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      Logical design of oral glucose ingestion pattern minimizing blood glucose in humans

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          Abstract

          Excessive increase in blood glucose level after eating increases the risk of macroangiopathy, and a method for not increasing the postprandial blood glucose level is desired. However, a logical design method of the dietary ingestion pattern controlling the postprandial blood glucose level has not yet been established. We constructed a mathematical model of blood glucose control by oral glucose ingestion in three healthy human subjects, and predicted that intermittent ingestion 30 min apart was the optimal glucose ingestion patterns that minimized the peak value of blood glucose level. We confirmed with subjects that this intermittent pattern consistently decreased the peak value of blood glucose level. We also predicted insulin minimization pattern, and found that the intermittent ingestion 30 min apart was optimal, which is similar to that of glucose minimization pattern. Taken together, these results suggest that the glucose minimization is achieved by suppressing the peak value of insulin concentration, rather than by enhancing insulin concentration. This approach could be applied to design optimal dietary ingestion patterns.

          Abstract

          The key points of this study are three-fold: The first point is the physiological impact. Intuitively, an ingestion pattern that minimizes the maximum blood glucose level is expected to be a slow, continuous ingestion. We found that the optimal minimization pattern was an intermittent pattern with 30 min intervals. The second point is the methodology. We constructed the mathematical model as a forward problem, and in turn, predicted input pattern by control the output pattern of interest as an inverse problem. The third point is the experiment design used to generate the mathematical model; dense time course data for six ingestion patterns combining of 3 doses of glucose level and 2 durations of ingestion.

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          Use of the oral glucose tolerance test to assess insulin release and insulin sensitivity.

          The oral glucose tolerance test (OGTT) has often been used to evaluate apparent insulin release and insulin resistance in various clinical settings. However, because insulin sensitivity and insulin release are interdependent, to what extent they can be predicted from an OGTT is unclear. We studied insulin sensitivity using the euglycemic-hyperinsulinemic clamp and insulin release using the hyperglycemic clamp in 104 nondiabetic volunteers who had also undergone an OGTT. Demographic parameters (BMI, waist-to-hip ratio, age) and plasma glucose and insulin values from the OGTT were subjected to multiple linear regression to predict the metabolic clearance rate (MCR) of glucose, the insulin sensitivity index (ISI), and first-phase (1st PH) and second-phase (2nd PH) insulin release as measured with the respective clamps. The equations predicting MCR and ISI contained BMI, insulin (120 min), and glucose (90 min) and were highly correlated with the measured MCR (r = 0.80, P < 0.00005) and ISI (r = 0.79, P < 0.00005). The equations predicting 1st PH and 2nd PH contained insulin (0 and 30 min) and glucose (30 min) and were also highly correlated with the measured 1st PH (r = 0.78, P < 0.00005) and 2nd PH (r = 0.79, P < 0.00005). The parameters predicted by our equations correlated better with the measured parameters than homeostasis model assessment for secretion and resistance, the delta30-min insulin/delta30-min glucose ratio for secretion and insulin (120 min) for insulin resistance taken from the OGTT. We thus conclude that predicting insulin sensitivity and insulin release with reasonable accuracy from simple demographic parameters and values obtained during an OGTT is possible. The derived equations should be used in various clinical settings in which the use of clamps or the minimal model would be impractical.
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            Defining the relationship between plasma glucose and HbA(1c): analysis of glucose profiles and HbA(1c) in the Diabetes Control and Complications Trial.

            To define the relationship between HbA(1c) and plasma glucose (PG) levels in patients with type 1 diabetes using data from the Diabetes Control and Complications Trial (DCCT). The DCCT was a multicenter, randomized clinical trial designed to compare intensive and conventional therapies and their relative effects on the development and progression of diabetic complications in patients with type 1 diabetes. Quarterly HbA(1c) and corresponding seven-point capillary blood glucose profiles (premeal, postmeal, and bedtime) obtained in the DCCT were analyzed to define the relationship between HbA(1c) and PG. Only data from complete profiles with corresponding HbA(1c) were used (n = 26,056). Of the 1,441 subjects who participated in the study, 2 were excluded due to missing data. Mean plasma glucose (MPG) was estimated by multiplying capillary blood glucose by 1.11. Linear regression analysis weighted by the number of observations per subject was used to correlate MPG and HbA(1c). Linear regression analysis, using MPG and HbA(1c) summarized by patient (n = 1,439), produced a relationship of MPG (mmol/l) = (1.98 . HbA(1c)) - 4.29 or MPG (mg/dl) = (35.6 . HbA(1c)) - 77.3, r = 0.82). Among individual time points, afternoon and evening PG (postlunch, predinner, postdinner, and bedtime) showed higher correlations with HbA(1c) than the morning time points (prebreakfast, postbreakfast, and prelunch). We have defined the relationship between HbA(1c) and PG as assessed in the DCCT. Knowing this relationship can help patients with diabetes and their healthcare providers set day-to-day targets for PG to achieve specific HbA(1c) goals.
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              An Overview of Evolutionary Algorithms for Parameter Optimization

