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Introduction: Hyperglycemia following meals is a recurring challenge for people with type 1 diabetes, and even the most advanced available automated systems currently require manual input of carbohydrate amounts. To progress toward fully automated systems, we present a novel control system that can automatically deliver priming boluses and/or anticipate eating behaviors to improve postprandial full closed-loop control. Methods: A model predictive control (MPC) system was enhanced by an automated bolus system reacting to early glucose rise and/or a multistage MPC (MS-MPC) framework to anticipate historical patterns. Priming was achieved by detecting large glycemic disturbances, such as meals, and delivering a fraction of the patient’s total daily insulin (TDI) modulated by the disturbance’s likelihood (bolus priming system [BPS]). In the anticipatory module, glycemic disturbance profiles were generated from historical data using clustering to group days with similar behaviors; the probability of each cluster is then evaluated at every controller step and informs the MS-MPC framework to anticipate each profile. We tested four configurations: MPC, MPC + BPS, MS-MPC, and MS-MPC + BPS in simulation to contrast the effect of each controller module. Results: Postprandial time in range was highest for MS-MPC + BPS: 60.73 ± 25.39%, but improved with each module: MPC + BPS: 56.95±25.83 and MS-MPC: 54.83 ± 26.00%, compared with MPC: 51.79 ± 26.12%. Exposure to hypoglycemia was maintained for all controllers (time below 70 mg/dL Conclusions: The BPS and anticipatory disturbance profiles improved blood glucose control and were most efficient when combined.

Ultra-rapid acting insulin analogs that could improve or even prevent postprandial hyperglycemia are now available for both research and clinical care. However, clear glycemic benefits remain elusive, especially when combined with automated insulin delivery (AID) systems. In this work, we study two insulin formulations in-silico and highlight adjustments of both open-loop and closed-loop insulin delivery therapies as a critical step to achieve clinically meaningful improvements.Subcutaneous insulin transport models for two faster analogs, Fiasp (Novo Nordisk, Bagsværd, Denmark) and AT247 (Arecor, Saffron Walden UK), were identified using data collected from prior clamp experiments, and integrated into the UVA/Padova type 1 diabetes (T1D) simulator. Pump therapy parameters and the aggressiveness of our full closed-loop algorithm were adapted to the new insulin pharmacokinetic and pharmacodynamic profiles through a sequence of in-silico studies. Finally, we assessed these analogs' glycemic impact with and without modified therapy parameters in simulated conditions designed to match clinical trial data.Simply switching to faster insulin analogs shows limited improvements in glycemic outcomes. However, when insulin acceleration is accompanied by therapy adaptation, clinical significance is found comparing Aspart with AT247 in open-loop (+5.1%); and Aspart versus Fiasp (+5.4%) or AT247 (+10.6%) in full closed-loop with no clinically significant differences in the exposure to hypoglycemia.In-silico results suggest that properly adjusting intensive insulin therapy profiles, or AID tuning, to faster insulin analogs is necessary to obtain clinically significant improvements in glucose control.

Background: The t:slim X2™ insulin pump with Control-IQ® technology from Tandem Diabetes Care is an advanced hybrid closed-loop system that was first commercialized in the United States in January 2020. Longitudinal glycemic outcomes associated with real-world use of this system have yet to be reported. Methods: A retrospective analysis of Control-IQ technology users who uploaded data to Tandem's t:connect® web application as of February 11, 2021 was performed. Users age ≥6 years, with >2 weeks of continuous glucose monitoring (CGM) data pre- and >12 months post-Control-IQ technology initiation were included in the analysis. Results: In total 9451 users met the inclusion criteria, 83% had type 1 diabetes, and the rest had type 2 or other forms of diabetes. The mean age was 42.6 ± 20.8 years, and 52% were female. Median percent time in automation was 94.2% [interquartile range, IQR: 90.1%–96.4%] for the entire 12-month duration of observation, with no significant changes over time. Of these users, 9010 (96.8%) had ≥75% of their CGM data available, that is, sufficient data for reliable computation of CGM-based glycemic outcomes. At baseline, median percent time in range (70–180 mg/dL) was 63.6 (IQR: 49.9%–75.6%) and increased to 73.6% (IQR: 64.4%–81.8%) for the 12 months of Control-IQ technology use with no significant changes over time. Median percent time

Background: It has been shown that insulin acceleration by itself might not be sufficient to see clear improvements in glycemic metrics, and insulin therapy may need to be adjusted to fully leverage the extra safety margin provided by faster pharmacokinetic (PK) and pharmacodynamic (PD) profiles. The objective of this work is to explore how to perform such adjustments on a commercially available automated insulin delivery (AID) system. Methods: Ultra-rapid lispro (URLi) is modeled within the UVA/Padova simulation platform using data from previously published clamp studies. The Control-IQ AID algorithm is selected as it leverages carbohydrate-to-insulin ratio (CR in g/U), correction factor (CF in mg/dL/U), and basal rate (BR in U/h) daily profiles that are fully customizable. An experiment roadmap is proposed to understand how to safely modify these profiles when switching from lispro to URLi. Results: Simulations show that a 7% decrease in CR (approximately an 8% increase in prandial insulin) and a 7.5% increase in BR lead to cumulative improvements in glucose control with URLi. Comparing with baseline metrics using lispro, a clinically significant increase in time in the range of 70 to 180 mg/dL (overall: 70.2%-75.2%, P < .001; 6 am-12 am: 62.4%-68.5%, P < .001) and a reduction in time below 70 mg/dL (overall: 1.8%-1.2%, P < .001; 6 am-12 am: 1.8%-1.3%, P < .001) were observed. Conclusion: Properly adjusting therapy parameters allows to fully leverage glucose control benefits provided by faster insulin analogues, opening opportunities to take another step forward into a next generation of more effective AID solutions.

