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Objective: The objective was to evaluate the safety and efficacy of ultra-rapid-acting insulin with the Boost and Ease-off features of the Cambridge hybrid closed-loop system., Methods: A secondary analysis of Boost and Ease-off from two double-blind, randomized, crossover hybrid closed-loop studies comparing (1) Fiasp to insulin aspart (n = 25), and (2) Lyumjev to insulin lispro (n = 26) was carried out. Mean glucose on initialization of Boost and Ease-off, change in glucose 60 and 120 minutes after initialization, duration and frequency of use, mean glucose, and time in, above, and below target glucose range were calculated for periods of Boost use, Ease-off use, or neither., Results: Participants used Boost for longer with Fiasp than insulin aspart (median [interquartile range, IQR] = 75 [53-125] minutes vs 60 [49-75] minutes; P = .01). Mean glucose on Boost initialization with Fiasp was 238 ± 62 mg/dL compared with 218 ± 45 mg/dL with insulin aspart ( P = .08). Fiasp use resulted in a greater glucose reduction 120 minutes after Boost initialization [-59 ± 34 mg/dL vs -43 ± 31 mg/dL; P = .02]. There were no statistically significant differences in sensor glucose endpoints during Boost or Ease-off periods between Fiasp and aspart. There were no statistically significant differences during Boost or Ease-off periods when comparing Lyumjev with insulin lispro. There were no safety issues when using Boost and Ease-off with ultra-rapid insulins., Conclusions: The use of Fiasp and Lyumjev during Boost or Ease-off resulted in comparable safety and efficacy to using insulin aspart and lispro., Competing Interests: Declaration of Conflicting InterestsThe author(s) declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: CB is a consultant at CamDiab and has received speaker honoraria from Ypsomed. MN has received travel grant support from Sanofi, Janssen, and Eli Lilly and was previously chair of the Young Diabetologists’ and Endocrinologists’ Forum in the United Kingdom, which uses unrestricted sponsorship from industry partners to deliver educational programs for health care professionals. SH serves as a member of Medtronic advisory board, is a director of Ask Diabetes Ltd, providing training and research support in health care settings, and reports having received training honoraria from Medtronic and Sanofi and consulting fees for CamDiab. MEW is a consultant to CamDiab and reports patents related to closed loop. JW has received speaker honoraria from Ypsomed and Novo Nordisk. HT receives consulting fees and speaker honoraria from Eli Lilly and Dexcom Inc, reports having received research support from Dexcom Inc, and participated in advisory groups for Medtronic, Abbott Diabetes Care, and Roche Diabetes Care. JKM is a member in the advisory boards of Abbott Diabetes Care, Becton-Dickinson/Embecta, Biomea, Eli Lilly, Medtronic, Novo Nordisk, Pharmasens, Roche Diabetes Care, Sanofi, and Viatris; received speaker honoraria from Abbott Diabetes Care, A. Menarini Diagnostics, Becton-Dickinson/Embecta, Boehringer-Ingelheim, Eli Lilly, MedTrust, Novo Nordisk, Roche Diabetes Care, Sanofi, and Ypsomed; and is shareholder of decide Clinical Software GmbH and elyte Diagnostics. LB reports having participated in advisory boards of Eli Lilly, Novo Nordisk, Oviva, Roche Diabetes Care, Sanofi, and Ypsomed and received speaker fees from Dexcom, Ypsomed, and Oviva. LL has received research support from Abbott Diabetes Care and Dexcom, participated in advisory groups for Abbott Diabetes Care, Insulet, Dexcom, Medtronic, and Roche Diabetes, and received fees for speaking from Sanofi, Insulet, Medtronic, and Abbott Diabetes Care. MLE has been a member of advisory panels and/or received speaker fees from Novo Nordisk, Eli Lilly, Sanofi, Abbott Diabetes Care, Dexcom, Medtronic, Ypsomed, Pila Pharma, and Zucara. RH reports having received speaker honoraria from Eli Lilly, Dexcom, and Novo Nordisk; receiving license fees from B. Braun; patents related to closed-loop; being consultant to Abbott Diabetes Care; and being director at CamDiab. CR, RL, and JMA report no conflicts of interest related to the present paper.

