Cited by 349Pancreatic function and diabetesDiabetes Management and ResearchDiabetes, Cardiovascular Risks, and Lipoproteins

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Coefficients of normal blood glucose regulation

Bolie, Victor W.·1961·Journal of Applied Physiology

DOI 10.1152/jappl.1961.16.5.783

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Abstract

A previously formulated glucose-insulin feed-back theory was simplified with appropriate assumptions for the purpose of determining which physiological sensitivity coefficients dominate the mathematical characteristics of the normal insulin and glucose tolerance curves. It was found from experimental data that these physiological coefficients approximate the well-known critical damping criteria of servomechanism theory. Correlations between theoretical and experimental results were made with some particular solutions of the necessary differential equations, obtained with the aid of an electronic analogue computer. Using a distribution volume of 17.5 liters for the 70-kg adult in three different methods of approach, it was found that the average coefficients of the insulin and glucose responses of the liver, pancreas, and peripheral tissues are approximately ⍺ = 0.780 unit/hr/unit, β = 0.208 unit/hr/g, γ = 4.34 g/hr/unit, and δ = 2.92 g/hr/g. Submitted on December 27, 1960

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Background
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The development of a nonlinear model to describe the blood glucose response for the determination of prandial insulin infusion.
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Mori, H., & Kitamura, S. (1988) · IFAC Proceedings Volumes · doi:10.1016/s1474-6670(17)54898-3
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Bergman, R. N., & Bucolo, R. J. (1973) · IFAC Proceedings Volumes · doi:10.1016/s1474-6670(17)68076-5
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REMOVED: Bibliography
Chauvet, G. A. (1986) · Elsevier eBooks · doi:10.1016/b978-0-08-041993-0.50025-2
References
(2019) · Elsevier eBooks · doi:10.1016/b978-0-12-815756-5.00023-0
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Modeling of OGTT Blood Glucose and Insulin Responses and Diagnostic Indices
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References
(2008) · Elsevier eBooks · doi:10.1016/b978-012160240-6.50013-2
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Authors: Bolie, V. W.
Affiliations: Iowa State University (US)
Corresponding author: Bolie, Victor W.
Venue: Journal of Applied Physiology
Publisher: American Physiological Society
Published: 1961-09-01
Volume: 16
Issue: 5
Pages: 783-788
ISSN: 0161-7567 (ISSN-L)
8750-7587
1522-1601
DOI: 10.1152/jappl.1961.16.5.783
OpenAlex: W2337101260
Cited by: 349
References: 0
Citations by year: 2026: 12 · 2025: 25 · 2024: 18 · 2023: 14 · 2022: 10 · 2021: 13 · 2020: 18 · 2019: 13 · 2018: 13 · 2017: 16 · 2016: 8 · 2015: 15 · 2014: 12 · 2013: 12 · 2012: 10
FWCI: 2.58
Type: journal article
Language: en
Open access: closed
Locations: Journal of Applied Physiology (publishedVersion)
PubMed (publishedVersion)
Topics: Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Pancreatic function and diabetes
Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes Management and Research
Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes, Cardiovascular Risks, and Lipoproteins
Keywords: blood glucose regulation, glucose tolerance curve, critical damping, volume of distribution, glucose-insulin dynamics
MeSH terms: Blood Glucose, Humans, Carbohydrate Metabolism

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Coefficients of normal blood glucose regulation

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