Enhancing automatic closed-loop glucose control in type 1 diabetes with an adaptive meal bolus calculator—in silico evaluation under intra-day variability
Open accessAbstract
Background and objective Current prototypes of closed-loop systems for glucose control in type 1 diabetes mellitus, also referred to as artificial pancreas systems, require a pre-meal insulin bolus to compensate for delays in subcutaneous insulin absorption in order to avoid initial post-prandial hyperglycemia. Computing such a meal bolus is a challenging task due to the high intra-subject variability of insulin requirements. Most closed-loop systems compute this pre-meal insulin dose by a standard bolus calculation, as is commonly found in insulin pumps. However, the performance of these calculators is limited due to a lack of adaptiveness in front of dynamic changes in insulin requirements. Despite some initial attempts to include adaptation within these calculators, challenges remain.Methods In this paper we present a new technique to automatically adapt the meal-priming bolus within an artificial pancreas. The technique consists of using a novel adaptive bolus calculator based on Case-Based Reasoning and Run-To-Run control, within a closed-loop controller. Coordination between the adaptive bolus calculator and the controller was required to achieve the desired performance. For testing purposes, the clinically validated Imperial College Artificial Pancreas controller was employed. The proposed system was evaluated against itself but without bolus adaptation. The UVa-Padova T1DM v3.2 system was used to carry out a three-month in silico study on 11 adult and 11 adolescent virtual subjects taking into account inter-and intra-subject variability of insulin requirements and uncertainty on carbohydrate intake.Results Overall, the closed-loop controller enhanced by an adaptive bolus calculator improves glycemic control when compared to its non-adaptive counterpart. In particular, the following statistically significant improvements were found (non-adaptive vs. adaptive). Adults: mean glucose 142.2 ± 9.4vs. 131.8 ± 4.2mg/dl; percentage time in target [70, 180]mg/dl, 82.0 ± 7.0vs. 89.5 ± 4.2; percentage time above target 17.7 ± 7.0vs. 10.2 ± 4.1. Adolescents: mean glucose 158.2 ± 21.4vs. 140.5 ± 13.0mg/dl; percentage time in target, 65.9 ± 12.9vs. 77.5 ± 12.2; percentage time above target, 31.7 ± 13.1vs. 19.8 ± 10.2. Note that no increase in percentage time in hypoglycemia was observed.Conclusion Using an adaptive meal bolus calculator within a closed-loop control system has the potential to improve glycemic control in type 1 diabetes when compared to its non-adaptive counterpart.
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Insulin sensitivity varies during the day.
“Since insulin is known to vary during the day [24], circadian variability of insulin sensitivity was introduced to the insulin sensitivity parameters (Sf1, Sf2 and Sf3) within each virtual patient by varying these parameters with respect to time of day using Eq (12):”
From A statistical virtual patient population for the glucoregulatory system in type 1 diabetes with integrated exercise model (2019) · cites this paper as [24]
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Case base maintenance of a personalized insulin dose recommender system for Type 1 Diabetes Mellitus
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- Authors
- Herrero, P., Bondia, J., Adewuyi, O., Pesl, P., El-Sharkawy, M., Reddy, M., Toumazou, C., Oliver, N., & Georgiou, P.
- Affiliations
- Imperial College London (GB)
- Imperial College Healthcare NHS Trust (GB)
- Corresponding author
- Herrero, Pau
- Venue
- Computer Methods and Programs in Biomedicine
- Publisher
- Elsevier BV
- Published
- 2017-07
- Volume
- 146
- Pages
- 125-131
- ISSN
- 0169-2607 (ISSN-L)
- PMC
- PMC6522376
- OpenAlex
- W2621159547
- Semantic Scholar
- 52716d3b7aafdd3497ff282f7c65e4b982d67076
- Cited by
- 63
- References
- 33
- Citations by year
- 2026: 2 · 2025: 4 · 2024: 3 · 2023: 4 · 2022: 11 · 2021: 6 · 2020: 8 · 2019: 11 · 2018: 12 · 2017: 2
- FWCI
- 6.52
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Computer Methods and Programs in Biomedicine (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- RiuNet (Universitat Politècnica de València) (submittedVersion, cc-by-nc-nd)
- Spiral (Imperial College London) (submittedVersion, cc-by-nc-nd)
- Funders
- Wellcome Trust (100921)
- Medical Research Council (MC_PC_12015)
- Topics
- Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes Management and Research
- Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes Management and Education
- Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes Treatment and Management
- Keywords
- type 1 diabetes, closed-loop glucose control, artificial pancreas, case-based reasoning, run-to-run control, glycemic control, computer simulation, adolescents, adults, time in target
- MeSH terms
- Algorithms, Blood Glucose, Diabetes Mellitus, Type 1, Humans, Hypoglycemic Agents, Insulin, Insulin Infusion Systems, Blood Glucose Self-Monitoring, Pancreas, Artificial
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