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2014 | OriginalPaper | Buchkapitel

Data-Driven Modeling of Diabetes Progression

verfasst von : Andrea DeGaetano, Simona Panunzi, Pasquale Palumbo, Claudio Gaz, Thomas Hardy

Erschienen in: Data-driven Modeling for Diabetes

Verlag: Springer Berlin Heidelberg

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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 β-cell protection. The mechanistic Diabetes Progression Model provides a credible tool by which long-term implications of anti-diabetic interventions can be evaluated.

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Metadaten
Titel
Data-Driven Modeling of Diabetes Progression
verfasst von
Andrea DeGaetano
Simona Panunzi
Pasquale Palumbo
Claudio Gaz
Thomas Hardy
Copyright-Jahr
2014
Verlag
Springer Berlin Heidelberg
DOI
https://doi.org/10.1007/978-3-642-54464-4_8

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