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

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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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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×
“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×
“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
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×
“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×
“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
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×
“, the stochastic deconvolution method [62].”
Self-citation
Tracer Kinetics in Biomedical Research
Cobelli, C., Foster, D., & TOFFOLO, G. M. (2002) · Springer eBooks · doi:10.1007/b112199 · Cited by 201
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×
“has been precisely estimated and has been validated by comparison with both multiple tracer [46] and glucose clamp [47] techniques.”
Self-citation
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 198
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
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×
“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
Self-citation
Model predictive control of glucose concentration in type I diabetic patients: An in silico trial
Magni, L., Raimondo, D., Man, C., Nicolao, G. D., Kovatchev, B., & Cobelli, C. (2009) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2009.04.003 · Cited by 190 · cited 30×
“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
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×
“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×
“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×
“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
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 179 · cited 1×
“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×
“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
Self-citation
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 178
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
Modelling Methodology for Physiology and Medicine
(2014) · Elsevier eBooks · doi:10.1016/c2012-0-06031-0 · Cited by 176
A model of glucose kinetics and their control by insulin, compartmental and noncompartmental approaches
Cobelli, C., Toffolo, G., & Ferrannini, E. (1984) · Mathematical Biosciences · doi:10.1016/0025-5564(84)90114-7 · Cited by 171 · cited 5×
“Glucose kinetics requires at least a two compartment description [21].”
InfluentialSelf-citation
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 165 · cited 2×
“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., Voskanyan, G., & Steil, G. (2009) · Journal of Diabetes Science and Technology · doi:10.1177/193229680900300508 · Cited by 165 · cited 1×
“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.”
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×
“An alternative approach relies on insulin-on-board (IOB) computation [251].”
Estimation of Future Glucose Concentrations with Subject-Specific Recursive Linear Models
Eren-Oruklu, M., Çinar, A., Quinn, L., & Smith, D. (2009) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2008.0065 · Cited by 164 · cited 1×
“eling techniques [227]–[233], but neural networks [234], [235] and other physiological structural models [236] have been used”
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
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×
“certain safety constraints already within the real-time control module [258], [269].”
A Closed-Loop Artificial Pancreas Using Model Predictive Control and a Sliding Meal Size Estimator
Lee, H., Buckingham, B., Wilson, D., & Bequette, B. (2009) · Journal of Diabetes Science and Technology · doi:10.1177/193229680900300511 · Cited by 150 · cited 3×
“context, a meal detection and estimation module [258], [276],”
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
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 148 · cited 1×
“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 148
Self-citation
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
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 146 · cited 2×
“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
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×
“In addition, from pre- and post-hepatic model parameters an index of hepatic extraction can also be calculated [108], [109].”
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×
“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×
“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×
“, -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
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
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 135 · cited 1×
“CGM [191]–[193] and charted guidelines for clinical use [194] and its future as a precursor to closed-loop control [195].”
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
Self-citation
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 133
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×
“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
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
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×
“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
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 128 · cited 1×
“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].”
Insulin sensitivity by oral glucose minimal models: validation against clamp.
Man, C. D., Yarasheski, K., Caumo, A., Robertson, H., Toffolo, G., Polonsky, K., & Cobelli, C. (2005) · American Journal of Physiology. Endocrinology and Metabolism · doi:10.1152/ajpendo.00076.2005 · Cited by 128 · cited 3×
“has been validated against the multiple tracer [57] and the glucose clamp [47] techniques.”
Self-citation
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×
“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×
“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×
“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
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 124 · cited 3×
“, -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
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×
“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×
“eling techniques [227]–[233], but neural networks [234], [235] and other physiological structural models [236] have been used”
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×
“[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×
“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×
“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×
“[104] during “up&down” intravenous glucose infusion and by Steil et al.”
Self-citation
Stable-Label Intravenous Glucose Tolerance Test Minimal Model
Avogaro, A., Bristow, J., Bier, D., Cobelli, C., & Toffolo, G. (1989) · Diabetes · doi:10.2337/diab.38.8.1048 · Cited by 107
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
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×
“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×
“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
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×
“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
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 102 · cited 1×
“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].”
