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

Ageing well with diabetes: the role of technology

Over the past two decades there has been a substantial rise in the adoption of diabetes therapeutic technology among children, adolescents and younger adults with type 1 diabetes, and its use is now also advocated for older individuals. Older people with diabetes are more prone to experience hypoglycaemia because of numerous predisposing factors and are at higher risk of hypoglycaemic events requiring third-party assistance as well as other adverse sequelae. Hypoglycaemia may also have long-term consequences, including cognitive impairment, frailty and disability. Diabetes in older people is often characterised by marked glucose variability related to age-associated changes such as variable appetite and levels of physical activity, comorbidities and polypharmacotherapy. Preventing hypoglycaemia and mitigating glucose excursions may have considerable positive impacts on physical and cognitive function and general well-being and may even prevent or improve frailty. Technology for older people includes continuous glucose monitoring systems, insulin pumps, automated insulin delivery systems and smart insulin pens. Clinical trials and real-world studies have shown that older people with diabetes benefit from technology in terms of glucose management, reductions in hypoglycaemic events, emergency department attendance and hospital admissions, and improvement in quality of life. However, ageing may bring physical impairments and other challenges that hinder the use of technology. Healthcare professionals should identify older adults with diabetes who may benefit from therapeutic technology and then adopt an individualised approach to education and follow-up for individuals and their caregivers. Future research should explore the impact of diabetes technology on outcomes relevant to older people with diabetes.

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.

Glucose Targets Using Continuous Glucose Monitoring Metrics in Older Adults With Diabetes: Are We There Yet?

The older population is increasing worldwide and up to 30% of older adults have diabetes. Older adults with diabetes are at risk of glucose-related acute and chronic complications. Recently, mostly in type 1 diabetes (T1D), continuous glucose monitoring (CGM) devices have proven beneficial in improving time in range (TIR glucose, 70-180 mg/dL or glucose 3.9-10 mmol/L), glycated hemoglobin (HbA1c), and in lowering hypoglycemia (time below range [TBR] glucose <70 mg/dL or glucose <3.9 mmol/L). The international consensus group formulated CGM glycemic targets relating to older adults with diabetes based on very limited data. Their recommendations, based on expert opinion, were aimed at mitigating hypoglycemia in all older adults. However, older adults with diabetes are a heterogeneous group, ranging from healthy to very complex frail individuals based on chronological, biological, and functional aging. Recent clinical trial and real-world data, mostly from healthy older adults with T1D, demonstrated that older adults often achieve CGM targets, including TIR recommended for non-vulnerable groups, but less often meet the recommended TBR <1%. Existing data also support that hypoglycemia avoidance may be more strongly related to minimization of glucose variability (coefficient of variation [CV]) rather than lower TIR. Very limited data are available for glucose goals in older adults adjusted for the complexity of their health status. Herein, we review the bidirectional associations between glucose and health status in older adults with diabetes; use of diabetes technologies, and their impact on glucose control; discuss current guidelines; and propose a new set of CGM targets for older adults with insulin-treated diabetes that are individualized for health and living status.

Exploring Technology's Influence on Health Behaviours and Well-being in Type 1 Diabetes: a Review

PURPOSE OF REVIEW

Maintaining positive health behaviours promotes better health outcomes for people with type 1 diabetes (T1D). However, implementing these behaviours may also lead to additional management burdens and challenges. Diabetes technologies, including continuous glucose monitoring systems, automated insulin delivery systems, and digital platforms, are being rapidly developed and widely used to reduce these burdens. Our aim was to review recent evidence to explore the influence of these technologies on health behaviours and well-being among adults with T1D and discuss future directions.

RECENT FINDINGS

Current evidence, albeit limited, suggests that technologies applied in diabetes self-management education and support (DSME/S), nutrition, physical activity (PA), and psychosocial care areas improved glucose outcomes. They may also increase flexibility in insulin adjustment and eating behaviours, reduce carb counting burden, increase confidence in PA, and reduce mental burden. Technologies have the potential to promote health behaviours changes and well-being for people with T1D. More confirmative studies on their effectiveness and safety are needed to ensure optimal integration in standard care practices.

Prolonged Use of an Automated Insulin Delivery System Improves Sleep in Long-Standing Type 1 Diabetes Complicated by Impaired Awareness of Hypoglycemia

BACKGROUND

This study assessed changes in actigraphy-estimated sleep and glycemic outcomes after initiating automated insulin delivery (AID).

