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OBJECTIVE: Evaluating the feasibility of closed-loop insulin delivery with a zone model predictive control (zone-MPC) algorithm designed for pregnancy complicated by type 1 diabetes (T1D). RESEARCH DESIGN AND METHODS: Pregnant women with T1D from 14 to 32 weeks gestation already using continuous glucose monitor (CGM) augmented pump therapy were enrolled in a 2-day multicenter supervised outpatient study evaluating pregnancy-specific zone-MPC based closed-loop control (CLC) with the interoperable artificial pancreas system (iAPS) running on an unlocked smartphone. Meals and activities were unrestricted. The primary outcome was the CGM percentage of time between 63 and 140 mg/dL compared with participants' 1-week run-in period. Early (2-h) postprandial glucose control was also evaluated. RESULTS: Eleven participants completed the study (age: 30.6 ± 4.1 years; gestational age: 20.7 ± 3.5 weeks; weight: 76.5 ± 15.3 kg; hemoglobin A1c: 5.6% ± 0.5% at enrollment). No serious adverse events occurred. Compared with the 1-week run-in, there was an increased percentage of time in 63–140 mg/dL during supervised CLC (CLC: 81.5%, run-in: 64%, P = 0.007) with less time >140 mg/dL (CLC: 16.5%, run-in: 30.8%, P = 0.029) and time 180 mg/dL (CLC: 4.9%, run-in: 13.1%, P = 0.032). Overnight glucose control was comparable, except for less time >250 mg/dL (CLC: 0%, run-in:3.9%, P = 0.030) and lower glucose standard deviation (CLC: 23.8 mg/dL, run-in:42.8 mg/dL, P = 0.007) during CLC. CONCLUSION: In this pilot study, use of the pregnancy-specific zone-MPC was feasible in pregnant women with T1D. Although the duration of our study was short and the number of participants was small, our findings add to the limited data available on the use of CLC systems during pregnancy (NCT04492566).

Decentralized sensing of analytes in remote locations is today a reality. However, the number of measurable analytes remains limited, mainly due to the requirement for time-consuming successive standard additions calibration used to address matrix effects and resulting in greatly delayed results, along with more complex and costly operation. This is particularly challenging in commonly used immunoassays of key biomarkers that typically require from 60 to 90 min for quantitation based on two standard additions, hence hindering their implementation for rapid and routine diagnostic applications, such as decentralized point-of-care (POC) insulin testing. In this work we have developed and demonstrated the theoretical framework for establishing a universal slope for direct calibration-free POC insulin immunoassays in serum samples using an electrochemical biosensor (developed originally for extended calibration by standard additions). The universal slope is presented as an averaged slope constant, relying on 68 standard additions-based insulin determinations in human sera. This new quantitative analysis approach offers reliable sample measurement without successive standard additions, leading to a dramatically simplified and faster assay (30 min vs 90 min when using 2 standard additions) and greatly reduced costs, without compromising the analytical performance while significantly reducing the analyses costs. The substantial improvements associated with the new universal slope concept have been demonstrated successfully for calibration-free measurements of serum insulin in 30 samples from individuals with type 1 diabetes using meticulous statistical analysis, supporting the prospects of applying this immunoassay protocol to routine decentralized POC insulin testing.

Current guidelines recommend 15-20 g of carbohydrate (CHO) for treatment of mild to moderate hypoglycemia. However, these guidelines do not account for reduced insulin during suspensions with predictive low-glucose suspend (PLGS). We assessed insulin suspensions, hypoglycemic events, and CHO treatment during a 20-h inpatient evaluation of an investigational system with a PLGS feature, including an overnight basal up-titration period to activate the PLGS. Among 10 adults with type 1 diabetes, there were 59 suspensions; 7 suspensions were associated with rescue CHO and 5 with hypoglycemia. Rescue treatment consisted of median 9 g CHO (range: 5-16 g), with no events requiring repeat CHO. No rescue CHO were given during or after insulin suspension for the overnight basal up-titration. To minimize rebound hyperglycemia and needless calorie intake from hypoglycemia overtreatment, updated guidance for PLGS systems should reflect possible need to reduce CHO amounts for hypoglycemia rescue associated with an insulin suspension. The clinical trial was registered with ClinicalTrials.gov (NCT03890003).

BACKGROUND: Automated insulin delivery (AID) systems have not been evaluated in the context of psychological and pharmacological stress in type 1 diabetes. Our objective was to determine glycemic control and insulin use with Zone Model Predictive Control (zone-MPC) AID system enhanced for states of persistent hyperglycemia versus sensor-augmented pump (SAP) during outpatient use, including in-clinic induced stress. MATERIALS AND METHODS: Randomized, crossover, 2-week trial of zone-MPC AID versus SAP in 14 adults with type 1 diabetes. In each arm, each participant was studied in-clinic with psychological stress induction (Trier Social Stress Test [TSST] and Socially Evaluated Cold Pressor Test [SECPT]), followed by pharmacological stress induction with oral hydrocortisone (total four sessions per participant). The main outcomes were 2-week continuous glucose monitor percent time in range (TIR) 70–180 mg/dL, and glucose and insulin outcomes during and overnight following stress induction. RESULTS: During psychological stress, AID decreased glycemic variability percentage by 13.4% (P = 0.009). During pharmacological stress, including the following overnight, there were no differences in glucose outcomes and total insulin between AID and physician-assisted SAP. However, with AID total user-requested insulin was lower by 6.9 U (P = 0.01) for pharmacological stress. Stress induction was validated by changes in heart rate and salivary cortisol levels. During the 2-week AID use, TIR was 74.4% (vs. SAP 63.1%, P = 0.001) and overnight TIR was 78.3% (vs. SAP 63.1%, P = 0.004). There were no adverse events. CONCLUSIONS: Zone-MPC AID can reduce glycemic variability and the need for user-requested insulin during pharmacological stress and can improve overall glycemic outcomes. Clinical Trial Identifier NCT04142229.

Background: Personal insulin pumps have shown to be effective in improving the quality of therapy for people with type 1 diabetes (T1D). However, the safety of this technology is limited by the possible infusion site failures, which are linked with hyperglycemia and ketoacidosis. Thanks to the large availability of collected data provided by modern therapeutic technologies, machine learning algorithms have the potential to provide new way to identify failures early and avert adverse events. Methods: A clinical dataset ( N = 20) is used to evaluate a novel method for detecting real-time infusion site failures using unsupervised anomaly detection algorithms, previously proposed and developed on in-silico data. An adapted feature engineering procedure is introduced to make the method able to operate in the absence of a closed-loop (CL) system and meal announcements. Results: In the optimal configuration, we obtained a performance of 0.75 Sensitivity (15 out of 20 total failures detected) and 0.08 FP/day, outperforming previously proposed literature algorithms. The algorithm was able to anticipate the replacement of the malfunctioning infusion sets by ~2 h on average. Conclusions: On the considered dataset, the proposed algorithm showed the potential to improve the safety of patients treated with sensor-augmented pump systems.

Objective: In this work, we design iterative algorithms for the delivery of long-acting (basal) and rapid-acting (bolus) insulin, respectively, for people with type 1 diabetes (T1D) on multiple-daily-injections (MDIs) therapy using feedback from self-monitoring of blood glucose (SMBG) measurements. Methods: Iterative learning control (ILC) updates basal therapy consisting of one long-acting insulin injection per day, while run-to-run (R2R) adapts meal bolus therapy via the update of the mealtime-specific insulin-to-carbohydrate ratio (CR). Updates are due weekly and are based upon sparse SMBG measurements. Results: Upon termination of the 20 weeks long in-silico trial, in a scenario characterized by meal carbohydrate (CHO) normally distributed with mean $\mu$ = [50, 75, 75] grams and standard deviation $\sigma$ = [5, 7, 7] grams, our strategy produced statistically significant improvements in time in range (70--180) [mg/dl], from 66.9(33.1) $\%$ to 93.6(6.7) $\%$ , $p$ = 0.02. Conclusions: Iterative learning shows potential to improve glycemic regulation over time by driving blood glucose closer to the recommended glycemic targets. Significance: Decision support systems (DSSs) and automated therapy advisors such as the one proposed here are expected to improve glycemic outcomes reducing the burden on patients on MDI therapy.

