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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: 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).

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.

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.

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.

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 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.