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Cobelli C, Kovatchev B. Developing the UVA/Padova Type 1 Diabetes Simulator: Modeling, Validation, Refinements, and Utility. J Diabetes Sci Technol 2023; 17:1493-1505. [PMID: 37743740 PMCID: PMC10658679 DOI: 10.1177/19322968231195081] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 09/26/2023]
Abstract
Arguably, diabetes mellitus is one of the best quantified human conditions. In the past 50 years, the metabolic monitoring technologies progressed from occasional assessment of average glycemia via HbA1c, through episodic blood glucose readings, to continuous glucose monitoring (CGM) producing data points every few minutes. The high-temporal resolution of CGM data enabled increasingly intensive treatments, from decision support assisting insulin injection or oral medication, to automated closed-loop control, known as the "artificial pancreas." Throughout this progress, mathematical models and computer simulation of the human metabolic system became indispensable for the technological progress of diabetes treatment, enabling every step, from assessment of insulin sensitivity via the now classic Minimal Model of Glucose Kinetics, to in silico trials replacing animal experiments, to automated insulin delivery algorithms. In this review, we follow these developments, beginning with the Minimal Model, which evolved through the years to become large and comprehensive and trigger a paradigm change in the design of diabetes optimization strategies: in 2007, we introduced a sophisticated model of glucose-insulin dynamics and a computer simulator equipped with a "population" of N = 300 in silico "subjects" with type 1 diabetes. In January 2008, in an unprecedented decision, the Food and Drug Administration (FDA) accepted this simulator as a substitute to animal trials for the pre-clinical testing of insulin treatment strategies. This opened the field for rapid and cost-effective development and pre-clinical testing of new treatment approaches, which continues today. Meanwhile, animal experiments for the purpose of designing new insulin treatment algorithms have been abandoned.
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Affiliation(s)
| | - Boris Kovatchev
- Center for Diabetes Technology,
University of Virginia, Charlottesville, VA, USA
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2
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Phenomenological-based model of glucose transport from liver to abdominal subcutaneous adipose tissue. J Theor Biol 2021; 530:110883. [PMID: 34478744 DOI: 10.1016/j.jtbi.2021.110883] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2021] [Revised: 08/09/2021] [Accepted: 08/23/2021] [Indexed: 11/22/2022]
Abstract
BACKGROUND A good treatment for type 1 diabetes mellitus (T1DM) requires accurate measurements of blood glucose levels. Continuous glucose monitors (CGM) measure the glucose concentration in the interstitial fluid of the abdominal subcutaneous adipose tissue. However, glucose measured in the abdominal interstitial fluid does not represent blood glucose concentrations accurately due to the complex blood transport through the body and glucose diffusion in interstitial fluid. METHODS To gain insight into this problem, a phenomenological-based semiphysical model (PBSM) of glucose transport by volumetric flow and diffusion from the bloodstream to interstitial fluid was constructed. A published 10-step modeling procedure was used to obtain a model for glucose transport time through the blood vessels and from the blood capillaries to the interstitial fluid, glucose diffusion within the interstitial fluid, and glucose diffusion through the semipermeable coating of the sensor needle. For this model, a healthy person is considered at rest with average parameters. RESULTS The simulations performed using the PBSM allow obtaining the glucose transport time from the liver to the sensor needle. In this way, it is possible to reconstruct an accurate dynamic measurement of blood glucose from the measurements in the interstitial fluid of the abdominal subcutaneous adipose tissue. CONCLUSIONS PBSMs with parameters interpretability illustrate the connection of glucose concentrations in the interstitial fluid with that currently in the blood. Implementing this model in a CGM will result in more reliable measurements of blood glucose levels for T1DM treatment.
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3
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Garcia-Tirado J, Lv D, Corbett JP, Colmegna P, Breton MD. Advanced hybrid artificial pancreas system improves on unannounced meal response - In silico comparison to currently available system. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2021; 211:106401. [PMID: 34560603 DOI: 10.1016/j.cmpb.2021.106401] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/19/2021] [Accepted: 09/01/2021] [Indexed: 06/13/2023]
Abstract
BACKGROUND AND OBJECTIVE Glycemic control, especially meal-related disturbance rejection, has proven to be a major challenge for people with type 1 diabetes. In this manuscript, we introduce a novel, personalized, advanced hybrid insulin infusion system (a.k.a. artificial pancreas) based on the Model Predictive Control (MPC) methodology to adjust insulin infusion while automatically rejecting uninformed meals. METHODS The proposed advanced hybrid closed-loop system relies on the integration of three key elements: (i) an adaptive personalized MPC control law that modulates the control strength depending on recent past control actions, glucose measurements, and its derivative, (ii) an automatic Bolus Priming System (BPS) that commands additional insulin injections safely upon the detection of enabling metabolic conditions (e.g., an unacknowledged meal), and (iii) a new hyperglycemia mitigation system to avoid prevailing hyperglycemia. The benefits of the proposed system are demonstrated through simulations and tests using the most up-to-date Type 1 UVA/Padova simulator as preclinical stage prior to in vivo clinical tests. We used a legacy algorithm (USS Virginia), currently used in clinical care, as a benchmark controller. RESULTS Overall, the proposed control strategy enhanced by an automatic BPS improves glycemic control when compared with an available system. When a large meal is not announced (80g CHO), the proposed controller outperformed the legacy controller in time-in-target-range TIR (postprandial and overnight) and time-in-tight-range TTR (overall, postprandial, and overnight). CONCLUSION The integration of a novel BPS into an advanced control system allowed to automatically reject unannounced meals. Exhaustive simulation studies indicated the safety and feasibility of the proposed controller to be deployed in human clinical trials.
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Affiliation(s)
- Jose Garcia-Tirado
- Center for Diabetes Technology, University of Virginia, Charlottesville, VA, USA.
| | - Dayu Lv
- Center for Diabetes Technology, University of Virginia, Charlottesville, VA, USA.
| | - John P Corbett
- Center for Diabetes Technology, University of Virginia, Charlottesville, VA, USA; Department of Engineering Systems and Environment, University of Virginia, Charlottesville, VA, USA.
| | - Patricio Colmegna
- Center for Diabetes Technology, University of Virginia, Charlottesville, VA, USA.
| | - Marc D Breton
- Center for Diabetes Technology, University of Virginia, Charlottesville, VA, USA.
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4
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Hughes J, Gautier T, Colmegna P, Fabris C, Breton MD. Replay Simulations with Personalized Metabolic Model for Treatment Design and Evaluation in Type 1 Diabetes. J Diabetes Sci Technol 2021; 15:1326-1336. [PMID: 33218280 PMCID: PMC8655285 DOI: 10.1177/1932296820973193] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
Abstract
BACKGROUND The capacity to replay data collected in real life by people with type 1 diabetes mellitus (T1DM) would lead to individualized (vs population) assessment of treatment strategies to control blood glucose and possibly true personalization. Patek et al introduced such a technique, relying on regularized deconvolution of a population glucose homeostasis model to estimate a residual additive signal and reproduce the experimental data; therefore, allowing the subject-specific replay of what-if scenarios by altering the model inputs (eg, insulin). This early method was shown to have a limited domain of validity. We propose and test in silico a similar approach and extend the method applicability. METHODS A subject-specific model personalization of insulin sensitivity and meal-absorption parameters is performed. The University of Virginia (UVa)/Padova T1DM simulator is used to generate experimental scenarios and test the ability of the methodology to accurately reproduce changes in glucose concentration to alteration in meal and insulin inputs. Method performance is assessed by comparing true (UVa/Padova simulator) and replayed glucose traces, using the mean absolute relative difference (MARD) and the Clarke error grid analysis (CEGA). RESULTS Model personalization led to a 9.08 and 6.07 decrease in MARD over a prior published method of replaying altered insulin scenarios for basal and bolus changes, respectively. Replay simulations achieved high accuracy, with MARD <10% and more than 95% of readings falling in the CEGA A-B zones for a wide range of interventions. CONCLUSIONS In silico studies demonstrate that the proposed method for replay simulation is numerically and clinically valid over broad changes in scenario inputs, indicating possible use in treatment optimization.
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Affiliation(s)
- Jonathan Hughes
- Center for Diabetes Technology,
University of Virginia, Charlottesville, VA, USA
| | - Thibault Gautier
- Center for Diabetes Technology,
University of Virginia, Charlottesville, VA, USA
| | - Patricio Colmegna
- Center for Diabetes Technology,
University of Virginia, Charlottesville, VA, USA
| | - Chiara Fabris
- Center for Diabetes Technology,
University of Virginia, Charlottesville, VA, USA
| | - Marc D Breton
- Center for Diabetes Technology,
University of Virginia, Charlottesville, VA, USA
- Marc D Breton, PhD, Center for Diabetes
Technology, University of Virginia, 560 Ray C Hunt Dr, Charlottesville, VA
22903, USA.
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5
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Frutiger A, Tanno A, Hwu S, Tiefenauer RF, Vörös J, Nakatsuka N. Nonspecific Binding-Fundamental Concepts and Consequences for Biosensing Applications. Chem Rev 2021; 121:8095-8160. [PMID: 34105942 DOI: 10.1021/acs.chemrev.1c00044] [Citation(s) in RCA: 89] [Impact Index Per Article: 29.7] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Nature achieves differentiation of specific and nonspecific binding in molecular interactions through precise control of biomolecules in space and time. Artificial systems such as biosensors that rely on distinguishing specific molecular binding events in a sea of nonspecific interactions have struggled to overcome this issue. Despite the numerous technological advancements in biosensor technologies, nonspecific binding has remained a critical bottleneck due to the lack of a fundamental understanding of the phenomenon. To date, the identity, cause, and influence of nonspecific binding remain topics of debate within the scientific community. In this review, we discuss the evolution of the concept of nonspecific binding over the past five decades based upon the thermodynamic, intermolecular, and structural perspectives to provide classification frameworks for biomolecular interactions. Further, we introduce various theoretical models that predict the expected behavior of biosensors in physiologically relevant environments to calculate the theoretical detection limit and to optimize sensor performance. We conclude by discussing existing practical approaches to tackle the nonspecific binding challenge in vitro for biosensing platforms and how we can both address and harness nonspecific interactions for in vivo systems.
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Affiliation(s)
- Andreas Frutiger
- Laboratory of Biosensors and Bioelectronics, Institute for Biomedical Engineering, ETH Zürich, Zürich CH-8092, Switzerland
| | - Alexander Tanno
- Laboratory of Biosensors and Bioelectronics, Institute for Biomedical Engineering, ETH Zürich, Zürich CH-8092, Switzerland
| | - Stephanie Hwu
- Laboratory of Biosensors and Bioelectronics, Institute for Biomedical Engineering, ETH Zürich, Zürich CH-8092, Switzerland
| | - Raphael F Tiefenauer
- Laboratory of Biosensors and Bioelectronics, Institute for Biomedical Engineering, ETH Zürich, Zürich CH-8092, Switzerland
| | - János Vörös
- Laboratory of Biosensors and Bioelectronics, Institute for Biomedical Engineering, ETH Zürich, Zürich CH-8092, Switzerland
| | - Nako Nakatsuka
- Laboratory of Biosensors and Bioelectronics, Institute for Biomedical Engineering, ETH Zürich, Zürich CH-8092, Switzerland
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6
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Montaser E, Díez JL, Bondia J. Glucose Prediction under Variable-Length Time-Stamped Daily Events: A Seasonal Stochastic Local Modeling Framework. SENSORS (BASEL, SWITZERLAND) 2021; 21:3188. [PMID: 34064325 PMCID: PMC8124701 DOI: 10.3390/s21093188] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/20/2021] [Revised: 04/23/2021] [Accepted: 04/28/2021] [Indexed: 11/16/2022]
Abstract
Accurate glucose prediction along a long-enough time horizon is a key component for technology to improve type 1 diabetes treatment. Subjects with diabetes might benefit from supervision and control systems that accurately predict risks and trigger corrective actions early enough with improved mitigation. However, large intra-patient variability poses big challenges to glucose prediction. In previous works by the authors, clustering and local modeling techniques with seasonal stochastic models proved to be efficient, allowing for good glucose prediction accuracy for long prediction horizons. Continuous glucose monitoring (CGM) data were partitioned into fixed-length postprandial time subseries and clustered with Fuzzy C-Means to collect similar behaviors, enforcing seasonality at each cluster after subseries concatenation. Then, seasonal stochastic models were identified for each cluster and local predictions were integrated into a global prediction. However, free-living conditions do not support the fixed-length partition of CGM data since daily events duration is variable. In this work, a new algorithm is provided to overcome this constraint, allowing better coping with patient's variability under variable-length time-stamped daily events in supervision and control applications. Besides predicted glucose, two real-time indices are additionally provided-a crispness index, indicating good representation of current glucose behavior by a single model, and a normality index, allowing for the detection of an abnormal glucose behavior (unusual according to registered historical data). The framework is tested in a proof-of-concept in silico study with ten patients over four month training data and two independent two month validation datasets, with and without abnormal behaviors, from the distribution version of the UVA/Padova simulator extended with diverse sources of intra-patient variability.
