Integrated closed-loop control (CLC), combining continuous glucose monitoring (CGM) with insulin pump (continuous subcutaneous insulin infusion [CSII]), known as artificial pancreas, can help optimize glycemic control in diabetes. We present a fundamental modular concept for CLC design, illustrated by clinical studies involving 11 adolescents and 27 adults at the Universities of Virginia, Padova, and Montpellier. We tested two modular CLC constructs: standard control to range (sCTR), designed to augment pump plus CGM by preventing extreme glucose excursions; and enhanced control to range (eCTR), designed to truly optimize control within near normoglycemia of 3.9–10 mmol/L. The CLC system was fully integrated using automated data transfer CGM→algorithm→CSII. All studies used randomized crossover design comparing CSII versus CLC during identical 22-h hospitalizations including meals, overnight rest, and 30-min exercise. sCTR increased significantly the time in near normoglycemia from 61 to 74%, simultaneously reducing hypoglycemia 2.7-fold. eCTR improved mean blood glucose from 7.73 to 6.68 mmol/L without increasing hypoglycemia, achieved 97% in near normoglycemia and 77% in tight glycemic control, and reduced variability overnight. In conclusion, sCTR and eCTR represent sequential steps toward automated CLC, preventing extremes (sCTR) and further optimizing control (eCTR). This approach inspires compelling new concepts: modular assembly, sequential deployment, testing, and clinical acceptance of custom-built CLC systems tailored to individual patient needs.

Introduction:In late February 2020, due to the spread of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), the Italian Government closed down all educational and sport activities. In March, it introduced further measures to stop the spread of coronavirus disease (COVID-19), placing the country in a state of almost complete lockdown. We report the impact of these restrictions on glucose control among people with type 1 diabetes (T1D). Methods: Data were collected on 33 individuals with T1D who were monitoring their glucose levels using a flash glucose monitoring device and remotely connected to the diabetes clinic on a cloud platform. We retrieved information on average glucose, standard deviation and percentage time in hypoglycaemia (\ 70 mg/ dl), glucose range (70-180 mg/dl) and hyperglycaemia ([ 180 mg/dl). We compared glycaemic measures collected during lockdown to those collected before the SARS-CoV-2 epidemic and to the periods immediately before lockdown. Results: In 20 patients who had stopped working and were at home as a result of the lockdown, overall glycaemic control improved during the first 7 days of the lockdown as compared to the weeks before the spread of SARS-CoV-2. Average glucose declined from 177 ± 45 mg/dl (week before lockdown) to 160 ± 40 mg/dl (lockdown; p = 0.005) and the standard deviation improved significantly. Time in range increased from 54.4 to 65.2% (p = 0.010), and time in hyperglycaemia decreased from 42.3 to 31.6% (p = 0.016). The number of scans per day remained unchanged. In 13 patients who continued working, none of the measures of glycaemic control changed during lockdown. Conclusion: Despite the limited possibility to exercise and the incumbent psychologic stress, glycaemic control improved in patients with T1D who stopped working during the lockdown, suggesting that slowing down routine daily activities can have beneficial effects on T1D management, at least in the short term.

OBJECTIVEWidespread use of carbohydrate counting is limited by its complex education. In this study we compared a Diabetes Interactive Diary (DID) with standard carbohydrate counting in terms of metabolic and weight control, time required for education, quality of life, and treatment satisfaction.RESEARCH DESIGN AND METHODSAdults with type 1 diabetes were randomly assigned to DID (group A, n = 67) or standard education (group B, n = 63) and followed for 6 months. A subgroup also completed the SF-36 Health Survey (SF-36) and World Health Organization-Diabetes Treatment Satisfaction Questionnaire (WHO-DTSQ) at each visit.RESULTSOf 130 patients (aged 35.7 ± 9.4 years; diabetes duration 16.5 ± 10.5 years), 11 dropped out. Time for education was 6 h (range 2–15 h) in group A and 12 h (2.5–25 h) in group B (P = 0.07). A1C reduction was similar in both groups (group A from 8.2 ± 0.8 to 7.8 ± 0.8% and group B from 8.4 ± 0.7 to 7.9 ± 1.1%; P = 0.68). Nonsignificant differences in favor of group A were documented for fasting blood glucose and body weight. No severe hypoglycemic episode occurred. WHO-DTSQ scores increased significantly more in group A (from 26.7 ± 4.4 to 30.3 ± 4.5) than in group B (from 27.5 ± 4.8 to 28.6 ± 5.1) (P = 0.04). Role Physical, General Health, Vitality, and Role Emotional SF-36 scores improved significantly more in group A than in group B.CONCLUSIONSDID is at least as effective as traditional carbohydrate counting education, allowing dietary freedom for a larger proportion of type 1 diabetic patients. DID is safe, requires less time for education, and is associated with lower weight gain. DID significantly improved treatment satisfaction and several quality-of-life dimensions.

CSII usage offers significant benefits over NPH-based MDI for individuals with Type 1 diabetes, with improvement in all significant metabolic parameters as well as in patients' quality of life. Additional studies are needed to compare CSII with glargine- and detemir-based MDI.