Background: Physical activity can cause glucose fluctuations both during and after it is performed, leading to hurdles in optimal insulin dosing in people with type 1 diabetes (T1D). We conducted a pilot clinical trial assessing the safety and feasibility of a physical activity-informed mealtime insulin bolus advisor that adjusts the meal bolus according to previous physical activity, based on step count data collected through an off-the-shelf physical activity tracker. Methods: Fifteen adults with T1D, each using a continuous glucose monitor (CGM) and an insulin pump with carbohydrate counting, completed two randomized crossover daily visits. Participants performed a 30 to 45-minute brisk walk before lunch and lunchtime insulin boluses were calculated based on either their standard therapy (ST) or the physical activity-informed bolus method. Post-lunch glycemic excursions were assessed using CGM readings. Results: There was no significant difference between visits in the time spent in hypoglycemia in the post-lunch period (median [IQR] standard: 0 [0]% vs physical activity-informed: 0 [0]%, P = NS). Standard therapy bolus yielded a higher time spent in 70 to 180 mg/dL target range (mean ± standard: 77% ± 27% vs physical activity-informed: 59% ± 31%, P = .03) yet, it was associated with a steeper negative slope in the early postprandial phase ( P = .032). Conclusions: Use of step count to adjust mealtime insulin following a walking bout has proved to be safe and feasible in a cohort of 15 T1D subjects. Physical activity-informed insulin dosing of meals eaten soon after a walking bout has a potential of mitigating physical activity related glucose reduction in the early postprandial phase.

Background: The capacity to replay data collected in real life by people with type 1 diabetes mellitus (T1DM) would lead to individualized (vs population) assessment of treatment strategies to control blood glucose and possibly true personalization. Patek et al introduced such a technique, relying on regularized deconvolution of a population glucose homeostasis model to estimate a residual additive signal and reproduce the experimental data; therefore, allowing the subject-specific replay of what-if scenarios by altering the model inputs (eg, insulin). This early method was shown to have a limited domain of validity. We propose and test in silico a similar approach and extend the method applicability. Methods: A subject-specific model personalization of insulin sensitivity and meal-absorption parameters is performed. The University of Virginia (UVa)/Padova T1DM simulator is used to generate experimental scenarios and test the ability of the methodology to accurately reproduce changes in glucose concentration to alteration in meal and insulin inputs. Method performance is assessed by comparing true (UVa/Padova simulator) and replayed glucose traces, using the mean absolute relative difference (MARD) and the Clarke error grid analysis (CEGA). Results: Model personalization led to a 9.08 and 6.07 decrease in MARD over a prior published method of replaying altered insulin scenarios for basal and bolus changes, respectively. Replay simulations achieved high accuracy, with MARD Conclusions: In silico studies demonstrate that the proposed method for replay simulation is numerically and clinically valid over broad changes in scenario inputs, indicating possible use in treatment optimization.

A growing number of individuals with type 1 diabetes are choosing to use “do-it-yourself” artificial pancreas systems (DIY APS) to support their diabetes self-management. Observational and self-report data of glycemic benefits of DIY APS are promising; however, without rigorous clinical trials or regulation from governing bodies, liability and user safety continue to be central concerns for stakeholders. Despite DIY APS having been used for several years now, there are no guidelines to assist users and healthcare professionals in addressing DIY APS use in routine clinical care. This commentary reports key stakeholders’ perspectives presented at the annual Advanced Technologies and Treatments in Diabetes conference in February 2020. Important considerations to inform the development of clinical care guidelines are also presented to generate further debate.

Objective: To assess the safety and efficacy of a simplified initialization for the Tandem t:slim X2 Control-IQ hybrid closed-loop system, using parameters based on total daily insulin (“MyTDI”) in...

Introduction We examined differences in hypoglycaemia risk between insulin glargine 300 U/mL (Gla-300) and insulin glargine 100 U/mL (Gla-100) in individuals with type 2 diabetes (T2DM) using the low blood glucose index (LBGI). Methods Daily profiles of self-monitored plasma glucose (SMPG) from the EDITION 2, EDITION 3 and SENIOR treat-to-target trials of Gla-300 versus Gla-100 were used to compute the LBGI, which is an established metric of hypoglycaemia risk. The analysis also examined documented (blood glucose readings 1.1) reported 4- to 8-fold more frequent DSH events than those at minimal risk (LBGI ≤ 1.1) (p ≤ 0.009). Conclusions The LBGI identified individuals with T2DM at risk for hypoglycaemia using SMPG data and correlated with the number of DSH events. Using the LBGI metric, a lower risk of hypoglycaemia with Gla-300 than Gla-100 was observed in all three trials. The finding that differences in LBGI are greater at night is consistent with previously published differences in the pharmacokinetic profiles of Gla-300 and Gla-100, which provides the physiological foundation for the presented results. Electronic Supplementary Material The online version of this article (10.1007/s13300-020-00808-y) contains supplementary material, which is available to authorized users.

OBJECTIVE Continuous glucose monitoring (CGM) improves diabetes management, but its reliability in individuals on hemodialysis is poorly understood and potentially affected by interstitial and intravascular volume variations. RESEARCH DESIGN AND METHODS We assessed the accuracy of a factory-calibrated CGM by using venous blood glucose measurements (vBGM) during hemodialysis sessions and self-monitoring blood glucose (SMBG) at home. RESULTS Twenty participants completed the protocol. The mean absolute relative difference of the CGM was 13.8% and 14.4%, when calculated on SMBG (n = 684) and on vBGM (n = 624), and 98.7% and 100% of values in the Parkes error grid A/B zones, respectively. Throughout 181 days of CGM monitoring, the median time in range (70–180 mg/dL) was 38.5% (interquartile range 29.3–57.9), with 28.7% (7.8–40.6) of the time >250 mg/dL. CONCLUSIONS The overall performance of a factory-calibrated CGM appears reasonably accurate and clinically relevant for use in practice by individuals on hemodialysis and health professionals to improve diabetes management.

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 >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 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.