Objective: Many hybrid closed-loop (HCL) systems struggle to manage unusually high glucose levels as experienced with intercurrent illness or pre-menstrually. Manual correction boluses may be needed, increasing hypoglycemia risk with overcorrection. The Cambridge HCL system includes a user-initiated algorithm intensification mode ("Boost"), activation of which increases automated insulin delivery by approximately 35%, while remaining glucose-responsive. In this analysis, we assessed the safety of "Boost" mode., Methods: We retrospectively analyzed data from closed-loop studies involving young children (1-7 years, n = 24), children and adolescents (10-17 years, n = 19), adults (≥24 years, n = 13), and older adults (≥60 years, n = 20) with type 1 diabetes. Outcomes were calculated per participant for days with ≥30 minutes of "Boost" use versus days with no "Boost" use. Participants with <10 "Boost" days were excluded. The main outcome was time spent in hypoglycemia <70 and <54 mg/dL., Results: Eight weeks of data for 76 participants were analyzed. There was no difference in time spent <70 and <54 mg/dL between "Boost" days and "non-Boost" days; mean difference: -0.10% (95% confidence interval [CI] -0.28 to 0.07; P = .249) time <70 mg/dL, and 0.03 (-0.04 to 0.09; P = .416) time < 54 mg/dL. Time in significant hyperglycemia >300 mg/dL was 1.39 percentage points (1.01 to 1.77; P < .001) higher on "Boost" days, with higher mean glucose and lower time in target range ( P < .001)., Conclusions: Use of an algorithm intensification mode in HCL therapy is safe across all age groups with type 1 diabetes. The higher time in hyperglycemia observed on "Boost" days suggests that users are more likely to use algorithm intensification on days with extreme hyperglycemic excursions., Competing Interests: Declaration of Conflicting InterestsThe author(s) declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: RH reports having received speaker honoraria from Eli Lilly, Dexcom, and Novo Nordisk; receiving license fees from BBraun; patents related to closed-loop; and being director at CamDiab. JW reports receiving speaker honoraria from Ypsomed. YR is a consultant at CamDiab. MEW is a consultant at CamDiab and reports patents related to closed-loop. CKB reports receiving consultancy fees from CamDiab and speaker honoraria from Ypsomed. SH reports speaker and advisory board fees from Dexcom, Medtronic, Sanofi, and Ypsomed; being director at ASK Diabetes Ltd; and receiving consulting/training fees from CamDiab. LB reports receiving research support from Dexcom and CamDiab. REJB reports receiving speaking honoraria from Eli Lilly and Springer Healthcare, and sitting as a voluntary unpaid member of the NovoNordisk UK Foundation Research Selection committee. FMC reports receiving speaker honoraria from Eli Lilly, Dexcom, and Novo Nordisk and Insulet, and consultancy fees from Abbott Diabetes Care. EF-R reports having received speaker honoraria from Eli Lilly and Novo Nordisk, serving on advisory boards for Eli Lilly and Sanofi. MLE is a clinical triallist with or has served on advisory boards or received speakers or writers fees from Medtronic, Dexcom, Abbott Diabetes Care, Roche, AstraZeneca, Novo Nordisk, Eli Lilly, Zucara, Pila Pharma, and Imcyse Pharma. SEH has received speaker honoraria by Eli Lilly, Vertex, Minimed Medtronic, Insulet, Ypsomed, and Sanofi. TMK reports having received speaker honoraria from Eli Lilly and Novo. LL has received personal fees from Abbott Diabetes Care, Dexcom, Insulet, Medtronic, Novo Nordisk, Sanofi, and Diabetes Care. JKM is a member on the advisory board of Boehringer Ingelheim, Becton-Dickinson, Eli Lilly, Medtronic, Prediktor A/S, Roche Diabetes Care, and Sanofi-Aventis, and received speaker honoraria from Abbott Diabetes Care, AstraZeneca, Becton-Dickinson, Dexcom, Eli Lilly, Mercke Sharp & Dohme, NovoNordisk, Roche Diabetes Care, Sanofi, Servier, and Ypsomed. TR reports receiving speaker honoraria from Novo Nordisk and consultancy fees from Abbott Diabetes Care. HT reports receiving research support from Dexcom and speaker honoraria from Eli Lilly. MT reports having received speaker honoraria from Eli Lilly, Novo Nordisk, and Medtronic and advisory board fees from Abbott Diabetes Care. BR-M has received speaker honoraria from Abbott Diabetes Care, Eli Lilly, Medtronic, Novo Nordisk, Roche Diabetes Care, Sanofi, and Menarini and has been on the advisory boards of Eli Lilly, Roche Diabetes Care, and Abbott Diabetes Care. CdB has received speaker honoraria from Minimed Medtronic, and has been member of their European Psychology and e-learning Advisory Board. JMA reports training fees from CamDiab. DE, WMM, PN, TP, AG, AT, KD, and NT have no disclosures.

