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Wadghiri MZ, Idri A, El Idrissi T, Hakkoum H. Ensemble blood glucose prediction in diabetes mellitus: A review. Comput Biol Med 2022; 147:105674. [PMID: 35716436 DOI: 10.1016/j.compbiomed.2022.105674] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2022] [Revised: 04/28/2022] [Accepted: 05/25/2022] [Indexed: 11/03/2022]
Abstract
Considering the complexity of blood glucose dynamics, the adoption of a single model to predict blood glucose level does not always capture the inter- and intra-patients' context changes. Ensembles are a set of machine learning techniques combining multiple single learners to find a better variance/bias trade-off and hence improve the prediction accuracy. The present paper aims to review the state of the art in predicting blood glucose using ensemble methods with regard to 8 criteria: publication year and sources, datasets used to train/evaluate the models, types of ensembles used, single learners involved to construct ensembles, combination schemes used to aggregate the base learners, metrics and validation methods adopted to assess the performance of ensembles, reported overall performance of the predictors and accuracy comparison of ensemble techniques with single models. A systematic literature review has been conducted in order to analyze and synthetize primary studies published between 2000 and 2020 in six digital libraries. A total of 32 primary papers were selected and reviewed with regard to eight review questions. The results show that ensembles have gained wider interest during the last years and improved in general the performance compared with other single models. However, multiple gaps have been identified concerning the ensembles construction process and the performance metrics used. Several recommendations have been made in this regard to design accurate ensembles for blood glucose level prediction.
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Affiliation(s)
- M Z Wadghiri
- Software Project Management Research Team, ENSIAS, Mohammed V University in Rabat, Morocco
| | - A Idri
- Software Project Management Research Team, ENSIAS, Mohammed V University in Rabat, Morocco; MSDA, Mohammed VI Polytechnic University, Benguerir, Morocco.
| | - Touria El Idrissi
- Software Project Management Research Team, ENSIAS, Mohammed V University in Rabat, Morocco
| | - Hajar Hakkoum
- Software Project Management Research Team, ENSIAS, Mohammed V University in Rabat, Morocco
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2
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Sharma A, Singh HP, Nilam. A methodical survey of mathematical model-based control techniques based on open and closed loop control approach for diabetes management. INT J BIOMATH 2022. [DOI: 10.1142/s1793524522500516] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Disturbance of blood sugar level is controlled through well-known biomechanical feedback loops: high levels of glucose in blood facilitate to release insulin from the pancreas which accelerates the absorption rate of cellular glucose. Low glucose levels encourage to release pancreatic glucagon which induces glycogen breakdown to glucose in the liver. These bio-control systems do not function properly in diabetic patients. Though the control of disease seems intuitively easy, in real life, due to many differences in structure by diet and fasting, exercise, medications, patient’s profile and other stressors, it is not that easy. The mathematical models of the glucose-insulin regulatory system follow the patient’s physiological conditions which make it difficult to identify and estimate all the model parameters. In this paper, we have given a systematic literature review on mathematical models of the diabetic patients, and various kinds of disease control techniques through the development of open and closed loop insulin deliver command system and optimization of exogenous insulin rate. It demonstrates the open and closed loop type model-based control strategies underlying the assumptions of the concerned models. The combination of mathematical model with control strategies such as genetic algorithm (GA), neural network (NN), sliding mode controller (SMC), model predictive controller (MPC), and fuzzy logic control (FLC) has been considered, which provides an overview of this area, highlighting the control profile over the diabetic model with promising clinical results, outlining key challenges, and identifying needs for the future research. Also, the significance of these control algorithms has been discussed in the presence of the noises, the controller’s robustness and various other disturbances. It provides substantial information on diabetes management through various control techniques.
