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 HbA1c 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 HbA1c 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 < 0.001). Time >180 mg/dL was lower in the CLC group than PLGS group (difference = −6.0%; 95% CI −8.4%, −3.7%; P < 0.001) while time <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 <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 < 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 < 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: To investigate the safety and efficacy of the addition of a trust index to enhanced Model Predictive Control (eMPC) Artificial Pancreas (AP) that works by adjusting the aggressiveness of the controller’s insulin delivery based on the confidence intervals around predictions of glucose trends. Methods: After one week of sensor-augmented pump (SAP) use, subjects completed a 48-hour AP admission that included 3 meals/day of at least 30 g CHO per meal, 1 hour of unannounced exercise and two overnight periods. Endpoints included sensor glucose percentage time 70-180 mg/dL, <70 mg/dL, >180 mg/dL, number of hypoglycemic events, and assessment of the trust index vs. standard eMPC glucose predictions. Results: Baseline characteristics for the 15 subjects (mean±SD) were age 46.1±17.8 years, HbA1c 7.2±1.0%, diabetes duration 26.8±17.6 years and TDD 35.5±16.4 units/day. Glycemic outcomes are reported in the table. Mean percent time 70-180 mg/dL (88.0±7.7%), <70 mg/dL (1.5±1.8%), and number of hypoglycemic events (0.6±0.6%) all showed statistically significant improvement during AP (p<0.001). On average, the trust index enhanced controller responsiveness to predicted hyper- and hypoglycemia by 26% (p<0.005). Glycemic Outcomes. eMPC Artificial Pancreas with Trust Index vs. Sensor-Augmented Pump.Glycemic Outcomes (Mean±SD)48-Hour AP SessionSAP Run-In Weekp-valuePercentage Time 70-180, mg/dL88.0±7.774.6±9.0<0.001Percentage Time 80-140, mg/dL60.1±13.445.2±10.30.004Percentage Time < 70, mg/dL1.5±1.87.8±5.8<0.001Percentage Time > 180, mg/dL10.5±7.717.6±10.40.081Mean CGM, mg/dL130.1±14.7135.1±18.8>0.5Standard Deviation, mg/dL32.9±6.150.3±10.5<0.001Number of daily hypoglycemic events (CGM <70 mg/dL for ≥ 15 minutes)0.6±0.61.7±0.9<0.001 Conclusions: eMPC AP with trust index additions achieved nearly 90% time in the target glucose range. Additional studies will further validate these results. Disclosure J.E. Pinsker: Research Support; Self; Insulet Corporation, Dexcom, Inc., Tandem Diabetes Care, Inc.. A.J. Laguna Sanz: None. M. Church: None. J. Lee: Employee; Self; Insulet Corporation. L. Lindsey: None. C.C. Andre: None. F.J. Doyle: Research Support; Self; Insulet Corporation, Roche Diagnostics Corporation, Xeris Pharmaceuticals, Inc., Dexcom, Inc., DreaMed Diabetes, Ltd., LifeScan, Inc.. Other Relationship; Self; ModeAGC. E. Dassau: Consultant; Self; Insulet Corporation. Research Support; Self; Insulet Corporation. Consultant; Self; Animas Corporation. Research Support; Self; Dexcom, Inc.. Speaker's Bureau; Self; Roche Diabetes Care Health and Digital Solutions. Research Support; Self; Roche Diabetes Care Health and Digital Solutions, Xeris Pharmaceuticals, Inc.. Consultant; Self; Eli Lilly and Company. Research Support; Self; Tandem Diabetes Care, Inc.. Other Relationship; Self; ModAGC. Research Support; Self; LifeScan, Inc., DreaMed Diabetes, Ltd..
