Assess the efficacy of inControl AP, a mobile closed-loop control (CLC) system. RESEARCH DESIGN AND METHODSThis 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 HbA 1c <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. RESULTSBetween November 2017 and May 2018, 127 participants were randomly assigned 1:1 to CLC (n 5 65) versus SAP (n 5 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 21.7% [95% CI 22.4, 21.0]; 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 23.0% [95% CI 26.1, 0.1]; P < 0.0001 for noninferiority). One severe hypoglycemic event occurred in the CLC group, which was unrelated to the study device. CONCLUSIONSIn 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.People with type 1 diabetes face a life-long optimization problem: limiting their exposure to hyperglycemia while simultaneously avoiding hypoglycemia (1). Classic studies have shown that many complications from diabetes are predicted by average glycemia, typically assessed by hemoglobin A 1c (HbA 1c ), and can be reduced with intensive insulin therapy (2,3); however, the risk for hypoglycemia remains the primary barrier to optimal glycemic control (1). At present, closed-loop control (CLC), known as the artificial pancreas, offers the best solution to this optimization problem: day-and-night real-time fine-tuning of insulin delivery by an automated system.In the past few years, the volume of CLC clinical trials increased dramatically. In 2018, the National Library of Medicine included 132 publications in the CLC field, and in the first 6 weeks of 2019 alone, 25 new articles were published. Research results are

To evaluate the contemporary prevalence of diabetic peripheral neuropathy (DPN) in participants with type 1 diabetes in the T1D Exchange Clinic Registry throughout the U.S. RESEARCH DESIGN AND METHODSDPN was assessed with the Michigan Neuropathy Screening Instrument Questionnaire (MNSIQ) in adults with ‡5 years of type 1 diabetes duration. A score of ‡4 defined DPN. Associations of demographic, clinical, and laboratory factors with DPN were assessed. RESULTSAmong 5,936 T1D Exchange participants (mean 6 SD age 39 6 18 years, median type 1 diabetes duration 18 years [interquartile range 11, 31], 55% female, 88% non-Hispanic white, mean glycated hemoglobin [HbA 1c ] 8.1 6 1.6% [65.3 6 17.5 mmol/mol]), DPN prevalence was 11%. Compared with those without DPN, DPN participants were older, had higher HbA 1c , had longer duration of diabetes, were more likely to be female, and were less likely to have a college education and private insurance (all P < 0.001). DPN participants also were more likely to have cardiovascular disease (CVD) (P < 0.001), worse CVD risk factors of smoking (P 5 0.008), hypertriglyceridemia (P 5 0.002), higher BMI (P 5 0.009), retinopathy (P 5 0.004), reduced estimated glomerular filtration rate (P 5 0.02), and Charcot neuroarthropathy (P 5 0.002). There were no differences in insulin pump or continuous glucose monitor use, although DPN participants were more likely to have had severe hypoglycemia (P 5 0.04) and/or diabetic ketoacidosis (P < 0.001) in the past 3 months. CONCLUSIONSThe prevalence of DPN in this national cohort with type 1 diabetes is lower than in prior published reports but is reflective of current clinical care practices. These data also highlight that nonglycemic risk factors, such as CVD risk factors, severe hypoglycemia, diabetic ketoacidosis, and lower socioeconomic status, may also play a role in DPN development.Diabetic neuropathy is a prevalent complication in patients with diabetes and a major cause of morbidity and mortality (1). Among the various forms of diabetic neuropathy, distal symmetric polyneuropathy (DPN) and diabetic autonomic neuropathies are by far the most studied (1).