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Affiliation(s)
- Ankit Sharma
- Department of Applied Mathematics, Delhi Technological University, Delhi 110042, India
| | | | - Nilam
- Department of Applied Mathematics, Delhi Technological University, Delhi 110042, India
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Fakhroleslam M, Bozorgmehry Boozarjomehry R. A multi‐objective optimal insulin bolus advisor for type 1 diabetes based on personalized model and daily diet. ASIA-PAC J CHEM ENG 2021. [DOI: 10.1002/apj.2651] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Affiliation(s)
- Mohammad Fakhroleslam
- Process Engineering Department, Faculty of Chemical Engineering Tarbiat Modares University Tehran Iran
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Deichmann J, Bachmann S, Burckhardt MA, Szinnai G, Kaltenbach HM. Simulation-Based Evaluation of Treatment Adjustment to Exercise in Type 1 Diabetes. Front Endocrinol (Lausanne) 2021; 12:723812. [PMID: 34489869 PMCID: PMC8417413 DOI: 10.3389/fendo.2021.723812] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/11/2021] [Accepted: 07/26/2021] [Indexed: 01/26/2023] Open
Abstract
Regular exercise is beneficial and recommended for people with type 1 diabetes, but increased glucose demand and changes in insulin sensitivity require treatment adjustments to prevent exercise-induced hypoglycemia. Several different adjustment strategies based on insulin bolus reductions and additional carbohydrate intake have been proposed, but large inter- and intraindividual variability and studies using different exercise duration, intensity, and timing impede a direct comparison of their effects. In this study, we use a mathematical model of the glucoregulatory system and implement published guidelines and strategies in-silico to provide a direct comparison on a single 'typical' person on a standard day with three meals. We augment this day by a broad range of exercise scenarios combining different intensity and duration of the exercise session, and different timing with respect to adjacent meals. We compare the resulting blood glucose trajectories and use summary measures to evaluate the time-in-range and risk scores for hypo- and hyperglycemic events for each simulation scenario, and to determine factors that impede prevention of hypoglycemia events. Our simulations suggest that the considered strategies and guidelines successfully minimize the risk for acute hypoglycemia. At the same time, all adjustments substantially increase the risk of late-onset hypoglycemia compared to no adjustment in many cases. We also find that timing between exercise and meals and additional carbohydrate intake during exercise can lead to non-intuitive behavior due to superposition of meal- and exercise-related glucose dynamics. Increased insulin sensitivity appears as a major driver of non-acute hypoglycemic events. Overall, our results indicate that further treatment adjustment might be required both immediately following exercise and up to several hours later, but that the intricate interplay between different dynamics makes it difficult to provide generic recommendations. However, our simulation scenarios extend substantially beyond the original scope of each model component and proper model validation is warranted before applying our in-silico results in a clinical setting.
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Affiliation(s)
- Julia Deichmann
- Department of Biosystems Science and Engineering and Swiss Institute of Bioinformatics (SIB), ETH Zurich, Basel, Switzerland
- Life Science Zurich Graduate School, Zurich, Switzerland
| | - Sara Bachmann
- Pediatric Endocrinology and Diabetology, University Children’s Hospital Basel, and Department of Clinical Research, University Hospital Basel, University of Basel, Basel, Switzerland
| | - Marie-Anne Burckhardt
- Pediatric Endocrinology and Diabetology, University Children’s Hospital Basel, and Department of Clinical Research, University Hospital Basel, University of Basel, Basel, Switzerland
| | - Gabor Szinnai