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Affiliation(s)
- Eslam Montaser
- Instituto Universitario de Automática e Informática Industrial, Universitat Politècnica de València, Camino de Vera, s/n, 46022 València, Spain; (E.M.); (J.-L.D.)
| | - José-Luis Díez
- Instituto Universitario de Automática e Informática Industrial, Universitat Politècnica de València, Camino de Vera, s/n, 46022 València, Spain; (E.M.); (J.-L.D.)
- Centro de Investigación Biomédica en Red de Diabetes y Enfermedades Metabólicas Asociadas (CIBERDEM), Instituto de Salud Carlos III, 28029 Madrid, Spain
| | - Jorge Bondia
- Instituto Universitario de Automática e Informática Industrial, Universitat Politècnica de València, Camino de Vera, s/n, 46022 València, Spain; (E.M.); (J.-L.D.)
- Centro de Investigación Biomédica en Red de Diabetes y Enfermedades Metabólicas Asociadas (CIBERDEM), Instituto de Salud Carlos III, 28029 Madrid, Spain
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7
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Fabris C, Kovatchev B. The closed‐loop artificial pancreas in 2020. Artif Organs 2020; 44:671-679. [DOI: 10.1111/aor.13704] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2020] [Accepted: 04/06/2020] [Indexed: 12/13/2022]
Affiliation(s)
- Chiara Fabris
- Center for Diabetes Technology University of Virginia Charlottesville VA USA
| | - Boris Kovatchev
- Center for Diabetes Technology University of Virginia Charlottesville VA USA
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8
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Kovatchev BP, Kollar L, Anderson SM, Barnett C, Breton MD, Carr K, Gildersleeve R, Oliveri MC, Wakeman CA, Brown SA. Evening and overnight closed-loop control versus 24/7 continuous closed-loop control for type 1 diabetes: a randomised crossover trial. Lancet Digit Health 2020; 2:e64-e73. [PMID: 32864597 PMCID: PMC7453908 DOI: 10.1016/s2589-7500(19)30218-3] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
Background Automated closed-loop control (CLC), known as the "artificial pancreas" is emerging as a treatment option for Type 1 Diabetes (T1D), generally superior to sensor-augmented insulin pump (SAP) treatment. It is postulated that evening-night (E-N) CLC may account for most of the benefits of 24-7 CLC; however, a direct comparison has not been done. Methods In this trial (NCT02679287), adults with T1D were randomised 1:1 to two groups, which followed different sequences of four 8-week sessions, resulting in two crossover designs comparing SAP vs E-N CLC and E-N CLC vs 24-7 CLC, respectively. Eligibility: T1D for at least 1 year, using an insulin pump for at least six months, ages 18 years or older. Primary hypothesis: E-N CLC compared to SAP will decrease percent time <70mg/dL (3.9mmol/L) measured by continuous glucose monitoring (CGM) without deterioration in HbA1c. Secondary Hypotheses: 24-7 CLC compared to SAP will increase CGM-measured time in target range (TIR, 70-180mg/dL; 3.9-10mmol/L) and will reduce glucose variability during the day. Findings Ninety-three participants were randomised and 80 were included in the analysis, ages 18-69 years; HbA1c levels 5.4-10.6%; 66% female. Compared to SAP, E-N CLC reduced overall time <70mg/dL from 4.0% to 2.2% () resulting in an absolute difference of 1.8% (95%CI: 1.2-2.4%), p<0.0001. This was accompanied by overall reduction in HbA1c from 7.4% at baseline to 7.1% at the end of study, resulting in an absolute difference of 0.3% (95% CI: 0.1-0.4%), p<0.0001. There were 5 severe hypoglycaemia adverse events attributed to user-directed boluses without malfunction of the investigational device, and no diabetic ketoacidosis events. Interpretation In type 1 diabetes, evening-night closed-loop control was superior to sensor-augmented pump therapy, achieving most of the glycaemic benefits of 24-7 closed-loop.
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Affiliation(s)
| | - Laura Kollar
- University of Virginia Center for Diabetes Technology, Charlottesville, VA USA
| | - Stacey M. Anderson
- University of Virginia Center for Diabetes Technology, Charlottesville, VA USA
| | - Charlotte Barnett
- University of Virginia Center for Diabetes Technology, Charlottesville, VA USA
| | - Marc D. Breton
- University of Virginia Center for Diabetes Technology, Charlottesville, VA USA
| | - Kelly Carr
- University of Virginia Center for Diabetes Technology, Charlottesville, VA USA
| | - Rachel Gildersleeve
- University of Virginia Center for Diabetes Technology, Charlottesville, VA USA
| | - Mary C. Oliveri
- University of Virginia Center for Diabetes Technology, Charlottesville, VA USA
| | | | - Sue A Brown
- Address for Correspondence: Sue A. Brown, M.D., University of Virginia, Center for Diabetes Technology, 560 Ray C. Hunt Drive, Second Floor, Charlottesville, VA, Tel: +1-434-982-0602,
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9
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Yu X, Rashid M, Feng J, Hobbs N, Hajizadeh I, Samadi S, Sevil M, Lazaro C, Maloney Z, Littlejohn E, Quinn L, Cinar A. Online Glucose Prediction Using Computationally Efficient Sparse Kernel Filtering Algorithms in Type-1 Diabetes. IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY : A PUBLICATION OF THE IEEE CONTROL SYSTEMS SOCIETY 2020; 28:3-15. [PMID: 32699492 PMCID: PMC7375403 DOI: 10.1109/tcst.2018.2843785] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
Streaming data from continuous glucose monitoring (CGM) systems enable the recursive identification of models to improve estimation accuracy for effective predictive glycemic control in patients with type-1 diabetes. A drawback of conventional recursive identification techniques is the increase in computational requirements, which is a concern for online and real-time applications such as the artificial pancreas systems implemented on handheld devices and smartphones where computational resources and memory are limited. To improve predictions in such computationally constrained hardware settings, efficient adaptive kernel filtering algorithms are developed in this paper to characterize the nonlinear glycemic variability by employing a sparsification criterion based on the information theory to reduce the computation time and complexity of the kernel filters without adversely deteriorating the predictive performance. Furthermore, the adaptive kernel filtering algorithms are designed to be insensitive to abnormal CGM measurements, thus compensating for measurement noise and disturbances. As such, the sparsification-based real-time model update framework can adapt the prediction models to accurately characterize the time-varying and nonlinear dynamics of glycemic measurements. The proposed recursive kernel filtering algorithms leveraging sparsity for improved computational efficiency are applied to both in-silico and clinical subjects, and the results demonstrate the effectiveness of the proposed methods.
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Affiliation(s)
- Xia Yu
- School of Information Science and Engineering, Northeastern University, Shenyang 110819, China
| | - Mudassir Rashid
- Department of Chemical and Biological Engineering, Illinois Institute of Technology, Chicago, IL 60616 USA
| | - Jianyuan Feng
- Department of Chemical and Biological Engineering, Illinois Institute of Technology, Chicago, IL 60616 USA
| | - Nicole Hobbs
- Department of Biomedical Engineering, Illinois Institute of Technology, Chicago, IL 60616 USA
| | - Iman Hajizadeh
- Department of Chemical and Biological Engineering, Illinois Institute of Technology, Chicago, IL 60616 USA
| | - Sediqeh Samadi
- Department of Chemical and Biological Engineering, Illinois Institute of Technology, Chicago, IL 60616 USA
| | - Mert Sevil
- Department of Biomedical Engineering, Illinois Institute of Technology, Chicago, IL 60616 USA
| | - Caterina Lazaro
- Department of Electrical and Computer Engineering, Illinois Institute of Technology, Chicago, IL 60616 USA
| | - Zacharie Maloney
- Department of Electrical and Computer Engineering, Illinois Institute of Technology, Chicago, IL 60616 USA
| | - Elizabeth Littlejohn
- Kovler Diabetes Center, Department of Pediatrics and Medicine, University of Chicago, Chicago, IL 60637 USA
| | - Laurie Quinn
- Department of Biobehavioral Health Science, College of Nursing, University of Illinois at Chicago, Chicago, IL 60612 USA
| | - Ali Cinar
- Department of Chemical and Biological Engineering, Illinois Institute of Technology, Chicago, IL 60616 USA, and also with the Department of Biomedical Engineering, Illinois Institute of Technology, Chicago, IL 60616 USA
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10
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Montaser E, Diez JL, Rossetti P, Rashid M, Cinar A, Bondia J. Seasonal Local Models for Glucose Prediction in Type 1 Diabetes. IEEE J Biomed Health Inform 2019; 24:2064-2072. [PMID: 31796419 DOI: 10.1109/jbhi.2019.2956704] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Linear empirical dynamic models have been widely used for blood glucose prediction and risks prevention in people with type 1 diabetes. More accurate blood glucose prediction models with longer prediction horizon (PH) are desirable to enable warnings to patients about imminent blood glucose changes with enough time to take corrective actions. In this study, a blood glucose prediction method is developed by integrating the predictions of a set of seasonal local models (each of them corresponding to different glucose profiles observed along historical data). In the modeling step, the number of sets and their corresponding glucose profiles characteristics are obtained by clustering techniques (Fuzzy C-Means). Then, Box-Jenkins methodology is used to identify a seasonal model for each set. Finally, blood glucose predictions of local models are integrated using different techniques. The proposed method is tested by using 18 60-h closed-loop experiments (including different exercise types and artificial pancreas strategies) and achieving mean absolute percentage error (MAPE) of 2.94%, 3.89%, 5.41%, 6.29% and 8.66% for 15-, 30-, 45-, 60-, and 90-min PHs, respectively.
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11
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Ogunnaike BA. 110th Anniversary: Process and Systems Engineering Perspectives on Personalized Medicine and the Design of Effective Treatment of Diseases. Ind Eng Chem Res 2019. [DOI: 10.1021/acs.iecr.9b04228] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Babatunde A. Ogunnaike
- Department of Chemical & Biomolecular Engineering, Department of Biomedical Engineering, University of Delaware, Newark, Delaware 19706, United States
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12
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Seo W, Lee YB, Lee S, Jin SM, Park SM. A machine-learning approach to predict postprandial hypoglycemia. BMC Med Inform Decis Mak 2019; 19:210. [PMID: 31694629 PMCID: PMC6833234 DOI: 10.1186/s12911-019-0943-4] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2019] [Accepted: 10/21/2019] [Indexed: 11/17/2022] Open
Abstract
BACKGROUND For an effective artificial pancreas (AP) system and an improved therapeutic intervention with continuous glucose monitoring (CGM), predicting the occurrence of hypoglycemia accurately is very important. While there have been many studies reporting successful algorithms for predicting nocturnal hypoglycemia, predicting postprandial hypoglycemia still remains a challenge due to extreme glucose fluctuations that occur around mealtimes. The goal of this study is to evaluate the feasibility of easy-to-use, computationally efficient machine-learning algorithm to predict postprandial hypoglycemia with a unique feature set. METHODS We use retrospective CGM datasets of 104 people who had experienced at least one hypoglycemia alert value during a three-day CGM session. The algorithms were developed based on four machine learning models with a unique data-driven feature set: a random forest (RF), a support vector machine using a linear function or a radial basis function, a K-nearest neighbor, and a logistic regression. With 5-fold cross-subject validation, the average performance of each model was calculated to compare and contrast their individual performance. The area under a receiver operating characteristic curve (AUC) and the F1 score were used as the main criterion for evaluating the performance. RESULTS In predicting a hypoglycemia alert value with a 30-min prediction horizon, the RF model showed the best performance with the average AUC of 0.966, the average sensitivity of 89.6%, the average specificity of 91.3%, and the average F1 score of 0.543. In addition, the RF showed the better predictive performance for postprandial hypoglycemic events than other models. CONCLUSION In conclusion, we showed that machine-learning algorithms have potential in predicting postprandial hypoglycemia, and the RF model could be a better candidate for the further development of postprandial hypoglycemia prediction algorithm to advance the CGM technology and the AP technology further.