AIM To assess the safety and efficacy of hybrid closed-loop (HCL) insulin delivery 24/7 versus only evening and night (E/N), and on extended 24/7 use, in free-living children with type 1 diabetes. MATERIALS AND METHODS Prepubertal children (n = 122; 49 females/73 males; age, 8.6 ± 1.6 years; diabetes duration, 5.2 ± 2.3 years; insulin pump use, 4.6 ± 2.5 years; HbA1c 7.7% ± 0.7%/61 ± 5 mmol/mol) from four centres were randomized for 24/7 versus E/N activation of the Tandem Control-IQ system for 18 weeks. Afterwards, all children used the activated system 24/7 for 18 more weeks. The primary outcome was the percentage of time spent in the 70-180 mg/dL glucose range (TIR). RESULTS HCL was active 94.1% and 51.1% of the time in the 24/7 and E/N modes, respectively. TIR from baseline increased more in the 24/7 versus the E/N mode (52.9% ± 9.5% to 67.3% ± 5.6% [+14.4%, 95% CI 12.4%-16.7%] vs. 55.1% ± 10.8% to 64.7% ± 7.0% [+9.6%, 95% CI 7.4%-11.6%]; P = .001). Mean percentage time below range was similarly reduced, from 4.2% and 4.6% to 2.7%, and the mean percentage time above range decreased more in the 24/7 mode (41.9% to 30.0% [-11.9%, 95% CI 9.7%-14.6%] vs. 39.8% to 32.6% [-7.2%, 95% CI 5.0%-9.9%]; P = .007). TIR increased through the whole range of baseline levels and always more with 24/7 use. The results were maintained during the extension phase in those initially on 24/7 use and improved in those with initial E/N use up to those with 24/7 use. Neither ketoacidosis nor severe hypoglycaemia occurred. CONCLUSIONS The current study shows the safety and efficacy of the Tandem Control-IQ system in free-living children with type 1 diabetes for both E/N and 24/7 use; 24/7 use shows better outcomes, sustained for up to 36 weeks with no safety issues.

OBJECTIVE Insulin dosing in type 1 diabetes (T1D) is oftentimes complicated by fluctuating insulin requirements driven by metabolic and psychobehavioral factors impacting individuals’ insulin sensitivity (IS). In this context, smart bolus calculators that automatically tailor prandial insulin dosing to the metabolic state of a person can improve glucose management in T1D. RESEARCH DESIGN AND METHODS Fifteen adults with T1D using continuous glucose monitors (CGMs) and insulin pumps completed two 24-h admissions in a hotel setting. During the admissions, participants engaged in an early afternoon 45-min aerobic exercise session, after which they received a standardized dinner meal. The dinner bolus was computed using a standard bolus calculator or smart bolus calculator informed by real-time IS estimates. Glucose control was assessed in the 4 h following dinner using CGMs and was compared between the two admissions. RESULTS The IS-informed bolus calculator allowed for a reduction in postprandial hypoglycemia as quantified by the low blood glucose index (2.02 vs. 3.31, P = 0.006) and percent time 180 mg/dL: 13.24% vs. 10.42%, P = 0.5; percent time >250 mg/dL: 2.08% vs. 1.19%, P = 0.317). In addition, the number of hypoglycemia rescue treatments was reduced from 12 to 7 with the use of the system. CONCLUSIONS The study shows that the proposed IS-informed bolus calculator is safe and feasible in adults with T1D, appropriately reducing postprandial hypoglycemia following an exercise-induced IS increase.

Patients with type 2 diabetes mellitus (T2DM) typically take blood glucose level lowering oral or injectable therapeutic agents to treat their condition. Titration and timing of administration of these agents can be difficult under optimal conditions. Largely because of these challenging tasks, less than half of patients with T2DM under therapy are reaching desired glycemic targets. Computer simulations have been shown in both types of diabetes to be powerful tools to design and test optimal therapies. However, the diversity of available therapeutic agents makes the construction of such a platform challenging. In this manuscript, we present a methodology to integrate pharmacokinetics (PK) and pharmacodynamics (PD) of anti-diabetic drugs into an existing T2DM population simulation platform to optimize therapy dosage and timing, and inform clinical trial designs; the mixture of insulin glargine and a glucagon-like peptide 1 receptor agonist (GLP1-RA) was used as an example. The platform was augmented with several drug-specific new/modified sub-models and the associated parameter distributions were derived from various blood measurements collected during clinical studies. The joint model parameter distribution of the augmented platform was obtained by fitting simulated glucose profiles on 2000 days of glucose sensor data in a novel Bayesian framework. The resulting platform was then validated by reproducing glucose distributions from a large clinical study, originally excluded from the training data. Finally, simulation experiments of optimal administration timing of the studied mixture were run.

This contribution presents an individualized multistage model predictive control (MS-MPC) algorithm for blood glucose (BG) stabilization and improved postprandial BG control for people with type 1 diabetes (T1D) with consistent meal patterns. The multistage formulation utilizes different meal patterns as disturbance realizations entering the glucose-insulin system, then assesses the best possible control input among all of the probable scenarios. The disturbance realizations, in the form of glucose rate of appearance traces, are estimated by using meal records (time and carbohydrate amount) as the input into an individualized oral model. Meal signatures are then clustered with the k-medoids algorithm to obtain meal patterns. Two approaches, a hybrid closed-loop (HCL) and fully closed-loop (FCL) MS-MPC were tested and compared with their respective control treatments (hybrid and fully automated MPC, respectively) using the complete in silico adult cohort of the FDA-accepted UVA/Padova metabolic simulator. Results confirm an improvement in both postprandial and overall percent time in 70-180 mg/dL 85.2 ± 15.5 v. 89.6 ± 12.2 and 94.1 ± 6.3 v. 95.7 ±5.0, respectively, using the HCL approach, and 37.8 ± 15.7 v. 63.4± 16.6 and 65.8 ± 12.7 v. 82.2± 9.2, using the FCL approach.P

