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Rubin-Falcone H, Lee JM, Wiens J. Learning control-ready forecasters for Blood Glucose Management. Comput Biol Med 2024; 180:108995. [PMID: 39126789 DOI: 10.1016/j.compbiomed.2024.108995] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2024] [Revised: 07/29/2024] [Accepted: 08/02/2024] [Indexed: 08/12/2024]
Abstract
Type 1 diabetes (T1D) presents a significant health challenge, requiring patients to actively manage their blood glucose (BG) levels through regular bolus insulin administration. Automated control solutions based on machine learning (ML) models could reduce the need for manual patient intervention. However, the accuracy of current models falls short of what is needed. This is due in part to the fact that these models are often trained on data collected using a basal bolus (BB) strategy, which results in substantial entanglement between bolus insulin and carbohydrate intake. Under standard training approaches, this entanglement can lead to inaccurate forecasts in a control setting, ultimately resulting in poor BG management. To address this, we propose a novel algorithm for training BG forecasters that disentangles the effects of insulin and carbohydrates. By exploiting correction bolus values and leveraging the monotonic effect of insulin on BG, our method accurately captures the independent effects of insulin and carbohydrates on BG. Using an FDA-approved simulator, we evaluated our approach on 10 individuals across 30 days of data. Our approach achieved on average higher time in range compared to standard approaches (81.1% [95% confidence interval (CI) 80.3,81.9] vs 53.6% [95%CI 52.7,54.6], p<0.001), indicating that our approach is able to reliably maintain healthy BG levels in simulated individuals, while baseline approaches are not. Utilizing proxy metrics, our approach also demonstrates potential for improved control on three real world datasets, paving the way for advancements in ML-based BG management.
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Affiliation(s)
- Harry Rubin-Falcone
- Division of Computer Science and Engineering, University of Michigan, 2260 Hayward St, Ann Arbor, 48109, MI, USA.
| | - Joyce M Lee
- Division of Pediatric Endocrinology, University of Michigan, 1540 E Hospital Dr, Ann Arbor, 48109, MI, USA
| | - Jenna Wiens
- Division of Computer Science and Engineering, University of Michigan, 2260 Hayward St, Ann Arbor, 48109, MI, USA
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2
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Filo M, Gupta A, Khammash M. Anti-windup strategies for biomolecular control systems facilitated by model reduction theory for sequestration networks. SCIENCE ADVANCES 2024; 10:eadl5439. [PMID: 39167660 PMCID: PMC11338268 DOI: 10.1126/sciadv.adl5439] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/06/2023] [Accepted: 07/11/2024] [Indexed: 08/23/2024]
Abstract
Robust perfect adaptation, a system property whereby a variable adapts to persistent perturbations at steady state, has been recently realized in living cells using genetic integral controllers. In certain scenarios, such controllers may lead to "integral windup," an adverse condition caused by saturating control elements, which manifests as error accumulation, poor dynamic performance, or instabilities. To mitigate this effect, we here introduce several biomolecular anti-windup topologies and link them to control-theoretic anti-windup strategies. This is achieved using a novel model reduction theory that we develop for reaction networks with fast sequestration reactions. We then show how the anti-windup topologies can be realized as reaction networks and propose intein-based genetic designs for their implementation. We validate our designs through simulations on various biological systems, including models of patients with type I diabetes and advanced biomolecular proportional-integral-derivative (PID) controllers, demonstrating their efficacy in mitigating windup effects and ensuring safety.
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Pathmanathan P, Aycock K, Badal A, Bighamian R, Bodner J, Craven BA, Niederer S. Credibility assessment of in silico clinical trials for medical devices. PLoS Comput Biol 2024; 20:e1012289. [PMID: 39116026 PMCID: PMC11309390 DOI: 10.1371/journal.pcbi.1012289] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2023] [Accepted: 07/01/2024] [Indexed: 08/10/2024] Open
Abstract
In silico clinical trials (ISCTs) are an emerging method in modeling and simulation where medical interventions are evaluated using computational models of patients. ISCTs have the potential to provide cost-effective, time-efficient, and ethically favorable alternatives for evaluating the safety and effectiveness of medical devices. However, ensuring the credibility of ISCT results is a significant challenge. This paper aims to identify unique considerations for assessing the credibility of ISCTs and proposes an ISCT credibility assessment workflow based on recently published model assessment frameworks. First, we review various ISCTs described in the literature, carefully selected to showcase the range of methodological options available. These studies cover a wide variety of devices, reasons for conducting ISCTs, patient model generation approaches including subject-specific versus 'synthetic' virtual patients, complexity levels of devices and patient models, incorporation of clinician or clinical outcome models, and methods for integrating ISCT results with real-world clinical trials. We next discuss how verification, validation, and uncertainty quantification apply to ISCTs, considering the range of ISCT approaches identified. Based on our analysis, we then present a hierarchical workflow for assessing ISCT credibility, using a general credibility assessment framework recently published by the FDA's Center for Devices and Radiological Health. Overall, this work aims to promote standardization in ISCTs and contribute to the wider adoption and acceptance of ISCTs as a reliable tool for evaluating medical devices.
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Affiliation(s)
- Pras Pathmanathan
- Center for Devices and Radiological Health, US Food and Drug Administration, Silver Spring, Maryland, United States of America
| | - Kenneth Aycock
- Center for Devices and Radiological Health, US Food and Drug Administration, Silver Spring, Maryland, United States of America
| | - Andreu Badal
- Center for Devices and Radiological Health, US Food and Drug Administration, Silver Spring, Maryland, United States of America
| | - Ramin Bighamian
- Center for Devices and Radiological Health, US Food and Drug Administration, Silver Spring, Maryland, United States of America
| | - Jeff Bodner
- Medtronic, PLC., Minneapolis, Minnesota, United States of America
| | - Brent A. Craven
- Center for Devices and Radiological Health, US Food and Drug Administration, Silver Spring, Maryland, United States of America
| | - Steven Niederer
- National Heart and Lung Institute, Imperial College, London, United Kingdom
- The Alan Turing Institute, London, United Kingdom
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4
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Visentin R, Schiavon M, Bonet J, Riz M, Wagenhuber B, Man CD. Tailoring the Padova Type 2 Diabetes Simulator for Treatment Guidance in Target Populations. IEEE Trans Biomed Eng 2024; 71:1780-1788. [PMID: 38198258 DOI: 10.1109/tbme.2024.3352153] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/12/2024]
Abstract
OBJECTIVE The Padova type 2 diabetes (T2D) simulator (T2DS) has been recently proposed to optimize T2D treatments including novel long-acting insulins. It consists of a physiological model and an in silico population describing glucose dynamics, derived from early-stage T2D subjects studied with sophisticated tracer-based experimental techniques. This limits T2DS domain of validity to this specific sub-population. Conversely, running simulations in insulin-naïve or advanced T2D subjects, would be more valuable. However, it is rarely possible or cost-effective to run complex experiments in such populations. Therefore, we propose a method for tuning the T2DS to any desired T2D sub-population using published clinical data. As case study, we extended the T2DS to insulin-naïve T2D subjects, who need to start insulin therapy to compensate the reduced insulin function. METHODS T2DS model was identified based on literature data of the target population. The estimated parameters were used to generate a virtual cohort of insulin-naïve T2D subjects (inC1). A model of basal insulin degludec (IDeg) was also incorporated into the T2DS to enable basal insulin therapy. The resulting tailored T2DS was assessed by simulating IDeg therapy initiation and comparing simulated vs. clinical trial outcomes. For further validation, this procedure was reiterated to generate a new cohort of insulin-naïve T2D (inC2) assuming inC1 as target population. RESULTS No statistically significant differences were found when comparing fasting plasma glucose and IDeg dose, neither in clinical data vs. inC1, nor inC1 vs. inC2. CONCLUSIONS The tuned T2DS allowed reproducing the main findings of clinical studies in insulin-naïve T2D subjects. SIGNIFICANCE The proposed methodology makes the Padova T2DS usable for supporting treatment guidance in target T2D populations.
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Forlenza GP, Tabatabai I, Lewis DM. Point-Counterpoint: The Need for Do-It-Yourself (DIY) Open Source (OS) AID Systems in Type 1 Diabetes Management. Diabetes Technol Ther 2024. [PMID: 38669472 DOI: 10.1089/dia.2024.0073] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 04/28/2024]
Abstract
In the last decade, technology developed by people with diabetes and their loved ones has added to the options for diabetes management. One such example is that of automated insulin delivery (AID) algorithms, which were created and shared as open source by people living with type 1 diabetes (T1D) years before commercial systems were first available. Now, numerous options for commercial systems exist in some countries, yet tens of thousands of people with diabetes are still choosing Open-Source AID (OS-AID), previously called "do-it-yourself" (DIY) systems, which are noncommercial versions of these open-source AID systems. In this article, we provide point and counterpoint perspectives regarding (1) safety and efficacy, (2) regulation and support, (3) user choice and flexibility, (4) access and affordability, and (5) patient and provider education, for open source and commercial AID systems. The perspectives reflected here include that of a person living with T1D who uses and has developed OS-AID systems, a physician-researcher based in the United States who conducts clinical trials to support development of commercial AID systems and supports people with diabetes using all types of AID, and an endocrinologist with T1D who uses both systems and treats people with diabetes using all types of AID.
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Affiliation(s)
- Gregory P Forlenza
- Barbara Davis Center, University of Colorado Anschutz Medical Campus, Aurora, Colorado, USA
| | - Ideen Tabatabai
- Barbara Davis Center, University of Colorado Anschutz Medical Campus, Aurora, Colorado, USA
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Chalumuri YR, Arabidarrehdor G, Tivay A, Sampson CM, Khan M, Kinsky M, Kramer GC, Hahn JO, Scully CG, Bighamian R. A Lumped-Parameter Model of the Cardiovascular System Response for Evaluating Automated Fluid Resuscitation Systems. IEEE ACCESS : PRACTICAL INNOVATIONS, OPEN SOLUTIONS 2024; 12:62511-62525. [PMID: 38872754 PMCID: PMC11170980 DOI: 10.1109/access.2024.3395008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/15/2024]
Abstract
Physiological closed-loop controlled (PCLC) medical devices, such as those designed for blood pressure regulation, can be tested for safety and efficacy in real-world clinical settings. However, relying solely on limited animal and clinical studies may not capture the diverse range of physiological conditions. Credible mathematical models can complement these studies by allowing the testing of the device against simulated patient scenarios. This research involves the development and validation of a low-order lumped-parameter mathematical model of the cardiovascular system's response to fluid perturbation. The model takes rates of hemorrhage and fluid infusion as inputs and provides hematocrit and blood volume, heart rate, stroke volume, cardiac output and mean arterial blood pressure as outputs. The model was calibrated using data from 27 sheep subjects, and its predictive capability was evaluated through a leave-one-out cross-validation procedure, followed by independent validation using 12 swine subjects. Our findings showed small model calibration error against the training dataset, with the normalized root-mean-square error (NRMSE) less than 10% across all variables. The mathematical model and virtual patient cohort generation tool demonstrated a high level of predictive capability and successfully generated a sufficient number of subjects that closely resembled the test dataset. The average NRMSE for the best virtual subject, across two distinct samples of virtual subjects, was below 12.7% and 11.9% for the leave-one-out cross-validation and independent validation dataset. These findings suggest that the model and virtual cohort generator are suitable for simulating patient populations under fluid perturbation, indicating their potential value in PCLC medical device evaluation.
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Affiliation(s)
- Yekanth Ram Chalumuri
- Department of Mechanical Engineering, University of Maryland, College Park, MD 20742, USA
| | - Ghazal Arabidarrehdor
- Department of Mechanical Engineering, University of Maryland, College Park, MD 20742, USA
| | - Ali Tivay
- Department of Mechanical Engineering, University of Maryland, College Park, MD 20742, USA
| | - Catherine M Sampson
- Department of Anesthesiology, The University of Texas Medical Branch, Galveston, TX 77555, USA
| | - Muzna Khan
- Department of Anesthesiology, The University of Texas Medical Branch, Galveston, TX 77555, USA
| | - Michael Kinsky
- Department of Anesthesiology, The University of Texas Medical Branch, Galveston, TX 77555, USA
| | - George C Kramer
- Department of Anesthesiology, The University of Texas Medical Branch, Galveston, TX 77555, USA
| | - Jin-Oh Hahn
- Department of Mechanical Engineering, University of Maryland, College Park, MD 20742, USA
| | - Christopher G Scully
- Office of Science and Engineering Laboratories, Center for Devices and Radiological Health, United States Food and Drug Administration, Silver Spring, MD 20993, USA
| | - Ramin Bighamian
- Office of Science and Engineering Laboratories, Center for Devices and Radiological Health, United States Food and Drug Administration, Silver Spring, MD 20993, USA
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Herrgårdh T, Simonsson C, Ekstedt M, Lundberg P, Stenkula KG, Nyman E, Gennemark P, Cedersund G. A multi-scale digital twin for adiposity-driven insulin resistance in humans: diet and drug effects. Diabetol Metab Syndr 2023; 15:250. [PMID: 38044443 PMCID: PMC10694923 DOI: 10.1186/s13098-023-01223-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/07/2023] [Accepted: 11/15/2023] [Indexed: 12/05/2023] Open
Abstract
BACKGROUND The increased prevalence of insulin resistance is one of the major health risks in society today. Insulin resistance involves both short-term dynamics, such as altered meal responses, and long-term dynamics, such as the development of type 2 diabetes. Insulin resistance also occurs on different physiological levels, ranging from disease phenotypes to organ-organ communication and intracellular signaling. To better understand the progression of insulin resistance, an analysis method is needed that can combine different timescales and physiological levels. One such method is digital twins, consisting of combined mechanistic mathematical models. We have previously developed a model for short-term glucose homeostasis and intracellular insulin signaling, and there exist long-term weight regulation models. Herein, we combine these models into a first interconnected digital twin for the progression of insulin resistance in humans. METHODS The model is based on ordinary differential equations representing biochemical and physiological processes, in which unknown parameters were fitted to data using a MATLAB toolbox. RESULTS The interconnected twin correctly predicts independent data from a weight increase study, both for weight-changes, fasting plasma insulin and glucose levels, and intracellular insulin signaling. Similarly, the model can predict independent weight-change data in a weight loss study with the weight loss drug topiramate. The model can also predict non-measured variables. CONCLUSIONS The model presented herein constitutes the basis for a new digital twin technology, which in the future could be used to aid medical pedagogy and increase motivation and compliance and thus aid in the prevention and treatment of insulin resistance.