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                Author and article information

                Contributors
                skuroda@bs.s.u-tokyo.ac.jp
                Journal
                NPJ Syst Biol Appl
                NPJ Syst Biol Appl
                NPJ Systems Biology and Applications
                Nature Publishing Group UK (London )
                2056-7189
                2 September 2019
                2 September 2019
                2019
                : 5
                : 31
                Affiliations
                [1 ]ISNI 0000 0001 2151 536X, GRID grid.26999.3d, Molecular Genetic Research Laboratory, Graduate School of Science, , The University of Tokyo, ; Tokyo, 113-0033 Japan
                [2 ]ISNI 0000 0001 2151 536X, GRID grid.26999.3d, Department of Biological Sciences, Graduate School of Science, , The University of Tokyo, ; Tokyo, 113-0033 Japan
                [3 ]ISNI 0000 0004 0372 2033, GRID grid.258799.8, Department of Systems Science, Graduate School of Informatics, , Kyoto University, ; Kyoto, 606-8501 Japan
                [4 ]Department of Neurosurgery, The University of Tokyo Hospital, The University of Tokyo, Tokyo, 113-0033 Japan
                [5 ]ISNI 0000 0001 2151 536X, GRID grid.26999.3d, Department of Rehabilitation, Graduate School of Medicine, , The University of Tokyo, ; Tokyo, 113-0033 Japan
                [6 ]ISNI 0000 0000 8863 9909, GRID grid.262576.2, Department of Mathematics, Graduate School of Science and Engineering, , Ritsumeikan University, ; Shiga, 525-8577 Japan
                [7 ]ISNI 0000 0001 2242 4849, GRID grid.177174.3, Division of Integrated Omics, Research Center for Transomics Medicine, Medical Institute of Bioregulation, , Kyushu University, ; Fukuoka, 812-8582 Japan
                [8 ]ISNI 0000 0001 2308 3329, GRID grid.9707.9, Metabolism and Nutrition Research Unit, Institute for Frontier Science Initiative, , Kanazawa University, ; Ishikawa, 920-8640 Japan
                [9 ]ISNI 0000 0004 1762 1436, GRID grid.257114.4, Faculty of Computer and Information Sciences, , Hosei University, ; Tokyo, 184-8584 Japan
                [10 ]ISNI 0000 0004 1754 9200, GRID grid.419082.6, CREST, Japan Science and Technology Agency, ; Tokyo, 113-0033 Japan
                [11 ]ISNI 0000 0000 8711 3200, GRID grid.257022.0, Present Address: Department of Integrated Sciences for Life, Graduate School of Integrated Sciences for Life, , Hiroshima University, ; Hiroshima, 739-8526 Japan
                Article
                108
                10.1038/s41540-019-0108-1
                6718521
                31508240
                f8354eb9-a96e-4140-960a-fdbf3becbff7
                © The Author(s) 2019

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 5 March 2019
                : 6 August 2019
                Funding
                Funded by: FundRef https://doi.org/10.13039/501100002241, MEXT | Japan Science and Technology Agency (JST);
                Award ID: JPMJCR12W3
                Award Recipient :
                Funded by: FundRef https://doi.org/10.13039/501100001691, MEXT | Japan Society for the Promotion of Science (JSPS);
                Award ID: 17H06300
                Award ID: 17H06299
                Award ID: 19K22860
                Award ID: 16K12508
                Award ID: 19K20382
                Award ID: 18K16578
                Award ID: 16H01551
                Award ID: 18H04801
                Award ID: 16H06577
                Award ID: 16H06577
                Award ID: 15KT0021
                Award ID: 15K00246
                Award Recipient :
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                © The Author(s) 2019

                systems biology,health care,mathematics and computing

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