Self-tuning insulin adjustment algorithm for type 1 diabetic patients based on multi-doses regime

Using Iterative Learning for Insulin Dosage Optimization in Multiple-Daily-Injections Therapy for People With Type 1 Diabetes

OBJECTIVE

In this work, we design iterative algorithms for the delivery of long-acting (basal) and rapid-acting (bolus) insulin, respectively, for people with type 1 diabetes (T1D) on multiple-daily-injections (MDIs) therapy using feedback from self-monitoring of blood glucose (SMBG) measurements.

METHODS

Iterative learning control (ILC) updates basal therapy consisting of one long-acting insulin injection per day, while run-to-run (R2R) adapts meal bolus therapy via the update of the mealtime-specific insulin-to-carbohydrate ratio (CR). Updates are due weekly and are based upon sparse SMBG measurements.

RESULTS

Upon termination of the 20 weeks long in-silico trial, in a scenario characterized by meal carbohydrate (CHO) normally distributed with mean μ = [50, 75, 75] grams and standard deviation σ = [5, 7, 7] grams, our strategy produced statistically significant improvements in time in range (70--180) [mg/dl], from 66.9(33.1) % to 93.6(6.7) %, p = 0.02.

CONCLUSIONS

Iterative learning shows potential to improve glycemic regulation over time by driving blood glucose closer to the recommended glycemic targets.

SIGNIFICANCE

Decision support systems (DSSs) and automated therapy advisors such as the one proposed here are expected to improve glycemic outcomes reducing the burden on patients on MDI therapy.

An extension to the compartmental model of type 1 diabetic patients to reproduce exercise periods with glycogen depletion and replenishment

The purpose of this work is to present the main interactions promoted by exercise and synthesize them into mathematical equations. It is intended to extend the ability of the compartmental glucose-insulin model introduced by Sorensen [1985. A physiologic model of glucose metabolism in man and its use to design and assess improved insulin therapies for diabetes. Ph.D. Dissertation, Chemical Engineering Department, MIT, Cambridge] to reproduce variations in the blood glucose concentration induced by exercise in diabetic patients and to complement the previous work by Lenart and Parker [2002. Modeling exercise effects in type I diabetic patients. In: Proceedings of the 15th Triennial World Congress, Barcelona, Spain] and Lenart, DiMascio and Parker [2002. Modeling glycogen-exercise interactions in type I diabetic patients. In: Proceedings of the A.I.Ch.E. Annual Meeting, Indianapolis, IN]. The immediate consequences of exercise are incorporated in this research: redistribution of blood flows, increments in peripheral glucose and insulin uptakes, and increment in hepatic glucose production. The extended model was verified with experimental data for light and moderate intensity exercise. In addition, data extrapolation was introduced to simulate heavy intensity exercise. The hepatic glycogen reservoir limits the peripheral glucose uptake for prolonged exercise. Therefore, the depletion and replenishment of hepatic glycogen were modeled, looking to reproduce the blood glucose levels for a type 1 diabetic patient during a normal day, with meal intakes, insulin infusions and/or boluses, and a predefined exercise regime. From the extensive simulation evaluation, it is found that the new exercise model provides a good approximation to the available experimental data from literature.

On hyperglicemic glucose basal levels in Type 1 Diabetes Mellitus from dynamic analysis

Compartmental-Physiological Models (CPM's) have been used to derive feedback controllers for the glucose regulation in Diabetes Mellitus (DM). Despite these important advances, there are two criticisms about the use of the CPM's in DM: (i) Can this class of model reproduce severe basal glucose levels (e.g., larger than 300 mg/dl)? and (ii) Does a CPM reproduce a distinct glucose level as its parameters change or is it unique even if its parameters change? This contribution aims these criticisms from the study of the parametric sensitivity of a CPM. The results exploit the analysis of the dynamic properties of the chosen CPM and permit to show that such model can reproduce distinct severe basal levels by modifying the values of the metabolic parameters, which agree with expectations on a realistic model. Mainly, the chosen CPM has been selected due to the following two reasons. (i) It includes the main organs related to the glucose metabolism in Type 1 Diabetes Mellitus (T1DM); as, for example, the liver, brain and kidney. (ii) It models metabolic phenomena as, for instance, the counter-regulatory effects by glucagon and the hepatic glucose uptake/production. Additionally, the chosen model has been recently used to design feedback controllers for the glucose regulation with very promissory results.

Fuzzy-based controller for glucose regulation in type-1 diabetic patients by subcutaneous route

This paper presents an advisory/control algorithm for a type-1 diabetes mellitus (TIDM) patient under an intensive insulin treatment based on a multiple daily injections regimen (MDIR). The advisory/control algorithm incorporates expert knowledge about the treatment of this disease by using Mamdani-type fuzzy logic controllers to regulate the blood glucose level (BGL). The overall control strategy is based on a two-loop feedback strategy to overcome the variability in the glucose-insulin dynamics from patient to patient. An inner-loop provides the amount of both rapid/short and intermediate/long acting insulin (RSAI and ILAI) formulations that are programmed in a three-shots daily basis before meals. The combined preparation is then injected by the patient through a subcutaneous route. Meanwhile, an outer-loop adjusts the maximum amounts of insulin provided to the patient in a time-scale of days. The outer-loop controller aims to work as a supervisor of the inner-loop controller. Extensive closed-loop simulations are illustrated, using a detailed compartmental model of the insulin-glucose dynamics in a TIDM patient with meal intake.