Background and Aims

The aim of this study is to evaluate whether T1D patients’ characteristics such as age, body weight (BW), Sex, and time in physical activity (TPA) have effect on the performance of the Diabeloop Artificial Pancreas (AP) based on data from clinical trial (NCT02987556).

Methods

Linear correlation was computed between age, BW, and TPA (1) vs time in range (TIR (2)), and time in hypoglycemia (TIHYPO (3)), to demonstrate that there is not a linear dependency between performance and age, BW, and TPA. To demonstrate that patient's sex has no effect on performance of the Diabeloop AP the Kruskal-Wallis H-test was computed to evaluate statistical difference.

The dataset is composed of 24 women and 39 men wearing the Diabeloop AP during 3 months. Patients’ characteristics were (mean, std): age (49.21, 13.36) years old, BW (70.11, 11.17) kg, and TPA (1.38, 1.68) %.

Results

There is no linear correlation between TIR and age (r = 0.01), BW (r = 0.25), and TPA (r = 0.02) neither between TIHYPO and age (r = 0.09), BW (r = 0.15), and TPA (r = 0.01). We observe that there is no significant difference between the performance reached by the Diabeloop AP for women and men (p value of 0.38 and 0.6 for TIR and TIHYPO respectively).

Conclusions

The Diabeloop AP allowed to correctly perform on a variety of T1D patients despite their age, BW, TPA and sex, showing that the Diabeloop’s algorithm is not biased by these factors.

Background and Aims

Clinical trials in free-living conditions is key in the development of an Artificial Pancreas (AP) for T1D subjects. Since the scenario plays a key role in the synthesis and validation of AP control algorithms, a probabilistic approach is proposed to automatically design meal scenarios. In particular, we exploit our real-life data to design realistic in silico scenarios.

Methods

The amount and time-of-day of ingested carbohydrates in a 1-month in 13 patients for a total of 1500 meals. have been considered. The joint distribution of these variables has been estimated via a copula function, in order to model their dependence. The use of a copula allows to generate Monte Carlo scenarios by drawing random samples, which represent a pair of amount and time-of-day.

Results

A Gaussian copula resulted suitable for the description of the dependence in the meal dataset with a p-value of 0.005 according to the χ² test based on Rosenblatt’s transformation. A bootstrap version of the test shows that the estimate of the Spearman correlation coefficient (ρ) is sufficiently accurate with respect to the correlation (ρ) directly computed from the data (ρ=0.13, ρ=0.12).

Conclusions

The availability of a copula statistical model able to represent the food habits of a T1D population allows to design realistic eating patterns to run in silico simulations under free-living conditions.

Background and Aims

Glucagon has received renewed interest, particularly in the development of a dual hormone artificial pancreas (AP). Slow subcutaneous (SC) dynamics motivates for exploration of the intraperitoneal (IP) space both for glucose sensing and hormone delivery. We previously investigated IP glucagon delivery in rats [1]. Now we compared glucose dynamics after IP and SC glucagon delivery in a swine model.

Methods

Ten anaesthetized, non-diabetic, somatostatin-analogue treated pigs (35–50 kg) were, in random order, given glucagon boluses of 0.6 µg/kg IP, 0.3 µg/kg IP, and 0.6 µg/kg SC. At last, 1 mg IP glucagon was given to test maximum glucose response.

Results

Only 17 of 28 IP boluses and nine of 10 SC boluses had a glucose increasing effect. We believe this is due to prolonged fasting causing depletion of hepatic glycogen. Hence, we excluded four pigs from further analysis. The mean maximum effect on glucose for the remaining six pigs was 2.4, 2.2 and 1.6 mmol/L for 0.6 µg/kg IP, 0.3 µg/kg IP and 0.6 µg/kg SC glucagon, respectively.

Glucose increase after 14 to 30 minutes was significantly larger for the 0.6 µg/kg IP bolus compared to the equally sized SC bolus. In some pigs, a marked “first-pass-effect” is observed after IP glucagon.

Conclusions

Results indicates that adequate glucose responses by IP glucagon is achieved by smaller doses, potentially avoiding side effects of glucagon treatment by resembling physiologic glucagon secretion and distribution [2].

Further data on glucagon levels in blood following the different boluses will be presented.

References:

Background and Aims

To regulate blood glucose of a diabetic patient; artificial pancreas is used to externally infuse insulin in the patient body. This work presents the design and analysis of the nonlinear controller that enables the automatic regulation of blood glucose level in type-1 diabetic patients.

Methods

We have proposed a Lyapunov based nonlinear Backstepping controller. In Berman’s Minimal Model, the meal disturbance phenomenon is considered as fixed value. One of the enhancements that we have introduced is the annexure of the variable meal disturbance as a dynamic state to the existing BMM. The asymptotic stability of the system is proven via mathematical analysis using Lyapunov theory.

Results

To demonstrate the performance of the proposed controller, simulations are carried out through MATLAB/Simulink and results of the proposed controller has been compared with PID controller.

Conclusions

The propsed nonlinear controller enables the automatic regulation of blood glucose level far better than PID controller.

