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. If the ISR can be reliably inferred from measurements, it could be used for understanding and clinically diagnosing problems with the glucose regulation system.Objecti...
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Frontiers Media S.A.
2022-08-01
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| Series: | Frontiers in Physiology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fphys.2022.893862/full |
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| author | Rammah M. Abohtyra Rammah M. Abohtyra Christine L. Chan David J. Albers Bruce J. Gluckman Bruce J. Gluckman Bruce J. Gluckman Bruce J. Gluckman |
| author_facet | Rammah M. Abohtyra Rammah M. Abohtyra Christine L. Chan David J. Albers Bruce J. Gluckman Bruce J. Gluckman Bruce J. Gluckman Bruce J. Gluckman |
| author_sort | Rammah M. Abohtyra |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-14T05:50:12Z |
| format | Article |
| id | doaj.art-2ef344addc5144469e2552ddb93f7aa6 |
| institution | Directory Open Access Journal |
| issn | 1664-042X |
| language | English |
| last_indexed | 2024-04-14T05:50:12Z |
| publishDate | 2022-08-01 |
| publisher | Frontiers Media S.A. |
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| series | Frontiers in Physiology |
| spelling | doaj.art-2ef344addc5144469e2552ddb93f7aa62022-12-22T02:09:08ZengFrontiers Media S.A.Frontiers in Physiology1664-042X2022-08-011310.3389/fphys.2022.893862893862Inferring Insulin Secretion Rate from Sparse Patient Glucose and Insulin MeasuresRammah M. Abohtyra0Rammah M. Abohtyra1Christine L. Chan2David J. Albers3Bruce J. Gluckman4Bruce J. Gluckman5Bruce J. Gluckman6Bruce J. Gluckman7Center for Neural Engineering, The Pennsylvania State University, University Park, PA, United StatesDepartment of Engineering Science and Mechanics, The Pennsylvania State University, University Park, PA, United StatesSection of Pediatric Endocrinology, University of Colorado School of Medicine, Aurora, CO, United StatesDepartment of Bioengineering, University of Colorado School of Medicine, Aurora, CO, United StatesCenter for Neural Engineering, The Pennsylvania State University, University Park, PA, United StatesDepartment of Engineering Science and Mechanics, The Pennsylvania State University, University Park, PA, United StatesDepartment of Neurosurgery, College of Medicine, The Pennsylvania State University, University Park, PA, United StatesDepartment of Biomedical Engineering, The Pennsylvania State University, University Park, PA, United StatesThe 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.https://www.frontiersin.org/articles/10.3389/fphys.2022.893862/fullestimation algorithmISR functioncompartment modelsinsulin and C-peptideOGTTand CSR/ISR molar ratio |
| spellingShingle | Rammah M. Abohtyra Rammah M. Abohtyra Christine L. Chan David J. Albers Bruce J. Gluckman Bruce J. Gluckman Bruce J. Gluckman Bruce J. Gluckman Inferring Insulin Secretion Rate from Sparse Patient Glucose and Insulin Measures Frontiers in Physiology estimation algorithm ISR function compartment models insulin and C-peptide OGTT and CSR/ISR molar ratio |
| title | Inferring Insulin Secretion Rate from Sparse Patient Glucose and Insulin Measures |
| title_full | Inferring Insulin Secretion Rate from Sparse Patient Glucose and Insulin Measures |
| title_fullStr | Inferring Insulin Secretion Rate from Sparse Patient Glucose and Insulin Measures |
| title_full_unstemmed | Inferring Insulin Secretion Rate from Sparse Patient Glucose and Insulin Measures |
| title_short | Inferring Insulin Secretion Rate from Sparse Patient Glucose and Insulin Measures |
| title_sort | inferring insulin secretion rate from sparse patient glucose and insulin measures |
| topic | estimation algorithm ISR function compartment models insulin and C-peptide OGTT and CSR/ISR molar ratio |
| url | https://www.frontiersin.org/articles/10.3389/fphys.2022.893862/full |
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