Background: Automated Insulin Delivery (AID) hybrid closed-loop systems have not been well studied in the context of prescribed meals. We evaluated performance of our interoperable artificial pancreas system (iAPS) in the at-home setting, running on an unlocked smartphone, with scheduled meal challenges in a randomized crossover trial. Methods: Ten adults with type 1 diabetes completed 2 weeks of AID-based control and 2 weeks of conventional therapy in random order where they consumed regular pasta or extra-long grain white rice as part of a complete dinner meal on six different occasions in both arms (each meal thrice in random order). Surveys assessed satisfaction with AID use. Results: Postprandial differences in conventional therapy were 10,919.0 mg/dL × min (95% confidence interval [CI] 3190.5–18,648.0, P = 0.009) for glucose area under the curve (AUC) and 40.9 mg/dL (95% CI 4.6–77.3, P = 0.03) for peak continuous glucose monitor glucose, with rice showing greater increases than pasta. White rice resulted in a lower estimate over pasta by a factor of 0.22 (95% CI 0.08–0.63, P = 0.004) for AUC under 70 mg/dL. These glycemic differences in both meal types were reduced under AID-based control and were not statistically significant, where 0–2 h insulin delivery decreased by 0.45 U for pasta (P = 0.001) and by 0.27 U for white rice (P = 0.01). Subjects reported high overall satisfaction with the iAPS. Conclusions: The AID system running on an unlocked smartphone improved postprandial glucose control over conventional therapy in the setting of challenging meals in the outpatient setting. Clinical Trial Registry: clinicaltrials.gov NCT03767790.

We propose a Kalman filter-based observer utilizing noisy remote compartment insulin measurements to estimate plasma insulin concentration. The design considers plant-model mismatch, sensor noise, as well as both uniform sampling intervals, mimicking infrequent continuous measurements, and non-uniform sampling intervals, mimicking infrequent on-demand measurements. The performance of the observer is demonstrated on ten in-silico subjects from the UVA/Padova simulator using real-life scenarios, including variability in sensor noise and variability in insulin pharmacokinetics. The proposed observer provides insight into the future use of insulin measurements for diabetes management.

BACKGROUND: Suboptimal glycemic control is associated with maternal and neonatal morbidity and mortality in pregnancy complicated by type 1 diabetes (T1D). Prospective analysis of continuous glucose monitoring (CGM) metrics, insulin pump settings, and insulin delivery can better characterize the changes in glycemic levels and insulin use throughout pregnancy with T1D. MATERIALS AND METHODS: Prescribed parameters, insulin delivery, carbohydrate intake, and CGM data for 25 pregnant women with T1D from three U.S. sites were collected. Participants enrolled before 17 weeks gestation and used personal insulin pumps and study CGM. Mean daily total, basal, and bolus insulin doses (units/kg), CGM time in range (TIR: 63–140 mg/dL), and pump-entered carbohydrates were analyzed for every 2-week gestational interval. Linear mixed-effects regression models were used to evaluate changes across gestational ages compared to 12–14 weeks. RESULTS: Basal insulin was higher during weeks 6–12 and 24–40. Daily bolus and total insulin were higher during weeks 20–40. Pump parameters were adjusted to intensify insulin therapy from 22 weeks onward. Average TIR across pregnancy was 59% ± 14%. Between 18 and 30 weeks, TIR was significantly lower, and time above range was significantly higher compared to the reference biweek. Time below target was lower between 22 and 34 weeks. Seven participants achieved >70% recommended TIR for pregnancy. Participants with maternal complications or infant neonatal intensive care unit admissions had lower TIR. CONCLUSION: While insulin dosing changed significantly with advancing gestation, most participants did not achieve >70% TIR. Customized anticipatory pump setting adjustments and automated systems aimed toward the designated TIR are needed to improve outcomes for this population. NCT03761615

Type 1 diabetes (T1D) increases the risk for pregnancy complications. Increased time in the pregnancy glucose target range (63-140 mg/dL as suggested by clinical guidelines) is associated with improved pregnancy outcomes that underscores the need for tight glycemic control. While closed-loop control is highly effective in regulating blood glucose levels in individuals with T1D, its use during pregnancy requires adjustments to meet the tight glycemic control and changing insulin requirements with advancing gestation. In this paper, we tailor a zone model predictive controller (zone-MPC), an optimization-based control strategy that uses model predictions, for use during pregnancy and verify its robustness in-silico through a broad range of scenarios. We customize the existing zone-MPC to satisfy pregnancy-specific glucose control objectives by having (i) lower target glycemic zones (i.e., 80-110 mg/dL daytime and 80-100 mg/dL overnight), (ii) more assertive correction bolus for hyperglycemia, and (iii) a control strategy that results in more aggressive postprandial insulin delivery to keep glucose within the target zone. The emphasis is on leveraging the flexible design of zone-MPC to obtain a controller that satisfies glycemic outcomes recommended for pregnancy based on clinical insight. To verify this pregnancy-specific zone-MPC design, we use the UVA/Padova simulator and conduct in-silico experiments on 10 subjects over 13 scenarios ranging from scenarios with ideal metabolic and treatment parameters for pregnancy to extreme scenarios with such parameters that are highly deviant from the ideal. All scenarios had three meals per day and each meal had 40 grams of carbohydrates. Across 13 scenarios, pregnancy-specific zone-MPC led to a 10.3 ± 5.3% increase in the time in pregnancy target range (baseline zone-MPC: 70.6 ± 15.0%, pregnancy-specific zone-MPC: 80.8 ± 11.3%, p < 0.001) and a 10.7 ± 4.8% reduction in the time above the target range (baseline zone-MPC: 29.0 ± 15.4%, pregnancy-specific zone-MPC: 18.3 ± 12.0, p < 0.001). There was no significant difference in the time below range between the controllers (baseline zone-MPC: 0.5 ± 1.2%, pregnancy-specific zone-MPC: 3.5 ± 1.9%, p = 0.1). The extensive simulation results show improved performance in the pregnancy target range with pregnancy-specific zone MPC, suggest robustness of the zone-MPC in tight glucose control scenarios, and emphasize the need for customized glucose control systems for pregnancy.

Aim: Overnight insulin dosing relies on glucose level and evening food intake, although PA also impacts glycemic levels. We assessed associations between antecedent energy expenditure (EE) from PA and nocturnal glycemia in youth with T1D. Methods: Youth completed 3-day PA logs (PA type, duration, intensity) and wore 3-day masked CGM (iPro™) quarterly for 18 months. We assigned a MET value to PA and calculated EE (kcal/day), using only complete 24-hour days. CGM metrics were calculated for days (6 AM-11:59 PM) and nights (12 AM-5:59 AM) when ≥50% of CGM data were available. Results: Youth (N=136, 48% male) were ages 8-17 years (M±SD 12.8±2.5), with T1D duration 5.9±3.1 years and daily insulin dose 0.9±0.3 U/kg; 73% used insulin pumps. Median (IQR) PA duration and EE were 60 (10-120) minutes/day and 287 (0-695) kcal/day, respectively. CGM metrics by day/night appear in Table. In partial correlations adjusted for multiple observations/person, higher EE was associated with lower mean glucose (r=-.12, p=.0001), more %time 180 (r=-.09, p=.006) that night. Time in range was unchanged. When youth EE was 180 (p=.03) that night. Conclusion: In youth with T1D, PA can reduce nocturnal hyperglycemia and increase nocturnal hypoglycemia. These findings can help tailor automated insulin delivery algorithms according to PA. Disclosure R. O. La banca: None. L. K. Volkening: None. K. Milaszewski: Consultant; Self; Lilly USA, LLC. E. Dassau: Consultant; Self; Eli Lilly and Company, Employee; Self; Eli Lilly and Company, Research Support; Self; Dexcom, Inc., Tandem Diabetes Care, Speaker’s Bureau; Self; Dexcom, Inc., Roche Diabetes Care, Stock/Shareholder; Self; Eli Lilly and Company. S. N. Mehta: None. L. M. Laffel: Consultant; Self; AstraZeneca, Boehringer Ingelheim International GmbH, Dexcom, Inc., Dompe, Insulogic LLC, Janssen Pharmaceuticals, Inc., Laxmi Therapeutic Devices, LifeScan, Lilly Diabetes, Medtronic, Provention Bio, Inc. Funding National Institutes of Health (K12DK094721, P30DK036836); Iacocca Family Foundation

Background As new diabetes technologies improve to better manage glucose levels, users' priorities for future technologies may shift to prioritize burden reduction and ease of use. We used qualitative methods to explore youth and parent desired features of an "ideal" artificial pancreas (AP) system. Methods We conducted semi-structured interviews with 39 youth, ages 10-25 years, and 44 parents. Interviews were audio-recorded, transcribed, and coded using thematic analysis. Results Youth (79% female, 82% non-Hispanic white) were (M±SD) ages 17.0±4.7 years, with diabetes for 9.4±4.9 years, and HbA1c of 8.4±1.1%; 79% were pump-treated and 82% used CGM. Of parents, 91% were mothers and 86% were non-Hispanic white. Participants suggested various ways in which an ideal AP system could reduce physical and emotional burdens of diabetes. Physical burdens could be reduced by lessening user responsibilities to manage glucose for food and exercise, and wear or carry devices. Emotional burden could be reduced by mitigating negative emotional reactions to sound and frequency of alerts, while increasing feelings of normalcy. Youth and parents differed in their suggestions to reduce emotional burden. Participants suggested features that would improve glycemia, but nearly always in the context of how the feature would directly reduce their diabetes-specific burden. Conclusions Although participants expressed interest in improving glucose levels, the pervasive desire among suggested features of an ideal AP system was to minimize the burden of diabetes. Understanding and addressing users' priorities to reduce physical and emotional burden will be necessary to enhance uptake and maintain use of future AP systems. This article is protected by copyright. All rights reserved.

