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

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.

Super Bolus-A Remedy for a High Glycemic Index Meal in Children with Type 1 Diabetes on Insulin Pump Therapy?-A Randomized, Double-Blind, Controlled Trial

BACKGROUND

This study aimed to compare whether a super bolus (SB) is a more efficient strategy than a normal bolus (NB) for high glycemic index (h-GI) meals in children with type 1 diabetes (T1D).

METHODS

A randomized, double-blind, crossover trial with an allocation ratio of 1:1, registered at ClinicalTrials.gov (NCT04019821). 72 children aged 10-18 years with T1D > 1 year, and on insulin pump therapy > 3 months were included. As an intervention, they ate a h-GI breakfast for the two following days and receive a prandial insulin bolus either in the form of SB or NB.

RESULTS

The SB group had lower glucose values during the observation time and lower glucose levels in 90th min (primary end point). The median time in range was also higher after SB. At the same time, more hypoglycemic episodes and a higher time below range were noted in this group. Almost 90% of them were the threshold value for initiating treatment for hypoglycemia and occurred near the end of observation period. More hyperglycemic episodes and over twice as much time in hyperglycemia were noted after NB.

CONCLUSIONS

Super bolus is an effective strategy to avoid postprandial hyperglycemia but the basal insulin suspension should be longer to avoid hypoglycemia (f.ex. 3 h).

Super Bolus: a remedy for a high glycemic index meal in children with type 1 diabetes on insulin pump therapy?-study protocol for a randomized controlled trial

Background

Postprandial hyperglycemia (PPH) is a common clinical problem among patients with type 1 diabetes (T1D), which is related to high glycemic index (h-GI) meals. The main problem is linked to high, sharp glycemic spikes following hypoglycemia after h-GI meal consumption. There is a lack of effective and satisfactory solutions for insulin dose adjustment to cover an h-GI meal. The goal of this research was to determine whether a Super Bolus is an effective strategy to prevent PPH and late hypoglycemia after an h-GI meal compared to a Normal Bolus.

Methods

A total of 72 children aged 10–18 years with T1D for at least 1 year and treated with continuous subcutaneous insulin infusion for more than 3 months will be enrolled in a double-blind, randomized, crossover clinical trial. The participants will eat a h-GI breakfast for the two following days and receive a prandial insulin bolus in the form of a Super Bolus 1 day and a Normal Bolus the next day. The glucose level 90 min after the administration of the prandial bolus will be the primary outcome measure. The secondary endpoints will refer to the glucose levels at 30, 60, 120, 150, and 180 min postprandially, the area under the blood glucose curve within 180 min postprandially, peak glucose level and the time to peak glucose level, glycemic rise, the mean amplitude of glycemic excursions, and the number of hypoglycemia episodes.

Discussion

There are still few known clinical studies on this type of bolus. A Super Bolus is defined as a 50% increase in prandial insulin dose compared to the dose calculated based on the individualized patient’s insulin-carbohydrate ratio and a simultaneous suspension of basal insulin for 2 h. Our patients reported the best experience with such a combination. A comprehensive and effective solution to this frequent clinical difficulty of PPH after an h-GI meal has not yet been found. The problem is known and important, and the presented solution is innovative and easy to apply in everyday life.

Trial registration

ClinicalTrials.gov NCT04019821

Glucose outcomes of a learning-type artificial pancreas with an unannounced meal in type 1 diabetes

BACKGROUND AND OBJECTIVES

Glycemic control with unannounced meals is the major challenge for artificial pancreas. In this study, we described the performance and safety of learning-type model predictive control (L-MPC) for artificial pancreas challenged by an unannounced meal in type 1 diabetes (T1D).

METHODS

This closed-loop (CL) system was tested in 29 T1D patients at one site in a 4 h inpatient open-label study. Participants used an L-MPC CL system for 6 days after 2-day system identification using open-loop (OL) insulin system. During the CL period, the L-MPC system was started from 8:00 am to noon each day. At 9:00 am, each participant consumed 50 g of carbohydrates with no prandial insulin bolus. At 9:30 am on CL-Day 4 or CL-Day 6, participants rode bicycles for 20 minutes or drank 50 ml of beer, in a random order.

