characterized by increased glomerular filtration rate (GFR), known as hyperfiltration, which is believed to be one of the main causes leading to renal injury in diabetes. Sodium-glucose cotransporter 2 inhibitors (SGLT2i) have been shown to be able to reverse hyperfiltration in some patients. We developed a mechanistic computational model of the kidney that explains the interplay of hyperglycemia and hyperfiltration and integrates the pharmacokinetics/ pharmacodynamics (PK/PD) of the SGLT2i dapagliflozin. Based on simulation results, we propose kidney growth as the necessary process for hyperfiltration progression. Further, the model indicates that renal SGLT1i could significantly improve hyperfiltration when added to SGTL2i. Integrated into a physiologically based PK/PD (PBPK/PD) Diabetes Platform, the model presents a powerful tool for aiding drug development, prediction of hyperfiltration risk, and allows the assessment of the outcomes of individualized treatments with SGLT1-inhibitors and SGLT2-inhibitors and their co-administration with insulin

Smart Sensors and Virtual Physiology Human Approach as a Basis of Personalized Therapies in Diabetes Mellitus

Diabetes mellitus (DM) has a growing incidence and prevalence in modern societies, pushed by the aging and change of life styles. Despite the huge resources dedicated to improve their quality of life, mortality and morbidity rates, these are still very poor. In this work, DM pathology is revised from clinical and metabolic points of view, as well as mathematical models related to DM, with the aim of justifying an evolution of DM therapies towards the correction of the physiological metabolic loops involved. We analyze the reliability of mathematical models, under the perspective of virtual physiological human (VPH) initiatives, for generating and integrating customized knowledge about patients, which is needed for that evolution. Wearable smart sensors play a key role in this frame, as they provide patient’s information to the models

Role of adipose tissue in the pathogenesis and treatment of metabolic syndrome

© Springer International Publishing Switzerland 2014. Adipocytes are highly specialized cells that play a major role in energy homeostasis in vertebrate organisms. Excess adipocyte size or number is a hallmark of obesity, which is currently a global epidemic. Obesity is not only the primary disease of fat cells, but also a major risk factor for the development of Type 2 diabetes, cardiovascular disease, hypertension, and metabolic syndrome (MetS). Today, adipocytes and adipose tissue are no longer considered passive participants in metabolic pathways. In addition to storing lipid, adipocytes are highly insulin sensitive cells that have important endocrine functions. Altering any one of these functions of fat cells can result in a metabolic disease state and dysregulation of adipose tissue can profoundly contribute to MetS. For example, adiponectin is a fat specific hormone that has cardio-protective and anti-diabetic properties. Inhibition of adiponectin expression and secretion are associated with several risk factors for MetS. For this purpose, and several other reasons documented in this chapter, we propose that adipose tissue should be considered as a viable target for a variety of treatment approaches to combat MetS

The Design and Validation of a Discrete-Event Simulator for Carbohydrate Metabolism in Humans

CarbMetSim is a discrete event simulator that tracks the changes of blood glucose level of a human subject after a timed series of diet and exercises activities. CarbMetSim implements wider aspects of carbohydrate metabolism in individuals to capture the average effect of various diet/exercise routines on the blood glucose level of diabetic patients. The simulator is implemented in an object-oriented paradigm, where key organs are represented as classes in the CarbMetSim. Key organs (stomach, intestine, portal vein, liver, kidney, muscles, adipose tissue, brain and heart) are implemented to the extent necessary to simulate their impact on the production and consumption of glucose. Metabolic pathways (glucose oxidation, glycolysis and gluconeogenesis) have been taken in account in the operation of various organs. In accordance with published research, the impact of insulin and insulin resistance on the operation of various organs/pathways is captured. CarbMetSim offers broad versatility to configure the insulin production ability, the average flux along various metabolic pathways and the impact of insulin resistance on different aspects of carbohydrate metabolism. However, the CarbMetSim project has not yet been finished. There are many aspects and metabolic pathways that have not been implemented or have been implemented in a simple manner. Also, additional validation is required before the simulator can be considered ready for use by people with Diabetes