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Affiliation(s)
- Tilda Herrgårdh
- Department of Biomedical Engineering, Linköping University, Linköping, Sweden
| | - Christian Simonsson
- Department of Biomedical Engineering, Linköping University, Linköping, Sweden
- Center for Medical Image Science and Visualization (CMIV), Linköping University, Linköping, Sweden
| | - Mattias Ekstedt
- Center for Medical Image Science and Visualization (CMIV), Linköping University, Linköping, Sweden
- Department of Health, Medicine, and Caring Sciences, Linköping University, Linköping, Sweden
| | - Peter Lundberg
- Center for Medical Image Science and Visualization (CMIV), Linköping University, Linköping, Sweden
- Department of Health, Medicine, and Caring Sciences, Linköping University, Linköping, Sweden
- Department of Radiation Physics, Linköping University, Linköping, Sweden
| | - Karin G Stenkula
- Department of Experimental Medical Science, Lund University, Lund, Sweden
| | - Elin Nyman
- Department of Biomedical Engineering, Linköping University, Linköping, Sweden
| | - Peter Gennemark
- Department of Biomedical Engineering, Linköping University, Linköping, Sweden
- Drug Metabolism and Pharmacokinetics, Research and Early Development, Cardiovascular, Renal and Metabolism (CVRM), AstraZeneca, BioPharmaceuticals R&D, Gothenburg, Sweden
| | - Gunnar Cedersund
- Department of Biomedical Engineering, Linköping University, Linköping, Sweden.
- Center for Medical Image Science and Visualization (CMIV), Linköping University, Linköping, Sweden.
- School of Medical Sciences, Faculty of Medicine and Health, Örebro University, Örebro, Sweden.
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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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9
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Moscoso-Vasquez M, Fabris C, Breton MD. Performance Effect of Adjusting Insulin Sensitivity for Model-Based Automated Insulin Delivery Systems. J Diabetes Sci Technol 2023; 17:1470-1481. [PMID: 37864340 PMCID: PMC10658700 DOI: 10.1177/19322968231206798] [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: 10/22/2023]
Abstract
BACKGROUND Model predictive control (MPC) has become one of the most popular control strategies for automated insulin delivery (AID) in type 1 diabetes (T1D). These algorithms rely on a prediction model to determine the best insulin dosing every sampling time. Although these algorithms have been shown to be safe and effective for glucose management through clinical trials, managing the ever-fluctuating relationship between insulin delivery and resulting glucose uptake (aka insulin sensitivity, IS) remains a challenge. We aim to evaluate the effect of informing an AID system with IS on the performance of the system. METHOD The University of Virginia (UVA) MPC control-based hybrid closed-loop (HCL) and fully closed-loop (FCL) system was used. One-day simulations at varying levels of IS were run with the UVA/Padova T1D Simulator. The AID system was informed with an estimated value of IS obtained through a mixed meal glucose tolerance test. Relevant controller parameters are updated to inform insulin dosing of IS. Performance of the HCL/FCL system with and without information of the changing IS was assessed using a novel performance metric penalizing the time outside the target glucose range. RESULTS Feedback in AID systems provides a certain degree tolerance to changes in IS. However, IS-informed bolus and basal dosing improve glycemic outcomes, providing increased protection against hyperglycemia and hypoglycemia according to the individual's physiological state. CONCLUSIONS The proof-of-concept analysis presented here shows the potentially beneficial effects on system performance of informing the AID system with accurate estimates of IS. In particular, when considering reduced IS, the informed controller provides increased protection against hyperglycemia compared with the naïve controller. Similarly, reduced hypoglycemia is obtained for situations with increased IS. Further tailoring of the adaptation schemes proposed in this work is needed to overcome the increased hypoglycemia observed in the more resistant cases and to optimize the performance of the adaptation method.
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Affiliation(s)
| | - 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
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10
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Zhu T, Li K, Georgiou P. Offline Deep Reinforcement Learning and Off-Policy Evaluation for Personalized Basal Insulin Control in Type 1 Diabetes. IEEE J Biomed Health Inform 2023; 27:5087-5098. [PMID: 37607154 DOI: 10.1109/jbhi.2023.3303367] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/24/2023]
Abstract
Recent advancements in hybrid closed-loop systems, also known as the artificial pancreas (AP), have been shown to optimize glucose control and reduce the self-management burdens for people living with type 1 diabetes (T1D). AP systems can adjust the basal infusion rates of insulin pumps, facilitated by real-time communication with continuous glucose monitoring. Deep reinforcement learning (DRL) has introduced new paradigms of basal insulin control algorithms. However, all the existing DRL-based AP controllers require extensive random online interactions between the agent and environment. While this can be validated in T1D simulators, it becomes impractical in real-world clinical settings. To this end, we propose an offline DRL framework that can develop and validate models for basal insulin control entirely offline. It comprises a DRL model based on the twin delayed deep deterministic policy gradient and behavior cloning, as well as off-policy evaluation (OPE) using fitted Q evaluation. We evaluated the proposed framework on an in silico dataset generated by the UVA/Padova T1D simulator, and the OhioT1DM dataset, a real clinical dataset. The performance on the in silico dataset shows that the offline DRL algorithm significantly increased time in range while reducing time below range and time above range for both adult and adolescent groups. Then, we used the OPE to estimate model performance on the clinical dataset, where a notable increase in policy values was observed for each subject. The results demonstrate that the proposed framework is a viable and safe method for improving personalized basal insulin control in T1D.
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Simalatsar A. Synthetic biomedical data generation in support of In Silico Clinical Trials. Front Big Data 2023; 6:1085571. [PMID: 37655113 PMCID: PMC10466133 DOI: 10.3389/fdata.2023.1085571] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Accepted: 07/10/2023] [Indexed: 09/02/2023] Open
Abstract
Living in the era of Big Data, one may advocate that the additional synthetic generation of data is redundant. However, to be able to truly say whether it is valid or not, one needs to focus more on the meaning and quality of data than on the quantity. In some domains, such as biomedical and translational sciences, data privacy still holds a higher importance than data sharing. This by default limits access to valuable research data. Intensive discussion, agreements, and conventions among different medical research players, as well as effective techniques and regulations for data anonymization, already made a big step toward simplification of data sharing. However, the situation with the availability of data about rare diseases or outcomes of novel treatments still requires costly and risky clinical trials and, thus, would greatly benefit from smart data generation. Clinical trials and tests on animals initiate a cyclic procedure that may involve multiple redesigns and retesting, which typically takes two or three years for medical devices and up to eight years for novel medicines, and costs between 10 and 20 million euros. The US Food and Drug Administration (FDA) acknowledges that for many novel devices, practical limitations require alternative approaches, such as computer modeling and engineering tests, to conduct large, randomized studies. In this article, we give an overview of global initiatives advocating for computer simulations in support of the 3R principles (Replacement, Reduction, and Refinement) in humane experimentation. We also present several research works that have developed methodologies of smart and comprehensive generation of synthetic biomedical data, such as virtual cohorts of patients, in support of In Silico Clinical Trials (ISCT) and discuss their common ground.
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Affiliation(s)
- Alena Simalatsar
- Institute of Systems Engineering, University of Applied Sciences and Arts - Western Switzerland, Sion, Switzerland
- SENSE - Innovation and Research Center, Sion, Switzerland
- SENSE - Innovation and Research Center, Lausanne, Switzerland
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Gomez LA, Toye AA, Hum RS, Kleinberg S. Simulating Realistic Continuous Glucose Monitor Time Series By Data Augmentation. J Diabetes Sci Technol 2023:19322968231181138. [PMID: 37350111 DOI: 10.1177/19322968231181138] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 06/24/2023]
Abstract
BACKGROUND Simulated data are a powerful tool for research, enabling benchmarking of blood glucose (BG) forecasting and control algorithms. However, expert created models provide an unrealistic view of real-world performance, as they lack the features that make real data challenging, while black-box approaches such as generative adversarial networks do not enable systematic tests to diagnose model performance. METHODS To address this, we propose a method that learns missingness and error properties of continuous glucose monitor (CGM) data collected from people with type 1 diabetes (OpenAPS, OhioT1DM, RCT, and Racial-Disparity), and then augments simulated BG data with these properties. On the task of BG forecasting, we test how well our method brings performance closer to that of real CGM data compared with current simulation practices for missing data (random dropout) and error (Gaussian noise, CGM error model). RESULTS Our methods had the smallest performance difference versus real data compared with random dropout and Gaussian noise when individually testing the effects of missing data and error on simulated BG in most cases. When combined, our approach was significantly better than Gaussian noise and random dropout for all data sets except OhioT1DM. Our error model significantly improved results on diverse data sets. CONCLUSIONS We find a significant gap between BG forecasting performance on simulated and real data, and our method can be used to close this gap. This will enable researchers to rigorously test algorithms and provide realistic estimates of real-world performance without overfitting to real data or at the expense of data collection.
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Affiliation(s)
| | | | - R Stanley Hum
- The Montreal Children's Hospital, McGill University Health Centre, Montreal, QC, Canada
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13
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Estremera E, Beneyto A, Cabrera A, Contreras I, Vehí J. Intermittent closed-loop blood glucose control for people with type 1 diabetes on multiple daily injections. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 236:107568. [PMID: 37137221 DOI: 10.1016/j.cmpb.2023.107568] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/01/2023] [Revised: 04/13/2023] [Accepted: 04/24/2023] [Indexed: 05/05/2023]
Abstract
BACKGROUND AND OBJECTIVES Recent advances in Automated Insulin Delivery systems have been shown to dramatically improve glycaemic control and reduce the risk of hypoglycemia in people with type 1 diabetes. However, they are complex systems that require specific training and are not affordable for most. Attempts to reduce the gap with closed-loop therapies using advanced dosing advisors have so far failed, mainly because they require too much human intervention. With the advent of smart insulin pens, one of the main constraints (having reliable bolus and meal information) disappears and new strategies can be employed. This is our starting hypothesis, which we have validated in a very demanding simulator. In this paper, we propose an intermittent closed-loop control system specifically intended for multiple daily injection therapy to bring the benefits of artificial pancreas to the application of multiple daily injections. METHODS The proposed control algorithm is based on model predictive control and integrates two patient-driven control actions. Correction insulin boluses are automatically computed and recommended to the patient to minimize the duration of hyperglycemia. Rescue carbohydrates are also triggered to avoid hypoglycemia episodes. The algorithm can adapt to different patient lifestyles with customizable triggering conditions, closing the gap between practicality and performance. The proposed algorithm is compared with conventional open-loop therapy, and its superiority is demonstrated through extensive in silico evaluations using realistic cohorts and scenarios. The evaluations were conducted in a cohort of 47 virtual patients. We also provide detailed explanations of the implementation, imposed constraints, triggering conditions, cost functions, and penalties for the algorithm. RESULTS The in-silico outcomes combining the proposed closed-loop strategy with slow-acting insulin analog injections at 09:00 h resulted in percentages of time in range (TIR) (70-180 mg/dL) of 69.5%, 70.6%, and 70.4% for glargine-100, glargine-300, and degludec-100, respectively, and injections at 20:00 h resulted in percentages of TIR of 70.5%, 70.3%, and 71.6%, respectively. In all the cases, the percentages of TIR were considerably higher than those obtained from the open-loop strategy, being only 50.7%, 53.9%, and 52.2% for daytime injection and 55.5%, 54.1%, and 56.9% for nighttime injection. Overall, the occurrence of hypoglycemia and hyperglycemia was notably reduced using our approach. CONCLUSIONS Event-triggering model predictive control in the proposed algorithm is feasible and may meet clinical targets for people with type 1 diabetes.
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Affiliation(s)
- Ernesto Estremera
- Department of Electrical, Electronic and Automatic Engineering, University of Girona, 17004 Girona, Spain.
| | - Aleix Beneyto
- Department of Electrical, Electronic and Automatic Engineering, University of Girona, 17004 Girona, Spain.
| | - Alvis Cabrera
- Department of Electrical, Electronic and Automatic Engineering, University of Girona, 17004 Girona, Spain.
| | - Iván Contreras
- Department of Electrical, Electronic and Automatic Engineering, University of Girona, 17004 Girona, Spain.
| | - Josep Vehí
- Department of Electrical, Electronic and Automatic Engineering, University of Girona, 17004 Girona, Spain; Centro de Investigación Biomédica en Red de Diabetes y Enfermedades Metabólicas Asociadas, Instituto de Salud Carlos III, Spain.
