Hybrid Closed Loop in Adults With Type 1 Diabetes and Severely Impaired Hypoglycemia Awareness.

BACKGROUND: Benefits of hybrid closed-loop (HCL) systems in a high-risk group with type 1 diabetes and impaired awareness of hypoglycemia (IAH) have not been well-explored. METHODS: Adults with Edmonton HYPO scores ≥1047 were randomized to 26-weeks HCL (MiniMed™ 670G) vs standard therapy (multiple daily injections or insulin pump) without continuous glucose monitoring (CGM) (control). Primary outcome was percentage CGM time-in-range (TIR; 70-180 mg/dL) at 23 to 26 weeks post-randomization. Major secondary endpoints included magnitude of change in counter-regulatory hormones and autonomic symptom responses to hypoglycemia at 26-weeks post-randomization. A post hoc analysis evaluated glycemia risk index (GRI) comparing HCL with control groups at 26 weeks post-randomization. RESULTS: Nine participants (median [interquartile range (IQR)] age 51 [41, 59] years; 44% male; enrolment HYPO score 1183 [1058, 1308]; Clarke score 6 [6, 6]; n = 5 [HCL]; n = 4 [control]) completed the study. Time-in-range was higher using HCL vs control (70% [68, 74%] vs 48% [44, 50%], P = .014). Time <70 mg/dL did not differ (HCL 3.8% [2.7, 3.9] vs control 6.5% [4.3, 8.6], P = .14) although hypoglycemia episode duration was shorter (30 vs 50 minutes, P < .001) with HCL. Glycemia risk index was lower with HCL vs control (38.1 [30.0, 39.2] vs 70.8 [58.5, 72.4], P = .014). Following 6 months of HCL use, greater dopamine (24.0 [12.3, 27.6] vs -18.5 [-36.5, -4.8], P = .014), and growth hormone (6.3 [4.6, 16.8] vs 0.5 [-0.8, 3.0], P = .050) responses to hypoglycemia were observed. CONCLUSIONS: Six months of HCL use in high-risk adults with severe IAH increased glucose TIR and improved GRI without increased hypoglycemia, and partially restored counter-regulatory responses. CLINICAL TRIAL REGISTRATION: ACTRN12617000520336.

Driving-related glucose patterns among older adults with type 1 diabetes.

Older adults with type 1 diabetes may face challenges driving safely. Glucose "above 5 to drive" is often recommended for insulin-treated diabetes to minimize hypoglycemia while driving. However, the effectiveness of this recommendation among older adults has not been evaluated. Older drivers with type 1 diabetes were assessed while using sensor-augmented insulin pumps during a 2-week clinical trial run-in. Twenty-three drivers (median age 69 years [IQR 65-72]; diabetes duration 37 years [20-45]) undertook 618 trips (duration 10 min [5-21]). Most trips (n = 535; 87%) were <30-min duration; nine trips (1.5%) exceeded 90 min and three trips (0.5%) exceeded 120 min. Pre-trip continuous glucose monitoring (CGM) was >5.0 mmol/L for 577 trips (93%) and none of these had CGM <3.9 mmol/L during driving (including eight trips >90 min and three trips >120 min). During 41 trips with pre-trip CGM ≤5.0 mmol/L, 11 trips had CGM <3.9 mmol/L. Seventy-one CGM alerts occurred during 60 trips (10%), of which 54/71 (76%) were unrelated to hypoglycemia. Our findings support a glucose "above-5-to-drive" recommendation to avoid CGM-detected hypoglycemia among older drivers, including for prolonged drives, and highlight the importance of active CGM low-glucose alerts to prevent hypoglycemia during driving. Driving-related CGM usability and alert functionality warrants investigation.

Automated insulin delivery among adults with type 1 diabetes for up to 2 years: a real-world, multicentre study.