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Affiliation(s)
- Wonju Seo
- Department of Creative IT engineering, POSTECH, 77, Cheongam-Ro, Nam-Gu, Pohang, 37673, Republic of Korea
| | - You-Bin Lee
- Division of Endocrinology and Metabolism, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81, Irwon-ro, Seoul, 06351, Republic of Korea
| | - Seunghyun Lee
- Department of Creative IT engineering, POSTECH, 77, Cheongam-Ro, Nam-Gu, Pohang, 37673, Republic of Korea
| | - Sang-Man Jin
- Division of Endocrinology and Metabolism, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81, Irwon-ro, Seoul, 06351, Republic of Korea.
| | - Sung-Min Park
- Department of Creative IT engineering, POSTECH, 77, Cheongam-Ro, Nam-Gu, Pohang, 37673, Republic of Korea.
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13
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Garcia-Tirado J, Colmegna P, Corbett JP, Ozaslan B, Breton MD. In Silico Analysis of an Exercise-Safe Artificial Pancreas With Multistage Model Predictive Control and Insulin Safety System. J Diabetes Sci Technol 2019; 13:1054-1064. [PMID: 31679400 PMCID: PMC6835197 DOI: 10.1177/1932296819879084] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
BACKGROUND Maintaining glycemic equilibrium can be challenging for people living with type 1 diabetes (T1D) as many factors (eg, length, type, duration, insulin on board, stress, and training) will impact the metabolic changes triggered by physical activity potentially leading to both hypoglycemia and hyperglycemia. Therefore, and despite the noted health benefits, many individuals with T1D do not exercise as much as their healthy peers. While technology advances have improved glucose control during and immediately after exercise, it remains one of the key limitations of artificial pancreas (AP) systems, largely because stopping insulin at the onset of exercise may not be enough to prevent impending, exercise-induced hypoglycemia. METHODS A hybrid AP algorithm with subject-specific exercise behavior recognition and anticipatory action is designed to prevent hypoglycemic events during and after moderate-intensity exercise. Our approach relies on a number of key innovations, namely, an activity informed premeal bolus calculator, personalized exercise pattern recognition, and a multistage model predictive control (MS-MPC) strategy that can transition between reactive and anticipatory modes. This AP design was evaluated on 100 in silico subjects from the most up-to-date FDA-accepted UVA/Padova metabolic simulator, emulating an outpatient clinical trial setting. Results with a baseline controller, a regular MPC (rMPC), are also included for comparison purposes. RESULTS In silico experiments reveal that the proposed MS-MPC strategy markedly reduces the number of exercise-related hypoglycemic events (8 vs 68). CONCLUSION An anticipatory mode for insulin administration of a monohormonal AP controller reduces the occurrence of hypoglycemia during moderate-intensity exercise.
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Affiliation(s)
- Jose Garcia-Tirado
- University of Virginia, Center for Diabetes Technology, Charlottesville, VA, USA
- Jose Garcia-Tirado, PhD, University of Virginia, Center for Diabetes Technology, 560 Ray C Hunt Dr, Charlottesville, VA 22903, USA.
| | - Patricio Colmegna
- University of Virginia, Center for Diabetes Technology, Charlottesville, VA, USA
- National Scientific and Technical Research Council, Buenos Aires, Argentina
| | - John P. Corbett
- University of Virginia, Center for Diabetes Technology, Charlottesville, VA, USA
- Department of Engineering Systems and Environment, University of Virginia, Charlottesville, VA, USA
| | - Basak Ozaslan
- University of Virginia, Center for Diabetes Technology, Charlottesville, VA, USA
- Department of Engineering Systems and Environment, University of Virginia, Charlottesville, VA, USA
| | - Marc D. Breton
- University of Virginia, Center for Diabetes Technology, Charlottesville, VA, USA
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14
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Pleus S, Ulbrich S, Zschornack E, Kamann S, Haug C, Freckmann G. Documentation of Skin-Related Issues Associated with Continuous Glucose Monitoring Use in the Scientific Literature. Diabetes Technol Ther 2019; 21:538-545. [PMID: 31335203 DOI: 10.1089/dia.2019.0171] [Citation(s) in RCA: 28] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Background: The average wear time of continuous glucose monitoring (CGM) systems steadily increased over the last years. Increased wear times are likely achieved by using adhesives with a longer adherence time, which may have a more pronounced effect on the skin than adhesives with shorter adherence time. Methods: In this project, a structured literature search was performed to assess how potential skin-related issues with CGM usage have been reported in scientific literature in the last 5 years. The literature search was performed with four search terms in the PubMed database. Results: In sum, 279 articles were analyzed. Skin-related issues were mentioned in 19.0% of articles (53 out of 279). With respect to articles mentioning skin-related issues, CGM performance or efficacy was the study's main topic of most of these studies, whereas the minority used CGM as "add-on" to assess other objectives. There was a varying degree in how detailed skin-related issues were described and no uniform structured documentation was given. While some articles only described findings, other articles already documented final diagnoses, such as contact dermatitis. Furthermore, inconsistent wordings for comparable issues were used. The most frequent issues were pain, itching, erythema, bleeding, bruising, and allergic reactions. Conclusion: To draw possible conclusions about the occurrence of skin-related issues during CGM use, more reports about skin-related issues in scientific literature are needed. A more detailed and uniformly structured documentation, possibly facilitated by a generally accepted guideline for structured descriptions, of skin-related issues could be helpful to enable clear differentiations between the described skin reactions.
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Affiliation(s)
- Stefan Pleus
- Institut für Diabetes-Technologie, Forschungs- und Entwicklungsgesellschaft mbH an der Universität Ulm, Ulm, Germany
| | - Sina Ulbrich
- Institut für Diabetes-Technologie, Forschungs- und Entwicklungsgesellschaft mbH an der Universität Ulm, Ulm, Germany
| | - Eva Zschornack
- Institut für Diabetes-Technologie, Forschungs- und Entwicklungsgesellschaft mbH an der Universität Ulm, Ulm, Germany
| | | | - Cornelia Haug
- Institut für Diabetes-Technologie, Forschungs- und Entwicklungsgesellschaft mbH an der Universität Ulm, Ulm, Germany
| | - Guido Freckmann
- Institut für Diabetes-Technologie, Forschungs- und Entwicklungsgesellschaft mbH an der Universität Ulm, Ulm, Germany
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15
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Garcia-Tirado J, Corbett JP, Boiroux D, Jørgensen JB, Breton MD. Closed-Loop Control with Unannounced Exercise for Adults with Type 1 Diabetes using the Ensemble Model Predictive Control. JOURNAL OF PROCESS CONTROL 2019; 80:202-210. [PMID: 32831483 PMCID: PMC7437946 DOI: 10.1016/j.jprocont.2019.05.017] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
This paper presents an individualized Ensemble Model Predictive Control (EnMPC) algorithm for blood glucose (BG) stabilization and hypoglycemia prevention in people with type 1 diabetes (T1D) who exercise regularly. The EnMPC formulation can be regarded as a simplified multi-stage MPC allowing for the consideration of N en scenarios gathered from the patient's recent behavior. The patient's physical activity behavior is characterized by an exercise-specific input signal derived from the deconvolution of the patient's continuous glucose monitor (CGM), accounting for known inputs such as meal, and insulin pump records. The EnMPC controller was tested in a cohort of in silico patients with representative inter-subject and intra-subject variability from the FDA-accepted UVA/Padova simulation platform. Results show a significant improvement on hypoglycemia prevention after 30 min of mild to moderate exercise in comparison to a similarly tuned baseline controller (rMPC); with a reduction in hypoglycemia occurrences (< 70 mg/dL), from 3.08% ± 3.55 with rMPC to 0.78% ± 2.04 with EnMPC (P < 0.05).
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Affiliation(s)
- Jose Garcia-Tirado
- Center for Diabetes Technology, University of Virginia, Charlottesville, VA
| | - John P. Corbett
- Center for Diabetes Technology, University of Virginia, Charlottesville, VA
- Department of Systems and Information Engineering, University of Virginia, Charlottesville, VA
| | - Dimitri Boiroux
- Department of Applied Mathematics and Computer Science, Technical University of Denmark, DK-2800 Kgs. Lyngby, Denmark
- Danish Diabetes Academy, DK-5000 Odense, Denmark
| | - John Bagterp Jørgensen
- Department of Applied Mathematics and Computer Science, Technical University of Denmark, DK-2800 Kgs. Lyngby, Denmark
| | - Marc D Breton
- Center for Diabetes Technology, University of Virginia, Charlottesville, VA
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16
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Abstract
Glycemic variability (GV) a well-established risk factor for hypoglycemia and a suspected risk factor for diabetes complications. GV is also a marker of the instability of a person's metabolic system, expressed as frequent high and low glucose excursions and overall volatile glycemic control. In this review, the author discusses topics related to the assessment, quantification, and optimal control of diabetes, including (1) the notion that optimal control of diabetes, that is, lowering of HbA1c-the commonly accepted gold-standard outcome-can be achieved only if accompanied by simultaneous reduction of GV; (2) assessment and visualization of the two principal dimensions of GV, amplitude and time, which is now possible via continuous glucose monitoring (CGM) and various metrics quantifying GV and the risks associated with hypo- and hyperglycemic excursions; and (3) the evolution of diabetes science and technology beyond quantifying GV and into the realm of GV control via pharmacological agents, for example, GLP-1 receptor agonists and DPP-4 inhibitors, which have pronounced variability-reducing effect, or real-time automated closed-loop systems commonly referred to as the "artificial pancreas." The author concludes that CGM allows close tracking over time, and therefore precise quantification, of glycemic variability in diabetes. The next step-optimal control of glucose fluctuations-is also taken by medications with pronounced GV-lowering effect primarily in type 2 diabetes, and by automated insulin delivery in type 1 diabetes. Contemporary CGM-based artificial pancreas systems use specific GV representations as input signals, and thus their main objective is to minimize GV and, from there, optimize glycemic control.
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Affiliation(s)
- Boris Kovatchev
- University of Virginia School of
Medicine and School of Engineering and Applied Sciences, UVA Center for Diabetes
Technology, Charlottesville, VA, USA
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17
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Abstract
Over the past 50 years, the diabetes technology field progressed remarkably through self-monitoring of blood glucose (SMBG), continuous subcutaneous insulin infusion (CSII), risk and variability analysis, mathematical models and computer simulation of the human metabolic system, real-time continuous glucose monitoring (CGM), and control algorithms driving closed-loop control systems known as the "artificial pancreas" (AP). This review follows these developments, beginning with an overview of the functioning of the human metabolic system in health and in diabetes and of its detailed quantitative network modeling. The review continues with a brief account of the first AP studies that used intravenous glucose monitoring and insulin infusion, and with notes about CSII and CGM-the technologies that made possible the development of contemporary AP systems. In conclusion, engineering lessons learned from AP research, and the clinical need for AP systems to prove their safety and efficacy in large-scale clinical trials, are outlined.
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Affiliation(s)
- Boris Kovatchev
- Center for Diabetes Technology, University of Virginia, Charlottesville, Virginia 22908
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18
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Bhattacharjee A, Easwaran A, Leow MKS, Cho N. Design of an online-tuned model based compound controller for a fully automated artificial pancreas. Med Biol Eng Comput 2019; 57:1437-1449. [PMID: 30895514 DOI: 10.1007/s11517-019-01972-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2018] [Accepted: 03/06/2019] [Indexed: 11/25/2022]
Abstract
This paper deals with the development of a control algorithm that can predict optimal insulin doses without patients' intervention in fully automated artificial pancreas system. An online-tuned model based compound controller comprising an online-tuned internal model control (IMC) algorithm and an enhanced IMC (eIMC) algorithm along with a meal detection module is proposed. Volterra models, used to develop IMC and eIMC algorithms, are developed online using recursive least squares (RLS) filter. The time domain kernels, computed online using RLS filter, are converted into frequency domain to obtain Volterra transfer function (VTF). VTFs are used to develop both IMC and eIMC algorithms. The compound controller is designed in such a way that eIMC predicts insulin doses when the glucose rate increase detector of meal detection module is positive, otherwise conventional IMC takes the control action. Experimental results show that the compound controller performs robustly in the presence of higher and irregular amounts of meal disturbances at random times, very high actuator and sensor noises and also with the variation in insulin sensitivity. The combination of compound control strategy and meal detection module compensates the shortcomings of both slow subcutaneous insulin action that causes postprandial hyperglycemia, and delayed peak of action that causes hypoglycaemia. Graphical Abstract A fully-automated artificial pancreas system containing glucose sensor, insulin pump and control algorithm. Block diagram showing the control algorithm i.e., online-tuned compound IMC comprising enhanced IMC, conventional IMC and meal detection module, developed in the present work.