Background: Maintaining glycemic equilibrium can be challenging for people living with type 1 diabetes (T1D) as many factors (eg, length, type, duration, insulin on board, stress, and training) will impact the metabolic changes triggered by physical activity potentially leading to both hypoglycemia and hyperglycemia. Therefore, and despite the noted health benefits, many individuals with T1D do not exercise as much as their healthy peers. While technology advances have improved glucose control during and immediately after exercise, it remains one of the key limitations of artificial pancreas (AP) systems, largely because stopping insulin at the onset of exercise may not be enough to prevent impending, exercise-induced hypoglycemia. Methods: A hybrid AP algorithm with subject-specific exercise behavior recognition and anticipatory action is designed to prevent hypoglycemic events during and after moderate-intensity exercise. Our approach relies on a number of key innovations, namely, an activity informed premeal bolus calculator, personalized exercise pattern recognition, and a multistage model predictive control (MS-MPC) strategy that can transition between reactive and anticipatory modes. This AP design was evaluated on 100 in silico subjects from the most up-to-date FDA-accepted UVA/Padova metabolic simulator, emulating an outpatient clinical trial setting. Results with a baseline controller, a regular MPC (rMPC), are also included for comparison purposes. Results: In silico experiments reveal that the proposed MS-MPC strategy markedly reduces the number of exercise-related hypoglycemic events (8 vs 68). Conclusion: An anticipatory mode for insulin administration of a monohormonal AP controller reduces the occurrence of hypoglycemia during moderate-intensity exercise.

Introduction: It is important to have accurate information regarding when individuals with type 1 diabetes have eaten and taken insulin to reconcile those events with their blood glucose levels throughout the day. Insulin pumps and connected insulin pens provide records of when the user injected insulin and how many carbohydrates were recorded, but it is often unclear when meals occurred. This project demonstrates a method to estimate meal times using a multiple hypothesis approach. Methods: When an insulin dose is recorded, multiple hypotheses were generated describing variations of when the meal in question occurred. As postprandial glucose values informed the model, the posterior probability of the truth of each hypothesis was evaluated, and from these posterior probabilities, an expected meal time was found. This method was tested using simulation and a clinical data set ( n = 11) and with either uniform or normally distributed ( μ = 0, σ = 10 or 20 minutes) prior probabilities for the hypothesis set. Results: For the simulation data set, meals were estimated with an average error of −0.77 (±7.94) minutes when uniform priors were used and −0.99 (±8.55) and −0.88 (±7.84) for normally distributed priors ( σ = 10 and 20 minutes). For the clinical data set, the average estimation error was 0.02 (±30.87), 1.38 (±21.58), and 0.04 (±27.52) for the uniform priors and normal priors ( σ = 10 and 20 minutes). Conclusion: This technique could be used to help advise physicians about the meal time insulin dosing behaviors of their patients and potentially influence changes in their treatment strategy.

To evaluate the effectiveness of predictive low glucose suspend (PLGS) systems within sensor-augmented insulin infusion pumps at preventing nocturnal hypoglycemia in patients with type 1 diabetes (DM1), we performed a systematic review and meta-analysis of randomized crossover trials. Pubmed and Google Scholar were searched for randomized crossover trials, published between January 2013 and July 2018, in nonpregnant outpatients with DM1, which compared event rates during PLGS overnight periods and non-PLGS overnight periods. The primary outcome was the proportion of overnight periods with one or more hypoglycemic measurement. When available, individual patient data were used to assess the effect of clustering measurements within patients. Four studies (272 patients, 10,735 patient-nights: 5422 PLGS and 5313 non-PLGS) were included in the meta-analysis. Two studies reported patient-level data that permitted assessment of the effect of clustering measurements within patients. The effect on the risk difference was minimal. The proportion of overnight periods with one or more episodes of hypoglycemia was 19.6% for the PLGS periods and 27.8% for the non-PLGS periods. Based on the pooled estimate, PLGS overnight periods were associated with an 8.8% lower risk of hypoglycemia (risk difference -0.088; 95% CI -0.119 to -0.056

Background: Typically, closed-loop control (CLC) studies excluded patients with significant hypoglycemia. We evaluated the effectiveness of hybrid CLC (HCLC) versus sensor-augmented pump (SAP) in reducing hypoglycemia in this high-risk population. Methods: Forty-four subjects with type 1 diabetes, 25 women, 37 ± 2 years old, HbA1c 7.4% ± 0.2% (57 ± 1.5 mmol/mol), diabetes duration 19 ± 2 years, on insulin pump, were enrolled at the University of Virginia (N = 33) and Stanford University (N = 11). Eligibility: increased risk of hypoglycemia confirmed by 1 week of blinded continuous glucose monitor (CGM); randomized to 4 weeks of home use of either HCLC or SAP. Primary/secondary outcomes: risk for hypoglycemia measured by the low blood glucose index (LBGI)/CGM-based time in ranges. Results: Values reported: mean ± standard deviation. From baseline to the final week of study: LBGI decreased more on HCLC (2.51 ± 1.17 to 1.28 ± 0.5) than on SAP (2.1 ± 1.05 to 1.79 ± 0.98), P

Objective: Hybrid closed-loop (HCL) artificial pancreas (AP) systems are now moving from research settings to widespread clinical use. In this study, the inControl algorithm developed by TypeZero Technologies was embedded to a commercial Tandem t:slim X2 insulin pump, now called Control-IQ, paired with a Dexcom G6 continuous glucose monitor and tested for superiority against sensor augmented pump (SAP) therapy. Both groups were physician-monitored throughout the clinical trial. Research Design and Methods: In a randomized controlled trial, 24 school-aged children (6–12 years) with type 1 diabetes (T1D) participated in a 3-day home-use trial at two sites: Stanford University and the Barbara Davis Center (50% girls, 9.6 ± 1.9 years of age, 4.5 ± 1.9 years of T1D, baseline hemoglobin A1c 7.35% ± 0.68%). Study subjects were randomized 1:1 at each site to either HCL AP therapy with the Control-IQ system or SAP therapy with remote monitoring. Results: The primary outcome, time in target range 70–180 mg/dL, using Control-IQ significantly improved (71.0% ± 6.6% vs. 52.8% ± 13.5%; P = 0.001) and mean sensor glucose (153.6 ± 13.5 vs. 180.2 ± 23.1 mg/dL; P = 0.003) without increasing hypoglycemia time