Background: The objective of this study was to assess the safety and performance of the Omnipod® personalized model predictive control (MPC) algorithm with variable glucose setpoints and moderate intensity exercise using an investigational device in adults with type 1 diabetes (T1D). Materials and Methods: A supervised 54-h hybrid closed-loop (HCL) study was conducted in a hotel setting after a 7-day outpatient standard treatment phase. Adults aged 18–65 years with T1D and HbA1c between 6.0% and 10.0% were eligible. Subjects completed two moderate intensity exercise sessions of >30 min duration on consecutive days: the first with the glucose set point increased from 130 to 150 mg/dL and the second with a temporary basal rate of 50%, both started 90 min pre-exercise. Primary endpoints were percentage time in hypoglycemia

Background: People with type 1 diabetes estimate meal carbohydrate content to accurately dose insulin, yet, protein and fat content of meals also influences postprandial glycemia. We examined accuracy of macronutrient content estimation via a novel phone app. Participant estimates were compared with expert nutrition analyses performed via the Remote Food Photography Method© (RFPM©). Methods: Data were collected through a novel phone app. Participants were asked to take photos of meals/snacks on the day of and day after scheduled exercise, enter carbohydrate estimates, and categorize meals as low, typical, or high protein and fat. Glycemia was measured via continuous glucose monitoring. Results: Participants (n = 48) were 15–68 years (34 ± 14 years); 40% were female. The phone app plus RFPM© analysis captured 88% ± 29% of participants' estimated total energy expenditure. The majority (70%) of both low-protein and low-fat meals were accurately classified. Only 22% of high-protein meals and 17% of high-fat meals were accurately classified. Forty-nine percent of meals with

AIMS: Participant-driven solutions may help youth and families better engage and maintain use of diabetes technologies. We explored innovative features and functionalities of an ideal artificial pancreas (AP) system suggested by youth with type 1 diabetes and parents. METHODS: Semi-structured interviews were conducted with 39 youth, ages 10–25 years, and 44 parents. Interviews were recorded, transcribed, and coded using thematic analysis. RESULTS: Youth (72% female, 82% non-Hispanic white) were (M±SD) ages 17.0±4.7 years, with diabetes for 9.4±4.9 years, and HbA1c of 68±11 mmol/mol (8.4±1.1%); 79% were pump-treated and 82% were CGM users. Of parents, 91% were mothers and 86% were non-Hispanic white, with a child 10.6±4.5 years old.Youth and parents suggested a variety of innovative features and functionalities for an ideal artificial pancreas system related to: 1) enhancing the appeal of user interface, 2) increasing automation of new glucose management functionalities, and 3) innovative and commercial add-ons for greater convenience. Youth and parents offered many similar suggestions, including integration of ketone testing, voice activation, and location-tracking into the system. Youth seemed more driven by increasing convenience and normalcy, while parents expressed more concerns with safety. CONCLUSIONS: Youth and parents expressed creative solutions for an ideal artificial pancreas system to increase ease of use, enhance normalcy, and reduce burden of management. Designers of artificial pancreas systems will likely benefit from incorporating the desired preferences by end users in order to optimize acceptance and usability by young persons with diabetes.

Aim: Insulin bolus doses are mainly based on ambient glucose level and planned carbohydrate (CHO) intake, although fat and protein content of food also impact glycemic excursions. We examined the impact of macronutrient intake on glycemic outcomes in youth with T1D. Methods: Youth (N=136, ages 8-17) with T1D provided diet (3-day food records) and glucose (3-day masked CGM [iPro™]) data every 3 months for 1 year (5 time points). Diet data were analyzed using Nutrition Data System for Research (NDSR). Macronutrient % intake was derived as the mean for each 3-day period. Glycemic outcomes were A1c and CGM metrics: glucose % time in range (TIR) 70-180 mg/dL, % time (T) 180, and glucose CV. Longitudinal mixed models assessed the effect of macronutrient intake on glycemic outcomes. Results: Youth (48% male) had a mean age of 12.8±2.5 years and T1D duration of 5.9±3.1 years; youth checked BGs 5.6±2.4 X/day and 73% used insulin pumps. At baseline, A1c=8.1±1.0%, %TIR=49±17% (11.8 hrs), %T180=44±20% (10.6 hrs), and CV=41±8%; macronutrient % intake was 48±5% CHO, 36±5% fat, and 16±2% protein. Over 1 year, differences in macronutrient intake affected glycemic outcomes (Table). Conclusions: Our data confirm the significant impact of fat and protein intake on glycemic outcomes. These findings offer an opportunity to tailor algorithms to assist with meal-time bolus dosing and automated insulin delivery systems. Disclosure R.O. La Banca: None. L.K. Volkening: None. E. Dassau: Consultant; Self; Eli Lilly and Company. Research Support; Self; Dexcom, Inc., DreaMed Diabetes, Tandem Diabetes Care, Xeris Pharmaceuticals, Inc. Speaker’s Bureau; Self; Roche Diabetes Care. Other Relationship; Self; Dexcom, Inc., Insulet Corporation, Roche Diabetes Care. S.N. Mehta: None. L.M. Laffel: Advisory Panel; Self; Roche Diabetes Care. Consultant; Self; Boehringer Ingelheim Pharmaceuticals, Inc., ConvaTec Inc., Dexcom, Inc., Insulet Corporation, Insulogic LLC, Janssen Pharmaceuticals, Inc., Lilly Diabetes, Novo Nordisk Inc., Sanofi US. Funding National Institutes of Health (P30DK036836, K12DK094721)

Insulin monitoring is clinically relevant for diabetes management. We present a first evaluation of an insulin immunosensor as a step towards point-of-care devices to enhance automated insulin delivery for T1D. The sensor is a novel, wearable, minimally invasive microneedle/microfluidic device for real-time insulin monitoring. To validate the sensor, a mid-study analysis was performed on 4 adults with T1D (43±12 years, 75% male) using insulin pumps. Insulin lispro or aspart was injected via syringe just prior to breakfast. Insulin levels were collected prior to injection and over the next 4h. Insulin quantification was performed using the participants’ serum samples extracted from venous blood. The change in real-time immunosensor-measured insulin levels was compared with values obtained by ELISA in a central lab. At each time point, a median insulin level was derived from replicate immunosensor samples. Concordance of immunosensor and ELISA insulin levels was assessed as a comparison of their changes from baseline (Figure). The mean absolute relative difference between immunosensor and venous insulin levels was 16%, 8%, and 18% at 1, 2, and 4h after the baseline measurement, respectively. The results of the 1st phase real-time insulin immunosensor evaluation are promising and may provide data to improve current insulin decay curve assumptions used in insulin-on-board approximations. Disclosure K.L. Wolkowicz: None. E. Vargas: None. H. Teymourian: None. F. Tehrani: None. J.E. Pinsker: Advisory Panel; Self; Medtronic. Consultant; Self; Eli Lilly and Company, Tandem Diabetes Care. Research Support; Self; Dexcom, Inc., Eli Lilly and Company, Insulet Corporation, Medtronic, Tandem Diabetes Care. Speaker’s Bureau; Self; Tandem Diabetes Care. M. Church: None. M. Piper: None. F.J. Doyle: Research Support; Self; DreaMed Diabetes, Tandem Diabetes Care, Xeris Pharmaceuticals, Inc. Stock/Shareholder; Self; Mode AGC. Other Relationship; Self; Dexcom, Inc., Insulet Corporation, Roche Diabetes Care. M. Patti: Consultant; Self; Fractyl Laboratories, Inc. Research Support; Self; Dexcom, Inc., Xeris Pharmaceuticals, Inc. Other Relationship; Self; Academy of Nutrition and Dietetics, American Diabetes Association, American Society of Metabolic and Bariatric Surgery, Endocrine Society, Insulet Corporation, King Abdullah International Medical Research Center, SUNY Downstate. L.M. Laffel: Advisory Panel; Self; Roche Diabetes Care. Consultant; Self; Boehringer Ingelheim Pharmaceuticals, Inc., ConvaTec Inc., Dexcom, Inc., Insulet Corporation, Insulogic LLC, Janssen Pharmaceuticals, Inc., Lilly Diabetes, Novo Nordisk Inc., Sanofi US. J. Wang: None. E. Dassau: Consultant; Self; Eli Lilly and Company. Research Support; Self; Dexcom, Inc., DreaMed Diabetes, Tandem Diabetes Care, Xeris Pharmaceuticals, Inc. Speaker’s Bureau; Self; Roche Diabetes Care. Other Relationship; Self; Dexcom, Inc., Insulet Corporation, Roche Diabetes Care. Funding The Leona M. and Harry B. Helmsley Charitable Trust (2018PG-TID061); Dexcom, Inc. (IIS-2019-052)

