Diabetes: Models, Signals, and Control

Cobelli, C.; Dalla Man, C.; Sparacino, G.; Magni, L.; De Nicolao, G.; Kovatchev, B.P.

doi:10.1109/rbme.2009.2036073

Cited by 511Diabetes Management and ResearchPancreatic function and diabetesDiabetes Management and Education

Diabetes: Models, Signals, and Control

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Cobelli, C., Dalla Man, C., Sparacino, G., Magni, L., De Nicolao, G., Kovatchev, B.P.·2009·IEEE Reviews in Biomedical Engineering

DOI 10.1109/rbme.2009.2036073PMC PMC2951686

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Abstract

The control of diabetes is an interdisciplinary endeavor, which includes a significant biomedical engineering component, with traditions of success beginning in the early 1960s. It began with modeling of the insulin-glucose system, and progressed to large-scale in silico experiments, and automated closed-loop control (artificial pancreas). Here, we follow these engineering efforts through the last, almost 50 years. We begin with the now classic minimal modeling approach and discuss a number of subsequent models, which have recently resulted in the first in silico simulation model accepted as substitute to animal trials in the quest for optimal diabetes control. We then review metabolic monitoring, with a particular emphasis on the new continuous glucose sensors, on the analyses of their time-series signals, and on the opportunities that they present for automation of diabetes control. Finally, we review control strategies that have been successfully employed in vivo or in silico, presenting a promise for the development of a future artificial pancreas and, in particular, discuss a modular architecture for building closed-loop control systems, including insulin delivery and patient safety supervision layers. We conclude with a brief discussion of the unique interactions between human physiology, behavioral events, engineering modeling and control relevant to diabetes.

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In silico testing has provided directions for clinical studies and ruling out of ineffective control scenarios in a cost-effective manner.
“in silico testing has provided directions for clinical studies and ruling out of ineffective control scenarios in a cost-effective manner. 1,2”
From The UVA/PADOVA Type 1 Diabetes Simulator: New Features (2014) · cites this paper as [1]
The development of artificial pancreas control algorithms in the past few years has been accelerated by computer simulation.
“The development of artificial pancreas control algorithms in the past few years has been accelerated by computer simulation; for example, in silico testing has provided directions for clinical studies and ruling out of ineffective control scenarios in a cost-effective manner. 1,2”
From The UVA/PADOVA Type 1 Diabetes Simulator: New Features (2014) · cites this paper as [1]