- Pediatric Endocrinology and Diabetology, University Children’s Hospital Basel, and Department of Clinical Research, University Hospital Basel, University of Basel, Basel, Switzerland
| | - Hans-Michael Kaltenbach
- Department of Biosystems Science and Engineering and Swiss Institute of Bioinformatics (SIB), ETH Zurich, Basel, Switzerland
- *Correspondence: Hans-Michael Kaltenbach,
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Alam W, Khan Q, Riaz RA, Akmeliawati R. Arbitrary-order sliding mode-based robust control algorithm for the developing artificial pancreas mechanism. IET Syst Biol 2020; 14:307-313. [PMID: 33399094 PMCID: PMC8687268 DOI: 10.1049/iet-syb.2018.5075] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2018] [Revised: 04/15/2020] [Accepted: 05/27/2020] [Indexed: 11/20/2022] Open
Abstract
In Diabetes Mellitus, the pancreas remains incapable of insulin administration that leads to hyperglycaemia, an escalated glycaemic concentration, which may stimulate many complications. To circumvent this situation, a closed-loop control strategy is much needed for the exogenous insulin infusion in diabetic patients. This closed-loop structure is often termed as an artificial pancreas that is generally established by the employment of different feedback control strategies. In this work, the authors have proposed an arbitrary-order sliding mode control approach for development of the said mechanism. The term, arbitrary, is exercised in the sense of its applicability to any n-order controllable canonical system. The proposed control algorithm affirms the finite-time effective stabilisation of the glucose-insulin regulatory system, at the desired level, with the alleviation of sharp fluctuations. The novelty of this work lies in the sliding manifold that incorporates indirect non-linear terms. In addition, the necessary discontinuous terms are filtered-out once before its employment to the plant, i.e. diabetic patient. The robustness, in the presence of external disturbances, i.e. meal intake is confirmed via rigorous mathematical stability analysis. In addition, the effectiveness of the proposed control strategy is ascertained by comparing the results with the standard literature.
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Affiliation(s)
- Waqar Alam
- Department of Electrical and Computer Engineering, COMSATS University Islamabad, Islamabad, Pakistan
| | - Qudrat Khan
- Center for Advanced Studies in Telecommunications (CAST), COMSATS University Islamabad, Islamabad, Pakistan.
| | - Raja Ali Riaz
- Department of Electrical and Computer Engineering, COMSATS University Islamabad, Islamabad, Pakistan
| | - Rini Akmeliawati
- School of Mechanical Engineering, The University of Adelaide, Adelaide, South Australia 5005, Australia
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Nath A, Deb D, Dey R, Das S. Blood glucose regulation in type 1 diabetic patients: an adaptive parametric compensation control-based approach. IET Syst Biol 2019; 12:219-225. [PMID: 30259867 PMCID: PMC8687408 DOI: 10.1049/iet-syb.2017.0093] [Citation(s) in RCA: 35] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022] Open
Abstract
Here, a direct adaptive control strategy with parametric compensation is adopted for an uncertain non‐linear model representing blood glucose regulation in type 1 diabetes mellitus patients. The uncertain parameters of the model are updated by appropriate design of adaptation laws using the Lyapunov method. The closed‐loop response of the plasma glucose concentration as well as external insulin infusion rate is analysed for a wide range of variation of the model parameters through extensive simulation studies. The result indicates that the proposed adaptive control scheme avoids severe hypoglycaemia and gives satisfactory performance under parametric uncertainty highlighting its ability to address the issue of inter‐patient variability.
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Affiliation(s)
- Anirudh Nath
- Electrical Engineering Department, National Institute of Technology, Silchar 788010, Assam, India.
