title: Personalized glucose-insulin model based on signal analysis.
abstract: 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.
authors.affiliation: Systems Research, Oterlekerweg 4, 1841 GP Stompetoren, The Netherlands. Electronic address: slgoede@kpnmail.nl.
authors.forename: Simon L
authors.initials: SL
authors.lastname: Goede
authors.affiliation: 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.
authors.forename: Bastiaan E
authors.initials: BE
authors.lastname: de Galan
authors.affiliation: 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.
authors.forename: Melvin Khee Shing
authors.initials: MKS
authors.lastname: Leow
mesh_terms: D000465:Algorithms; D001786:Blood Glucose / Q000378:metabolism*; D003922:Diabetes Mellitus, Type 1 / Q000097:blood* / Q000188:drug therapy; D003924:Diabetes Mellitus, Type 2 / Q000097:blood* / Q000188:drug therapy; D005947:Glucose / Q000378:metabolism; D006706:Homeostasis; D006801:Humans; D007004:Hypoglycemic Agents / Q000008:administration & dosage; D007328:Insulin / Q000008:administration & dosage*; D008954:Models, Biological; D057285:Precision Medicine / Q000379:methods*
chemical_list: D001786:Blood Glucose; D007004:Hypoglycemic Agents; D007328:Insulin; D005947:Glucose
keywords: Appearance profile; Electrical network model; Model identification; Personalized target; Simulation; Validation