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Insulin Kinetics in Type-1 Diabetes: Continuous and Bolus Delivery of Rapid Acting Insulin
Wilinska, M. E., Chassin, L. J., Schaller, H. C., Schaupp, L., Pieber, T. R., & Hovorka, R. (2004) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2004.839639 · Cited by 236 · cited 2× · see citing works
“The model, coupled to a subcutaneous insulin kinetic model [142], consists of 11 dif-”
Pathogenesis of Pre-Diabetes
Bock, G., Dalla Man, C., Campioni, M., Chittilapilly, E., Basu, R., Toffolo, G., Cobelli, C., & Rizza, R. (2006) · Diabetes · doi:10.2337/db06-0319 · Cited by 230 · see citing works
Self-citation
Motivations and Methods for Analyzing Pulsatile Hormone Secretion
Veldhuis, J. D., Keenan, D. M., & Pincus, S. M. (2008) · Endocrine Reviews · doi:10.1210/er.2008-0005 · Cited by 223 · cited 2× · see citing works
“It has been established that the ability of glucose to entrain ultradian insulin secretion patterns is disturbed in diabetic and prediabetic individuals [157]–[159], but the exact role of oscillations in the glucose regulation systems is still under investigation [160].”
Pulsatile Insulin Secretion: Detection, Regulation, and Role in Diabetes
Pørksen, N., Hollingdal, M., Juhl, C., Butler, P., Veldhuis, J. D., & Schmitz, O. (2002) · Diabetes · doi:10.2337/diabetes.51.2007.s245 · Cited by 223 · cited 2× · see citing works
“Specific to diabetes, it has been reported that insulin secretion patterns are more irregular in healthy older and obese individuals and patients with prediabetes or type II diabetes mellitus than in young, nonobese, nondiabetic subjects [156]–[159].”
Lilly lecture 1989. Toward physiological understanding of glucose tolerance. Minimal-model approach
Bergman, R. N. (1989) · Diabetes · doi:10.2337/diabetes.38.12.1512 · Cited by 215 · cited 3× · see citing works
“Only later this remote compartment was experimentally proven in dog studies to be the interstitial fluid [27].”
An Improved PID Switching Control Strategy for Type 1 Diabetes
Marchetti, G., Barolo, M., Jovanovic, L. G., Zisser, H., & Seborg, D. E. (2008) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2008.915665 · Cited by 212 · cited 2× · see citing works
“this direction by [261], where the authors present a controller”
Identification of the docked granule pool responsible for the first phase of glucose-stimulated insulin secretion.
Daniel, S., Noda, M., Straub, S. G., & Sharp, G. W. G. (1999) · Diabetes · doi:10.2337/diabetes.48.9.1686 · Cited by 210 · see citing works
Evaluating the Efficacy of Closed-Loop Glucose Regulation via Control-Variability Grid Analysis
Magni, L., Raimondo, D. M., Man, C. D., Breton, M. D., Patek, S. D., Nicolao, G. D., Cobelli, C., & Kovatchev, B. P. (2008) · Journal of Diabetes Science and Technology · doi:10.1177/193229680800200414 · Cited by 207 · cited 4× · see citing works
“Preliminary to a clinical study, the controller can be applied to an in silico population, representative of the real population, and the performance assessed on the CVGA.”
InfluentialSelf-citation
Run-to-Run Control in Semiconductor Manufacturing
(2010) · CRC Press eBooks · doi:10.1201/9781420040661 · Cited by 206 · cited 1× · see citing works
“This type of problem, called run-to-run (R2R) control, has been extensively studied in the control of chemical and manufacturing processes [293].”
Detection and characterization of peaks and estimation of instantaneous secretory rate for episodic pulsatile hormone secretion
Oerter, K. E., Guardabasso, V., & Rodbard, D. (1986) · Computers and Biomedical Research · doi:10.1016/0010-4809(86)90014-5 · Cited by 204 · see citing works
Nonparametric input estimation in physiological systems: Problems, methods, and case studies
Nicolao, G. D., Sparacino, G., & Cobelli, C. (1997) · Automatica · doi:10.1016/s0005-1098(96)00254-3 · Cited by 204 · cited 1× · see citing works
“, the stochastic deconvolution method [62].”
Self-citation
Minimal model estimation of glucose absorption and insulin sensitivity from oral test: validation with a tracer method
Dalla Man, C., Caumo, A., Basu, R., Rizza, R., Toffolo, G., & Cobelli, C. (2004) · American Journal of Physiology-Endocrinology and Metabolism · doi:10.1152/ajpendo.00319.2003 · Cited by 198 · cited 1× · see citing works
“has been precisely estimated and has been validated by comparison with both multiple tracer [46] and glucose clamp [47] techniques.”
Self-citation
Model-based blood glucose control for type 1 diabetes via parametric programming
Dua, V., Doyle, F. J., & Pistikopoulos, E. N. (2006) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2006.878075 · Cited by 198 · see citing works
Metabolic and electrical oscillations: partners in controlling pulsatile insulin secretion
Bertram, R., Sherman, A., & Satin, L. S. (2007) · American Journal of Physiology-Endocrinology and Metabolism · doi:10.1152/ajpendo.00359.2007 · Cited by 196 · see citing works
Validation of the Archimedes Diabetes Model
Eddy, D. M., & Schlessinger, L. (2003) · Diabetes Care · doi:10.2337/diacare.26.11.3102 · Cited by 192 · cited 1× · see citing works
“However, in this review we have to limit our scope; thus, we will not be able to discuss modeling in the areas of patient management [9], [10] and health care intervention strategies [11]–[14], or in the emerging area of systems biology [15], [16].”
Roles of Glucose Transport and Glucose Phosphorylation in Muscle Insulin Resistance of NIDDM
Bonadonna, R. C., Bonora, E., Prato, S. D., Saccomani, M. P., Cobelli, C., Natali, A., Frascerra, S., Pecori, N., Ferrannini, E., Bier, D., et al. (1996) · Diabetes · doi:10.2337/diab.45.7.915 · Cited by 191 · see citing works
Self-citation
Alarms Based on Real-Time Sensor Glucose Values Alert Patients to Hypo- and Hyperglycemia: The Guardian Continuous Monitoring System
Bode, B. W., Gross, K. M., Rikalo, N., Schwartz, S. L., Wahl, T. O., Page, C. W., Gross, T. M., & Mastrototaro, J. J. (2004) · Diabetes Technology & Therapeutics · doi:10.1089/152091504773731285 · Cited by 190 · cited 2× · see citing works
“These methods have rapidly evolved from a concept [238] to implementation in CGM devices, such as the GuardianRT (Medtronic, Norhtridge, CA) [239] and the Freestyle Navigator (Abbott Diabetes Care, Alameda, CA) [178].”
Models of subcutaneous insulin kinetics. A critical review
Nucci, G., & Cobelli, C. (2000) · Computer Methods and Programs in Biomedicine · doi:10.1016/s0169-2607(00)00071-7 · Cited by 189 · cited 1× · see citing works
“modeled and quantitative models exist [142], [285], [286] which allowed both the estimation of the timing and duration of insulin”
Self-citation
Closed-loop artificial pancreas using subcutaneous glucose sensing and insulin delivery and a model predictive control algorithm: the Virginia experience
Clarke, W. L., Anderson, S., Breton, M., Patek, S., Kashmer, L., & Kovatchev, B. (2009) · Journal of Diabetes Science and Technology · doi:10.1177/193229680900300506 · Cited by 185 · cited 4× · see citing works
“This can be done for both PID regulators [261] and MPC ones [143], [255]–[257].”
InfluentialSelf-citation
An integrated mathematical model of the dynamics of blood glucose and its hormonal control
Cobelli, C., Federspil, G., Pacini, G., Salvan, A., & Scandellari, C. (1982) · Mathematical Biosciences · doi:10.1016/0025-5564(82)90050-5 · Cited by 182 · see citing works
Self-citation
A Mathematical Model of the Glucose-tolerance test
Ackerman, E., Rosevear, J. W., & McGuckin, W. F. (1964) · Physics in Medicine and Biology · doi:10.1088/0031-9155/9/2/307 · Cited by 180 · cited 1× · see citing works
“With various minor modifications this model has been used in conjunction with intravenous injections or infusions [29]–[31] and also during an oral glucose tolerance test [32]–[34] to obtain a four-parameter representation of glucose metabolism in various states of glucose intolerance, including diabetes.”
Modeling Phasic Insulin Release
Nesher, R., & Cerasi, E. (2002) · Diabetes · doi:10.2337/diabetes.51.2007.s53 · Cited by 179 · cited 1× · see citing works
“for granules having different thresholds [149], but Grodsky”
Two-Hour Seven-Sample Oral Glucose Tolerance Test and Meal Protocol
Dalla Man, C., Campioni, M., Polonsky, K. S., Basu, R., Rizza, R. A., Toffolo, G., & Cobelli, C. (2005) · Diabetes · doi:10.2337/diabetes.54.11.3265 · Cited by 179 · see citing works
Self-citation
Estimating false-positive and false-negative errors in analyses of hormonal pulsatility
Cauter, E. V. (1988) · American Journal of Physiology-Endocrinology and Metabolism · doi:10.1152/ajpendo.1988.254.6.e786 · Cited by 177 · see citing works