| | - Dipankar Deb
- Electrical Engineering Department, Institute of Infrastructure Technology Research and Management, Ahmedabad 380026, Gujarat, India
| | - Rajeeb Dey
- Electrical Engineering Department, National Institute of Technology, Silchar 788010, Assam, India
| | - Sipon Das
- Electrical Engineering Department, National Institute of Technology, Silchar 788010, Assam, India
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Woldaregay AZ, Årsand E, Walderhaug S, Albers D, Mamykina L, Botsis T, Hartvigsen G. Data-driven modeling and prediction of blood glucose dynamics: Machine learning applications in type 1 diabetes. Artif Intell Med 2019; 98:109-134. [DOI: 10.1016/j.artmed.2019.07.007] [Citation(s) in RCA: 54] [Impact Index Per Article: 10.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2018] [Revised: 08/22/2018] [Accepted: 07/19/2019] [Indexed: 10/26/2022]
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Woldaregay AZ, Årsand E, Botsis T, Albers D, Mamykina L, Hartvigsen G. Data-Driven Blood Glucose Pattern Classification and Anomalies Detection: Machine-Learning Applications in Type 1 Diabetes. J Med Internet Res 2019; 21:e11030. [PMID: 31042157 PMCID: PMC6658321 DOI: 10.2196/11030] [Citation(s) in RCA: 58] [Impact Index Per Article: 11.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2018] [Revised: 11/27/2018] [Accepted: 01/30/2019] [Indexed: 01/23/2023] Open
Abstract
Background Diabetes mellitus is a chronic metabolic disorder that results in abnormal blood glucose (BG) regulations. The BG level is preferably maintained close to normality through self-management practices, which involves actively tracking BG levels and taking proper actions including adjusting diet and insulin medications. BG anomalies could be defined as any undesirable reading because of either a precisely known reason (normal cause variation) or an unknown reason (special cause variation) to the patient. Recently, machine-learning applications have been widely introduced within diabetes research in general and BG anomaly detection in particular. However, irrespective of their expanding and increasing popularity, there is a lack of up-to-date reviews that materialize the current trends in modeling options and strategies for BG anomaly classification and detection in people with diabetes. Objective This review aimed to identify, assess, and analyze the state-of-the-art machine-learning strategies and their hybrid systems focusing on BG anomaly classification and detection including glycemic variability (GV), hyperglycemia, and hypoglycemia in type 1 diabetes within the context of personalized decision support systems and BG alarm events applications, which are important constituents for optimal diabetes self-management. Methods A rigorous literature search was conducted between September 1 and October 1, 2017, and October 15 and November 5, 2018, through various Web-based databases. Peer-reviewed journals and articles were considered. Information from the selected literature was extracted based on predefined categories, which were based on previous research and further elaborated through brainstorming. Results The initial results were vetted using the title, abstract, and keywords and retrieved 496 papers. After a thorough assessment and screening, 47 articles remained, which were critically analyzed. The interrater agreement was measured using a Cohen kappa test, and disagreements were resolved through discussion. The state-of-the-art classes of machine learning have been developed and tested up to the task and achieved promising performance including artificial neural network, support vector machine, decision tree, genetic algorithm, Gaussian process regression, Bayesian neural network, deep belief network, and others. Conclusions Despite the complexity of BG dynamics, there are many attempts to capture hypoglycemia and hyperglycemia incidences and the extent of an individual’s GV using different approaches. Recently, the advancement of diabetes technologies and continuous accumulation of self-collected health data have paved the way for popularity of machine learning in these tasks. According to the review, most of the identified studies used a theoretical threshold, which suffers from inter- and intrapatient variation. Therefore, future studies should consider the difference among patients and also track its temporal change over time. Moreover, studies should also give more emphasis on the types of inputs used and their associated time lag. Generally, we foresee that these developments might encourage researchers to further develop and test these systems on a large-scale basis.
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Affiliation(s)
| | - Eirik Årsand
- Norwegian Centre for E-health Research, University Hospital of North Norway, Tromsø, Norway
| | - Taxiarchis Botsis
- The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - David Albers
- Department of Biomedical Informatics, Columbia University, New York, NY, United States
| | - Lena Mamykina
- Department of Biomedical Informatics, Columbia University, New York, NY, United States
| | - Gunnar Hartvigsen
- Department of Computer Science, University of Tromsø - The Arctic University of Norway, Tromsø, Norway
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Delavari H, Heydarinejad H, Baleanu D. Adaptive fractional-order blood glucose regulator based on high-order sliding mode observer. IET Syst Biol 2019; 13:43-54. [DOI: 10.1049/iet-syb.2018.5016] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2018] [Revised: 04/24/2018] [Accepted: 06/11/2018] [Indexed: 12/24/2022] Open
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10
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Agent-Based Modeling of Immune Response to Study the Effects of Regulatory T Cells in Type 1 Diabetes. Processes (Basel) 2018. [DOI: 10.3390/pr6090141] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022] Open
Abstract
Regulatory T cells (Tregs) have an important role in self-tolerance. Understanding the functions of Tregs is important for preventing or slowing the progress of Type 1 Diabetes. We use a two-dimensional (2D) agent-based model to simulate immune response in mice and test the effects of Tregs in tissue protection. We compared the immune response with and without Tregs, and also tested the effects of Tregs from different sources or with different functions. The results show that Tregs can inhibit the proliferation of effector T cells by inhibiting antigens presenting via dendritic cells (DCs). Although the number and function of Tregs affect the inhibition, a small number of Tregs compared to CD4+ T cells can effectively protect islets in pancreatic tissue. Finally, we added Tregs to the system in the middle phase of the immune response. The simulation results show that Tregs can inhibit the production of effector CD8+ T cells and maintain a good environment for β cell regeneration.