Background and Aims

Diabetes Type 1 is caused when body is deficient of required insulin quantity to maintain blood glucose level; due to unavailability of Pancreatic
beta cells. Artificial Pancreas facilitates Type 1 diabetes Mellitus to have automatic stabilization of blood glucose level using some controller. In this research work, we have proposed Backstepping Sliding Mode Controller for Artificial Pancreas in Type 1 diabetes Mellitus.

Methods

We have used Extended Bergman’s Minimal Model that presents the relation between glucose and insulin for Type 1 Diabetic patient with fixed known meal disturbance but have meal disturbance as a 4th state. Then we designed Lyapunov based robust backstepping nonlinear controller for stabilization of blood-glucose level.

Results

The analysis through Lyapunov theory proves the global asymptotic stability of the system. The proposed controller has been compared with PID controller in MATLAB/Simulink.

Conclusions

The performance of the proposed controller has been proved to be far better than conventional PID controller.

Background and Aims

There is a complex relationship between sleep and diabetes. Most research reported focuses on changes in insulin sensitivity and type 2 diabetes. Relationships between sleep and daytime glycemic control in people with type 1 diabetes (T1D) must be understood in a similar quantitative way to incorporate new modules into a multivariable artificial pancreas (mAP) to achieve better glycemic regulation.

Methods

Subjects with T1D ages 18-65 are monitored for the weekdays of three weeks. Each participant wore an at-home automatic sleep-staging device and a CGM. Participants maintained constant activity schedules and meal compositions across the study. Quantitative descriptive features, including insulin sensitivity, were developed with the meal, insulin and CGM data along with the corresponding previous night of sleep data. K-means clustering and linear regression was used to determine relationships between sleep characteristics and daytime glycemic control. Analysis was done across the entire group and on each individual participant.

Results

When analyzed together, there were no common effects sleep characteristics had on daytime glycemic control. However, when analyzed individually, participants all had distinct clusters of data that showed sleep influenced their next day glycemic regulation. Furthermore, most individuals exhibited unique relationships with differing sleep quality characteristics including measures such as sleep efficiency, time in light sleep and total sleep time.

Conclusions

These results suggest that sleep has distinct individualized effects in people with T1D. These results show a potential need for personalized sleep effect models to be implemented in mAP systems to enhance daytime glycemic control.

Background and Aims

To study if motivational interviewing (MI) added to standard educational care (SE) improves vascular health in adolescents with poorly controlled type 1 diabetes.

Methods

47 adolescents with type 1 diabetes of at least 2 years duration and HbA1c > 75 mmol/mol (> 9.0%) on two visits were randomized to MI+SE or SE, clinicaltrials.gov; NCT02637154.

Results

39 adolescents (20 MI + SE) completed the study. At 12 months, vascular health parameter changes were not statistically significantly different between MI + SE and SE (carotid-femoral pulse-wave velocity (PWV): mean difference 0.052 m/s (95% CI -0.395 – 0.500, p=0.81); carotid-radial PWV: 0.118 m/s (95% -0.478 – 0.713, p=0.69), carotid intima-media thickness (IMT): 0.002 mm (95% CI -0.37 – 0.40, p=0.93), systolic blood pressure (SBP) z-score: 0.495 (95% CI -0.099 – 1.09, p=0.10). At baseline, duration of type 1 diabetes was associated with radial IMT (r=0.430, p=0.007) and cfPWV (r=0.373, p=0.018), and carotid, femoral and brachial IMT were correlated with CGM-SD (r=0.440, p=0.017; r=0.377, p=0.048; r=0.387, p=0.038). There was an inverse association between CGM time-in-range (3.9-10.0 mmol/L) and crPWV (r=-0.476, p=0.022) changes. SBP change was associated with BMI change (r=0.374, p=0.019) and IMT change (r=0.461, p=0.016 for carotid IMT; r=0.498, p=0.010 for femoral IMT).

Conclusions

There was no effect of MI added to SE on vascular health parameters. Although disease duration and glycemic control were associated with vascular health at baseline, there were only limited associations between glycemic control and vascular health parameter changes. Vascular health parameter changes were interrelated suggesting clustering of cardiovascular risk.

Background and Aims

The objective of this study was to assess the cost-effectiveness of retrospective CGM (rCGM) in people with type 2 diabetes (T2D).

Methods

The IQVIA CORE Diabetes model was used to perform cost-effectiveness analyses over patient lifetimes. Clinical data were sourced from the single-arm before/after ADJUST study. Type 2 patients already on insulin were equipped with a rCGM device. The use of the rCGM was associated with a reduction in HbA1c of -1.3%, from 9.4% (79 mmol/mol) at baseline to 8.1% (65 mmol/mol) at 12 months. Cost data, expressed in 2018 euros (EUR), were obtained from Portuguese reference prices and the published literature. A 5% discount rate was applied to both clinical and economic outcomes.