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14
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Zou Y, Chu Z, Guo J, Liu S, Ma X, Guo J. Minimally invasive electrochemical continuous glucose monitoring sensors: Recent progress and perspective. Biosens Bioelectron 2023; 225:115103. [PMID: 36724658 DOI: 10.1016/j.bios.2023.115103] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2022] [Revised: 12/25/2022] [Accepted: 01/23/2023] [Indexed: 01/26/2023]
Abstract
Diabetes and its complications are seriously threatening the health and well-being of hundreds of millions of people. Glucose levels are essential indicators of the health conditions of diabetics. Over the past decade, concerted efforts in various fields have led to significant advances in glucose monitoring technology. In particular, the rapid development of continuous glucose monitoring (CGM) based on electrochemical sensing principles has great potential to overcome the limitations of self-monitoring blood glucose (SMBG) in continuously tracking glucose trends, evaluating diabetes treatment options, and improving the quality of life of diabetics. However, the applications of minimally invasive electrochemical CGM sensors are still limited owing to the following aspects: i) invasiveness, ii) short lifespan, iii) biocompatibility, and iv) calibration and prediction. In recent years, the performance of minimally invasive electrochemical CGM systems (CGMSs) has been significantly improved owing to breakthrough developments in new materials and key technologies. In this review, we summarize the history of commercial CGMSs, the development of sensing principles, and the research progress of minimally invasive electrochemical CGM sensors in reducing the invasiveness of implanted probes, maintaining enzyme activity, and improving the biocompatibility of the sensor interface. In addition, this review also introduces calibration algorithms and prediction algorithms applied to CGMSs and describes the application of machine learning algorithms for glucose prediction.
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Affiliation(s)
- Yuanyuan Zou
- University of Electronic Science and Technology of China, 611731, Chengdu, China
| | - Zhengkang Chu
- School of Sensing Science and Engineering, Shanghai Jiaotong University, Shanghai, China
| | - Jiuchuan Guo
- University of Electronic Science and Technology of China, 611731, Chengdu, China; Chongqing Medical University, 400016, Chongqing, China
| | - Shan Liu
- Department of Laboratory Medicine, Sichuan Academy of Medical Sciences and Sichuan Provincial People's Hospital, University of Electronic Science and Technology, Chengdu, 610072, China.
| | - Xing Ma
- School of Materials Science and Engineering, Harbin Institute of Technology (Shenzhen), Shenzhen, 518055, China.
| | - Jinhong Guo
- Chongqing Medical University, 400016, Chongqing, China; School of Sensing Science and Engineering, Shanghai Jiaotong University, Shanghai, China.
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15
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E-DES-PROT: A novel computational model to describe the effects of amino acids and protein on postprandial glucose and insulin dynamics in humans. iScience 2023; 26:106218. [PMID: 36895641 PMCID: PMC9989689 DOI: 10.1016/j.isci.2023.106218] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2022] [Revised: 01/27/2023] [Accepted: 02/13/2023] [Indexed: 02/19/2023] Open
Abstract
Current computational models of whole-body glucose homeostasis describe physiological processes by which insulin regulates circulating glucose concentrations. While these models perform well in response to oral glucose challenges, interaction with other nutrients that impact postprandial glucose metabolism, such as amino acids (AAs), is not considered. Here, we developed a computational model of the human glucose-insulin system, which incorporates the effects of AAs on insulin secretion and hepatic glucose production. This model was applied to postprandial glucose and insulin time-series data following different AA challenges (with and without co-ingestion of glucose), dried milk protein ingredients, and dairy products. Our findings demonstrate that this model allows accurate description of postprandial glucose and insulin dynamics and provides insight into the physiological processes underlying meal responses. This model may facilitate the development of computational models that describe glucose homeostasis following the intake of multiple macronutrients, while capturing relevant features of an individual's metabolic health.
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16
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Renard E. Automated insulin delivery systems: from early research to routine care of type 1 diabetes. Acta Diabetol 2023; 60:151-161. [PMID: 35994106 DOI: 10.1007/s00592-022-01929-5] [Citation(s) in RCA: 17] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/11/2022] [Accepted: 06/22/2022] [Indexed: 01/24/2023]
Abstract
Automated insulin delivery (AID) systems, so-called closed-loop systems or artificial pancreas, are based upon the concept of insulin supply driven by blood glucose levels and their variations according to body glucose needs, glucose intakes and insulin action. They include a continuous glucose monitoring device which provides a signal to a control algorithm tuning insulin delivery from an infusion pump. The control algorithm is the key of the system since it commands insulin administration in order to maintain blood glucose in a predefined target range and close to a near-normal glucose level. The last two decades have shown dramatic advances toward the use in free life of AID systems for routine care of type 1 diabetes through step-by-step demonstrations of feasibility, safety and efficacy in successive hospital, transitional and outpatient trials. Because of the constraints of pharmacokinetics and dynamics of subcutaneous insulin delivery, the currently available AID systems are all 'hybrid' or 'semi-automated' insulin delivery systems with a need of meal and exercise announcements in order to anticipate rapid glucose variations through pre-meal bolus or pre-exercise reduction of infusion rate. Nevertheless, these AID systems significantly improve time spent in a near-normal range with a reduction of the risk of hypoglycemia and the mental load of managing diabetes in everyday life, representing a milestone in insulin therapy. Expected progression toward fully automated, further miniaturized and integrated, possibly implantable on long-term and more physiological closed-loop systems paves the way for a functional cure of type 1 diabetes.
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Affiliation(s)
- Eric Renard
- Department of Endocrinology, Diabetes, Nutrition, Montpellier University Hospital, Montpellier, France.
- INSERM Clinical Investigation Centre CIC 1411, Montpellier, France.
- Department of Physiology, Institute of Functional Genomics, CNRS, INSERM, University of Montpellier, Montpellier, France.
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17
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In Silico Evaluation of the Medtronic 780G System While Using the GS3 and Its Calibration-Free Successor, the G4S Sensor. Ann Biomed Eng 2023; 51:211-224. [PMID: 36125605 DOI: 10.1007/s10439-022-03079-9] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2022] [Accepted: 09/06/2022] [Indexed: 01/13/2023]
Abstract
In silico simulation studies using 5807 virtual patients with insulin dependent diabetes have been conducted to estimate the risk and efficacy with the closed-loop 780G pump when switching between Medtronic Guardian Sensor 3 (GS3) and Medtronic Guardian 4 Sensor (G4S), next generation calibration free glucose sensor. To demonstrate by utilizing a case study that captures the merits of in silico studies with single hormone insulin dependent virtual patients that include variability in pharmacokinetics/pharmacodynamics, age, gender, insulin sensitivity and BMIs. Also, to show that in silico studies can uniquely isolate the effect of a single variable on clinical outcomes. Simulation studies results were compared to clinical and commercial data and were separated by age groups and pump settings. The commercial data, the clinical study data and the simulation studies predicted that switching between GS3 to G4S will introduce a change in glucose average, percentage time between 70 and 180 mg/dL, and percentage time below 70 mg/dL of: 5.2, 3.4, and 3.1 mg/dL, - 1.1, 0.2, and - 1.1%, and - 0.6, - 1.0, and - 0.3%, respectively. We demonstrated that our simulation studies were able to predict the difference in glycemic outcomes when switching between different sensors in real world setting, better than a small clinical controlled study. As predicted, switching between GS3 and G4S sensors with the 780G system does not introduce clinical risk and maintain the clinical outcomes of the sensor. We demonstrated the ability of insulin dependent diabetes virtual patients to predict clinical outcomes and to augment or even replace some small clinical studies.
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18
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Liu W, Chen T, Liang B, Wang Y, Jin H. In-silico evaluation of an artificial pancreas achieving automatic glycemic control in patients with type 1 diabetes. Front Endocrinol (Lausanne) 2023; 14:1115436. [PMID: 36793281 PMCID: PMC9922739 DOI: 10.3389/fendo.2023.1115436] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/04/2022] [Accepted: 01/20/2023] [Indexed: 02/02/2023] Open
Abstract
Artificial pancreas (AP) is a useful tool for maintaining the blood glucose (BG) of patients with type 1 diabetes (T1D) within the euglycemic range. An intelligent controller has been developed based on general predictive control (GPC) for AP. This controller exhibits good performance with the UVA/Padova T1D mellitus simulator approved by the US Food and Drug Administration. In this work, the GPC controller was further evaluated under strict conditions, including a pump with noise and error, a CGM sensor with noise and error, a high carbohydrate intake, and a large population of 100 in-silico subjects. Test results showed that the subjects are in high risk for hypoglycemia. Thus, an insulin on board (IOB) calculator, as well as an adaptive control weighting parameter (AW) strategy, was introduced. The percentage of time spent in euglycemic range of the in-silico subjects was 86.0% ± 5.8%, and the patient group had a low risk of hypoglycemia with the GPC+IOB+AW controller. Moreover, the proposed AW strategy is more effective in hypoglycemia prevention and does not require any personalized data compared with the IOB calculator. Thus, the proposed controller realized an automatic control of the BG of patients with T1D without meal announcements and complex user interaction.
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Affiliation(s)
| | | | | | | | - Haoyu Jin
- *Correspondence: Haoyu Jin, ; Wenping Liu,
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19
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Ubl M, Koutny T, Della Cioppa A, De Falco I, Tarantino E, Scafuri U. Distributed Assessment of Virtual Insulin-Pump Settings Using SmartCGMS and DMMS.R for Diabetes Treatment. SENSORS (BASEL, SWITZERLAND) 2022; 22:9445. [PMID: 36502149 PMCID: PMC9739839 DOI: 10.3390/s22239445] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Revised: 11/28/2022] [Accepted: 11/29/2022] [Indexed: 06/17/2023]
Abstract
Diabetes is a heterogeneous group of diseases that share a common trait of elevated blood glucose levels. Insulin lowers this level by promoting glucose utilization, thus avoiding short- and long-term organ damage due to the elevated blood glucose level. A patient with diabetes uses an insulin pump to dose insulin. The pump uses a controller to compute and dose the correct amount of insulin to keep blood glucose levels in a safe range. Insulin-pump controller development is an ongoing process aiming at fully closed-loop control. Controllers entering the market must be evaluated for safety. We propose an evaluation method that exploits an FDA-approved diabetic patient simulator. The method evaluates a Cartesian product of individual insulin-pump parameters with a fine degree of granularity. As this is a computationally intensive task, the simulator executes on a distributed cluster. We identify safe and risky combinations of insulin-pump parameter settings by applying the binomial model and decision tree to this product. As a result, we obtain a tool for insulin-pump settings and controller safety assessment. In this paper, we demonstrate the tool with the Low-Glucose Suspend and OpenAPS controllers. For average ± standard deviation, LGS and OpenAPS exhibited 1.7 ± 0.6% and 3.2 ± 1.8% of local extrema (i.e., good insulin-pump settings) out of all the entire Cartesian products, respectively. A continuous region around the best-discovered settings (i.e., the global extremum) of the insulin-pump settings spread across 4.0 ± 1.1% and 4.1 ± 1.3% of the Cartesian products, respectively.
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Affiliation(s)
- Martin Ubl
- Department of Computer Science and Engineering, University of West Bohemia, Technicka 18, 330 01 Pilsen, Czech Republic
| | - Tomas Koutny
- Department of Computer Science and Engineering, New Technologies for Information Society, University of West Bohemia, Technicka 18, 330 01 Pilsen, Czech Republic
| | - Antonio Della Cioppa
- Natural Computation Lab, Department of Information Engineering, Electrical Engineering and Applied Mathematics, University of Salerno, Via Giovanni Paolo II 132, 84084 Fisciano, Italy
| | - Ivanoe De Falco
- ICAR-National Research Council of Italy, Via P. Castellino, 80131 Naples, Italy
| | - Ernesto Tarantino
- ICAR-National Research Council of Italy, Via P. Castellino, 80131 Naples, Italy
| | - Umberto Scafuri
- ICAR-National Research Council of Italy, Via P. Castellino, 80131 Naples, Italy
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20
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O'Donovan SD, Erdős B, Jacobs DM, Wanders AJ, Thomas EL, Bell JD, Rundle M, Frost G, Arts ICW, Afman LA, van Riel NAW. Quantifying the contribution of triglycerides to metabolic resilience through the mixed meal model. iScience 2022; 25:105206. [PMID: 36281448 DOI: 10.1016/j.isci.2022.105206] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2022] [Revised: 09/01/2022] [Accepted: 09/22/2022] [Indexed: 11/26/2022] Open
Abstract
Despite the pivotal role played by elevated circulating triglyceride levels in the pathophysiology of cardio-metabolic diseases many of the indices used to quantify metabolic health focus on deviations in glucose and insulin alone. We present the Mixed Meal Model, a computational model describing the systemic interplay between triglycerides, free fatty acids, glucose, and insulin. We show that the Mixed Meal Model can capture deviations in the post-meal excursions of plasma glucose, insulin, and triglyceride that are indicative of features of metabolic resilience; quantifying insulin resistance and liver fat; validated by comparison to gold-standard measures. We also demonstrate that the Mixed Meal Model is generalizable, applying it to meals with diverse macro-nutrient compositions. In this way, by coupling triglycerides to the glucose-insulin system the Mixed Meal Model provides a more holistic assessment of metabolic resilience from meal response data, quantifying pre-clinical metabolic deteriorations that drive disease development in overweight and obesity.