Aim: Current automated insulin delivery systems utilize insulin pumps and CGMs, both of which impose multiple alerts/alarms upon users. Patients and parents must respond to such notifications in order to effectively manage glucose levels. We interviewed children, teens, and young adults with T1D and parents of youth with T1D to explore preferences for alerting features of an ideal AP system. Methods: Semi-structured interviews were conducted with 39 youth, ages 10-25 years with T1D duration ≥1 year, and 28 parents at 2 diabetes centers. Interview transcripts were coded and underwent content analysis. Youth (72% female, 82% white) were (M±SD) age 17.0±4.7 years, with T1D duration 9.4±4.9 years, and A1c 8.4±1.1%; 79% used an insulin pump and 82% used a CGM. Of parents, 96% parents were mothers and 89% were white. Results: Youth and parents both endorsed a desire for the following 5 alerting features: 1) personalized sounds and volumes for different treatment situations (e.g., low vs. high glucose levels, insulin delivery issues), 2) custom schedules and glucose thresholds to account for different activities or times of day/night, 3) pleasant sounds to avoid negative emotional reactions, 4) flexible share settings (e.g., which alerts followers receive), and 5) consistent and personalized overnight alerts. Some youth wanted the option to turn off alerts completely. Parents wanted certainty that they would receive share alerts and suggested adding more share options (e.g., confirmation of insulin coverage for food or correction dose for high glucose), stating that such notifications may lessen their emotional distress and worry for their child’s safety. Conclusions: Youth and parents emphasized need for customizable alerts and control over alert settings to reduce physical and emotional burdens of diabetes care. AP designs that provide comprehensive, customizable alert options are essential to reduce care burden for youth with T1D and their parents. Disclosure L. Roethke: None. P.V. Commissariat: None. J.L. Finnegan: None. L.K. Volkening: None. D.A. Butler: None. E. Dassau: Consultant; Self; Eli Lilly and Company. Research Support; Self; Dexcom, Inc., DreaMed Diabetes, Tandem Diabetes Care, Xeris Pharmaceuticals, Inc. Speaker’s Bureau; Self; Roche Diabetes Care. Other Relationship; Self; Dexcom, Inc., Insulet Corporation, Roche Diabetes Care. S.A. Weinzimer: Consultant; Spouse/Partner; Tandem Diabetes Care. Consultant; Self; Zealand Pharma A/S. Speaker’s Bureau; Self; Insulet Corporation. L.M. Laffel: Advisory Panel; Self; Roche Diabetes Care. Consultant; Self; Boehringer Ingelheim Pharmaceuticals, Inc., ConvaTec Inc., Dexcom, Inc., Insulet Corporation, Insulogic LLC, Janssen Pharmaceuticals, Inc., Lilly Diabetes, Novo Nordisk Inc., Sanofi US. Funding National Institutes of Health (P30DK036836, DP3DK113511, DP3DK104057, K12DK094721, T32DK007260)

PLGS systems have been demonstrated to limit hypoglycemia. Reduced insulin during suspensions may avoid the need for rescue CHO or lessen the amount of CHO needed. The approximately 20-hour inpatient evaluation of the Lilly investigational AID system’s PLGS feature, including a basal up-titration period to activate the PLGS and investigator-directed administration of rescue CHO, allowed assessment of hypoglycemia prevention and treatment requirements. Ten subjects with type 1 diabetes (40% male, mean age 39.0±13.0 years, A1C 7.2±0.6%, and insulin usage 0.6±0.2 U/kg/day) were studied. There were 59 suspensions, with all subjects experiencing suspensions during which CHO were not administered. Only six suspensions were associated with rescue CHO and five suspensions were associated with hypoglycemia (CGM glucose Disclosure J.E. Pinsker: Advisory Panel; Self; Medtronic. Consultant; Self; Eli Lilly and Company, Tandem Diabetes Care. Research Support; Self; Dexcom, Inc., Eli Lilly and Company, Insulet Corporation, Medtronic, Tandem Diabetes Care. Speaker’s Bureau; Self; Tandem Diabetes Care. A. Bartee: None. M. Katz: Employee; Self; Eli Lilly and Company. Stock/Shareholder; Self; Eli Lilly and Company. A. LaLonde: Employee; Self; Eli Lilly and Company. Stock/Shareholder; Self; Eli Lilly and Company. R. Jones: Employee; Self; Eli Lilly and Company. Stock/Shareholder; Self; Eli Lilly and Company. E. Dassau: Consultant; Self; Eli Lilly and Company. Research Support; Self; Dexcom, Inc., DreaMed Diabetes, Tandem Diabetes Care, Xeris Pharmaceuticals, Inc. Speaker’s Bureau; Self; Roche Diabetes Care. Other Relationship; Self; Dexcom, Inc., Insulet Corporation, Roche Diabetes Care. H. Wolpert: Employee; Self; Eli Lilly and Company.

Aim: Most youth with T1D meet neither nutrition guidelines nor glycemic goals. We assessed macronutrient content of meals/snacks and impact of daily number of meals/snacks on glycemic outcomes in youth with T1D over 1 year. Methods: Youth completed 3-day food records (2 week days/1 weekend day) and wore 3-day masked CGM (iPro™) every 3 months for 1 year. Diet data were reviewed by 2 RDs and analyzed using Nutrition Data System for Research (NDSR). Glycemic outcomes were A1c and CGM metrics (glucose % time in range [TIR] 70-180 mg/dL, % time [T] 180, and glucose CV [SD/mean]). Longitudinal mixed models assessed effect of number of daily meals/snacks on glycemic outcomes. Results: Youth (N=136, ages 8-17, 48% male) were ages 12.8±2.5 years with T1D duration 5.9±3.1 years and daily insulin dose 0.9±0.3 U/kg; 73% used insulin pumps. Number of meals/snacks ranged from 1-9 with most youth (69%) reporting 3-4 meals/snacks daily. Macronutrient intake varied by meal/snack (Figure). An increase of 1 meal/snack per day led to a 0.1% decrease in A1c (p=.001), 2.4% (35 minutes/day) more TIR (p=.0006), and 3.1% (45 minutes/day) less T>180 (p=.0001); T Conclusions: In youth with T1D, eating frequent meals/snacks may lead to lower A1c, more TIR, and less hyperglycemia. These findings may be due to dividing energy intake across the day with more frequent bolus dosing, given predominance of carbohydrates in meals/snacks. Disclosure R.O. La Banca: None. L.K. Volkening: None. E. Dassau: Consultant; Self; Eli Lilly and Company. Research Support; Self; Dexcom, Inc., DreaMed Diabetes, Tandem Diabetes Care, Xeris Pharmaceuticals, Inc. Speaker’s Bureau; Self; Roche Diabetes Care. Other Relationship; Self; Dexcom, Inc., Insulet Corporation, Roche Diabetes Care. S.N. Mehta: None. L.M. Laffel: Advisory Panel; Self; Roche Diabetes Care. Consultant; Self; Boehringer Ingelheim Pharmaceuticals, Inc., ConvaTec Inc., Dexcom, Inc., Insulet Corporation, Insulogic LLC, Janssen Pharmaceuticals, Inc., Lilly Diabetes, Novo Nordisk Inc., Sanofi US. Funding National Institutes of Health (P30DK036836, K12DK094721)

