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Background: We evaluated the effect of meloxicam on insulin lispro pharmacokinetics and glucose pharmacodynamics over 10 days of continuous subcutaneous insulin infusion (CSII) at one infusion site in people with type 1 diabetes (T1D). Method: This phase 1, randomized, double-blind, single-center, two-way crossover study enrolled adults with T1D for ≥1 year on stable CSII for ≥3 months. Participants randomly received U100 insulin lispro and LY900027 (U100 insulin lispro + 0.25 mg/mL meloxicam). Primary end points were area under the insulin lispro curve from 0 to 5 hours (AUCIns.0-5h) after bolus administration prior to a mixed-meal tolerance test (MMTT) and maximum observed concentration of insulin lispro (CIns.max) on days 5, 7, and 10, versus day 3 (baseline). Results: A total of 20 participants were randomized. Insulin absorption was accelerated for insulin lispro and LY900027 from days 1 to 7. The AUCIns.0-5h was significantly lower on day 10 versus day 3 for LY900027 (−19%) and insulin lispro (−14%); the AUCIns.0-5h did not differ significantly between treatments. The CIns.max increased with LY900027 and insulin lispro (by ~14%-23% and ~16%-51%) on days 5, 7, and 10 versus day 3. The CIns.max of LY900027 was ~14%-23% lower than insulin lispro CIns.max on days 7 and 10 ( P ≤ .0805). Accelerated insulin absorption and a modest loss of total insulin exposure led to a loss of MMTT glycemic control at later time points. Conclusions: The pharmacokinetics of insulin changed over catheter wear time even when an anti-inflammatory agent was present. Postprandial glycemic control was adversely affected by the accelerated insulin absorption and decreased insulin exposure.

OBJECTIVE: There are no commercially available hybrid closed-loop insulin delivery systems customized to achieve pregnancy-specific glucose targets in the United States. This study aimed to evaluate the feasibility and performance of at-home use of a zone model predictive controller based closed-loop insulin delivery system customized for pregnancies complicated by type 1 diabetes (CLC-P). RESEARCH DESIGN AND Methods: Pregnant women with type 1 diabetes using insulin pumps were enrolled in the second or early third trimester. After study sensor wear collecting run-in data on personal pump therapy and two days of supervised training, participants used CLC-P targeting 80-110 mg/dL during the day and 80-100 mg/dL overnight running on an unlocked smartphone at home. Meals and activities were unrestricted throughout the trial. The primary outcome was the continuous glucose monitoring percentage of time in the target range 63-140 mg/dL versus run-in. Results: Ten participants (HbA1c 5.8±0.6%) used the system from mean gestational age of 23.7±3.5 weeks. Mean percentage time in range increased 14.1 percentage points, equivalent to 3.4 hours per day, compared to run-in (run-in: 64.5±16.3% versus CLC-P: 78.6±9.2%, P=0.002). During CLC-P use, there was significant decrease in both time over 140 mg/dL (P=0.033) and the hypoglycemic ranges of less than 63 mg/dL and 54 mg/dL (P=0.037 for both). Nine participants exceeded consensus goals of above 70% time in range during CLC-P use. ConclusionS: The results show that the extended use of CLC-P at home until delivery is feasible. Larger, randomized studies are indicated to further evaluate system efficacy and pregnancy outcomes.

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).

The ability to continuously monitor the concentration of specific molecules in the body is a long-sought goal of biomedical research. For this purpose, interstitial fluid (ISF) was proposed as the ideal target biofluid because its composition can rapidly equilibrate with that of systemic blood, allowing the assessment of molecular concentrations that reflect full-body physiology. In the past, continuous monitoring in ISF was enabled by microneedle sensor arrays. Yet, benchmark microneedle sensors can only detect molecules that undergo redox reactions, which limits the ability to sense metabolites, biomarkers, and therapeutics that are not redox-active. To overcome this barrier, here, we expand the scope of these devices by demonstrating the first use of microneedle-supported electrochemical, aptamer-based (E-AB) sensors. This platform achieves molecular recognition based on affinity interactions, vastly expanding the scope of molecules that can be sensed. We report the fabrication of microneedle E-AB sensor arrays and a method to regenerate them for multiple uses. In addition, we demonstrate continuous molecular measurements using these sensors in flow systems in vitro using single and multiplexed microneedle array configurations. Translation of the platform to in vivo measurements is possible as we demonstrate with a first E-AB measurement in the ISF of a rodent. The encouraging results reported in this work should serve as the basis for future translation of microneedle E-AB sensor arrays to biomedical research in preclinical animal models.

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: 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.

