Combining basal-bolus insulin infusion for tight postprandial glucose control: an in silico evaluation in adults, children, and adolescents

The Effectiveness of Superbolus on Postprandial Blood Glucose Management of Pregnant Women With Type 1 Diabetes

AIM

Pregnancies of women with pregestational diabetes are at risk of after-meal glucose peaks and late after-meal hypoglycemia, particularly at breakfast. We aimed to explore the effectiveness of a specific feature of insulin pump therapy called superbolus in preventing these glucose swings.

METHODS

In this retrospective observational study, we analyzed continuous glucose monitoring data of patients with type 1 diabetes in pregnancy who were advised to use superbolus to manage their breakfast. Some of the postprandial basal insulin delivery was partially reduced and delivered instead as additional insulin bolus on top of a normal bolus. Outcomes of interest were one hour after breakfast glucose levels, the time in glucose range for after breakfast period, the number of late hypoglycemic episodes.

RESULTS

Overall, 21 consecutive pregnant women with type 1 diabetes (mean age 34.3 ± 5.5 years, mean pregestational body mass index 23.7 ± 4.7 kg/m2, HbA1c levels during pregnancy 6.1 ± 0.6%) were studied. Superbolus reduced after breakfast glucose peaks (one hour after breakfast glucose levels 130 ± 17 mg/dL vs 123 ± 10 mg/dL before and after superbolus use, respectively, P = .01), improved the time in glucose range for after breakfast period (70.4% vs 50.8%, P = .001), and reduced the number of late hypoglycemic episodes (3 [1-5] vs 1 [0-2], P< .0001).

CONCLUSION

Superbolus was effective in avoiding after-meal glucose peaks, increased postprandial glucose time in target, without late hypoglycemia occurrence. It represents a valid option for the treatment of pregnant women with type 1 diabetes using insulin pump.

Open-loop glucose control: Automatic IOB-based super-bolus feature for commercial insulin pumps

BACKGROUND AND OBJECTIVE

Although there has been significant progress towards closed-loop type 1 diabetes mellitus (T1DM) treatments, most diabetic patients still treat this metabolic disorder in an open-loop manner, based on insulin pump therapy (basal and bolus insulin infusion). This paper presents a method for automatic insulin bolus shaping based on insulin-on-board (IOB) as an alternative to conventional bolus dosing.

METHODS

The methodology presented allows the pump to generate the so-called super-bolus (SB) employing a two-compartment IOB dynamic model. The extra amount of insulin to boost the bolus and the basal cutoff time are computed using the duration of insulin action (DIA). In this way, the pump automatically re-establishes basal insulin when IOB reaches its basal level. Thus, detrimental transients caused by manual or a-priori computations are avoided.

RESULTS

The potential of this method is illustrated via in-silico trials over a 30 patients cohort in single meal and single day scenarios. In the first ones, improvements were found (standard treatment vs. automatic SB) both in percentage time in euglycemia (75g meal: 81.9 ± 15.59 vs. 89.51 ± 11.95, ρ ≃ 0; 100g meal: 75.12 ± 18.23 vs. 85.46 ± 14.96, ρ ≃ 0) and time in hypoglecymia (75g meal: 5.92 ± 14.48 vs. 0.97 ± 4.15, ρ=0.008; 100g meal: 9.5 ± 17.02 vs. 1.85 ± 7.05, ρ=0.014). In a single day scenario, considering intra-patient variability, the time in hypoglycemia was reduced (9.57 ± 14.48 vs. 4.21 ± 6.18, ρ=0.028) and improved the time in euglycemia (79.46 ± 17.46 vs. 86.29 ± 11.73, ρ=0.007).

CONCLUSIONS

The automatic IOB-based SB has the potential of a better performance in comparison with the standard treatment, particularly for high glycemic index meals with high carbohydrate content. Both glucose excursion and time spent in hypoglycemia were reduced.