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Oviedo S, Vehí J, Calm R, Armengol J. A review of personalized blood glucose prediction strategies for T1DM patients. INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN BIOMEDICAL ENGINEERING 2017; 33:e2833. [PMID: 27644067 DOI: 10.1002/cnm.2833] [Citation(s) in RCA: 107] [Impact Index Per Article: 15.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/15/2016] [Revised: 09/15/2016] [Accepted: 09/16/2016] [Indexed: 06/06/2023]
Abstract
This paper presents a methodological review of models for predicting blood glucose (BG) concentration, risks and BG events. The surveyed models are classified into three categories, and they are presented in summary tables containing the most relevant data regarding the experimental setup for fitting and testing each model as well as the input signals and the performance metrics. Each category exhibits trends that are presented and discussed. This document aims to be a compact guide to determine the modeling options that are currently being exploited for personalized BG prediction.
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Affiliation(s)
- Silvia Oviedo
- Institut d'Informàtica i Aplicacions, Parc Científic i Tecnològic de la Universitat de Girona, 17003, Girona, Spain
| | - Josep Vehí
- Institut d'Informàtica i Aplicacions, Universitat de Girona, Campus Montilivi, Edifici P4, 17071, Girona, Spain
| | - Remei Calm
- Institut d'Informàtica i Aplicacions, Universitat de Girona, Campus Montilivi, Edifici P4, 17071, Girona, Spain
| | - Joaquim Armengol
- Institut d'Informàtica i Aplicacions, Universitat de Girona, Campus Montilivi, Edifici P4, 17071, Girona, Spain
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Ashworth WB, Davies NA, Bogle IDL. A Computational Model of Hepatic Energy Metabolism: Understanding Zonated Damage and Steatosis in NAFLD. PLoS Comput Biol 2016; 12:e1005105. [PMID: 27632189 PMCID: PMC5025084 DOI: 10.1371/journal.pcbi.1005105] [Citation(s) in RCA: 32] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2015] [Accepted: 08/12/2016] [Indexed: 12/20/2022] Open
Abstract
In non-alcoholic fatty liver disease (NAFLD), lipid build-up and the resulting damage is known to occur more severely in pericentral cells. Due to the complexity of studying individual regions of the sinusoid, the causes of this zone specificity and its implications on treatment are largely ignored. In this study, a computational model of liver glucose and lipid metabolism is presented which treats the sinusoid as the repeating unit of the liver rather than the single hepatocyte. This allows for inclusion of zonated enzyme expression by splitting the sinusoid into periportal to pericentral compartments. By simulating insulin resistance (IR) and high intake diets leading to the development of steatosis in the model, we identify key differences between periportal and pericentral cells accounting for higher susceptibility to pericentral steatosis. Secondly, variation between individuals is seen in both susceptibility to steatosis and in its development across the sinusoid. Around 25% of obese individuals do not show excess liver fat, whilst 16% of lean individuals develop NAFLD. Furthermore, whilst pericentral cells tend to show higher lipid levels, variation is seen in the predominant location of steatosis from pericentral to pan-sinusoidal or azonal. Sensitivity analysis was used to identify the processes which have the largest effect on both total hepatic triglyceride levels and on the sinusoidal location of steatosis. As is seen in vivo, steatosis occurs when simulating IR in the model, predominantly due to increased uptake, along with an increase in de novo lipogenesis. Additionally, concentrations of glucose intermediates including glycerol-3-phosphate increased when simulating IR due to inhibited glycogen synthesis. Several differences between zones contributed to a higher susceptibility to steatosis in