Brown, Sue A.

Objective: To assess the efficacy and safety of closed-loop control (CLC) insulin delivery system in adolescents and young adults with type 1 diabetes. Research Design and Methods: Prespecified subanalysis of outcomes in adolescents and young adults aged 14–24 years old with type 1 diabetes in a previously published 6-month multicenter randomized trial. Participants were randomly assigned 2:1 to CLC (Tandem Control-IQ) or sensor augmented pump (SAP, various pumps+Dexcom G6 CGM) and followed for 6 months. Results: Mean age of the 63 participants was 17 years, median type 1 diabetes duration was 7 years, and mean baseline HbA1c was 8.1%. All 63 completed the trial. Time in range (TIR) increased by 13% with CLC versus decreasing by 1% with SAP (adjusted treatment group difference = +13% [+3.1 h/day]; 95% confidence interval [CI] 9–16, P < 0.001), which largely reflected a reduction in time >180 mg/dL (adjusted difference −12% [−2.9 h/day], P < 0.001). Time <70 mg/dL decreased by 1.6% with CLC versus 0.3% with SAP (adjusted difference −0.7% [−10 min/day], 95% CI −1.0% to −0.2%, P = 0.002). CLC use averaged 89% of the time for 6 months. The mean adjusted difference in HbA1c after 6 months was 0.30% in CLC versus SAP (95% CI −0.67 to +0.08, P = 0.13). There was one diabetic ketoacidosis episode in the CLC group. Conclusions: CLC use for 6 months was substantial and associated with improved TIR and reduced hypoglycemia in adolescents and young adults with type 1 diabetes. Thus, CLC has the potential to improve glycemic outcomes in this challenging age group. The clinical trial was registered with ClinicalTrials.gov (NCT03563313).

Background: Data are limited on the need for and benefits of pump setting optimization with automated insulin delivery. We examined clinical management of a closed-loop control (CLC) system and its relationship to glycemic outcomes. Materials and Methods: We analyzed personal parameter adjustments in 168 participants in a 6-month multicenter trial of CLC with Control-IQ versus sensor-augmented pump (SAP) therapy. Preset parameters (BR = basal rates, CF = correction factors, CR = carbohydrate ratios) were optimized at randomization, 2 and 13 weeks, for safety issues, participant concerns, or initiation by participants' usual diabetes care team. Time in range (TIR 70–180 mg/dL) was compared in the week before and after parameter changes. Results: In 607 encounters for parameter changes, there were fewer adjustments for CLC than SAP (3.4 vs. 4.1/participant). Adjustments involved BR (CLC 69%, SAP 80%), CR (CLC 68%, SAP 50%), CF (CLC 44%, SAP 41%), and overnight parameters (CLC 62%, SAP 75%). TIR before and after adjustments was 71.2% and 71.3% for CLC and 61.0% and 62.9% for SAP. The highest baseline HbA 1c CLC subgroup had the largest TIR improvement (51.2% vs. 57.7%). When a CR was made more aggressive in the CLC group, postprandial time >180 mg/dL was 43.1% before the change and 36.0% after the change. The median postprandial time <70 mg/dL before making CR less aggressive was 1.8%, and after the change was 0.7%. Conclusions: No difference in TIR was detected with parameter changes overall, but they may have an effect in higher HbA 1c subgroups or following user-directed boluses, suggesting that changes may matter more in suboptimal control or during discrete periods of the day. Clinical Trials Registration number: NCT03563313.

