Twenty-Five Hours of Fasting with Automated Insulin Delivery in Youth with Type 1 Diabetes.

The Official Journal of ATTD Advanced Technologies & Treatments for Diabetes Conference 22‐25 February 2023 I Berlin & Online

To evaluate the outcomes of prolonged religious Jewish fasting in individuals with type 1 diabetes using automated insulin delivery (AID) systems. This cross-sectional, non-interventional study assessed the effects of a 25-hour Jewish fast in individuals using AID systems. Data was collected on the day of the fast, one week before, and one week after. The study included data from 109 fasting days involving 80 adolescents and young adults with type 1 diabetes. The mean age of participants was 17.4 ± 4.1 years; 47.5% were male, and the average duration of diabetes was 7.2 ± 4.3 years. A total of 67.6% of participants modified their AID system settings during the fasting period, with the most common modification being a change in the target glucose level. Overall, 71.5% completed the fast without complications. Fasts were primarily broken because of sensor-detected hypoglycemia. No cases of severe hypoglycemia or diabetic ketoacidosis were reported during or after the fasting period. During the fast, the mean blood glucose level was 135 ± 28.6 mg/dL, time in range (70-180 mg/dL) was 80.7%, and time spent in hypoglycemia (<70 mg/dL) was 2.6%. Prolonged fasting appears to be safe for adolescents and young adults with type 1 diabetes using AID systems. However, individualized adjustments to system settings are often necessary to maintain glycemic stability during fasting. To our knowledge, this is the first report of the effects of using an AID system during Jewish religious fasting.

BackgroundAutomated insulin delivery is the treatment of choice in adults with type 1 diabetes. Data are needed on the efficacy and safety of automated insulin delivery for children and youth with diabetes and elevated glycated hemoglobin levels.MethodsIn this multicenter, open-label randomized controlled trial, we assigned patients with type 1 diabetes in a 1:1 ratio either to use an automated insulin delivery system (MiniMed 780G) or to receive usual diabetes care of multiple daily injections or non-automated pump therapy (control). The patients were children and youth (defined as 7 to 25 years of age) with elevated glycemia (glycated hemoglobin ≥8.5% with no upper limit). The primary outcome was the baseline-adjusted between-group difference in glycated hemoglobin at 13 weeks.ResultsA total of 80 patients underwent randomization (37 to automated insulin delivery and 43 to control) and all patients completed the trial. At 13 weeks, the mean (±SD) glycated hemoglobin decreased from 10.5±1.9% to 8.1±1.8% in the automated insulin delivery group but remained relatively consistent in the control group, changing from 10.4±1.6% to 10.6±1.8% (baseline-adjusted between-group difference, −2.5 percentage points; 95% confidence interval [CI], −3.1 to −1.8; P<0.001). Patients in the automated insulin delivery group spent on average 8.4 hours more in the target glucose range of 70 to 180 mg/dl than those in the control group. One severe hypoglycemia event and two diabetic ketoacidosis events occurred in the control group, with no such events in the automated insulin delivery group.ConclusionsIn this trial of 80 children and youth with elevated glycated hemoglobin, automated insulin delivery significantly reduced glycated hemoglobin compared with usual diabetes care, without resulting in severe hypoglycemia or diabetic ketoacidosis events. (Funded by Lions Clubs New Zealand District 202F and others; Australian New Zealand Clinical Trials Registry number, ACTRN12622001454763.)