Objective: Prior reports using the validated Barriers to Physical Activity in Diabetes (type 1) [BAPAD1] scale showed fear of hypoglycemia was the strongest barrier to regular physical activity (PA) in people with type 1 diabetes (T1D). This was before the higher prevalence of continuous glucose monitoring (CGM) use today. Methods: Twenty adults with T1D enrolling in an exercise tracking study completed the BAPAD1, reporting perceived barriers to regular PA on a scale of 1 (extremely unlikely) to 7 (extremely likely). All used insulin pumps. Fifteen of the 20 were current CGM users. Results: Baseline characteristics were age 44.9±15.0 years, F/M 12/8, HbA1c 6.8±0.7%, and diabetes duration 22.9±15.9 years. Mean BAPAD1 score was 2.55±1.05. The highest scores were for risk of hypoglycemia (4.00±1.78) and work schedule (3.70±2.00), with current CGM users reporting higher overall scores than non-CGM users (2.71±1.04 vs. 2.10±1.07, p=0.28). Significantly higher scores were reported by CGM users for “The fear of being tired” (p=0.01), “The fact that you have diabetes” (p=0.03), and “The location of a gym” (p=0.04) (Table 1). Greater barrier scores for work schedule were associated with lower age (r=-0.5, p=0.02). Conclusions: CGM use was not associated with lower perceived barriers to regular PA, suggesting additional interventions beyond providing ways to measure glucose are needed to reduce these barriers in people with T1D. Disclosure C.C. Andre: None. Y.C. Kudva: Advisory Panel; Self; Novo Nordisk Inc. Other Relationship; Self; Dexcom, Inc., Roche Diabetes Care, Tandem Diabetes Care. V. Dadlani: None. M. Cescon: None. S.K. McCrady-Spitzer: None. M. Church: None. C. Reid: None. K. Kumari: None. D. Choudhary: None. F.J. Doyle: Consultant; Self; ModeAGC. Other Relationship; Self; Insulet Corporation. E. Dassau: Consultant; Self; Eli Lilly and Company, Insulet Corporation. Research Support; Self; Dexcom, Inc., DreaMed Diabetes, Ltd., Insulet Corporation, Roche Diabetes Care, Tandem Diabetes Care, Xeris Pharmaceuticals, Inc. Speaker's Bureau; Self; Roche Diabetes Care. Other Relationship; Self; ModAGC. J.E. Pinsker: Research Support; Self; Ascensia Diabetes Care, Dexcom, Inc., Insulet Corporation, LifeScan, Inc., Roche Diabetes Care, Tandem Diabetes Care. Speaker's Bureau; Self; Tandem Diabetes Care. Funding JDRF; The Leona M. and Harry B. Helmsley Charitable Trust (2-SRA-2017-503-M-B); Dexcom, Inc. (IIS-2017-043)
Background: Food choices are essential to successful glycemic control for people with diabetes. We compared the impact of three carbohydrate-rich meals on the postprandial glycemic response in adults with type 1 diabetes (T1D). Methods: We performed a randomized crossover study in 12 adults with T1D (age 58.7 ± 14.2 years, baseline hemoglobin A1c 7.5% ± 1.3%) comparing the postprandial glycemic response to three meals using continuous glucose monitoring: (1) "higher protein" pasta containing 10 g protein/serving, (2) regular pasta with 7 g protein/serving, and (3) extra-long grain white rice. All meals contained 42 g carbohydrate; were served with homemade tomato sauce, green salad, and balsamic dressing; and were repeated twice in random order. After their insulin bolus, subjects were observed in clinic for 5 h. Linear mixed effects models were used to assess the glycemic response. Results: Compared with white rice, peak glucose levels were significantly lower for higher protein pasta (-32.6 mg/dL; 95% CI -48.4 to -17.2; P < 0.001) and regular pasta (-43.2 mg/dL, 95% CI -58.7 to -27.7; P < 0.001). The difference between the two types of pastas did not reach statistical significance (-11 mg/dL; 95% CI -24.1 to 3.4; P = 0.17). Total glucose area under the curve was also significantly higher for white rice compared with both pastas (P < 0.001 for both comparisons). Conclusions: This exploratory study concluded that different food types of similar macronutrient content (e.g., rice and pasta) generate significantly different postprandial glycemic responses in persons with T1D. These results provide useful insights into the impact of food choices on and optimization of glucose control. Clinical Trial Registry: clinicaltrials.gov NCT03362151.
