Inferring Insulin Secretion Rate from Sparse Patient Glucose and Insulin Measures

• Published in: Frontiers in physiology Vol. 13; p. 893862
• Main Authors: Abohtyra, Rammah M., Chan, Christine L., Albers, David J., Gluckman, Bruce J.
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Media S.A 03.08.2022
• Subjects: and CSR/ISR molar ratio; compartment models; estimation algorithm; insulin and C-peptide; ISR function; OGTT; Physiology; ISR function; compartment models; estimation algorithm; insulin and C-peptide; OGTT; and CSR/ISR molar ratio
• ISSN: 1664-042X; 1664-042X
• DOI: 10.3389/fphys.2022.893862

The insulin secretion rate (ISR) contains information that can provide a personal, quantitative understanding of endocrine function. If the ISR can be reliably inferred from measurements, it could be used for understanding and clinically diagnosing problems with the glucose regulation system. Objective : This study aims to develop a model-based method for inferring a parametrization of the ISR and related physiological information among people with different glycemic conditions in a robust manner. The developed algorithm is applicable for both dense or sparsely sampled plasma glucose/insulin measurements, where sparseness is defined in terms of sampling time with respect to the fastest time scale of the dynamics. Methods: An algorithm for parametrizing and validating a functional form of the ISR for different compartmental models with unknown but estimable ISR function and absorption/decay rates describing the dynamics of insulin accumulation was developed. The method and modeling applies equally to c-peptide secretion rate (CSR) when c-peptide is measured. Accuracy of fit is reliant on reconstruction error of the measured trajectories, and when c-peptide is measured the relationship between CSR and ISR. The algorithm was applied to data from 17 subjects with normal glucose regulatory systems and 9 subjects with cystic fibrosis related diabetes (CFRD) in which glucose, insulin and c-peptide were measured in course of oral glucose tolerance tests (OGTT). Results: This model-based algorithm inferred parametrization of the ISR and CSR functional with relatively low reconstruction error for 12 of 17 control and 7 of 9 CFRD subjects. We demonstrate that when there are suspect measurements points, the validity of excluding them may be interrogated with this method. Significance: A new estimation method is available to infer the ISR and CSR functional profile along with plasma insulin and c-peptide absorption rates from sparse measurements of insulin, c-peptide, and plasma glucose concentrations. We propose a method to interrogate and exclude potentially erroneous OGTT measurement points based on reconstruction errors.