Background: Understanding qualities that make up trust in AID systems is imperative for successful development of systems built for long-term use. Previous research has demonstrated that trust is associated with: (a) better glycemic outcomes, (b) decreased self-management burdens, and (c) continued use of therapy. Method: In December 2018, 5,037 individuals with diabetes responded to an online survey administered by dQ&amp;A. We analyzed a subgroup of 386 survey participants who reported on their use of an AID system (The t:slim X2™ insulin pump with Basal-IQ® technology from Tandem Diabetes Care® = 116 ; The MiniMed 670G insulin pump system from Medtronic = 270). Results: Hierarchical multiple regression analysis was used to ascertain the significance of ease of use (device level) and psychosocial (individual level) predictors of trust in AID systems. There were no issues with multicollinearity (all VIFs &amp;lt; 3). In Model 1, ease of use accounted for 45% of variance in trust, F (3,382) = 106.74, p &amp;lt; .001. After psychosocial predictors were added in Model 2, variance accounted for was 64%, explaining an additional 19% of unique variance in trust, F (3,379) = 65.46, p &amp;lt; .001. Conclusion: These findings suggest that both ease of use and psychosocial outcomes are distinctly informative and instrumental in the development of trust in AID systems. Disclosure M.L. Manning: Employee; Self; Tandem Diabetes Care. M. McElwee-Malloy: Employee; Self; Tandem Diabetes Care. K.C. Stoner: Other Relationship; Self; Multiple companies and organizations in the diabetes field (greater than 10). S. Habif: Employee; Self; Tandem Diabetes Care.

Automated insulin delivery (AID) systems adapt insulin delivery via a predictive algorithm integrated with continuous glucose monitoring and an insulin pump. Automated insulin delivery has become standard of care for glycemic management of people with type 1 diabetes (T1D) outside pregnancy, leading to improvements in time in range, with lower risk for hypoglycemia and improved treatment satisfaction. The use of AID facilitates optimal preconception care, thus more women of reproductive age are becoming pregnant while using AID. The effectiveness and safety in pregnant populations of using AID systems with algorithms for non-pregnant populations may be impacted by requirements for lower glucose targets and existence of increased insulin resistance during gestation. The CamAPS FX is the only AID system approved for use in pregnancy. A large randomized controlled trial (RCT) with this AID system demonstrated a 10.5% increase in time in pregnancy range (an additional 2.5 hours/day) compared with standard insulin therapy in pregnant women with T1D with a baseline glycated hemoglobin A1c (HbA1c) ≥48 mmol/mol (6.5%). A RCT of AID not approved for use in pregnancy (MiniMed 780G) has also demonstrated some benefits of AID compared with standard insulin therapy with improved time in pregnancy range overnight (24 minutes), less hypoglycemia, and improved treatment satisfaction. There is also increasing evidence that AID can be safely continued during delivery and postpartum, while maintaining glycemic goals with lower risk for hypoglycemia. More AID systems are needed with flexible glucose targets in the pregnancy range and possibly with algorithms that better adapt to changing insulin requirements. More evidence is needed on the impact of AID on maternal and neonatal outcomes. We review the current evidence on the use of AID in pregnancy and postpartum.

ATTD 2021 Invited Speaker Abstracts.

This study investigates glucose conditions preceding and following various hypoglycemia levels in individuals with type 1 diabetes using open-source automated insulin delivery (AID) systems. It also seeks to evaluate relationships between hypoglycemia and subsequent glycemic variability. Methods: Analysis of continuous glucose monitor (CGM) data from 122 adults with type 1 diabetes using open-source AID from the OpenAPS Data Commons was conducted. This study comprehensively analyzed the effects of hypoglycemia on glycemic variability, covering time periods before and after hypoglycemia. Results: Glucose variability normalization post-hypoglycemia can take up to 48 h, with severe hypoglycemia (41–50 mg/dL) linked to prolonged normalization. A cyclical pattern was observed where hypoglycemia predisposes individuals to further hypoglycemia, even with AID system use. A rise in glucose levels often precedes hypoglycemia, followed by an elevated mean time above range (TAR) post-hypoglycemia, indicating a ‘rebound’ effect. The experimental results are further validated on T1DEXI data (n = 222), originating from commercial AID systems. Different hypoglycemia categorization approaches did not show significant differences in glycemic variability outcomes. The level of hypoglycemia does influence the pattern of subsequent glucose fluctuations. Conclusion: Hypoglycemia, especially at lower levels, significantly impacts subsequent glycemic variability, even with the use of open-source AID systems. This should be studied further with a broader set of commercial AID systems to understand if these patterns are true of all types of AID systems. If these patterns occur in all types of AID systems, it underscores potential opportunities for enhancements in AID algorithms and highlights the importance of educating healthcare providers and people with diabetes about post-hypoglycemia glucose variability.