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Affiliation(s)
- Shauna D O'Donovan
- Division of Human Nutrition and Health, Wageningen University, Wageningen, the Netherlands.,Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, the Netherlands.,Eindhoven Artifical Intelligence Systems Institute (EAISI), Eindhoven University of Technology, Eindhoven, the Netherlands
| | - Balázs Erdős
- Maastricht Centre for Systems Biology (MaCSBio), Maastricht University, Maastricht, the Netherlands
| | - Doris M Jacobs
- Unilever Global Food Innovation Centre, Bronland 14, 6708WH Wageningen, the Netherlands
| | - Anne J Wanders
- Unilever Global Food Innovation Centre, Bronland 14, 6708WH Wageningen, the Netherlands
| | - E Louise Thomas
- Research Center for Optimal Health, School of Life Sciences, University of Westminster, London, UK
| | - Jimmy D Bell
- Research Center for Optimal Health, School of Life Sciences, University of Westminster, London, UK
| | - Milena Rundle
- Division of Diabetes, Endocrinology, and Metabolism, Department of Medicine, Imperial College London, London, UK
| | - Gary Frost
- Division of Diabetes, Endocrinology, and Metabolism, Department of Medicine, Imperial College London, London, UK
| | - Ilja C W Arts
- Maastricht Centre for Systems Biology (MaCSBio), Maastricht University, Maastricht, the Netherlands
| | - Lydia A Afman
- Division of Human Nutrition and Health, Wageningen University, Wageningen, the Netherlands
| | - Natal A W van Riel
- Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, the Netherlands.,Eindhoven Artifical Intelligence Systems Institute (EAISI), Eindhoven University of Technology, Eindhoven, the Netherlands
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21
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de Farias JLCB, Bessa WM. Intelligent Control with Artificial Neural Networks for Automated Insulin Delivery Systems. Bioengineering (Basel) 2022; 9:664. [PMID: 36354574 PMCID: PMC9687429 DOI: 10.3390/bioengineering9110664] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2023] Open
Abstract
Type 1 diabetes mellitus is a disease that affects millions of people around the world. Recent progress in embedded devices has allowed the development of artificial pancreas that can pump insulin subcutaneously to automatically regulate blood glucose levels in diabetic patients. In this work, a Lyapunov-based intelligent controller using artificial neural networks is proposed for application in automated insulin delivery systems. The adoption of an adaptive radial basis function network within the control scheme allows regulation of blood glucose levels without the need for a dynamic model of the system. The proposed model-free approach does not require the patient to inform when they are going to have a meal and is able to deal with inter- and intrapatient variability. To ensure safe operating conditions, the stability of the control law is rigorously addressed through a Lyapunov-like analysis. In silico analysis using virtual patients are provided to demonstrate the effectiveness of the proposed control scheme, showing its ability to maintain normoglycemia in patients with type 1 diabetes mellitus. Three different scenarios were considered: one long- and two short-term simulation studies. In the short-term analyses, 20 virtual patients were simulated for a period of 7 days, with and without prior basal therapy, while in the long-term simulation, 1 virtual patient was assessed over 63 days. The results show that the proposed approach was able to guarantee a time in the range above 95% for the target glycemia in all scenarios studied, which is in fact well above the desirable 70%. Even in the long-term analysis, the intelligent control scheme was able to keep blood glucose metrics within clinical care standards: mean blood glucose of 119.59 mg/dL with standard deviation of 32.02 mg/dL and coefficient of variation of 26.78%, all below the respective reference values.
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22
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Personalized hybrid artificial pancreas using unidirectional sliding-modes control algorithm. Biocybern Biomed Eng 2022. [DOI: 10.1016/j.bbe.2022.10.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
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23
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Viroonluecha P, Egea-Lopez E, Santa J. Evaluation of blood glucose level control in type 1 diabetic patients using deep reinforcement learning. PLoS One 2022; 17:e0274608. [PMID: 36099285 PMCID: PMC9469983 DOI: 10.1371/journal.pone.0274608] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2022] [Accepted: 08/30/2022] [Indexed: 11/18/2022] Open
Abstract
Diabetes mellitus is a disease associated with abnormally high levels of blood glucose due to a lack of insulin. Combining an insulin pump and continuous glucose monitor with a control algorithm to deliver insulin is an alternative to patient self-management of insulin doses to control blood glucose levels in diabetes mellitus patients. In this work, we propose a closed-loop control for blood glucose levels based on deep reinforcement learning. We describe the initial evaluation of several alternatives conducted on a realistic simulator of the glucoregulatory system and propose a particular implementation strategy based on reducing the frequency of the observations and rewards passed to the agent, and using a simple reward function. We train agents with that strategy for three groups of patient classes, evaluate and compare it with alternative control baselines. Our results show that our method is able to outperform baselines as well as similar recent proposals, by achieving longer periods of safe glycemic state and low risk.
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Affiliation(s)
- Phuwadol Viroonluecha
- Universidad Politecnica de Cartagena (UPCT), Department of Information Technologies and Communications, Cartagena, Spain
- * E-mail:
| | - Esteban Egea-Lopez
- Universidad Politecnica de Cartagena (UPCT), Department of Information Technologies and Communications, Cartagena, Spain
| | - Jose Santa
- Universidad Politecnica de Cartagena (UPCT), Department of Electronics, Computer Technology and Projects, Cartagena, Spain
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24
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Silfvergren O, Simonsson C, Ekstedt M, Lundberg P, Gennemark P, Cedersund G. Digital twin predicting diet response before and after long-term fasting. PLoS Comput Biol 2022; 18:e1010469. [PMID: 36094958 PMCID: PMC9499255 DOI: 10.1371/journal.pcbi.1010469] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2021] [Revised: 09/22/2022] [Accepted: 08/04/2022] [Indexed: 11/30/2022] Open
Abstract
Today, there is great interest in diets proposing new combinations of macronutrient compositions and fasting schedules. Unfortunately, there is little consensus regarding the impact of these different diets, since available studies measure different sets of variables in different populations, thus only providing partial, non-connected insights. We lack an approach for integrating all such partial insights into a useful and interconnected big picture. Herein, we present such an integrating tool. The tool uses a novel mathematical model that describes mechanisms regulating diet response and fasting metabolic fluxes, both for organ-organ crosstalk, and inside the liver. The tool can mechanistically explain and integrate data from several clinical studies, and correctly predict new independent data, including data from a new study. Using this model, we can predict non-measured variables, e.g. hepatic glycogen and gluconeogenesis, in response to fasting and different diets. Furthermore, we exemplify how such metabolic responses can be successfully adapted to a specific individual’s sex, weight, height, as well as to the individual’s historical data on metabolite dynamics. This tool enables an offline digital twin technology. Fasting and diet are central components of prevention against cardiovascular disease. Unfortunately, there is little consensus regarding which diet schemes are optimal. This is partially because different clinical studies contribute with different non-connected pieces of knowledge, which have not been fully integrated into a useful and interconnected big picture. In principle, mathematical models describing meal responses could be used for such an integration. However, today’s models still lack critical mechanisms, such as protein metabolism and a dynamic glycogen regulation. Herein, we present a) a new expanded model structure including these mechanisms; b) a set of parameters which can simultaneously describe a wide array of complementary estimation data, in both healthy and diabetic populations; c) a personalisation-script, which allows these generic parameters to be tuned to an individual/sub-population, using demographics (age, weight, height, diabetes status) and historic metabolic data. We exemplify how this personalisation can be used to predict new independent data, including a new clinical study, where a qualitatively new prediction is validated: that an oral protein tolerance test gives a clear response in plasma glucose, after, but not before, a 48h fasting period. Our combined model, parameters, and fitting script lay the foundation for an offline digital twin.
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Affiliation(s)
- Oscar Silfvergren
- Department of Biomedical Engineering, IMT, Linköping University, Linköping, Sweden
| | - Christian Simonsson
- Department of Biomedical Engineering, IMT, Linköping University, Linköping, Sweden
- Center for Medical Image Science and Visualisation, Linköping University, Linköping, Sweden
| | - Mattias Ekstedt
- Center for Medical Image Science and Visualisation, Linköping University, Linköping, Sweden
- Department of Health, Medicine, and Caring Sciences, Linköping University, Linköping, Sweden
| | - Peter Lundberg
- Center for Medical Image Science and Visualisation, Linköping University, Linköping, Sweden
- Department of Medical Radiation Physics, and Department of Health, Medicine and Caring Sciences, Linköping University, Linköping, Sweden
| | - Peter Gennemark
- Department of Biomedical Engineering, IMT, Linköping University, Linköping, Sweden
- Drug Metabolism and Pharmacokinetics, Research and Early Development, Cardiovascular, Renal and Metabolism (CVRM), BioPharmaceuticals R&D, AstraZeneca, Gothenburg, Sweden
| | - Gunnar Cedersund
- Department of Biomedical Engineering, IMT, Linköping University, Linköping, Sweden
- Center for Medical Image Science and Visualisation, Linköping University, Linköping, Sweden
- * E-mail:
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25
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Cobelli C, Dalla Man C. Minimal and Maximal Models to Quantitate Glucose Metabolism: Tools to Measure, to Simulate and to Run in Silico Clinical Trials. J Diabetes Sci Technol 2022; 16:1270-1298. [PMID: 34032128 PMCID: PMC9445339 DOI: 10.1177/19322968211015268] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
Abstract
Several models have been proposed to describe the glucose system at whole-body, organ/tissue and cellular level, designed to measure non-accessible parameters (minimal models), to simulate system behavior and run in silico clinical trials (maximal models). Here, we will review the authors' work, by putting it into a concise historical background. We will discuss first the parametric portrait provided by the oral minimal models-building on the classical intravenous glucose tolerance test minimal models-to measure otherwise non-accessible key parameters like insulin sensitivity and beta-cell responsivity from a physiological oral test, the mixed meal or the oral glucose tolerance tests, and what can be gained by adding a tracer to the oral glucose dose. These models were used in various pathophysiological studies, which we will briefly review. A deeper understanding of insulin sensitivity can be gained by measuring insulin action in the skeletal muscle. This requires the use of isotopic tracers: both the classical multiple-tracer dilution and the positron emission tomography techniques are discussed, which quantitate the effect of insulin on the individual steps of glucose metabolism, that is, bidirectional transport plasma-interstitium, and phosphorylation. Finally, we will present a cellular model of insulin secretion that, using a multiscale modeling approach, highlights the relations between minimal model indices and subcellular secretory events. In terms of maximal models, we will move from a parametric to a flux portrait of the system by discussing the triple tracer meal protocol implemented with the tracer-to-tracee clamp technique. This allows to arrive at quasi-model independent measurement of glucose rate of appearance (Ra), endogenous glucose production (EGP), and glucose rate of disappearance (Rd). Both the fast absorbing simple carbs and the slow absorbing complex carbs are discussed. This rich data base has allowed us to build the UVA/Padova Type 1 diabetes and the Padova Type 2 diabetes large scale simulators. In particular, the UVA/Padova Type 1 simulator proved to be a very useful tool to safely and effectively test in silico closed-loop control algorithms for an artificial pancreas (AP). This was the first and unique simulator of the glucose system accepted by the U.S. Food and Drug Administration as a substitute to animal trials for in silico testing AP algorithms. Recent uses of the simulator have looked at glucose sensors for non-adjunctive use and new insulin molecules.
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Affiliation(s)
- Claudio Cobelli
- Department of Woman and Child’s Health University of Padova, Padova, Italy
- Claudio Cobelli, PhD, Department of Woman and Child’s Health, University of Padova, Via N. Giustiniani, 3, Padova 35128, Italy.
| | - Chiara Dalla Man
- Department of Information Engineering, University of Padova, Padova, Italy
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Visentin R, Cobelli C, Dalla Man C. A software interface for in silico testing of type 2 diabetes treatments. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2022; 223:106973. [PMID: 35792365 DOI: 10.1016/j.cmpb.2022.106973] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/12/2021] [Revised: 06/09/2022] [Accepted: 06/21/2022] [Indexed: 06/15/2023]
Abstract
BACKGROUND AND OBJECTIVE The increasing incidence of diabetes continuously stimulates the research on new antidiabetic drugs. Computer simulation can save time and costs, alleviating the need of animal trials and providing useful information for optimal experiment design and drug dosing. We recently presented a type 2 diabetes (T2D) simulator as tool for in silico testing of new molecules and guiding treatment optimization. Here we present a user-friendly interface aimed to increase the usability of the simulator. METHOD The simulator, based on a large-scale glucose, insulin, and C-peptide model and equipped with 100 virtual subjects well describing system dynamics in a real T2D population, is extended to incorporate pharmacokinetics/pharmacodynamics (PK/PD) of a drug of interest. A graphical interface is developed on top of the simulator, allowing an easy design of in silico experiments: specifically, it is possible to select the population size to test, design the experiment (crossover or parallel), its duration and the sampling grid, choose glucose and insulin doses, and define treatment PK/PD and dose administered. The simulator also provides the outcome metrics requested by the user, and performs statistical comparisons among treatments and/or placebo. RESULTS To illustrate the potential of the simulator, we provided a case study using metformin and liraglutide. Literature-based PK/PD models of metformin and liraglutide have been incorporated in the simulator, by modulating key drug-sensitive model parameters. An in silico placebo-controlled trial has been done by simulating a three-arm meal tolerance test with subjects receiving placebo, metformin 850 mg, liraglutide 1.80 mg, respectively. The obtained results are in agreement with the clinical evidences, in terms of main glucose, insulin, and C-peptide outcome metrics. CONCLUSIONS We developed a user-friendly software interface for the T2D simulator to support the design and test of new antidiabetic drugs and treatments. This increases the simulator usability, making it suitable also for users who have low experience with computer programming.