Aim: Maintaining in-range glucose on days with PA is challenging for youth with T1D, often requiring fine-tuning of diet and insulin. We examined how PA and macronutrient intake impact glycemic outcomes in youth with T1D. Methods: Youth and parents completed 3-day PA and diet records and youth wore 3-day masked CGM (iPro) every 3 months for 18 months. Days were classified as active (≥60 min PA) or inactive. Diet data were analyzed using Nutrition Data System for Research (NDSR) . Daytime (6 AM-11:59 PM) CGM glucometrics were: % time (T) in range (TIR) 70-180 mg/dL, %T180, and glucose CV. Analyses included complete days for PA, diet, and CGM data. Separate longitudinal mixed models for daily carb, fat, and protein intake (adjusted for age, T1D duration, sex, pump vs. MDI) , assessed changes in glucometrics on active vs. inactive days. Results: Youth (N=136, 49% male, 73% pump users) were ages 8-17 yrs (12.9±2.6) with T1D duration 6.0±3.1 yrs and daily insulin 0.9±0.3 U/kg. At baseline, A1c was 8.0±0.9%, daytime %TIR 50±22% (12.0 hrs) , %T180 44±25% (10.6 hrs) , CV 37±11%; macronutrient intake was 49±9% carb, 35±8% fat, 16±4% protein. In all models of macronutrient intakes, %TIR increased by ∼5.3% (76 min; p=.01) with pump use; by 2.8% (40 min; p=.02) on active days; by 1.4% (20 min; p=.03) with 10% increase in carb intake; and by 1.7% (24 min; p=.02) with 10% decrease in fat intake. PA and carb intake did not impact %T180; %T>180 decreased by ∼5.6% (81 min; p=.02) with pump use; by 1.7% (24 min; p=.02) with 10% increase in CHO intake; and by 2.2% (32 min; p=.01) with 10% decrease in fat intake. CV decreased by 1.3% on inactive days (p=.04) ; by 1.1% with 10% decrease in CHO intake (p Conclusion: PA and macronutrient intake have varying effects on glycemic outcomes. The findings reinforce the need to tailor insulin dosing algorithms for diet and PA. Disclosure R.O.La banca: None. L.K.Volkening: None. E.Dassau: Employee; Eli Lilly and Company, Research Support; Dexcom, Inc., JDRF, National Institute of Diabetes and Digestive and Kidney Diseases, Stock/Shareholder; Eli Lilly and Company. S.N.Mehta: None. L.M.Laffel: Advisory Panel; Medtronic, Roche Diabetes Care, Consultant; Boehringer Ingelheim International GmbH, Dexcom, Inc., Dompé, Insulet Corporation, Janssen Pharmaceuticals, Inc., Lilly Diabetes, Novo Nordisk, Provention Bio, Inc. Funding National Institutes of Health (K12DK094721, P30DK036836) ; Iacocca Foundation

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: 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.

Background Intraperitoneal insulin delivery has proven to safely overcome a major limit of subcutaneous delivery—meal announcement—and has been able to optimize glycemic control in adults under controlled experimental conditions. In addition, intraperitoneal delivery avoids peripheral hyperinsulinemia resulting from the subcutaneous route and restores a physiological liver gradient. Methods Relying on a unique data set of intraperitoneal closed-loop insulin delivery obtained with a Model Predictive Controller (MPC), we develop a compartmental model of intraperitoneal insulin kinetics, which, once included in the UVa/Padova T1D simulator, will facilitate the investigation of various control strategies, for example, the simpler Proportional Integral Derivative controller versus MPC. Results Intraperitoneal insulin kinetics can be described with a 2-compartment model including liver and plasma. Conclusion Intraperitoneal insulin transit is fast enough to render irrelevant the addition of a peritoneal compartment, proving the peritoneum being a virtual—not actual—transit space for insulin delivery.

Background: The estimation of available active insulin remains a limitation of automated insulin delivery systems. Currently, insulin pumps calculate active insulin using mathematical decay curves, while quantitative measurements of insulin would explicitly provide person-specific PK insulin dynamics to assess remaining active insulin more accurately, permitting more effective glucose control. Methods: We performed the first clinical evaluation of an insulin immunosensor chip, providing near real-time measurements of insulin levels. In this study, we sought to determine the accuracy of the novel insulin sensor and assess its therapeutic risk and benefit by presenting a new tool developed to indicate the potential therapeutic consequences arising from inaccurate insulin measurements. Results: Nine adult participants with type-1 diabetes completed the study. The change from baseline in immunosensor-measured insulin levels was compared with values obtained by standard enzyme-linked immunosorbant assay (ELISA) after preprandial injection of insulin. The point-of-care quantification of insulin levels revealed similar temporal trends as those from the laboratory insulin ELISA. The results showed that 70% of the paired immunosensor-reference values were concordant, which suggests that the patient could take action safely based on insulin concentration obtained by the novel sensor. Conclusions: This proposed technology and preliminary feasibility evaluation show encouraging results for near real-time evaluation of insulin levels, with the potential to improve diabetes management. Real-time measurements of insulin provide person-specific insulin dynamics that could be used to make more informed decisions regarding insulin dosing, thus helping to prevent hypoglycemia and improve diabetes outcomes.

