Analytical Basal-State Model of the Glucose, Insulin, and C-Peptide Systems for Type 2 Diabetes

We present a mechanistic mathematical model of the basal state for type 2 diabetes mellitus (T2DM) in an analytical form and illustrate its use for in silico basal-state and dynamic studies. At the core of the basal-state model is a quartic equation that expresses the basal plasma glucose concentrat...

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Main Authors: Ched C. Chichester, Munekazu Yamakuchi, Kazunori Takenouchi, Teruto Hashiguchi, Drew N. Maywar
Format: Article
Language:English
Published: MDPI AG 2025-05-01
Series:Bioengineering
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Online Access:https://www.mdpi.com/2306-5354/12/5/553
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author Ched C. Chichester
Munekazu Yamakuchi
Kazunori Takenouchi
Teruto Hashiguchi
Drew N. Maywar
author_facet Ched C. Chichester
Munekazu Yamakuchi
Kazunori Takenouchi
Teruto Hashiguchi
Drew N. Maywar
author_sort Ched C. Chichester
collection DOAJ
description We present a mechanistic mathematical model of the basal state for type 2 diabetes mellitus (T2DM) in an analytical form and illustrate its use for in silico basal-state and dynamic studies. At the core of the basal-state model is a quartic equation that expresses the basal plasma glucose concentration solely in terms of model parameters. This analytical model avoids a computationally intensive numerical solver and is illustrated by an investigation of how glucose-utilization parameters impact basal glucose, insulin, insulin-dependent utilization, and hepatic extraction, leveraging median parameter values of early-stage T2DM. Furthermore, the presented basal-state model ensures accurate execution of the corresponding dynamic model, which contains basal quantities within its derivative functions; erroneous, unintended dynamics in plasma glucose, insulin, and C-peptide are illustrated using an incorrect basal glucose value. The presented basal model enables efficient and accurate basal-state and dynamic studies, facilitating the understanding of T2DM pathophysiology and the development of T2DM diagnosis, treatment, and management strategies.
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spelling doaj-art-85abf16e88d2497185e27fb9fbb995382025-08-20T03:14:40ZengMDPI AGBioengineering2306-53542025-05-0112555310.3390/bioengineering12050553Analytical Basal-State Model of the Glucose, Insulin, and C-Peptide Systems for Type 2 DiabetesChed C. Chichester0Munekazu Yamakuchi1Kazunori Takenouchi2Teruto Hashiguchi3Drew N. Maywar4Department of Electrical and Microelectronic Engineering, Rochester Institute of Technology, Rochester, NY 14623, USADepartment of Laboratory and Vascular Medicine, Kagoshima University Graduate School of Medical and Dental Sciences, Kagoshima 890-8520, JapanDepartment of Laboratory and Vascular Medicine, Kagoshima University Graduate School of Medical and Dental Sciences, Kagoshima 890-8520, JapanDepartment of Laboratory and Vascular Medicine, Kagoshima University Graduate School of Medical and Dental Sciences, Kagoshima 890-8520, JapanDepartment of Electrical and Microelectronic Engineering, Rochester Institute of Technology, Rochester, NY 14623, USAWe present a mechanistic mathematical model of the basal state for type 2 diabetes mellitus (T2DM) in an analytical form and illustrate its use for in silico basal-state and dynamic studies. At the core of the basal-state model is a quartic equation that expresses the basal plasma glucose concentration solely in terms of model parameters. This analytical model avoids a computationally intensive numerical solver and is illustrated by an investigation of how glucose-utilization parameters impact basal glucose, insulin, insulin-dependent utilization, and hepatic extraction, leveraging median parameter values of early-stage T2DM. Furthermore, the presented basal-state model ensures accurate execution of the corresponding dynamic model, which contains basal quantities within its derivative functions; erroneous, unintended dynamics in plasma glucose, insulin, and C-peptide are illustrated using an incorrect basal glucose value. The presented basal model enables efficient and accurate basal-state and dynamic studies, facilitating the understanding of T2DM pathophysiology and the development of T2DM diagnosis, treatment, and management strategies.https://www.mdpi.com/2306-5354/12/5/553mechanistic modelingtype 2 diabetes mellitusbasal stateglucoseinsulinC-peptide
spellingShingle Ched C. Chichester
Munekazu Yamakuchi
Kazunori Takenouchi
Teruto Hashiguchi
Drew N. Maywar
Analytical Basal-State Model of the Glucose, Insulin, and C-Peptide Systems for Type 2 Diabetes
Bioengineering
mechanistic modeling
type 2 diabetes mellitus
basal state
glucose
insulin
C-peptide
title Analytical Basal-State Model of the Glucose, Insulin, and C-Peptide Systems for Type 2 Diabetes
title_full Analytical Basal-State Model of the Glucose, Insulin, and C-Peptide Systems for Type 2 Diabetes
title_fullStr Analytical Basal-State Model of the Glucose, Insulin, and C-Peptide Systems for Type 2 Diabetes
title_full_unstemmed Analytical Basal-State Model of the Glucose, Insulin, and C-Peptide Systems for Type 2 Diabetes
title_short Analytical Basal-State Model of the Glucose, Insulin, and C-Peptide Systems for Type 2 Diabetes
title_sort analytical basal state model of the glucose insulin and c peptide systems for type 2 diabetes
topic mechanistic modeling
type 2 diabetes mellitus
basal state
glucose
insulin
C-peptide
url https://www.mdpi.com/2306-5354/12/5/553
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