BACKGROUND AND AIMS: Automated insulin delivery (AID) improves glycaemia among people with type 1 diabetes in clinical trials and overseas real-world studies. Whether improvements are sustained beyond 12 months in the real world, and whether they occur in the Australian context, has not yet been established. We aimed to observe, up to 2 years, the effectiveness of initiating first-generation AID for type 1 diabetes management. METHODS: Retrospective, real-world, observational study using medical records, conducted across five sites in Australia. Adults with type 1 diabetes, who had AID initiated between February 2019 and December 2021, were observed for 6-24 months after initiation (until June 2022). Outcomes examined included glucose metrics assessed by glycated haemoglobin (HbA1c ) and continuous glucose monitoring (CGM), safety and therapy continuation. RESULTS: Ninety-four adults were studied (median age 39 years (interquartile range, IQR: 31-51); pre-initiation HbA1c 7.8% (7.2-8.6)). After AID initiation, HbA1c decreased by mean 0.5 percentage points (95% confidence interval (CI): -0.7 to -0.2) at 3 months (P < 0.001); CGM time in range 3.9-10.0 mmol/L increased by 11 percentage points (9-14) at 1 month (P < 0.001); these improvements were maintained up to 24 months (all P < 0.02). Median CGM time below 3.9 mmol/L was <1.5% pre- and post-AID initiation. The subgroup with pre-initiation HbA1c above 8.5% had the greatest HbA1c improvement (-1.4 percentage points (-1.8 to -1.1) at 3 months). Twelve individuals (13%) discontinued AID, predominantly citing difficulties with CGM. During the 150 person-years observed, four diabetes-related emergencies were documented: three severe hypoglycaemic events and one hyperglycaemic event without ketoacidosis. CONCLUSIONS: Early glucose improvements were observed after real-world AID initiation, sustained up to 2 years, without excess adverse events. The greatest benefits were observed among individuals with highest glycaemia before initiation. Future-generation systems with increased user-friendliness may enhance therapy continuation.

The diabetes management experiences questionnaire: Psychometric validation among adults with type 1 diabetes.

AIMS: To examine the psychometric properties of the Diabetes Management Experiences Questionnaire (DME-Q). Adapted from the validated Glucose Monitoring Experiences Questionnaire, the DME-Q captures satisfaction with diabetes management irrespective of treatment modalities. METHODS: The DME-Q was completed by adults with type 1 diabetes as part of a randomized controlled trial comparing hybrid closed loop (HCL) to standard therapy. Most psychometric properties were examined with pre-randomization data (n = 149); responsiveness was examined using baseline and 26-week follow-up data (n = 120). RESULTS: Pre-randomization, participants' mean age was 44 ± 12 years, 52% were women. HbA1c was 61 ± 11 mmol/mol (7.8 ± 1.0%), diabetes duration was 24 ± 12 years and 47% used an insulin pump prior to the trial. A forced three-factor analysis revealed three expected domains, that is, 'Convenience', 'Effectiveness' and 'Intrusiveness', and a forced one-factor solution was also satisfactory. Internal consistency reliability was strong for the three subscales ( α range = 0.74-0.84) and 'Total satisfaction' ( α = 0.85). Convergent validity was demonstrated with moderate correlations between DME-Q 'Total satisfaction' and diabetes distress (PAID: rs = -0.57) and treatment satisfaction (DTSQ; rs = 0.58). Divergent validity was demonstrated with a weak correlation with prospective/retrospective memory (PRMQ: rs = -0.16 and - 0.13 respectively). Responsiveness was demonstrated, as participants randomized to HCL had higher 'Effectiveness' and 'Total satisfaction' scores than those randomized to standard therapy. CONCLUSIONS: The 22-item DME-Q is a brief, acceptable, reliable measure with satisfactory structural and construct validity, which is responsive to intervention. The DME-Q is likely to be useful for evaluation of new pharmaceutical agents and technologies in research and clinical settings.

What difference does sleep make? Continuous glucose monitoring metrics during fixed-overnight time versus sleep periods among older adults with type 1 diabetes.

Hypoglycaemia during sleep is a common and clinically important issue for people living with insulin-treated diabetes. Continuous glucose monitoring devices can help to identify nocturnal hypoglycaemia and inform treatment strategies. However, sleep is generally inferred, with diabetes researchers and physicians using a fixed-overnight period as a proxy for sleep-wake status when analysing and interpretating continuous glucose monitoring data. No study to date has validated such an approach with established sleep measures. Continuous glucose monitoring and research-grade actigraphy devices were worn and sleep diaries completed for 2 weeks by 28 older adults (mean age 67 years [SD 5]; 17 (59%) women) with type 1 diabetes. Using continuous glucose monitoring data from a total of 356 nights, fixed-overnight (using the recommended period of 00:00 hours-06:00 hours) and objectively-measured sleep periods were compared. The fixed-overnight period approach missed a median 57 min per night (interquartile range: 49-64) of sleep for each participant, including five continuous glucose monitoring-detected hypoglycaemia episodes during objectively-measured sleep. Twenty-seven participants (96%) had at least 1 night with continuous glucose monitoring time-in-range and time-above-range discrepancies both ≥ 10 percentage points, a clinically significant discrepancy. The utility of fixed-overnight time continuous glucose monitoring as a proxy for sleep-awake continuous glucose monitoring is inadequate as it consistently excludes actual sleep time, obscures glycaemic patterns, and misses sensor hypoglycaemia episodes during sleep. The use of validated measures of sleep to aid interpretation of continuous glucose monitoring data is encouraged.