pericentral cells in the model simulations. Firstly, the periportal zonation of both glycogen synthase and the oxidative phosphorylation enzymes meant that the build-up of glucose intermediates was less severe in the periportal hepatocyte compartments. Secondly, the periportal zonation of the enzymes mediating β-oxidation and oxidative phosphorylation resulted in excess fats being metabolised more rapidly in the periportal hepatocyte compartments. Finally, the pericentral expression of de novo lipogenesis contributed to pericentral steatosis when additionally simulating the increase in sterol-regulatory element binding protein 1c (SREBP-1c) seen in NAFLD patients in vivo. The hepatic triglyceride concentration was predicted to be most sensitive to inter-individual variation in the activity of enzymes which, either directly or indirectly, determine the rate of free fatty acid (FFA) oxidation. The concentration was most strongly dependent on the rate constants for β-oxidation and oxidative phosphorylation. It also showed moderate sensitivity to the rate constants for processes which alter the allosteric inhibition of β-oxidation by acetyl-CoA. The predominant sinusoidal location of steatosis meanwhile was most sensitive variations in the zonation of proteins mediating FFA uptake or triglyceride release as very low density lipoproteins (VLDL). Neither the total hepatic concentration nor the location of steatosis showed strong sensitivity to variations in the lipogenic rate constants.
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Affiliation(s)
- William B. Ashworth
- Institute of Liver and Digestive Health, University College London, London, United Kingdom
- Department of Chemical Engineering, University College London, London, United Kingdom
- CoMPLEX, University College London, London, United Kingdom
| | - Nathan A. Davies
- Institute of Liver and Digestive Health, University College London, London, United Kingdom
| | - I. David L. Bogle
- Department of Chemical Engineering, University College London, London, United Kingdom
- * E-mail:
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13
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Ashworth W, Perez-Galvan C, Davies N, Bogle IDL. Liver function as an engineering system. AIChE J 2016. [DOI: 10.1002/aic.15292] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Affiliation(s)
- William Ashworth
- Centre for Process Systems Engineering, Dept. of Chemical Engineering; University College London, London WC1E 7JE, U.K
- Institute for Liver and Digestive Health, Division of Medicine, University College London, Royal Free Campus, London NW3 2PF, U.K
- COMPLEX (Centre for Mathematics and Physics in the Life Sciences and Experimental Biology); University College London, London WC1E 6BT, U.K
| | - Carlos Perez-Galvan
- Centre for Process Systems Engineering, Dept. of Chemical Engineering; University College London, London WC1E 6BT, U.K
| | - Nathan Davies
- Institute for Liver and Digestive Health, Division of Medicine, University College London, Royal Free Campus, London NW3 2PF, U.K
| | - Ian David Lockhart Bogle
- Centre for Process Systems Engineering, Dept. of Chemical Engineering; University College London, London WC1E 7JE, U.K
- COMPLEX (Centre for Mathematics and Physics in the Life Sciences and Experimental Biology); University College London, London WC1E 6BT, U.K
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Cescon M, Johansson R, Renard E. Subspace-based linear multi-step predictors in type 1 diabetes mellitus. Biomed Signal Process Control 2015. [DOI: 10.1016/j.bspc.2014.09.012] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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15
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Balakrishnan NP, Samavedham L, Rangaiah GP. Personalized mechanistic models for exercise, meal and insulin interventions in children and adolescents with type 1 diabetes. J Theor Biol 2014; 357:62-73. [DOI: 10.1016/j.jtbi.2014.04.038] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2013] [Revised: 03/29/2014] [Accepted: 04/30/2014] [Indexed: 11/15/2022]