Modelling Methodology for Physiology and Medicine
(2014) · Elsevier eBooks · doi:10.1016/c2012-0-06031-0 · Cited by 176 · see citing works
Analysis, Modeling, and Simulation of the Accuracy of Continuous Glucose Sensors
Breton, M. D., & Kovatchev, B. P. (2008) · Journal of Diabetes Science and Technology · doi:10.1177/193229680800200517 · Cited by 173 · see citing works
Self-citation
Model predictive control of glucose concentration in type I diabetic patients: An in silico trial
Magni, L., Raimondo, D. M., Man, C. D., Nicolao, G. D., Kovatchev, B. P., & Cobelli, C. (2009) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2009.04.003 · Cited by 173 · cited 30× · see citing works
“Finally, there may be constraints on the manipulated variables (insulin delivery rates by the pump is greater than zero and less than some maximal value) (50) (51) and also on the controlled ones (glycemia in the admissible range) (52) The rationale behind MPC is rather simple: at each time point we compute the sequen…”
InfluentialSelf-citation
Bolus Calculator: A Review of Four “Smart” Insulin Pumps
Zisser, H., Robinson, L., Bevier, W. C., Dassau, E., Ellingsen, C., Doyle, F. J., & Jovanovic, L. G. (2008) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2007.0284 · Cited by 164 · cited 1× · see citing works
“An alternative approach relies on insulin-on-board (IOB) computation [251].”
β-Cell Function Across the Spectrum of Glucose Tolerance in Obese Youth
Weiss, R., Caprio, S., Trombetta, M., Taksali, S. E., Tamborlane, W. V., & Bonadonna, R. C. (2005) · Diabetes · doi:10.2337/diabetes.54.6.1735 · Cited by 164 · cited 2× · see citing works
“Hepatic insulin extraction can then be calculated from SR and profiles [106].”
Identification of Intraday Metabolic Profiles during Closed-Loop Glucose Control in Individuals with Type 1 Diabetes
Kanderian, S. S., WEINZIMER, S. A., Voskanyan, G. R., & Steil, G. M. (2009) · Journal of Diabetes Science and Technology · doi:10.1177/193229680900300508 · Cited by 162 · cited 1× · see citing works
“Although the confidence in their results is certainly higher than that obtained using as simulator the glucose minimal model [25] (extensively misused for this purpose and recently being the core of the Medtronic Virtual Patient [139]), the impact has been very modest.”
Closing the Loop: The Adicol Experience
Hovorka, R., Chassin, L. J., Wilinska, M. E., Canonico, V., Akwi, J. A., Federici, M. O., Massi‐Benedetti, M., Hutzli, I., Zaugg, C., Kaufmann, H., et al. (2004) · Diabetes Technology & Therapeutics · doi:10.1089/152091504774197990 · Cited by 153 · see citing works
Safety Constraints in an Artificial Pancreatic β Cell: An Implementation of Model Predictive Control with Insulin on Board
Ellingsen, C., Dassau, E., Zisser, H., Grosman, B., Percival, M. W., Jovanovic, L. G., & Doyle, F. J. (2009) · Journal of Diabetes Science and Technology · doi:10.1177/193229680900300319 · Cited by 153 · cited 1× · see citing works
“certain safety constraints already within the real-time control module [258], [269].”
A model of glucose kinetics and their control by insulin, compartmental and noncompartmental approaches
Cobelli, C., TOFFOLO, G. M., & Ferrannini, E. (1984) · Mathematical Biosciences · doi:10.1016/0025-5564(84)90114-7 · Cited by 151 · cited 5× · see citing works
“Glucose kinetics requires at least a two compartment description [21].”
InfluentialSelf-citation
Closed-Loop Control of Artificial Pancreatic $\beta$ -Cell in Type 1 Diabetes Mellitus Using Model Predictive Iterative Learning Control
Wang, Y., Dassau, E., & Doyle, F. J. (2009) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2009.2024409 · Cited by 150 · see citing works
Estimation of Future Glucose Concentrations with Subject-Specific Recursive Linear Models
Eren-Oruklu, M., Çınar, A., Quinn, L. T., & SMITH, D. P. (2009) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2008.0065 · Cited by 149 · cited 1× · see citing works
“eling techniques [227]–[233], but neural networks [234], [235] and other physiological structural models [236] have been used”
Interstitial fluid glucose dynamics during insulin-induced hypoglycaemia
Steil, G. M., Rebrin, K., Hariri, F., Jinagonda, S., Tadros, S. S., Darwin, C. H., & Saad, M. F. (2005) · Diabetologia · doi:10.1007/s00125-005-1852-x · Cited by 148 · see citing works
A Closed-Loop Artificial Pancreas Using Model Predictive Control and a Sliding Meal Size Estimator
Lee, H., Buckingham, B. A., Wilson, D. M., & Bequette, B. W. (2009) · Journal of Diabetes Science and Technology · doi:10.1177/193229680900300511 · Cited by 147 · cited 3× · see citing works
“context, a meal detection and estimation module [258], [276],”
Metabolic Syndrome and Robustness Tradeoffs
Kitano, H., Oda, K., Kimura, T., Matsuoka, Y., CSETE, M. E., Doyle, J. C., & Muramatsu, M. (2004) · Diabetes · doi:10.2337/diabetes.53.suppl_3.s6 · Cited by 147 · cited 1× · see citing works
“However, in this review we have to limit our scope; thus, we will not be able to discuss modeling in the areas of patient management [9], [10] and health care intervention strategies [11]–[14], or in the emerging area of systems biology [15], [16].”
Contribution of Hepatic and Extrahepatic Insulin Resistance to the Pathogenesis of Impaired Fasting Glucose
Bock, G., Chittilapilly, E. G., Basu, R., TOFFOLO, G. M., Cobelli, C., Chandramouli, V., Landau, B. R., & Rizza, R. A. (2007) · Diabetes · doi:10.2337/db06-1776 · Cited by 146 · see citing works
Self-citation
A minimal model of insulin secretion and kinetics to assess hepatic insulin extraction.
Toffolo, G., Campioni, M., Basu, R., Rizza, R., & Cobelli, C. (2006) · American Journal of Physiology. Endocrinology and Metabolism · doi:10.1152/ajpendo.00473.2004 · Cited by 145 · cited 1× · see citing works
“In addition, from pre- and post-hepatic model parameters an index of hepatic extraction can also be calculated [108], [109].”
Self-citation
Algorithmic Evaluation of Metabolic Control and Risk of Severe Hypoglycemia in Type 1 and Type 2 Diabetes Using Self-Monitoring Blood Glucose Data
Kovatchev, B. P., Cox, D. J., Kumar, A., Gonder-Frederick, L. A., & Clarke, W. L. (2003) · Diabetes Technology & Therapeutics · doi:10.1089/152091503322527021 · Cited by 145 · cited 2× · see citing works
“The LBGI, which is based on the left branch of the BG Risk Function r(BG) has been validated as an excellent predictor of future significant hypoglycemia [186], [187].”
Self-citation
Postprandial Suppression of Glucagon Secretion Depends on Intact Pulsatile Insulin Secretion
Meier, J. J., Kjems, L. L., Veldhuis, J. D., Lefèbvre, P., & Butler, P. C. (2006) · Diabetes · doi:10.2337/diabetes.55.04.06.db05-1449 · Cited by 144 · cited 1× · see citing works
“ApEn has been utilized to probe the regulation of several hormones including growth hormone [175], aldosterone [176], cortisol [177], insulin [178], and glucagon [179].”
Detection of a Meal Using Continuous Glucose Monitoring
Dassau, E., Bequette, B. W., Buckingham, B. A., & Doyle, F. J. (2008) · Diabetes Care · doi:10.2337/dc07-1293 · Cited by 143 · cited 1× · see citing works
“context, a meal detection and estimation module [258], [276],”
Estimation of insulin sensitivity and glucose clearance from minimal model: new insights from labeled IVGTT
Cobelli, C., Pacini, G., TOFFOLO, G. M., & Saccà, L. (1986) · American Journal of Physiology-Endocrinology and Metabolism · doi:10.1152/ajpendo.1986.250.5.e591 · Cited by 140 · cited 1× · see citing works
“, -glucose) label, was first interpreted with one [48]–[51] and, subsequently, with a two-compartment minimal model [52], [53], [55], [56] which has been shown to be more accurate.”
Self-citation
Closed-Loop Artificial Pancreas Using Subcutaneous Glucose Sensing and Insulin Delivery and a Model Predictive Control Algorithm: Preliminary Studies in Padova and Montpellier
Bruttomesso, D., Farret, A., Costa, S., Marescotti, M. C., Vettore, M., Avogaro, A., Tiengo, A., Man, C. D., Place, J., Facchinetti, A., et al. (2009) · Journal of Diabetes Science and Technology · doi:10.1177/193229680900300504 · Cited by 140 · see citing works
Self-citation
Validation of mathematical models of complex endocrine-metabolic systems. A case study on a model of glucose regulation
Cobelli, C., & Mari, A. (1983) · Medical & Biological Engineering & Computing · doi:10.1007/bf02442625 · Cited by 140 · see citing works
Self-citation
Clinical Application of Emerging Sensor Technologies in Diabetes Management: Consensus Guidelines for Continuous Glucose Monitoring (CGM)