Real-world lived experience of older adults with type 1 diabetes after an automated insulin delivery trial.

AIMS: First-generation closed-loop automated insulin delivery improves glycaemia and psychosocial outcomes among older adults with type 1 diabetes in clinical trials. However, no study has previously assessed real-world lived experience of older adults using closed-loop therapy outside a trial environment. METHODS: Semi-structured interviews were conducted with older adults who were pre-existing insulin pump users and previously completed the OldeR Adult Closed-Loop (ORACL) randomised trial. Interviews focused on perceptions of diabetes technology use, and factors influencing decisions regarding continuation. RESULTS: Twenty-eight participants, mean age 70 years (SD 5), were interviewed at median 650 days (IQR 608-694) after their final ORACL trial visit. At interview, 23 participants (82%) were still using a commercial closed-loop system (requiring manual input for prandial insulin bolus doses). Themes discussed in interviews relating to closed-loop system use included sustained psychosocial benefits, cost and retirement considerations and usability frustrations relating to sensor accuracy and system alarms. Of the five participants who had discontinued, reasons included cost, continuous glucose monitoring-associated difficulties and usability frustrations. Cost was the largest consideration regarding continued use; most participants considered the increased ease of diabetes management to be worth the associated costs, though cost was prohibitive for some. CONCLUSIONS: Almost 2 years after completing a closed-loop clinical trial, closed-loop automated insulin delivery remains the preferred type 1 diabetes therapy for the majority of older adult participants. Chronological age is not a barrier to real-world successful use of diabetes technology. Identifying age-related barriers, and solutions, to diabetes technology use among older adults is warranted.

Continuous Glucose Monitoring-Based Composite Metrics: A Review and Assessment of Performance in Recent-Onset and Long-Duration Type 1 Diabetes.

This study examined correlations between continuous glucose monitoring (CGM)-based composite metrics and standard glucose metrics within CGM data sets from individuals with recent-onset and long-duration type 1 diabetes. First, a literature review and critique of published CGM-based composite metrics was undertaken. Second, composite metric results were calculated for the two CGM data sets and correlations with six standard glucose metrics were examined. Fourteen composite metrics met selection criteria; these metrics focused on overall glycemia (n = 8), glycemic variability (n = 4), and hypoglycemia (n = 2), respectively. Results for the two diabetes cohorts were similar. All eight metrics focusing on overall glycemia strongly correlated with glucose time in range; none strongly correlated with time below range. The eight overall glycemia-focused and two hypoglycemia-focused composite metrics were all sensitive to automated insulin delivery therapeutic intervention. Until a composite metric can adequately capture both achieved target glycemia and hypoglycemia burden, the current two-dimensional CGM assessment approach may offer greatest clinical utility.

Impact of Missing Data on the Accuracy of Glucose Metrics from Continuous Glucose Monitoring Assessed Over a 2-Week Period.

Objective: To explore the impact of missing data on the accuracy of continuous glucose monitoring (CGM) metrics collected for a 2-week period in a clinical trial. Research Design and Methods: Simulations were conducted to examine the effect of various patterns of missingness on the accuracy of CGM metrics as compared with a "complete" data set. The proportion of missing data, the "block size" in which the data were missing, and the missing mechanism were modified for each "scenario." The degree of agreement between simulated and "true" glycemic measures under each scenario was presented as R2. Results: Under all missing patterns, R2 declined as the proportion of missing data increased, however, as the "block size" of missing data increased, the percentage of missing data had a more pronounced effect on the agreement between measures. For a 14-day CGM data set to be considered representative for percentage time in range (%TIR), at least 70% of CGM data should be available over at least 10 days (R2 > 0.9). Skewed outcome measures, such as percentage time below range and coefficient of variation, were more affected by missing data than the less skewed measures (%TIR, percentage time above range, mean glucose). Conclusions: Both the degree and pattern of missing data impact upon the accuracy of recommended CGM-derived glycemic measures. In planning research, an understanding of patterns of missing data in the study population is required to gauge the likely effects of missing data on outcome accuracy. Trial registration number: Australian New Zealand Clinic Trials Registry ACTRN12616000753459.

Lived experience of older adults with type 1 diabetes using closed-loop automated insulin delivery in a randomised trial.

