PMID- 28039012
OWN - NLM
STAT- MEDLINE
DCOM- 20170925
LR  - 20181118
IS  - 1095-8541 (Electronic)
IS  - 0022-5193 (Linking)
VI  - 419
DP  - 2017 Apr 21
TI  - Personalized glucose-insulin model based on signal analysis.
PG  - 333-342
LID - S0022-5193(16)30428-3 [pii]
LID - 10.1016/j.jtbi.2016.12.018 [doi]
AB  - Glucose plasma measurements for diabetes patients are generally presented as a 
      glucose concentration-time profile with 15-60min time scale intervals. This 
      limited resolution obscures detailed dynamic events of glucose appearance and 
      metabolism. Measurement intervals of 15min 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 MMTT 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.
CI  - Copyright (c) 2017 Elsevier Ltd. All rights reserved.
FAU - Goede, Simon L
AU  - Goede SL
AD  - Systems Research, Oterlekerweg 4, 1841 GP Stompetoren, The Netherlands. 
      Electronic address: slgoede@kpnmail.nl.
FAU - de Galan, Bastiaan E
AU  - de Galan BE
AD  - Department of General Internal Medicine of Radboud University Nijmegen Medical 
      Centre, Postbus 9101, 6500 HB Nijmegen, The Netherlands. Electronic address: 
      B.deGalan@aig.umcn.nl.
FAU - Leow, Melvin Khee Shing
AU  - Leow MKS
AD  - Dept of Endocrinology, Tan Tock Seng Hospital, Singapore 308433, Office of 
      Clinical Sciences, Duke-NUS Graduate Medical School, Singapore Lee Kong Chian 
      School of Medicine, Nanyang Technological University, Singapore. Electronic 
      address: melvin_leow@nuhs.edu.sg.
LA  - eng
PT  - Journal Article
DEP - 20161228
PL  - England
TA  - J Theor Biol
JT  - Journal of theoretical biology
JID - 0376342
RN  - 0 (Blood Glucose)
RN  - 0 (Hypoglycemic Agents)
RN  - 0 (Insulin)
RN  - IY9XDZ35W2 (Glucose)
SB  - IM
MH  - Algorithms
MH  - Blood Glucose/*metabolism
MH  - Diabetes Mellitus, Type 1/*blood/drug therapy
MH  - Diabetes Mellitus, Type 2/*blood/drug therapy
MH  - Glucose/metabolism
MH  - Homeostasis
MH  - Humans
MH  - Hypoglycemic Agents/administration & dosage
MH  - Insulin/*administration & dosage
MH  - *Models, Biological
MH  - Precision Medicine/*methods
OTO - NOTNLM
OT  - Appearance profile
OT  - Electrical network model
OT  - Model identification
OT  - Personalized target
OT  - Simulation
OT  - Validation
EDAT- 2017/01/01 06:00
MHDA- 2017/09/26 06:00
CRDT- 2017/01/01 06:00
PHST- 2016/09/20 00:00 [received]
PHST- 2016/12/04 00:00 [revised]
PHST- 2016/12/26 00:00 [accepted]
PHST- 2017/01/01 06:00 [pubmed]
PHST- 2017/09/26 06:00 [medline]
PHST- 2017/01/01 06:00 [entrez]
AID - S0022-5193(16)30428-3 [pii]
AID - 10.1016/j.jtbi.2016.12.018 [doi]
PST - ppublish
SO  - J Theor Biol. 2017 Apr 21;419:333-342. doi: 10.1016/j.jtbi.2016.12.018. Epub 2016 
      Dec 28.