Data-Driven Modeling of Diabetes Progression
A realistic representation of the long-term physiologic adaptation to developing insulin resistance would facilitate the effective design of clinical trials evaluating diabetes prevention or disease modification therapies. In the present work, a realistic
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Abstract A realistic representation of the long-term physiologic adaptation to developing insulin resistance would facilitate the effective design of clinical trials evaluating diabetes prevention or disease modification therapies. In the present work, a realistic, robust description of the evolution of the compensation of the glucose-insulin system in healthy and diabetic individuals, with particular attention to the physiological compensation to worsening insulin resistance is formulated, its physiological assumptions are presented, and its performance over the span of a lifetime is simulated. Model-based simulations of the long-term evolution of the disease and of its response to therapeutic interventions are consistent with the transient benefits observed with conventional therapies, and with promising effects of radical improvement of insulin sensitivity (as by metabolic surgery) or of b-cell protection. The mechanistic Diabetes Progression Model provides a credible tool by which long-term implications of anti-diabetic interventions can be evaluated.
Keywords Glucose Insulin resistance Beta cell mass Mathematical models Type 2 diabetes mellitus
A. DeGaetano S. Panunzi (&) C. Gaz Consiglio Nazionale delle Ricerche, Istituto di Analisi dei Sistemi ed Informatica ‘‘A. Ruberti’’ - Laboratorio di Biomatematica, UCSC-Largo A. Gemelli, 8, 00168 Roma, Italy e-mail: [email protected] P. Palumbo Consiglio Nazionale delle Ricerche, Istituto di Analisi dei Sistemi ed Informatica ‘‘A. Ruberti’’, Viale Manzoni, 30, Roma 00185, Italy T. Hardy Lilly Research Laboratories, Eli Lilly and Company, Lilly Corporate Center, Indianapolis, IN, USA
V. Marmarelis and G. Mitsis (eds.), Data-driven Modeling for Diabetes, Lecture Notes in Bioengineering, DOI: 10.1007/978-3-642-54464-4_8, Springer-Verlag Berlin Heidelberg 2014
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1 Introduction The glucose-insulin system is a time-honored topic for the development of mathematical models, starting from the early days of Bolie [1] and Ackerman et al. [2]. Part of the attention devoted to this specific physiological system over the years is due to its embodying, in its most skeletal interpretation, a relatively simple, clear-cut feedback mechanism: when glycemia increases, the pancreas secretes insulin which brings glycemia down. If the system were this simple, however, mathematical physiologists would probably not have found material for continuing scientific investigation for more than 50 years to date. In fact, the control is complicated by internal nonlinearities and delays, by external influences (notably through the link with energy consumption and lipid metabolism) and by the superposition of different levels of neural and hormonal regulation (e.g. through the sympathetic system and through the incretins mechanism). Modeling the regulation of glycemia through its main controller, the hormone insulin, may satisfy different purposes. On one hand, understanding short-term (minutes to hours) regulation is relevant to the cond
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