AIM: To explore the lived experience of older adults with type 1 diabetes using closed-loop automated insulin delivery, an area previously receiving minimal attention. METHODS: Semi-structured interviews were conducted with adults aged 60 years or older with long-duration type 1 diabetes who participated in a randomised, open-label, two-stage crossover trial comparing first-generation closed-loop therapy (MiniMed 670G) versus sensor-augmented pump therapy. Interview recordings were transcribed, thematically analysed and assessed. RESULTS: Twenty-one older adults participated in interviews after using closed-loop therapy. Twenty were functionally independent, without frailty or major cognitive impairment; one was dependent on caregiver assistance, including for diabetes management. Quality of life benefits were identified, including improved sleep and reduced diabetes-related psychological burden, in the context of experiencing improved glucose levels. Gaps between expectations and reality of closed-loop therapy were also experienced, encountering disappointment amongst some participants. The cost was perceived as a barrier to continued closed-loop access post-trial. Usability issues were identified, such as disruptive overnight alarms and sensor inaccuracy. CONCLUSIONS: The lived experience of older adults without frailty or major cognitive impairment using first-generation closed-loop therapy was mainly positive and concordant with glycaemic benefits found in the trial. Older adults' lived experience using automated insulin delivery beyond trial environments requires exploration; moreover, the usability needs of older adults should be considered during future device development.

Glucose profiles of older adults with type 1 diabetes using sensor-augmented pump therapy in Australia: pre-randomisation results from the ORACL study.

BACKGROUND: Older adults with type 1 diabetes are recommended modified glucose targets. However, data on the effects of diabetes technology in older age are scarce. We assessed older adults established on sensor-augmented insulin pump therapy during clinical trial run-in and compared their continuous glucose monitoring (CGM) profiles with consensus recommendations. We aimed to provide insight into the applicability of currently recommended CGM-based targets while accounting for current Diabetes UK guidelines. METHODS: In this analysis, adults aged 60 years or older with type 1 diabetes with a duration of at least 10 years and entering the Older Adult Closed Loop (ORACL) trial were studied. The trial was done at two tertiary hospitals in Australia. Individuals who were independent with diabetes self-management, as well as those receiving caregiver assistance for their diabetes management, were eligible for inclusion. Participants underwent baseline clinical assessment, which included medical history and examination, testing for frailty, functional ability, cognitive functioning, psychosocial wellbeing, and subjective sleep quality; fasting venous blood samples were collected for C-peptide, glucose, and glycated haemoglobin A1c measurement. Sensor-augmented pumps, carbohydrate-counting education, and diabetes education were provided to participants by diabetes nurse educators, dietitians, and endocrinologists experienced in type 1 diabetes clinical care. CGM data were subsequently collected for 2 weeks during sensor-augmented pump therapy. The ORACL trial is registered with the Australian New Zealand Clinical Trial Registry, ACTRN12619000515190. FINDINGS: Our analysis included all 30 participants who completed the ORACL trial run-in-19 (63%) women and 11 (37%) men (mean age 67 years [SD 5], median diabetes duration 38 years [IQR 20-47], and insulin total daily dose 0·55 units [0·41-0·66] per kg bodyweight). Ten (33%) of 30 participants had impaired hypoglycaemia awareness and six (20%) were pre-frail; none were frail. The median CGM time in range 3·9-10·0 mmol/L was 71% (IQR 64-79). The time spent with glucose above 10·0 mmol/L was 27% (18-35) and above 13·9 mmol/L was 3·9% (2·4-10·2). The time with glucose below 3·9 mmol/L was 2·0% (1·2-3·1) and the time below 3·0 mmol/L was 0·2% (0·1-0·4). Only two (7%) of 30 participants met all CGM-based consensus recommendations modified for older adults. Time in hypoglycaemia was lower among the 16 participants with predictive low-glucose alerts enabled than among the 14 participants not using predictive low-glucose alerts (median difference -1·1 percentage points [95% CI -2·0 to -0·1]; p=0·038). This difference was even greater overnight (-2·3 percentage points [-3·2 to -1·0]; p=0·0018). One serious adverse event occurred (elective cardiac stent). INTERPRETATION: Using sensor-augmented pumps after multidisciplinary education, this group of older adults without frailty achieved a time in range far exceeding minimum consensus recommendations. However, the current stringent hypoglycaemia recommendations for all older adults were not met. Predictive low alerts could reduce hypoglycaemia, particularly overnight. Investigation into the effectiveness of CGM-based targets that consider frailty, functional status, and diabetes therapies for older adults is warranted. FUNDING: JDRF and Diabetes Australia.

Late Afternoon Vigorous Exercise Increases Postmeal but Not Overnight Hypoglycemia in Adults with Type 1 Diabetes Managed with Automated Insulin Delivery.