Hirsch, I. B., Armstrong, D. U., Bergenstal, R. M., Buckingham, B. A., Childs, B. P., Clarke, W. L., Peters, A. L., & Wolpert, H. A. (2008) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2008.0016 · Cited by 136 · cited 1× · see citing works
“CGM [191]–[193] and charted guidelines for clinical use [194] and its future as a precursor to closed-loop control [195].”
A probabilistic approach to glucose prediction and insulin dose adjustment: description of metabolic model and pilot evaluation study
Andreassen, S., Benn, J. J., Hovorka, R., Olesen, K. G., & Carson, E. R. (1994) · Computer Methods and Programs in Biomedicine · doi:10.1016/0169-2607(94)90052-3 · Cited by 135 · see citing works
Estimation of β-Cell Sensitivity From Intravenous Glucose Tolerance Test C-Peptide Data: Knowledge of the Kinetics Avoids Errors in Modeling the Secretion
TOFFOLO, G. M., Grandi, F. D., & Cobelli, C. (1995) · Diabetes · doi:10.2337/diab.44.7.845 · Cited by 134 · see citing works
Self-citation
Preventing Hypoglycemia Using Predictive Alarm Algorithms and Insulin Pump Suspension
Buckingham, B. A., Cobry, E. C., Clinton, P. K., Gage, V., Caswell, K., Kunselman, E. L., Cameron, F., & Chase, H. P. (2009) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2008.0032 · Cited by 132 · cited 1× · see citing works
“This approach has been shown to reduce the risk of nocturnal hypoglycemia [242], [250].”
Lack of control by glucose of ultradian insulin secretory oscillations in impaired glucose tolerance and in non-insulin-dependent diabetes mellitus.
O'Meara, N. M., Sturis, J., Van Cauter, E., & Polonsky, K. S. (1993) · Journal of Clinical Investigation · doi:10.1172/jci116560 · Cited by 132 · see citing works
Prediction of Severe Hypoglycemia
Cox, D. J., Gonder-Frederick, L., Ritterband, L., Clarke, W., & Kovatchev, B. P. (2007) · Diabetes Care · doi:10.2337/dc06-1386 · Cited by 129 · see citing works
Self-citation
The hot IVGTT two-compartment minimal model: indexes of glucose effectiveness and insulin sensitivity
Vicini, P., Caumo, A., & Cobelli, C. (1997) · American Journal of Physiology-Endocrinology and Metabolism · doi:10.1152/ajpendo.1997.273.5.e1024 · Cited by 129 · cited 3× · see citing works
“, -glucose) label, was first interpreted with one [48]–[51] and, subsequently, with a two-compartment minimal model [52], [53], [55], [56] which has been shown to be more accurate.”
Self-citation
Predicting Subcutaneous Glucose Concentration in Humans: Data-Driven Glucose Modeling
Gani, A., Gribok, A. V., Rajaraman, S., Ward, W. K., & Jaques Reifman, A. (2008) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2008.2005937 · Cited by 129 · cited 1× · see citing works
“eling techniques [227]–[233], but neural networks [234], [235] and other physiological structural models [236] have been used”
Effects of type 2 diabetes on insulin secretion, insulin action, glucose effectiveness, and postprandial glucose metabolism.
Basu, A., Dalla Man, C., Basu, R., Toffolo, G., Cobelli, C., & Rizza, R. A. (2009) · Diabetes Care · doi:10.2337/dc08-1826 · Cited by 129 · see citing works
Self-citation
Insulin sensitivity by oral glucose minimal models: validation against clamp
Man, C. D., Yarasheski, K. E., Caumo, A., Robertson, H., TOFFOLO, G. M., Polonsky, K. S., & Cobelli, C. (2005) · American Journal of Physiology-Endocrinology and Metabolism · doi:10.1152/ajpendo.00076.2005 · Cited by 128 · cited 3× · see citing works
“has been validated against the multiple tracer [57] and the glucose clamp [47] techniques.”
Self-citation
Can We Really Close the Loop and How Soon? Accelerating the Availability of an Artificial Pancreas: A Roadmap to Better Diabetes Outcomes
Kowalski, A. J. (2009) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2009.0031 · Cited by 127 · cited 1× · see citing works
“Recently, it has been proposed that implementation of “semi closed-loop” glucose control, only using the safety module, without the real-time control, could offer a first step towards full-closed loop control [253].”
Development of a Neural Network for Prediction of Glucose Concentration in Type 1 Diabetes Patients
Pappada, S. M., Cameron, B. D., & Rosman, P. M. (2008) · Journal of Diabetes Science and Technology · doi:10.1177/193229680800200507 · Cited by 125 · cited 1× · see citing works
“eling techniques [227]–[233], but neural networks [234], [235] and other physiological structural models [236] have been used”
Risk Analysis of Blood Glucose Data: A Quantitative Approach to Optimizing the Control of Insulin Dependent Diabetes
Kovatchev, B. P., Straume, M., Cox, D. J., & Farhy, L. S. (2000) · Computational and Mathematical Methods in Medicine · doi:10.1080/10273660008833060 · Cited by 125 · cited 2× · see citing works
“27 visualizes this concept by a 72-hour CGM trace of a patient with T1DM: each CGM reading is processed in two steps: 1) application of the symmetrization formula [184], and 2) application of the quadratic risk function that assigns increasing weights to larger BG deviations towards hypo- or hyper-glycemia [185].”
Self-citation
Primary Defects in β-Cell Function Further Exacerbated by Worsening of Insulin Resistance Mark the Development of Impaired Glucose Tolerance in Obese Adolescents
Cali, A. M. G., Man, C. D., Cobelli, C., Dziura, J., Seyal, A., Shaw, M., Allen, K., Chen, S., & Caprio, S. (2009) · Diabetes Care · doi:10.2337/dc08-1274 · Cited by 125 · cited 1× · see citing works
“For instance, the battery of oral glucose, C-peptide, and insulin models have been used in studying: the effect of age and gender on glucose metabolism [59]; the effect of anti-aging drugs [110]; the influence of ethnicity [111]; insulin sensitivity and beta-cell function in nondiabetic [112] and obese [113] adolescen…”
Self-citation
Predictive Monitoring for Improved Management of Glucose Levels
Jaques Reifman, A., Rajaraman, S., Gribok, A. V., & Ward, W. K. (2007) · Journal of Diabetes Science and Technology · doi:10.1177/193229680700100405 · Cited by 123 · cited 2× · see citing works
“Furthermore, the prediction algorithm of [228] has been”
Diabetes mellitus modeling and short-term prediction based on blood glucose measurements
Ståhl, F. L., & Johansson, R. (2008) · Mathematical Biosciences · doi:10.1016/j.mbs.2008.10.008 · Cited by 122 · cited 1× · see citing works
“eling techniques [227]–[233], but neural networks [234], [235] and other physiological structural models [236] have been used”
Minimal model SGoverestimation and SIunderestimation: improved accuracy by a Bayesian two-compartment model
Cobelli, C., Caumo, A., & Omenetto, M. (1999) · American Journal of Physiology-Endocrinology and Metabolism · doi:10.1152/ajpendo.1999.277.3.e481 · Cited by 122 · cited 1× · see citing works
“A two compartment glucose minimal model has been proposed [41], [42], but this requires a priori knowledge on the two glucose exchange parameters [21], [23], [43], [44].”
Self-citation
Evaluation of Factors Affecting CGMS Calibration
Diabetes Research In Children Network (Direcnet) Study Group (2006) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2006.8.318 · Cited by 116 · cited 1× · see citing works
“[200], analyzed the improvement in CGMS sensor accuracy”
The cost-effectiveness of different management strategies for Type I diabetes: a Swiss perspective
Palmer, A. J., Weiss, C., Sendi, P., Neeser, K., Brandt, A., Singh, G., Wenzel, H., & Spinas, G. A. (2000) · Diabetologia · doi:10.1007/s001250050003 · Cited by 115 · cited 1× · see citing works
“However, in this review we have to limit our scope; thus, we will not be able to discuss modeling in the areas of patient management [9], [10] and health care intervention strategies [11]–[14], or in the emerging area of systems biology [15], [16].”
A run-to-run control strategy to adjust basal insulin infusion rates in type 1 diabetes
Palerm, C. C., Zisser, H., Jovanovic, L. G., & Doyle, F. J. (2007) · Journal of Process Control · doi:10.1016/j.jprocont.2007.07.010 · Cited by 113 · cited 1× · see citing works
“The first applications to glucose control regarded the iterative adjustment of the basal and boluses forming the conventional open-loop therapy [280]–[282].”
Quantitative indexes of β-cell function during graded up&down glucose infusion from C-peptide minimal models
Toffolo, G., Breda, E., Cavaghan, M. K., Ehrmann, D. A., Polonsky, K. S., & Cobelli, C. (2001) · American Journal of Physiology-Endocrinology and Metabolism · doi:10.1152/ajpendo.2001.280.1.e2 · Cited by 112 · cited 1× · see citing works
“[104] during “up&down” intravenous glucose infusion and by Steil et al.”