OBJECTIVE Parental sleep quality may contribute to glycemic control in youth with type 1 diabetes. In this article we present sleep analysis from a multicenter, randomized trial of children ages 6–13 years with type 1 diabetes evaluating the Tandem Control-IQ (CIQ) hybrid closed-loop (HCL) system. RESEARCH DESIGN AND METHODS Pittsburgh Sleep Quality Index (PSQI) scores were assessed at baseline to identify parents as “poor” sleepers (PSQI >5). Glycemic and psycho-behavioral outcomes before and after CIQ use were analyzed in poor sleepers (n = 49) and their children. RESULTS Nocturnal time in range (P < 0.001) and time hyperglycemic (P < 0.001), Hypoglycemia Fear Survey for Parents score (P < 0.001), Problem Areas in Diabetes scale score (P < 0.001), PSQI score (P < 0.001), and Hypoglycemia Fear Survey for Children score (P = 0.025) significantly improved. Of poor sleepers, 27 became good sleepers (PSQI score CONCLUSIONS Use of CIQ in youth with type 1 diabetes ages 6–13 years significantly improved sleep and psychosocial measures in parent poor sleepers, coinciding with improvements in child nocturnal glycemia, highlighting the relationship between HCL systems and parent sleep quality.

Objective: Meals are a major hurdle to glycemic control in type 1 diabetes (T1D). Our objective was to test a fully-automated closed-loop control (CLC) system in the absence of announcement of carbohydrate ingestion among adolescents with T1D, who are known to commonly omit meal announcement. Research Design and Methods: Eighteen adolescents with T1D (age 15.6±1.7 years; HbA1c 7.4%±1.5; 9F/9M) participated in a randomized crossover clinical trial comparing our legacy hybrid CLC system (USS-Virginia) with a novel fully-automated CLC system (RocketAP), during two 46h supervised admissions (each with one announced and one unannounced dinner), following 2 weeks of data collection. Primary outcome was the percent time-in-range 70-180mg/dL (TIR) following the unannounced meal, with secondary outcomes related to additional CGM-based metrics. Results: Both TIR and time-in-tight-range 70-140mg/dL (TTR) were significantly higher using RocketAP than using USS-Virginia during the 6h following the unannounced meal (83% [64-93] vs. 53% [40-71]; p=0.004 and 49% [41-59] vs. 27% [22-36]; p=0.002, respectively), primarily driven by reduced time-above-range (TAR >180mg/dL 17% [1.3-34] vs. 47% [28-60]), with no increase in time-below-range (TBR Conclusions: A new fully-automated CLC system with automatic prandial dosing was proven to be safe and feasible and outperformed our legacy USS-Virginia in an adolescent population with and without meal announcement.

Background: Researchers have extensively used metabolic simulators as a fast, inexpensive, and safe way of testing novel treatment strategies. This work aims to bring this technology to people with T1D to enable unique patient/data interactions. Methods: A 5-week pilot study was carried out in 15 adults with T1D using Control-IQ technology (age 36±13 years, HbA1c 6.5±0.7%) to evaluate acceptance of the proposed Web-Based Simulation Tool (WST). The study consisted of 1 week of observation (Phase 1) and 4 weeks of interaction with WST (Phase 2). Data were automatically collected via Tandem Diabetes Care t:connect web application, and used to generate personalized models of the participants’ glucose metabolism. Results: Success rate in generating models was 86.4%, achieving an average MARD of 7.4±3.2%. Interaction time was 15.8±10.7 min per week. Comparing Phases 1 and 2, no variation was detected in time in range 70-180 mg/dl (80.1 [70.4,89.6]% vs. 80 [69.7,87.9]%). Time in 70-250 mg/dl increased slightly (94.2 [90.3,95.7]% vs. 96.2 [92.1,97.9]%), especially overnight (92.8 [88.3,98.2]% vs. 97.2 [91.8,99.6]%), and for participants who modified their pump settings based on WST simulations (90 [88.7,92.4]% vs. 94.5 [87.1,99.6]%). One subject tested COVID positive during the study and was excluded from this analysis due to abnormal hyperglycemia. Analysis of Diabetes Distress Scale (DDS)-17 pre and post-system use shows a reduction in diabetes-related distress (2.2 [1.7,3.4] vs. 2 [1.7,2.4]). Trust, ease of use, and usefulness scores were 80 [60,80]%, 60 [60,80]%, and 80 [60,80]%, respectively. During the follow up interviews, 10 participants reported they enjoyed using WST and would implement it into their diabetes management; 2 did not like the system but see the potential of it for other people; 3 participants did not like the system at all. Conclusions: Evidence from this study suggests that simulation technologies may empower people with T1D, making them more confident in their diabetes self-management. Disclosure P. Colmegna: None. A. Bisio: None. R. Mcfadden: None. C. A. Wakeman: Stock/Shareholder; Self; Dexcom, Inc., Tandem Diabetes Care. M. C. Oliveri: None. R. Nass: None. M. D. Breton: Consultant; Self; ADOCIA, Dexcom, Inc., Research Support; Self; Arecor, Dexcom, Inc., Novo Nordisk A/S, Tandem Diabetes Care, Speaker’s Bureau; Self; Arecor, Tandem Diabetes Care, Stock/Shareholder; Self; Dexcom, Inc., Insulet Corporation, Tandem Diabetes Care. Funding JDRF (2-APF-2019-737-A-N)

