Welcome to the ATTD 2023 Interactive Program

Background and Aims

Rapid improvement in A1c may worsen diabetic retinopathy (DR). However, the progression of DR and factors affecting it after initiation of AID systems, which are known to reduce A1c in T1D are unknown.

Methods

In this retrospective, longitudinal study, demographics, A1c, and eye exams were retrieved from electronic medical records of T1D adults who initiated AID between January 2020 and January 2022. DR worsening was defined as an increase in ETDRS scores and/or qualitative eye examination from the eye exam prior to AID initiation to the first eye exam following AID use.

Results

Of 152 T1D adults, 42 (28%) had DR worsening over mean 1.6 years. Those with DR worsening had higher baseline A1c, LDL, and eGFR (Table 1). In mixed models, there was no significant change in ETDRS score in either eye by time interval (p=0.3 for OS, p=0.4 for OD) or by A1c over time (p=0.5 for OS, p=0.4 for OD). In a logistic regression adjusted for age, duration, and sex, higher baseline A1c was associated with 2-fold increased risk for DR worsening (OR=2.1 [1.34-3.04], p=0.0008). Patients with LDL>100 and A1c >8% (n=16) had 3-fold increased risk for DR worsening (OR=3.33 [1.12-9.91], p=0.03).

Conclusions

Higher baseline A1c and LDL were associated with worsening of retinopathy after initiation of the AID system in adults with T1D. Further research is needed to evaluate the needed frequency of eye examination in high-risk patients.

Background and Aims

To evaluate the feasibility of outpatient extended duration use of a closed-loop glucose control system during pregnancies complicated by type 1 diabetes (T1D).

Methods

Ten pregnant people with T1D using insulin pump therapy were enrolled in the study at three US sites (NCT04492566). After a run-in week on their own therapy and supervised 48-hour training on the interoperable artificial pancreas system (iAPS) running a Zone-Model Predictive Control (zone-MPC) algorithm, participants continued using iAPS at home for the remainder of their pregnancy. The system was customized for stricter glycemic targets for pregnancy using glycemic target zones of 80-110 mg/dL during the day and 80-100 mg/dL overnight, with assertive insulin delivery in the postprandial period. The primary outcome was sensor glucose time in range (TIR) for pregnancy 63-140 mg/dL as per international consensus guidelines. Meals and physical activity were unrestricted.

Results

All participants completed the trial. Mean duration of iAPS use was 13.9±3.9 weeks. Participants had a mean age of 32.6±4.3 years, gestational age of 22.0±3.4 weeks, weight of 75.7±17.2 kg, and HbA1c of 5.8±0.6% at screening. Mean sensor TIR 63-140 mg/dL was 78.6±9.2% on iAPS, compared to 64.5±16.3% for the week prior in open loop at home (p=0.002). Nine out of 10 participants had >70% time in range during their time of system use. No system related safety concerns were observed.

Conclusions

A pregnancy-specific zone-MPC system demonstrated glycemic effectiveness for home-use during pregnancies with T1D.

Background and Aims

The most advanced commercially available technology for type 1 diabetes (T1D) is the insulin-only artificial pancreas (AP). We created a dual-hormone (insulin and glucagon) AP for individuals with T1D. We aimed to assess the efficacy of our dual-hormone (DH) AP compared to our single-hormone (SH) AP in adolescents with T1D.

Methods

In this 26-h, two-period, randomized, crossover, inpatient study involving 11 adolescents with T1D (nine males [82%], mean±SD age 14.8±1.4 years, diabetes duration 5.7±2.3 years) we used two configurations of our DiaCon AP: DH and SH. Each visit included an overnight stay, meals/snacks, and a bout of exercise. We hypothesized that DH would perform superiorly to SH. The primary endpoint was percentage time spent with sensor glucose values below range (TBR <3.9 mmol/L) during AP control.