OBJECTIVE Achieving optimal glycemic control for many individuals with type 1 diabetes (T1D) remains challenging, even with the advent of newer management tools, including continuous glucose monitoring (CGM). Modern management of T1D generates a wealth of data; however, use of these data to optimize glycemic control remains limited. We evaluated the impact of a CGM-based decision support system (DSS) in patients with T1D using multiple daily injections (MDI). RESEARCH DESIGN AND METHODS The studied DSS included real-time dosing advice and retrospective therapy optimization. Adults and adolescents (age &gt;15 years) with T1D using MDI were enrolled at three sites in a 14-week randomized controlled trial of MDI + CGM + DSS versus MDI + CGM. All participants (N = 80) used degludec basal insulin and Dexcom G5 CGM. CGM-based and patient-reported outcomes were analyzed. Within the DSS group, ad hoc analysis further contrasted active versus nonactive DSS users. RESULTS No significant differences were detected between experimental and control groups (e.g., time in range [TIR] +3.3% with CGM vs. +4.4% with DSS). Participants in both groups reported lower HbA1c (−0.3%; P = 0.001) with respect to baseline. While TIR may have improved in both groups, it was statistically significant only for DSS; the same was apparent for time spent &lt;60 mg/dL. Active versus nonactive DSS users showed lower risk of and exposure to hypoglycemia with system use. CONCLUSIONS Our DSS seems to be a feasible option for individuals using MDI, although the glycemic benefits associated with use need to be further investigated. System design, therapy requirements, and target population should be further refined prior to use in clinical care.

Background: Using sensor augmented pump data, late or missed meal boluses have shown a strong correlation with higher A1c levels, particularly in adolescents. With the Type Zero InControl Phone connected to a Dexcom G5 sensor and Novo insulin pens with memory and connectivity it is now possible to evaluate for late and missed meal boluses in MDI users. Methods: For this study 326 days of CGM data were evaluated from 24 subjects (mean age 33, range 15-59 years). Seven days of data were analyzed on starting the system and 7 days 1 month later. Overall 1,173 meals were evaluated. Meals were determined by either the subject manually recording the meal, or if the CGM was &gt;70 mg/dl and there was a &gt;70 mg/dl rise within 2 hours. A late meal bolus was defined when the CGM increased &gt;50 mg/dl from baseline prior to an insulin dose. A missed meal bolus was defined by no insulin dose within 2 hours from the start of the CGM rise. Results: 27% (range 8 to 55%) of meals had either a late or missed meal boluses: 13% (range 2-30%) were late and 14% (range 2 to 38%) were missed. There was no correlation with age or gender. There was a positive correlation with the percentage of missed meal boluses and A1c levels (p=0.019), but no correlation with the percentage of late meal boluses and A1c levels. There were no significant differences in missed or late meal boluses when comparing the first week of use to using the system one month later. Conclusion: The rate of late or missed meal boluses is high for both adults and adolescents using MDI therapy, and missed meal boluses correlated with higher A1c levels. Having this information may provide significant insight to patients and help clinicians provide advice to MDI patients. Disclosure L.M. Norlander: None. S. Anderson: Research Support; Self; Medtronic. Consultant; Self; Senseonics. C.J. Levy: Advisory Panel; Self; Medtronic MiniMed, Inc.. Research Support; Self; Lexicon Pharmaceuticals, Inc., Dexcom, Inc.. L. Ekhlaspour: None. D.W. Lam: None. L. Hsu: None. S.E. Loebner: None. S.J. Ogyaadu: None. G. O'Malley: None. C.M. Levister: None. M.D. Breton: Stock/Shareholder; Self; TypeZero Technologies, Inc.. Speaker's Bureau; Self; Ascensia Diabetes Care. Consultant; Self; Sanofi. Speaker's Bureau; Self; Roche Diabetes Care Health and Digital Solutions. Research Support; Self; Dexcom, Inc., Ascensia Diabetes Care, Senseonics. B. Buckingham: Advisory Panel; Self; Novo Nordisk Inc., ConvaTec Inc.. Research Support; Self; Medtronic, Insulet Corporation, Dexcom, Inc., Tandem Diabetes Care, Inc.. Consultant; Self; Tandem Diabetes Care, Inc., Becton, Dickinson and Company.

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