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.

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.

Artificial pancreas (AP) systems are designed to automate glucose management for patients with type 1 diabetes. In this work, we propose an adaptive disturbance rejection control approach for AP systems to achieve safe and effective glucose regulation. The controller is built within the framework of active disturbance rejection control, but incorporates safety operation constraints, and glucose- and velocity-dependent parameter adaptation modules for the key control parameters. In silico performance comparison between the proposed controller and an adaptive zone model predictive controller (MPC) (Shi, Dassau, and Doyle III, 2019a) is conducted on the 10-adult cohort of the FDA-accepted UVA/Padova T1DM simulator. For both announced and unannounced meals, the controller achieves comparable glucose regulation performance in terms of mean glucose (134.9 mg/dL vs. 135.4 mg/dL, p

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: A smartphone-based automated insulin delivery (AID) controller device can facilitate use of interoperable components and acceptance in adolescents and children. Methods: Pediatric participants (N = 20, 8F) with type 1 diabetes were enrolled in three sequential age-based cohorts: adolescents (12-Results: During AID, there was no more than one BG Conclusions: The smartphone-based AID was feasible and safe in sequentially younger cohorts of adolescents and children. ClinicalTrials.gov: NCT04255381 ( https://clinicaltrials.gov/ct2/show/NCT04255381 )

This paper introduces methods to estimate aspects of physical activity and sedentary behavior from three-axis accelerometer data collected with a wrist-worn device at a sampling rate of 32 [Hz] on adults with type 1 diabetes (T1D) in free-living conditions. In particular, we present two methods able to detect and grade activity based on its intensity and individual fitness as sedentary, mild, moderate or vigorous, and a method that performs activity classification in a supervised learning framework to predict specific user behaviors. Population results for activity level grading show multi-class average accuracy of 99.99%, precision of 98.0 ± 2.2%, recall of 97.9 ± 3.5% and F1 score of 0.9 ± 0.0. As for the specific behavior prediction, our best performing classifier, gave population multi-class average accuracy of 92.43 ± 10.32%, precision of 92.94 ± 9.80%, recall of 92.20 ± 10.16% and F1 score of 92.56 ± 9.94%. Our investigation showed that physical activity and sedentary behavior can be detected, graded and classified with good accuracy and precision from three-axial accelerometer data collected in free-living conditions on people with T1D. This is particularly significant in the context of automated glucose control systems for diabetes, in that the methods we propose have the potential to inform changes in treatment parameters in response to the intensity of physical activity, allowing patients to meet their glycemic targets.

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

Aim: PA is an important part of diabetes management. ADA recommends that youth with T1D participate daily in ≥60 minutes of moderate to vigorous PA. We prospectively assessed daily PA in youth with T1D and compared demographic and diabetes variables according to frequency of PA ≥60 minutes/day. Methods: Youth (N=125, ages 8-17) with T1D completed 3-day PA records every 3 months for 18 months. Diabetes treatment data and A1c were collected at the same time. For each youth, we calculated the % of days with ≥60 minutes PA and compared those with ≥60 minutes PA on Results: Youth (50% male, 90% white, 74% pump-treated) had a mean±SD age 12.8±2.5 years, T1D duration 6.0±3.2 years, and A1c 8.2±0.9%. Youth had a median of 16 (IQR 12-17) PA record days. The % of days with ≥60 minutes PA ranged from 0 to 100% (median 53% [IQR 35-71%]), with 59% of youth reporting ≥60 minutes PA on ≥50% of days. Youth with ≥60 minutes PA on ≥50% of days were younger (12.4 vs. 13.5 years, p=.02), had shorter T1D duration (5.2 vs. 7.0 years, p=.002), lower daily insulin dose (0.86 vs. 1.00 u/kg, p=.002), and lower A1c (8.1 vs. 8.4%, p=.03) than youth with ≥60 minutes PA on Conclusion: A substantial proportion of youth with T1D do not exercise per recommendations. Given associations of PA with lower A1c, additional efforts to support exercise management in T1D are needed along with support for PA throughout the year. Disclosure R. O. La banca: None. L. K. Volkening: None. H. Desrochers: None. 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

