Inferring Insulin Secretion Rate From Sparse Patient Glucose and Insulin Measures
The insulin secretion rate (ISR) contains information that can provide a personal, quantitative understanding of endocrine function.
In Abohtyra, et al, 2022 (https://www.frontiersin.org/articles/10.3389/fphys.2022.893862), we developed a model-based method for inferring a parametrization of the ISR and related physiological information 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. The method and modeling applies equally to c-peptide secretion (CSR) and 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.
This archive includes example code of the proposed algorithm, along with the code used to validate the method on model-generated data.
|Work Title||Inferring Insulin Secretion Rate From Sparse Patient Glucose and Insulin Measures|
|License||CC BY 4.0 (Attribution)|
|Publication Date||June 21, 2022|
|Publisher Identifier (DOI)||
|Deposited||July 07, 2022|
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