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 (, 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.


  • ReadMe.txt

    size: 974 Bytes | mime_type: text/plain | date: 2022-07-07 | sha256: 7bc6df2


Work Title Inferring Insulin Secretion Rate From Sparse Patient Glucose and Insulin Measures
Open Access
  1. Bruce Gluckman
  2. Rammah Abohtyra
License CC BY 4.0 (Attribution)
Work Type Article
  1. David Albers
Publication Date June 21, 2022
DOI doi:10.26207/e8rf-e082
Publisher Identifier (DOI)
  1. 10.3389/fphys.2022.893862
Deposited July 07, 2022




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Work History

Version 1

  • Created
  • Updated
  • Updated Acknowledgments Show Changes
    • David Albers
  • Added Creator Bruce Gluckman
  • Added Creator Rammah Abohtyra
  • Added ReadMe.txt
  • Updated License Show Changes
  • Published