OBJECTIVE Limited information is available about glycemic outcomes with a closed-loop control (CLC) system compared with a predictive low-glucose suspend (PLGS) system. RESEARCH DESIGN AND METHODS After 6 months of use of a CLC system in a randomized trial, 109 participants with type 1 diabetes (age range, 14–72 years; mean HbA1c, 7.1% [54 mmol/mol]) were randomly assigned to CLC (N = 54, Control-IQ) or PLGS (N = 55, Basal-IQ) groups for 3 months. The primary outcome was continuous glucose monitor (CGM)-measured time in range (TIR) for 70–180 mg/dL. Baseline CGM metrics were computed from the last 3 months of the preceding study. RESULTS All 109 participants completed the study. Mean ± SD TIR was 71.1 ± 11.2% at baseline and 67.6 ± 12.6% using intention-to-treat analysis (69.1 ± 12.2% using per-protocol analysis excluding periods of study-wide suspension of device use) over 13 weeks on CLC vs. 70.0 ± 13.6% and 60.4 ± 17.1% on PLGS (difference = 5.9%; 95% CI 3.6%, 8.3%; P &lt; 0.001). Time &gt;180 mg/dL was lower in the CLC group than PLGS group (difference = −6.0%; 95% CI −8.4%, −3.7%; P &lt; 0.001) while time &lt;54 mg/dL was similar (0.04%; 95% CI −0.05%, 0.13%; P = 0.41). HbA1c after 13 weeks was lower on CLC than PLGS (7.2% [55 mmol/mol] vs. 7.5% [56 mmol/mol], difference −0.34% [−3.7 mmol/mol]; 95% CI −0.57% [−6.2 mmol/mol], −0.11% [1.2 mmol/mol]; P = 0.0035). CONCLUSIONS Following 6 months of CLC, switching to PLGS reduced TIR and increased HbA1c toward their pre-CLC values, while hypoglycemia remained similarly reduced with both CLC and PLGS.

OBJECTIVE Assess the efficacy of inControl AP, a mobile closed-loop control (CLC) system. RESEARCH DESIGN AND METHODS This protocol, NCT02985866, is a 3-month parallel-group, multicenter, randomized unblinded trial designed to compare mobile CLC with sensor-augmented pump (SAP) therapy. Eligibility criteria were type 1 diabetes for at least 1 year, use of insulin pumps for at least 6 months, age ≥14 years, and baseline HbA1c &lt;10.5% (91 mmol/mol). The study was designed to assess two coprimary outcomes: superiority of CLC over SAP in continuous glucose monitor (CGM)–measured time below 3.9 mmol/L and noninferiority in CGM-measured time above 10 mmol/L. RESULTS Between November 2017 and May 2018, 127 participants were randomly assigned 1:1 to CLC (n = 65) versus SAP (n = 62); 125 participants completed the study. CGM time below 3.9 mmol/L was 5.0% at baseline and 2.4% during follow-up in the CLC group vs. 4.7% and 4.0%, respectively, in the SAP group (mean difference −1.7% [95% CI −2.4, −1.0]; P &lt; 0.0001 for superiority). CGM time above 10 mmol/L was 40% at baseline and 34% during follow-up in the CLC group vs. 43% and 39%, respectively, in the SAP group (mean difference −3.0% [95% CI −6.1, 0.1]; P &lt; 0.0001 for noninferiority). One severe hypoglycemic event occurred in the CLC group, which was unrelated to the study device. CONCLUSIONS In meeting its coprimary end points, superiority of CLC over SAP in CGM-measured time below 3.9 mmol/L and noninferiority in CGM-measured time above 10 mmol/L, the study has demonstrated that mobile CLC is feasible and could offer certain usability advantages over embedded systems, provided the connectivity between system components is stable.