Automated Insulin Delivery (AID) hybrid closed-loop systems have not been well studied in the context of psychological and physiological stress. We evaluated our interoperable artificial pancreas system with in-clinic stress challenges in a randomized crossover trial. Fourteen adults with type 1 diabetes were randomized to 2 weeks of AID-based control and two weeks of sensor augmented pump (SAP)l therapy at home in random order. The AID system used the Zone-Model Predictive Control algorithm with a continuous function of glucose velocity and insulin-on-board to gradually increase insulin infusion under conditions of sustained hyperglycemia. During each two-week period, in-clinic stress assessments for psychological stress (TSST and SECPT) and pharmacological stress (hydrocortisone 40-20-20 mg) were performed. Ten subjects completed the study per protocol, with 48 stress sessions completed in 12 subjects. AID increased time 70-180mg/dL from 63.1 to 74.4% (p=0.001), decreased time 180mg/dL from 35.4 to 24.8% (p=0.001) during the 2 weeks outpatient use. AID decreased glycemic variability as measured by CV (p=0.037) and time Disclosure R. Kaur: None. Y. C. Kudva: Research Support; Self; Dexcom, Inc. S. Deshpande: None. J. E. Pinsker: Advisory Panel; Self; Medtronic, Consultant; Self; Eli Lilly and Company, Tandem Diabetes Care, Research Support; Self; Dexcom, Inc., Insulet Corporation, Medtronic, Tandem Diabetes Care. S. K. Mccrady-spitzer: None. D. Desjardins: None. M. Church: None. W. K. Kremers: Research Support; Self; AstraZeneca, Biogen, Roche Pharma. F. J. Doyle: Advisory Panel; Self; Mode AGC, Other Relationship; Self; Dexcom, Inc., Insulet Corporation, Roche Diabetes Care. 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. Funding JDRF (2-SRA-2017-503-M-B); The Leona M. and Harry B. Helmsley Charitable Trust; Dexcom, Inc. (IIS-2019-017)

Pregnancy complicated by type 1 diabetes (T1D) is associated with increased maternal and fetal morbidity and mortality. We report results from the first clinical trial testing a closed-loop control (CLC) system specifically designed for pregnant women with T1D in the United States. Eight pregnant women with T1D participated in a 48-hour clinical trial at three US sites (NCT04492566). Women were enrolled in the second trimester after organogenesis was completed. CLC sessions used the interoperable artificial pancreas system (iAPS) running a Zone-Model Predictive Control algorithm designed for stricter glycemic targets for pregnancy. Glycemic target zones were 80-110 mg/dL during the day and 80-100 mg/dL overnight, with assertive insulin delivery in the postprandial period. The primary outcome was sensor glucose time in range (TIR) for pregnancy 63-140 mg/dL as per international consensus guidelines. All enrolled subjects completed the trial. Participants had a mean age of 29.3±3.5 years, gestational age of 22.2±3.6 weeks, weight of 77±12.2 kg, and HbA1c of 5.7±0.5%. Mean sensor TIR 63-140 mg/dL was 79.3±12.8% during CLC, compared to 60.8±18.8% for the week prior in open loop at home (p=0.03, Table 1). Unrestricted carbohydrate intake at meals ranged from 7 to 78 grams during the CLC sessions. No severe hypoglycemia or adverse events occurred. CLC in pregnant women with T1D is safe and effective. Disclosure B. Ozaslan: None. M. Church: None. M. Plesser: None. S. K. Mccrady-spitzer: None. C. Bakus: None. S. J. Ogyaadu: None. K. Nelson: None. C. Reid: None. S. Deshpande: None. W. K. Kremers: Research Support; Self; AstraZeneca, Biogen, Roche Pharma. F. J. Doyle: Advisory Panel; Self; Mode AGC, Other Relationship; Self; Dexcom, Inc., Insulet Corporation, Roche Diabetes Care. C. J. Levy: Advisory Panel; Self; Dexcom, Inc., Eli Lilly and Company, Employee; Spouse/Partner; AbbVie Inc., Other Relationship; Self; Dexcom, Inc., Research Support; Self; Abbott Diabetes, Dexcom, Inc., Insulet Corporation, Tandem Diabetes Care. B. Rosenn: None. Y. C. Kudva: Research Support; Self; Dexcom, Inc. 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. J. E. Pinsker: Advisory Panel; Self; Medtronic, Consultant; Self; Eli Lilly and Company, Tandem Diabetes Care, Research Support; Self; Dexcom, Inc., Insulet Corporation, Medtronic, Tandem Diabetes Care. G. O’malley: Research Support; Self; Abbott Diabetes, Dexcom, Inc., Horizon Therapeutics plc, Tandem Diabetes Care. R. Kaur: None. K. N. Castorino: Consultant; Self; Dexcom, Inc., Research Support; Self; Abbott Diabetes, Abbott Laboratories, Dexcom, Inc., Drawbridge Health, Inc., Lilly Diabetes, Medtronic, Novo Nordisk Inc. C. Levister: Research Support; Self; Abbott Diabetes, Insulet Corporation. M. Trinidad: None. D. Desjardins: None. Funding National Institutes of Health (R01DK120358); Dexcom, Inc. (AP-2020-014)

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.