The target time in range (TIR) for pregnant women with type 1 diabetes (T1D) by the International Consensus on TIR is 70% between 63-140 mg/dL. This is difficult to achieve and is rarely met in the literature. Insulin use increases during pregnancy, but prospective data and guidance on pump setting adjustments are limited. Insulin pump delivery and CGM data for 17 T1D women from 3 U.S. sites were prospectively collected every 2 weeks as part of the LOIS-P trial. Subjects enrolled before 17 weeks gestational age (GA) and wore personal pumps and study Dexcom G6 CGM. Changes in mean daily total, basal and bolus doses per kilogram, and TIR for every 2 weeks GA are reported, and linear mixed effects regression models are used for evaluation across trimesters. Enrollment HbA1C was 6.4±0.8%. Total daily dose increased from 0.68 to 0.73 to 0.98 U/kg during the first, second and third trimesters, respectively (p70% TIR during pregnancy. Time below target was 5%, 4% and 2%. Doses trended upwards around 24 weeks GA. Postpartum doses decreased significantly. While insulin doses were increased significantly across pregnancy, most subjects did not achieve > 70% TIR. Systems that are customized to this population’s targets with changing insulin sensitivity are needed. Disclosure G. O’Malley: Research Support; Self; Abbott, Dexcom, Inc. B. Ozaslan: None. C. Levister: None. M. Trinidad: None. K.N. Castorino: Research Support; Self; Abbott, Dexcom, Inc., Medtronic, Mylan, Novo Nordisk Inc. D. Desjardins: None. M. Church: None. B.H. Smith: None. S.J. Ogyaadu: None. M. Piper: None. C. Reid: None. S.K. McCrady-Spitzer: None. R. Kaur: None. W.K. Kremers: Research Support; Self; AstraZeneca. F.J. Doyle: Research Support; Self; DreaMed Diabetes, Tandem Diabetes Care, Xeris Pharmaceuticals, Inc. Stock/Shareholder; Self; Mode AGC. Other Relationship; Self; Dexcom, Inc., Insulet Corporation, Roche Diabetes Care. J.E. Pinsker: Advisory Panel; Self; Medtronic. Consultant; Self; Eli Lilly and Company, Tandem Diabetes Care. Research Support; Self; Dexcom, Inc., Eli Lilly and Company, Insulet Corporation, Medtronic, Tandem Diabetes Care. Speaker’s Bureau; Self; Tandem Diabetes Care. C.J. Levy: Consultant; Self; Dexcom, Inc. Employee; Spouse/Partner; Allergan plc. Research Support; Self; Abbott, Dexcom, Inc., Insulet Corporation. B. Rosenn: None. Y.C. Kudva: Research Support; Self; Dexcom, Inc., Roche Diabetes Care. Other Relationship; Self; Abbott. E. Dassau: Consultant; Self; Eli Lilly and Company. Research Support; Self; Dexcom, Inc., DreaMed Diabetes, Tandem Diabetes Care, Xeris Pharmaceuticals, Inc. Speaker’s Bureau; Self; Roche Diabetes Care. Other Relationship; Self; Dexcom, Inc., Insulet Corporation, Roche Diabetes Care. Funding National Institutes of Health (R01DK120358); Dexcom, Inc. (AP-2018-016)

OBJECTIVE Limited information is available about glycemic outcomes with a closed-loop control (CLC) system compared with a predictive low-glucose suspend (PLGS) system. RESEARCH DESIGN AND METHODS After 6 months of use of a CLC system in a randomized trial, 109 participants with type 1 diabetes (age range, 14–72 years; mean HbA1c, 7.1% [54 mmol/mol]) were randomly assigned to CLC (N = 54, Control-IQ) or PLGS (N = 55, Basal-IQ) groups for 3 months. The primary outcome was continuous glucose monitor (CGM)-measured time in range (TIR) for 70–180 mg/dL. Baseline CGM metrics were computed from the last 3 months of the preceding study. RESULTS All 109 participants completed the study. Mean ± SD TIR was 71.1 ± 11.2% at baseline and 67.6 ± 12.6% using intention-to-treat analysis (69.1 ± 12.2% using per-protocol analysis excluding periods of study-wide suspension of device use) over 13 weeks on CLC vs. 70.0 ± 13.6% and 60.4 ± 17.1% on PLGS (difference = 5.9%; 95% CI 3.6%, 8.3%; P < 0.001). Time >180 mg/dL was lower in the CLC group than PLGS group (difference = −6.0%; 95% CI −8.4%, −3.7%; P < 0.001) while time CONCLUSIONS Following 6 months of CLC, switching to PLGS reduced TIR and increased HbA1c toward their pre-CLC values, while hypoglycemia remained similarly reduced with both CLC and PLGS.

Background: This study assessed the safety and performance of the Omnipod® personalized model predictive control (MPC) algorithm using an investigational device in adults with type 1 diabetes in response to overestimated and missed meal boluses and extended boluses for high-fat meals. Materials and Methods: A supervised 54-h hybrid closed-loop (HCL) study was conducted in a hotel setting after a 7-day outpatient open-loop run-in phase. Adults aged 18–65 years with type 1 diabetes and HbA1c 6.0%–10.0% were eligible. Primary endpoints were percentage time in hypoglycemia

Background: We investigated the safety and efficacy of the addition of a trust index to enhanced Model Predictive Control (eMPC) Artificial Pancreas (AP) that works by adjusting the responsiveness of the controller's insulin delivery based on the confidence intervals around predictions of glucose trends. This constitutes a dynamic adaptation of the controller's parameters in contrast with the widespread AP implementation of individualized fixed controller tuning. Materials and Methods: After 1 week of sensor-augmented pump (SAP) use, subjects completed a 48-h AP admission that included three meals/day (carbohydrate range 29–57 g/meal), a 1-h unannounced brisk walk, and two overnight periods. Endpoints included sensor glucose percentage time 70–180, 180 mg/dL, number of hypoglycemic events, and assessment of the trust index versus standard eMPC glucose predictions. Results: Baseline characteristics for the 15 subjects who completed the study (mean ± SD) were age 46.1 ± 17.8 years, HbA1c 7.2% ± 1.0%, diabetes duration 26.8 ± 17.6 years, and total daily dose (TDD) 35.5 ± 16.4 U/day. Mean sensor glucose percent time 70–180 mg/dL (88.0% ± 8.0% vs. 74.6% ± 9.4%)

Background: The safety and feasibility of the OmniPod personalized model predictive control (MPC) algorithm in adult, adolescent, and pediatric patients with type 1 diabetes were investigated. Methods: This multicenter, observational trial included a 1-week outpatient sensor-augmented pump open-loop phase and a 36-h inpatient hybrid closed-loop (HCL) phase with announced meals ranging from 30 to 90 g of carbohydrates and limited physical activity. Patients aged 6–65 years with HbA1c between 6.0% and 10.0% were eligible. The investigational system included a modified version of OmniPod, the Dexcom G4 505 Share® AP System, and the personalized MPC algorithm running on a tablet computer. Primary endpoints included sensor glucose percentage of time in hypoglycemia 250 mg/dL. Additional glycemic targets were assessed. Results: The percentage of time 250 mg/dL was 8.0 (7.5), 3.6 (3.7), 4.9 (6.3), and 6.7 (5.6) in the study groups, respectively. Percentage of time in the target range of 70–180 mg/dL was 69.5 (14.4), 73.0 (15.0), 72.6 (15.5), and 70.1 (12.3), respectively. Conclusions: The OmniPod personalized MPC algorithm performed well and was safe during day and night use in adult, adolescent, and pediatric patients with type 1 diabetes. Longer term studies will assess the safety and performance of the algorithm under free living conditions with extended use.

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.

Type 1 diabetes mellitus (T1DM) is a metabolic disorder characterized by the destruction of the pancreatic beta cells. With this disease, the body is no longer able to produce insulin, leading to chronically high concentrations of glucose in the blood (hyperglycemia) [1]. People with T1DM manage the disease by administering exogenous insulin doses as determined by multiple daily measurements of the blood glucose (BG) concentration; however, this approach requires significant effort from the patient and often achieves suboptimal results, leading to short- and long-term health complications [2]-[5].

Measurement of glycated hemoglobin (HbA1c) has been the traditional method for assessing glycemic control. However, it does not reflect intra- and interday glycemic excursions that may lead to acute events (such as hypoglycemia) or postprandial hyperglycemia, which have been linked to both microvascular and macrovascular complications. Continuous glucose monitoring (CGM), either from real-time use (rtCGM) or intermittently viewed (iCGM), addresses many of the limitations inherent in HbA1c testing and self-monitoring of blood glucose. Although both provide themeans to move beyond the HbA1c measurement as the sole marker of glycemic control, standardized metrics for analyzing CGM data are lacking. Moreover, clear criteria for matching people with diabetes to themost appropriate glucose monitoring methodologies, as well as standardized advice about howbest to use the new information they provide, have yet to be established. In February 2017, the Advanced Technologies & Treatments for Diabetes (ATTD) Congress convened an international panel of physicians, researchers, and individuals with diabetes who are expert in CGM technologies to address these issues. This article summarizes the ATTD consensus recommendations and represents the current understanding of how CGM results can affect outcomes.