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Affiliation(s)
- Roberto Visentin
- Department of Information Engineering, University of Padova, Padova, Italy
| | - Claudio Cobelli
- Department of Woman and Child's Health, University of Padova, Padova, Italy
| | - Chiara Dalla Man
- Department of Information Engineering, University of Padova, Padova, Italy.
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McHugh AD, Chase JG, Knopp JL, Ormsbee JJ, Kulawiec DG, Merry TL, Murphy R, Shepherd PR, Burden HJ, Docherty PD. The Impact of Exogenous Insulin Input on Calculating Hepatic Clearance Parameters. J Diabetes Sci Technol 2022; 16:945-954. [PMID: 33478257 PMCID: PMC9264438 DOI: 10.1177/1932296820986878] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
OBJECTIVE Model-based metabolic tests require accurate identification of subject-specific parameters from measured assays. Insulin assays are used to identify insulin kinetics parameters, such as general and first-pass hepatic clearances. This study assesses the impact of intravenous insulin boluses on parameter identification precision. METHOD Insulin and C-peptide data from two intravenous glucose tolerance test (IVGTT) trials of healthy adults (N = 10 × 2; denoted A and B), with (A) and without (B) insulin modification, were used to identify insulin kinetics parameters using a grid search. Monte Carlo analysis (N = 1000) quantifies variation in simulation error for insulin assay errors of 5%. A region of parameter values around the optimum was identified whose errors are within variation due to assay error. A smaller optimal region indicates more precise practical identifiability. Trial results were compared to assess identifiability and precision. RESULTS Trial B, without insulin modification, has optimal parameter regions 4.7 times larger on average than Trial A, with 1-U insulin bolus modification. Ranges of optimal parameter values between trials A and B increase from 0.04 to 0.12 min-1 for hepatic clearance and from 0.07 to 0.14 for first-pass clearance on average. Trial B's optimal values frequently lie outside physiological ranges, further indicating lack of distinct identifiability. CONCLUSIONS A small 1-U insulin bolus improves identification of hepatic clearance parameters by providing a smaller region of optimal parameter values. Adding an insulin bolus in metabolic tests can significantly improve identifiability and outcome test precision. Assay errors necessitate insulin modification in clinical tests to ensure identifiability and precision.
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Affiliation(s)
- Alexander D. McHugh
- Centre for Bioengineering, Department of
Mechanical Engineering, University of Canterbury, Christchurch, New Zealand
- Alexander D. McHugh, BE(Hons),
Centre for Bioengineering, Department of Mechanical Engineering,
University of Canterbury, Level 5 Civil/Mechanical Building, Private Bag 4800,
Christchurch, 8140, New Zealand.
| | - J. Geoffrey Chase
- Centre for Bioengineering, Department of
Mechanical Engineering, University of Canterbury, Christchurch, New Zealand
| | - Jennifer L. Knopp
- Centre for Bioengineering, Department of
Mechanical Engineering, University of Canterbury, Christchurch, New Zealand
| | - Jennifer J. Ormsbee
- Centre for Bioengineering, Department of
Mechanical Engineering, University of Canterbury, Christchurch, New Zealand
| | - Diana G. Kulawiec
- Department of Biomedical Engineering,
Rochester Institute of Technology, Rochester, NY, USA
| | - Troy L. Merry
- Discipline of Nutrition, Faculty of
Medical and Health Sciences, University of Auckland, Auckland, New Zealand
- Maurice Wilkins Centre for Molecular
Biodiscovery, University of Auckland, Auckland, New Zealand
| | - Rinki Murphy
- Discipline of Nutrition, Faculty of
Medical and Health Sciences, University of Auckland, Auckland, New Zealand
| | - Peter R. Shepherd
- Discipline of Nutrition, Faculty of
Medical and Health Sciences, University of Auckland, Auckland, New Zealand
| | - Hannah J. Burden
- Discipline of Nutrition, Faculty of
Medical and Health Sciences, University of Auckland, Auckland, New Zealand
| | - Paul D. Docherty
- Centre for Bioengineering, Department of
Mechanical Engineering, University of Canterbury, Christchurch, New Zealand
- Institute for Technical Medicine,
Furtwangen University, Villingen-Schwenningen, Germany
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Hettiarachchi C, Malagutti N, Nolan C, Daskalaki E, Suominen H. A Reinforcement Learning Based System for Blood Glucose Control without Carbohydrate Estimation in Type 1 Diabetes: In Silico Validation. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2022; 2022:950-956. [PMID: 36086458 DOI: 10.1109/embc48229.2022.9871054] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Type 1 Diabetes (T1D) is a chronic autoimmune disease, which requires the use of exogenous insulin for glucose regulation. In current hybrid closed-loop systems, meal entry is manual which adds cognitive burden to the persons living with T1D. In this study, we proposed a control system based on Proximal Policy Optimisation (PPO) that controls both basal and bolus insulin infusion and only requires meal announcement, thus eliminating the need for carbohydrate estimation. We evaluated the system on a challenging meal scenario, using an open-source simulator based on the UVA/Padova 2008 model and achieved a mean Time in Range value of 65% for the adult subject cohort, while maintaining a moderate hypoglycemic and hyperglycemic risk profile. The approach shows promise and welcomes further research towards the translation to a real-life artificial pancreas. Clinical relevance- This was an in-silico analysis towards the development of an autonomous artificial pancreas system for glucose control. The proposed system show promise in eliminating the need for estimating the carbohydrate content in meals.
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Rios YY, García-Rodríguez JA, Sanchez EN, Alanis AY, Ruiz-Velázquez E, Pardo Garcia A. Treatment for T1DM patients by a neuro-fuzzy inverse optimal controller including multi-step prediction. ISA TRANSACTIONS 2022; 126:203-212. [PMID: 34446285 DOI: 10.1016/j.isatra.2021.07.045] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/14/2019] [Revised: 07/05/2021] [Accepted: 07/30/2021] [Indexed: 06/13/2023]
Abstract
Diabetes Mellitus is a serious metabolic condition for global health associations. Recently, the number of adults, adolescents and children who have developed Type 1 Diabetes Mellitus (T1DM) has increased as well as the mortality statistics related to this disease. For this reason, the scientific community has directed research in developing technologies to reduce T1DM complications. This contribution is related to a feedback control strategy for blood glucose management in population samples of ten virtual adult subjects, adolescents and children. This scheme focuses on the development of an inverse optimal control (IOC) proposal which is integrated by neural identification, a multi-step prediction (MSP) strategy, and Takagi-Sugeno (T-S) fuzzy inference to shape the convenient insulin infusion in the treatment of T1DM patients. The MSP makes it possible to estimate the glucose dynamics 15 min in advance; therefore, this estimation allows the Neuro-Fuzzy-IOC (NF-IOC) controller to react in advance to prevent hypoglycemic and hyperglycemic events. The T-S fuzzy membership functions are defined in such a way that the respective inferences change basal infusion rates for each patient's condition. The results achieved for scenarios simulated in Uva/Padova virtual software illustrate that this proposal is suitable to maintain blood glucose levels within normoglycemic values (70-115 mg/dL); furthermore, this level remains less than 250 mg/dL during the postprandial event. A comparison between a simple neural IOC (NIOC) and the proposed NF-IOC is carried out using the analysis for control variability named CVGA chart included in the Uva/Padova software. This analysis highlights the improvement of the NF-IOC treatment, proposed in this article, on the NIOC approach because each subject is located inside safe zones for the entire duration of the simulation.
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Affiliation(s)
- Y Yuliana Rios
- GAICO, Grupo de Automatización y Control, Universidad Tecnológica de Bolívar, Cartagena de Indias, Bolívar, Colombia.
| | - J A García-Rodríguez
- CUCEI, Electronics and Computing Division, Universidad de Guadalajara, Guadalajara, Jalisco, Mexico
| | - Edgar N Sanchez
- CINVESTAV, Electrical Engineering Department, Zapopan, Jalisco, Mexico
| | - Alma Y Alanis
- CUCEI, Electronics and Computing Division, Universidad de Guadalajara, Guadalajara, Jalisco, Mexico
| | - E Ruiz-Velázquez
- CUCEI, Electronics and Computing Division, Universidad de Guadalajara, Guadalajara, Jalisco, Mexico
| | - Aldo Pardo Garcia
- A&C, Grupo de Automatización y Control, Universidad de Pamplona, Pamplona, Norte de Santander, Colombia
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A genetic mammalian proportional-integral feedback control circuit for robust and precise gene regulation. Proc Natl Acad Sci U S A 2022; 119:e2122132119. [PMID: 35687671 PMCID: PMC9214505 DOI: 10.1073/pnas.2122132119] [Citation(s) in RCA: 21] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022] Open
Abstract
To survive in the harsh environments they inhabit, cells have evolved sophisticated regulatory mechanisms that can maintain a steady internal milieu or homeostasis. This robustness, however, does not generally translate to engineered genetic circuits, such as the ones studied by synthetic biology. Here, we introduce an implementation of a minimal and universal gene regulatory motif that produces robust perfect adaptation for mammalian cells, and we improve on it by enhancing the precision of its regulation. The processes that keep a cell alive are constantly challenged by unpredictable changes in its environment. Cells manage to counteract these changes by employing sophisticated regulatory strategies that maintain a steady internal milieu. Recently, the antithetic integral feedback motif has been demonstrated to be a minimal and universal biological regulatory strategy that can guarantee robust perfect adaptation for noisy gene regulatory networks in Escherichia coli. Here, we present a realization of the antithetic integral feedback motif in a synthetic gene circuit in mammalian cells. We show that the motif robustly maintains the expression of a synthetic transcription factor at tunable levels even when it is perturbed by increased degradation or its interaction network structure is perturbed by a negative feedback loop with an RNA-binding protein. We further demonstrate an improved regulatory strategy by augmenting the antithetic integral motif with additional negative feedback to realize antithetic proportional–integral control. We show that this motif produces robust perfect adaptation while also reducing the variance of the regulated synthetic transcription factor. We demonstrate that the integral and proportional–integral feedback motifs can mitigate the impact of gene expression burden, and we computationally explore their use in cell therapy. We believe that the engineering of precise and robust perfect adaptation will enable substantial advances in industrial biotechnology and cell-based therapeutics.
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Camerlingo N, Vettoretti M, Del Favero S, Facchinetti A, Choudhary P, Sparacino G. Generation of post-meal insulin correction boluses in type 1 diabetes simulation models for in-silico clinical trials: More realistic scenarios obtained using a decision tree approach. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2022; 221:106862. [PMID: 35597208 DOI: 10.1016/j.cmpb.2022.106862] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/04/2022] [Revised: 04/19/2022] [Accepted: 05/07/2022] [Indexed: 06/15/2023]
Abstract
BACKGROUND AND OBJECTIVE In type 1 diabetes (T1D) research, in-silico clinical trials (ISCTs) notably facilitate the design/testing of new therapies. Published simulation tools embed mathematical models of blood glucose (BG) and insulin dynamics, continuous glucose monitoring (CGM) sensors, and insulin treatments, but lack a realistic description of some aspects of patient lifestyle impacting on glucose control. Specifically, to effectively simulate insulin correction boluses, required to treat post-meal hyperglycemia (BG > 180 mg/dL), the timing of the bolus may be influenced by subjects' behavioral attitudes. In this work, we develop an easily interpretable model of the variability of correction bolus timing observed in real data, and embed it into a popular simulation tool for ISCTs. METHODS Using data collected in 196 adults with T1D monitored in free-living conditions, we trained a decision tree (DT) model to classify whether a correction bolus is injected in a future time window, based on predictors collected back in time, related to CGM data, previous insulin boluses and subject's characteristics. The performance was compared to that of a logistic regression classifier with LASSO regularization (LC), trained on the same dataset. After validation, the DT was embedded within a popular T1D simulation tool and an ISCT was performed to compare the simulated correction boluses against those observed in a subset of data not used for model training. RESULTS The DT provided better classification performance (accuracy: 0.792, sensitivity: 0.430, specificity: 0.878, precision: 0.455) than the LC and presented good interpretability. The most predictive features were related to CGM (and its temporal variations), time since the last insulin bolus, and time of the day. The correction boluses simulated by the DT, after implementation in the simulation tool, showed a good agreement with real-world data. CONCLUSIONS The DT developed in this work represents a simple set of rules to mimic the same timing of correction boluses observed on real data. The inclusion of the model in simulation tools allows investigators to perform ISCTs that more realistically represent the patient behavior in taking correction boluses and the post-prandial BG response. In the future, more complex models can be investigated.
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Affiliation(s)
- N Camerlingo
- Department of Information Engineering, University of Padova, Via G. Gradenigo 6B, Padova 35131, Italy
| | - M Vettoretti
- Department of Information Engineering, University of Padova, Via G. Gradenigo 6B, Padova 35131, Italy
| | - S Del Favero
- Department of Information Engineering, University of Padova, Via G. Gradenigo 6B, Padova 35131, Italy
| | - A Facchinetti
- Department of Information Engineering, University of Padova, Via G. Gradenigo 6B, Padova 35131, Italy
| | - P Choudhary
- Department of Diabetes, Leicester Diabetes Centre, University of Leicester, Gwendolen Rd, Leicester LE5 4PW, United Kingdom
| | - G Sparacino
- Department of Information Engineering, University of Padova, Via G. Gradenigo 6B, Padova 35131, Italy.