As wearable healthcare monitoring systems advance, there is immense potential for biological sensing to enhance the management of type 1 diabetes (T1D). The aim of this work is to describe the ongoing development of biomarker analytes in the context of T1D. Technological advances in transdermal biosensing offer remarkable opportunities to move from research laboratories to clinical point‐of‐care applications. In this review, a range of analytes, including glucose, insulin, glucagon, cortisol, lactate, epinephrine, and alcohol, as well as ketones such as beta‐hydroxybutyrate, will be evaluated to determine the current status and research direction of those analytes specifically relevant to T1D management, using both in‐vitro and on‐body detection. Understanding state‐of‐the‐art developments in biosensing technologies will aid in bridging the gap from bench‐to‐clinic T1D analyte measurement advancement.

The human insulin-glucose metabolism is a time-varying process, which is partly caused by the changing insulin sensitivity of the body. This insulin sensitivity follows a circadian rhythm and its effects should be anticipated by any automated insulin delivery system. This paper presents an extension of our previous work on automated insulin delivery by developing a controller suitable for humans with Type 1 Diabetes Mellitus. Furthermore, we enhance the controller with a new kernel function for the Gaussian Process and deal with noisy measurements, as well as, the noisy training data for the Gaussian Process, arising therefrom. This enables us to move the proposed control algorithm, a combination of Model Predictive Controller and a Gaussian Process, closer towards clinical application. Simulation results on the University of Virginia/Padova FDA-accepted metabolic simulator are presented for a meal schedule with random carbohydrate sizes and random times of carbohydrate uptake to show the performance of the proposed control scheme.

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.

Objective: This study analysis was designed to examine the 24-h effects of exercise on glycemic control as measured by continuous glucose monitoring (CGM). Methods: Individuals with type 1 diabetes (ages: 15–68 years; hemoglobin A1c: 7.5% ± 1.5% [mean ± standard deviation (SD)]) were randomly assigned to complete twice-weekly aerobic, high-intensity interval, or resistance-based exercise sessions in addition to their personal exercise sessions for a period of 4 weeks. Exercise was tracked with wearables and glucose concentrations assessed using CGM. An exercise day was defined as a 24-h period after the end of exercise, while a sedentary day was defined as any 24-h period with no recorded exercise ≥10 min long. Sedentary days start at least 24 h after the end of exercise. Results: Mean glucose was lower (150 ± 45 vs. 166 ± 49 mg/dL, P = 0.01), % time in range [70–180 mg/dL] higher (62% ± 23% vs. 56% ± 25%, P = 0.03), % time >180 mg/dL lower (28% ± 23% vs. 37% ± 26%, P = 0.01), and % time

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)

Exercise increases nocturnal hypoglycemia risk which may impact sleep in T1D. Adults with T1D (n=25; 19 CSII; age 33±14yrs; HbA1c 7.6±1.3%; T1D duration 15yrs [IQR: 10, 22]) were divided into 3 exercise groups: aerobic, interval, or resistance, performed twice/week using instructional videos for 4 weeks. Participants used CGM, and wore an activity/sleep monitor (Garmin Vivosmart 3). Sleep duration was lower on nights following interval exercise compared to nights following sedentary days (p=0.04, Figure 1a). Participants experienced hypoglycemia earlier in the night following interval or aerobic exercise, compared to sedentary or resistance exercise (Figure 1b). Exercise types differentially impact sleep loss and nocturnal hypoglycemia. Disclosure P.G. Jacobs: Consultant; Self; SFC Fluidics. Research Support; Self; Dexcom, Inc. Stock/Shareholder; Self; Pacific Diabetes Technologies. Other Relationship; Self; AgaMatrix. Z. Li: None. P. Calhoun: Stock/Shareholder; Self; Dexcom, Inc. R.L. Gal: None. R. Beck: None. J.R. Castle: Advisory Panel; Self; Novo Nordisk Inc. Consultant; Self; ADOCIA, Dexcom, Inc., Zealand Pharma A/S. Other Relationship; Self; Pacific Diabetes Technologies, Roche Diabetes Care. M.A. Clements: Consultant; Self; Glooko, Inc. Other Relationship; Self; Glooko, Inc. 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. 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.B. Gillingham: None. C.K. Martin: Advisory Panel; Self; EHE Health, NaturallySlim. Consultant; Self; Florida Hospital, Gila Therapeutics, OpenFit, Zafgen, Inc. Research Support; Self; Egg Board, Helmsley Trust, IDEA Public Schools, National Institutes of Health, NIHR, Patient-Centered Outcomes Research Institute, USDA, Weight Watchers International, Inc. Other Relationship; Self; Academy of Nutrition and Dietetics, Louisiana State Univ./Pennington Biomedical Research Center. M.R. Rickels: Consultant; Self; Semma Therapeutics, Inc. Research Support; Self; Xeris Pharmaceuticals, Inc. S.R. Patton: None. M. Riddell: Advisory Panel; Self; Zucara Therapeutics Inc. Consultant; Self; Lilly Diabetes. Research Support; Self; Dexcom, Inc., Insulet Corporation. Speaker’s Bureau; Self; Medtronic, Novo Nordisk A/S. Funding The Leona M. and Harry B. Helmsley Charitable Trust (T1-DEXI Main Study)

