TY - JOUR
T1 - Minimally-Invasive and Efficient Method to Accurately Fit the Bergman Minimal Model to Diabetes Type 2
AU - Gallardo-Hernández, Ana Gabriela
AU - González-Olvera, Marcos A.
AU - Castellanos-Fuentes, Medardo
AU - Escobar, Jésica
AU - Revilla-Monsalve, Cristina
AU - Hernandez-Perez, Ana Luisa
AU - Leder, Ron
N1 - Publisher Copyright:
© 2022, The Author(s) under exclusive licence to Biomedical Engineering Society.
PY - 2022/6
Y1 - 2022/6
N2 - Introduction: Diabetes mellitus is a global burden that is expected to grow 25 % by 2030. This will increase the need for prevention, diagnosis and treatment of diabetes. Animal and individualized in silico models will allow understanding and compensation for inter and intra-individual differences in treatment and management strategies for diabetic patients. The method presented here can advance the concept of personalized medicine. Methods: Twenty experiments were performed with Sprague-Dawley rats with streptozotocin induced experimental diabetes in which the insulin-glucose response curve was recorded over 60–100 min using only an insulin pump and a percutaneous glucose sensor. The information was used to fit the five-parameter Bergman Minimal Model to the experimental results using a genetic algorithm with a root-mean-squared optimization rule. Results: The Bergman Minimal Model parameters were estimated with high accuracy, low prediction bias, and low average root-mean-squared error of 15.27 mg/dl glucose. Conclusions: This study demonstrates a simple method to accurately parameterize the Bergman Minimal Model. We used Sprague–Dawley rats since their physiology is close to that of humans. The parameters can be used to objectively characterize the physiological severity of diabetes. In this way, planned treatments can compensate for natural variations of conditions both inter and intra patients. Changes in parameters indicate the patient’s diabetic condition using values of glucose effectiveness (SG= p1) and insulin sensitivity (SI= p3/ p2). Quantifying the diabetic patient’s condition is consistent with the trend toward personalized medicine. Parameter values can also be used to explain atypical research results of other studies and increase understanding of diabetes.
AB - Introduction: Diabetes mellitus is a global burden that is expected to grow 25 % by 2030. This will increase the need for prevention, diagnosis and treatment of diabetes. Animal and individualized in silico models will allow understanding and compensation for inter and intra-individual differences in treatment and management strategies for diabetic patients. The method presented here can advance the concept of personalized medicine. Methods: Twenty experiments were performed with Sprague-Dawley rats with streptozotocin induced experimental diabetes in which the insulin-glucose response curve was recorded over 60–100 min using only an insulin pump and a percutaneous glucose sensor. The information was used to fit the five-parameter Bergman Minimal Model to the experimental results using a genetic algorithm with a root-mean-squared optimization rule. Results: The Bergman Minimal Model parameters were estimated with high accuracy, low prediction bias, and low average root-mean-squared error of 15.27 mg/dl glucose. Conclusions: This study demonstrates a simple method to accurately parameterize the Bergman Minimal Model. We used Sprague–Dawley rats since their physiology is close to that of humans. The parameters can be used to objectively characterize the physiological severity of diabetes. In this way, planned treatments can compensate for natural variations of conditions both inter and intra patients. Changes in parameters indicate the patient’s diabetic condition using values of glucose effectiveness (SG= p1) and insulin sensitivity (SI= p3/ p2). Quantifying the diabetic patient’s condition is consistent with the trend toward personalized medicine. Parameter values can also be used to explain atypical research results of other studies and increase understanding of diabetes.
KW - Genetic algorithm
KW - Glucose effectiveness
KW - Individualized diabetes treatment
KW - Insulin sensitivity
UR - http://www.scopus.com/inward/record.url?scp=85124093513&partnerID=8YFLogxK
U2 - 10.1007/s12195-022-00719-x
DO - 10.1007/s12195-022-00719-x
M3 - Artículo
C2 - 35611162
AN - SCOPUS:85124093513
SN - 1865-5025
VL - 15
SP - 267
EP - 279
JO - Cellular and Molecular Bioengineering
JF - Cellular and Molecular Bioengineering
IS - 3
ER -