Inferring Insulin Secretion Rate From Sparse Patient Glucose and Insulin Measures

Abstract

ABSTRACTThe insulin secretion rate (ISR) contains information that can provide a personal, quantitative understanding of endocrine function. The ISR, if it can be reliably inferred from sparse measurements, could be used both for understanding the source of a problem with glucose regulation system and for monitoring people with diabetes.ObjectiveThis study aims to develop a model-based method for inferring ISR and related physiological information among people with different glycemic conditions in a robust manner.MethodsAn algorithm for estimating and validating ISR for different compartmental models with unknown but estimable ISR function and absorption/decay rates describing the dynamics of insulin and C-peptide accumulation was developed. 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).ResultsThe model-based algorithm was able to successfully estimate ISR for a diverse set of patients. For CFRD subjects, due to high maximum observed glucose values, we observed better estimation for the degradation rates than for healthy patients. Even though ISR and C-peptide secretion rate (CSR) have different physiological characteristics and nonlinear relations with plasma glucose concentration, our model-based algorithm reproduced the expected linear relationship between ISR and CSR. And the estimated ISR can differentiate normal and CFRD patients: the ISR for individuals with CFRD is substantially lower compared with the ISR for individuals’ normal glucose regulatory systems.SignificanceA new estimation method is available to infer the ISR profile, plasma insulin, and C-peptide absorption rates from sparse measurements of insulin, C-peptide, and plasma glucose concentrations.