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Affiliation(s)
| | | | - Melvin Khee-Shing Leow
- Nanyang Technological University, Singapore, Singapore.,Department of Endocrinology, Tan Tock Seng Hospital, Singapore, Singapore.,Singapore Institute for Clinical Sciences, A*STAR, Singapore, Singapore.,Office of Clinical Sciences, Duke-NUS Graduate Medical School, Singapore, Singapore.,Lee Kong Chian School of Medicine-Imperial College London, London, SW7 2DD, UK
| | - Namjoon Cho
- Nanyang Technological University, Singapore, Singapore
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19
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Anderson SM, Dassau E, Raghinaru D, Lum J, Brown SA, Pinsker JE, Church MM, Levy C, Lam D, Kudva YC, Buckingham B, Forlenza GP, Wadwa RP, Laffel L, Doyle FJ, DeVries JH, Renard E, Cobelli C, Boscari F, Del Favero S, Kovatchev BP. The International Diabetes Closed-Loop Study: Testing Artificial Pancreas Component Interoperability. Diabetes Technol Ther 2019; 21:73-80. [PMID: 30649925 PMCID: PMC6354594 DOI: 10.1089/dia.2018.0308] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Abstract
BACKGROUND Use of artificial pancreas (AP) requires seamless interaction of device components, such as continuous glucose monitor (CGM), insulin pump, and control algorithm. Mobile AP configurations also include a smartphone as computational hub and gateway to cloud applications (e.g., remote monitoring and data review and analysis). This International Diabetes Closed-Loop study was designed to demonstrate and evaluate the operation of the inControl AP using different CGMs and pump modalities without changes to the user interface, user experience, and underlying controller. METHODS Forty-three patients with type 1 diabetes (T1D) were enrolled at 10 clinical centers (7 United States, 3 Europe) and 41 were included in the analyses (39% female, >95% non-Hispanic white, median T1D duration 16 years, median HbA1c 7.4%). Two CGMs and two insulin pumps were tested by different study participants/sites using the same system hub (a smartphone) during 2 weeks of in-home use. RESULTS The major difference between the system components was the stability of their wireless connections with the smartphone. The two sensors achieved similar rates of connectivity as measured by percentage time in closed loop (75% and 75%); however, the two pumps had markedly different closed-loop adherence (66% vs. 87%). When connected, all system configurations achieved similar glycemic outcomes on AP control (73% [mean] time in range: 70-180 mg/dL, and 1.7% [median] time <70 mg/dL). CONCLUSIONS CGMs and insulin pumps can be interchangeable in the same Mobile AP system, as long as these devices achieve certain levels of reliability and wireless connection stability.
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Affiliation(s)
- Stacey M. Anderson
- Center for Diabetes Technology, Department of Medicine, University of Virginia
- Address correspondence to: Stacey M. Anderson, MD, Center for Diabetes Technology, Department of Medicine, University of Virginia, PO Box 400888, Charlottesville, VA 22903
| | - Eyal Dassau
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, Massachusetts
- Sansum Diabetes Research Institute, Santa Barbara, California
- Joslin Diabetes Center, Harvard Medical School, Boston, Massachusetts
| | | | - John Lum
- Jaeb Center for Health Research, Tampa, Florida
| | - Sue A. Brown
- Center for Diabetes Technology, Department of Medicine, University of Virginia
| | | | - Mei Mei Church
- Sansum Diabetes Research Institute, Santa Barbara, California
| | - Carol Levy
- Division of Endocrinology, Icahn School of Medicine at Mount Sinai, New York City, New York
| | - David Lam
- Division of Endocrinology, Icahn School of Medicine at Mount Sinai, New York City, New York
| | - Yogish C. Kudva
- Division of Endocrinology, Diabetes, Metabolism and Nutrition, Department of Internal Medicine, Mayo Clinic, Rochester, Minnesota
| | - Bruce Buckingham
- Division of Pediatric Endocrinology and Diabetes, Department of Pediatrics, Stanford University School of Medicine, Stanford, California
| | - Gregory P. Forlenza
- Barbara Davis Center for Diabetes, University of Colorado, Anschutz Medical Campus, Aurora, Colorado
| | - R. Paul Wadwa
- Barbara Davis Center for Diabetes, University of Colorado, Anschutz Medical Campus, Aurora, Colorado
| | - Lori Laffel
- Joslin Diabetes Center, Harvard Medical School, Boston, Massachusetts
| | - Francis J. Doyle
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, Massachusetts
| | - J. Hans DeVries
- Academic Medical Center, University of Amsterdam, Amsterdam, Netherlands
| | - Eric Renard
- Department of Endocrinology, Diabetes, Nutrition, Montpellier University Hospital, Montpellier, France
- INSERM 1411 Clinical Investigation Center, Institute of Functional Genomics, UMR CNRS 5203/INSERM U1191, University of Montpellier, Montpellier, France
| | - Claudio Cobelli
- Department of Information Engineering, University of Padova, Padova, Italy
| | | | - Simone Del Favero
- Department of Information Engineering, University of Padova, Padova, Italy
| | - Boris P. Kovatchev
- Center for Diabetes Technology, Department of Medicine, University of Virginia
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20
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Abstract
Good glucose management through an insulin dose regime based on the metabolism of glucose helps millions of people worldwide manage their diabetes. Since Banting and Best extracted insulin, glucose management has improved due to the introduction of insulin analogues that act from 30 minutes to 28 days, improved insulin dose regimes, and portable glucose meters, with a current focus on alternative sampling sites that are less invasive. However, a piece of the puzzle is still missing-the ability to measure insulin directly in a Point-of-Care device. The ability to measure both glucose and insulin concurrently will enable better glucose control by providing an improved estimate for insulin sensitivity, minimizing variability in control, and maximizing safety from hypoglycaemia. However, direct detection of free insulin has provided a challenge due to the size of the molecule, the low concentration of insulin in blood, and the selectivity against interferants in blood. This review summarizes current insulin detection methods from immunoassays to analytical chemistry, and sensors. We also discuss the challenges and potential of each of the methods towards Point-of-Care insulin detection.
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21
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Brown S, Raghinaru D, Emory E, Kovatchev B. First Look at Control-IQ: A New-Generation Automated Insulin Delivery System. Diabetes Care 2018; 41:2634-2636. [PMID: 30305346 PMCID: PMC6245207 DOI: 10.2337/dc18-1249] [Citation(s) in RCA: 51] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/18/2018] [Accepted: 09/08/2018] [Indexed: 02/03/2023]
Abstract
OBJECTIVE To pilot test a new closed-loop control technology to validate it for a further large clinical trial. RESEARCH DESIGN AND METHODS The t:slim X2 insulin pump with Control-IQ Technology (Tandem Diabetes Care) includes a Dexcom G6 sensor and a closed-loop algorithm implemented in the pump that 1) automates insulin correction boluses, 2) has a dedicated hypoglycemia safety system, and 3) gradually intensifies control overnight, aiming for blood glucose levels of approximately 100-120 mg/dL every morning. RESULTS Five patients with type 1 diabetes (mean age 52.8 years, 2/3 male/female, mean A1C 6.5%) used Control-IQ in an outpatient setting (hotel) for approximately 37 h. During the closed loop, mean glucose was 129 mg/dL (135/121 mg/dL during the day/night), time within target range 70-180 mg/dL was 87% (82%/94% during the day/night), and time <60 mg/dL was 1.1% (2.0%/0.0% during the day/night). CONCLUSIONS Following this pilot trial, Control-IQ was deployed in several studies, including the large-scale National Institutes of Health International Diabetes Closed-Loop (iDCL) Trial.
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Affiliation(s)
- Sue Brown
- Center for Diabetes Technology, University of Virginia, Charlottesville, VA.,Division of Endocrinology and Metabolism, University of Virginia, Charlottesville, VA
| | | | - Emma Emory
- Center for Diabetes Technology, University of Virginia, Charlottesville, VA
| | - Boris Kovatchev
- Center for Diabetes Technology, University of Virginia, Charlottesville, VA
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22
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Kovatchev B. Automated closed-loop control of diabetes: the artificial pancreas. Bioelectron Med 2018; 4:14. [PMID: 32232090 PMCID: PMC7098217 DOI: 10.1186/s42234-018-0015-6] [Citation(s) in RCA: 38] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2018] [Accepted: 10/08/2018] [Indexed: 12/28/2022] Open
Abstract
The incidence of Diabetes Mellitus is on the rise worldwide, which exerts enormous health toll on the population and enormous pressure on the healthcare systems. Now, almost hundred years after the discovery of insulin in 1921, the optimization problem of diabetes is well formulated as maintenance of strict glycemic control without increasing the risk for hypoglycemia. External insulin administration is mandatory for people with type 1 diabetes; various medications, as well as basal and prandial insulin, are included in the daily treatment of type 2 diabetes. This review follows the development of the Diabetes Technology field which, since the 1970s, progressed remarkably through continuous subcutaneous insulin infusion (CSII), mathematical models and computer simulation of the human metabolic system, real-time continuous glucose monitoring (CGM), and control algorithms driving closed-loop control systems known as the "artificial pancreas" (AP). All of these developments included significant engineering advances and substantial bioelectronics progress in the sensing of blood glucose levels, insulin delivery, and control design. The key technologies that enabled contemporary AP systems are CSII and CGM, both of which became available and sufficiently portable in the beginning of this century. This powered the quest for wearable home-use AP, which is now under way with prototypes tested in outpatient studies during the past 6 years. Pivotal trials of new AP technologies are ongoing, and the first hybrid closed-loop system has been approved by the FDA for clinical use. Thus, the closed-loop AP is well on its way to become the digital-age treatment of diabetes.
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Affiliation(s)
- Boris Kovatchev
- Center for Diabetes Technology, University of Virginia, P.O. Box 400888, Charlottesville, VA 22908 USA
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23
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Abstract
Over recent years there has been an explosion in availability of technical devices to support diabetes self-management. But with this technology revolution comes new hurdles. On paper, the available diabetes technologies should mean that the vast majority of people with type 1 diabetes have optimal glycemic control and are using their preferred therapy choices. Yet, it does not appear to be universally the case. In parallel, suboptimal glycemic control remains stubbornly widespread. Barriers to improvement include access to technology, access to expert diabetes health care professionals, and prohibitive insurance costs. Until access can be improved to ensure the technologies are available and usable by those that need them, there are many people with diabetes who are still losing out.
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Affiliation(s)
- Katharine D. Barnard
- Bournemouth University, Bournemouth,
UK
- Katharine D. Barnard, PhD, Bournemouth
University, Royal London House, Christchurch Road, Bournemouth BH1 3LT.
| | - Marc D. Breton
- University of Virginia School of
Medicine, Charlottesville, VA, USA
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24
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Garcia-Tirado J, Zuluaga-Bedoya C, Breton MD. Identifiability Analysis of Three Control-Oriented Models for Use in Artificial Pancreas Systems. J Diabetes Sci Technol 2018; 12:937-952. [PMID: 30095007 PMCID: PMC6134618 DOI: 10.1177/1932296818788873] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
OBJECTIVE Our aim is to analyze the identifiability of three commonly used control-oriented models for glucose control in patients with type 1 diabetes (T1D). METHODS Structural and practical identifiability analysis were performed on three published control-oriented models for glucose control in patients with type 1 diabetes (T1D): the subcutaneous oral glucose minimal model (SOGMM), the intensive control insulin-nutrition-glucose (ICING) model, and the minimal model control-oriented (MMC). Structural identifiability was addressed with a combination of the generating series (GS) approach and identifiability tableaus whereas practical identifiability was studied by means of (1) global ranking of parameters via sensitivity analysis together with the Latin hypercube sampling method (LHS) and (2) collinearity analysis among parameters. For practical identifiability and model identification, continuous glucose monitor (CGM), insulin pump, and meal records were selected from a set of patients (n = 5) on continuous subcutaneous insulin infusion (CSII) that underwent a clinical trial in an outpatient setting. The performance of the identified models was analyzed by means of the root mean square (RMS) criterion. RESULTS A reliable set of identifiable parameters was found for every studied model after analyzing the possible identifiability issues of the original parameter sets. According to an importance factor ([Formula: see text]), it was shown that insulin sensitivity is not the most influential parameter from the dynamical point of view, that is, is not the parameter impacting the outputs the most of the three models, contrary to what is assumed in the literature. For the test data, the models demonstrated similar performance with most RMS values around 20 mg/dl (min: 15.64 mg/dl, max: 51.32 mg/dl). However, MMC failed to identify the model for patient 4. Also, considering the three models, the MMC model showed the higher parameter variability when reidentified every 6 hours. CONCLUSION This study shows that both structural and practical identifiability analysis need to be considered prior to the model identification/individualization in patients with T1D. It was shown that all the studied models are able to represent the CGM data, yet their usefulness in a hypothetical artificial pancreas could be a matter of debate. In spite that the three models do not capture all the dynamics and metabolic effects as a maximal model (ie, our FDA-accepted UVa/Padova simulator), SOGMM and ICING appear to be more appealing than MMC regarding both the performance and parameter variability after reidentification. Although the model predictions of ICING are comparable to the ones of the SOGMM model, the large parameter set makes the model prone to overfitting if all parameters are identified. Moreover, the existence of a high nonlinear function like [Formula: see text] prevents the use of tools from the linear systems theory.
