Background: Automated Insulin Delivery (AID) devices in type 1 diabetes (T1D) are becoming standard of care, and the need for adaptive AID systems is now well recognized. However, very little is ...known about how self-treatment behaviors should be adapted to the action of an AID system, especially with hybrid closed-loop (HCL) solutions that still require people with T1D to make several treatment decisions each day.
Methods: A safety and feasibility 8-week study with a randomized two-arm parallel group design was conducted. Both control and experimental groups used a web-based simulation tool, and a commercial AID system (Control-IQ®) enhanced with an auto-titration module (ATM) that adjusted insulin therapy parameters weekly. The experimental group had access to a behavioral adaptation module (BAM) that provided up-to-date risk assessment from CGM data. Primary outcome: Safety assessment of ATM and BAM. Secondary glycemic outcomes: CGM-based metrics computed from the 2-week baseline period and last 2 weeks of treatment data.
Results: Thirty Control-IQ® users completed all study procedures, 17 women and 13 men, age: 40±14 years, diabetes duration: 23±14 years, HbA1c: 6.6%±0.5%. No severe hypoglycemia, DKA, or other serious adverse events were reported. No significant changes were observed in time in 70-180 mg/dL. Changes in time in 70-140 mg/dL and mild hyperglycemia 180-250 mg/dL were significantly different between control (-3.1% and +3.1%) and experimental (+3.6% and -0.7%) groups (P=0.and 0.04) . Overnight time in 70-140 mg/dL increased significantly in the experimental group (6.8%, 95% CI 0.6%,12.9%, P=0.03) . Time < 70 mg/dL decreased in both groups, but significantly only in the control group (-0.9%, 95% CI -1.6%,-0.3%, P=0.vs. -0.1%, 95% CI -0.7%,0.5%, P=0.71) .
Conclusions: Results from this pilot study suggest that combining HCL AP with behavioral adaptation feedback to the user is safe and effective.
Disclosure
P.Colmegna: None. R.Mcfadden: None. C.Fabris: None. B.Lobo: Other Relationship; Dexcom, Inc., Research Support; Dexcom, Inc. M.C.Oliveri: None. R.Nass: None. S.A.Brown: Research Support; Dexcom, Inc., Insulet Corporation, Roche Diagnostics USA, Tandem Diabetes Care, Inc., Tolerion, Inc. B.Kovatchev: Other Relationship; Dexcom, Inc., Johnson & Johnson, Novo Nordisk, Sanofi, Research Support; Dexcom, Inc., Novo Nordisk, Tandem Diabetes Care, Inc., Speaker's Bureau; Dexcom, Inc., Tandem Diabetes Care, Inc.
Funding
National Institutes of Health (2R01DK085623-10)
Background: Researchers have extensively used metabolic simulators as a fast, inexpensive, and safe way of testing novel treatment strategies. This work aims to bring this technology to people with ...T1D to enable unique patient/data interactions.
Methods: A 5-week pilot study was carried out in 15 adults with T1D using Control-IQ technology (age 36±13 years, HbA1c 6.5±0.7%) to evaluate acceptance of the proposed Web-Based Simulation Tool (WST). The study consisted of 1 week of observation (Phase 1) and 4 weeks of interaction with WST (Phase 2). Data were automatically collected via Tandem Diabetes Care t:connect web application, and used to generate personalized models of the participants’ glucose metabolism.
Results: Success rate in generating models was 86.4%, achieving an average MARD of 7.4±3.2%. Interaction time was 15.8±10.7 min per week. Comparing Phases 1 and 2, no variation was detected in time in range 70-180 mg/dl (80.1 70.4,89.6% vs. 80 69.7,87.9%). Time in 70-250 mg/dl increased slightly (94.2 90.3,95.7% vs. 96.2 92.1,97.9%), especially overnight (92.8 88.3,98.2% vs. 97.2 91.8,99.6%), and for participants who modified their pump settings based on WST simulations (90 88.7,92.4% vs. 94.5 87.1,99.6%). One subject tested COVID positive during the study and was excluded from this analysis due to abnormal hyperglycemia.
Analysis of Diabetes Distress Scale (DDS)-17 pre and post-system use shows a reduction in diabetes-related distress (2.2 1.7,3.4 vs. 2 1.7,2.4). Trust, ease of use, and usefulness scores were 80 60,80%, 60 60,80%, and 80 60,80%, respectively. During the follow up interviews, 10 participants reported they enjoyed using WST and would implement it into their diabetes management; 2 did not like the system but see the potential of it for other people; 3 participants did not like the system at all.
Conclusions: Evidence from this study suggests that simulation technologies may empower people with T1D, making them more confident in their diabetes self-management.
Disclosure
P. Colmegna: None. A. Bisio: None. R. Mcfadden: None. C. A. Wakeman: Stock/Shareholder; Self; Dexcom, Inc., Tandem Diabetes Care. M. C. Oliveri: None. R. Nass: None. M. D. Breton: Consultant; Self; ADOCIA, Dexcom, Inc., Research Support; Self; Arecor, Dexcom, Inc., Novo Nordisk A/S, Tandem Diabetes Care, Speaker’s Bureau; Self; Arecor, Tandem Diabetes Care, Stock/Shareholder; Self; Dexcom, Inc., Insulet Corporation, Tandem Diabetes Care.
