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Personalized glucose-insulin model based on signal analysis. (English) Zbl 1370.92046

Summary: 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.

MSC:

92C40 Biochemistry, molecular biology
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[1] Bergman, R. N.; Ider, Y. Z.; Bowden, C. R.; Cobelli, C., Quantitative estimation of insulin sensitivity, Am. J. Physiol., 236, E667-E677, (1979)
[2] Chee, F.; Fernando, T., Lecture notes in control and information sciences, close-loop control of blood glucose, (2007), Springer Verlag, (ISBN: 978-3-540-74030-8 (Print)(978-3-540-74031-5)
[3] Chen, W.; Ding, H.; Feng, P., IACP: a sequence-based tool for identifying anticancer peptides, Oncotarget, 2016, 7, 16895-16909, (2016)
[4] Cheng, X.; Zhao, S. G.; Xiao, X., IATC-misf: a multi-label classifier for predicting the classes of anatomical therapeutic chemicals, Bioinformatics, 2016, (2016)
[5] Chou, K. C., Impacts of bioinformatics to medicinal chemistry, Med. Chem., 2015, 11, 218-234, (2015)
[6] Cobelli, C.; Dalla Man, C.; Toffolo, G.; Basu, R.; Vella, A.; Rizza, R., The oral minimal model method, Diabetes, 63, 4, 1203-1213, (2014)
[7] Engwerda, E. C.E.; Tack, C. J.; De Galan, B. E., Needle-free jet injection of, Diabetes Care, 36, 3436-3441, (2013)
[8] Gaohua, L.; Kimura, H., A mathematical model of brain glucose homeostasis, Theor. Biol. Med. Model., 6, 26, (2009)
[9] Hovorka, R.; Canonico, V.; Chassin, L. J.; Haueter, U.; Massi-Benedetti, M.; Federici, M. O.; Pieber, T. R.; Schaller, H. C.; Schaupp, L.; Vering, T.; Wilinska, M. E., Nonlinear model predictive control of glucose concentration in subjects with type 1 diabetes, Physiol. Meas., 25, 2004, 905-920, (2004)
[10] Jia, J.; Liu, Z.; Xiao, X., Icar-psecp: identify carbonylation sites in proteins by monto Carlo sampling and incorporating sequence coupled effects into general pseaac, Oncotarget, 2016, 7, 34558-34570, (2016)
[11] Jia, J.; Zhang, L.; Liu, Z., Psumo-CD: predicting sumoylation sites in proteins with covariance discriminant algorithm by incorporating sequence-coupled effects into general pseaac, Bioinformatics, 2016, 32, 3133-3141, (2016)
[12] Johansen J., 2014. Graph 4.4.2 A graphical mathematical tool. Last updated: Available as a free download from 〈http://www.padowan.dk/graph/〉.
[13] Kissler, S. M.; Cichowitz, C.; Sankaranarayanan, S.; Bortz, D. M., J. Theor. Biol., 359, 101-111, (2014), (21 October)
[14] Kovatchev, B. P.; Breton, M.; Dalla Man Cand Cobelli, C., In silico preclinical trials: A proof of concept in closed-loop control of type 1 diabetes, J. Diabetes Sci. Technol., 3, 1, (2009), (January 2009)
[15] Liu, L.; Ma, Y.; Wang, R. L., Find novel dual-agonist drugs for treating type 2 diabetes by means of cheminformatics, Drug Des. Dev. Ther., 2013, 7, 279-287, (2013)
[16] Ma, Y.; Wang, S. Q.; Xu, W. R., Design novel dual agonists for treating type-2 diabetes by targeting peroxisome proliferator-activated receptors with core hopping approach, PLoS ONE, 2012, 7, e38546, (2012)
[17] Min, B. G.; Woo, E. J. Lee; Min, H. K.; H, K., Separation of physiological factors influencing kinetics in diabetic patients using computer simulation method, (1984), J. Korean Med. Assoc., 27, 1, (1984)
[18] Min, G. B.; Woo, E. J., An electrical equivalent circuit model of glucose-insulin kinetics during intravenous glucose tolerance test in dogs and man, Math. Model. Elsevier, 7, 9−12, 1187-1193, (1986)
[19] Palumbo, P.; Ditlevsen, S.; Bertuzzi, A.; De Gaetano, A., Mathematical modeling of the glucose-insulin system: a review, Math. Biosci., 244, 69-81, (2013) · Zbl 1280.92023
[20] Qiu, W. R.; Sun, B. Q.; Xiao, X., Iphos-pseevo: identifying human phosphorylated proteins by incorporating evolutionary information into general pseaac via grey system theory, Mol. Inform., 2016, (2016)
[21] Qiu, W. R.; Sun, B. Q.; Xiao, X., Ihyd-psecp: identify hydroxyproline and hydroxylysine in proteins by incorporating sequence-coupled effects into general pseaac, Oncotarget, 2016, 7, 44310-44321, (2016)
[22] Qiu, W. R.; Xiao, X.; Xu, Z. H., Iphos-pseen: identifying phosphorylation sites in proteins by fusing different pseudo components into an ensemble classifier, Oncotarget, 2016, 7, 51270-51283, (2016)
[23] Rangaiah, G. P.; Samavedham, L.; Balakrishnan, N. P., Personalized mechanistic models for exercise, meal, and insulin interventions in children and adolescents with type 1 diabetes, J. Theor. Biol., 21, 62-73, (2014) · Zbl 1412.92143
[24] Schenkman A.L., 2005. Transient analysis of Electrical Power Circuits Handbook. Springer USA ISBN 978-0-387-28799-7, Doi: 〈http://doi.org/10.1007/0-387-28799-X〉 〈https://download.e-bookshelf.de/download/0000/0008/76/L-G〉.
[25] SIMetrix Technologies Ltd, 2014. Circuit Simulation, 78 Chapel Street, Thatcham, Berkshire, RG18 4QN United Kingdom, 〈http:/www.simetrix.co.uk〉.
[26] Steil, G. M., Algorithms for a closed-loop artificial pancreas: the case for proportional-integral-derivative control, J. Diabetes Sci. Technol. Vol., 7, 6, (2013), (November 2013)
[27] Wang, J. F.; Wei, D. Q.; Li, L., 3D structure modeling of cytochrome P450 2C19 and its implication for personalized drug design, Biochem Biophys. Res Commun. (BBRC), (2007), (Corrigendum: ibid, 2007, Vol. 357, 330) (2007, 355, 513-519)
[28] Wang, J. F.; Wei, D. Q.; Chen, C., Molecular modeling of two CYP2C19 SNPs and its implications for personalized drug design, Protein Pept. Lett., 2008, 15, 27-32, (2008)
[29] Wang, J. F.; Wei, D. Q.; Li, L., Review: pharmacogenomics and personalized use of drugs, Curr. Top. Med. Chem., 2008, 8, 1573-1579, (2008)
[30] Zhang, C. J.; Tang, H.; Li, W. C., Iori-human: identify human origin of replication by incorporating dinucleotide physicochemical properties into pseudo nucleotide composition, Oncotarget, 2016, 7, 69783-69793, (2016)
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