Introduction and Objective: Fasting is a tradition in many religions and a lifestyle choice associated with health benefits. On Yom Kippur, the holiest day in Judaism, many individuals observe a 25-hour fast. This study evaluated glycemic control, insulin delivery, and ketone levels in individuals with T1D who chose to fast using Automated Insulin Delivery (AID) systems. Methods: The study included 54 adolescents and young adults: 34 enrolled prospectively with capillary ketone measurement instructions, and 20 retrospectively. Ketones were measured at the end of the 25-hour fast and on a regular morning. Data were collected from medical records, device uploads, and structured questionnaire interviews. Results: Among the 54 participants (mean age 17.3 ± 3.3 years, AID use 1.5 ± 0.8 years, HbA1c 6.8 ± 1%, TIR 71.2 ± 13.7%), 34 used Medtronic 780G, 10 Tandem Control-IQ, and 10 open-source systems. The most common adjustments were a 150 mg/dL exercise target, sleep mode, and basal/target modifications, respectively, for each system, while 11 participants made no changes. Ten mild hypoglycemic events occurred postprandially after the pre-fast meal, treated with 10.4 ± 7.1 grams of carbs, with all continuing fasting. The median ketone measurement, recorded by 31 participants, was 0.4 (0.3, 0.7) mmol/l, compared to 0.1 (0, 0.1) mmol/l on the morning of a regular day. The mild ketosis observed is likely due to fasting and, in a few cases, disruptions in insulin delivery. The average sensor glucose at the end of the fast was 115 ± 39 mg/dL. No instances of severe hypoglycemia or other serious adverse events were reported during any of the fasts. Conclusion: AID systems enable safe fasting with individualized adjustments and effective insulin delivery to suppress ketonuria. Disclosure R. Nimri: Advisory Panel; Tandem Diabetes Care, Inc. Speaker's Bureau; Novo Nordisk, Sanofi, Insulet Corporation, Medscape. Stock/Shareholder; DreaMed Diabetes. Employee; DreaMed Diabetes. Research Support; Geffen Medical. K. Smuel Zilberberg: None. R. Bello: None. N. Fisch-Shvalb: None. M. Phillip: Advisory Panel; Medtronic. Other Relationship; Eli Lilly and Company, Sanofi. Advisory Panel; Sanofi. Other Relationship; Novo Nordisk. Advisory Panel; Novo Nordisk. Other Relationship; Pfizer Inc. Advisory Panel; Pfizer Inc, Insulet Corporation. Other Relationship; AstraZeneca. Advisory Panel; LifeScan Diabetes Institute. Other Relationship; Dompé, Provention Bio, Inc, Provention Bio, Inc, Microbion, Microbion, OPKO, LUMOS. Advisory Panel; embecta, Tandem Diabetes Care, Inc. Consultant; Qulab Medical. Stock/Shareholder; Dreamed Diabetes. Other Relationship; Dreamed Diabetes. Stock/Shareholder; NG Solutions. Other Relationship; NG Solutions.

While automated insulin delivery (AID) systems have multiple well-established benefits outside of pregnancy and are widely used in non-pregnant individuals with type 1 diabetes (T1D), none of the commercially available AID systems in North America are approved for use during pregnancy. Use of commercially available AID systems off-label in pregnancy is currently limited by: (1) glucose targets higher than the fasting glucose target range recommended during pregnancy and (2) algorithms which were not designed for the dynamic changes in insulin resistance which occur across gestation. However, as AID use in the general population expands, many individuals will opt to continue using these systems off-label during pregnancy, and thus, guidance for providers regarding AID use and optimization during pregnancy is of the utmost importance. A cornerstone to the effective use of AID systems is the systematic and accurate interpretation of continuous glucose monitoring (CGM) data. One obstacle to the use of both CGM and AID systems by obstetric providers is the lack of comfort with CGM interpretation. We therefore present here: (1) a systematic approach to CGM interpretation during pregnancy and (2) practical guidance regarding AID use during pregnancy for individuals who opt to use commercially available AID systems off-label during pregnancy after consideration of individualized risks and benefits.

How can we reach the target of glucose control in type 1 diabetes?