Funding
JDRF (2-APF-2019-737-A-N)
Insulin dosing in type 1 diabetes (T1D) is oftentimes complicated by fluctuating insulin requirements driven by metabolic and psychobehavioral factors impacting individuals' insulin sensitivity (IS). ...In this context, smart bolus calculators that automatically tailor prandial insulin dosing to the metabolic state of a person can improve glucose management in T1D.
Fifteen adults with T1D using continuous glucose monitors (CGMs) and insulin pumps completed two 24-h admissions in a hotel setting. During the admissions, participants engaged in an early afternoon 45-min aerobic exercise session, after which they received a standardized dinner meal. The dinner bolus was computed using a standard bolus calculator or smart bolus calculator informed by real-time IS estimates. Glucose control was assessed in the 4 h following dinner using CGMs and was compared between the two admissions.
The IS-informed bolus calculator allowed for a reduction in postprandial hypoglycemia as quantified by the low blood glucose index (2.02 vs. 3.31,
= 0.006) and percent time <70 mg/dL (8.48% vs. 15.18%,
= 0.049), without increasing hyperglycemia (high blood glucose index: 3.13 vs. 2.09,
= 0.075; percent time >180 mg/dL: 13.24% vs. 10.42%,
= 0.5; percent time >250 mg/dL: 2.08% vs. 1.19%,
= 0.317). In addition, the number of hypoglycemia rescue treatments was reduced from 12 to 7 with the use of the system.
The study shows that the proposed IS-informed bolus calculator is safe and feasible in adults with T1D, appropriately reducing postprandial hypoglycemia following an exercise-induced IS increase.
Physical activity is a major challenge to glycemic control for people with type 1 diabetes. Moderate-intensity exercise often leads to steep decreases in blood glucose and hypoglycemia that ...closed-loop control systems have so far failed to protect against, despite improving glycemic control overall.
Fifteen adults with type 1 diabetes (42 ± 13.5 years old; hemoglobin A
6.6% ± 1.0%; 10F/5M) participated in a randomized crossover clinical trial comparing two hybrid closed-loop (HCL) systems, a state-of-the-art hybrid model predictive controller and a modified system designed to anticipate and detect unannounced exercise (APEX), during two 32-h supervised admissions with 45 min of planned moderate activity, following 4 weeks of data collection. Primary outcome was the number of hypoglycemic episodes during exercise. Continuous glucose monitor (CGM)-based metrics and hypoglycemia are also reported across the entire admissions.
The APEX system reduced hypoglycemic episodes overall (9 vs. 33;
= 0.02), during exercise (5 vs. 13;
= 0.04), and in the 4 h following (2 vs. 11;
= 0.02). Overall CGM median percent time <70 mg/dL decreased as well (0.3% vs. 1.6%;
= 0.004). This protection was obtained with no significant increase in time >180 mg/dL (18.5% vs. 16.6%,
= 0.15). Overnight control was notable for both systems with no hypoglycemia, median percent in time 70-180 mg/dL at 100% and median percent time 70-140 mg/dL at ∼96% for both.
A new closed-loop system capable of anticipating and detecting exercise was proven to be safe and feasible and outperformed a state-of-the-art HCL, reducing participants' exposure to hypoglycemia during and after moderate-intensity physical activity. ClinicalTrials.gov NCT03859401.
Typically, closed-loop control (CLC) studies excluded patients with significant hypoglycemia. We evaluated the effectiveness of hybrid CLC (HCLC) versus sensor-augmented pump (SAP) in reducing ...hypoglycemia in this high-risk population.
Forty-four subjects with type 1 diabetes, 25 women, 37 ± 2 years old, HbA1c 7.4% ± 0.2% (57 ± 1.5 mmol/mol), diabetes duration 19 ± 2 years, on insulin pump, were enrolled at the University of Virginia (
= 33) and Stanford University (
= 11). Eligibility: increased risk of hypoglycemia confirmed by 1 week of blinded continuous glucose monitor (CGM); randomized to 4 weeks of home use of either HCLC or SAP. Primary/secondary outcomes: risk for hypoglycemia measured by the low blood glucose index (LBGI)/CGM-based time in ranges.
Values reported: mean ± standard deviation. From baseline to the final week of study: LBGI decreased more on HCLC (2.51 ± 1.17 to 1.28 ± 0.5) than on SAP (2.1 ± 1.05 to 1.79 ± 0.98),
< 0.001; percent time below 70 mg/dL (3.9 mmol/L) decreased on HCLC (7.2% ± 5.3% to 2.0% ± 1.4%) but not on SAP (5.8% ± 4.7% to 4.8% ± 4.5%),
= 0.001; percent time within the target range 70-180 mg/dL (3.9-10 mmol/L) increased on HCLC (67.8% ± 13.5% to 78.2% ± 10%) but decreased on SAP (65.6% ± 12.9% to 59.6% ± 16.5%),
< 0.001; percent time above 180 mg/dL (10 mmol/L) decreased on HCLC (25.1% ± 15.3% to 19.8% ± 10.1%) but increased on SAP (28.6% ± 14.6% to 35.6% ± 17.6%),
= 0.009. Mean glucose did not change significantly on HCLC (144.9 ± 27.9 to 143.8 ± 14.4 mg/dL 8.1 ± 1.6 to 8.0 ± 0.8 mmol/L) or SAP (152.5 ± 24.3 to 162.4 ± 28.2 8.5 ± 1.4 to 9.0 ± 1.6),
= ns.
