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Objective: To determine the safety and efficacy of an automated unified safety system (USS) in providing overnight closed-loop (OCL) control in children and adolescents with type 1 diabetes attending diabetes summer camps. Research Design and Methods: The Diabetes Assistant (DIAS) USS used the Dexcom G4 Platinum glucose sensor (Dexcom) and t:slim insulin pump (Tandem Diabetes Care). An initial inpatient study was completed for 12 participants to evaluate safety. For the main camp study, 20 participants with type 1 diabetes were randomized to either OCL or sensor-augmented therapy (control conditions) per night over the course of a 5- to 6-day diabetes camp. Results: Subjects completed 54 OCL nights and 52 control nights. On an intention-to-treat basis, with glucose data analyzed regardless of system status, the median percent time in range, from 70-150 mg/dL, was 62% (29, 87) for OCL nights versus 55% (25, 80) for sensor-augmented pump therapy (P = 0.233). A per-protocol analysis allowed for assessment of algorithm performance. The median percent time in range, from 70-150 mg/dL, was 73% (50, 89) for OCL nights (n = 41) versus 52% (24, 83) for control conditions (n = 39) (P = 0.037). There was less time spent in the hypoglycemic range <50, <60, and <70 mg/dL during OCL compared with the control period (P = 0.019, P = 0.009, and P = 0.023, respectively). Conclusions: The DIAS USS algorithm is effective in improving time spent in range as well as reducing nocturnal hypoglycemia during the overnight period in children and adolescents with type 1 diabetes in a diabetes camp setting. [ABSTRACT FROM AUTHOR]

OBJECTIVE Inpatient studies suggest that model predictive control (MPC) is one of the most promising algorithms for artificial pancreas (AP). So far, outpatient trials have used hypo/hyperglycemia-mitigation ormedical-expert systems. In this study, we report the first wearable AP outpatient study based on MPC and investigate specifically its ability to control postprandial glucose, one of the major challenges in glucose control. RESEARCH DESIGN AND METHODS A new modular MPC algorithm has been designed focusing on meal control. Six type 1 diabetes mellitus patients underwent 42-h experiments: sensor-augmented pump therapy in the first 14 h (open-loop) and closed-loop in the remaining 28 h. RESULTS MPC showed satisfactory dinner control versus open-loop: time-in-target (70-180 mg/dL) 94.83 vs. 68.2% and time-in-hypo 1.25 vs. 11.9%. Overnight control was also satisfactory: time-in-target 89.4 vs. 85.0% and time-in-hypo: 0.00 vs. 8.19%. CONCLUSIONS This outpatient study confirms inpatient evidence of suitability of MPC-based strategies for AP. These encouraging results pave the way to randomized crossover outpatient studies. [ABSTRACT FROM AUTHOR]

OBJECTIVE To evaluate the efficacy of a portable, wearable, wireless artificial pancreas system (the Diabetes Assistant [DiAs] running the Unified Safety System) on glucose control at home in overnight-only and 24/7 closed-loop control (CLC) modes in patients with type 1 diabetes. RESEARCH DESIGN AND METHODS At six clinical centers in four countries, 30 participants 18–66 years old with type 1 diabetes (43% female, 96% non-Hispanic white, median type 1 diabetes duration 19 years, median A1C 7.3%) completed the study. The protocol included a 2-week baseline sensor-augmented pump (SAP) period followed by 2 weeks of overnight-only CLC and 2 weeks of 24/7 CLC at home. Glucose control during CLC was compared with the baseline SAP. RESULTS Glycemic control parameters for overnight-only CLC were improved during the nighttime period compared with baseline for hypoglycemia (time <70 mg/dL, primary end point median 1.1% vs. 3.0%; P < 0.001), time in target (70–180 mg/dL: 75% vs. 61%; P < 0.001), and glucose variability (coefficient of variation: 30% vs. 36%; P < 0.001). Similar improvements for day/night combined were observed with 24/7 CLC compared with baseline: 1.7% vs. 4.1%, P < 0.001; 73% vs. 65%, P < 0.001; and 34% vs. 38%, P < 0.001, respectively. CONCLUSIONS CLC running on a smartphone (DiAs) in the home environment was safe and effective. Overnight-only CLC reduced hypoglycemia and increased time in range overnight and increased time in range during the day; 24/7 CLC reduced hypoglycemia and increased time in range both overnight and during the day. Compared with overnight-only CLC, 24/7 CLC provided additional hypoglycemia protection during the day.