METHODS

Ten adults with long-standing type 1 diabetes and impaired awareness of hypoglycemia (IAH) participated in an 18-month clinical trial assessing an AID intervention on hypoglycemia and counter-regulatory mechanisms. Data from eight participants (median age = 58 years) with concurrent wrist actigraph and continuous glucose monitoring (CGM) data were used in the present analyses. Actigraphs and CGM measured sleep and glycemic control at baseline (one week) and months 3, 6, 9, 12, 15, and 18 (three weeks) following AID initiation. HypoCount software integrated actigraphy with CGM data to separate wake and sleep-associated glycemic measures. Paired sample t-tests and Cohen's d effect sizes modeled changes and their magnitude in sleep, glycemic control, IAH (Clarke score), hypoglycemia severity (HYPO score), hypoglycemia exposure (CGM), and glycemic variability (lability index [LI]; CGM coefficient-of-variation [CV]) from baseline to 18 months.

RESULTS

Sleep improved from baseline to 18 months (shorter sleep latency [P < .05, d = 1.74], later sleep offset [P < .05, d = 0.90], less wake after sleep onset [P < .01, d = 1.43]). Later sleep onset (d = 0.74) and sleep midpoint (d = 0.77) showed medium effect sizes. Sleep improvements were evident from 12 to 15 months after AID initiation and were preceded by improved hypoglycemia awareness (Clarke score [d = 1.18]), reduced hypoglycemia severity (HYPO score [d = 2.13]), reduced sleep-associated hypoglycemia (percent time glucose was < 54 mg/dL, < 60 mg/dL,< 70 mg/dL; d = 0.66-0.81), and reduced glucose variability (LI, d = 0.86; CV, d = 0.62).

CONCLUSION

AID improved sleep initiation and maintenance. Improved awareness of hypoglycemia, reduced hypoglycemia severity, hypoglycemia exposure, and glucose variability preceded sleep improvements.This trial is registered with ClinicalTrials.gov NCT03215914 https://clinicaltrials.gov/ct2/show/NCT03215914.

Type 1 Diabetes and Aging

The number of older adults with type 1 diabetes (T1D) is increasing due to an overall increase in life expectancy and improvement in diabetes management and treatment of complications. They are a heterogeneous cohort due to the dynamic process of aging and the presence of comorbidities and diabetes-related complications. A high risk for hypoglycemia unawareness and severe hypoglycemia has been described. Periodic assessment of health status and adjustment of glycemic goals to mitigate hypoglycemia is imperative. Continuous glucose monitoring, insulin pump, and hybrid closed-loop systems are promising tools to improve glycemic control and mitigate hypoglycemia in this age group.

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.

Efficacy of Control-IQ Technology in a General Endocrine Clinic

OBJECTIVE

Recent advances in technology have allowed for the expanded use of hybrid closed-loop insulin pump therapy and automated insulin delivery systems for the management of diabetes mellitus. We assessed the outcomes of introducing Tandem t:slim X2 with the Control-IQ technology in a general endocrine clinic.

METHODS

Data from 66 adults with type 1 (n = 61) and type 2 (n = 5) diabetes mellitus were aggregated for analysis. Patients were either transitioned from traditional insulin pump therapy or multiple daily injection therapy to Tandem t:slim X2 with the Control-IQ technology from January 2020 to June 2021. The assessed clinical end points included changes in time below range, time above range, and time in target range. Changes in hemoglobin A1C before and after Control-IQ technology implementation were noted. The primary outcome was a change in time in target range with the Control-IQ technology.

RESULTS

There was a significant increase in time in target range when comparing pre- and post-Control-IQ technology (49.5% vs 63.3%, P < .0003) values. There was a reduction in time above range (46.8% vs 34.9%, P < .0013), a decrease in time below range (4.0% vs 1.7%, P = .017), and a decrease in hemoglobin A1C after transitioning to the Control-IQ technology (7.7% [61 mmol/mol] vs 7.1% [54 mmol/mol], P < .017). The patient dropout rate was low (7%).

CONCLUSION

The Control-IQ technology system was effective in reducing hyperglycemia while increasing time in target range and decreasing hypoglycemia. This technology is a useful and effective addition to the growing number of automated insulin delivery systems. The clinical outcomes mirror the results found in the key adult pivotal trials.