Compared with SAP therapy, HCLC reduced the risk and frequency of hypoglycemia, while improving time in target range and reducing hyperglycemia in people at moderate to high risk of hypoglycemia.
Context: Ghrelin, an acylated peptide hormone secreted from the gut, regulates appetite and metabolism. Elucidating its pattern of secretion in the fed and fasted states is important in the face of ...the obesity epidemic.
Objective: Our objective was to examine changes in circulating ghrelin and des-acyl ghrelin in response to meals and fasting using newly developed two-site sandwich assays and sample preservation protocols to allow specific detection of full-length forms.
Design: Ten-minute sampling was done for 26.5 h during a fed admission with standardized meals and on a separate admission during the final 24 h of a 61.5-h fast and continuing for 2.5 h after terminating the fast.
Setting: The study was conducted at the University Hospital General Clinical Research Center.
Participants: Eight male volunteers participated, mean ± sd age 24.5 ± 3.7 yr and body mass index 24 ± 2.1 kg/m2.
Main Outcome Measures: Ten-minute sampling profiles were assessed for ghrelin and des-acyl ghrelin, fed and fasting.
Results: In the fed state, ghrelin and des-acyl ghrelin showed similar dynamics; both were sharply inhibited by meals and increased at night. During fasting, ghrelin decreased to nadir levels seen postprandially, and des-acyl ghrelin remained near peak levels seen preprandially. Total full-length ghrelin (acyl plus des-acyl) levels remained unchanged.
Conclusions: Meals inhibited secretion of both ghrelin and des-acyl ghrelin, yet long-term fasting inhibited acylation but not total secretion. Acylation may be regulated independently of secretion by nutrient availability in the gut or by esterases that cleave the acyl group. These studies highlight the importance of stringent conditions for sample collection and evaluation of full-length ghrelin and des-acyl ghrelin using specific two-site assays.
Background and Aim: Studies have increasingly identified sleep disturbances in people with T1D. Parents of young children with T1D may also be a particularly vulnerable population to sleep ...disturbances. Anecdotally, it is reported that use of CLC improves sleep quality and quantity in users but objective data on indirect users are not available. This study assesses sleep outcomes of CLC compared to SAP therapy in parents of children with T1D.
Methods: Thirteen parents and their young children (ages 6-10) on insulin pump therapy were enrolled. Children completed an initial 4-week study with SAP using their own pump and a study CGM (Dexcom G6) followed by a 4-week phase of CLC (Control-IQ, Tandem Diabetes Care). Sleep was assessed in parents using actigraphy watches worn the last 10 days of each study phase. The Pittsburgh Sleep Quality Index (PSQI) questionnaire and the Hypoglycemia Fear Survey (HFS) were administered at baseline (BL) and following each study phase.
Results: A statistically significant decrease was noted from BL to the end of the CLC phase of PSQI score (p=0.009) and the behavioral (p=0.005) and worry (p=0.045) HFS subscales. Even though statistical power was reduced by the number of participants, actigraphy data showed strong trends on several variables including an average increase of 14 minutes of total sleep time from SAP to CLC (p=0.068) and in people with high PSQI scores at BL (>median) a higher sleep efficiency (p=0.098) and lower wake after sleep onset (p=0.100). Notably, children significantly reduced their time spent in hypoglycemia (<70 mg/dl) at night from SAP to CLC (p=0.015).
Conclusions: Though preliminary, these results suggest that use of CLC has a positive impact on both the quality and quantity of sleep in parents of children with T1D. A decrease in fear of hypoglycemia, endorsed by fewer nocturnal hypoglycemic episodes, might be associated with sleep improvement since previous studies have reported poorer sleep quality in parents with high levels of fear of hypoglycemia.
Disclosure
A. Bisio: None. S.A. Brown: Research Support; Self; Dexcom, Inc., Insulet Corporation, Roche Diabetes Care, Tandem Diabetes Care, Tolerion, Inc. R. McFadden: None. H. Bonner: None. P.L. Yu: None. D.R. Cherñavvsky: Employee; Self; Dexcom, Inc. M.D. DeBoer: Research Support; Self; Tandem Diabetes Care. O. Khurshid: None. N. Kurtz: None. M.C. Oliveri: None. M. Pajewski: None. M. Schoelwer: Research Support; Self; Tandem Diabetes Care. M.K. Voelmle: Consultant; Self; Dexcom, Inc. C.A. Wakeman: Stock/Shareholder; Self; Dexcom, Inc., Tandem Diabetes Care. L. Gonder-Frederick: Other Relationship; Self; HFS-Global, LLC.
Funding
Virginia Research Investment Fund
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