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Glucose Homeostasis: Pancreatic Islets and Insulin Secretion

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The pancreatic islets comprising only 1%-2% of the volume are highly vascularized and innervated mini-organs. They contain five endocrine cell types, including β cells that secrete insulin, which is synthesized as a single polypeptide chain, preproinsulin, processed to proinsulin, and finally to insulin and C-peptide. This process is complex and regulated, involving the Golgi complex, the endoplasmic reticulum, and the secretory granules of the β cell.
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Differentiation of Human Pluripotent Stem Cells into Insulin-Producing Islet Clusters

Differentiation of Human Pluripotent Stem Cells into Insulin-Producing Islet Clusters

Published on: June 23, 2023

Model individualization for artificial pancreas.

Mirko Messori¹, Chiara Toffanin¹, Simone Del Favero²

¹Department of Civil Engineering and Architecture, University of Pavia, Pavia, Italy.

Computer Methods and Programs in Biomedicine

|July 19, 2016

Summary

This summary is machine-generated.

Two novel methods were developed to create personalized glucose-insulin models for type 1 diabetes management. These models improve the effectiveness of artificial pancreas systems by accounting for individual patient differences.

Keywords:

Constrained optimization Linear systems Model predictive control Nonparametric identification Type 1 diabetes

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Area of Science:

Biomedical Engineering
Control Systems
Endocrinology

Background:

Inter-subject variability in type 1 diabetes mellitus complicates automatic blood glucose control.
Non-individualized models lead to ineffective insulin responses and control laws.
Developing individualized control laws for artificial pancreas systems is an ongoing research challenge.

Purpose of the Study:

To introduce and evaluate two novel identification approaches for individualizing linear glucose-insulin models.
To address the challenge of patient-specific insulin responses in automatic blood glucose control.
To enhance the design of personalized control laws for artificial pancreas applications.

Main Methods:

Employed a black-box, kernel-based nonparametric identification approach.
Utilized a gray-box identification technique with constrained optimization, based on a linearized average virtual patient model.
Validated models using in silico data from simulated clinical protocols, assessing prediction performance with metrics like R-squared and RMSE.

Main Results:

Both identification approaches successfully generated linear individualized glucose-insulin models for virtual patients.
The identified individualized models demonstrated significantly improved simulation performance compared to a standard average model.
Enhanced model accuracy is crucial for effective artificial pancreas control.

Conclusions:

The proposed identification methods show strong potential for creating patient-specific glucose-insulin models.
These models are valuable for designing individualized control laws for artificial pancreas systems.
Personalized modeling is key to advancing artificial pancreas technology for type 1 diabetes.