Background: As type 2 diabetes (T2D) progresses, intensification to combination therapies, such as iGlarLixi (a fixed-ratio GLP-1 RA and basal insulin combination), may be required. Here a simulation study was used to assess the effect of iGlarLixi administration timing (am vs pm) on blood sugar profiles. Methods: Models of lixisenatide were built with a selection procedure, optimizing measurement fits and model complexity, and were included in a pre-existing T2D simulation platform containing glargine models. With the resulting tool, a simulated trial was conducted with 100 in-silico participants with T2D. Individuals were given iGLarLixi either before breakfast or before an evening meal for 2 weeks and daily glycemic profiles were analyzed. In the model, breakfast was considered the largest meal of the day. Results: A similar percentage of time within 24 hours was spent with blood sugar levels between 70 to 180 mg/dL when iGlarLixi was administered pre-breakfast or pre-evening meal (73% vs 71%, respectively). Overall percent of time with blood glucose levels above 180 mg/dL within a 24-hour period was similar when iGlarLixi was administered pre-breakfast or pre-evening meal (26% vs 28%, respectively). Rates of hypoglycemia were low in both regimens, with a blood glucose concentration of below 70 mg/dL only observed for 1% of the 24-hour time period for either timing of administration. Conclusions: Good efficacy was observed when iGlarlixi was administered pre-breakfast; however, administration of iGlarlixi pre-evening meal was also deemed to be effective, even though in the model the size of the evening meal was smaller than that of the breakfast.

OBJECTIVE Meals are a major hurdle to glycemic control in type 1 diabetes (T1D). Our objective was to test a fully automated closed-loop control (CLC) system in the absence of announcement of carbohydrate ingestion among adolescents with T1D, who are known to commonly omit meal announcement. RESEARCH DESIGN AND METHODS Eighteen adolescents with T1D (age 15.6 ± 1.7 years; HbA1c 7.4 ± 1.5%; 9 females/9 males) participated in a randomized crossover clinical trial comparing our legacy hybrid CLC system (Unified Safety System Virginia [USS]-Virginia) with a novel fully automated CLC system (RocketAP) during two 46-h supervised admissions (each with one announced and one unannounced dinner), following 2 weeks of data collection. Primary outcome was the percentage time-in-range 70–180 mg/dL (TIR) following the unannounced meal, with secondary outcomes related to additional continuous glucose monitoring-based metrics. RESULTS Both TIR and time-in-tight-range 70–140 mg/dL (TTR) were significantly higher using RocketAP than using USS-Virginia during the 6 h following the unannounced meal (83% [interquartile range 64–93] vs. 53% [40–71]; P = 0.004 and 49% [41–59] vs. 27% [22–36]; P = 0.002, respectively), primarily driven by reduced time-above-range (TAR >180 mg/dL: 17% [1.3–34] vs. 47% [28–60]), with no increase in time-below-range (TBR CONCLUSIONS A new fully automated CLC system with automatic prandial dosing was proven to be safe and feasible and outperformed our legacy USS-Virginia in an adolescent population with and without meal announcement.

Background and objective: Glycemic control, especially meal-related disturbance rejection, has proven to be a major challenge for people with type 1 diabetes. In this manuscript, we introduce a novel, personalized, advanced hybrid insulin infusion system (a.k.a. artificial pancreas) based on the Model Predictive Control (MPC) methodology to adjust insulin infusion while automatically rejecting uninformed meals. Methods: The proposed advanced hybrid closed-loop system relies on the integration of three key elements: (i) an adaptive personalized MPC control law that modulates the control strength depending on recent past control actions, glucose measurements, and its derivative, (ii) an automatic Bolus Priming System (BPS) that commands additional insulin injections safely upon the detection of enabling metabolic conditions (e.g., an unacknowledged meal), and (iii) a new hyperglycemia mitigation system to avoid prevailing hyperglycemia. The benefits of the proposed system are demonstrated through simulations and tests using the most up-to-date Type 1 UVA/Padova simulator as preclinical stage prior to in vivo clinical tests. We used a legacy algorithm (USS Virginia), currently used in clinical care, as a benchmark controller. Results: Overall, the proposed control strategy enhanced by an automatic BPS improves glycemic control when compared with an available system. When a large meal is not announced (80g CHO), the proposed controller outperformed the legacy controller in time-in-target-range TIR (postprandial and overnight) and time-in-tight-range TTR (overall, postprandial, and overnight). Conclusion: The integration of a novel BPS into an advanced control system allowed to automatically reject unannounced meals. Exhaustive simulation studies indicated the safety and feasibility of the proposed controller to be deployed in human clinical trials.

Background: Studies of closed-loop control (CLC) in patients with type 1 diabetes (T1D) consistently demonstrate improvements in glycemic control as measured by increased time-in-range (TIR) 70–180 mg/dL. However, clinical predictors of TIR in users of CLC systems are needed. Materials and Methods: We analyzed data from 100 children aged 6–13 years with T1D using the Tandem Control-IQ CLC system during a randomized trial or subsequent extension phase. Continuous glucose monitor data were collected at baseline and during 12–16 weeks of CLC use. Participants were stratified into quartiles of TIR on CLC to compare clinical characteristics. Results: TIR for those in the first, second, third, and fourth quartiles was 54%, 65%, 71%, and 78%, respectively. Lower baseline TIR was associated with lower TIR on CLC (r = 0.69, P

Objective: To further evaluate the safety and efficacy of the Control-IQ closed loop control (CLC) system in children with type 1 diabetes. Research Design and Methods: Following a 16-week randomized clinical trial (RCT) comparing CLC with sensor augmented pump (SAP) therapy in 101 children age 6 to 13 years old with type 1 diabetes, 22 participants in the SAP group initiated use of the CLC system (referred to as SAP-CLC cohort), and 78 participants in the CLC group continued use of CLC (CLC-CLC cohort) for 12 weeks. Results: In the SAP-CLC cohort, mean percentage of time in range 70-180 mg/dL (TIR) increased from 55±13% using SAP during the RCT to 65±10% using CLC (P70% plus time Conclusions: This further evaluation of the Control-IQ CLC system supports the findings of the preceding RCT that use of a closed-loop system can safely improve glycemic control in children 6 to 13 years old with type 1 diabetes from the first day of use and demonstrates that these improvements can be sustained through 28 weeks of use.