Aim: To compare evening and overnight hypoglycemia risk after late afternoon exercise with a nonexercise control day in adults with type 1 diabetes using automated insulin delivery (AID). Methods: Thirty adults with type 1 diabetes using AID (Minimed 670G) performed in random order 40 min high intensity interval aerobic exercise (HIE), resistance (RE), and moderate intensity aerobic exercise (MIE) exercise each separated by >1 week. The closed-loop set-point was temporarily increased 2 h pre-exercise and a snack eaten if plasma glucose was ≤126 mg/dL pre-exercise. Exercise commenced at ∼16:00. A standardized meal was eaten at ∼20:40. Hypoglycemic events were defined as a continuous glucose monitor (CGM) reading <70 mg/dL for ≥15 min. Four-hour postevening meal and overnight (00:00-06:00) CGM metrics for exercise were compared with the prior nonexercise day. Results: There was no severe hypoglycemia. Between 00:00 and 06:00, the proportion of nights with hypoglycemia did not differ postexercise versus control for HIE (18% vs. 11%; P = 0.688), RE (4% vs. 14%; P = 0.375), and MIE (7% vs. 14%; P = 0.625). Time in range (TIR) (70-180 mg/dL), >75% for all nights, did not differ between exercise conditions and control. Hypoglycemia episodes postmeal after exercise versus control did not differ for HIE (22% vs. 7%; P = 0.219) and MIE (10% vs. 14%; P > 0.999), but were greater post-RE (39% vs. 10%; P = 0.012). Conclusions: Overnight TIR was excellent with AID without increased hypoglycemia postexercise between 00:00 and 06:00 compared with nonexercise days. In contrast, hypoglycemia risk was increased after the first meal post-RE, suggesting the importance of greater vigilance and specific guidelines for meal-time dosing, particularly with vigorous RE. ACTRN12618000905268.

Hybrid closed-loop therapy with a first-generation system increases confidence and independence in diabetes management in youth with type 1 diabetes.

AIM: Hybrid closed-loop (HCL) therapy improves glycaemic control in adolescents with type 1 diabetes; however, little is known about their lived experience using these systems. The aim of this study was to explore the lived experiences of youth with type 1 diabetes using HCL therapy, and their parents, to provide insight into their lived experiences. METHODS: Adolescents and young adults aged 12-25 years, who used Medtronic MiniMed™ 670G HCL system during a 6-month randomised clinical trial, and their parents, were invited to participate in a semi-structured interview at the end of the study. Open-ended questions were used to explore the lived experiences of families using HCL. The interviews were audio-recorded, transcribed and analysed using thematic analysis to determine the main themes. RESULTS: In all, 17 young people with type 1 diabetes mean ± SD age: 17.5 ± 4.2 years, diabetes duration: 11.0 ± 4.9 years and HbA1c 64 ± 9 mmol/mol (8.0 ± 0.8%) and 10 parents were interviewed. Three themes were identified: (1) 'Developing confidence and trust in the system', (2) 'Reduction in anxiety' and (3) 'Issues with device'. They reported a positive experience using HCL, with improvements in glucose levels and increased independence with diabetes management. However, frustration around the number of alarms and notifications associated with the system were also identified as issues. CONCLUSION: Both youth and parents acknowledged the benefits of this first-generation HCL system in improving glycaemic outcomes and in providing flexibility and independence. These lived experiences provide valuable information in the introduction and provision of targeted education with HCL therapy.

Closed-Loop Insulin Delivery Effects on Glycemia During Sleep and Sleep Quality in Older Adults with Type 1 Diabetes: Results from the ORACL Trial.

Sleep-related effects of closed-loop therapy among older adults with type 1 diabetes have not been well established. In the OldeR Adult Closed-Loop (ORACL) randomized, crossover trial of first-generation closed-loop therapy (MiniMed 670G), participants wore actigraphy and completed sleep diaries for 14-day periods at stage end. During objectively measured sleep (actigraphy) with closed-loop versus sensor-augmented pump therapy, glucose time-in-range 70-180 mg/dL (3.9-10.0 mmol/L) was greater (90.3% vs. 78.7%, respectively; difference 8.2 percentage points [95% confidence interval {CI} 1.5 to 13.0]; P = 0.008), and there were fewer sensor hypoglycemia episodes (18 vs. 43, respectively; incident rate ratio 0.40 [95% CI 0.20 to 0.55]; P = 0.007). Sleep quality recorded daily was worse with closed-loop therapy (P = 0.006); Pittsburgh Sleep Quality Index did not differ. There were 30% more system alarms during monitored sleep with closed-loop therapy (P < 0.001). First-generation closed-loop therapy has important glycemic benefits during sleep for older adults, with deterioration in some sleep quality measures. Sleep quality warrants prioritization and investigation during advancement of closed-loop technology.