Development of an effective artificial pancreas (AP) controller to deliver insulin autonomously to people with type 1 diabetes mellitus is a difficult task. In this paper, three enhancements to a clinically validated AP model predictive controller (MPC) are proposed that address major challenges facing automated blood glucose control, and are then evaluated by both in silico tests and clinical trials. First, the core model of insulin-blood glucose dynamics utilized in the MPC is expanded with a medically inspired personalization scheme to improve controller responses in the face of inter- and intra-individual variations in insulin sensitivity. Next, the asymmetric nature of the short-term consequences of hypoglycemia versus hyperglycemia is incorporated in an asymmetric weighting of the MPC cost function. Finally, an enhanced dynamic insulin-on-board algorithm is proposed to minimize the likelihood of controller-induced hypoglycemia following a rapid rise of blood glucose due to rescue carbohydrate load with accompanying insulin suspension. Each advancement is evaluated separately and in unison through in silico trials based on a new clinical protocol, which incorporates induced hyper- and hypoglycemia to test robustness. The advancements are also evaluated in an advisory mode (simulated) testing of clinical data. The combination of the three proposed advancements show statistically significantly improved performance over the nonpersonalized controller without any enhancements across all metrics, displaying increased time in the 70–180 mg/dL safe glycemic range (76.9 versus 68.8%) and the 80–140 mg/dL euglycemic range (48.1 versus 44.5%), without a statistically significant increase in instances of hypoglycemia. The proposed advancements provide safe control action for AP applications, personalizing and improving controller performance without the need for extensive model identification processes.

OBJECTIVE Assess the efficacy of inControl AP, a mobile closed-loop control (CLC) system. RESEARCH DESIGN AND METHODS This protocol, NCT02985866, is a 3-month parallel-group, multicenter, randomized unblinded trial designed to compare mobile CLC with sensor-augmented pump (SAP) therapy. Eligibility criteria were type 1 diabetes for at least 1 year, use of insulin pumps for at least 6 months, age ≥14 years, and baseline HbA1c RESULTS Between November 2017 and May 2018, 127 participants were randomly assigned 1:1 to CLC (n = 65) versus SAP (n = 62); 125 participants completed the study. CGM time below 3.9 mmol/L was 5.0% at baseline and 2.4% during follow-up in the CLC group vs. 4.7% and 4.0%, respectively, in the SAP group (mean difference −1.7% [95% CI −2.4, −1.0]; P < 0.0001 for superiority). CGM time above 10 mmol/L was 40% at baseline and 34% during follow-up in the CLC group vs. 43% and 39%, respectively, in the SAP group (mean difference −3.0% [95% CI −6.1, 0.1]; P < 0.0001 for noninferiority). One severe hypoglycemic event occurred in the CLC group, which was unrelated to the study device. CONCLUSIONS In meeting its coprimary end points, superiority of CLC over SAP in CGM-measured time below 3.9 mmol/L and noninferiority in CGM-measured time above 10 mmol/L, the study has demonstrated that mobile CLC is feasible and could offer certain usability advantages over embedded systems, provided the connectivity between system components is stable.

Background: Food choices are essential to successful glycemic control for people with diabetes. We compared the impact of three carbohydrate-rich meals on the postprandial glycemic response in adults with type 1 diabetes (T1D). Methods: We performed a randomized crossover study in 12 adults with T1D (age 58.7 ± 14.2 years, baseline hemoglobin A1c 7.5% ± 1.3%) comparing the postprandial glycemic response to three meals using continuous glucose monitoring: (1) “higher protein” pasta containing 10 g protein/serving, (2) regular pasta with 7 g protein/serving, and (3) extra-long grain white rice. All meals contained 42 g carbohydrate; were served with homemade tomato sauce, green salad, and balsamic dressing; and were repeated twice in random order. After their insulin bolus, subjects were observed in clinic for 5 h. Linear mixed effects models were used to assess the glycemic response. Results: Compared with white rice, peak glucose levels were significantly lower for higher protein pasta (−32.6 mg/dL; 95% CI −48.4 to −17.2; P

Current artificial pancreas systems (AP) operate via subcutaneous (SC) glucose sensing and SC insulin delivery. Due to slow diffusion and transport dynamics across the interstitial space, even the most sophisticated control algorithms in on-body AP systems cannot react fast enough to maintain tight glycemic control under the effect of exogenous glucose disturbances caused by ingesting meals or performing physical activity. Recent efforts made towards the development of an implantable AP have explored the utility of insulin infusion in the intraperitoneal (IP) space: a region within the abdominal cavity where the insulin-glucose kinetics are observed to be much more rapid than the SC space. In this paper, a series of canine experiments are used to determine the dynamic association between IP insulin boluses and plasma glucose levels. Data from these experiments are employed to construct a new mathematical model and to formulate a closed-loop control strategy to be deployed on an implantable AP. The potential of the proposed controller is demonstrated via in-silico experiments on an FDA-accepted benchmark cohort: the proposed design significantly outperforms a previous controller designed using artificial data (time in clinically acceptable glucose range: 97.3±1.5% vs. 90.1±5.6%). Furthermore, the robustness of the proposed closed-loop system to delays and noise in the measurement signal (for example, when glucose is sensed subcutaneously) and deleterious glycemic changes (such as sudden glucose decline due to physical activity) is investigated. The proposed model based on experimental canine data leads to the generation of more effective control algorithms and is a promising step towards fully automated and implantable artificial pancreas systems.

Objective: Prior reports using the validated Barriers to Physical Activity in Diabetes (type 1) [BAPAD1] scale showed fear of hypoglycemia was the strongest barrier to regular physical activity (PA) in people with type 1 diabetes (T1D). This was before the higher prevalence of continuous glucose monitoring (CGM) use today. Methods: Twenty adults with T1D enrolling in an exercise tracking study completed the BAPAD1, reporting perceived barriers to regular PA on a scale of 1 (extremely unlikely) to 7 (extremely likely). All used insulin pumps. Fifteen of the 20 were current CGM users. Results: Baseline characteristics were age 44.9±15.0 years, F/M 12/8, HbA1c 6.8±0.7%, and diabetes duration 22.9±15.9 years. Mean BAPAD1 score was 2.55±1.05. The highest scores were for risk of hypoglycemia (4.00±1.78) and work schedule (3.70±2.00), with current CGM users reporting higher overall scores than non-CGM users (2.71±1.04 vs. 2.10±1.07, p=0.28). Significantly higher scores were reported by CGM users for “The fear of being tired” (p=0.01), “The fact that you have diabetes” (p=0.03), and “The location of a gym” (p=0.04) (Table 1). Greater barrier scores for work schedule were associated with lower age (r=-0.5, p=0.02). Conclusions: CGM use was not associated with lower perceived barriers to regular PA, suggesting additional interventions beyond providing ways to measure glucose are needed to reduce these barriers in people with T1D. Disclosure C.C. Andre: None. Y.C. Kudva: Advisory Panel; Self; Novo Nordisk Inc. Other Relationship; Self; Dexcom, Inc., Roche Diabetes Care, Tandem Diabetes Care. V. Dadlani: None. M. Cescon: None. S.K. McCrady-Spitzer: None. M. Church: None. C. Reid: None. K. Kumari: None. D. Choudhary: None. F.J. Doyle: Consultant; Self; ModeAGC. Other Relationship; Self; Insulet Corporation. E. Dassau: Consultant; Self; Eli Lilly and Company, Insulet Corporation. Research Support; Self; Dexcom, Inc., DreaMed Diabetes, Ltd., Insulet Corporation, Roche Diabetes Care, Tandem Diabetes Care, Xeris Pharmaceuticals, Inc. Speaker's Bureau; Self; Roche Diabetes Care. Other Relationship; Self; ModAGC. J.E. Pinsker: Research Support; Self; Ascensia Diabetes Care, Dexcom, Inc., Insulet Corporation, LifeScan, Inc., Roche Diabetes Care, Tandem Diabetes Care. Speaker's Bureau; Self; Tandem Diabetes Care. Funding JDRF; The Leona M. and Harry B. Helmsley Charitable Trust (2-SRA-2017-503-M-B); Dexcom, Inc. (IIS-2017-043)