Background: CamAPS FX is a hybrid closed-loop smartphone app used to manage type one diabetes. The closed-loop algorithm has a default target glucose of 5.8 mmol/L (104.5 mg/dL), but users can select personal glucose targets (adjustable between 4.4 mmol/L and 11.0 mmol/L [79 mg/dL and 198 mg/dL, respectively])., Method: In this post-hoc analysis, we evaluated the impact of personal glucose targets on glycemic control using data from participants in five randomized controlled trials., Results: Personal glucose targets were widely used, with 20.3% of all days in the data set having a target outside the default target bin (5.5-6.0 mmol/L [99-108 mg/dL]). Personal glucose targets >6.5 mmol/L (117 mg/dL) were associated with significantly less time in target range (3.9-10.0 mmol/L [70-180 mg/dL]; 6.5-7.0 mmol/L [117-126 mg/dL]: mean difference = -3.2 percentage points [95% CI: -5.3 to -1.2; P < .001]; 7.0-7.5 mmol/L [126-135 mg/dL]: -10.8 percentage points [95% CI: -14.1 to -7.6; P < .001]). Personal targets >6.5 mmol/L (117 mg/dL) were associated with significantly lower time (<3.9 mmol/L [<70 mg/dL]; 6.5-7.0 mmol/L [117-126 mg/dL]: -1.85 percentage points [95% CI: -2.37 to -1.34; P < .001]; 7.0-7.5 mmol/L [126-135 mg/dL]: -2.68 percentage points [95% CI: -3.49 to -1.86; P < .001])., Conclusions: Discrete study populations showed differences in glucose control when applying similar personal targets., Competing Interests: Declaration of Conflicting InterestsThe author(s) declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: C.K.B. has received consulting fees from CamDiab and speaker honoraria from Ypsomed. J.W. has received speaker honoraria from Ypsomed. J.M.A. has received consulting fees from CamDiab. S.H. serves as a member of Sigma (Dexcom) and Medtronic advisory boards; is a consultant for CamDiab and a director of Ask Diabetes Ltd., providing training and research support in healthcare settings; and reports having received training honoraria from Medtronic, Sanofi, and Ypsomed. M.E.W. reports receiving license fees from B. Braun, patents related to closed-loop systems, and being a consultant at CamDiab. C.d.B. reports having received speaker honoraria from Medtronic and has served on the EU psychology e-learning board of Medtronic. E.F.-R. reports having received speaker honoraria from Medtronic, Eli Lilly and Company, Novo Nordisk, and Sanofi and serving on the advisory board for Eli Lilly and Company. S.E.H. has received speaker honoraria from Medtronic, Eli Lilly, Ypsomed, and Insulet. T.M.K. reports having received speaker honoraria from Eli Lilly, Merck Serono, and Novo Nordisk. B.R.-M. has received speaker honoraria from Abbott Diabetes Care, Eli Lilly, Medtronic, Novo Nordisk, Roche Diabetes Care, Sanofi, and Menarini and has been on the advisory boards of Eli Lilly, Roche Diabetes Care, and Abbott Diabetes Care. T.L.R. has received consultancy fees from Abbott Diabetes Care and speaker honoraria from Novo Nordisk. R.E.J.B. reports having received speaking honoraria from Eli Lilly and Springer Healthcare and sits as an unpaid member of the Novo Nordisk UK Research Foundation grant and selection committee. L.D. has received honoraria for taking part in an Interactive Advisory and Advocacy Forum on CGM Use in Pediatric Clinical Practice from Dexcom and has received conference fees from Novo Nordisk. H.T. reports having received research support from Dexcom and speaker honoraria from Eli Lilly and Dexcom. L.L. reports having received speaker honoraria from Animas, Abbott, Insulet, Medtronic, Novo Nordisk, Roche, and Sanofi; was on advisory panels for Animas, Abbott, Novo Nordisk, Dexcom, Medtronic, Sanofi, and Roche; and received research support from Novo Nordisk and Dexcom. M.L.E. reports having received speaker honoraria from Eli Lilly and Company, Novo Nordisk, Abbott Diabetes Care, Medtronic, AstraZeneca, and Ypsomed and acting on advisory boards for Medtronic, Novo Nordisk, Zucara Therapeutics, Pila Pharma, and Abbott Diabetes Care. J.K.M. is a member of the advisory boards of Abbott Diabetes Care, BD, Boehringer Ingelheim, Eli Lilly and Company, Medtronic, Novo Nordisk AS, Prediktor A/S, Roche Diabetes Care, and Sanofi; received speaker honoraria from Abbott Diabetes Care, AstraZeneca, Dexcom, Eli Lilly and Company, Menarini Diagnostics, Novo Nordisk A/S, Roche Diabetes Care, Servier, and Ypsomed; and is a cofounder and shareholder of decide Clinical Software Ltd. R.H. reports having received speaker honoraria from Eli Lilly and Company, Dexcom, and Novo Nordisk; receiving license fees from Medtronic; receiving patents related to closed-loop systems; and being the director at CamDiab. M.F., A.T., F.M.C., A.G., D.E., N.T., N.D., and L.B. have no conflict of interest to disclose.