There is an unmet need for a modular artificial pancreas (AP) system for clinical trials within the existing regulatory framework to further AP research projects from both academia and industry. We designed, developed, and tested the interoperable artificial pancreas system (iAPS) smartphone app that can interface wirelessly with leading continuous glucose monitors (CGM), insulin pump devices, and decision-making algorithms while running on an unlocked smartphone.After algorithm verification, hazard and mitigation analysis, and complete system verification of iAPS, six adults with type 1 diabetes completed 1 week of sensor-augmented pump (SAP) use followed by 48 h of AP use with the iAPS, a Dexcom G5 CGM, and either a Tandem or Insulet insulin pump in an investigational device exemption study. The AP system was challenged by participants performing extensive walking without exercise announcement to the controller, multiple large meals eaten out at restaurants, two overnight periods, and multiple intentional connectivity interruptions.Even with these intentional challenges, comparison of the SAP phase with the AP study showed a trend toward improved time in target glucose range 70-180 mg/dL (78.8% vs. 83.1%; P = 0.31), and a statistically significant reduction in time below 70 mg/dL (6.1% vs. 2.2%; P = 0.03). The iAPS system performed reliably and showed robust connectivity with the peripheral devices (99.8% time connected to CGM and 94.3% time in closed loop) while requiring limited user intervention.The iAPS system was safe and effective in regulating glucose levels under challenging conditions and is suitable for use in unconstrained environments.
Antimicrobial peptides are an emerging class of antibiotics that present a series of advantageous characteristics such as wide structural variety, broad spectrum of activity, and low propensity to select for resistance. They are found in all classes of life as defense molecules. A group of peptides derived from the protein Bothropstoxin-I has been previously studied as an alternative treatment against multi-drug-resistant bacteria. The peptide p-BthTX-I (sequence: KKYRYHLKPFCKK) and its homodimer, linked by disulfide oxidation through the residues of Cys11 and the serum degradation product [sequence: (KKYRYHLKPFC)2], were evaluated and showed similar antimicrobial activity. In this study, we synthesized an analogue of p-BthTX-I that uses the strategy of Fmoc-Lys(Fmoc)-OH in the C-terminal region for dimerization and tryptophan for all aromatic amino acids to provide better membrane interactions. This analogue, named p-BthW, displayed potent antibacterial activity at lower concentrations and maintained the same hemolytic levels as the original molecule. Our assessment revealed that p-BthW has a quick in vitro bactericidal action and prolonged post-antibiotic effect, comparable to the action of polymyxin B. The mode of action of p-BthW seems to rely not only on membrane depolarization but also on necrosis-like effects, especially in Gram-negative bacteria. Overall, the remarkable results regarding the propensity to develop resistance reaffirmed the great potential of the developed molecule.
Background: Closed-loop control (CLC) has been shown to improve glucose time in range and other glucose metrics; however, randomized trials >3 months comparing CLC with sensor-augmented pump (SAP) therapy are limited. We recently reported glucose control outcomes from the 6-month international Diabetes Closed-Loop (iDCL) trial; we now report patient-reported outcomes (PROs) in this iDCL trial. Methods: Participants were randomized 2:1 to CLC (N = 112) versus SAP (N = 56) and completed questionnaires, including Hypoglycemia Fear Survey, Diabetes Distress Scale (DDS), Hypoglycemia Awareness, Hypoglycemia Confidence, Hyperglycemia Avoidance, and Positive Expectancies of CLC (INSPIRE) at baseline, 3, and 6 months. CLC participants also completed Diabetes Technology Expectations and Acceptance and System Usability Scale (SUS). Results: The Hypoglycemia Fear Survey Behavior subscale improved significantly after 6 months of CLC compared with SAP. DDS did not differ except for powerless subscale scores, which worsened at 3 months in SAP. Whereas Hypoglycemia Awareness and Hyperglycemia Avoidance did not differ between groups, CLC participants showed a tendency toward improved confidence in managing hypoglycemia. The INSPIRE questionnaire showed favorable scores in the CLC group for teens and parents, with a similar trend for adults. At baseline and 6 months, CLC participants had high positive expectations for the device with Diabetes Technology Acceptance and SUS showing high benefit and low burden scores. Conclusion: CLC improved some PROs compared with SAP. Participants reported high benefit and low burden with CLC. Clinical Trial Identifier: NCT03563313.