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)

Pregnancies in type 1 diabetes (T1D) are high-risk, and data in the U.S. are limited regarding clinical and CGM based hypoglycemia throughout pregnancy while on sensor augmented insulin pump (SAP). Pregnant women with T1D on insulin pump ≤ 16 wks gestation were enrolled at 3 U.S. centers and used study Dexcom G6. We analyzed episodes of severe hypoglycemia (SH) and days with > 20 hrs of CGM for biochemical hypoglycemia (BH) based on international consensus guidelines ( Disclosure R. Kaur: None. B.H. Smith: 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. B. Ozaslan: None. G. O’Malley: Research Support; Self; Abbott, Dexcom, Inc. M. Trinidad: None. D. Desjardins: None. K.N. Castorino: Research Support; Self; Abbott, Dexcom, Inc., Medtronic, Mylan, Novo Nordisk Inc. C. Levister: None. C. Reid: None. S.K. McCrady-Spitzer: None. S.J. Ogyaadu: None. M. Church: None. M. Piper: None. W.K. Kremers: Research Support; Self; AstraZeneca. B. Rosenn: None. C.J. Levy: Consultant; Self; Dexcom, Inc. Employee; Spouse/Partner; Allergan plc. Research Support; Self; Abbott, Dexcom, Inc., Insulet Corporation. 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. Y.C. Kudva: Research Support; Self; Dexcom, Inc., Roche Diabetes Care. Other Relationship; Self; Abbott. Funding National Institutes of Health (122358); Dexcom, Inc. (AP-2018-016)

People with type 1 diabetes (T1D) have difficulty controlling glucose during exercise. Different types, intensities, and durations of exercise impact glucose differently. There is currently no algorithm that can accurately predict glucose changes during aerobic, resistance, interval, and free-living exercise. A support vector regression (SVR) model was designed to predict the maximum change in glucose during exercise. The model was trained and validated on 30-minute exercise sessions collected twice weekly over 4 weeks from 33 people with T1D (age 33±13 years, BMI 26.3±2.9 kg/m2, 18±12 years since diagnosis). Participants completed study assigned aerobic (n = 40), resistance (n= 33), or interval (n=20) exercise sessions in addition to their typical exercise regimen (n = 254). Participants wore a Dexcom (G5 or G6) or Medtronic CGM and used their own insulin pen (n = 10) or pump to administer insulin. The exercise information and food consumed was reported using a custom app. Heart rate, accelerometry, and sleep metrics were acquired using a Garmin vivosmart watch. Training was done on 90% of the observations while 10% of data were used for testing using 10-fold cross validation. The mean absolute error (MAE) of the model-predicted change in glucose during exercise was 23.59 mg/dL. The SVR algorithm may be useful within a decision support tool to help people with T1D better manage their glucose levels during exercise. Disclosure G. Young: None. Z. Li: None. P. Calhoun: Stock/Shareholder; Self; Dexcom, Inc. R.L. Gal: None. R. Beck: None. J.R. Castle: Advisory Panel; Self; Novo Nordisk Inc. Consultant; Self; ADOCIA, Dexcom, Inc., Zealand Pharma A/S. Other Relationship; Self; Pacific Diabetes Technologies, Roche Diabetes Care. M.A. Clements: Consultant; Self; Glooko, Inc. Other Relationship; Self; Glooko, Inc. 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. 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.B. Gillingham: None. C.K. Martin: Advisory Panel; Self; EHE Health, NaturallySlim. Consultant; Self; Florida Hospital, Gila Therapeutics, OpenFit, Zafgen, Inc. Research Support; Self; Egg Board, Helmsley Trust, IDEA Public Schools, National Institutes of Health, NIHR, Patient-Centered Outcomes Research Institute, USDA, Weight Watchers International, Inc. Other Relationship; Self; Academy of Nutrition and Dietetics, Louisiana State Univ./Pennington Biomedical Research Center. M.R. Rickels: Consultant; Self; Semma Therapeutics, Inc. Research Support; Self; Xeris Pharmaceuticals, Inc. S.R. Patton: None. M. Riddell: Advisory Panel; Self; Zucara Therapeutics Inc. Consultant; Self; Lilly Diabetes. Research Support; Self; Dexcom, Inc., Insulet Corporation. Speaker’s Bureau; Self; Medtronic, Novo Nordisk A/S. P.G. Jacobs: Consultant; Self; SFC Fluidics. Research Support; Self; Dexcom, Inc. Stock/Shareholder; Self; Pacific Diabetes Technologies. Other Relationship; Self; AgaMatrix. Funding Exercise in Diabetes Initiative (T1-DEXI Main Study)