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16
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Liu SW, Huang HP, Lin CH, Chien IL. Modified control algorithms for patients with type 1 diabetes mellitus undergoing exercise. J Taiwan Inst Chem Eng 2014. [DOI: 10.1016/j.jtice.2014.06.005] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
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17
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Ghosh S. A differential evolution based approach for estimating minimal model parameters from IVGTT data. Comput Biol Med 2014; 46:51-60. [PMID: 24529205 DOI: 10.1016/j.compbiomed.2013.12.014] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2013] [Revised: 12/23/2013] [Accepted: 12/25/2013] [Indexed: 10/25/2022]
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18
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Ståhl F, Johansson R, Renard E. Ensemble Glucose Prediction in Insulin-Dependent Diabetes. DATA-DRIVEN MODELING FOR DIABETES 2014. [DOI: 10.1007/978-3-642-54464-4_2] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
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19
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Balakrishnan NP, Samavedham L, Rangaiah GP. Personalized Hybrid Models for Exercise, Meal, and Insulin Interventions in Type 1 Diabetic Children and Adolescents. Ind Eng Chem Res 2013. [DOI: 10.1021/ie402531k] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
| | | | - Gade Pandu Rangaiah
- Department of Chemical and
Biomolecular Engineering, National University of Singapore, Singapore
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20
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Stahl F, Johansson R, Renard E. Bayesian combination of multiple plasma glucose predictors. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2013; 2012:2839-44. [PMID: 23366516 DOI: 10.1109/embc.2012.6346555] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
This paper presents a novel on-line approach of merging multiple different predictors of plasma glucose into a single optimized prediction. Various different predictors are merged by recursive weighting into a single prediction using regularized optimization. The approach is evaluated on 12 data sets of type I diabetes data, using three parallel predictors. The performance of the combined prediction is better, or in par, with the best predictor for each evaluated data set. The results suggest that the outlined method could be a suitable way to improve prediction performance when using multiple predictors, or as a means to reduce the risk associated with definite a priori model selection.
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Affiliation(s)
- F Stahl
- Dept. Automatic Control, Lund University, PO Box 118, SE22100 Lund Sweden.
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Ajmera I, Swat M, Laibe C, Le Novère N, Chelliah V. The impact of mathematical modeling on the understanding of diabetes and related complications. CPT-PHARMACOMETRICS & SYSTEMS PHARMACOLOGY 2013; 2:e54. [PMID: 23842097 PMCID: PMC3731829 DOI: 10.1038/psp.2013.30] [Citation(s) in RCA: 82] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/02/2012] [Accepted: 04/18/2013] [Indexed: 12/20/2022]
Abstract
Diabetes is a chronic and complex multifactorial disease caused by persistent hyperglycemia and for which underlying pathogenesis is still not completely understood. The mathematical modeling of glucose homeostasis, diabetic condition, and its associated complications is rapidly growing and provides new insights into the underlying mechanisms involved. Here, we discuss contributions to the diabetes modeling field over the past five decades, highlighting the areas where more focused research is required.