Background: A composite metric for the quality of glycemia from continuous glucose monitor (CGM) tracings could be useful for assisting with basic clinical interpretation of CGM data., Methods: We assembled a data set of 14-day CGM tracings from 225 insulin-treated adults with diabetes. Using a balanced incomplete block design, 330 clinicians who were highly experienced with CGM analysis and interpretation ranked the CGM tracings from best to worst quality of glycemia. We used principal component analysis and multiple regressions to develop a model to predict the clinician ranking based on seven standard metrics in an Ambulatory Glucose Profile: very low-glucose and low-glucose hypoglycemia; very high-glucose and high-glucose hyperglycemia; time in range; mean glucose; and coefficient of variation., Results: The analysis showed that clinician rankings depend on two components, one related to hypoglycemia that gives more weight to very low-glucose than to low-glucose and the other related to hyperglycemia that likewise gives greater weight to very high-glucose than to high-glucose. These two components should be calculated and displayed separately, but they can also be combined into a single Glycemia Risk Index (GRI) that corresponds closely to the clinician rankings of the overall quality of glycemia (r = 0.95). The GRI can be displayed graphically on a GRI Grid with the hypoglycemia component on the horizontal axis and the hyperglycemia component on the vertical axis. Diagonal lines divide the graph into five zones (quintiles) corresponding to the best (0th to 20th percentile) to worst (81st to 100th percentile) overall quality of glycemia. The GRI Grid enables users to track sequential changes within an individual over time and compare groups of individuals., Conclusion: The GRI is a single-number summary of the quality of glycemia. Its hypoglycemia and hyperglycemia components provide actionable scores and a graphical display (the GRI Grid) that can be used by clinicians and researchers to determine the glycemic effects of prescribed and investigational treatments.

Objective: The objective was to describe a novel composite continuous glucose monitoring index (COGI) and to evaluate its utility, in adults with type 1 diabetes, during hybrid closed-loop (HCL) therapy and multiple daily injections (MDI) therapy combined with real-time continuous glucose monitoring (CGM)., Methods: COGI consists of three key components of glucose control as assessed by CGM: Time in range (TIR), time below range (TBR), and glucose variability (GV) (weighted by 50%, 35% and 15%). COGI ranges from 0 to 100, where 1% increase of time <3.9 mmol/L (<70 mg/dl) is equivalent to 4.7% reduction of TIR between 3.9-10 mmol/L (70-180 mg/dl), and 0.5 mmol/L (9 mg/dl) increase in standard deviation is equivalent to 3% reduction in TIR., Results: Continuous subcutaneous insulin infusion (CSII) users with HbA1c >7.5-10%, had significantly higher COGI during 12 weeks of HCL compared to sensor-augmented pump therapy, mean (SD), 60.3 (8.6) versus 69.5 (6.9), P < .001. Similarly, in CSII users with HbA1c <7.5%, HCL improved COGI from 59.9 (11.2) to 74.8 (6.6), P < .001. In MDI users with HbA1c >7.5% to 9.9%, use of real-time CGM led to improved COGI, 49.8 (14.2) versus 58.2 (9.1), P < .0001. In MDI users with impaired awareness of hypoglycemia, use of real-time CGM led to improved COGI, 53.4 (12.2) versus 66.7 (11.1), P < .001., Conclusions: COGI summarizes three key aspects of CGM data into a concise metric that could be utilized to evaluate the quality of glucose control and to demonstrate the incremental benefit of a wide range of treatment modalities.