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Affiliation(s)
- Jose Garcia-Tirado
- Center for Diabetes Technology, University of Virginia, Charlottesville, VA, USA
| | - Christian Zuluaga-Bedoya
- Dynamic Processes Research Group KALMAN, Universidad Nacional de Colombia, Medellín, Antioquia, Colombia
| | - Marc D. Breton
- Center for Diabetes Technology, University of Virginia, Charlottesville, VA, USA
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25
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Hajizadeh I, Rashid M, Samadi S, Feng J, Sevil M, Hobbs N, Lazaro C, Maloney Z, Brandt R, Yu X, Turksoy K, Littlejohn E, Cengiz E, Cinar A. Adaptive and Personalized Plasma Insulin Concentration Estimation for Artificial Pancreas Systems. J Diabetes Sci Technol 2018; 12:639-649. [PMID: 29566547 PMCID: PMC6154239 DOI: 10.1177/1932296818763959] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
BACKGROUND The artificial pancreas (AP) system, a technology that automatically administers exogenous insulin in people with type 1 diabetes mellitus (T1DM) to regulate their blood glucose concentrations, necessitates the estimation of the amount of active insulin already present in the body to avoid overdosing. METHOD An adaptive and personalized plasma insulin concentration (PIC) estimator is designed in this work to accurately quantify the insulin present in the bloodstream. The proposed PIC estimation approach incorporates Hovorka's glucose-insulin model with the unscented Kalman filtering algorithm. Methods for the personalized initialization of the time-varying model parameters to individual patients for improved estimator convergence are developed. Data from 20 three-days-long closed-loop clinical experiments conducted involving subjects with T1DM are used to evaluate the proposed PIC estimation approach. RESULTS The proposed methods are applied to the clinical data containing significant disturbances, such as unannounced meals and exercise, and the results demonstrate the accurate real-time estimation of the PIC with the root mean square error of 7.15 and 9.25 mU/L for the optimization-based fitted parameters and partial least squares regression-based testing parameters, respectively. CONCLUSIONS The accurate real-time estimation of PIC will benefit the AP systems by preventing overdelivery of insulin when significant insulin is present in the bloodstream.
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Affiliation(s)
- Iman Hajizadeh
- Department of Chemical and Biological
Engineering, Illinois Institute of Technology, Chicago, IL, USA
| | - Mudassir Rashid
- Department of Chemical and Biological
Engineering, Illinois Institute of Technology, Chicago, IL, USA
| | - Sediqeh Samadi
- Department of Chemical and Biological
Engineering, Illinois Institute of Technology, Chicago, IL, USA
| | - Jianyuan Feng
- Department of Chemical and Biological
Engineering, Illinois Institute of Technology, Chicago, IL, USA
| | - Mert Sevil
- Department of Biomedical Engineering,
Illinois Institute of Technology, Chicago, IL, USA
| | - Nicole Hobbs
- Department of Biomedical Engineering,
Illinois Institute of Technology, Chicago, IL, USA
| | - Caterina Lazaro
- Department of Electrical and Computer
Engineering, Illinois Institute of Technology, Chicago, IL, USA
| | - Zacharie Maloney
- Department of Biomedical Engineering,
Illinois Institute of Technology, Chicago, IL, USA
| | - Rachel Brandt
- Department of Biomedical Engineering,
Illinois Institute of Technology, Chicago, IL, USA
| | - Xia Yu
- School of Information Science and
Technology, Northeastern University, Shenyang, China
| | - Kamuran Turksoy
- Department of Biomedical Engineering,
Illinois Institute of Technology, Chicago, IL, USA
| | - Elizabeth Littlejohn
- Department of Pediatrics and Medicine,
Section of Endocrinology, Kovler Diabetes Center, University of Chicago, Chicago,
IL, USA
| | - Eda Cengiz
- Department of Pediatrics, Yale
University School of Medicine, New Haven, CT, USA
| | - Ali Cinar
- Department of Chemical and Biological
Engineering, Illinois Institute of Technology, Chicago, IL, USA
- Department of Biomedical Engineering,
Illinois Institute of Technology, Chicago, IL, USA
- Ali Cinar, PhD, Illinois Institute of
Technology, Department of Chemical and Biological Engineering, 10 W 33rd St,
Chicago, IL 60616, USA.
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26
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Bisker G, Bakh NA, Lee MA, Ahn J, Park M, O’Connell EB, Iverson NM, Strano MS. Insulin Detection Using a Corona Phase Molecular Recognition Site on Single-Walled Carbon Nanotubes. ACS Sens 2018; 3:367-377. [PMID: 29359558 DOI: 10.1021/acssensors.7b00788] [Citation(s) in RCA: 56] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
Corona phase molecular recognition (CoPhMoRe) is a technique whereby an external, adsorbed phase around a colloidal nanoparticle is selected such that its molecular conformation or interaction recognizes a specific target analyte. In this work, we employ a high-throughput screening of a library of poly(ethylene glycol) (PEG)-conjugated lipids adsorbed onto near-infrared fluorescent single-walled carbon nanotubes to discover a corona phase selective for insulin. We find that a C16-PEG(2000 Da)-ceramide causes a 62% fluorescent intensity decrease of the (10,2) chirality nanotube in the presence of 20 μg/mL insulin. The insulin protein has no prior affinity toward the C16-PEG(2000 Da)-ceramide molecules in free solution, verified by isothermal titration calorimetry, and the interaction occurs only upon their adsorption onto the single-walled carbon nanotube scaffolds. Testing a panel of proteins originating from human blood as well as short 7 amino acid fragments of the insulin peptide rules out nonselective recognition mechanisms such as molecular weight, isoelectric point, and hydrophobicity-based detection. Interestingly, longer fragments of isolated α- and β-peptide chains of insulin are detected by the construct, albeit with lower affinity compared to that of the intact insulin protein, suggesting that the construct recognizes insulin in its native form and conformation. Finally, the insulin recognition and the quantification of its solution concentration were demonstrated both in buffer and in blood serum, showing that the CoPhMoRe construct works in this complex environment despite the presence of potential nonspecific adsorption. Our results further motivate the search for nonbiological synthetic recognition sites and open up a new path for continuous insulin monitoring in vivo with the hope of improving glycemic control in closed-loop artificial pancreas systems.
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Affiliation(s)
| | | | | | | | | | | | - Nicole M. Iverson
- Department
of Biological Systems Engineering, University of Nebraska—Lincoln, 223 L.W. Chase Hall, Lincoln, Nebraska 68583, United States
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27
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Chase JG, Preiser JC, Dickson JL, Pironet A, Chiew YS, Pretty CG, Shaw GM, Benyo B, Moeller K, Safaei S, Tawhai M, Hunter P, Desaive T. Next-generation, personalised, model-based critical care medicine: a state-of-the art review of in silico virtual patient models, methods, and cohorts, and how to validation them. Biomed Eng Online 2018; 17:24. [PMID: 29463246 PMCID: PMC5819676 DOI: 10.1186/s12938-018-0455-y] [Citation(s) in RCA: 84] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2017] [Accepted: 02/12/2018] [Indexed: 01/17/2023] Open
Abstract
Critical care, like many healthcare areas, is under a dual assault from significantly increasing demographic and economic pressures. Intensive care unit (ICU) patients are highly variable in response to treatment, and increasingly aging populations mean ICUs are under increasing demand and their cohorts are increasingly ill. Equally, patient expectations are growing, while the economic ability to deliver care to all is declining. Better, more productive care is thus the big challenge. One means to that end is personalised care designed to manage the significant inter- and intra-patient variability that makes the ICU patient difficult. Thus, moving from current "one size fits all" protocolised care to adaptive, model-based "one method fits all" personalised care could deliver the required step change in the quality, and simultaneously the productivity and cost, of care. Computer models of human physiology are a unique tool to personalise care, as they can couple clinical data with mathematical methods to create subject-specific models and virtual patients to design new, personalised and more optimal protocols, as well as to guide care in real-time. They rely on identifying time varying patient-specific parameters in the model that capture inter- and intra-patient variability, the difference between patients and the evolution of patient condition. Properly validated, virtual patients represent the real patients, and can be used in silico to test different protocols or interventions, or in real-time to guide care. Hence, the underlying models and methods create the foundation for next generation care, as well as a tool for safely and rapidly developing personalised treatment protocols over large virtual cohorts using virtual trials. This review examines the models and methods used to create virtual patients. Specifically, it presents the models types and structures used and the data required. It then covers how to validate the resulting virtual patients and trials, and how these virtual trials can help design and optimise clinical trial. Links between these models and higher order, more complex physiome models are also discussed. In each section, it explores the progress reported up to date, especially on core ICU therapies in glycemic, circulatory and mechanical ventilation management, where high cost and frequency of occurrence provide a significant opportunity for model-based methods to have measurable clinical and economic impact. The outcomes are readily generalised to other areas of medical care.
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Affiliation(s)
- J. Geoffrey Chase
- Department of Mechanical Engineering, Centre for Bio-Engineering, University of Canterbury, Private Bag 4800, Christchurch, New Zealand
| | - Jean-Charles Preiser
- Department of Intensive Care, Erasme University of Hospital, 1070 Brussels, Belgium
| | - Jennifer L. Dickson
- Department of Mechanical Engineering, Centre for Bio-Engineering, University of Canterbury, Private Bag 4800, Christchurch, New Zealand
| | - Antoine Pironet
- GIGA In Silico Medicine, University of Liege, 4000 Liege, Belgium
| | - Yeong Shiong Chiew
- Department of Mechanical Engineering, School of Engineering, Monash University Malaysia, 47500 Selangor, Malaysia
| | - Christopher G. Pretty
- Department of Mechanical Engineering, Centre for Bio-Engineering, University of Canterbury, Private Bag 4800, Christchurch, New Zealand
| | - Geoffrey M. Shaw
- Department of Intensive Care, Christchurch Hospital, Christchurch, New Zealand
| | - Balazs Benyo
- Department of Control Engineering and Information Technology, Budapest University of Technology and Economics, Budapest, Hungary
| | - Knut Moeller
- Department of Biomedical Engineering, Institute of Technical Medicine, Furtwangen University, Villingen-Schwenningen, Germany
| | - Soroush Safaei
- Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand
| | - Merryn Tawhai
- Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand
| | - Peter Hunter
- Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand
| | - Thomas Desaive
- GIGA In Silico Medicine, University of Liege, 4000 Liege, Belgium
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Kovatchev B. The artificial pancreas in 2017: The year of transition from research to clinical practice. Nat Rev Endocrinol 2018; 14:74-76. [PMID: 29286043 DOI: 10.1038/nrendo.2017.170] [Citation(s) in RCA: 32] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Affiliation(s)
- Boris Kovatchev
- Center for Diabetes Technology, University of Virginia, POBox 400888, Charlottesville, Virginia 22908, USA
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Yu X, Turksoy K, Rashid M, Feng J, Frantz N, Hajizadeh I, Samadi S, Sevil M, Lazaro C, Maloney Z, Littlejohn E, Quinn L, Cinar A. Model-Fusion-Based Online Glucose Concentration Predictions in People with Type 1 Diabetes. CONTROL ENGINEERING PRACTICE 2018; 71:129-141. [PMID: 29276347 PMCID: PMC5736323 DOI: 10.1016/j.conengprac.2017.10.013] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/13/2023]
Abstract
Accurate predictions of glucose concentrations are necessary to develop an artificial pancreas (AP) system for people with type 1 diabetes (T1D). In this work, a novel glucose forecasting paradigm based on a model fusion strategy is developed to accurately characterize the variability and transient dynamics of glycemic measurements. To this end, four different adaptive filters and a fusion mechanism are proposed for use in the online prediction of future glucose trajectories. The filter fusion mechanism is developed based on various prediction performance indexes to guide the overall output of the forecasting paradigm. The efficiency of the proposed model fusion based forecasting method is evaluated using simulated and clinical datasets, and the results demonstrate the capability and prediction accuracy of the data-based fusion filters, especially in the case of limited data availability. The model fusion framework may be used in the development of an AP system for glucose regulation in patients with T1D.