Many women with type 1 DM report change in glucose (GLU) control and insulin (INS) dosing throughout the menstrual cycle (MC). No trials have reported the effect of closed loop control (CLC) on INS delivery across the MC. Women enrolled in Phase 1 of the IDCL hybrid CLC trial logged MCs to estimate late luteal and early follicular dates. Data from 64 MC across 27 women (13 CLC), 7 using hormonal contraception, aged 28±11 years, weight 67 kg (IQR: 61-80) was analyzed for diurnal GLU control and CLC INS delivery. MC phase did not impact trends toward increased time in range with CLC. There were no differences in GLU and INS variables. While median CLC INS delivery did not change across the MC, there was intrasubject variability across the MC. CLC has the potential to elucidate individual dosing variability and may mitigate reported change in GLU control across the MC. Disclosure C.J. Levy: Advisory Panel; Self; Novo Nordisk A/S, Sanofi. Employee; Spouse/Partner; Allergan. G. OMalley: None. S.A. Brown: Research Support; Self; Ascensia Diabetes Care, Dexcom, Inc., Roche Diabetes Care, Tandem Diabetes Care. D. Raghinaru: None. Y.C. Kudva: Advisory Panel; Self; Novo Nordisk Inc. Other Relationship; Self; Dexcom, Inc., Roche Diabetes Care, Tandem Diabetes Care. E. Dassau: Consultant; Self; Eli Lilly and Company, Insulet Corporation. Research Support; Self; Dexcom, Inc., DreaMed Diabetes, Ltd., Insulet Corporation, Roche Diabetes Care, Tandem Diabetes Care, Xeris Pharmaceuticals, Inc. Speaker’s Bureau; Self; Roche Diabetes Care. Other Relationship; Self; ModAGC. B. Kovatchev: Advisory Panel; Self; Sanofi. Board Member; Self; TypeZero Technologies, Inc. Consultant; Self; Sanofi, Tandem Diabetes Care. Research Support; Self; Dexcom, Inc., Roche Diabetes Care, Tandem Diabetes Care. Speaker’s Bureau; Self; Dexcom, Inc. Stock/Shareholder; Self; TypeZero Technologies, Inc. Other Relationship; Self; Johnson & Johnson, Sanofi. S. Anderson: Research Support; Self; Medtronic MiniMed, Inc. Funding National Institutes of Health

Performing bioassay formats based on enzyme and antibody recognition reactions with a single detection chip remains an unmet challenge owing to the different requirements of such bioassays. Herein, we describe a dual-marker biosensor chip, integrating enzyme and antibody-based assays for simultaneous electrochemical measurements of insulin (I) and glucose (G). Simultaneous G/I sensing has been realized by addressing key fabrication and operational challenges associated with the different assay requirements and surface chemistry. The I immunosensor relies on a peroxidase-labeled sandwich immunoassay, while G is monitored through reaction with glucose oxidase. The dual diabetes biomarker chip offers selective and reproducible detection of picomolar I and millimolar G concentrations in a single microliter sample droplet within less than 30 min, including direct measurements in whole blood and saliva samples. The resulting integrated enzymatic-immunoassay biosensor chip opens a new realm in point-of-care multiplexed biomarker detection.

International audience; Background: Use of artificial pancreas (AP) requires seamless interaction of device components, such as continuous glucose monitor (CGM), insulin pump, and control algorithm. Mobile AP configurations also include a smartphone as computational hub and gateway to cloud applications (e.g., remote monitoring and data review and analysis). This International Diabetes Closed-Loop study was designed to demonstrate and evaluate the operation of the inControl AP using different CGMs and pump modalities without changes to the user interface, user experience, and underlying controller.Methods: Forty-three patients with type 1 diabetes (T1D) were enrolled at 10 clinical centers (7 United States, 3 Europe) and 41 were included in the analyses (39% female, >95% non-Hispanic white, median T1D duration 16 years, median HbA1c 7.4%). Two CGMs and two insulin pumps were tested by different study participants/sites using the same system hub (a smartphone) during 2 weeks of in-home use.Results: The major difference between the system components was the stability of their wireless connections with the smartphone. The two sensors achieved similar rates of connectivity as measured by percentage time in closed loop (75% and 75%); however, the two pumps had markedly different closed-loop adherence (66% vs. 87%). When connected, all system configurations achieved similar glycemic outcomes on AP control (73% [mean] time in range: 70–180 mg/dL, and 1.7% [median] time

Background: There is an unmet need for a modular artificial pancreas (AP) system for clinical trials within the existing regulatory framework to further AP research projects from both academia and industry. We designed, developed, and tested the interoperable artificial pancreas system (iAPS) smartphone app that can interface wirelessly with leading continuous glucose monitors (CGM), insulin pump devices, and decision-making algorithms while running on an unlocked smartphone. Methods: After algorithm verification, hazard and mitigation analysis, and complete system verification of iAPS, six adults with type 1 diabetes completed 1 week of sensor-augmented pump (SAP) use followed by 48 h of AP use with the iAPS, a Dexcom G5 CGM, and either a Tandem or Insulet insulin pump in an investigational device exemption study. The AP system was challenged by participants performing extensive walking without exercise announcement to the controller, multiple large meals eaten out at restaurants, two overnight periods, and multiple intentional connectivity interruptions. Results: Even with these intentional challenges, comparison of the SAP phase with the AP study showed a trend toward improved time in target glucose range 70–180 mg/dL (78.8% vs. 83.1%; P = 0.31), and a statistically significant reduction in time below 70 mg/dL (6.1% vs. 2.2%; P = 0.03). The iAPS system performed reliably and showed robust connectivity with the peripheral devices (99.8% time connected to CGM and 94.3% time in closed loop) while requiring limited user intervention. Conclusions: The iAPS system was safe and effective in regulating glucose levels under challenging conditions and is suitable for use in unconstrained environments.

Here we describe the development of a dual electrochemical immunosensor microchip for simultaneous detection of insulin (I) and cortisol (C) biomarkers that can enhance the ability to improve glucose regulation using automated insulin delivery. The successful realization of the simultaneous I and C measurements has been realized by integrating different enzymatically-tagged competitive and sandwich immunoassay formats on a single chip platform. The insulin detection is based on a peroxidase (HRP)-labeled sandwich assay whereas the cortisol detection relies on an alkaline phosphatase (ALP)-labeled competitive immunoassay. The attractive analytical performance of the dual marker immunosensor, with no apparent cross-talk, was achieved through systematic optimization of the incubation and amperometric detection of the different captured enzyme tags. Evaluation of dual biosensor chip in untreated serum samples indicated favorable simultaneous detection of picomolar (pM) insulin and nanomolar (nM) cortisol concentrations in a single microliter sample droplet within less than 25min. The new dual immunosensor chip offers considerable promise for frequent decentralized testing of I and C towards a tighter glycemic control and improved management of diabetes.

Background: Insulin infusion set failure resulting in prolonged hyperglycemia or diabetic ketoacidosis can occur with pump therapy in type 1 diabetes. Set failures are frequently characterized by variable and unpredictable patterns of increasing glucose values despite increased insulin infusion. Early detection may minimize the risk of prolonged hyperglycemia, an important consideration for automated insulin delivery and closed-loop applications. Methods: A novel algorithm designed to alert the patient to the onset of infusion set failure was developed based upon continuous glucose sensor values and insulin delivered from an insulin pump. The method was calibrated on 12 weeks of infusion set wear without failures recorded by 4 patients in ambulatory conditions and prospectively validated on 18 weeks of infusion set wear with and without failures belonging to 9 other subjects in ambulatory conditions. Results: The algorithm, evaluated retrospectively, identified a failure 2.52 ± 1.91 days ahead of the actual event as recorded by the clinical team, corresponding to 50% sensitivity, 66% specificity and 55% accuracy. If set failure alarms had been activated in real time, the average time >180 mg/dl would be reduced from 82.7 ± 40.9 hours/week/subject (without alarm) to 58.8 ± 31.1 hours/week/subject (with alarm), corresponding to a potential 29% reduction in time spent >180mg/dl. Conclusion: The proposed method for early detection of infusion set failure based on glucose sensor and insulin data demonstrated favorable results on retrospective data and may be implemented as an additional safeguard in a future fully automated closed-loop system.

Objective: Zone model predictive control (MPC) has been proven to be an efficient approach to closed-loop insulin delivery in clinical studies. In this paper, we aim to safely reduce mean glucose levels by proposing control penalty adaptation in the cost function of zone MPC. Methods: A zone MPC method with a dynamic cost function that updates its control penalty parameters in real time according to the predicted glucose and its rate of change is developed. The proposed method is evaluated on the entire 100-adult cohort of the FDA-accepted UVA/Padova T1DM simulator and compared with the zone MPC tested in an extended outpatient study. Results: For unannounced meals, the proposed method leads to statistically significant improvements in terms of mean glucose (153.8 mg/dL vs. 159.0 mg/dL; $\boldsymbol p ) and percentage time in $[70, 180]$ mg/dL ( $\text{70.5}\%$ vs. $\text{66.3}\%$ ; $\boldsymbol p ) without increasing the risk of hypoglycemia. Performance for announced meals is similar to that obtained without adaptation. The proposed method also behaves properly and safely for scenarios of moderate meal-bolus and basal rate mismatches, as well as simulated unannounced exercise. Advisory-mode analysis based on clinical data indicates that the method can reduce glucose levels through suggesting additional safe amounts of insulin on top of those suggested by the zone MPC used in the study. Conclusion: The proposed method leads to improved glucose control without increasing hypoglycemia risks. Significance: The results validate the feasibility of improving glucose regulation through glucose- and velocity-dependent control penalty adaptation in MPC design.