Results

rCGM was associated with a quality-adjusted life-year (QALY) gain of 0.09 per patient based on their remaining life expectancy (ca 24 years) but with higher overall costs 616 EUR, due to the costs of rCGM and related visits. This led to an incremental cost-effectiveness ratio (ICER) of EUR 6,765 per QALY gained. Use of rCGM would lower the cumulative incidence of diabetes-related complications. Higher rCGM acquisition costs were partially offset by reduced complication costs. Extensive sensitivity analysis on key drivers confirmed the robustness of results.

Conclusions

rCGM was associated with improved glycemic control and quality of life in peoples with T2D with elevated HbA1c and already on insulin. rCGM is a cost-effective management tool for people with T2D in Portugal.

Background and Aims

The relationship between intermediate-term glycemic variability and hypoglycemia is well unknown, therefore, we analyzed that relationship using continuous glucose monitor (CGM) data.

Methods

We cross-sectionally analyzed CGM (FreeStyle Libre Pro) data for 97 patients with type 2 diabetes whose 24 h glucose levels were measured continuously for 13 days during hospitalization for type 2 diabetes treatment. Values over a span of 13 days for all glycemic variability and hypoglycemia metrics were evaluated. We have proposed novel glycemic variability metrics as follows: mean of daily difference 1 (MODD1) ÷ mean glucose level × 100 (MODD1/mean), mean absolute glucose (MAG) ÷ mean glucose level × 100 (MAG/mean), and glycemic variability percentage (GVP) ÷ mean glucose level × 100 (GVP/mean).

Results

The standard deviation (SD), MODD1, MAG, GVP, and the mean glucose level significantly negatively correlated with the percentage of time in the hypoglycemic range (< 70 mg/dL) [TIR < 70] (r = -0.32 – -0.75, p = 0.002 ~ < 0.001). Coefficient of variation (CV) tended to correlate with TIR < 70 positively. MODD1/mean, MAG/mean, and GVP/mean significantly positively correlated with TIR < 70. CV, MODD1/mean, MAG/mean, and GVP/mean significantly positively correlated with the percentage of time in the hypoglycemic range (< 54 mg/dL) [TIR < 54] (Table).

Conclusions

Intermediate-term glycemic variability which is divided by the mean glucose level may predict hypoglycemia.

Background and Aims

We analysed data obtained from electronic patient records from inpatients with diabetes admitted to a large university hospital.

Methods

The study was conducted using electronic patient record data from Oxford University Hospitals NHS Foundation Trust. The dataset contains hospital admission data for patients with diabetes. We define a biochemical hypoglycemic episode as any blood glucose measurement < 4mmol/l and a clinically significant hypoglycemic episode as any blood glucose measurement <3mmol/l. Any two or more than two consecutive low blood glucose within a 4-hour time window are considered as one hypoglycemic episode.

Results

We analyzed data obtained from 17,658 inpatients with diabetes [1,696 type 1 diabetes, 14,006 type 2 diabetes, 9,277 males, age 66(18) years, mean(SD)] who underwent 32,758 hospital admissions between 2014 and 2018. We identified all the biochemical and clinically significant hypoglycemic episodes during these admissions. The incidence of biochemical hypoglycemia was 21.5% and that of clinically significant hypoglycemia was 9.6%. Major findings from the data analysis include: Recurrent biochemical and clinically significant hypoglycemia happened during 50% and 39% of hospital admissions with at least one hypoglycemic episode; Patients on metformin alone had the lowest incidence of hypoglycemia(8%) comparing to those on rapid analogue, long analogue and human rapid insulin at the same time, with the highest incidence (53%); Incidence of biochemical hypoglycaemia in type 1 diabetes(37%) doubles that in type 2 diabetes(18%).

Conclusions

Retrospective analysis of data from electronic patient records helps gain clinical understanding about inpatient hypoglycaemia and may improve inpatient glycaemic control through targeting high-risk hypo-prone inpatients.

Background and Aims

Aim: to assess the variability of glycemia (GV) in patients with type 2 diabetes on the background of a low-calorie diet with the inclusion of a specialized food (SF) with a modified carbohydrate profile.

Methods

Materials and methods: 38 women with type 2 diabetes and obesity (BMI on average 38.1 ± 0.89 kg/m²) aged 37 to 69 years were examined. All patients receiving standard hypoglycemic therapy were assessed for GV using the continuous glucose monitoring system from Medtronic for 6 days: 3 days against the background of a low-calorie diet (1,500 kcal / day) and 3 days against the background of a low-calorie diet with the inclusion of a SF for medical nutrition. SF was included in the hypocaloric diet in the form of a drink in the amount of 200 ml for a second breakfast instead of a carbohydrate-containing dish.

Results

Results: It is shown that the inclusion of SF in the hypocaloric diet was accompanied by a statistically significant decrease in the level of maximum and average glycemia. For the majority of patients over the entire observation period, the average glycemia in the afternoon and in the evening was higher than at night, reflecting the natural effect of meals on glycemia.

Conclusions

Conclusions: modification of the hypocaloric diet due to the inclusion of SF with a modified carbohydrate profile helps to reduce some indicators of hepatitis B in patients with type 2 diabetes.