&lt;p dir="ltr"&gt;OBJECTIVE To determine how diabetes technologies, including continuous glucose monitoring and automated insulin delivery systems, impact glycemic metrics, prevalence of severe hypoglycemic events (SHEs), and impaired awareness of hypoglycemia (IAH) in people with type 1 diabetes in a real-world setting within the US. RESEARCH DESIGN AND METHODS This retrospective, observational study with cross-sectional elements enrolled participants aged ≥18 years from the T1D Exchange Registry/online community. Participants completed a one-time online survey describing glycemic metrics, SHEs, and IAH. The primary objective was to determine the proportions of participants who reported achieving glycemic targets (as assessed by self-reported hemoglobin A1c) and had SHEs and/or IAH. Additional subgroup analyses were performed focusing on the impact of continuous glucose monitoring and insulin delivery modality. RESULTS 2,074 individuals with type 1 diabetes were enrolled (mean ± SD age: 43.0 ± 15.6 years; duration of type 1 diabetes: 26.3 ± 15.3 years). The majority of participants (91.7%) were using continuous glucose monitoring with one-half (50.8%) incorporating automated insulin delivery. Despite high use of diabetes technologies, only 57.7% reported achieving glycemic targets (hemoglobin A1c &lt;7%). SHE and IAH still occurred, with approximately 20% of respondents experiencing at least one SHE within 12 months and 30.7% (95% CI: 28.7, 32.7) reporting IAH, regardless of continuous glucose monitoring or automated insulin delivery use. CONCLUSIONS Despite use of advanced diabetes technologies, a high proportion of people with type 1 diabetes don’t achieve glycemic targets and continue to experience SHEs and IAH, suggesting an ongoing need for improved treatment strategies.&lt;/p&gt;

As increasing numbers of people with insulin-managed diabetes use automated insulin delivery (AID) systems or seek such technologies, healthcare providers are faced with a steep learning curve. Healthcare providers need to understand how to support these technologies to help inform shared decision making, discussing available options, implementing them in the clinical setting, and guiding users in special situations. At the same time, there is a growing diversity of commercial and open source automated insulin delivery systems that are evolving at a rapid pace. This practical guide seeks to provide a conversational framework for healthcare providers to first understand and then jointly assess AID system options with users and caregivers. Using this framework will help HCPs in learning how to evaluate potential new commercial or open source AID systems, while also providing a guide for conversations to help HCPs to assess the readiness and understanding of users for AID systems. The choice of an AID system is not as simple as whether the system is open source or commercially developed, and indeed there are multiple criteria to assess when choosing an AID system. Most importantly, the choices and preferences of the person living with diabetes should be at the center of any decision around the ideal automated insulin delivery system or any other diabetes technology. This framework highlights issues with AID use that may lead to burnout or perceived failures or may otherwise cause users to abandon the use of AID. It discusses the troubleshooting of basic AID system operation and discusses more advanced topics regarding how to maximize the time spent on AID systems, including how to optimize settings and behaviors for the best possible outcomes with AID technology for people with insulin-requiring diabetes. This practical approach article demonstrates how healthcare providers will benefit from assessing and better understanding all available AID system options to enable them to best support each individual.

The primary goal of managing type1 diabetes mellitus (T1D) is to achieve glycemic control and prevent both acute and chronic complications. In recent years, automated insulin delivery (AID) systems, such as the 780G AID system, have significantly improved glycemic control and patient safety. Despite being the most advanced treatment option, AID initiation is often delayed until the honeymoon stage (partial remission phase). This study evaluated the impact of initiating MiniMed™ 780G at diagnosis on metabolic control and glycemic metrics in children newly diagnosed with T1D. It compares early AID initiation with continuous glucose monitoring (CGM) and multiple daily injection (MDI) therapy over a 1-year follow-up period. This retrospective study included children and adolescents (age range 0.87-17.72years) newly diagnosed with T1D between January 2023 and August 2024. Ten patients who were initiated on AID therapy at diagnosis were included, with eight patients completing a 1-year follow-up. Data from these eight patients and seven patients on CGM + MDI therapy were analyzed at baseline and at 3, 6, and 12months. The mean age at diagnosis was 6.98 ± 3.22years (0.87-9.82) for the AID group and 9.77 ± 4.89years (3.70-17.72) for the CGM + MDI group (p = 0.14). The AID system was initiated at an average of 3.33 ± 7.73days (2-23) after diagnosis, while sensor use in the CGM + MDI group began an average of 17.37 ± 8.86days (1-29) after diagnosis. At 12months, mean hemoglobin A1c (HbA1c) was 6.10% (43 mmol/mol) in the AID group compared with 7.73% (61 mmol/mol) in the CGM + MDI group. Time in range (TIR) was 79.0% vs. 50.7%, and time above range (TAR) was 13.4% vs. 30.7%, based on 2-week CGM data prior to the 12-month visit (p = 0.02, p = 0.009, p = 0.02). No case of diabetic ketoacidosis or severe hypoglycemia was reported during the follow-up period. This study highlights the potential benefits of initiating AID therapy at the time of diagnosis, offering novel insights into its safety and efficacy in the early management of T1D. These findings suggest that early initiation of AID therapy at the time of diagnosis is feasible and may improve glycemic outcomes.