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Renuse S, Benson LM, Vanderboom PM, Ruchi FNU, Yadav YR, Johnson KL, Brown BC, Peterson JA, Basu R, McCormick DJ, Pandey A, Basu A. 13C 15N: glucagon-based novel isotope dilution mass spectrometry method for measurement of glucagon metabolism in humans. Clin Proteomics 2022; 19:16. [PMID: 35590248 PMCID: PMC9118570 DOI: 10.1186/s12014-022-09344-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2021] [Accepted: 02/24/2022] [Indexed: 11/14/2022] Open
Abstract
Background Glucagon serves as an important regulatory hormone for regulating blood glucose concentration with tight feedback control exerted by insulin and glucose. There are critical gaps in our understanding of glucagon kinetics, pancreatic α cell function and intra-islet feedback network that are disrupted in type 1 diabetes. This is important for translational research applications of evolving dual-hormone (insulin + glucagon) closed-loop artificial pancreas algorithms and their usage in type 1 diabetes. Thus, it is important to accurately measure glucagon kinetics in vivo and to develop robust models of glucose-insulin-glucagon interplay that could inform next generation of artificial pancreas algorithms. Methods Here, we describe the administration of novel 13C15N heavy isotope-containing glucagon tracers—FF glucagon [(Phe 6 13C9,15N; Phe 22 13C9,15N)] and FFLA glucagon [(Phe 6 13C9,15N; Phe 22 13C9,15N; Leu 14 13C6,15N; Ala 19 13C3)] followed by anti-glucagon antibody-based enrichment and LC–MS/MS based-targeted assays using high-resolution mass spectrometry to determine levels of infused glucagon in plasma samples. The optimized assay results were applied for measurement of glucagon turnover in subjects with and without type 1 diabetes infused with isotopically labeled glucagon tracers. Results The limit of quantitation was found to be 1.56 pg/ml using stable isotope-labeled glucagon as an internal standard. Intra and inter-assay variability was < 6% and < 16%, respectively, for FF glucagon while it was < 5% and < 23%, respectively, for FFLA glucagon. Further, we carried out a novel isotope dilution technique using glucagon tracers for studying glucagon kinetics in type 1 diabetes. Conclusions The methods described in this study for simultaneous detection and quantitation of glucagon tracers have clinical utility for investigating glucagon kinetics in vivo in humans. Supplementary Information The online version contains supplementary material available at 10.1186/s12014-022-09344-2.
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Affiliation(s)
- Santosh Renuse
- Department of Laboratory Medicine and Pathology, Mayo Clinic, 200 First ST SW, Rochester, MN, 55905, USA.,Center for Individualized Medicine, Mayo Clinic, Rochester, MN, 55905, USA
| | - Linda M Benson
- Mayo Genomics Facility-Proteomics Core, Mayo Clinic, Rochester, MN, 55905, USA
| | - Patrick M Vanderboom
- Department of Laboratory Medicine and Pathology, Mayo Clinic, 200 First ST SW, Rochester, MN, 55905, USA
| | - F N U Ruchi
- Division of Endocrinology, Center of Diabetes Technology, University of Virginia School of Medicine, Charlottesville, VA, 22903, USA
| | - Yogesh R Yadav
- Division of Endocrinology, Center of Diabetes Technology, University of Virginia School of Medicine, Charlottesville, VA, 22903, USA
| | - Kenneth L Johnson
- Mayo Genomics Facility-Proteomics Core, Mayo Clinic, Rochester, MN, 55905, USA
| | - Benjamin C Brown
- Mayo Genomics Facility-Proteomics Core, Mayo Clinic, Rochester, MN, 55905, USA
| | - Jane A Peterson
- Mayo Genomics Facility-Proteomics Core, Mayo Clinic, Rochester, MN, 55905, USA
| | - Rita Basu
- Division of Endocrinology, Center of Diabetes Technology, University of Virginia School of Medicine, Charlottesville, VA, 22903, USA
| | - Daniel J McCormick
- Department of Biochemistry and Molecular Biology, Mayo Clinic, Rochester, MN, 55905, USA
| | - Akhilesh Pandey
- Department of Laboratory Medicine and Pathology, Mayo Clinic, 200 First ST SW, Rochester, MN, 55905, USA. .,Center for Individualized Medicine, Mayo Clinic, Rochester, MN, 55905, USA. .,Mayo Genomics Facility-Proteomics Core, Mayo Clinic, Rochester, MN, 55905, USA. .,Department of Biochemistry and Molecular Biology, Mayo Clinic, Rochester, MN, 55905, USA. .,Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, MN, 55905, USA. .,Manipal Academy of Higher Education, Manipal, Karnataka, 576104, India.
| | - Ananda Basu
- Division of Endocrinology, Center of Diabetes Technology, University of Virginia School of Medicine, Charlottesville, VA, 22903, USA.
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McHugh AD, Chase JG, Knopp JL, Zhou T, Holder-Pearson L. Determining Losses in Jet Injection Subcutaneous Insulin Delivery: A Model-Based Approach. J Diabetes Sci Technol 2022:19322968221085032. [PMID: 35343255 DOI: 10.1177/19322968221085032] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
OBJECTIVE Accurate, safe glycemic management requires reliable delivery of insulin doses. Insulin can be delivered subcutaneously for action over a longer period of time. Needle-free jet injectors provide subcutaneous (SC) delivery without requiring needle use, but the volume of insulin absorbed varies due to losses associated with the delivery method. This study employs model-based methods to determine the expected proportion of active insulin present from a needle-free SC dose. METHODS Insulin, C-peptide, and glucose assay data from a frequently sampled insulin-modified oral glucose tolerance test trial with 2U SC insulin delivery, paired with a well-validated metabolic model, predict metabolic outcomes for N = 7 healthy adults. Subject-specific nonlinear hepatic clearance profiles are modeled over time using third-order basis splines with knots located at assay times. Hepatic clearance profiles are constrained within a physiological rate of change, and relative to plasma glucose profiles. Insulin loss proportions yielding optimal insulin predictions are then identified, quantifying delivery losses. RESULTS Optimal parameter identification suggests losses of up to 22% of the nominal 2U SC dose. The degree of loss varies between subjects and between trials on the same subject. Insulin fit accuracy improves where loss greater than 5% is identified, relative to where delivery loss is not modeled. CONCLUSIONS Modeling shows needle-free SC jet injection of a nominal dose of insulin does not necessarily provide metabolic action equivalent to total dose, and this availability significantly varies between trials. By quantifying and accounting for variability of jet injection insulin doses, better glycemic management outcomes using SC jet injection may be achieved.
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Affiliation(s)
- Alexander D McHugh
- Centre for Bioengineering, Department of Mechanical Engineering, University of Canterbury, Christchurch, New Zealand
| | - J Geoffrey Chase
- Centre for Bioengineering, Department of Mechanical Engineering, University of Canterbury, Christchurch, New Zealand
| | - Jennifer L Knopp
- Centre for Bioengineering, Department of Mechanical Engineering, University of Canterbury, Christchurch, New Zealand
| | - Tony Zhou
- Centre for Bioengineering, Department of Mechanical Engineering, University of Canterbury, Christchurch, New Zealand
| | - Lui Holder-Pearson
- Centre for Bioengineering, Department of Mechanical Engineering, University of Canterbury, Christchurch, New Zealand
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Ozaslan B, Deshpande S, Doyle FJ, Dassau E. Zone-MPC Automated Insulin Delivery Algorithm Tuned for Pregnancy Complicated by Type 1 Diabetes. Front Endocrinol (Lausanne) 2022; 12:768639. [PMID: 35392357 PMCID: PMC8982146 DOI: 10.3389/fendo.2021.768639] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/31/2021] [Accepted: 12/30/2021] [Indexed: 01/13/2023] Open
Abstract
Type 1 diabetes (T1D) increases the risk for pregnancy complications. Increased time in the pregnancy glucose target range (63-140 mg/dL as suggested by clinical guidelines) is associated with improved pregnancy outcomes that underscores the need for tight glycemic control. While closed-loop control is highly effective in regulating blood glucose levels in individuals with T1D, its use during pregnancy requires adjustments to meet the tight glycemic control and changing insulin requirements with advancing gestation. In this paper, we tailor a zone model predictive controller (zone-MPC), an optimization-based control strategy that uses model predictions, for use during pregnancy and verify its robustness in-silico through a broad range of scenarios. We customize the existing zone-MPC to satisfy pregnancy-specific glucose control objectives by having (i) lower target glycemic zones (i.e., 80-110 mg/dL daytime and 80-100 mg/dL overnight), (ii) more assertive correction bolus for hyperglycemia, and (iii) a control strategy that results in more aggressive postprandial insulin delivery to keep glucose within the target zone. The emphasis is on leveraging the flexible design of zone-MPC to obtain a controller that satisfies glycemic outcomes recommended for pregnancy based on clinical insight. To verify this pregnancy-specific zone-MPC design, we use the UVA/Padova simulator and conduct in-silico experiments on 10 subjects over 13 scenarios ranging from scenarios with ideal metabolic and treatment parameters for pregnancy to extreme scenarios with such parameters that are highly deviant from the ideal. All scenarios had three meals per day and each meal had 40 grams of carbohydrates. Across 13 scenarios, pregnancy-specific zone-MPC led to a 10.3 ± 5.3% increase in the time in pregnancy target range (baseline zone-MPC: 70.6 ± 15.0%, pregnancy-specific zone-MPC: 80.8 ± 11.3%, p < 0.001) and a 10.7 ± 4.8% reduction in the time above the target range (baseline zone-MPC: 29.0 ± 15.4%, pregnancy-specific zone-MPC: 18.3 ± 12.0, p < 0.001). There was no significant difference in the time below range between the controllers (baseline zone-MPC: 0.5 ± 1.2%, pregnancy-specific zone-MPC: 3.5 ± 1.9%, p = 0.1). The extensive simulation results show improved performance in the pregnancy target range with pregnancy-specific zone MPC, suggest robustness of the zone-MPC in tight glucose control scenarios, and emphasize the need for customized glucose control systems for pregnancy.
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Affiliation(s)
| | | | | | - Eyal Dassau
- Harvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, Boston, MA, United States
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Builes-Montaño CE, Lema-Perez L, Garcia-Tirado J, Alvarez H. Main glucose hepatic fluxes in healthy subjects predicted from a phenomenological-based model. Comput Biol Med 2022; 142:105232. [DOI: 10.1016/j.compbiomed.2022.105232] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2021] [Revised: 01/08/2022] [Accepted: 01/09/2022] [Indexed: 11/28/2022]
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Alonso-Bastida A, Adam-Medina M, Posada-Gómez R, Salazar-Piña DA, Osorio-Gordillo GL, Vela-Valdés LG. Dynamic of Glucose Homeostasis in Virtual Patients: A Comparison between Different Behaviors. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:716. [PMID: 35055537 PMCID: PMC8775377 DOI: 10.3390/ijerph19020716] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/06/2021] [Revised: 12/23/2021] [Accepted: 01/01/2022] [Indexed: 02/04/2023]
Abstract
This work presents a mathematical model of homeostasis dynamics in healthy individuals, focusing on the generation of conductive data on glucose homeostasis throughout the day under dietary and physical activity factors. Two case studies on glucose dynamics for populations under conditions of physical activity and sedentary lifestyle were developed. For this purpose, two types of virtual populations were generated, the first population was developed according to the data of a total of 89 physical persons between 20 and 75 years old and the second was developed using the Monte Carlo approach, obtaining a total of 200 virtual patients. In both populations, each participant was classified as an active or sedentary person depending on the physical activity performed. The results obtained demonstrate the capacity of virtual populations in the generation of in-silico approximations similar to those obtained from in-vivo studies. Obtaining information that is only achievable through specific in-vivo experiments. Being a tool that generates information for the approach of alternatives in the prevention of the development of type 2 Diabetes.
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Affiliation(s)
- Alexis Alonso-Bastida
- Electronic Engineering Department, TecNM/CENIDET, Cuernavaca 62490, Morelos, Mexico; (M.A.-M.); (G.-L.O.-G.); (L.G.V.-V.)
| | - Manuel Adam-Medina
- Electronic Engineering Department, TecNM/CENIDET, Cuernavaca 62490, Morelos, Mexico; (M.A.-M.); (G.-L.O.-G.); (L.G.V.-V.)
| | | | | | - Gloria-Lilia Osorio-Gordillo
- Electronic Engineering Department, TecNM/CENIDET, Cuernavaca 62490, Morelos, Mexico; (M.A.-M.); (G.-L.O.-G.); (L.G.V.-V.)
| | - Luis Gerardo Vela-Valdés
- Electronic Engineering Department, TecNM/CENIDET, Cuernavaca 62490, Morelos, Mexico; (M.A.-M.); (G.-L.O.-G.); (L.G.V.-V.)
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Schmitzer J, Strobel C, Blechschmidt R, Tappe A, Peuscher H. Efficient Closed Loop Simulation of Do-It-Yourself Artificial Pancreas Systems. J Diabetes Sci Technol 2022; 16:61-69. [PMID: 34328030 PMCID: PMC8721541 DOI: 10.1177/19322968211032249] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/04/2022]
Abstract
BACKGROUND Numerical simulations, also referred to as in silico trials, are nowadays the first step toward approval of new artificial pancreas (AP) systems. One suitable tool to run such simulations is the UVA/Padova Type 1 Diabetes Metabolic Simulator (T1DMS). It was used by Toffanin et al. to provide data about safety and efficacy of AndroidAPS, one of the most wide-spread do-it-yourself AP systems. However, the setup suffered from slow simulation speed. The objective of this work is to speed up simulation by implementing the algorithm directly in MATLAB®/Simulink®. METHOD Firstly, AndroidAPS is re-implemented in MATLAB® and verified. Then, the function is incorporated into T1DMS. To evaluate the new setup, a scenario covering 2 days in real time is run for 30 virtual patients. The results are compared to those presented in the literature. RESULTS Unit tests and integration tests proved the equivalence of the new implementation and the original AndroidAPS code. Simulation of the scenario required approximately 15 minutes, corresponding to a speed-up factor of roughly 1000 with respect to real time. The results closely resemble those presented by Toffanin et al. Discrepancies were to be expected because a different virtual population was considered. Also, some parameters could not be extracted from and harmonized with the original setup. CONCLUSIONS The new implementation facilitates extensive in silico trials of AndroidAPS due to the significant reduction of runtime. This provides a cheap and fast means to test new versions of the algorithm before they are shared with the community.