Results

Overall, DH and SH performed similarly for the following outcomes (median [IQR]): TBR 1.6 [0.0-2.4] vs. 1.28 [0.16-3.19]%, p=0.999; time in range (TIR; 3.9-10.0 mmol/L) 68.4 [48.7-76.8] vs. 75.7 [69.8-87.1]%, p=0.079; and time above range (TAR; >10.0 mmol/L) 28.1 [18.1-49.8] vs. 23.3 [12.3-27.2]%, p=0.101. Insulin administration was similar between the two arms (p=0.954). Glucagon administration varied between participants (median [IQR]; 549 [229, 1034]µg/26-h). Compared to DH, SH performed superiorly overnight (TIR 73.0 [53.9, 87.4] vs. 96.5 [84.3, 100]%, p=0.019 and TAR 27.0 [12.6, 42.6] vs. 0 [0.0, 10.0]%, p=0.018) and around exercise (TIR 64.5 [50.0, 91.9] vs. 83.9 [80.6, 100.0]%, p=0.018).

Conclusions

In this study, DH did not perform superiorly to SH. Limited knowledge regarding individual glucagon sensitivity may have influenced the performance of the DiaCon DH-configuration.

Background and Aims

The objective of this study was to evaluate the glycemic outcomes of one-year Advanced Hybrid Closed Loop (AHCL) Minimed 780G system in children and adolescents with Type 1 Diabetes (T1D) previously treated with Multiple Daily Injections (MDI).

Methods

The prospective open label single-arm, single-center, clinical investigation was conducted at Sidra Medicine in Qatar study and enrolled 34 individuals aged 7-18 years with T1D >1 year, on MDI with self-monitoring of blood glucose or continuous glucose monitoring, with no prior pump experience, and baseline HbA1c <12.5% (<113 mmol/mol). Patients were followed for one year and HbA1c was obtained, and pump data was collected every 3-months during the study.

Results

All 34 participants (age 12.5 ± 3.7, female 53%), who initiated AHCL completed one-year of automated insulin delivery. The participants used the sensor 94.3±5.6% of the time with Auto Mode usage 91.2±7.1% during 12 months of AHCL system use. HbA1c decreased from 8.6±1.7% (70±18.6 mmol/mol) at baseline, to 6.5±0.7% (48±7.7 mmol/mol) at 3 months (p=0.001) and remained stable to 7.0±0.7 (53±7.7 mmol/mol) at 12 months (p=0.002). TIR (70-180mg/dL) increased from 42.1±18.7% at baseline to 78.8±6.1% during the first 3-months and remained above 75% during the 12 months of HCL use. Remote follow-up visits were noted in 41% of the participants between 3 and 12 months of HCL initiation.

Conclusions

The glycemic outcomes can be improved using AHCL system in individuals previously treated with MDI and maintained over the one year following automated insulin delivery. Remote follow ups should be offered to individuals on AHLC system.

Background and Aims

To manage postprandial hyperglycemia, premeal bolusing via carb-counting is the current standard of care for individuals with type 1 diabetes (T1D). However, counting carbs and announcing them prior to eating introduces a significant burden. An AID system with an auto meal-bolus feature that eliminates manual mealtime bolusing was studied in a feasibility trial of adults with T1D.

Methods

The system included the MiniMed™ 780G pump and a smartphone-paired smartwatch with the KLUE application (app) that detects eating and drinking gestures. A smartphone algorithm converted gestures to carb amounts that were transmitted to the pump for automatic bolusing. For 6 days, subjects (N=17, aged 18-75 years) used the system with the KLUE app disabled while traditional carb-counting and -entry were completed (Baseline). Thereafter, the KLUE app was enabled for 5 days and carb-counting/carb-entry were prohibited (Study). Subjects were given the same 8 test meals (6 solid and 2 liquid) of varying caloric and carb size during both phases. Otherwise, there were no other meal restrictions.

Results

The figure shows absolute percentage of time in range (%TIR, 70-180 mg/dL) during the baseline and study phase for all 17 subjects, along with the population average. There was no significant difference in any overall or 4-hour postprandial glycemic outcome between the phases (Table 1).

Conclusions

Data suggest that the novel AID system maintains glycemic control, including %TIR, similar to that observed with manual meal bolusing. By eliminating the burden of carb-counting, the new system may improve quality of life in persons with T1D.

Background and Aims

This session provides an overview of the ways that the Open Source Community has adapted the oref1 codebase, created by Dana Lewis and Scott Leibrand, to try and eliminate the need to manually manage mealtime insulin and remove the onus to bolus.

