Background and Aims

A model of exercise effect on glucose utilization (GU), in healthy (ND) and type 1 diabetes subjects (T1D) has been recently proposed (Romeres et al. ATTD 2019, ADA 2019). It assumed that exercise acts both on insulin-independent (IIGU) and insulin-dependent GU (IDGU). The model was able to fit plasma glucose concentration during different clamp conditions. The aim of this study was to validate model-derived against model-independent measurement of GU.

Methods

Six ND (age=28.2±4.2 yrs., BMI=23.6±1.0 kg/m², VO_2max=36.6±2.9 ml/kg/min) and six T1D (mean±SE age=29±3 yrs., BMI=28.3±2.0 kg/m², VO_2max=28.7±2.9 ml/kg/min) were studied with a glucose clamp during, before and after a 60min exercise session at 65% VO_2max, on three visits: euglycemia-low insulin; euglycemia-high insulin and hyperglycemia-low insulin in random order.

Glucose turnover was measured with the isotope dilution technique using [6,6-²H₂]glucose and the model-independent GU was then calculated.

The model of exercise effect on GU was incorporated in the two-compartment model of glucose kinetics assuming an exercise-induced rapid effect on IIGU and a delayed effect on IDGU.

Results

Model predicted GU matches the model-independent GU (Figure 1), before, during and after the exercise session, both in ND and T1D.

Conclusions

We validated the model of exercise effect on GU against gold-standard data. The model is suitable to be incorporated into the UVa/Padova T1D simulator. This will permit in silico optimization of insulin therapy for artificial pancreas algorithms during physical activity.

Background and Aims

Positive and negative psychosocial outcomes for individuals using diabetes technology occur. In hybrid closed-loop (HCL) systems, most studies were short-term with limited real-world application. We conducted a 12-month observational real-world study of youth and young adults with type 1 diabetes (T1D) using the Medtronic 670G HCL system for routine T1D care.

Methods

HCL use data, glycemic outcomes, and indicators of fear of hypoglycemia [FOH; Hypoglycemia Fear Survey (HFS)] and distress [Problem Areas in Diabetes (PAID)] were collected at baseline and every 3 months for one year. Associations between changes in FOH and distress and demographic and clinical measures were examined.

Results

Ninety youth and young adults (mean 14.5yrs, range 2-25yrs, mean HbA1c 8.7±1.7%) participated. At 6 months, HbA1c was significantly higher in participants who scored above the median change in HFS-behavior score (9.7±2.0% vs 8.5±1.1%, p=0.018) and in those whose HFS-behavior scores increased compared to those whose scores decreased (9.7±2.0% vs 8.5±1.2%, p=0.037). At 12 months, increasing distress was associated with increasing time in auto mode (p=0.039). There were no other associations between FOH and distress, and time in auto mode, time in range, time hypoglycemic, T1D duration, age, or previous technology use.

Conclusions

Behavioral manifestation of FOH is associated with worse HbA1c in youth and young adults with T1D. More diabetes distress after one year was associated with more time in auto mode, but not increased time in range. This association may be related to the high amount of user involvement necessary to maintain auto mode.

Background and Aims

Each type 1 diabetes patient undergoes a recurrent process of glycemic variation described by a sequence of CGM daily profiles. The goals of this study is to develop a methodology to approximate this process based on clustering of CGM daily profiles. The new technology is designed to evaluate the dynamics of glycemic control and is applicable to treatment optimization and decision support.

Methods

We use data from iDCL Protocol 3 (NCT03563313) which generated over 30,000 CGM daily profiles and compared Closed Loop Control (CLC) vs. Sensor Augmented Pump (SAP). Using unsupervised algorithm daily CGM profiles were classified in 3 clusters with the following cluster centers:

Results

The table below presents the classification of CGM daily profiles in the 3 clusters for SAP and CLC groups. All study participants went through all clusters but CLC resulted in 19.1% more time in tight control, and less time in both average control and hyperglycemia.

Conclusions

A novel technique was developed to classify daily CGM profiles into separable clusters. While each person goes through all/most clusters during their routin, clusters sequences differentiate treatment modalities, CLC vs SAP.

Background and Aims

We evaluated the function of the bihormonal iLet bionic pancreas delivering dasiglucagon when compared to the insulin-only iLet in a home-use study in adults with T1D.

Methods

Ten subjects used the bihormonal and insulin-only configurations of the iLet for one week each in random order. The bihormonal iLet delivered 4 mg/dl dasiglucagon, a glucagon analog stable in aqueous solution (Zealand Pharma). Sessions were initiated by entering the body weight; the iLet autonomously and continuously adapts to individual insulin needs. The primary outcome was iLet performance targets: CGM glucose (CGMG) readings capture ≥80%, drug delivery channel(s) availability ≥95%, and ratio of delivered to attempted doses 0.95 to 1.05. Secondary outcomes included measures of glycemic control.

Results

The iLet met the specified performance targets in both configurations. For the bihormonal vs. insulin-only configuration median percent time with CGMG < 54 mg/dL was 0.2% [0,0.3] vs. 0.6% [0.2,1.1] (p=0.16); mean daily carbohydrate to treat hypoglycemia was 13.0±9.6 vs. 16.1±13.0 grams/day; mean CGMG was 139±11 vs. 149±13 mg/dL (p <0.01); mean percent time within the 70-180 mg/dl range was 79±9% vs. 71±8% (p<0.01); and percentage of subjects with mean CGMG <154 mg/dl was 90% vs. 50%. There were no infusion set occlusions or site reactions.

Conclusions

The iLet performed to specifications in both the bihormonal and insulin-only configurations. Glycemic outcomes were very similar to those achieved with previous hardware implementations of the bionic pancreas. Dasiglucagon was well tolerated and the bihormonal iLet achieved similar glycemic outcomes to those previously achieved with freshly reconstituted human glucagon.