Self-citation
A Mathematical Model for the Glucose Induced Insulin Release in Man
Cerasi, E., Fick, G. H., & Rudemo, M. (1974) · European Journal of Clinical Investigation · doi:10.1111/j.1365-2362.1974.tb00403.x · Cited by 107 · see citing works
Entrainment of pulsatile insulin secretion by oscillatory glucose infusion.
Sturis, J., Cauter, E. V., Blackman, J. D., & Polonsky, K. S. (1991) · Journal of Clinical Investigation · doi:10.1172/jci115015 · Cited by 106 · cited 1× · see citing works
“Peakdetection methods provide information on pulse amplitude and location [161]–[164], while spectral/correlation analysis seeks to identify cycles in time series [165], [166].”
Run-to-Run Tuning of Model Predictive Control for Type 1 Diabetes Subjects: In Silico Trial
Magni, L., Forgione, M., Toffanin, C., Man, C. D., Kovatchev, B. P., Nicolao, G. D., & Cobelli, C. (2009) · Journal of Diabetes Science and Technology · doi:10.1177/193229680900300512 · Cited by 106 · cited 3× · see citing works
“More recently, R2R control has been applied also to the tuning of the controller parameters [254] where adjustment of the controller aggressiveness is considered.”
Self-citation
Dose-dependent effects of oral and intravenous glucose on insulin secretion and clearance in normal humans
Tillil, H., Shapiro, E. T., Miller, M. A., Karrison, T., Frank, B. H., Galloway, J. A., Rubenstein, A. H., & Polonsky, K. S. (1988) · American Journal of Physiology-Endocrinology and Metabolism · doi:10.1152/ajpendo.1988.254.3.e349 · Cited by 105 · see citing works
Compartmental Analysis in Biology and Medicine, 2nd edition.
Faddy, M. J., & Jacquez, J. A. (1987) · Biometrics · doi:10.2307/2531562 · Cited by 104 · cited 1× · see citing works
“Multiple tracer dilution data can be interpreted with both linear distributed parameter (see reviews [18] and [71] and compartmental organ models [72].”
An Integrated Model for Glucose and Insulin Regulation in Healthy Volunteers and Type 2 Diabetic Patients Following Intravenous Glucose Provocations
Silber, H. E., Jauslin, P. M., Frey, N., Gieschke, R., Simonsson, U. S. H., & Karlsson, M. O. (2007) · The Journal of Clinical Pharmacology · doi:10.1177/0091270007304457 · Cited by 103 · see citing works
Kinetic Modeling of the Glucoregulatory System to Improve Insulin Therapy
Salzsieder, E., Albrecht, G., Fischer, U., & Freyse, E. (1985) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.1985.325500 · Cited by 101 · see citing works
A simulation model of glucose regulation in the critically ill
Hovorka, R., Chassin, L. J., Ellmerer, M., Plank, J., & Wilinska, M. E. (2008) · Physiological Measurement · doi:10.1088/0967-3334/29/8/008 · Cited by 101 · cited 1× · see citing works
“In view of this, another inclusion criterion is the validation platform: we will only consider control algorithms validated in clinical in vivo trials or in advanced in silico experiments, which provide accurate description of dynamic phenomena and/or incorporate interindividual variability [137], [244], [245].”
Hepatic glucose production during the labeled IVGTT: estimation by deconvolution with a new minimal model
Caumo, A., & Cobelli, C. (1993) · American Journal of Physiology-Endocrinology and Metabolism · doi:10.1152/ajpendo.1993.264.5.e829 · Cited by 101 · cited 1× · see citing works
“, -glucose) label, was first interpreted with one [48]–[51] and, subsequently, with a two-compartment minimal model [52], [53], [55], [56] which has been shown to be more accurate.”
Self-citation
Stable-Label Intravenous Glucose Tolerance Test Minimal Model
Avogaro, A., Bristow, J. D., Bier, D. M., Cobelli, C., & TOFFOLO, G. M. (1989) · Diabetes · doi:10.2337/diab.38.8.1048 · Cited by 100 · see citing works
Self-citation
Assessment of β‐cell function during the oral glucose tolerance test by a minimal model of insulin secretion
Cretti, A., Lehtovirta, M., Bonora, E., Brunato, B., Zenti, M., Tosi, F., Caputo, M., Caruso, B., Groop, L., Muggeo, M., et al. (2001) · European Journal of Clinical Investigation · doi:10.1046/j.1365-2362.2001.00827.x · Cited by 99 · cited 2× · see citing works
“[100] describes insulin secretion with the static component of glucose control of the C-peptide minimal model; thus, it is characterized by a delay but does not include any dynamic, i.”
Glucose Transport in Human Skeletal Muscle: The In Vivo Response to Insulin
Bonadonna, R., Saccomani, M., Seely, L., Zych, K., Ferrannini, E., Cobelli, C., & DeFronzo, R. (1993) · Diabetes · doi:10.2337/diab.42.1.191 · Cited by 99 · cited 1× · see citing works
“Consequently, this model allowed us to study the enhancing effect of insulin on muscle glucose transport parameters in nondiabetic subjects [73] and identified the presence of a localized defect in insulin control in non-insulin-dependent diabetic (NIDD) patients [74].”
Self-citation
Adaptive closed-loop control provides blood-glucose regulation using dual subcutaneous insulin and glucagon infusion in diabetic swine
El-Khatib, F. H., Jiang, J., & Damiano, E. R. (2007) · Journal of Diabetes Science and Technology · doi:10.1177/193229680700100208 · Cited by 99 · see citing works
Measurements of Cytoplasmic Ca2+ in Islet Cell Clusters Show That Glucose Rapidly Recruits β-Cells and Gradually Increases the Individual Cell Response
Jonkers, F. C., & Henquin, J. (2001) · Diabetes · doi:10.2337/diabetes.50.3.540 · Cited by 99 · cited 3× · see citing works
“Later, Jonkers and Henquin [150] showed that the number of active cells is a sigmoidal function of the glucose concentration, as assumed by Grodsky [120] or the threshold distribution.”
Influential
Run-to-Run Control in Semiconductor Manufacturing
Drain, D. C. (2002) · Journal of Quality Technology · doi:10.1080/00224065.2002.11980181 · Cited by 99 · see citing works
Multiple tracer dilution estimates of D- and 2-deoxy-D-glucose uptake by the heart
Kuikka, J. T., Levin, M., & Bassingthwaighte, J. B. (1986) · American Journal of Physiology-Heart and Circulatory Physiology · doi:10.1152/ajpheart.1986.250.1.h29 · Cited by 98 · cited 2× · see citing works
“The only application to glucose metabolism of distributed parameter models has been in an isolated and perfused heart [70].”
Minimal modeling, partition analysis, and the estimation of insulin sensitivity.
Bergman, R. N., & Cobelli, C. (1980) · PubMed · Cited by 98 · see citing works
Self-citation
Disorderly and nonstationary insulin secretion in relatives of patients with NIDDM
Schmitz, O., Pørksen, N. K., Nyholm, B., Skjærbæk, C. B., Butler, P. C., Veldhuis, J. D., & Pincus, S. M. (1997) · American Journal of Physiology-Endocrinology and Metabolism · doi:10.1152/ajpendo.1997.272.2.e218 · Cited by 96 · cited 2× · see citing works
“It has been established that the ability of glucose to entrain ultradian insulin secretion patterns is disturbed in diabetic and prediabetic individuals [157]–[159], but the exact role of oscillations in the glucose regulation systems is still under investigation [160].”
Non-insulin-mediated glucose disappearance in subjects with IDDM. Discordance between experimental results and minimal model analysis.
Quon, M. J., Cochran, C., Taylor, S. I., & Eastman, R. C. (1994) · Diabetes · doi:10.2337/diab.43.7.890 · Cited by 96 · cited 1× · see citing works
“, using one instead of two compartments during the highly dynamic IVGTT perturbation, can introduce bias in glucose effectiveness and insulin sensitivity, being over- and under-estimated, respectively [38]–[40].”
The Extended Kalman Filter for Continuous Glucose Monitoring
Knobbe, E. J., & Buckingham, B. A. (2005) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2005.7.15 · Cited by 95 · cited 2× · see citing works
“was done by Knobbe and Buckingham [201].”
Increased disorderliness and amplified basal and pulsatile aldosterone secretion in patients with primary aldosteronism
Siragy, H. M., Vieweg, W. V. R., Pincus, S. M., & Veldhuis, J. D. (1995) · The Journal of Clinical Endocrinology & Metabolism · doi:10.1210/jcem.80.1.7829626 · Cited by 95 · cited 1× · see citing works
“ApEn has been utilized to probe the regulation of several hormones including growth hormone [175], aldosterone [176], cortisol [177], insulin [178], and glucagon [179].”
Hypoglycemia Prediction and Detection Using Optimal Estimation
Palerm, C. C., Willis, J. P., Desemone, J., & Bequette, B. W. (2005) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2005.7.3 · Cited by 94 · cited 1× · see citing works
“In particular, it can be used to predict hypoglycemia [274], [275] and possibly trigger safety”
