Nonlinear model predictive control of glucose concentration in subjects with type 1 diabetes
Abstract
A nonlinear model predictive controller has been developed to maintain normoglycemia in subjects with type 1 diabetes during fasting conditions such as during overnight fast. The controller employs a compartment model, which represents the glucoregulatory system and includes submodels representing absorption of subcutaneously administered short-acting insulin Lispro and gut absorption. The controller uses Bayesian parameter estimation to determine time-varying model parameters. Moving target trajectory facilitates slow, controlled normalization of elevated glucose levels and faster normalization of low glucose values. The predictive capabilities of the model have been evaluated using data from 15 clinical experiments in subjects with type 1 diabetes. The experiments employed intravenous glucose sampling (every 15 min) and subcutaneous infusion of insulin Lispro by insulin pump (modified also every 15 min). The model gave glucose predictions with a mean square error proportionally related to the prediction horizon with the value of 0.2 mmol L(-1) per 15 min. The assessment of clinical utility of model-based glucose predictions using Clarke error grid analysis gave 95% of values in zone A and the remaining 5% of values in zone B for glucose predictions up to 60 min (n = 1674). In conclusion, adaptive nonlinear model predictive control is promising for the control of glucose concentration during fasting conditions in subjects with type 1 diabetes.
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The Hovorka model and the UVAPadova model were originally formulated to represent the physiological behaviour of T1DM individuals, and no modification is necessary for the purpose of the present work.
“On the contrary, the Hovorka model and the UVAPadova model were originally formulated to represent the physiological behaviour of T1DM individuals, and no modification is necessary for the purpose of the present work.”
From A comparison among three maximal mathematical models of the glucose-insulin system (2021) · cites this paper as [18]
Preliminary testing on the virtual populations showed that the insulin kinetics model published in Hovorka et al. [11] better reflects the physiological characteristics of adults with T1D.
“Our preliminary testing on the virtual populations showed that the insulin kinetics model published in Hovorka et al. [11] better reflects the physiological characteristics of adults with T1D.”
From A statistical virtual patient population for the glucoregulatory system in type 1 diabetes with integrated exercise model (2019) · cites this paper as Hovorka et al. [11]
The glucose kinetics model was presented in Hovorka et al. [11] and Jacobs et al. [14].
“The glucose kinetics model, which estimates blood glucose with respect to insulin and glucagon actions and non-insulin mediated glucose uptake, was presented in Hovorka et al. [11] and Jacobs et al. [14]”
From A statistical virtual patient population for the glucoregulatory system in type 1 diabetes with integrated exercise model (2019) · cites this paper as Hovorka et al. [11]
The nominal values given for Sf1, Sf2, and Sf3 from Eq (2) were derived for people without T1D.
“The nominal values given for Sf1, Sf2, and Sf3 from Eq (2) were derived for people without T1D [11].”
From A statistical virtual patient population for the glucoregulatory system in type 1 diabetes with integrated exercise model (2019) · cites this paper as Hovorka et al. [11]
The Hovorka model is used primarily for the development of control algorithms.
“The Hovorka model is the simplest of the three models, well documented and used primarily for the development of control algorithms.”
From A comparison among three maximal mathematical models of the glucose-insulin system (2021) · cites this paper as [18]
The Hovorka model is well documented.
“The Hovorka model is the simplest of the three models, well documented and used primarily for the development of control algorithms.”
From A comparison among three maximal mathematical models of the glucose-insulin system (2021) · cites this paper as [18]
The Hovorka model is the simplest of the three models.
“The Hovorka model is the simplest of the three models, well documented and used primarily for the development of control algorithms.”
From A comparison among three maximal mathematical models of the glucose-insulin system (2021) · cites this paper as [18]
The Hovorka model and the UVAPadova Simulator describe only the glucose metabolism of a subject with type 1 diabetes.
“While the Hovorka model and the UVAPadova Simulator only describe the glucose metabolism of a subject with type 1 diabetes, the Sorensen model was formulated to simulate the behaviour of both normal and diabetic individuals.”
From A comparison among three maximal mathematical models of the glucose-insulin system (2021) · cites this paper as [18]
The model of the subcutaneous compartments used is equivalent to that present in the Hovorka model.
“The model of the subcutaneous compartments used is equivalent to that present in the Hovorka model.”
From A comparison among three maximal mathematical models of the glucose-insulin system (2021) · cites this paper as [18]
The parameters of the Hovorka and UVA/Padova models are indicated for a diabetic individual.
“The parameters of the Hovorka and UVA/Padova models are instead indicated for a diabetic individual, but the more compact formulation of the Hovorka model requires fewer modifications to obtain a good fit.”
From A comparison among three maximal mathematical models of the glucose-insulin system (2021) · cites this paper as [18]
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References 44 · Cited by 1,274
- Authors
- Hovorka, R., Canonico, V., Chassin, L. J., Haueter, U., Massi-Benedetti, M., Federici, M. O., Pieber, T. R., Schaller, H. C., Schaupp, L., Vering, T., & Wilinska, M. E.
- Affiliations
- University of Cambridge (GB)
- University of Perugia (IT)
- University of Graz (AT)
- Venue
- Physiological Measurement
- Publisher
- IOP Publishing
- Published
- 2004-07-23
- Volume
- 25
- Issue
- 4
- Pages
- 905-920
- ISSN
- 0967-3334 (ISSN-L)
- 1361-6579
- OpenAlex
- W1964693647
- Cited by
- 1,274
- References
- 44
- Citations by year
- 2026: 44 · 2025: 59 · 2024: 60 · 2023: 77 · 2022: 58 · 2021: 78 · 2020: 60 · 2019: 86 · 2018: 96 · 2017: 81 · 2016: 44 · 2015: 61 · 2014: 65 · 2013: 60 · 2012: 66
- FWCI
- 15.77
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Physiological Measurement (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes Management and Research
- Physical Sciences › Engineering › Control and Systems Engineering › Advanced Control Systems Optimization
- Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes Treatment and Management
- Keywords
- type 1 diabetes, nonlinear model predictive control, glucose concentration control, normoglycemia, compartmental model, insulin lispro, subcutaneous insulin infusion, Bayesian parameter estimation, Clarke error grid, overnight fasting, insulin pump
- MeSH terms
- Blood Glucose, Diabetes Mellitus, Type 1, Forecasting, Humans, Hypoglycemic Agents, Insulin, Models, Theoretical, Insulin Lispro
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