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Affiliation(s)
- Jana Schmitzer
- Institute for Medical Engineering and Mechatronics, Ulm University of Applied Sciences, Ulm, Germany
| | - Carolin Strobel
- Institute for Medical Engineering and Mechatronics, Ulm University of Applied Sciences, Ulm, Germany
| | - Ronald Blechschmidt
- Institute for Medical Engineering and Mechatronics, Ulm University of Applied Sciences, Ulm, Germany
| | - Adrian Tappe
- AndroidAPS.org, Software Development, Linz, Austria
| | - Heiko Peuscher
- Institute for Medical Engineering and Mechatronics, Ulm University of Applied Sciences, Ulm, Germany
- Heiko Peuscher, Dr.-Ing., Institute for Medical Engineering and Mechatronics, Ulm University of Applied Sciences, Albert-Einstein-Allee 55, Ulm, 89081, Germany.
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Olçomendy L, Cassany L, Pirog A, Franco R, Puginier E, Jaffredo M, Gucik-Derigny D, Ríos H, Ferreira de Loza A, Gaitan J, Raoux M, Bornat Y, Catargi B, Lang J, Henry D, Renaud S, Cieslak J. Towards the Integration of an Islet-Based Biosensor in Closed-Loop Therapies for Patients With Type 1 Diabetes. Front Endocrinol (Lausanne) 2022; 13:795225. [PMID: 35528003 PMCID: PMC9072637 DOI: 10.3389/fendo.2022.795225] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/14/2021] [Accepted: 02/25/2022] [Indexed: 01/01/2023] Open
Abstract
In diabetes mellitus (DM) treatment, Continuous Glucose Monitoring (CGM) linked with insulin delivery becomes the main strategy to improve therapeutic outcomes and quality of patients' lives. However, Blood Glucose (BG) regulation with CGM is still hampered by limitations of algorithms and glucose sensors. Regarding sensor technology, current electrochemical glucose sensors do not capture the full spectrum of other physiological signals, i.e., lipids, amino acids or hormones, relaying the general body status. Regarding algorithms, variability between and within patients remains the main challenge for optimal BG regulation in closed-loop therapies. This work highlights the simulation benefits to test new sensing and control paradigms which address the previous shortcomings for Type 1 Diabetes (T1D) closed-loop therapies. The UVA/Padova T1DM Simulator is the core element here, which is a computer model of the human metabolic system based on glucose-insulin dynamics in T1D patients. That simulator is approved by the US Food and Drug Administration (FDA) as an alternative for pre-clinical testing of new devices and closed-loop algorithms. To overcome the limitation of standard glucose sensors, the concept of an islet-based biosensor, which could integrate multiple physiological signals through electrical activity measurement, is assessed here in a closed-loop insulin therapy. This investigation has been addressed by an interdisciplinary consortium, from endocrinology to biology, electrophysiology, bio-electronics and control theory. In parallel to the development of an islet-based closed-loop, it also investigates the benefits of robust control theory against the natural variability within a patient population. Using 4 meal scenarios, numerous simulation campaigns were conducted. The analysis of their results then introduces a discussion on the potential benefits of an Artificial Pancreas (AP) system associating the islet-based biosensor with robust algorithms.
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Affiliation(s)
- Loïc Olçomendy
- Univ. Bordeaux, CNRS, Bordeaux INP, IMS, UMR 5218, Talence, France
| | - Louis Cassany
- Univ. Bordeaux, CNRS, Bordeaux INP, IMS, UMR 5218, Talence, France
| | - Antoine Pirog
- Univ. Bordeaux, CNRS, Bordeaux INP, IMS, UMR 5218, Talence, France
| | - Roberto Franco
- Tecnológico Nacional de México/I.T. La Laguna, Torreón, Mexico
| | | | | | | | - Héctor Ríos
- Tecnológico Nacional de México/I.T. La Laguna, Torreón, Mexico
- Cátedras CONACYT, Ciudad de México, Mexico
| | | | - Julien Gaitan
- Univ. Bordeaux, CNRS, CBMN, UMR 5248, Pessac, France
| | | | - Yannick Bornat
- Univ. Bordeaux, CNRS, Bordeaux INP, IMS, UMR 5218, Talence, France
| | - Bogdan Catargi
- Univ. Bordeaux, CNRS, CBMN, UMR 5248, Pessac, France
- Bordeaux Hospitals, Endocrinology and Metabolic Diseases Unit, Bordeaux, France
| | - Jochen Lang
- Univ. Bordeaux, CNRS, CBMN, UMR 5248, Pessac, France
| | - David Henry
- Univ. Bordeaux, CNRS, Bordeaux INP, IMS, UMR 5218, Talence, France
| | - Sylvie Renaud
- Univ. Bordeaux, CNRS, Bordeaux INP, IMS, UMR 5218, Talence, France
| | - Jérôme Cieslak
- Univ. Bordeaux, CNRS, Bordeaux INP, IMS, UMR 5218, Talence, France
- *Correspondence: Jérôme Cieslak,
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GLYFE: review and benchmark of personalized glucose predictive models in type 1 diabetes. Med Biol Eng Comput 2021; 60:1-17. [PMID: 34751904 DOI: 10.1007/s11517-021-02437-4] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2021] [Accepted: 08/20/2021] [Indexed: 10/19/2022]
Abstract
Due to the sensitive nature of diabetes-related data, preventing them from being easily shared between studies, and the wide discrepancies in their data processing pipeline, progress in the field of glucose prediction is hard to assess. To address this issue, we introduce GLYFE (GLYcemia Forecasting Evaluation), a benchmark of machine learning-based glucose predictive models. We present the accuracy and clinical acceptability of nine different models coming from the literature, from standard autoregressive to more complex neural network-based models. These results are obtained on two different datasets, namely UVA/Padova Type 1 Diabetes Metabolic Simulator (T1DMS) and Ohio Type-1 Diabetes Mellitus (OhioT1DM), featuring artificial and real type 1 diabetic patients respectively. By providing extensive details about the data flow as well as by providing the whole source code of the benchmarking process, we ensure the reproducibility of the results and the usability of the benchmark by the community. Those results serve as a basis of comparison for future studies. In a field where data are hard to obtain, and where the comparison of results from different studies is often irrelevant, GLYFE gives the opportunity of gathering researchers around a standardized common environment.
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Sun X, Rashid M, Hobbs N, Askari MR, Brandt R, Shahidehpour A, Cinar A. Prior Informed Regularization of Recursively Updated Latent-Variables-Based Models with Missing Observations. CONTROL ENGINEERING PRACTICE 2021; 116:104933. [PMID: 34539101 PMCID: PMC8443145 DOI: 10.1016/j.conengprac.2021.104933] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Many data-driven modeling techniques identify locally valid, linear representations of time-varying or nonlinear systems, and thus the model parameters must be adaptively updated as the operating conditions of the system vary, though the model identification typically does not consider prior knowledge. In this work, we propose a new regularized partial least squares (rPLS) algorithm that incorporates prior knowledge in the model identification and can handle missing data in the independent covariates. This latent variable (LV) based modeling technique consists of three steps. First, a LV-based model is developed on the historical time series data. In the second step, the missing observations in the new incomplete data sample are estimated. Finally, the future values of the outputs are predicted as a linear combination of estimated scores and loadings. The model is recursively updated as new data are obtained from the system. The performance of the proposed rPLS and rPLS with exogenous inputs (rPLSX) algorithms are evaluated by modeling variations in glucose concentration (GC) of people with Type 1 diabetes (T1D) in response to meals and physical activities for prediction windows up to one hour, or 12 sampling instances, into the future. The proposed rPLS family of GC prediction models are evaluated with both in-silico and clinical experiment data and compared with the performance of recursive time series and kernel-based models. The root mean squared error (RMSE) with simulated subjects in the multivariable T1D simulator where physical activity effects are incorporated in GC variations are 2.52 and 5.81 mg/dL for 30 and 60 mins ahead predictions (respectively) when information for all meals and physical activities are used, increasing to 2.70 and 6.54 mg/dL (respectively) when meals and activities occurred, but the information is with-held from the modeling algorithms. The RMSE is 10.45 and 14.48 mg/dL for clinical study with prediction horizons of 30 and 60 mins, respectively. The low RMSE values demonstrate the effectiveness of the proposed rPLS approach compared to the conventional recursive modeling algorithms.
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Affiliation(s)
- Xiaoyu Sun
- 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
| | - Nicole Hobbs
- Department of Biomedical Engineering, Illinois Institute of Technology, Chicago, IL 60616 USA
| | - Mohammad Reza Askari
- Department of Chemical and Biological Engineering, Illinois Institute of Technology, Chicago, IL 60616 USA
| | - Rachel Brandt
- Department of Biomedical Engineering, Illinois Institute of Technology, Chicago, IL 60616 USA
| | - Andrew Shahidehpour
- Department of Chemical and Biological Engineering, Illinois Institute of Technology, Chicago, IL 60616 USA
| | - Ali Cinar
- Department of Chemical and Biological Engineering, Illinois Institute of Technology, Chicago, IL 60616 USA
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Visentin R, De Lazzari M. A Novel Method for Generation of In Silico Subjects with Type 2 Diabetes. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2021; 2021:1380-1383. [PMID: 34891542 DOI: 10.1109/embc46164.2021.9629678] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
A type 2 diabetes (T2D) simulator has been recently proposed for supporting drug development and treatment optimization. This tool consists of a physiological model of glucose/insulin/C-peptide dynamics and a virtual cohort of T2D subjects (i.e., random extractions of model parameterizations from a joint parameter distribution) well describing both average and variability realistic T2D dynamics . However, the state-of-art procedure to get a reliable virtual population requires some post-processing after subject extraction, in order to discard implausible behaviors. We propose an improved method for virtual subjects' generation to overcome this burdensome task. To do so, we first assessed a refined joint parameter distribution, from which extracting a number of subjects, greater than the target population size. Then, target-size subsets are undersampled from the large cohort. The final virtual population is selected among the subsets as the one maximizing the similarity with T2D data and model parameter distribution, by means of measurement' outcome metrics and Euclidian distance (Δ), respectively. In the final population, almost all the outcome metrics are statistically identical to the clinical counterparts (p-value>0.05) and model parameters' distribution differs by ~5-10% from that derived from data. The methodology described here is flexible, thus resulting suitable for different T2D stages and type 1 diabetes, as well.Clinical Relevance- A straightforward subjects' generation would ease the availability of tailored in silico trials for testing diabetes treatment in a specific population.
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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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43
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Fabris C, Gautier T, Breton M. Automated Adaptation of Insulin Treatment in Type 1 Diabetes. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2021; 2021:5039-5042. [PMID: 34892339 DOI: 10.1109/embc46164.2021.9630191] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
Abstract
Individuals with type 1 diabetes (T1D) need life-long insulin therapy to compensate for the lack of endogenous insulin due to the autoimmune damage to pancreatic beta-cells. Treatment is based on basal and bolus insulin, to cover fasting and postprandial periods, respectively, according to three insulin dosing parameters: basal rate (BR), carbohydrate-to-insulin ratio (CR), and correction factor (CF). Suboptimal BR, CR, and CF profiles leading to incorrect insulin dosing may be the cause of undesired glycemic events, which carry dangerous short-term and long-term effects. Therefore, correct tuning of these parameters is of the utmost importance. In this work, we propose a new algorithm to optimize insulin dosing parameters in individuals with T1D who use a continuous glucose monitor and an insulin pump. The algorithm was tested using the University of Virginia/Padova T1D Simulator and led to an improvement in the quality of glycemic control. Future efforts will be devoted to test the algorithm in human clinical trials.
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Parvinian B, Bighamian R, Scully CG, Hahn JO, Pathmanathan P. Credibility Assessment of a Subject-Specific Mathematical Model of Blood Volume Kinetics for Prediction of Physiological Response to Hemorrhagic Shock and Fluid Resuscitation. Front Physiol 2021; 12:705222. [PMID: 34603074 PMCID: PMC8481867 DOI: 10.3389/fphys.2021.705222] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2021] [Accepted: 08/23/2021] [Indexed: 11/30/2022] Open
Abstract
Subject-specific mathematical models for prediction of physiological parameters such as blood volume, cardiac output, and blood pressure in response to hemorrhage have been developed. In silico studies using these models may provide an effective tool to generate pre-clinical safety evidence for medical devices and help reduce the size and scope of animal studies that are performed prior to initiation of human trials. To achieve such a goal, the credibility of the mathematical model must be established for the purpose of pre-clinical in silico testing. In this work, the credibility of a subject-specific mathematical model of blood volume kinetics intended to predict blood volume response to hemorrhage and fluid resuscitation during fluid therapy was evaluated. A workflow was used in which: (i) the foundational properties of the mathematical model such as structural identifiability were evaluated; (ii) practical identifiability was evaluated both pre- and post-calibration, with the pre-calibration results used to determine an optimal splitting of experimental data into calibration and validation datasets; (iii) uncertainty in model parameters and the experimental uncertainty were quantified for each subject; and (iv) the uncertainty was propagated through the blood volume kinetics model and its predictive capability was evaluated via validation tests. The mathematical model was found to be structurally identifiable. Pre-calibration identifiability analysis led to splitting the 180 min of time series data per subject into 50 and 130 min calibration and validation windows, respectively. The average root mean squared error of the mathematical model was 12.6% using the calibration window of (0 min, 50 min). Practical identifiability was established post-calibration after fixing one of the parameters to a nominal value. In the validation tests, 82 and 75% of the subject-specific mathematical models were able to correctly predict blood volume response when predictive capability was evaluated at 180 min and at the time when amount of infused fluid equals fluid loss.