Automated Insulin Delivery with SGLT2i Combination Therapy in Type 1 Diabetes I Garcia-Tirado J SP 1 sp , Farhy L SP 1,2 sp , Nass R SP 2 sp , Kollar L SP 1 sp , Clancy-Oliveri M SP 1 sp , Basu R SP 1,2 sp , Kovatchev B SP 1 sp , Basu A SP 1,2 sp i I SP 1 sp Center for Diabetes Technology, University of Virginia, Charlottesville, VA; SP 2 sp Division of Endocrinology and Metabolism, Department of Medicine, University of Virginia, Charlottesville, VA i I Diabetes Technol Ther 2022; i B I 24 i b I : 461-470 i Background Use of sodium-glucose cotransporter 2 inhibitors (SGLT2is) as adjunct therapy to insulin in type 1 diabetes (T1D) has been previously studied. Empagliflozin add-on therapy to closed-loop insulin delivery in type 1 diabetes: a 2 × 2 factorial randomized crossover trial. Closed-loop (CL) control of diabetes, known as the "artificial pancreas", or automated insulin delivery (AID), is no longer a strictly research subject. Closed-loop therapy plus empagliflozin improved time in target range compared to SAP therapy plus empagliflozin by 6.1% but by 17.5% for the combination of closed-loop therapy and empagliflozin compared to SAP therapy plus placebo. [Extracted from the article]

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 custombuilt CLC systems tailored to individual patient needs. [ABSTRACT FROM AUTHOR]

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Abstract: Scientific and industrial effort is now increasingly focused on the development of closed-loop control systems (artificial pancreas) to control glucose metabolism of people with diabetes, particularly type 1 diabetes mellitus. The primary prerequisite to a successful artificial pancreas, and to optimal diabetes control in general, is the continuous glucose monitor (CGM), which measures glucose levels frequently (e.g., every 5 min). Thus, a CGM collects detailed glucose time series, which carry significant information about the dynamics of glucose fluctuations. However, a CGM assesses blood glucose indirectly via subcutaneous determinations. As a result, two types of analytical problems arise for the retrieval and interpretation of CGM data: (1) the order and the timing of CGM readings and (2) sensor errors, time lag, and deviations from BG need to be accounted for. In order to improve the quality of information extracted from CGM data, we suggest several analytical and data visualization methods. These analyses evaluate CGM errors, assess risks associated with glucose variability, quantify glucose system stability, and predict glucose fluctuation. All analyses are illustrated with data collected using MiniMed CGMS (Medtronic, Northridge, CA) and Freestyle Navigator (Abbott Diabetes Care, Alameda, CA). It is important to remember that traditional statistics do not work well with CGM data because consecutive CGM readings are highly interdependent. In conclusion, advanced analysis and visualization of CGM data allow for evaluation of dynamical characteristics of diabetes and reveal clinical information that is inaccessible via standard statistics, which do not take into account the temporal structure of data. The use of such methods has the potential to enable optimal glycemic control in diabetes and, in the future, artificial pancreas systems. [Copyright &y& Elsevier]

OBJECTIVE -- The purpose of this study was to compare the numerical and clinical accuracy of four continuous glucose monitors (CGMs): Guardian, DexCom, Navigator, and Glucoday. RESEARCH DESIGN AND METHODS -- Accuracy data for the four CGMs were collected in two studies: Study 1 enrolled 14 adults with type 1 diabetes at the University of Virginia (UVA), Charlottesville, Virginia; study 2 enrolled 20 adults with type 1 diabetes at the Profil Institute for Metabolic Research, Neuss, Germany. All participants underwent hyperinsulinemic clamps including 1.5-2 h of maintained euglycemia at 5.6 mmol/l followed by descent into hypoglycemia, sustained hypoglycemia at 2.5 mmol/l for 30 min, and recovery. Reference blood glucose sampling was performed every 5 min. The UVA study tested Guardian, DexCom, and Navigator simultaneously; the Profil study tested Glucoday. RESULTS -- Regarding numerical accuracy, during euglycemia, the mean absolute relative differences (MARDs) of Guardian, DexCom, Navigator, and Glucoday were 15.2, 21.2, 15.3, and 15.6%, respectively. During hypoglycemia, the MARDs were 16.1, 21.5, 10.3, and 17.5%, respectively. Regarding clinical accuracy, continuous glucose-error grid analysis (CG-EGA) revealed 98.9, 98.3, 98.6, and 95.5% zones A + B hits in euglycemia. During hypoglycemia, zones A + B hits were 84.4, 97.0, and 96.2% for Guardian, Navigator, and Glucoday, respectively. Because of frequent loss of sensitivity, there were insufficient hypoglycemic DexCom data to perform CG-EGA. CONCLUSIONS-- The numerical accuracy of Guardian, Navigator, and Glucoday was comparable, with an advantage to the Navigator in hypoglycemia; the numerical errors of the DexCom were ∼30% larger. The clinical accuracy of the four sensors was similar in euglycemia and was higher for the Navigator and Glucoday in hypoglycemia. [ABSTRACT FROM AUTHOR]