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)

Background: There are limited contemporaneous data about outcomes in pregnancies with pre-existing type 1 diabetes (T1D) in the U.S. Objective: To determine maternal and neonatal outcomes in pregnancies with preexisting T1D and pregnancies in age, parity and BMI matched healthy subjects. Methods: We retrospectively studied 32 pregnancies in patients with preexisting T1D matched for age (mean 25.83 years), gravidity/parity {G1P1 (66), G2P1 (19), G3P1 (9) and G4P1 (6) % respectively} and BMI with controls (n=32) during a 5 year period from 01/01/2015 at Mayo Clinic, Rochester. Results: In 32 T1D pregnancies, 4 (12.5%) congenital defects (CD) were observed in neonates, 1-dysplastic ribs with thumb hypoplasia and 3-congentinal heart defects leading to death in one in comparison to no congenital defect or death in controls. CD pregnancies were complicated by severe pre-eclampsia in all 4 and poor glycemic control (HbA1c >8) in 3. There was a significant difference in Apgar score at 1 min between T1D pregnancies and controls (p= Conclusion: Pregnancies in T1D continue to be high risk pregnancies and need tight glycemic control with a more advanced system such as closed loop insulin delivery during pregnancy to improve maternal and neonatal outcomes. Disclosure R. Kaur: None. S. Rizvi: None. M. Trinidad: None. B.H. Smith: None. S. Batthula: None. S.K. McCrady-Spitzer: None. C. Reid: None. D. Desjardins: None. G. O’Malley: Research Support; Self; Abbott, Dexcom, Inc. K.N. Castorino: Research Support; Self; Abbott, Dexcom, Inc., Medtronic, Mylan, Novo Nordisk Inc. 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. W.K. Kremers: Research Support; Self; AstraZeneca. 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. C.J. Levy: Consultant; Self; Dexcom, Inc. Employee; Spouse/Partner; Allergan plc. Research Support; Self; Abbott, Dexcom, Inc., Insulet Corporation. Y.C. Kudva: Research Support; Self; Dexcom, Inc., Roche Diabetes Care. Other Relationship; Self; Abbott.

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)

Aim: Although hybrid closed loop insulin delivery systems simplify self-care with automated insulin delivery, it remains necessary for patients to enter planned carbohydrate intake for meals and snacks. We interviewed children, teens, and young adults with T1D and parents of youth with T1D about their willingness to trade off limiting carb intake to 50g if this would remove need to manually bolus for each meal/snack. Methods: Semi-structured interviews were conducted with 39 youth, ages 10-25 years, and 44 parents of youth at 2 U.S. diabetes centers. Interviews were audio-recorded, transcribed, and coded using thematic 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% were pump users and 82% were CGM users. Of parents, 86% were white and 91% were mothers. Results: Most youth and parents strongly preferred to manually bolus for meals/snacks rather than use a fully automated system that requires limiting carb intake at each meal/snack; many stated they did not want to feel restricted. However, both youth and parents said they would like automatic coverage for meals/snacks of Conclusions: Youth and parents agreed that a fully automated system that did not require manual bolusing would reduce physical and mental burdens of care, but not if it limited carb intake in order for the system to work effectively. AP designers should address patient and parent aversions to dietary restrictions in future AP devices. Disclosure P.V. Commissariat: None. L. Roethke: None. J.L. Finnegan: None. L.K. Volkening: None. D.E. McGill: 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)

Automated Insulin Delivery (AID) hybrid closed-loop systems have not been well studied in the context of prescribed meals. We evaluated our interoperable artificial pancreas system (iAPS) with scheduled meal challenges in a randomized crossover trial in an at-home setting. Ten adults with type 1 diabetes completed two weeks of AID-based control and two weeks of conventional therapy (sensor-augmented pump/predictive low-glucose suspend) at home in random order. During each period, subjects consumed pasta or white rice as part of a complete dinner meal on six different occasions (each meal three times in random order). The AID system increased time in range 70-180 mg/dL from 70.6 to 74.0% (3.4, 95% CI -2.3 to 9.1, p=0.22), while sensor time The AID system improved postprandial glucose control over conventional therapy in the handling of challenging meals in the at-home setting. 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. S. Deshpande: None. M. Church: None. M. Piper: None. C.C. Andre: None. J.S. Massa: 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. D.M. Eisenberg: Consultant; Self; Barilla Center For Food and Nutrition, Italy. 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 Barilla Center for Food & Nutrition Foundation; National Institutes of Health (DP3DK104057, DP3DK113511); Harvard Accelerator; Dexcom, Inc. (IIS-2018-019)

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.