Greater disorderliness of ACTH and cortisol release accompanies pituitary-dependent Cushing's disease
van den Berg, G., Pincus, S. M., Veldhuis, J. D., Frölich, M., & Roelfsema, F. (1997) · European Journal of Endocrinology · doi:10.1530/eje.0.1360394 · Cited by 93 · cited 1× · see citing works
“ApEn has been utilized to probe the regulation of several hormones including growth hormone [175], aldosterone [176], cortisol [177], insulin [178], and glucagon [179].”
Comparison of Accuracy and Safety of the SEVEN and the Navigator Continuous Glucose Monitoring Systems
Garg, S. K., Smith, J. G. W., Beatson, C. R., Lopez-Baca, B., Voelmle, M. K., & Gottlieb, P. A. (2009) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2008.0109 · Cited by 93 · see citing works
Graphical and Numerical Evaluation of Continuous Glucose Sensing Time Lag
Kovatchev, B. P., Shields, D., & Breton, M. D. (2009) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2008.0044 · Cited by 91 · cited 1× · see citing works
“of the direction of BG change [196], [207], [208].”
Self-citation
Undermodeling affects minimal model indexes: insights from a two-compartment model
Caumo, A., Vicini, P., Zachwieja, J. J., Avogaro, A., Yarasheski, K. E., Bier, D. M., & Cobelli, C. (1999) · American Journal of Physiology-Endocrinology and Metabolism · doi:10.1152/ajpendo.1999.276.6.e1171 · Cited by 91 · cited 1× · see citing works
“For instance, in [122] and [123] the consequences of undermodeling the glucose system by using the classic IVGTT single compartment minimal model has been studied by using a richer IVGTT two compartment glucose-insulin simulation model.”
Self-citation
The kinetics of insulin in man. I. General aspects
Ferrannini, E., & Cobelli, C. (1987) · Diabetes/Metabolism Reviews · doi:10.1002/dmr.5610030201 · Cited by 90 · cited 2× · see citing works
“In the physiological concentration range (up to 100–150 U/ml) where insulin kinetic is approximately linear, various linear compartmental models have been proposed [84], following the landmark model of Sherwin et al.”
Self-citation
Pancreatic β-Cell Responsiveness during Meal Tolerance Test: Model Assessment in Normal Subjects and Subjects with Newly Diagnosed Noninsulin-Dependent Diabetes Mellitus1
Hovorka, R., Chassin, L. J., Luzio, S. D., Playle, R., & Owens, D. R. (1998) · The Journal of Clinical Endocrinology & Metabolism · doi:10.1210/jcem.83.3.4646 · Cited by 90 · see citing works
Hypoglycemia Detection and Prediction Using Continuous Glucose Monitoring—A Study on Hypoglycemic Clamp Data
Palerm, C. C., & Bequette, B. W. (2007) · Journal of Diabetes Science and Technology · doi:10.1177/193229680700100505 · Cited by 89 · see citing works
Impact of pulsatility on the ensemble orderliness (approximate entropy) of neurohormone secretion
Veldhuis, J. D., Johnson, M. L., Veldhuis, O. L., Straume, M., & Pincus, S. M. (2001) · American Journal of Physiology-Regulatory, Integrative and Comparative Physiology · doi:10.1152/ajpregu.2001.281.6.r1975 · Cited by 88 · cited 1× · see citing works
“, in hormone secretion studies, the discrimination power of ApEn from plasma concentration time series can be influenced by their kinetics [180], [181].”
Muscle glucose transport and phosphorylation in type 2 diabetic, obese nondiabetic, and genetically predisposed individuals
Pendergrass, M., Bertoldo, A., Bonadonna, R., Nucci, G., Mandarino, L., Cobelli, C., & DeFronzo, R. A. (2007) · American Journal of Physiology-Endocrinology and Metabolism · doi:10.1152/ajpendo.00617.2005 · Cited by 87 · cited 2× · see citing works
“Among these, it was possible to show that the insulin control on both transmembrane transport and phosphorylation flux in subjects affected by NIDDM is much less efficient with respect to nondiabetic subjects [76], [77].”
Self-citation
Kinetic modeling of [ 18 F]FDG in skeletal muscle by PET: a four-compartment five-rate-constant model
Bertoldo, A., Peltoniemi, P., Oikonen, V., Knuuti, J. M., Nuutila, P., & Cobelli, C. (2001) · American Journal of Physiology-Endocrinology and Metabolism · doi:10.1152/ajpendo.2001.281.3.e524 · Cited by 86 · cited 1× · see citing works
“In order to provide rate constants of transport and phosphorylation of F FDG in skeletal muscle, a four-compartment model (plasma, extracellular, tissue F FDG, and F FDG ) with five rate constants has been proposed [81].”
Self-citation
Validation of simple and complex models in physiology and medicine
Cobelli, C., Carson, E. R., Finkelstein, L., & Leaning, M. S. (1984) · American Journal of Physiology-Regulatory, Integrative and Comparative Physiology · doi:10.1152/ajpregu.1984.246.2.r259 · Cited by 86 · cited 1× · see citing works
“Although strategies for validation of a complex (versus simple) model have been delineated [17], [119], the difficulties of the problem remain.”
Self-citation
Increased Disorderliness of Basal Insulin Release, Attenuated Insulin Secretory Burst Mass, and Reduced Ultradian Rhythmicity of Insulin Secretion in Older Individuals
Meneilly, G. S., Ryan, A. S., Veldhuis, J. D., & Elahi, D. (1997) · The Journal of Clinical Endocrinology & Metabolism · doi:10.1210/jcem.82.12.4457 · Cited by 85 · cited 1× · see citing works
“It has been established that the ability of glucose to entrain ultradian insulin secretion patterns is disturbed in diabetic and prediabetic individuals [157]–[159], but the exact role of oscillations in the glucose regulation systems is still under investigation [160].”
In Silico Preclinical Trials: Methodology and Engineering Guide to Closed-Loop Control in Type 1 Diabetes Mellitus
Patek, S. D., Bequette, B. W., Breton, M. D., Buckingham, B. A., Dassau, E., Doyle, F. J., Lum, J. W., Magni, L., & Zisser, H. (2009) · Journal of Diabetes Science and Technology · doi:10.1177/193229680900300207 · Cited by 84 · see citing works
Self-citation
Control to Range for Diabetes: Functionality and Modular Architecture
Kovatchev, B. P., Patek, S. D., Dassau, E., Doyle, F. J., Magni, L., Nicolao, G. D., & Cobelli, C. (2009) · Journal of Diabetes Science and Technology · doi:10.1177/193229680900300509 · Cited by 83 · cited 2× · see citing works
“In order to classify and organize the review of the literature, we refer to a recent paper that has proposed a layered architecture for artificial pancreas systems [249] (Fig.”
Self-citation
Evaluation of Portal/Peripheral Route and of Algorithms for Insulin Delivery in the Closed-Loop Control of Glucose in Diabetes - A Modeling Study
Cobelli, C., & Ruggeri, A. (1983) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.1983.325203 · Cited by 82 · see citing works
Self-citation
Introduction to Modeling in Physiology and Medicine
(2008) · Elsevier eBooks · doi:10.1016/b978-0-12-160240-6.x5001-7 · Cited by 80 · cited 4× · see citing works
“Although strategies for validation of a complex (versus simple) model have been delineated [17], [119], the difficulties of the problem remain.”
Influential
The Future of Continuous Glucose Monitoring: Closed Loop
Hovorka, R. (2008) · Current Diabetes Reviews · doi:10.2174/157339908785294479 · Cited by 80 · cited 1× · see citing works
“CGM [191]–[193] and charted guidelines for clinical use [194] and its future as a precursor to closed-loop control [195].”
Reduced glucose effectiveness associated with reduced insulin release: an artifact of the minimal-model method
Finegood, D. T., & Tzur, D. (1996) · American Journal of Physiology-Endocrinology and Metabolism · doi:10.1152/ajpendo.1996.271.3.e485 · Cited by 80 · see citing works
Run-to-Run Control of Meal-Related Insulin Dosing
Zisser, H., Jovanovič, L., Doyle, F., Ospina, P., & Owens, C. (2005) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2005.7.48 · Cited by 78 · cited 1× · see citing works
“The first applications to glucose control regarded the iterative adjustment of the basal and boluses forming the conventional open-loop therapy [280]–[282].”
An Integrated Glucose‐Insulin Model to Describe Oral Glucose Tolerance Test Data in Type 2 Diabetics
Jauslin, P. M., Silber, H. E., Frey, N., Gieschke, R., Simonsson, U. S. H., Jorga, K. M., & Karlsson, M. O. (2007) · The Journal of Clinical Pharmacology · doi:10.1177/0091270007302168 · Cited by 78 · see citing works
A stochastic deconvolution method to reconstruct insulin secretion rate after a glucose stimulus
Sparacino, G., & Cobelli, C. (1996) · IEEE Transactions on Biomedical Engineering · doi:10.1109/10.488799 · Cited by 76 · cited 1× · see citing works
“Deconvolution methods have been applied to estimate the secretion profile in various physiological states [89], [90] and during both intravenous and oral glucose tolerance tests [91]–[93].”