RESULTS

As the primary outcome, TIR on CL-Day 3 was 65.2±23.3%, which was 9.8 points higher (95% CI 1.8 to 17.8; P = 0.019) than that on CL-Day 1. The time of glucose >10 mmol/L was decreased by 11.0% (95% CI -18.7 to 3.3; P = 0.007), and mean glucose level was decreased by 1.1 mmol/L (95% CI -1.1 to 0.5; P = 0.000). The total daily insulin dosage showed no significant difference (-0.1U, 95% CI -1.34 to 1.32; P = 0.982). Compared with OL-Day1 with a postprandial bolus, the TIR was increased by 13.7 points (95% CI 1.4 to 26.0; P = 0.030), the time of glucose >10 mmol/L and the mean glucose level were also decreased. Compared with the exercise day (CL-Day E, 62.0 ± 23.3%; P = 0.347) or alcohol day (CL-Day A, 64.0 ± 23.6%; P = 0.756), there was no statistically significant difference in terms of TIR, time of glucose >10 mmol/L and mean glucose level. No severe hypoglycemic events occurred and hypoglycemic episodes were not increased by using closed-loop insulin system.

CONCLUSION

The L-MPC CL insulin system achieved good glycemic control challenged by an unannounced meal.

Artificial Intelligence in Decision Support Systems for Type 1 Diabetes

Type 1 diabetes (T1D) is a chronic health condition resulting from pancreatic beta cell dysfunction and insulin depletion. While automated insulin delivery systems are now available, many people choose to manage insulin delivery manually through insulin pumps or through multiple daily injections. Frequent insulin titrations are needed to adequately manage glucose, however, provider adjustments are typically made every several months. Recent automated decision support systems incorporate artificial intelligence algorithms to deliver personalized recommendations regarding insulin doses and daily behaviors. This paper presents a comprehensive review of computational and artificial intelligence-based decision support systems to manage T1D. Articles were obtained from PubMed, IEEE Xplore, and ScienceDirect databases. No time period restrictions were imposed on the search. After removing off-topic articles and duplicates, 562 articles were left to review. Of those articles, we identified 61 articles for comprehensive review based on algorithm evaluation using real-world human data, in silico trials, or clinical studies. We grouped decision support systems into general categories of (1) those which recommend adjustments to insulin and (2) those which predict and help avoid hypoglycemia. We review the artificial intelligence methods used for each type of decision support system, and discuss the performance and potential applications of these systems.

Control-Oriented Model With Intra-Patient Variations for an Artificial Pancreas

In this work, a low-order model designed for glucose regulation in Type 1 Diabetes Mellitus (T1DM) is obtained from the UVA/Padova metabolic simulator. It captures not only the nonlinear behavior of the glucose-insulin system, but also intra-patient variations related to daily insulin sensitivity ( S_I) changes. To overcome the large inter-subject variability, the model can also be personalized based on a priori patient information. The structure is amenable for linear parameter varying (LPV) controller design, and represents the dynamics from the subcutaneous insulin input to the subcutaneous glucose output. The efficacy of this model is evaluated in comparison with a previous control-oriented model which in turn is an improvement of previous models. Both models are compared in terms of their open- and closed-loop differences with respect to the UVA/Padova model. The proposed model outperforms previous T1DM control-oriented models, which could potentially lead to more robust and reliable controllers for glycemia regulation.

Glycemic Index, Extended Bolusing, and Diabetes Education in Insulin Pump Therapy (GLIDE: A Pilot Study)

Background: There is no published evidence on whether advanced bolus education affects outcomes in insulin pump-treated type 1 diabetes. We assess the feasibility of delivering a clinical education program on rates of digestion and bolusing, and to assess its preliminary impact. Methods: An interactive education session on glycemic index (GI), extended bolusing, and superbolusing was developed and assessed in a nonrandomized single-arm study for 12 weeks. Insulin pump-treated participants with type 1 diabetes were recruited. Glucose outcomes were assessed by blinded continuous glucose monitoring after the consumption of high-fat and high-GI test meal. The primary outcome measure was 8-h glucose area under the curve (AUC) after high-fat meals, before and after intervention. Secondary outcomes included time spent in hypoglycemia, quality of life, treatment satisfaction, HbA1c, frequency of use of extended boluses, and postprandial AUC. Results: Eleven participants completed the study [mean (SD) age 42.3 (12.8) years, baseline HbA1c 57.3 (10.0) mmol/mol, duration of diabetes 19.5 (9.9) years]. AUC for glucose after test meals did not differ significantly after education except for in the first 2 h after the high-GI meal [precourse 83.1 (0.23-88.9), postcourse 5.38 (-16.2 to 50.8)]. Percentage time spent in hypoglycemia (<3.9 and <3.3 mmol/L) fell at week 12 compared with baseline [5.8 (IQR 2.1-8.3) % to 4.3 (IQR 2.1-5.4) %, P = 0.013, and 2.9 (IQR 1.2-3.9) % to 1.6 (IQR 0.7-2.4) %, P = 0.029, respectively]. Conclusion: Delivering an education program to support advanced boluses is feasible and may reduce exposure to hypoglycemia. A further trial is required to confirm the findings.