Introduction: Patients and their families and medical providers have assumed that psychologic stress impacts glucose control in T1D (Type 1 Diabetes) though studies providing confirmatory evidence in real world settings are, to our knowledge, lacking. We hypothesized that self-reported psychologic stress worsens glucose control in T1D. Method: We studied 20 adults with T1D on continuous glucose monitor (CGM), sensor augmented insulin pump (SAP) prospectively at 2 clinical research centers. Patients reported psychological stress through stress diaries for 5 weeks on a severity scale of 1-7 using hard copy logs including time of onset and offset of stress and severity. For analytic purpose, grades 1-4 are classified as mild and grades 5-7 as severe. Results: Baseline characteristics were age 44.9±15.0 years, F/M 12/8, HbA1c 6.8 ± 0.7%, and diabetes duration of 22.9±15.9 years. We analyzed glucose variability during days of stress versus days without stress. During a 24 hour period, patients experienced less hypoglycemia during days with stress versus days without stress (p value 0.03). During the 5 week period, patients reported 23 ± 19.5 events. We analyzed the impact of self-reported stress on CGM data streams after excluding stress events associated with missing CGM data, nocturnal events (from 12 MN to 6 AM, too few events) and events for which subjects did not provide duration of stress. Thus, we analyzed 19.5 ± 17 events per patient from 6AM to 12MN. From 6 AM to 12 MN, the episodes lasted 179 ± 255 minutes with 83 % episodes being mild/moderate and 17% moderate/ severe. Number of CGM readings during daytime stress episodes were 717± 1120 compared to 8768± 1238 during non-stress periods. Impact of stress from 6 AM to 12 MN (Mid-Night) on CGM glucose was analyzed using matched paired t test. Mean glucose (160.6±41.9 vs 148.3± 28.6) and SD (53.2 ±17.7 vs 56.1±14.6) did not show a difference; however % of time spent below 70 mg/dl was less (4 ± 5) in patients during stressful periods compared to times without stress (6.3± 5.5, P value 0.02). Conclusions: To our knowledge, this is the first study attempting to analyze the impact of self-reported stress using daily stress diaries on CGM data streams in T1D patients on SAP. The study revealed significant challenges experienced by patients in reporting adequate data. Self-reported stress was not associated with hyperglycemia. However, days of self-reported stress and periods during patients reported stress were characterized by less hypoglycemia on CGM data streams.

The significant risks associated with pregnancies complicated by type 1 diabetes (T1D) were first recognized in the medical literature in the mid-twentieth century. Stringent glycemic control with hemoglobin A1c (HbA1c) values ideally less than 6% has been shown to improve maternal and fetal outcomes. The management options for pregnant women with T1D in the modern era include a variety of technologies to support self-care. Although self-monitoring of blood glucose (SMBG) and multiple daily injections (MDI) are often the recommended management options during pregnancy, many people with T1D utilize a variety of different technologies, including continuous glucose monitoring (CGM), continuous subcutaneous insulin infusion (CSII), and CSII including automated delivery or suspension algorithms. These systems have yielded invaluable diagnostic and therapeutic capabilities and have the potential to benefit this understudied higher-risk group. A recent prospective, multicenter study evaluating pregnant patients with T1D revealed that CGM significantly improves maternal glycemic parameters, is associated with fewer adverse neonatal outcomes, and minimizes burden. Outcome data for CSII, which is approved for use in pregnancy and has been utilized for several decades, remain mixed. Current evidence, although limited, for commercially available and emerging technologies for the management of T1D in pregnancy holds promise for improving patient and fetal outcomes.妊娠合并1型糖尿病(T1D)的重大风险在20世纪中叶的医学文献中被首次认识到。严格控制血糖，理想情况下使血红蛋白A1c(HbA1c)低于6%，已被证明可以改善母婴结局。在现代，对于患有T1D孕妇的管理选择包括各种支持自我护理的技术。虽然妊娠期自我血糖监测(self-monitoring of blood glucose，SMBG)和每日多次注射(multiple daily injections，MDI)通常是推荐的治疗方案，许多T1D患者使用各种不同的技术，包括连续血糖监测(continuous glucose monitoring，CGM)、连续皮下胰岛素输注(continuous subcutaneous insulin infusion，CSII)以及包括自动给药或暂停算法的CSII。这些系统已经产生了无价的诊断和治疗能力，并有可能使这一研究较少的高危群体受益。最近一项评估T1D孕妇的前瞻性多中心研究显示，CGM显著改善母亲的血糖参数，与较少的不良新生儿结局相关，并最大限度地减轻了负担。CSII被批准用于妊娠，并已使用了几十年，但其结果仍然喜忧参半。目前的证据虽然有限，但商业上可获得的和新兴的妊娠T1D管理技术对改善患者和胎儿的结局还是有希望的。.

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