It will look at the various approaches taken to identify meals and manage glucose variation as a result, and the various mechanisms available to users to tune and adapt the approaches to attempt to get the best outcomes.

Methods

A review of the different codebases developed on top of oref1 to identify how developers and users have adapted the code to provide "hands-free" meal time management, and data gathered from users via survey to determine the effectiveness of the adaptations.

Results

Multiple different adapatations are being used by different user groups to successfully aid meal-time management. There are clear benefits and drawbacks to the different approaches taken but in general, users are finding that carbohydrate measurement and entry are not required, and in many cases, remembering to mealtime bolus may also not be required.

Conclusions

Within "Expert systems" such as oref1, it is possible to adapt the codebase to allow signals to be identified that negate the need to calculate mealtime carbohydrate amounts and boluses, and allow a user to manage mealtimes with far less mealtime intervention than has previously been seen with hybrid closed loop systems.

Background and Aims

The technology of 3D printing of bionic organs gives an opportunity to solve the problems of classical transplantology. After printing, the bionic pancreas requires an optimal environment conditioning the process of its maturation, which consists in the colonization of the produced vessels with endothelial cells and the tubularization of the endothelium within the microcirculation. After the maturation process is completed, it is necessary to evaluate the functionality and safety of the organ.

Methods

10 procedures of maturation and bionic pancreas evaluation were performed using a novel, dedicated bioreactor. 5 pancreas contained human beta cells and 5 pancreas contained porcian islets. The mean pancreatic maturation time was 23 hours (2-72 hours). The effectiveness of adhesion of endothelial cells to the vascular wall and tubularization of endothelial cells was assessed by immunohistochemistry. The GSIS test was performed by automatically. The integrity of the vascular system was assessed by maintaining a pressure of 190 mmHg for 5 minutes.

Results

The adhesion of endothelial cells to the bioink was observed after 1 hour. Tubularization of the endothelium was observed after 48 hours. Insulin secretion upon stimulation with glucose was observed without delay to the control (beta cells or islets) and the insulin concentration during the observation showed a constant ratio compared to the control, but without a clear peak at high glucose concentration.

Conclusions

The use of a dedicated bioreactor enables safety during the bionic maturation process of the organ, while allowing for an effective assessment of the organ's functionality and the tightness of the vascular system.

Background and Aims

Hybrid automated insulin delivery (AID) systems are not expected to respond optimally to prandial excursion without manual dosing. Thus, we can hypothesize that the achieved glycemic control will rely on user adherence to meal boluses. We aim to quantify this relationship using data collected during real-life use of a commercial hybrid AID system.

Methods

Linear regression models of daily time between 70-180 mg/dL (TIR) and coefficient of variation (CV) were used to account for patients' average number of daily manual insulin doses (aMD), daily deviations from aMD (ΔMD=MD-aMD), and their interaction term (X=aMD·ΔMD); using 1,577,988 days of continuous glucose monitoring (CGM) and insulin data collected from 18,690 Control-IQ users with type 1 diabetes: F/M: 55%/45%, age, weight, and total daily insulin ranges were [1-92y], [24.9-140.0kg], and [6.3-269.8U], respectively.

Results

For the TIR model, the intercept term (TIR at 0 manual bolus) was 61.91%, and aMD, ΔMD, and X coefficients were 1.57%, -3.83%, and 0.21%, respectively. For the CV model, the intercept term was 31.83%, and aMD, ΔMD, and X coefficients were -0.40%, 0.64%, and -0.023%, respectively. All p-levels<0.0001.

Conclusions

aMD coefficient signs confirm that glycemic control improves with increased daily bolus average numbers (+1.57% of TIR and -0.40% of CV per additional bolus); but positive deviation from this average is associated with degraded control (-3.83% of TIR and +0.64% of CV), potentially related to difficulties regulating glucose on unusual days. The interaction terms show that this latest effect is primarily observed in individuals with low daily bolus averages.

Background and Aims

Objective: Qualitative meal-size estimation has been proposed instead of quantitative carbohydrate (CHO) counting with automated insulin delivery. We aimed to assess the non-inferiority of qualitative meal-size estimation strategy.