Background: In type 1 diabetes (T1D) continuous subcutaneous insulin infusion (CSII) use has been associated with lower risks for mortality and diabetes-related complications when compared to multiple daily injections. There are limited data on the effect of CSII on blood pressure (BP). We aimed to evaluate the relationship between CSII initiation and BP changes., Methods: We performed a retrospective, single-center, observational study including all adults with T1D who initiated CSII between 2001 and 2014. Mean systolic BP levels were calculated from values obtained up to 15 months before and up 15 months after CSII initiation., Results: We studied 436 adults (46% male; mean [95% CI] age: 45 [36, 54] years, duration of diabetes: 20.3 [19.1, 21.5] years). CSII was associated with the following changes (post minus pre CSII levels) in mean (95% CI) SBP and DBP levels, respectively: whole cohort (N = 436): -2 (-1, -3), P < .001 and -2 (-1, -2), P < .001; those taking stable antihypertensive medication (n = 118): -4 (-6, -2), P < .001 and -2 (-3, -1), P < .001; antihypertensive treatment-naïve patients (n = 279): -1 (-2, 0), P = .147 and -1 (-2, 0), P = .002. In multivariate regression analysis including age, sex, diabetes duration, pre-CSII BP, HbA1c and weight changes, only pre-CSII BP showed an independent association with BP drop in all patients' groups. Once adjusted for pre-CSII BP there was no significant difference between those on antihypertensive drugs and antihypertensive treatment-naïve individuals., Conclusions: We showed BP reduction in association with CSII therapy particularly in those on antihypertensive medication. These original findings may partly explain the cardiovascular and mortality benefits associated with CSII therapy.

Objective: The objective was to explore psychosocial experiences of closed loop technology for adults, children, and adolescents with type 1 diabetes and their parents taking part in two multicenter, free-living, randomized crossover home studies., Methods: Participants using insulin pump therapy were randomized to either 12 weeks of automated closed-loop glucose control, then 12 weeks of sensor augmented insulin pump therapy (open loop), or vice versa. Closed loop was used for 24 hours by adults and overnight only by children and adolescents. Participants completed the Diabetes Technology Questionnaire (DTQ) periodically and shared their views in semistructured interviews. This analysis characterizes the impact of the technology, positive and negative aspects of living with the device, alongside participants' expectations, hopes, and anxieties., Results: Participants were 32 adults, age 38.6 ± 9.6 years, 55% male, and 26 children, mean age 12 years (range 6-18 years), 54% male. DTQ results indicated moderately favorable impact of, and satisfaction with, both open and closed loop interventions, but little evidence of a comparative advantage of either. Key positive themes included perceived improved blood glucose control, improved general well-being, particularly on waking, improved sleep, reduced burden of diabetes, and visibility of data. Key negative themes included having to carry around the equipment and dislike of the pump and second cannula (ie, sensor) inserted., Conclusions: Overall, participants reported a positive experience of the closed loop technology. Results are consistent with previous research with size of equipment continuing to be a problem. Progress is being made in the usability of the closed-loop system.

The objective was to assess the reliability of a novel automated closed-loop glucose control system developed within the AP@home consortium in adults with type 1 diabetes. Eight adults with type 1 diabetes on insulin pump therapy (3 men; ages 40.5 ± 14.3 years; HbA1c 8.2 ± 0.8%) participated in an open-label, single-center, single-arm, 12-hour overnight study performed at the clinical research facility. A standardized evening meal (80 g CHO) accompanied by prandial insulin boluses were given at 19:00 followed by an optional snack of 15 g at 22:00 without insulin bolus. Automated closed-loop glucose control was started at 19:00 and continued until 07:00 the next day. Basal insulin delivery (Accu-Chek Spirit, Roche) was automatically adjusted by Cambridge model predictive control algorithm, running on a purpose-built embedded device, based on real-time continuous glucose monitor readings (Dexcom G4 Platinum). Closed-loop system was operational as intended over 99% of the time. Overnight plasma glucose levels (22:00 to 07:00) were within the target range (3.9 to 8.0 mmol/l) for 75.4% (37.5, 92.9) of the time without any time spent in hypoglycemia (<3.9 mmol/l). Mean overnight glucose was 7.8 ± 1.3 mmol/l. For the entire 12-hour closed-loop period (19:00 until 07:00) plasma glucose levels were within the target range (3.9 to 10.0 mmol/l) for 84.4% (63.3, 100) of time. There were no adverse events noted during the trial. We observed a high degree of reliability of the automated closed-loop system. The time spent in target glucose level overnight was comparable to results of previously published studies. Further developments to miniaturize the system for home studies are warranted., (© 2014 Diabetes Technology Society.)