Introduction: Automated insulin delivery (AID) systems offer superior glycaemic control compared to non-AID in children with type 1 diabetes, yet their performance during real-life challenges, such as summer camps with physical activity, remains underexplored. This study evaluated AID efficacy based on time range (70–180 mg/dL), comparing AID systems against sensor-augmented pump therapy (SAP) during a summer camp in children with type 1 diabetes. Methods: Data were collected from a 14-day diabetes camp (July 2024) involving 26 children (mean + SD age 10 ± 1.3 years), using Medtronic MiniMed 780G (n = 13), mylife CamAPS FX (n = 7), or SAP (n = 6). Continuous glucose monitoring (CGM)-derived metrics for the two AID systems and SAP were compared by means of t tests or Mann-Whitney U tests (p ≤ 0.05). Results: Both AID systems showed a similar time in range over the camp (primary endpoint, 75.5 ± 7.5% for MiniMed 780G vs. 71.1 ± 11.16% for CamAPS FX; p = 0.30). No significant differences were found for other glycaemic metrics or insulin dosage. Overnight, MiniMed 780G had less time below 54 mg/dL (0.0% [IQR: 0.0; 0.0%]) than CamAPS FX (0.4% [IQR: 0.0; 0.7%]; p = 0.024). SAP had significantly lower time in range than both AID systems (75.0% [IQR: 70.0; 81.0%] vs. 56.0% [IQR: 55.0; 66.0%]; p = 0.006). A positive correlation was found between coefficient of variation and the total number of steps (r = 0.39; p = 0.0459). Conclusions: Despite the camp’s challenges, both AID systems were safe and effective, meeting recommended CGM-derived treatment targets. Furthermore, AID systems showed superior glycaemic control compared to SAP.

Older adults with type 1 diabetes (≥65 years) are often under-represented in clinical trials of automated insulin delivery (AID) systems. We sought to test the efficacy of a recently FDA-approved AID system in this population. Participants with type 1 diabetes used sensor-augmented pump (SAP) therapy for four weeks and then used an AID system (Control-IQ) for four weeks. In addition to glucose control variables, patient-reported outcomes (PRO) were assessed with questionnaires and sleep parameters were assessed by actigraphy. Fifteen older adults (mean age 68.7 ± 3.3, HbA1c of 7.0 ± 0.8) completed the pilot trial. Glycemic outcomes improved during AID compared to SAP. During AID use, mean glucose was 146.0 mg/dL; mean percent time in range (TIR, 70-180 mg/dL) was 79.6%; median time below 70 mg/dL was 1.1%. The AID system was in use 92.6% ± 7.0% of the time. Compared to SAP, while participants were on AID the TIR increased significantly (+10%, P = .002) accompanied by a reduction in both time above 180 mg/dL (-6.9%, P = .005) and below 70 mg/dl (-0.4%, P = .053). Diabetes-related distress decreased significantly while using AID (P = .028), but sleep parameters remained unchanged. Use of this AID system in older adults improved glycemic control with high scores in ease of use, trust, and usability. Participants reported an improvement in diabetes distress with AID use. There were no significant changes in sleep.