Objective: Physical activity is a major challenge to glycemic control for people with type 1 diabetes. Moderate-intensity exercise often leads to steep decreases in blood glucose and hypoglycemia that closed-loop control systems have so far failed to protect against, despite improving glycemic control overall. Research Design and Methods: Fifteen adults with type 1 diabetes (42 ± 13.5 years old; hemoglobin A(1c) 6.6% ± 1.0%; 10F/5M) participated in a randomized crossover clinical trial comparing two hybrid closed-loop (HCL) systems, a state-of-the-art hybrid model predictive controller and a modified system designed to anticipate and detect unannounced exercise (APEX), during two 32-h supervised admissions with 45 min of planned moderate activity, following 4 weeks of data collection. Primary outcome was the number of hypoglycemic episodes during exercise. Continuous glucose monitor (CGM)-based metrics and hypoglycemia are also reported across the entire admissions. Results: The APEX system reduced hypoglycemic episodes overall (9 vs. 33; P = 0.02), during exercise (5 vs. 13; P = 0.04), and in the 4 h following (2 vs. 11; P = 0.02). Overall CGM median percent time 180 mg/dL (18.5% vs. 16.6%, P = 0.15). Overnight control was notable for both systems with no hypoglycemia, median percent in time 70–180 mg/dL at 100% and median percent time 70–140 mg/dL at ∼96% for both. Conclusions: A new closed-loop system capable of anticipating and detecting exercise was proven to be safe and feasible and outperformed a state-of-the-art HCL, reducing participants' exposure to hypoglycemia during and after moderate-intensity physical activity. ClinicalTrials.gov NCT03859401.

Background: Glycemic control is particularly challenging for toddlers and preschoolers with type 1 diabetes (T1D), and data on the use of closed-loop systems in this age range are limited. Materials and Methods: We studied use of a modified investigational version of the Tandem t:slim X2 Control-IQ system in children aged 2 to 5 years during 48 h in an outpatient supervised hotel (SH) setting followed by 3 days of home use to examine the safety of this system in young children. Meals and snacks were not restricted and boluses were estimated per parents' usual routine. At least 30 min of daily exercise was required during the SH phase. All participants were remotely monitored by study staff while on closed-loop in addition to monitoring by at least one parent throughout the study. Results: Twelve participants diagnosed with T1D for at least 3 months with mean age 4.7 ± 1.0 years (range 2.0–5.8 years) and hemoglobin A1c of 7.3% ± 0.8% were enrolled at three sites. With use of Control-IQ, the percentage of participants meeting our prespecified goals of less than 6% time below 70 mg/dL and less than 40% time above 180 mg/dL increased from 33% to 83%. Control-IQ use significantly improved percent time in range (70–180 mg/dL) compared to baseline (71.3 ± 12.5 vs. 63.7 ± 15.1, P = 0.016). All participants completed the study with no adverse events. Conclusions: In this brief pilot study, use of the modified Control-IQ system was safe in 2–5-year-old children with T1D and improved glycemic control.

Outcomes are regularly compared across studies frequently leading to inaccurate conclusions due to sampling differences. We introduce distribution-adjusting methods allowing for more adequate cross-study comparisons. To illustrate these methods, we use time-in-range 70-180mg/dl (TIR) from two closed loop system pivotal clinical trials: CLS1 for MiniMed 670G (Medtronic), and CLS2 for Control-IQ (Tandem). TIR was the key outcome measure for both, but baseline TIRs were different (Table 1). Furthermore, CLS1 did not have a control group; rendering the otherwise best option, a comparison of the experimental vs. control deltas, inapplicable. A second-best option is to compare the increments from baseline to active-treatment TIRs, which causes two opposing statistical artifacts: (1) with higher baseline, active-treatment TIR would be higher, and (2) with higher baseline, the TIR increment would be lower due to a ceiling effect. Three methods were used to mitigate these artifacts by matching baseline TIRs distributions: sample truncation, weighted resampling, and baseline cumulative distribution functions mapping. All three resulted in similar Adjusted TIRs (Table 1). Comparing results across studies is possible, if limited to the relative improvements from baseline to active treatment. However, comparing absolute results will not be accurate without proper baseline adjustments. Disclosure M.D. Breton: Research Support; Self; Dexcom, Inc., Novo Nordisk A/S, Sanofi, Tandem Diabetes Care. Speaker’s Bureau; Self; Dexcom, Inc., Tandem Diabetes Care. R. Beck: None. R.M. Bergenstal: Consultant; Self; Ascensia Diabetes Care, Johnson & Johnson. Other Relationship; Self; Abbott, Dexcom, Inc., Hygieia, Lilly Diabetes, Medtronic, Novo Nordisk A/S, Onduo, Roche Diabetes Care, Sanofi, UnitedHealth Group. B. Kovatchev: Advisory Panel; Self; Dexcom, Inc. Research Support; Self; Dexcom, Inc., Tandem Diabetes Care. Speaker’s Bureau; Self; Dexcom, Inc., Sanofi, Tandem Diabetes Care. Other Relationship; Self; Dexcom, Inc., Johnson & Johnson, Sanofi. Funding National Institute of Diabetes and Digestive and Kidney Diseases (UC4DK108483)

Background: Limited information is available about glycemic outcomes with closed-loop control (CLC) compared with predictive-low glucose suspend (PLGS). Methods: After 6 months of use of a CLC system in a randomized trial, 109 participants with type 1 diabetes (age range 14 to 72 years, mean HbA1c 7.1% [54 mmol/mol]) were randomly assigned to CLC (N=54, Control-IQ) or PLGS (N=55, Basal-IQ) for 3 months. Primary outcome was CGM-measured time in range (TIR 70-180mg/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 intent-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 versus 70.0±13.6% and 60.4±17.1% on PLGS (difference = +5.9%, 95%CI +3.6 to +8.3; P180mg/dL was lower in the CLC group than PLGS group (difference = -6.0%, 95%CI -8.4 to -3.7, p Conclusion: Following 6 months of CLC, switching to PLGS reduced TIR and increased HbA1c towards their pre-CLC values while hypoglycemia remained similarly reduced with both CLC and PLGS.