Self-citation
Episodes of Severe Hypoglycemia in Type 1 Diabetes Are Preceded and Followed within 48 Hours by Measurable Disturbances in Blood Glucose1
Kovatchev, B. P., Cox, D. J., Farhy, L. S., Straume, M., Gonder-Frederick, L. A., & Clarke, W. L. (2000) · The Journal of Clinical Endocrinology & Metabolism · doi:10.1210/jcem.85.11.6999 · Cited by 76 · cited 1× · see citing works
“0, respectively [187], and has been used for short term prediction of hypoglycemia as well [188], [189].”
Self-citation
Overestimation of minimal model glucose effectiveness in presence of insulin response is due to undermodeling
Cobelli, C., Bettini, F., Caumo, A., & Quon, M. J. (1998) · American Journal of Physiology-Endocrinology and Metabolism · doi:10.1152/ajpendo.1998.275.6.e1031 · Cited by 74 · cited 1× · see citing works
“, using one instead of two compartments during the highly dynamic IVGTT perturbation, can introduce bias in glucose effectiveness and insulin sensitivity, being over- and under-estimated, respectively [38]–[40].”
Self-citation
Effect of input excitation on the quality of empirical dynamic models for type 1 diabetes
Finan, D. A., Palerm, C. C., Doyle, F. J., Seborg, D. E., Zisser, H., Bevier, W. C., & Jovanovič, L. (2009) · AIChE Journal · doi:10.1002/aic.11699 · Cited by 72 · cited 1× · see citing works
“of the inputs [254], [266] (see also [267] for further optimal de-”
A Biopsychobehavioral Model of Risk of Severe Hypoglycemia
Gonder-Frederick, L. A., Cox, D. J., Kovatchev, B. P., Schlundt, D. G., & Clarke, W. L. (1997) · Diabetes Care · doi:10.2337/diacare.20.4.661 · Cited by 71 · see citing works
Self-citation
Continuous Glucose Sensors: Continuing Questions about Clinical Accuracy
Clarke, W. L., & Kovatchev, B. (2007) · Journal of Diabetes Science and Technology · doi:10.1177/193229680700100510 · Cited by 71 · cited 1× · see citing works
“measurement [216]–[219] and may be reaching a physiological”
Self-citation
Failure of physiological plasma glucose excursions to entrain high-frequency pulsatile insulin secretion in type 2 diabetes.
Hollingdal, M., Juhl, C. B., Pincus, S. M., Sturis, J., Veldhuis, J. D., Polonsky, K. S., Pørksen, N., & Schmitz, O. (2000) · Diabetes · doi:10.2337/diabetes.49.8.1334 · Cited by 71 · see citing works
Reconstruction of Glucose in Plasma from Interstitial Fluid Continuous Glucose Monitoring Data: Role of Sensor Calibration
Facchinetti, A., Sparacino, G., & Cobelli, C. (2007) · Journal of Diabetes Science and Technology · doi:10.1177/193229680700100504 · Cited by 69 · cited 1× · see citing works
“models have been studied as well [204], [205].”
Self-citation
Tracer Kinetics in Biomedical Research
Cobelli, C., Foster, D., & TOFFOLO, G. M. (2002) · Springer eBooks · doi:10.1007/b112199 · Cited by 68 · see citing works
Self-citation
Probabilistic Evolving Meal Detection and Estimation of Meal Total Glucose Appearance
Cameron, F., Niemeyer, G., & Buckingham, B. A. (2009) · Journal of Diabetes Science and Technology · doi:10.1177/193229680900300505 · Cited by 65 · cited 1× · see citing works
“[277] could greatly help the controller reacting properly to the”
A mathematical model for the control mechanism of free fatty acid-glucose metabolism in normal humans
Srinivasan, R., Kadish, A. H., & Sridhar, R. (1970) · Computers and Biomedical Research · doi:10.1016/0010-4809(70)90021-2 · Cited by 65 · cited 1× · see citing works
“, for assessing different control algorithms and different insulin infusion routes [122], [127]–[138].”
Regulation of cAMP dynamics by Ca2+ and G protein-coupled receptors in the pancreatic β-cell: a computational approach
Fridlyand, L. E., Harbeck, M. C., Roe, M. W., & Philipson, L. H. (2007) · American Journal of Physiology-Cell Physiology · doi:10.1152/ajpcell.00555.2006 · Cited by 65 · see citing works
Integrating model-based decision support in a multi-modal reasoning system for managing type 1 diabetic patients
Stefania Montani, B., Magni, P., Bellazzi, R., Larizza, C., Roudsari, A., & Carson, E. R. (2003) · Artificial Intelligence in Medicine · doi:10.1016/s0933-3657(03)00045-9 · Cited by 65 · cited 1× · see citing works
“However, in this review we have to limit our scope; thus, we will not be able to discuss modeling in the areas of patient management [9], [10] and health care intervention strategies [11]–[14], or in the emerging area of systems biology [15], [16].”
Evaluation of the Effect of Gain on the Meal Response of an Automated Closed-Loop Insulin Delivery System
Panteleon, A. E., Loutseiko, M., Steil, G. M., & Rebrin, K. (2006) · Diabetes · doi:10.2337/db05-1346 · Cited by 65 · see citing works
Overnight Closed-Loop Insulin Delivery with Model Predictive Control: Assessment of Hypoglycemia and Hyperglycemia Risk Using Simulation Studies
Wilinska, M. E., Budiman, E. S., Taub, M. B., Elleri, D., Allen, J. M., Acerini, C. L., Dunger, D. B., & Hovorka, R. (2009) · Journal of Diabetes Science and Technology · doi:10.1177/193229680900300514 · Cited by 65 · cited 3× · see citing works
“This can be done for both PID regulators [261] and MPC ones [143], [255]–[257].”
Modeling of Calibration Effectiveness and Blood-to-Interstitial Glucose Dynamics as Potential Confounders of the Accuracy of Continuous Glucose Sensors during Hyperinsulinemic Clamp
King, C., Anderson, S. M., Breton, M. D., Clarke, W. L., & Kovatchev, B. P. (2007) · Journal of Diabetes Science and Technology · doi:10.1177/193229680700100302 · Cited by 65 · cited 1× · see citing works
“models have been studied as well [204], [205].”
Self-citation
Is an Automatic Pump Suspension Feature Safe for Children with Type 1 Diabetes? An Exploratory Analysis with a Closed-Loop System
Cengiz, E., Swan, K. L., Tamborlane, W. V., Steil, G. M., Steffen, A. T., & WEINZIMER, S. A. (2009) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2008.0102 · Cited by 64 · cited 1× · see citing works
“This approach has been shown to reduce the risk of nocturnal hypoglycemia [242], [250].”
Glucose Estimation and Prediction through Meal Responses Using Ambulatory Subject Data for Advisory Mode Model Predictive Control
Gillis, R., Palerm, C. C., Zisser, H., Jovanovic, L. G., Seborg, D. E., & Doyle, F. J. (2007) · Journal of Diabetes Science and Technology · doi:10.1177/193229680700100605 · Cited by 64 · cited 1× · see citing works
“servation are needed (see [143], [273], and [292]).”
Continuous Glucose Monitoring with Glucose Sensors: Calibration and Assessment Criteria
Lodwig, V., & Heinemann, L. (2003) · Diabetes Technology & Therapeutics · doi:10.1089/152091503322250596 · Cited by 64 · see citing works
Five-compartment model of insulin kinetics and its use to investigate action of chloroquine in NIDDM
Hovorka, R., Powrie, J., Smith, G. D., Sönksen, P. H., Carson, E. R., & Jones, R. H. (1993) · American Journal of Physiology-Endocrinology and Metabolism · doi:10.1152/ajpendo.1993.265.1.e162 · Cited by 64 · cited 1× · see citing works
“[86], which also incorporates a description of insulin kinetic at the receptor level.”
An Online Self-Tunable Method to Denoise CGM Sensor Data
Facchinetti, A., Sparacino, G., & Cobelli, C. (2009) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2009.2033264 · Cited by 62 · cited 2× · see citing works
“Monte Carlo simulation of [225] and obtained by adding,”
Self-citation
Evaluation of Insulin Sensitivity and β-Cell Function Indexes Obtained From Minimal Model Analysis of a Meal Tolerance Test
Steil, G. M., Hwu, C.-M., Janowski, R., Hariri, F., Jinagouda, S. D., Darwin, C. H., Tadros, S. S., Rebrin, K., & Saad, M. F. (2004) · Diabetes · doi:10.2337/diabetes.53.5.1201 · Cited by 62 · cited 1× · see citing works
“[105] for describing hyperglycemic clamp C-peptide data as well as meals, thus providing further independent evidence for its validity.”
Dipeptidyl Peptidase-4 Inhibition by Vildagliptin and the Effect on Insulin Secretion and Action in Response to Meal Ingestion in Type 2 Diabetes
Man, C. D., Bock, G., Giesler, P. D., Serra, D. B., Ligueros‐Saylan, M., Foley, J. E., Camilleri, M., TOFFOLO, G. M., Cobelli, C., Rizza, R. A., et al. (2008) · Diabetes Care · doi:10.2337/dc08-1512 · Cited by 62 · see citing works
Self-citation
A model to measure insulin effects on glucose transport and phosphorylation in muscle: a three-tracer study
Saccomani, M. P., Bonadonna, R. C., Bier, D. M., DeFronzo, R. A., & Cobelli, C. (1996) · American Journal of Physiology-Endocrinology and Metabolism · doi:10.1152/ajpendo.1996.270.1.e170 · Cited by 62 · see citing works
Self-citation