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Affiliation(s)
- I Ajmera
- 1] BioModels Group, EMBL - European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, UK [2] Multidiscipinary Centre for Integrative Biology (MyCIB), School of Biosciences, University of Nottingham, Loughborough, UK
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Maheshwari V, Rangaiah GP, Samavedham L. Multiobjective Framework for Model-based Design of Experiments to Improve Parameter Precision and Minimize Parameter Correlation. Ind Eng Chem Res 2013. [DOI: 10.1021/ie400133m] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Vaibhav Maheshwari
- Department of Chemical & Biomolecular Engineering, National University of Singapore, Singapore 117576
| | - Gade Pandu Rangaiah
- Department of Chemical & Biomolecular Engineering, National University of Singapore, Singapore 117576
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23
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Abbes IB, Richard PY, Lefebvre MA, Guilhem I, Poirier JY. A closed-loop artificial pancreas using a proportional integral derivative with double phase lead controller based on a new nonlinear model of glucose metabolism. J Diabetes Sci Technol 2013; 7:699-707. [PMID: 23759403 PMCID: PMC3869138 DOI: 10.1177/193229681300700315] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Abstract
BACKGROUND Most closed-loop insulin delivery systems rely on model-based controllers to control the blood glucose (BG) level. Simple models of glucose metabolism, which allow easy design of the control law, are limited in their parametric identification from raw data. New control models and controllers issued from them are needed. METHODS A proportional integral derivative with double phase lead controller was proposed. Its design was based on a linearization of a new nonlinear control model of the glucose-insulin system in type 1 diabetes mellitus (T1DM) patients validated with the University of Virginia/Padova T1DM metabolic simulator. A 36 h scenario, including six unannounced meals, was tested in nine virtual adults. A previous trial database has been used to compare the performance of our controller with their previous results. The scenario was repeated 25 times for each adult in order to take continuous glucose monitoring noise into account. The primary outcome was the time BG levels were in target (70-180 mg/dl). RESULTS Blood glucose values were in the target range for 77% of the time and below 50 mg/dl and above 250 mg/dl for 0.8% and 0.3% of the time, respectively. The low blood glucose index and high blood glucose index were 1.65 and 3.33, respectively. CONCLUSION The linear controller presented, based on the linearization of a new easily identifiable nonlinear model, achieves good glucose control with low exposure to hypoglycemia and hyperglycemia.
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Affiliation(s)
- Ilham Ben Abbes
- Supelec/I.E.T.R., Avenue de la Boulaie, Cesson-Sévigné Cedex, France.
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24
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Liu SW, Huang HP, Lin CH, Chien IL. Fuzzy-Logic-Based Supervisor of Insulin Bolus Delivery for Patients with Type 1 Diabetes Mellitus. Ind Eng Chem Res 2013. [DOI: 10.1021/ie301621u] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Shih-Wei Liu
- Department of Chemical Engineering, National Taiwan University, Taipei 10617, Taiwan
| | - Hsiao-Ping Huang
- Department of Chemical Engineering, National Taiwan University, Taipei 10617, Taiwan
| | - Chia-Hung Lin
- Division of Endocrinology and
Metabolism, Department of Internal Medicine, Chang Gung Memorial Hospital, Tao-Yuan 33305, Taiwan
| | - I-Lung Chien
- Department of Chemical Engineering, National Taiwan University, Taipei 10617, Taiwan
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26
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Balakrishnan NP, Rangaiah GP, Samavedham L. Personalized blood glucose models for exercise, meal and insulin interventions in type 1 diabetic children. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2012; 2012:1250-1253. [PMID: 23366125 DOI: 10.1109/embc.2012.6346164] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/01/2023]
Abstract
Modern healthcare is rapidly evolving towards a personalized, predictive, preventive and participatory approach of treatment to achieve better quality of life (QoL) in patients. Identification of personalized blood glucose (BG) prediction models incorporating the lifestyle interventions can help in devising optimal patient specific exercise, food, and insulin prescriptions, which in turn can prevent the risk of frequent hypoglycemic episodes and other diabetes complications. Hence, we propose a modeling methodology based on multi-input single-output time series models, to develop personalized BG models for 12 type 1 diabetic (T1D) children, using the clinical data from Diabetes Research in Children's Network. The multiple inputs needed to develop the proposed models were rate of perceived exertion (RPE) values (which quantify the exercise intensity), carbohydrate absorption dynamics, basal insulin infusion and bolus insulin absorption kinetics. Linear model classes like Box-Jenkins (1 patient), state space (1 patient) and process transfer function models (7 patients) of different orders were found to be the most suitable as the personalized models for 9 patients, whereas nonlinear Hammerstein-Wiener models of different orders were found to be the personalized models for 3 patients. Hence, inter-patient variability was captured by these models as each patient follows a different personalized model.
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Affiliation(s)
- Naviyn P Balakrishnan
- National University of Singapore, Department of Chemical & Biomolecular Engineering, Singapore.
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