Methods

Research Design and Methods: We did a two-center randomized crossover non-inferiority trial to compare 3 weeks of automated insulin delivery with (i) carbohydrate counting and (ii) qualitative meal-size estimation in adults with type 1 diabetes. Qualitative meal-size estimation categories were low, medium, high, or very high CHO and were defined as <30 g, 30-60 g, 60-90 g, and >90 g of CHO, respectively. Prandial insulin boluses were calculated as the individualized insulin-to-CHO ratios x 15, 35, 65, and 95, respectively. Closed-loop algorithms were otherwise identical in the two arms. The primary outcome was time in range 3.9–10.0 mmol/L, with a pre-defined non-inferiority margin of 4%.

Results

Results: 30 participants completed the study (20 females, age 44 (SD 17) years, A1c 7.4 (0.7%)). The time in 3.9-10.0 mmol/L was 74.1% (10.0%) with carbohydrate counting and 70.5% (11.2%) with qualitative meal-size estimation; mean difference -3.6% (8.3%; non-inferiority p=0.78). Times <3.9 mmol/L and <3.0 mmol/L were low (< 1.6% and < 0.2%) in both two arms. Automated basal insulin delivery was higher in the qualitative meal-size estimation arm (34.6 vs. 32.6 u/day, p=0.003).

Conclusions

Conclusions: Though QMS method achieved a high time in range and low time in hypoglycemia, non-inferiority was not confirmed. The qualitative meal-size estimation method may benefit from larger prandial boluses and more responsive post-meal automatic basal delivery.

Background and Aims

To determine real-life glycemic outcomes in a large cohort of pediatric patients after switching from a predictive low glucose suspend (PLGS) system to automated insulin delivery (AID) as well as to explore changes in insulin delivery and bolusing behaviors with the use of AID.

Methods

CGM and pump data were analyzed from 2,340 pediatric patients with T1D (age 1-13 years) using PLGS (Basal-IQ) for one month followed by AID (Control-IQ) for three months. Glycemic outcomes and changes in insulin delivery were compared across age groups (0-5 years, 6-10 years, 11-13 years).

Results

Glycemic control improved within the first month of AID use and was sustained through month three with time in the target range increasing 13% (Table). Hypoglycemia remained low and was marginally higher in the youngest age group. Fewer manual boluses were entered per day on AID compared to PLGS, however the number of carbohydrate boluses did not change. The youngest children had the most carbohydrate bolus entries. Only a small percentage of children (2.3%) entered 1 or fewer carbohydrate boluses per day at three months. Total daily insulin (TDI) increased across all age groups due primarily to an increase in basal insulin delivery, although basal insulin remained <50% of the TDI.

Conclusions

Glycemic outcomes significantly improved in a large cohort of young children on AID in the real world, similar to published study outcomes. Patterns of insulin delivery and bolusing behaviors change slightly with AID use.

Background and Aims

This qualitative study examined views of healthcare professionals (HCPs) on open source Artificial Pancreas Systems (APS) in type 1 diabetes care.

Methods

Nineteen HCPs were recruited through professional networks to cover various regions of the Netherlands. Eight diabetes specialized nurses, eight endocrinologists, two technical physicians and one physician assistant were interviewed. Topics of the semi-structured interviews included interaction with people with diabetes (PWD), perceived healthcare needs and (ideal) image of future diabetes care. Individual and thematic content analyses were performed (open, axial, selective coding).

Results

HCPs interacted with M = 4.7 (SD = 3.4) PWD using open source APS. Overall, participants felt free to discuss open source APS, provided that the initiative came from PWD. HCP assistance to (potential) users was limited: many considered extensive assistance to be ‘off-limits’. Barriers to engage with PWD about the systems laid on the personal (sense of losing control), legal (fear of liability/adverse events) and practical level (lack of manufacturer support). Perceived healthcare needs of PWD involved in open source APS included openness, understanding, cooperation and autonomy. HCPs expected open source APS to remain used by a motivated and skilled minority and hoped for the systems to become redundant by increased accessibility of commercial systems. In the meantime, education on open source APS was desired.

Conclusions

Although HCPs experienced freedom to discuss open source APS, they also felt reluctant and insufficiently knowledgeable to start the conversation and to extensively support PWD involved in these systems. A more proactive attitude requires better education on open source APS.