Background: Physical activity presents a significant challenge for glycemic control in individuals with type 1 diabetes. As accurate glycemic predictions are key to successful automated decision-making systems (eg, artificial pancreas, AP), the inclusion of additional physiological variables in the estimation of the metabolic state may improve the glucose prediction accuracy during exercise. Methods: Predictor-based subspace identification is applied to a dynamic glucose prediction model including heart rate measurements along with variables representing the carbohydrate consumption and insulin boluses. To demonstrate the improvement in prediction ability due to the additional heart rate variable, the performance of the proposed modeling technique is evaluated with (SID-HR) and without heart rate (SID-2) as an additional input using experimental data involving adolescents at ski camp. Furthermore, the performance of the proposed approach is compared to that of the metabolic state observer (MSO) model currently used in the University of Virginia AP algorithm. Results: The addition of heart rate in the subspace-based model (SID-HR) yields a statistically significant improvement in the root-mean-square error compared to the SID-2 model ( P < .001) and the standard MSO ( P < .001). Furthermore, the SID-HR model performed favorably in comparison to the SID-2 and MSO models after accounting for its increased complexity. Conclusions: Directly considering the effects of physical activity levels on glycemic dynamics through the inclusion of heart rate as an additional input variable in the glucose dynamics model improves the glucose prediction accuracy. The proposed methodology could improve exercise-informed model-based predictive control algorithms in artificial pancreas systems.

Background: Considering current insulin action profiles and the nature of glycemic responses to insulin, there is an acute need for longer term, accurate, blood glucose predictions to inform insuli...

Neural networks present a useful framework for learning complex dynamics, and are increasingly being considered as components to closed loop predictive control algorithms. However, if they are to be utilized in such safety-critical advisory settings, they must be provably "conformant" to the governing scientific (biological, chemical, physical) laws which underlie the modeled process. Unfortunately, this is not easily guaranteed as neural network models are prone to learn patterns which are artifacts of the conditions under which the training data is collected, which may not necessarily conform to underlying physiological laws. In this work, we utilize a formal range-propagation based approach for checking whether neural network models for predicting future blood glucose levels of individuals with type-1 diabetes are monotonic in terms of their insulin inputs. These networks are increasingly part of closed loop predictive control algorithms for "artificial pancreas" devices which automate control of insulin delivery for individuals with type-1 diabetes. Our approach considers a key property that blood glucose levels must be monotonically decreasing with increasing insulin inputs to the model. Multiple representative neural network models for blood glucose prediction are trained and tested on real patient data, and conformance is tested through our verification approach. We observe that standard approaches to training networks result in models which violate the core relationship between insulin inputs and glucose levels, despite having high prediction accuracy. We propose an approach that can learn conformant models without much loss in accuracy.

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 investigate the relationship of body mass index (BMI) to differing glycemic responses to psychological stress in patients with type 1 diabetes. Methods: Continuous blood glucose monitor (CGM) data were collected for 1 week from a total of 37 patients with BMI ranging from 21.5-39.4 kg/m2 (mean = 28.2 ± 4.9). Patients reported daily stress levels (5-point Likert-type scale, 0 = none, 4 = extreme), physical activity, carbohydrate intake, insulin boluses and basal rates. Daily reported carbohydrates, total insulin bolus, and average blood glucose (BG from CGM) were compared among patients based on their BMI levels on days with different stress levels. In addition, daily averages of a model-based “effectiveness index” (quantifying the combined impact of insulin and carbohydrate on glucose levels) were defined and compared across stress levels to capture meal and insulin independent glycemic changes. Results: Analyses showed that patient BMI likely moderated stress related glycemic changes. Linear mixed effect model results were significant for the stress-BMI interaction on both behavioral and behavior-independent glycemic changes. Across participants, under stress, an increase was observed in daily carbohydrate intake and effectiveness index at higher BMI. There was no significant interactive effect on daily insulin or average BG. Conclusion: Findings suggest that (1) stress has both behavioral and nonbehavioral glycemic effects on T1D patients and (2) the direction and magnitude of these effects are potentially influenced by level of stress and patient BMI. Possibly responsible for these observed effects are T1D/BMI related alterations in endocrine response.

Automated Insulin Delivery systems, a.k.a. artificial pancreas, are rapidly becoming available to many patients with type 1 diabetes around the world. As the transition from controlled clinical tri...

Artificial pancreas (AP) systems have initially been designed for and tested in teens and adults, but there is evidence that an AP system with additional support and safety systems could greatly benefit younger children with type 1 diabetes (T1D).Five pediatric endocrinologists and 15 parents of children aged 5-8 years with T1D participated in a total of four focus groups. Focus groups investigated current diabetes technology use and acceptance, as well as possible modifications to the current adult AP system, which would allow for safe and successful use in younger children. Modifications discussed include child-specific functionality for input tasks, safety features, and monitoring capabilities.Participant suggestions included the following: passcodes for differential access to AP features by parents, ancillary caregivers, and the child; preset early, intermediate, and advanced child access categories; maximal customization for general and alarm settings; simplified meal screens utilizing the AP' corrective blood glucose (BG) ability; automated exercise mode; spoken and dictated messaging capabilities; emergency contacts; treatment instructions for the child and caregiver; remote monitoring website and application; animated continuous glucose monitor BG trace; gamification, such as rewarding diabetes-friendly behaviors; and comprehensive training of all individuals involved in the child's diabetes care.Parents and physicians were eager for AP applications to be available for younger children, but stressed that a modified system could better serve this group's needs for safety and improved diabetes-related communication. The diverse and emerging needs of 5-8-year olds require flexible and customizable systems for T1D management.