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Affiliation(s)
- Xia Yu
- School of Information Science and Engineering, Northeastern University, Shenyang 110819, PR China
| | - Kamuran Turksoy
- Department of Biomedical Engineering, Illinois Institute of Technology, Chicago, IL 60616, USA
| | - Mudassir Rashid
- Department of Chemical and Biological Engineering, Illinois Institute of Technology, Chicago, IL 60616, USA
| | - Jianyuan Feng
- Department of Chemical and Biological Engineering, Illinois Institute of Technology, Chicago, IL 60616, USA
| | - Nicole Frantz
- Department of Biomedical Engineering, Illinois Institute of Technology, Chicago, IL 60616, USA
| | - Iman Hajizadeh
- Department of Chemical and Biological Engineering, Illinois Institute of Technology, Chicago, IL 60616, USA
| | - Sediqeh Samadi
- Department of Chemical and Biological Engineering, Illinois Institute of Technology, Chicago, IL 60616, USA
| | - Mert Sevil
- Department of Biomedical Engineering, Illinois Institute of Technology, Chicago, IL 60616, USA
| | - Caterina Lazaro
- Department of Electrical and Computer Engineering, Illinois Institute of Technology, Chicago, IL 60616, USA
| | - Zacharie Maloney
- Department of Biomedical Engineering, Illinois Institute of Technology, Chicago, IL 60616, USA
| | - Elizabeth Littlejohn
- Department of Pediatrics and Medicine, Kovler Diabetes Center, University of Chicago, Chicago, IL 60637, USA
| | - Laurie Quinn
- Department of Biobehavioral Health Science, College of Nursing, University of Illinois at Chicago, Chicago, IL 60612, USA
| | - Ali Cinar
- Department of Biomedical Engineering, Illinois Institute of Technology, Chicago, IL 60616, USA
- Department of Chemical and Biological Engineering, Illinois Institute of Technology, Chicago, IL 60616, USA
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Yang R, Wu M, Lin S, Nargund RP, Li X, Kelly T, Yan L, Dai G, Qian Y, Dallas-Yang Q, Fischer PA, Cui Y, Shen X, Huo P, Feng DD, Erion MD, Kelley DE, Mu J. A glucose-responsive insulin therapy protects animals against hypoglycemia. JCI Insight 2018; 3:97476. [PMID: 29321379 DOI: 10.1172/jci.insight.97476] [Citation(s) in RCA: 31] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2017] [Accepted: 12/05/2017] [Indexed: 01/24/2023] Open
Abstract
Hypoglycemia is commonly associated with insulin therapy, limiting both its safety and efficacy. The concept of modifying insulin to render its glucose-responsive release from an injection depot (of an insulin complexed exogenously with a recombinant lectin) was proposed approximately 4 decades ago but has been challenging to achieve. Data presented here demonstrate that mannosylated insulin analogs can undergo an additional route of clearance as result of their interaction with endogenous mannose receptor (MR), and this can occur in a glucose-dependent fashion, with increased binding to MR at low glucose. Yet, these analogs retain capacity for binding to the insulin receptor (IR). When the blood glucose level is elevated, as in individuals with diabetes mellitus, MR binding diminishes due to glucose competition, leading to reduced MR-mediated clearance and increased partitioning for IR binding and consequent glucose lowering. These studies demonstrate that a glucose-dependent locus of insulin clearance and, hence, insulin action can be achieved by targeting MR and IR concurrently.
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31
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Dassau E, Pinsker JE, Kudva YC, Brown SA, Gondhalekar R, Dalla Man C, Patek S, Schiavon M, Dadlani V, Dasanayake I, Church MM, Carter RE, Bevier WC, Huyett LM, Hughes J, Anderson S, Lv D, Schertz E, Emory E, McCrady-Spitzer SK, Jean T, Bradley PK, Hinshaw L, Laguna Sanz AJ, Basu A, Kovatchev B, Cobelli C, Doyle FJ. Twelve-Week 24/7 Ambulatory Artificial Pancreas With Weekly Adaptation of Insulin Delivery Settings: Effect on Hemoglobin A 1c and Hypoglycemia. Diabetes Care 2017; 40:1719-1726. [PMID: 29030383 PMCID: PMC5711334 DOI: 10.2337/dc17-1188] [Citation(s) in RCA: 44] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/14/2017] [Accepted: 09/14/2017] [Indexed: 02/03/2023]
Abstract
OBJECTIVE Artificial pancreas (AP) systems are best positioned for optimal treatment of type 1 diabetes (T1D) and are currently being tested in outpatient clinical trials. Our consortium developed and tested a novel adaptive AP in an outpatient, single-arm, uncontrolled multicenter clinical trial lasting 12 weeks. RESEARCH DESIGN AND METHODS Thirty adults with T1D completed a continuous glucose monitor (CGM)-augmented 1-week sensor-augmented pump (SAP) period. After the AP was started, basal insulin delivery settings used by the AP for initialization were adapted weekly, and carbohydrate ratios were adapted every 4 weeks by an algorithm running on a cloud-based server, with automatic data upload from devices. Adaptations were reviewed by expert study clinicians and patients. The primary end point was change in hemoglobin A1c (HbA1c). Outcomes are reported adhering to consensus recommendations on reporting of AP trials. RESULTS Twenty-nine patients completed the trial. HbA1c, 7.0 ± 0.8% at the start of AP use, improved to 6.7 ± 0.6% after 12 weeks (-0.3, 95% CI -0.5 to -0.2, P < 0.001). Compared with the SAP run-in, CGM time spent in the hypoglycemic range improved during the day from 5.0 to 1.9% (-3.1, 95% CI -4.1 to -2.1, P < 0.001) and overnight from 4.1 to 1.1% (-3.1, 95% CI -4.2 to -1.9, P < 0.001). Whereas carbohydrate ratios were adapted to a larger extent initially with minimal changes thereafter, basal insulin was adapted throughout. Approximately 10% of adaptation recommendations were manually overridden. There were no protocol-related serious adverse events. CONCLUSIONS Use of our novel adaptive AP yielded significant reductions in HbA1c and hypoglycemia.
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Affiliation(s)
- Eyal Dassau
- Harvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA.,William Sansum Diabetes Center, Santa Barbara, CA
| | | | | | - Sue A Brown
- Center for Diabetes Technology, University of Virginia, Charlottesville, VA
| | - Ravi Gondhalekar
- Harvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA.,William Sansum Diabetes Center, Santa Barbara, CA
| | - Chiara Dalla Man
- Department of Information Engineering, University of Padova, Padova, Italy
| | - Steve Patek
- Center for Diabetes Technology, University of Virginia, Charlottesville, VA
| | - Michele Schiavon
- Department of Information Engineering, University of Padova, Padova, Italy
| | | | - Isuru Dasanayake
- William Sansum Diabetes Center, Santa Barbara, CA.,Department of Chemical Engineering, University of California, Santa Barbara, Santa Barbara, CA
| | | | - Rickey E Carter
- Department of Health Sciences Research, Mayo Clinic, Rochester, MN
| | | | - Lauren M Huyett
- William Sansum Diabetes Center, Santa Barbara, CA.,Department of Chemical Engineering, University of California, Santa Barbara, Santa Barbara, CA
| | - Jonathan Hughes
- Center for Diabetes Technology, University of Virginia, Charlottesville, VA
| | - Stacey Anderson
- Center for Diabetes Technology, University of Virginia, Charlottesville, VA
| | - Dayu Lv
- Center for Diabetes Technology, University of Virginia, Charlottesville, VA
| | - Elaine Schertz
- Center for Diabetes Technology, University of Virginia, Charlottesville, VA
| | - Emma Emory
- Center for Diabetes Technology, University of Virginia, Charlottesville, VA
| | | | - Tyler Jean
- William Sansum Diabetes Center, Santa Barbara, CA
| | | | - Ling Hinshaw
- Endocrine Research Unit, Mayo Clinic, Rochester, MN
| | - Alejandro J Laguna Sanz
- Harvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA.,William Sansum Diabetes Center, Santa Barbara, CA
| | - Ananda Basu
- Endocrine Research Unit, Mayo Clinic, Rochester, MN
| | - Boris Kovatchev
- Center for Diabetes Technology, University of Virginia, Charlottesville, VA
| | - Claudio Cobelli
- Department of Information Engineering, University of Padova, Padova, Italy
| | - Francis J Doyle
- Harvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA .,William Sansum Diabetes Center, Santa Barbara, CA
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32
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Fath M, Danne T, Biester T, Erichsen L, Kordonouri O, Haahr H. Faster-acting insulin aspart provides faster onset and greater early exposure vs insulin aspart in children and adolescents with type 1 diabetes mellitus. Pediatr Diabetes 2017; 18:903-910. [PMID: 28165180 DOI: 10.1111/pedi.12506] [Citation(s) in RCA: 42] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/20/2016] [Revised: 01/06/2017] [Accepted: 01/06/2017] [Indexed: 01/26/2023] Open
Abstract
BACKGROUND Faster-acting insulin aspart (faster aspart) is insulin aspart (IAsp) in a new formulation with additional excipients (L-arginine and niacinamide). In adults, faster aspart provides faster onset and greater early exposure and action vs IAsp. AIM This randomized, double-blind, 2-period crossover trial investigated the pharmacological properties of faster aspart vs IAsp in 12 children (6-11 years), 13 adolescents (12-17 years), and 15 adults (18-64 years) with type 1 diabetes mellitus. METHODS Subjects received 0.2 U/kg subcutaneous dosing (mean of 8.3, 12.8, and 15.6 U, respectively) immediately prior to a standardized meal (17.3 g carbohydrate/100 mL; amount adjusted by body weight). RESULTS Consistently across age groups, onset of appearance occurred approximately twice-as-fast (5-7 minutes earlier) and early exposure (AUCIAsp,0-30min ; area under the IAsp curve from 0 to 30 minutes) was greater (by 78%-147%) for faster aspart vs IAsp, with no treatment differences in total exposure (AUCIAsp,0-t ) or maximum concentration (C max ). Two-hour postmeal plasma glucose excursion was reduced for faster aspart vs IAsp (although only reaching statistical significance in children). In accordance with the absolute dose administered for each age group, AUCIAsp,0-t for faster aspart was lower in children (estimated ratio children/adults [95% confidence interval]: 0.59 [0.50;0.69], P < .001) and adolescents (0.78 [0.67;0.90], P = .002) vs adults. No age group differences were seen in C max (0.91 [0.70;1.17], P = .445, and 0.99 [0.77;1.26], P = .903). The age effect on AUCIAsp,0-t and C max did not differ statistically significantly between treatments. Faster aspart and IAsp were well-tolerated. CONCLUSION The current findings in children and adolescents suggest a potential for faster aspart to improve postprandial glycemia over current rapid-acting insulins also in younger age groups. http://ClinicalTrials.gov identifier: NCT02035371.
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Affiliation(s)
- Maryam Fath
- Diabetes Centre for Children and Adolescents, Kinder- und Jugendkrankenhaus AUF DER BULT, Hannover, Germany
| | - Thomas Danne
- Diabetes Centre for Children and Adolescents, Kinder- und Jugendkrankenhaus AUF DER BULT, Hannover, Germany
| | - Torben Biester
- Diabetes Centre for Children and Adolescents, Kinder- und Jugendkrankenhaus AUF DER BULT, Hannover, Germany
| | | | - Olga Kordonouri
- Diabetes Centre for Children and Adolescents, Kinder- und Jugendkrankenhaus AUF DER BULT, Hannover, Germany
| | - Hanne Haahr
- Clinical Pharmacology, Novo Nordisk A/S, Søborg, Denmark
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33
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Breton MD, Cherñavvsky DR, Forlenza GP, DeBoer MD, Robic J, Wadwa RP, Messer LH, Kovatchev BP, Maahs DM. Closed-Loop Control During Intense Prolonged Outdoor Exercise in Adolescents With Type 1 Diabetes: The Artificial Pancreas Ski Study. Diabetes Care 2017; 40:1644-1650. [PMID: 28855239 PMCID: PMC5711335 DOI: 10.2337/dc17-0883] [Citation(s) in RCA: 102] [Impact Index Per Article: 14.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/03/2017] [Accepted: 08/04/2017] [Indexed: 02/03/2023]
Abstract
OBJECTIVE Intense exercise is a major challenge to the management of type 1 diabetes (T1D). Closed-loop control (CLC) systems (artificial pancreas) improve glycemic control during limited intensity and short duration of physical activity (PA). However, CLC has not been tested during extended vigorous outdoor exercise common among adolescents. RESEARCH DESIGN AND METHODS Skiing presents unique metabolic challenges: intense prolonged PA, cold, altitude, and stress/fear/excitement. In a randomized controlled trial, 32 adolescents with T1D (ages 10-16 years) participated in a 5-day ski camp (∼5 h skiing/day) at two sites: Wintergreen, VA, and Breckenridge, CO. Participants were randomized to the University of Virginia CLC system or remotely monitored sensor-augmented pump (RM-SAP). The CLC and RM-SAP groups were coarsely paired by age and hemoglobin A1c (HbA1c). All subjects were remotely monitored 24 h per day by the study physicians and clinical team. RESULTS Compared with physician-monitored open loop, percent time in range (70-180 mg/dL) improved using CLC: 71.3 vs. 64.7% (+6.6% [95% CI 1-12]; P = 0.005), with maximum effect late at night. Hypoglycemia exposure and carbohydrate treatments were improved overall (P = 0.001 and P = 0.007) and during the daytime with strong ski level effects (P = 0.0001 and P = 0.006); ski/snowboard proficiency was balanced between groups but with a very strong site effect: naive in Virginia and experienced in Colorado. There was no adverse event associated with CLC; the participants' feedback was overwhelmingly positive. CONCLUSIONS CLC in adolescents with T1D improved glycemic control and reduced exposure to hypoglycemia during prolonged intensive winter sport activities, despite the added challenges of cold and altitude.