Zone model predictive control has proven to be an effective closed-loop method to regulate blood glucose for people with type 1 diabetes (T1D). In this paper, we present a universal model-free optimization scheme for adapting the zone for T1D patients individually. The adaptation is based on a clinical glycemic risk index named relative regularized glycemic penalty index (rrGPI), which is calculated from glucose measurements by a continuous glucose monitor. The scheme's objective is to minimize rrGPI by simultaneously modulating a controller's blood glucose target zone's upper bound and lower bound. The adaptation mechanism is based on extremum seeking control, in which the zone boundaries are driven by gradient estimation obtained by continuously sinusoidally modulating and demodulating the rrGPI readings. To improve the adaptation method's robustness against uncertainties, a decaying feedback gain and a vanishing dither signal are employed. in-silico trials suggested that the personalized optimized zone can be reached within a week of adaptation. Both for announced and unannounced meals, the proposed method outperforms the fixed zone [80, 140] mg/dL, which has been employed in the authors’ clinical trials. It is also shown that the developed method has strong robustness against real-life uncertainties.

People with type 1 diabetes are required to manually input a feedforward bolus of insulin to compensate for glycemic excursions due to the macronutrient content of ingested meals. Precisely assessing macronutrient contents of complex food types is extremely difficult and time-consuming, whereas inaccurate dietary assessment may result in poor glycemic outcomes. To alleviate this burden, we propose a deep learning based assistive tool that automatically estimates the macronutrient content via real-time image recognition. Concretely, the user provides an image of their meal along with an estimated serving size, based on which a deep convolutional neural network (CNN) predicts the food category and subsequently queries a nutritional database to obtain the macronutrient content. This deep learning framework is integrated with an artificial pancreas (AP) system, and equipped with explicit safety constraints. Upon constraint violation, no automatic feedforward bolus is provided, and appropriate corrective insulin boluses are computed solely using the AP's intrinsic feedback control algorithm. Numerical simulations are performed to demonstrate the potential of the proposed methodology. Glucose is maintained within the safe zone of 70–180 mg/dL for $91.76\pm 7.20\%$ of the time, which is a sianificant improvement over control with unannounced meals ( $78.78\pm 12.14\%$ ). Although glycemic outcomes are moderately lower with deep learning assist than with full meal announcement ( $97.29\pm 2.91\%$ ), the proposed method offers the advantage of increased autonomy by leveraging machine intelligence to facilitate macronutrient estimation without compromising safety. Robustness testing is performed by considering meal size estimation errors of up to ±40%: our method exhibits a small reduction (

Background:As evidence emerges that artificial pancreas systems improve clinical outcomes for patients with type 1 diabetes, the burden of this disease will hopefully begin to be alleviated for many patients and caregivers. However, reliance on automated insulin delivery potentially means patients will be slower to act when devices stop functioning appropriately. One such scenario involves an insulin infusion site failure, where the insulin that is recorded as delivered fails to affect the patient’s glucose as expected. Alerting patients to these events in real time would potentially reduce hyperglycemia and ketosis associated with infusion site failures.Methods:An infusion site failure detection algorithm was deployed in a randomized crossover study with artificial pancreas and sensor-augmented pump arms in an outpatient setting. Each arm lasted two weeks. Nineteen participants wore infusion sets for up to 7 days. Clinicians contacted patients to confirm infusion site failures detected by the algorithm and instructed on set replacement if failure was confirmed.Results:In real time and under zone model predictive control, the infusion site failure detection algorithm achieved a sensitivity of 88.0% (n = 25) while issuing only 0.22 false positives per day, compared with a sensitivity of 73.3% (n = 15) and 0.27 false positives per day in the SAP arm (as indicated by retrospective analysis). No association between intervention strategy and duration of infusion sets was observed ( P = .58).Conclusions:As patient burden is reduced by each generation of advanced diabetes technology, fault detection algorithms will help ensure that patients are alerted when they need to manually intervene.Clinical Trial Identifier:www.clinicaltrials.gov,NCT02773875

Aims To evaluate physicians' adjustments of insulin pump settings based on continuous glucose monitoring (CGM) for patients with type 1 diabetes and to compare these to automated insulin dose adjustments. Methods A total of 26 physicians from 16 centres in Europe, Israel and South America participated in the study. All were asked to adjust insulin dosing based on insulin pump, CGM and glucometer downloads of 15 patients (mean age 16.2 ± 4.3 years, six female, mean glycated haemoglobin 8.3 ± 0.9% [66.8 ± 7.3 mmol/mol]) gathered over a 3-week period. Recommendations were compared for the relative changes in the basal, carbohydrate to insulin ratio (CR) and correction factor (CF) plans among physicians and among centres and also between the physicians and an automated algorithm, the Advisor Pro (DreaMed Diabetes Ltd, Petah Tikva, Israel). Study endpoints were the percentage of comparison points for which there was full agreement on the trend of insulin dose adjustments (same trend), partial agreement (increase/decrease vs no change) and full disagreement (opposite trend). Results The percentages for full agreement between physicians on the trend of insulin adjustments of the basal, CR and CF plans were 41 ± 9%, 45 ± 11% and 45.5 ± 13%, and for complete disagreement they were 12 ± 7%, 9.5 ± 7% and 10 ± 8%, respectively. Significantly similar results were found between the physicians and the automated algorithm. The algorithm magnitude of insulin dose change was at least equal to or less than that proposed by the physicians. Conclusions Physicians provide different insulin dose recommendations based on the same datasets. The automated advice of the Advisor Pro did not differ significantly from the advice given by the physicians in the direction or magnitude of the insulin dosing.

International audience; To compare intraperitoneal (IP) to subcutaneous (SC) insulin delivery in an artificial pancreas (AP). RESEARCH DESIGN AND METHODS: Ten adults with type 1 diabetes participated in a non-randomized, non-blinded sequential AP study using the same SC glucose sensing and Zone Model Predictive Control (ZMPC) algorithm adjusted for insulin clearance. On first admission, subjects underwent closed-loop control with SC delivery of a fast-acting insulin analogue for 24 hours. Following implantation of a DiaPort IP insulin delivery system, the identical 24-hour trial was performed with IP regular insulin delivery. The clinical protocol included 3 unannounced meals with 70, 40 and 70 g carbohydrate, respectively. Primary endpoint was time spent with blood glucose (BG) in the range of 80 to 140 mg/dL (4.4-7.7 mmol/L). RESULTS: Percent of time spent within the 80 to 140 mg/dL range was significantly higher for IP delivery than for SC delivery: 39.8 \textpm 7.6 vs 25.6 \textpm 13.1 ( P = .03). Mean BG (mg/dL) and percent of time spent within the broader 70 to 180 mg/dL range were also significantly better for IP insulin: 151.0 \textpm 11.0 vs 190.0 \textpm 31.0 ( P = .004) and 65.7 \textpm 9.2 vs 43.9 \textpm 14.7 ( P = .001), respectively. Superiority of glucose control with IP insulin came from the reduced time spent in hyperglycaemia (\textgreater180 mg/dL: 32.4 \textpm 8.9 vs 53.5 \textpm 17.4, P = .014; \textgreater250 mg/dL: 5.9 \textpm 5.6 vs 23.0 \textpm 11.3, P = .0004). Higher daily doses of insulin (IU) were delivered with the IP route (43.7 \textpm 0.1 vs 32.3 \textpm 0.1, P \textless .001) with no increased percent time spent \textless70 mg/dL (IP: 2.5 \textpm 2.9 vs SC: 4.1 \textpm 5.3, P = .42). CONCLUSIONS: Glycaemic regulation with fully-automated AP delivering IP insulin was superior to that with SC insulin delivery. This pilot study provides proof-of-concept for an AP system combining a ZMPC algorithm with IP insulin delivery.

Physical activity is an important determinant of glucose variability in type 1 diabetes (T1D). It has been incorporated as a nonglucose input into closed-loop control (CLC) protocols for T1D during the last 4 years mainly by 3 research groups in single center based controlled clinical trials involving a maximum of 18 subjects in any 1 study. Although physical activity data capture may have clinical benefit in patients with T1D by impacting cardiovascular fitness and optimal body weight achievement and maintenance, limited number of such studies have been conducted to date. Clinical trial registries provide information about a single small sample size 2 center prospective study incorporating physical activity data input to modulate closed-loop control in T1D that are seeking to build on prior studies. We expect an increase in such studies especially since the NIH has expanded support of this type of research with additional grants starting in the second half of 2015. Studies (1) involving patients with other disorders that have lasted 12 weeks or longer and tracked physical activity and (2) including both aerobic and resistance activity may offer insights about the user experience and device optimization even as single input CLC heads into real-world clinical trials over the next few years and nonglucose input is introduced as the next advance.