Personalized glucose-insulin model based on signal analysis

被引:4
作者
Goede, Simon L. [1 ]
de Galan, Bastiaan E. [2 ]
Leow, Melvin Khee Shing [3 ,4 ]
机构
[1] Syst Res, Oterlekerweg 4, NL-1841 GP Stompetoren, Netherlands
[2] Radboud Univ Nijmegen, Med Ctr, Dept Gen Internal Med, Postbus 9101, NL-6500 HB Nijmegen, Netherlands
[3] Tan Tock Seng Hosp, Dept Endocrinol, Singapore 308433, Singapore
[4] Nanyang Technol Univ, Off Clin Sci, Duke NUS Grad Med Sch, Singapore Lee Kong Chian Sch Med, Singapore, Singapore
关键词
Appearance profile; Model identification; Electrical network model; Simulation; Personalized target; Validation;
D O I
10.1016/j.jtbi.2016.12.018
中图分类号
Q [生物科学];
学科分类号
07 ; 0710 ; 09 ;
摘要
Glucose plasma measurements for diabetes patients are generally presented as a glucose concentration-time profile with 15-60 min time scale intervals. This limited resolution obscures detailed dynamic events of glucose appearance and metabolism. Measurement intervals of 15 min or more could contribute to imperfections in present diabetes treatment. High resolution data from mixed meal tolerance tests (MMTT) for 24 type 1 and type 2 diabetes patients were used in our present modeling. We introduce a model based on the physiological properties of transport, storage and utilization. This logistic approach follows the principles of electrical network analysis and signal processing theory. The method mimics the physiological equivalent of the glucose homeostasis comprising the meal ingestion, absorption via the gastrointestinal tract (GIT) to the endocrine nexus between the liver, pancreatic alpha and beta cells. This model demystifies the metabolic 'black box' by enabling in silico simulations and fitting of individual responses to clinical data. Five-minute intervals MMIT data measured from diabetic subjects result in two independent model parameters that characterize the complete glucose system response at a personalized level. From the individual data measurements, we obtain a model which can be analyzed with a standard electrical network simulator for diagnostics and treatment optimization. The insulin dosing time scale can be accurately adjusted to match the individual requirements of characterized diabetic patients without the physical burden of treatment.
引用
收藏
页码:333 / 342
页数:10
相关论文
共 50 条
[41]   Identification of an integrated mathematical model of standard oral glucose tolerance test for characterization of insulin potentiation in health [J].
Burattini, Roberto ;
Morettini, Micaela .
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE, 2012, 107 (02) :248-261
[42]   Cascaded analysis of signal and noise propagation through a heterogeneous breast model [J].
Mainprize, James G. ;
Yaffe, Martin J. .
MEDICAL PHYSICS, 2010, 37 (10) :5243-5250
[43]   Population PK/PD analysis of metformin using the signal transduction model [J].
Chae, Jung-woo ;
Baek, In-hwan ;
Lee, Byung-yo ;
Cho, Seong-kwon ;
Kwon, Kwang-il .
BRITISH JOURNAL OF CLINICAL PHARMACOLOGY, 2012, 74 (05) :815-823
[44]   Compilation and Analysis of Web-Based Orthopedic Personalized Predictive Tools: A Scoping Review [J].
Curtin, Patrick ;
Conway, Alexandra ;
Martin, Liu ;
Lin, Eugenia ;
Jayakumar, Prakash ;
Swart, Eric .
JOURNAL OF PERSONALIZED MEDICINE, 2020, 10 (04) :1-20
[45]   Time-Series-Based Personalized Lane-Changing Decision-Making Model [J].
Ye, Ming ;
Pu, Lei ;
Li, Pan ;
Lu, Xiangwei ;
Liu, Yonggang .
SENSORS, 2022, 22 (17)
[46]   Double Deep Q-learning Based on Personalized Thermal Comfort Model for HVAC Optimization [J].
Zhou, Hanchen ;
Wang, Di ;
Xu, Zhanbo ;
Jia, Qing-Shan .
2024 IEEE 20TH INTERNATIONAL CONFERENCE ON AUTOMATION SCIENCE AND ENGINEERING, CASE 2024, 2024, :3262-3267
[47]   Ontology Based Personalized Modeling for Type 2 Diabetes Risk Analysis: An Integrated Approach [J].
Verma, Anju ;
Fiasche, Maurizio ;
Cuzzola, Maria ;
Iacopino, Pasquale ;
Morabito, Francesco C. ;
Kasabov, Nikola .
NEURAL INFORMATION PROCESSING, PT 2, PROCEEDINGS, 2009, 5864 :360-+
[48]   Personalized Breast Cancer Screening: A Risk Prediction Model Based on Women Attending BreastScreen Norway [J].
Louro, Javier ;
Roman, Marta ;
Moshina, Nataliia ;
Olstad, Camilla F. ;
Larsen, Marthe ;
Sagstad, Silje ;
Castells, Xavier ;
Hofvind, Solveig .
CANCERS, 2023, 15 (18)
[49]   Model based robustness analysis of an ion-exchange chromatography step [J].
Jakobsson, Niklas ;
Degerman, Marcus ;
Stenborg, Emelie ;
Nilsson, Bernt .
JOURNAL OF CHROMATOGRAPHY A, 2007, 1138 (1-2) :109-119
[50]   Research on rejection magnetic levitation model simulation and wavelet analysis of its signal [J].
Kong Deshan ;
Jiang Dong ;
Zhao Yanchao ;
Liu Xukun ;
Wang Deyu .
PROCEEDINGS OF 2019 14TH IEEE INTERNATIONAL CONFERENCE ON ELECTRONIC MEASUREMENT & INSTRUMENTS (ICEMI), 2019, :1701-1707