Objectives: This study examined the dynamics of postprandial glucose (PPG) and symptoms among adults with type 2 diabetes mellitus (T2DM) in their natural environment. Using a hand-held computer for 70 trials, 36 adults with T2DM rated symptoms, performed tests, and measured their blood glucose (BG). Results: The mean peak PPG value was 11.3 mmol/L achieved 2-3 hours after meal, while highest symptom ratings and cognitive slowing were observed within the first hour after meal, at a time corresponding to the steepest slope of PPG increase. Thus, we hypothesized that postprandial symptoms maybe related to a higher rate of BG increase, which was confirmed by high correlations (r = 0.5-0.75) between symptom ratings and BG rate of increase. Conclusions: We conclude that postprandial symptom elevation is related to the rate of BG increase, thus treatments designed to limit the magnitude of post-meal BG fluctuation may reduce its symptomatic and cognitive consequences. [ABSTRACT FROM AUTHOR]

Formulates the basic growth hormone (GH) secretion pattern in the male rat in a simplified construction arising from the primary consensus interactions in the system among GH, GH-releasing hormone (GHRH) and the release-inhibiting hormone somatostatin (SRIF). GHRH injections during GH peaks and troughs; Effect of SRIF antagonism; Simulation of SRIF by an exogenous GH bolus.

This study continues our research to further validate the idea that ADHD (Attention Deficit/Hyperactivity Disorder) interferes with transition from one task to another and this interference can be quantified by a Consistency Index (CI) derived from a specific mathematical representation of EEG data. We reanalyze 32 previously reported data sets present new data for 35 boys and girls, ages 7–12, ADHD or control. Each data set contains EEG, recorded and digitized while participants perform consecutive 10-min tasks: video, reading, and math. For boys, the CI in ADHD was four times lower than in controls, p < .005, for girls this difference was two times, p < .05. ADHD/control classification based on the CI coincided with the DSM-IV criteria for 88% of the boys and for 67% of the girls. Post hoc analysis indicated that the classification utility of the CI diminished with age. A CI below 40% could be a discriminating, reliable, and reproducible marker of ADHD in young boys. [ABSTRACT FROM AUTHOR]

The primary diagnostic procedure for Attention-Deficit/Hyperactivity Disorder (ADHD) is the clinical interview, because psychological, neuropsychological, and neurological tests to date have not had sufficient specificity. Currently, there is no objective means to measure severity of ADHD, or the extent to which it is benefited by various dosages of medication. We recently reported that a certain EEG profile, the Consistency Index, occurring during the transition between two easy cognitive tasks clearly differentiated ADHD from non-ADHD boys between the ages of 8 and 12. The current study replicated this with older males (19–25) using different tasks, and a double blind, placebo versus Ritalin® controlled crossover design. Seven ADHD subjects were found to have a significantly lower Consistency Index than 6 non-ADHD males while transitioning from 2 Simple tasks during placebo condition, while only the ADHD subjects demonstrated a significant improvement in their Consistency Index while on Ritalin®. Similar but nonsignificant trends were observed while transitioning across Hard tasks. [ABSTRACT FROM AUTHOR]