Background: In 2008–2009, the first multinational study was completed comparing closed-loop control (artificial pancreas) to state-of-the-art open-loop therapy in adults with type 1 diabetes mellitus (T1DM). Methods: The design of the control algorithm was done entirely in silico, i.e., using computer simulation experiments with N = 300 synthetic “subjects” with T1DM instead of traditional animal trials. The clinical experiments recruited 20 adults with T1DM at the Universities of Virginia (11); Padova, Italy (6); and Montpellier, France (3). Open-loop and closed-loop admission was scheduled 3–4 weeks apart, continued for 22 h (14.5 h of which were in closed loop), and used a continuous glucose monitor and an insulin pump. The only difference between the two sessions was that insulin dosing was performed by the patient under a physician's supervision during open loop, whereas insulin dosing was performed by a control algorithm during closed loop. Results: In silico design resulted in rapid (less than 6 months compared to years of animal trials) and cost-effective system development, testing, and regulatory approvals in the United States, Italy, and France. In the clinic, compared to open-loop, closed-loop control reduced nocturnal hypoglycemia (blood glucose below 3.9 mmol/liter) from 23 to 5 episodes (p < .01) and increased the amount of time spent overnight within the target range (3.9 to 7.8 mmol/liter) from 64% to 78% (p = .03). Conclusions: In silico experiments can be used as viable alternatives to animal trials for the preclinical testing of insulin treatment strategies. Compared to open-loop treatment under identical conditions, closed-loop control improves the overnight regulation of diabetes.

No abstract

OBJECTIVETo evaluate the feasibility of a wearable artificial pancreas system, the Diabetes Assistant (DiAs), which uses a smart phone as a closed-loop control platform.RESEARCH DESIGN AND METHODSTwenty patients with type 1 diabetes were enrolled at the Universities of Padova, Montpellier, and Virginia and at Sansum Diabetes Research Institute. Each trial continued for 42 h. The United States studies were conducted entirely in outpatient setting (e.g., hotel or guest house); studies in Italy and France were hybrid hospital–hotel admissions. A continuous glucose monitoring/pump system (Dexcom Seven Plus/Omnipod) was placed on the subject and was connected to DiAs. The patient operated the system via the DiAs user interface in open-loop mode (first 14 h of study), switching to closed-loop for the remaining 28 h. Study personnel monitored remotely via 3G or WiFi connection to DiAs and were available on site for assistance.RESULTSThe total duration of proper system communication functioning was 807.5 h (274 h in open-loop and 533.5 h in closed-loop), which represented 97.7% of the total possible time from admission to discharge. This exceeded the predetermined primary end point of 80% system functionality.CONCLUSIONSThis study demonstrated that a contemporary smart phone is capable of running outpatient closed-loop control and introduced a prototype system (DiAs) for further investigation. Following this proof of concept, future steps should include equipping insulin pumps and sensors with wireless capabilities, as well as studies focusing on control efficacy and patient-oriented clinical outcomes.

AimsTo assess the accuracy and reliability of the two most widely used continuous glucose monitoring (CGM) systems.MethodsWe studied the Dexcom®G4 Platinum (DG4P; Dexcom, San Diego, CA, USA) and Medtronic Paradigm Veo Enlite system (ENL; Medtronic, Northridge, CA, USA) CGM systems, in 24 patients with type 1 diabetes. The CGM systems were tested during 6-day home use and a nested 6-h clinical research centre (CRC) visit. During the CRC visit, frequent venous blood glucose samples were used as reference while patients received a meal with an increased insulin bolus to induce an aggravated postprandial glucose nadir. At home, patients performed at least six reference capillary blood measurements per day. A Wilcoxon signed-rank test was performed using all data points ≥15 min apart.ResultsThe overall mean absolute relative difference (MARD) value [standard deviation (s.d.)] measured at the CRC was 13.6 (11.0)% for the DG4P and 16.6 (13.5)% for the ENL [p < 0.0002, confidence interval of difference (CI Δ) 1.7–4.3%, n = 530]. The overall MARD assessed at home was 12.2 (12.0)% for the DG4P and 19.9 (20.5)% for the ENL (p < 0.0001, CI Δ = 5.8–8.7%, n = 839). During the CRC visit, the MARD in the hypoglycaemic range [≤3.9 mmol/l (70 mg/dl)], was 17.6 (12.2)% for the DG4P and 24.6 (18.8)% for the ENL (p = 0.005, CI Δ 3.1–10.7%, n = 117). Both sensors showed higher MARD values during hypoglycaemia than during euglycaemia [3.9–10 mmol/l (70–180 mg/dl)]: for the DG4P 17.6 versus 13.0% and for the ENL 24.6 versus 14.2%.ConclusionsDuring circumstances of intended use, including both a CRC and home phase, the ENL was noticeably less accurate than the DG4P sensor. Both sensors showed lower accuracy in the hypoglycaemic range. The DG4P was less affected by this negative effect of hypoglycaemia on sensor accuracy than was the ENL.