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Affiliation(s)
- Bahram Parvinian
- Department of Mechanical Engineering, University of Maryland College Park, College Park, MD, United States
| | - Ramin Bighamian
- Office of Science and Engineering Laboratories, Food and Drug Administration, Silver Spring, MD, United States
| | - Christopher George Scully
- Office of Science and Engineering Laboratories, Food and Drug Administration, Silver Spring, MD, United States
| | - Jin-Oh Hahn
- Department of Mechanical Engineering, University of Maryland College Park, College Park, MD, United States
| | - Pras Pathmanathan
- Office of Science and Engineering Laboratories, Food and Drug Administration, Silver Spring, MD, United States
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Batmani Y, Khodakaramzadeh S, Moradi P. Automatic Artificial Pancreas Systems Using an Intelligent Multiple-Model PID Strategy. IEEE J Biomed Health Inform 2021; 26:1708-1717. [PMID: 34587104 DOI: 10.1109/jbhi.2021.3116376] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
In this paper, an individualized intelligent multiple-model technique is proposed to design automatic artificial pancreas (AP) systems for the glycemic regulation of type 1 diabetic patients. At first, using the multiple-model concept, the insulin-glucose regulatory system is mathematically identified by constructing some local models. In this step, trade-offs between the number of local models and the complexity of the overall closed-loop system are made by defining and solving a bi-objective optimization problem. Then, optimal AP systems are designed by tuning a bank of proportionalintegralderivative (PID) controllers via the genetic algorithm (GA). A fuzzy gain scheduling strategy is employed to determine the participation percentages of the PID controllers in the control action. Finally, two safety mechanisms, called insulin on board (IOB) constraint and pump shut-off, are installed in the AP systems to enhance their performance. To assess the proposed AP systems, in silico experiments are performed on virtual patients of the UVA/Padova metabolic simulator. The obtained results reveal that the proposed intelligent multiple-model methodology leads to AP systems with limited hyperglycemia and no severe hypoglycemia.
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46
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Wang W, Wang S, Geng Y, Qiao Y, Wu T. An OGI model for personalized estimation of glucose and insulin concentration in plasma. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2021; 18:8499-8523. [PMID: 34814309 DOI: 10.3934/mbe.2021420] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Plasma glucose concentration (PGC) and plasma insulin concentration (PIC) are two essential metrics for diabetic regulation, but difficult to be measured directly. Often, PGC and PIC are estimated from continuous glucose monitoring and insulin delivery data. Nevertheless, the inter-individual variability and external disturbance (e.g. carbohydrate intake) bring challenges for accurate estimations. This study is to estimate PGC and PIC adaptively by identifying personalized parameters and external disturbances. An observable glucose-insulin (OGI) dynamic model is established to describe insulin absorption, glucose regulation, and glucose transport. The model parameters and disturbances can be extended to observable state variables and be identified dynamically by Bayesian filtering estimators. Two basic Gaussian noise based Bayesian filtering estimators, extended Kalman filtering (EKF) and unscented Kalman filtering (UKF), are implemented. Recognizing the prevalence of non-Gaussian noise, in this study, two new filtering estimators: particle filtering with Gaussian noise (PFG), and particle filtering with mixed non-Gaussian noise (PFM) are designed and implemented. The proposed OGI model in conjunction with the estimators is evaluated using the data from 30 in-silico subjects and 10 human participants. For in-silico subjects, the OGI with PFM estimator has the ability to estimate PIC and PGC adaptively, achieving RMSE of PIC 9.49±3.81 mU/L, and PGC 0.89±0.19 mmol/L. For human, the OGI with PFM has the promise to identify disturbances (95.46%±0.65% accurate rate of meal identification). OGI model provides a way to fully personalize the parameters and external disturbances in real time, and has potential clinical utility for artificial pancreas.
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Affiliation(s)
- Weijie Wang
- School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, China
| | - Shaoping Wang
- School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, China
- Beijing Advanced Innovation Center for Big Data-based Precision Medicine, Beihang University, Beijing 100191, China
| | - Yixuan Geng
- School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, China
| | - Yajing Qiao
- School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, China
| | - Teresa Wu
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University and College of Medicine, Mayo Clinic, Tempe AZ 85281, the USA
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Pompa M, Panunzi S, Borri A, De Gaetano A. A comparison among three maximal mathematical models of the glucose-insulin system. PLoS One 2021; 16:e0257789. [PMID: 34570804 PMCID: PMC8476045 DOI: 10.1371/journal.pone.0257789] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2021] [Accepted: 09/13/2021] [Indexed: 11/24/2022] Open
Abstract
The most well-known and widely used mathematical representations of the physiology of a diabetic individual are the Sorensen and Hovorka models as well as the UVAPadova Simulator. While the Hovorka model and the UVAPadova Simulator only describe the glucose metabolism of a subject with type 1 diabetes, the Sorensen model was formulated to simulate the behaviour of both normal and diabetic individuals. The UVAPadova model is the most known model, accepted by the FDA, with a high level of complexity. The Hovorka model is the simplest of the three models, well documented and used primarily for the development of control algorithms. The Sorensen model is the most complete, even though some modifications were required both to the model equations (adding useful compartments for modelling subcutaneous insulin delivery) and to the parameter values. In the present work several simulated experiments, such as IVGTTs and OGTTs, were used as tools to compare the three formulations in order to establish to what extent increasing complexity translates into richer and more correct physiological behaviour. All the equations and parameters used for carrying out the simulations are provided.
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Affiliation(s)
- Marcello Pompa
- CNR-IASI, Laboratorio di Biomatematica, Consiglio Nazionale delle Ricerche, Istituto di Analisi dei Sistemi ed Informatica, Rome, Italy
- Università Cattolica del Sacro Cuore Rome, Rome, Italy
| | - Simona Panunzi
- CNR-IASI, Laboratorio di Biomatematica, Consiglio Nazionale delle Ricerche, Istituto di Analisi dei Sistemi ed Informatica, Rome, Italy
| | - Alessandro Borri
- CNR-IASI, Laboratorio di Biomatematica, Consiglio Nazionale delle Ricerche, Istituto di Analisi dei Sistemi ed Informatica, Rome, Italy
| | - Andrea De Gaetano
- CNR-IASI, Laboratorio di Biomatematica, Consiglio Nazionale delle Ricerche, Istituto di Analisi dei Sistemi ed Informatica, Rome, Italy
- CNR-IRIB, Consiglio Nazionale delle Ricerche, Istituto per la Ricerca e l’Innovazione Biomedica Palermo, Palermo, Italy
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Schiavon M, Cobelli C, Dalla Man C. Modeling Intraperitoneal Insulin Absorption in Patients with Type 1 Diabetes. Metabolites 2021; 11:metabo11090600. [PMID: 34564415 PMCID: PMC8465342 DOI: 10.3390/metabo11090600] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2021] [Revised: 08/27/2021] [Accepted: 09/01/2021] [Indexed: 11/16/2022] Open
Abstract
Standard insulin therapy to treat type 1 diabetes (T1D) consists of exogenous insulin administration through the subcutaneous (SC) tissue. Despite recent advances in insulin formulations, the SC route still suffers from delays and large inter/intra-subject variability that limiting optimal glucose control. Intraperitoneal (IP) insulin administration, despite its higher invasiveness, was shown to represent a valid alternative to the SC one. To date, no mathematical model describing the absorption and distribution of insulin after IP administration is available. Here, we aim to fill this gap by using data from eight patients with T1D, treated by implanted IP pump, studied in a hospitalized setting, with frequent measurements of plasma insulin and glucose concentration. A battery of models describing insulin kinetics after IP administration were tested. Model comparison and selection were performed based on model ability to predict the data, precision of parameters and parsimony criteria. The selected model assumed that the insulin absorption from the IP space was described by a linear, two-compartment model, coupled with a two-compartment model of whole-body insulin kinetics with hepatic insulin extraction controlled by hepatic insulin. Future developments include model incorporation into the UVa/Padova T1D Simulator for testing open- and closed-loop therapies with IP insulin administration.
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Affiliation(s)
- Michele Schiavon
- Department of Information Engineering, University of Padova, 35131 Padova, Italy;
| | - Claudio Cobelli
- Department of Woman and Child’s Health, University of Padova, 35128 Padova, Italy;
| | - Chiara Dalla Man
- Department of Information Engineering, University of Padova, 35131 Padova, Italy;
- Correspondence:
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Olcomendy L, Pirog A, Lebreton F, Jaffredo M, Cassany L, Gucik Derigny D, Cieslak J, Henry D, Lang J, Catargi B, Raoux M, Bornat Y, Renaud S. Integrating an Islet-Based Biosensor in the Artificial Pancreas: In Silico Proof-of-Concept. IEEE Trans Biomed Eng 2021; 69:899-909. [PMID: 34469288 DOI: 10.1109/tbme.2021.3109096] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
OBJECTIVE Current treatment of type 1 diabetes by closed-loop approaches depends on continuous glucose monitoring. However, glucose readings alone are insufficient for an artificial pancreas to truthfully restore glucose homeostasis where additional physiological regulators of insulin secretion play a considerable role. Previously, we have developed an electrophysiological biosensor of pancreatic islet activity, which integrates these additional regulators through electrical measurement. This work aims at investigating the performance of the biosensor in a blood glucose control loop, to establish an in silico proof-of-concept. METHODS Two islet algorithm models were identified on experimental data recorded with the biosensor. First, we validated electrical measurement as a means to exploit the inner regulation capabilities of islets for intravenous glucose measurement and insulin infusion. Then, an artificial pancreas integrating the islet-based biosensor was compared to standard treatment approaches using subcutaneous routes. The closed-loop simulations were performed in the UVA/Padova T1DM Simulator where a series of realistic meal scenarios were applied to virtual diabetic patients. RESULTS With intravenous routes, the endogenous islet algorithms successfully restored glucose homeostasis for all patient categories (mean time in range exceeds 90%) while mitigating the risk of adverse glycaemic events (mean BGI < 2). Using subcutaneous routes, the biosensor-based artificial pancreas was as performing as standard treatments, and outperformed them under challenging conditions. CONCLUSION This work validates the concept of using pancreatic islets algorithms in an artificial pancreas in silico. SIGNIFICANCE Pancreatic islet endogenous algorithms obtained via an electrophysiological biosensor successfully regulate blood glucose levels of virtual type 1 diabetic patients.
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Felizardo V, Garcia NM, Pombo N, Megdiche I. Data-based algorithms and models using diabetics real data for blood glucose and hypoglycaemia prediction - A systematic literature review. Artif Intell Med 2021; 118:102120. [PMID: 34412843 DOI: 10.1016/j.artmed.2021.102120] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2020] [Revised: 05/24/2021] [Accepted: 05/25/2021] [Indexed: 10/21/2022]
Abstract
BACKGROUND AND AIM Hypoglycaemia prediction play an important role in diabetes management being able to reduce the number of dangerous situations. Thus, it is relevant to present a systematic review on the currently available prediction algorithms and models for hypoglycaemia (or hypoglycemia in US English) prediction. METHODS This study aims to systematically review the literature on data-based algorithms and models using diabetics real data for hypoglycaemia prediction. Five electronic databases were screened for studies published from January 2014 to June 2020: ScienceDirect, IEEE Xplore, ACM Digital Library, SCOPUS, and PubMed. RESULTS Sixty-three eligible studies were retrieved that met the inclusion criteria. The review identifies the current trend in this topic: most of the studies perform short-term predictions (82.5%). Also, the review pinpoints the inputs and shows that information fusion is relevant for hypoglycaemia prediction. Regarding data-based models (80.9%) and hybrid models (19.1%) different predictive techniques are used: Artificial neural network (22.2%), ensemble learning (27.0%), supervised learning (20.6%), statistic/probabilistic (7.9%), autoregressive (7.9%), evolutionary (6.4%), deep learning (4.8%) and adaptative filter (3.2%). Artificial Neural networks and hybrid models show better results. CONCLUSIONS The data-based models for blood glucose and hypoglycaemia prediction should be able to provide a good balance between the applicability and performance, integrating complementary data from different sources or from different models. This review identifies trends and possible opportunities for research in this topic.
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Affiliation(s)
- Virginie Felizardo
- Instituto de Telecomunicações, Covilhã, Portugal; Universidade da Beira Interior, Covilhã, Portugal.
| | - Nuno M Garcia
- Instituto de Telecomunicações, Covilhã, Portugal; Universidade da Beira Interior, Covilhã, Portugal.
| | - Nuno Pombo
- Instituto de Telecomunicações, Covilhã, Portugal; Universidade da Beira Interior, Covilhã, Portugal.
| | - Imen Megdiche
- IRIT, Institut de Recherche en Informatique de Toulouse, Toulouse University, France.
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