Background: With the proliferation of continuous glucose monitoring (CGM), a number of metrics were developed to assess quality of glycemic control. Many of them are highly correlated. Thus, we aim to identify the principal dimensions of glycemic control-a minimal set of metrics, necessary and sufficient for comprehensive assessment of diabetes management. Methods: Seventy-five thousand five hundred sixty-three 2-week CGM profiles recorded in six studies by 790 individuals with type 1 or type 2 diabetes were used to compute mean glucose (MG), percentage time >180 mg/dL (TAR), >250 mg/dL (TAR2), <70 mg/dL (TBR), <54 mg/dL (TBR2), and coefficient of variation (CV). The true dimensionality of the glycemic-metric space was identified in a training set (53,380 profiles) and validated in an independent test set (22,183 profiles). Results: Correlation analysis identified two blocks of metrics-(MG, TAR, TAR2) and (TBR, TBR2, CV)-each with high internal correlation, but insignificant between-block correlation, suggesting that the true dimensionality of the glycemic-metric space is 2. Principal component analysis confirmed two essential metrics quantifying exposure to hyperglycemia (i.e., treatment efficacy) and risk for hypoglycemia (i.e., treatment safety), and explaining ∼90% of the variance in the training and test data. Conclusion: Two essential metrics, treatment efficacy and treatment safety, are necessary and sufficient to characterize glycemic control in diabetes. Thus, quantitatively, diabetes treatment optimization is reduced to a two-dimensional problem, meaning that minimizing both exposure to hyperglycemia and risk for hypoglycemia will lead to improvement in any other metric of glycemic control. [ABSTRACT FROM AUTHOR]

Background: While it is well recognized that an automated insulin delivery (AID) algorithm should adapt to changes in physiology, it is less understood that the individual would also have to adapt to the AID system. The adaptive biobehavioral control (ABC) method presented here attempts to compensate for this deficiency by including AID into an information cloud-based ecosystem. Methods: The Web Information Tool (WIT) implements the ABC concept via the following: (1) a Physiological Adaptation Module (PAM) that tracks metabolic changes and adapts AID parameters accordingly and (2) a Behavioral Adaptation Module (BAM) that provides information feedback. The safety of WIT (primary outcome) was assessed in an 8-week randomized, two-arm parallel pilot study. All participants used the Control-IQ® AID system enhanced with PAM, but only those in the Experimental group had access to BAM. Secondary glycemic outcomes were computed using the 2-week baseline period and the last 2 weeks of treatment. Results: Thirty participants with type 1 diabetes (T1D) completed all study procedures (17 female/13 male; age: 40 ± 14 years; HbA1c: 6.6% ± 0.5%). No severe hypoglycemia, DKA, or other serious adverse events were reported. Comparing the Experimental and Control groups, no significant difference was observed in time in range (70–180 mg/dL): 74.6% vs 73.8%, adjusted mean difference: 2.65%, 95% CI (−1.12%,6.41%), P = 0.161. Time in 70–140 mg/dL was significantly higher in the Experimental group: 50.7% vs 49.2%, 5.71% (0.44%,10.97%), P = 0.035, without increased time below range: 0.54% (−0.09%,1.17%), P = 0.089. Conclusion: The results demonstrate that it is safe to integrate an AID system into the WIT ecosystem. Validation in a full-scale study is ongoing. [ABSTRACT FROM AUTHOR]

Attention-Deficit/Hyperactivity Disorder (ADHD) is reported to have an incidence of 3–5%, and is associated with a variety of interpersonal, academic, and social problem behaviors. There is controversy as to whether ADHD is a learned behavioral or brain dysfunction. Research has explored a variety of measures to assess behavioral and brain dysfunctions in this population, with no consistent and clearly diagnostic results. We investigated whether a new psychometric and a new electroencephalographic procedure would clearly differentiate ADHD. The psychometric was based on DSM-IV criteria and the EEG measure was based on the assumption that ADHD interferes with cognitive transition from one discrete task to another. Parents of four ADHD boys (ages 8–12) and four age- and interest-matched non-ADHD boys completed the ADHD Symptom Inventory, while their sons' EEG was monitored during viewing of a video and reading of a book. For the ADHD boys, this was repeated a second time, 3 months later, to assess test-retest reliability. Both the psychometric and the EEG measures clearly differentiated the two samples (p's < .01) with no overlap in scores, were reliable over 3 months (r = .87), and were significantly correlated with one another (r = .85). While a small sample size, these robust, related and reliable findings suggest that both the psychometric and the psychophysiological EEG measures deserve further replication and exploration. [ABSTRACT FROM AUTHOR]