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Affiliation(s)
- Marc D Breton
- Center for Diabetes Technology, University of Virginia, Charlottesville, VA
| | | | - Gregory P Forlenza
- Barbara Davis Center for Childhood Diabetes, University of Colorado Denver, Aurora, CO
| | - Mark D DeBoer
- Center for Diabetes Technology, University of Virginia, Charlottesville, VA
| | - Jessica Robic
- Center for Diabetes Technology, University of Virginia, Charlottesville, VA
| | - R Paul Wadwa
- Barbara Davis Center for Childhood Diabetes, University of Colorado Denver, Aurora, CO
| | - Laurel H Messer
- Barbara Davis Center for Childhood Diabetes, University of Colorado Denver, Aurora, CO
| | - Boris P Kovatchev
- Center for Diabetes Technology, University of Virginia, Charlottesville, VA
| | - David M Maahs
- Barbara Davis Center for Childhood Diabetes, University of Colorado Denver, Aurora, CO.,Department of Pediatrics, Stanford University, Stanford, CA
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Viceconti M, Cobelli C, Haddad T, Himes A, Kovatchev B, Palmer M. In silico assessment of biomedical products: The conundrum of rare but not so rare events in two case studies. Proc Inst Mech Eng H 2017; 231:455-466. [PMID: 28427321 DOI: 10.1177/0954411917702931] [Citation(s) in RCA: 34] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
In silico clinical trials, defined as "The use of individualized computer simulation in the development or regulatory evaluation of a medicinal product, medical device, or medical intervention," have been proposed as a possible strategy to reduce the regulatory costs of innovation and the time to market for biomedical products. We review some of the the literature on this topic, focusing in particular on those applications where the current practice is recognized as inadequate, as for example, the detection of unexpected severe adverse events too rare to be detected in a clinical trial, but still likely enough to be of concern. We then describe with more details two case studies, two successful applications of in silico clinical trial approaches, one relative to the University of Virginia/Padova simulator that the Food and Drug Administration has accepted as possible replacement for animal testing in the preclinical assessment of artificial pancreas technologies, and the second, an investigation of the probability of cardiac lead fracture, where a Bayesian network was used to combine in vivo and in silico observations, suggesting a whole new strategy of in silico-augmented clinical trials, to be used to increase the numerosity where recruitment is impossible, or to explore patients' phenotypes that are unlikely to appear in the trial cohort, but are still frequent enough to be of concern.
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Affiliation(s)
- Marco Viceconti
- 1 Department of Mechanical Engineering, INSIGNEO Institute for in silico Medicine, The University of Sheffield, Sheffield, UK
| | - Claudio Cobelli
- 2 Department of Information Engineering, University of Padova, Padova, Italy
| | | | | | - Boris Kovatchev
- 4 Center for Diabetes Technology, The University of Virginia, Charlottesville, VA, USA
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Montaser E, Díez JL, Bondia J. Stochastic Seasonal Models for Glucose Prediction in the Artificial Pancreas. J Diabetes Sci Technol 2017; 11:1124-1131. [PMID: 29039207 PMCID: PMC5951060 DOI: 10.1177/1932296817736074] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
BACKGROUND Linear empirical dynamic models have been widely used for glucose prediction. The extension of the concept of seasonality, characteristic of other domains, is explored here for the improvement of prediction accuracy. METHODS Twenty time series of 8-hour postprandial periods (PP) for a same 60g-carbohydrate meal were collected from a closed-loop controller validation study. A single concatenated time series was produced representing a collection of data from similar scenarios, resulting in seasonality. Variability in the resulting time series was representative of worst-case intrasubject variability. Following a leave-one-out cross-validation, seasonal and nonseasonal autoregressive integrated moving average models (SARIMA and ARIMA) were built to analyze the effect of seasonality in the model prediction accuracy. Further improvement achieved from the inclusion of insulin infusion rate as exogenous variable was also analyzed. Prediction horizons (PHs) from 30 to 300 min were considered. RESULTS SARIMA outperformed ARIMA revealing a significant role of seasonality. For a 5-h PH, average MAPE was reduced in 26.62%. Considering individual runs, the improvement ranged from 6.3% to 54.52%. In the best-performing case this reduction amounted to 29.45%. The benefit of seasonality was consistent among different PHs, although lower PHs benefited more, with MAPE reduction over 50% for PHs of 60 and 120 minutes, and over 40% for 180 min. Consideration of insulin infusion rate into the seasonal model further improved performance, with a 61.89% reduction in MAPE for 30-min PH and reductions over 20% for PHs over 180 min. CONCLUSIONS Seasonality improved model accuracy allowing for the extension of the PH significantly.
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Affiliation(s)
- Eslam Montaser
- Instituto Universitario de Automática e Informática Industrial, Universitat Politècnica de València, València, Spain
| | - José-Luis Díez
- Instituto Universitario de Automática e Informática Industrial, Universitat Politècnica de València, València, Spain
| | - Jorge Bondia
- Instituto Universitario de Automática e Informática Industrial, Universitat Politècnica de València, València, Spain
- Jorge Bondia, PhD, Departamento de Ingeniería de Sistemas y Automática, Universitat Politècnica de València, C/ Camí de Vera, s/n, 46022 Valencia, Spain.
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Kahkoska AR, Mayer-Davis EJ, Hood KK, Maahs DM, Burger KS. Behavioural implications of traditional treatment and closed-loop automated insulin delivery systems in Type 1 diabetes: applying a cognitive restraint theory framework. Diabet Med 2017; 34. [PMID: 28626906 PMCID: PMC5647213 DOI: 10.1111/dme.13407] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Abstract
As the prevalence of obesity in Type 1 diabetes rises, the effects of emerging therapy options should be considered in the context of both weight and glycaemic control outcomes. Artificial pancreas device systems will 'close the loop' between blood glucose monitoring and automated insulin delivery and may transform day-to-day dietary management for people with Type 1 diabetes in multiple ways. In the present review, we draw directly from cognitive restraint theory to consider unintended impacts that closed-loop systems may have on ingestive behaviour and food intake. We provide a brief overview of dietary restraint theory and its relation to weight status in the general population, discuss the role of restraint in traditional Type 1 diabetes treatment, and lastly, use this restraint framework to discuss the possible behavioural implications and opportunities of closed-loop systems in the treatment of Type 1 diabetes. We hypothesize that adopting closed-loop systems will lift the diligence and restriction that characterizes Type 1 diabetes today, thus requiring a transition from a restrained eating behaviour to a non-restrained eating behaviour. Furthermore, we suggest this transition be leveraged as an opportunity to teach people lifelong eating behaviour to promote healthy weight status by incorporating education and cognitive reappraisal. Our aim was to use a transdisciplinary approach to highlight critical aspects of the emerging closed-loop technologies relating to eating behaviour and weight effects and to promote discussion of strategies to optimize long-term health in Type 1 diabetes via two key outcomes: glycaemic control and weight management.
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Affiliation(s)
- A R Kahkoska
- Department of Nutrition, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC
| | - E J Mayer-Davis
- Department of Nutrition, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC
| | - K K Hood
- Division of Pediatric Endocrinology, Stanford University, Stanford, CA, USA
| | - D M Maahs
- Division of Pediatric Endocrinology, Stanford University, Stanford, CA, USA
| | - K S Burger
- Department of Nutrition, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC
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Piemonte V, Capocelli M, De Santis L, Maurizi AR, Pozzilli P. A Novel Three-Compartmental Model for Artificial Pancreas: Development and Validation. Artif Organs 2017; 41:E326-E336. [DOI: 10.1111/aor.12980] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2017] [Revised: 04/09/2017] [Accepted: 05/17/2017] [Indexed: 12/12/2022]
Affiliation(s)
- Vincenzo Piemonte
- Faculty of Engineering, University Campus Biomedico of Rome; Rome Italy
| | - Mauro Capocelli
- Faculty of Engineering, University Campus Biomedico of Rome; Rome Italy
| | - Luca De Santis
- Faculty of Engineering, University Campus Biomedico of Rome; Rome Italy
| | - Anna Rita Maurizi
- Faculty of Engineering, University Campus Biomedico of Rome; Rome Italy
| | - Paolo Pozzilli
- Faculty of Engineering, University Campus Biomedico of Rome; Rome Italy
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38
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Hajizadeh I, Rashid M, Turksoy K, Samadi S, Feng J, Frantz N, Sevil M, Cengiz E, Cinar A. Plasma Insulin Estimation in People with Type 1 Diabetes Mellitus. Ind Eng Chem Res 2017. [DOI: 10.1021/acs.iecr.7b01618] [Citation(s) in RCA: 38] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
| | | | | | | | | | | | | | - Eda Cengiz
- Department
of Pediatrics, Yale University School of Medicine, New Haven, Connecticut 06437-2411, United States
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39
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Abstract
As intensive treatment to lower levels of HbA1c characteristically results in an increased risk of hypoglycaemia, patients with diabetes mellitus face a life-long optimization problem to reduce average levels of glycaemia and postprandial hyperglycaemia while simultaneously avoiding hypoglycaemia. This optimization can only be achieved in the context of lowering glucose variability. In this Review, I discuss topics that are related to the assessment, quantification and optimal control of glucose fluctuations in diabetes mellitus. I focus on markers of average glycaemia and the utility and/or shortcomings of HbA1c as a 'gold-standard' metric of glycaemic control; the notion that glucose variability is characterized by two principal dimensions, amplitude and time; measures of glucose variability that are based on either self-monitoring of blood glucose data or continuous glucose monitoring (CGM); and the control of average glycaemia and glucose variability through the use of pharmacological agents or closed-loop control systems commonly referred to as the 'artificial pancreas'. I conclude that HbA1c and the various available metrics of glucose variability reflect the management of diabetes mellitus on different timescales, ranging from months (for HbA1c) to minutes (for CGM). Comprehensive assessment of the dynamics of glycaemic fluctuations is therefore crucial for providing accurate and complete information to the patient, physician, automated decision-support or artificial-pancreas system.
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Affiliation(s)
- Boris P Kovatchev
- University of Virginia School of Medicine, 1215 Lee Street, Charlottesvile, Virginia 22908, USA
- The School of Engineering and Applied Sciences, University of Virginia, Thornton Hall, P.O. Box 400259, Charlottesville, Virginia 22904-4259, USA
- Center for Diabetes Technology, University of Virginia School of Medicine, Ivy Translational Research Building, 560 Ray C. Hunt Drive, Charlottesville, Virginia 22903-2981, USA
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40
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Abstract
In recent years, continuous intraperitoneal insulin infusion (CIPII) has become a favored treatment alternative for patients with subcutaneous insulin resistance, mainly due to its ability of mimicking physiological conditions of insulin absorption. CIPII has been shown to improve glycemic control as well as to reduce hypoglycemic events and to lead to increased patient satisfaction and quality of life (QoL). Among CIPII delivery systems, Diaport stands out due to its low side effects, its demonstrated clinical efficacy and the potential for integration into closed-loop systems.
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Affiliation(s)
| | | | - Oliver Schnell
- Sciarc Institute, Baierbrunn, Germany
- Forschergruppe Diabetes e.V., Munich-Neuherberg, Germany
- Oliver Schnell, MD, Forschergruppe Diabetes e.V., Ingolstädter Landstraße 1, 85764 Munich-Neuherberg, Germany.