Objective: The objective was to investigate the relationship of body mass index (BMI) to differing glycemic responses to psychological stress in patients with type 1 diabetes. Methods: Continuous blood glucose monitor (CGM) data were collected for 1 week from a total of 37 patients with BMI ranging from 21.5-39.4 kg/m 2 (mean = 28.2 ± 4.9). Patients reported daily stress levels (5-point Likert-type scale, 0 = none, 4 = extreme), physical activity, carbohydrate intake, insulin boluses and basal rates. Daily reported carbohydrates, total insulin bolus, and average blood glucose (BG from CGM) were compared among patients based on their BMI levels on days with different stress levels. In addition, daily averages of a model-based “effectiveness index” (quantifying the combined impact of insulin and carbohydrate on glucose levels) were defined and compared across stress levels to capture meal and insulin independent glycemic changes. Results: Analyses showed that patient BMI likely moderated stress related glycemic changes. Linear mixed effect model results were significant for the stress-BMI interaction on both behavioral and behavior-independent glycemic changes. Across participants, under stress, an increase was observed in daily carbohydrate intake and effectiveness index at higher BMI. There was no significant interactive effect on daily insulin or average BG. Conclusion: Findings suggest that (1) stress has both behavioral and nonbehavioral glycemic effects on T1D patients and (2) the direction and magnitude of these effects are potentially influenced by level of stress and patient BMI. Possibly responsible for these observed effects are T1D/BMI related alterations in endocrine response.

OBJECTIVE Artificial pancreas (AP) systems are best positioned for optimal treatment of type 1 diabetes (T1D) and are currently being tested in outpatient clinical trials. Our consortium developed and tested a novel adaptive AP in an outpatient, single-arm, uncontrolled multicenter clinical trial lasting 12 weeks. RESEARCH DESIGN AND METHODS Thirty adults with T1D completed a continuous glucose monitor (CGM)-augmented 1-week sensor-augmented pump (SAP) period. After the AP was started, basal insulin delivery settings used by the AP for initialization were adapted weekly, and carbohydrate ratios were adapted every 4 weeks by an algorithm running on a cloud-based server, with automatic data upload from devices. Adaptations were reviewed by expert study clinicians and patients. The primary end point was change in hemoglobin A1c (HbA1c). Outcomes are reported adhering to consensus recommendations on reporting of AP trials. RESULTS Twenty-nine patients completed the trial. HbA1c, 7.0 ± 0.8% at the start of AP use, improved to 6.7 ± 0.6% after 12 weeks (−0.3, 95% CI −0.5 to −0.2, P &lt; 0.001). Compared with the SAP run-in, CGM time spent in the hypoglycemic range improved during the day from 5.0 to 1.9% (−3.1, 95% CI −4.1 to −2.1, P &lt; 0.001) and overnight from 4.1 to 1.1% (−3.1, 95% CI −4.2 to −1.9, P &lt; 0.001). Whereas carbohydrate ratios were adapted to a larger extent initially with minimal changes thereafter, basal insulin was adapted throughout. Approximately 10% of adaptation recommendations were manually overridden. There were no protocol-related serious adverse events. CONCLUSIONS Use of our novel adaptive AP yielded significant reductions in HbA1c and hypoglycemia.

Background: The relationship between daily psychological stress and BG fluctuations in type 1 diabetes (T1DM) is unclear. More research is needed to determine if stress-related BG changes should be considered in glucose control algorithms. This study in the usual free-living environment examined relationships among routine daily stressors and BG profile measures generated from CGM readings. Methods: A total of 33 participants with T1DM on insulin pumps wore a CGM device for 1 week and recorded daily ratings of psychological stress, carbohydrates, and insulin boluses. Results: Within-subjects ANCOVAs found a significant relationship between daily stress and indices of BG variability/instability ( r = .172 to .185, P = .011 to .018, r 2 = 2.97% to 3.43%), increased % time in hypoglycemia ( r = .153, P = .036, r 2 = 2.33%) and decreased carbohydrate consumption ( r = –.157, P = .031, r 2 = 2.47%). Models accounted for more variance for individuals reporting the highest daily stress. There was no relationship between stress and mean daily glucose or low/high glucose risk indices. Conclusions: These preliminary findings suggest that naturally occurring daily stressors can be associated with increased glucose instability and hypoglycemia, as well as decreased food consumption. In addition, findings support the hypothesis that some individuals are more metabolically reactive to stress. More rigorous studies using CGM technology are needed to understand whether the impact of daily stress on BG is clinically meaningful and if it is a behavioral factor that should be considered in glucose control systems for some individuals.