Suspension of basal insulin to avoid hypoglycemia in type 1 diabetes treated with insulin pump

Treatment with continuous s.c. insulin infusion (CSII) provides better glycemic control and lower risk of hypoglycemia than conventional therapy with multiple daily insulin injections. These benefits have been related to a more reliable absorption and an improved pharmacokinetic profile of insulin delivered through CSII therapy. However, even for patients treated with CSII, exaggerated postmeal hyperglycemic excursions and late postabsorptive hypoglycemia can still constitute a therapeutic challenge. Two female patients with type 1 diabetes who began treatment with CSII required to increase their previous breakfast insulin-to-carbohydrate ratio in order to achieve postprandial glycemic goals. However, they simultaneously presented recurrent episodes of late hypoglycemia several hours after breakfast bolus. Advancing the timing of the bolus was ineffective and bothersome for patients. In both cases, the best therapeutic option was to set a basal insulin rate of zero units per hour during 6 h after breakfast. Even so, they need to routinely take a midmorning snack with 10–20 g of carbohydrates to avoid late postabsorptive hypoglycemia. They have been using this insulin schedule for about 3 years without complications. The action of prandial insulin delivered through insulin pumps can be inappropriately delayed for the requirements of some patients. Although suspension of basal rate can be an acceptable therapeutic alternative for them, these cases demonstrate that new strategies to improve the bioavailability of prandial insulin infused through CSII are still needed.

Evaluation of a novel continuous glucose monitoring-based method for mealtime insulin dosing--the iBolus--in subjects with type 1 diabetes using continuous subcutaneous insulin infusion therapy: a randomized controlled trial

OBJECTIVE

Prandial insulin dosing is an empirical practice associated frequently with poor reproducibility in postprandial glucose response. Based on continuous glucose monitoring (CGM), a method for prandial insulin administration (iBolus) is presented and evaluated for people with type 1 diabetes using CSII therapy.

SUBJECTS AND METHODS

An individual patient's model for a 5-h postprandial period was obtained from 6-day ambulatory CGM and used for iBolus calculation in 12 patients with type 1 diabetes. In a double-blind, crossover study each patient underwent four meal tests with 40 g or 100 g of carbohydrates (CHOs), both on two occasions. For each meal, the iBolus or the traditional bolus (tBolus) was given before mealtime (t(0)) in a randomized order. We measured the postprandial glycemic response as the area under the curve of plasma glucose (AUC-PG(0-5h)) and variability as the individual coefficient of variation (CV) of AUC-PG(0-5h). The contribution of the insulin-to-CHO ratio, CHO, plasma glucose at t(0) (PG(t0)), and insulin dose to AUC-PG(0-5h) and its CV was also investigated.

RESULTS

AUC-PG(0-5h) was similar with either bolus for 40-g (iBolus vs. tBolus, 585.5±127.5 vs. 689.2±180.7 mg/dL·h) or 100-g (752.1±237.7 vs. 760.0±263.2 mg/dL·h) CHO meals. A multiple regression analysis revealed a significant model only for the tBolus, with PG(t0) being the best predictor of AUC-PG(0-5h) explaining approximately 50% of the glycemic response. Observed variability was greater with the iBolus (CV, 16.7±15.3% vs. 10.1±12.5%) but independent of the factors studied.

CONCLUSIONS

A CGM-based algorithm for calculation of prandial insulin is feasible, although it does not reduce unpredictability of individual glycemic responses. Causes of variability need to be identified and analyzed for further optimization of postprandial glycemic control.

Improving the computational effort of set-inversion-based prandial insulin delivery for its integration in insulin pumps

OBJECTIVE

Set-inversion-based prandial insulin delivery is a new model-based bolus advisor for postprandial glucose control in type 1 diabetes mellitus (T1DM). It automatically coordinates the values of basal-bolus insulin to be infused during the postprandial period so as to achieve some predefined control objectives. However, the method requires an excessive computation time to compute the solution set of feasible insulin profiles, which impedes its integration into an insulin pump. In this work, a new algorithm is presented, which reduces computation time significantly and enables the integration of this new bolus advisor into current processing features of smart insulin pumps.

METHODS

A new strategy was implemented that focused on finding the combined basal-bolus solution of interest rather than an extensive search of the feasible set of solutions. Analysis of interval simulations, inclusion of physiological assumptions, and search domain contractions were used. Data from six real patients with T1DM were used to compare the performance between the optimized and the conventional computations.

RESULTS

In all cases, the optimized version yielded the basal-bolus combination recommended by the conventional method and in only 0.032% of the computation time. Simulations show that the mean number of iterations for the optimized computation requires approximately 3.59 s at 20 MHz processing power, in line with current features of smart pumps.

CONCLUSIONS

A computationally efficient method for basal-bolus coordination in postprandial glucose control has been presented and tested. The results indicate that an embedded algorithm within smart insulin pumps is now feasible. Nonetheless, we acknowledge that a clinical trial will be needed in order to justify this claim.