Flagged first, then by citations

Research works printed but not linked: 10

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Authors: Cobelli, C., Dalla Man, C., Sparacino, G., Magni, L., De Nicolao, G., & Kovatchev, B. P.
Affiliations: University of Padua (IT)
University of Pavia (IT)
University of Virginia (US)
ORCID: Cobelli, C.
Sparacino, G.
Magni, L.
Kovatchev, B.P.
Venue: IEEE Reviews in Biomedical Engineering
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Published: 2009
Volume: 2
Pages: 54-96
ISSN: 1937-3333 (ISSN-L)
1941-1189
DOI: 10.1109/rbme.2009.2036073
PMC: PMC2951686
OpenAlex: W2171613573
Cited by: 511
References: 304
Citations by year: 2026: 13 · 2025: 27 · 2024: 11 · 2023: 17 · 2022: 25 · 2021: 27 · 2020: 32 · 2019: 27 · 2018: 39 · 2017: 42 · 2016: 29 · 2015: 40 · 2014: 44 · 2013: 39 · 2012: 43
FWCI: 24.04
Type: journal article
Language: en
Open access: green
www.ncbi.nlm.nih.gov
Licence: ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html (vor)
Locations: IEEE Reviews in Biomedical Engineering (publishedVersion)
PubMed (publishedVersion)
PubMed Central (acceptedVersion)
Padua Research Archive (University of Padova) (submittedVersion)
Funders: National Institutes of Health (5r01dk051562-23)
National Institute of Diabetes and Digestive and Kidney Diseases (R01-DK-051562)
Topics: Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes Management and Research
Health Sciences › Medicine › Surgery › Pancreatic function and diabetes
Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes Management and Education
Keywords: diabetes management, computer simulation, artificial pancreas, feedback control, continuous glucose monitoring, modular control architecture

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Diabetes: Models, Signals, and Control

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