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41
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Wieringa FP, Broers NJH, Kooman JP, Van Der Sande FM, Van Hoof C. Wearable sensors: can they benefit patients with chronic kidney disease? Expert Rev Med Devices 2017; 14:505-519. [PMID: 28612635 DOI: 10.1080/17434440.2017.1342533] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
INTRODUCTION This article ponders upon wearable medical measurement devices in relation to Chronic Kidney Disease (CKD) and its' associated comorbidities - and whether these might benefit CKD-patients. We aimed to map the intersection(s) of nephrology and wearable sensor technology to help technologists understand medical aspects, and clinicians to understand technological possibilities that are available (or soon will become so). Areas covered: A structured literature search on main comorbidities and complications CKD patients suffer from, was used to steer mini-reviews on wearable sensor technologies clustered around 3 themes being: Cardiovascular-related, diabetes-related and physical fitness/frailty. This review excludes wearable dialysis - although also strongly enabled by miniaturization - because that highly important theme deserves separate in-depth reviewing. Expert commentary: Continuous progress in integrated electronics miniaturization enormously lowered price, size, weight and energy consumption of electronic sensors, processing power, memory and wireless connectivity. These combined factors boost opportunities for wearable medical sensors. Such devices can be regarded as enablers for: Remote monitoring, influencing human behaviour (exercise, dietary), enhanced home care, remote consults, patient education and peer networks. However, to make wearable medical devices succeed, the challenge to fit them into health care structures will be dominant over the challenge to realize the bare technologies themselves.
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Affiliation(s)
- Fokko Pieter Wieringa
- a imec The Netherlands - Wearable Health Solutions , Eindhoven , The Netherlands.,b Maastricht University , Faculty of Health, Medicine and Life Sciences , Maastricht , The Netherlands
| | | | - Jeroen Peter Kooman
- c Maastricht UMC+ - Internal Medicine , Division of Nephrology , Maastricht , The Netherlands
| | - Frank M Van Der Sande
- c Maastricht UMC+ - Internal Medicine , Division of Nephrology , Maastricht , The Netherlands
| | - Chris Van Hoof
- a imec The Netherlands - Wearable Health Solutions , Eindhoven , The Netherlands.,d Katholieke Universiteit Leuven-ESAT , Leuven , Belgium
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42
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Scaramuzza AE, Arnaldi C, Cherubini V, Piccinno E, Rabbone I, Toni S, Tumini S, Candela G, Cipriano P, Ferrito L, Lenzi L, Tinti D, Cohen O, Lombardo F. Use of the predictive low glucose management (PLGM) algorithm in Italian adolescents with type 1 diabetes: CareLink™ data download in a real-world setting. Acta Diabetol 2017; 54:317-319. [PMID: 27744516 DOI: 10.1007/s00592-016-0927-0] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/14/2016] [Accepted: 10/04/2016] [Indexed: 10/20/2022]
Affiliation(s)
- Andrea E Scaramuzza
- Department of Pediatrics, ASST Fatebenefratelli Sacco, Ospedale Luigi Sacco, University of Milano, Via G.B. Grassi 74, 20157, Milan, Italy.
| | - Claudia Arnaldi
- UOS Diabetologia Pediatrica ASL Viterbo, Via E. Fermi 10, 01100, Viterbo, Italy
| | - Valentino Cherubini
- Division of Pediatric and Adolescent Diabetes, Department of Women's and Children's Health, AOU Salesi Hospital, Via Corridoni 11, 60123, Ancona, Italy
| | - Elvira Piccinno
- Division of Pediatric and Adolescent Endocrinology and Diabetes, Hospital Giovanni XXIII, Via Amendola 207, 70123, Bari, Italy
| | - Ivana Rabbone
- Department of Pediatrics, University of Turin, Piazza Polonia 94, 10126, Turin, Italy
| | - Sonia Toni
- Juvenile Diabetes Center, Anna Meyer Children's Hospital, Via Pieraccini 24, 50132, Florence, Italy
| | - Stefano Tumini
- Center of Pediatric Diabetology, University of Chieti, 66100, Chieti, Italy
| | - Gliberto Candela
- Department of Pediatrics, University of Messina, Via Consolare Valeria 1, 98125, Messina, Italy
| | - Paola Cipriano
- Center of Pediatric Diabetology, University of Chieti, 66100, Chieti, Italy
| | - Lucia Ferrito
- Division of Pediatric and Adolescent Diabetes, Department of Women's and Children's Health, AOU Salesi Hospital, Via Corridoni 11, 60123, Ancona, Italy
| | - Lorenzo Lenzi
- Juvenile Diabetes Center, Anna Meyer Children's Hospital, Via Pieraccini 24, 50132, Florence, Italy
| | - Davide Tinti
- Department of Pediatrics, University of Turin, Piazza Polonia 94, 10126, Turin, Italy
| | - Ohad Cohen
- Institute of Endocrinology, Sheba Medical Center, Derech Sheba 2, 52621, Tel-Hashomer, Ramat Gan, Israel
| | - Fortunato Lombardo
- Department of Pediatrics, University of Messina, Via Consolare Valeria 1, 98125, Messina, Italy
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Kovatchev B, Cheng P, Anderson SM, Pinsker JE, Boscari F, Buckingham BA, Doyle FJ, Hood KK, Brown SA, Breton MD, Chernavvsky D, Bevier WC, Bradley PK, Bruttomesso D, Del Favero S, Calore R, Cobelli C, Avogaro A, Ly TT, Shanmugham S, Dassau E, Kollman C, Lum JW, Beck RW. Feasibility of Long-Term Closed-Loop Control: A Multicenter 6-Month Trial of 24/7 Automated Insulin Delivery. Diabetes Technol Ther 2017; 19:18-24. [PMID: 27982707 DOI: 10.1089/dia.2016.0333] [Citation(s) in RCA: 92] [Impact Index Per Article: 13.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/02/2023]
Abstract
BACKGROUND In the past few years, the artificial pancreas-the commonly accepted term for closed-loop control (CLC) of blood glucose in diabetes-has become a hot topic in research and technology development. In the summer of 2014, we initiated a 6-month trial evaluating the safety of 24/7 CLC during free-living conditions. RESEARCH DESIGN AND METHODS Following an initial 1-month Phase 1, 14 individuals (10 males/4 females) with type 1 diabetes at three clinical centers in the United States and one in Italy continued with a 5-month Phase 2, which included 24/7 CLC using the wireless portable Diabetes Assistant (DiAs) developed at the University of Virginia Center for Diabetes Technology. Median subject characteristics were age 45 years, duration of diabetes 27 years, total daily insulin 0.53 U/kg/day, and baseline HbA1c 7.2% (55 mmol/mol). RESULTS Compared with the baseline observation period, the frequency of hypoglycemia below 3.9 mmol/L during the last 3 months of CLC was lower: 4.1% versus 1.3%, P < 0.001. This was accompanied by a downward trend in HbA1c from 7.2% (55 mmol/mol) to 7.0% (53 mmol/mol) at 6 months. HbA1c improvement was correlated with system use (Spearman r = 0.55). The user experience was favorable with identified benefit particularly at night and overall trust in the system. There were no serious adverse events, severe hypoglycemia, or diabetic ketoacidosis. CONCLUSION We conclude that CLC technology has matured and is safe for prolonged use in patients' natural environment. Based on these promising results, a large randomized trial is warranted to assess long-term CLC efficacy and safety.
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Affiliation(s)
- Boris Kovatchev
- 1 University of Virginia Center for Diabetes Technology, Charlottesville, Virginia
| | - Peiyao Cheng
- 2 Jaeb Center for Health Research , Tampa, Florida
| | - Stacey M Anderson
- 1 University of Virginia Center for Diabetes Technology, Charlottesville, Virginia
| | | | | | - Bruce A Buckingham
- 5 Division of Pediatric Endocrinology and Diabetes, Department of Pediatrics, Stanford University School of Medicine , Stanford, California
| | - Francis J Doyle
- 6 Department of Chemical Engineering, University of California , Santa Barbara, Santa Barbara, California
- 7 Harvard John A. Paulson School of Engineering and Applied Sciences, Harvard University , Cambridge, Massachusetts
| | - Korey K Hood
- 5 Division of Pediatric Endocrinology and Diabetes, Department of Pediatrics, Stanford University School of Medicine , Stanford, California
| | - Sue A Brown
- 1 University of Virginia Center for Diabetes Technology, Charlottesville, Virginia
| | - Marc D Breton
- 1 University of Virginia Center for Diabetes Technology, Charlottesville, Virginia
| | - Daniel Chernavvsky
- 1 University of Virginia Center for Diabetes Technology, Charlottesville, Virginia
| | - Wendy C Bevier
- 3 William Sansum Diabetes Center , Santa Barbara, California
| | - Paige K Bradley
- 3 William Sansum Diabetes Center , Santa Barbara, California
| | | | | | | | | | | | - Trang T Ly
- 5 Division of Pediatric Endocrinology and Diabetes, Department of Pediatrics, Stanford University School of Medicine , Stanford, California
| | - Satya Shanmugham
- 5 Division of Pediatric Endocrinology and Diabetes, Department of Pediatrics, Stanford University School of Medicine , Stanford, California
| | - Eyal Dassau
- 6 Department of Chemical Engineering, University of California , Santa Barbara, Santa Barbara, California
- 7 Harvard John A. Paulson School of Engineering and Applied Sciences, Harvard University , Cambridge, Massachusetts
| | | | - John W Lum
- 2 Jaeb Center for Health Research , Tampa, Florida
| | - Roy W Beck
- 2 Jaeb Center for Health Research , Tampa, Florida
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44
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Krentz AJ, Hompesch M. Glucose: archetypal biomarker in diabetes diagnosis, clinical management and research. Biomark Med 2016; 10:1153-1166. [DOI: 10.2217/bmm-2016-0170] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022] Open
Abstract
The clinical utility of diabetes biomarkers can be considered in terms of diagnosis, management and prediction of long-term vascular complications. Glucose satisfies all of these requirements. Thresholds of hyperglycemia diagnostic of diabetes reflect inflections that confer a risk of developing long-term microvascular complications. Degrees of hyperglycemia (impaired fasting glucose, impaired glucose tolerance) that lie below the diagnostic threshold for diabetes identify individuals at risk of progression to diabetes and/or development of atherothrombotic cardiovascular disease. Self-measured glucose levels usefully complement hemoglobin A1c levels to guide daily management decisions. Continuous glucose monitoring provides detailed real-time data that is of value in clinical decision making, assessing response to new diabetes drugs and the development of closed-loop artificial pancreas technology.
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Affiliation(s)
- Andrew J Krentz
- Institute for Translational Medicine, Clore Life Sciences, University of Buckingham, Hunter Street, Buckingham, MK18 1EG, UK
- Profil Institute for Clinical Research, 855 3rd Avenue Suite 4400, Chula Vista, CA 91911, USA
| | - Marcus Hompesch
- Profil Institute for Clinical Research, 855 3rd Avenue Suite 4400, Chula Vista, CA 91911, USA
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45
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Affiliation(s)
- Enrique Campos-Náñez
- Center for Diabetes Technology, University of Virginia Health System , Charlottesville, Virginia
| | - Boris P Kovatchev
- Center for Diabetes Technology, University of Virginia Health System , Charlottesville, Virginia
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Cefalu WT, Boulton AJM, Tamborlane WV, Moses RG, LeRoith D, Greene EL, Hu FB, Bakris G, Wylie-Rosett J, Rosenstock J, Weinger K, Blonde L, de Groot M, Rich SS, D'Alessio D, Riddle MC, Reynolds L. Diabetes Care: "Lagniappe" and "Seeing Is Believing"! Diabetes Care 2016; 39:1069-71. [PMID: 27631957 PMCID: PMC5013720 DOI: 10.2337/dc16-0891] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/03/2023]
Affiliation(s)
- William T Cefalu
- Pennington Biomedical Research Center, Louisiana State University, Baton Rouge, LA
| | | | | | | | - Derek LeRoith
- Division of Endocrinology, Diabetes and Bone Disease, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Eddie L Greene
- Division of Nephrology and Hypertension, Mayo Clinic, Rochester, MN
| | - Frank B Hu
- Departments of Nutrition and Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA
| | - George Bakris
- ASH Comprehensive Hypertension Center, Department of Medicine, Division of Endocrinology, Diabetes and Metabolism, The University of Chicago Medicine, Chicago, IL
| | - Judith Wylie-Rosett
- Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY
| | - Julio Rosenstock
- Dallas Diabetes and Endocrine Center at Medical City, Dallas, TX
| | - Katie Weinger
- Joslin Diabetes Center, Harvard Medical School, Boston, MA
| | - Lawrence Blonde
- Ochsner Diabetes Clinical Research Unit, Frank Riddick Diabetes Institute, Department of Endocrinology, Ochsner Medical Center, New Orleans, LA
| | - Mary de Groot
- Indiana University School of Medicine, Indianapolis, IN
| | - Stephen S Rich
- Department of Public Health Sciences, University of Virginia, Charlottesville, VA
| | - David D'Alessio
- Division of Endocrinology, Diabetes and Metabolism, Duke University, Durham, NC
| | - Matthew C Riddle
- Division of Endocrinology, Diabetes and Clinical Nutrition, Oregon Health & Science University, Portland, OR
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