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
Hepatic glucose production during the labeled IVGTT: estimation by deconvolution with a new minimal model
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Assessment of β‐cell function during the oral glucose tolerance test by a minimal model of insulin secretion
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Influential
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Disorderly and nonstationary insulin secretion in relatives of patients with NIDDM
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The Extended Kalman Filter for Continuous Glucose Monitoring
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Hypoglycemia Prediction and Detection Using Optimal Estimation
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Undermodeling affects minimal model indexes: insights from a two-compartment model
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“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
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×
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The kinetics of insulin in man. I. General aspects
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Pancreatic β-Cell Responsiveness during Meal Tolerance Test: Model Assessment in Normal Subjects and Subjects with Newly Diagnosed Noninsulin-Dependent Diabetes Mellitus1
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Hypoglycemia Detection and Prediction Using Continuous Glucose Monitoring—A Study on Hypoglycemic Clamp Data
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Impact of pulsatility on the ensemble orderliness (approximate entropy) of neurohormone secretion
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Increased Disorderliness of Basal Insulin Release, Attenuated Insulin Secretory Burst Mass, and Reduced Ultradian Rhythmicity of Insulin Secretion in Older Individuals
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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×
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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×
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Self-citation
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
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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×
“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
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Introduction to Modeling in Physiology and Medicine
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“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
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“CGM [191]–[193] and charted guidelines for clinical use [194] and its future as a precursor to closed-loop control [195].”
An Integrated Glucose‐Insulin Model to Describe Oral Glucose Tolerance Test Data in Type 2 Diabetics
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“The first applications to glucose control regarded the iterative adjustment of the basal and boluses forming the conventional open-loop therapy [280]–[282].”
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×
“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×
“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×
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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 73 · cited 1×
“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
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×
“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
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×
“models have been studied as well [204], [205].”
Self-citation
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., Seborg, D., & Doyle, F. (2007) · Journal of Diabetes Science and Technology · doi:10.1177/193229680700100605 · Cited by 67 · cited 1×
“servation are needed (see [143], [273], and [292]).”
Regulation of cAMP dynamics by Ca2+ and G protein-coupled receptors in the pancreatic β-cell: a computational approach
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Integrating model-based decision support in a multi-modal reasoning system for managing type 1 diabetic patients
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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
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×
“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×
“models have been studied as well [204], [205].”
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 64 · cited 1×
“[277] could greatly help the controller reacting properly to the”
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×
“This approach has been shown to reduce the risk of nocturnal hypoglycemia [242], [250].”
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 64 · cited 1×
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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
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×
“[86], which also incorporates a description of insulin kinetic at the receptor level.”
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 63 · cited 1×
“[105] for describing hyperglycemic clamp C-peptide data as well as meals, thus providing further independent evidence for its validity.”
Insulin release in impaired glucose tolerance: oral minimal model predicts normal sensitivity to glucose but defective response times.
Breda, E., Toffolo, G., Polonsky, K., & Cobelli, C. (2002) · Diabetes · doi:10.2337/diabetes.51.2007.s227 · Cited by 62 · cited 1×
“In particular, both a rate of change of glucose component of insulin secretion and a delay between glucose stimulus and beta-cell response have been shown to be necessary to fit the data [103].”
Self-citation
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×
“Monte Carlo simulation of [225] and obtained by adding,”
Self-citation
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
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
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The UVA/PADOVA Type 1 Diabetes Simulator: New Features
Man, C. D., Micheletto, F., Lv, D., Breton, M., Kovatchev, B., & Cobelli, C. (2014) · Journal of Diabetes Science and Technology · doi:10.1177/1932296813514502 · Cited by 791
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Cobelli, C., Renard, E., & Kovatchev, B. (2011) · Diabetes · doi:10.2337/db11-0654 · Cited by 600
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Closed-loop insulin delivery from bench to clinical practice
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Breton, M., Farret, A., Bruttomesso, D., Anderson, S., Magni, L., Patek, S., Dalla Man, C., Place, J., Demartini, S., Del Favero, S., et al. (2012) · Diabetes · doi:10.2337/db11-1445 · Cited by 315
Self-citation

See all 511

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 › Endocrinology, Diabetes and Metabolism › 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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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

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Diabetes: Models, Signals, and Control

Abstract

Overview

Integrity signals

Verification

Claims the paper makes

Claims other papers make about this one

Self-check

Citations

Cites

Cited by

Related

Metadata

Record

About these checks