At-Home Use of a Pregnancy-Specific Zone-MPC Closed-Loop System for Pregnancies Complicated by Type 1 Diabetes: A Single-Arm, Observational Multicenter Study.

OBJECTIVE: There are no commercially available hybrid closed-loop insulin delivery systems customized to achieve pregnancy-specific glucose targets in the U.S. 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 2 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 h per day, compared with 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 needed to further evaluate system efficacy and pregnancy outcomes.

Glucose Rate-of-Change and Insulin-on-Board Jointly Weighted Zone Model Predictive Control.

We present design and evaluation of closed-loop insulin delivery using zone model predictive control (MPC) featuring an adaptive weighting scheme to address prolonged hyperglycemia due to changes in insulin sensitivity, underdelivery from profile mismatch, and meal composition. In the MPC cost function, the penalty on predicted glucose deviation from the upper zone boundary is weighted by a joint function of predicted glucose rate-of-change (ROC) and insulin-on-board (IOB). The asymmetric weighting gradually increases when glucose ROC and IOB were jointly low, independent of glucose magnitude, to limit hyperglycemia while aggressively reduces for negative glucose ROC to avoid hypoglycemia. The proposed controller was evaluated using two simulation scenarios: an induced resistance scenario and a nominal scenario to highlight the performance over a reference zone MPC with glucose ROC weighting only. The continuous adaption scheme resulted in consistent improvement for the entire glucose range without incurring additional risk of hypoglycemia. For the induced resistance and no feedforward bolus scenario, the percent time in 70-180 mg/dL was higher (53.5% versus 48.9%, p<0.001) with larger improvement in the overnight percent time in tighter glucose range 70-140 mg/dL (70.9% versus 52.9%, p<0.001). The results from extensive simulations, as well as clinical validation in three different outpatient studies demonstrate the utility and safety of the proposed zone MPC.

Feasibility and Preliminary Safety of Smartphone-Based Automated Insulin Delivery in Adolescents and Children With Type 1 Diabetes.

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-<18 years, n = 8, 5F), school-age (8-<12 years, n = 7, 2F), and young children (2-<8 years, n = 5, 1F). Participants used the interoperable artificial pancreas system (iAPS) and zone model predictive control (MPC) on an unlocked smartphone for 48 hours, consumed unrestricted meals of their choice, and engaged in various unannounced exercises. Primary outcomes and stopping criteria were defined using fingerstick blood glucose (BG) data; secondary outcomes compared continuous glucose monitoring (CGM) data with preceding sensor augmented pump (SAP) therapy. RESULTS: During AID, there was no more than one BG <50 mg/dL except in one young child participant; no instance of more than two episodes of BG ≥300 mg/dL lasting longer than 2 hours; and no adverse events. Despite large meals (total of 404.9 grams of carbs) and unannounced exercise (total of 182 minutes), overall CGM percent time in range (TIR) of 70 to 180 mg/dL during AID was statistically similar to SAP (63.5% vs 57.3%, respectively, P = .145). Overnight glucose standard deviation was 43 mg/dL (vs SAP 57.9 mg/dL, P = .009) and coefficient of variation was 25.7% (vs SAP 34.9%, P < .001). The percent time in closed-loop mode and connected to the CGM was 92.7% and 99.6%, respectively. Surveys indicated that participants and parents/guardians were satisfied with the system. 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).

Outpatient Randomized Crossover Comparison of Zone Model Predictive Control Automated Insulin Delivery with Weekly Data Driven Adaptation Versus Sensor-Augmented Pump: Results from the International Diabetes Closed-Loop Trial 4.

Background: Automated insulin delivery (AID) systems have proven effective in increasing time-in-range during both clinical trials and real-world use. Further improvements in outcomes for single-hormone (insulin only) AID may be limited by suboptimal insulin delivery settings. Methods: Adults (≥18 years of age) with type 1 diabetes were randomized to either sensor-augmented pump (SAP) (inclusive of predictive low-glucose suspend) or adaptive zone model predictive control AID for 13 weeks, then crossed over to the other arm. Each week, the AID insulin delivery settings were sequentially and automatically updated by an adaptation system running on the study phone. Primary outcome was sensor glucose time-in-range 70-180 mg/dL, with noninferiority in percent time below 54 mg/dL as a hierarchical outcome. Results: Thirty-five participants completed the trial (mean age 39 ± 16 years, HbA1c at enrollment 6.9% ± 1.0%). Mean time-in-range 70-180 mg/dL was 66% with SAP versus 69% with AID (mean adjusted difference +2% [95% confidence interval: -1% to +6%], P = 0.22). Median time <70 mg/dL improved from 3.0% with SAP to 1.6% with AID (-1.5% [-2.4% to -0.5%], P = 0.002). The adaptation system decreased initial basal rates by a median of 4% (-8%, 16%) and increased initial carbohydrate ratios by a median of 45% (32%, 59%) after 13 weeks. Conclusions: Automated adaptation of insulin delivery settings with AID use did not significantly improve time-in-range in this very well-controlled population. Additional study and further refinement of the adaptation system are needed, especially in populations with differing degrees of baseline glycemic control, who may show larger benefits from adaptation.

Feasibility of Closed-Loop Insulin Delivery with a Pregnancy-Specific Zone Model Predictive Control Algorithm.

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 <63 mg/dL (CLC: 2.0%, run-in:5.2%, P = 0.039). There was also less time <54 mg/dL (CLC: 0.7%, run-in:1.6%, P = 0.030) and >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).

Optimizing Behavioral Interventions to Regulate Gestational Weight Gain With Sequential Decision Policies Using Hybrid Model Predictive Control.

Excessive gestational weight gain is a significant public health concern that has been the recent focus of control systems-based interventions. Healthy Mom Zone (HMZ) is an intervention study that aims to develop and validate an individually-tailored and "intensively adaptive" intervention to manage weight gain for pregnant women with overweight or obesity using control engineering approaches. This paper presents how Hybrid Model Predictive Control (HMPC) can be used to assign intervention dosages and consequently generate a prescribed intervention with dosages unique to each individuals needs. A Mixed Logical Dynamical (MLD) model enforces the requirements for categorical (discrete-level) doses of intervention components and their sequential assignment into mixed-integer linear constraints. A comprehensive system model that integrates energy balance and behavior change theory, using data from one HMZ participant, is used to illustrate the workings of the HMPC-based control system for the HMZ intervention. Simulations demonstrate the utility of HMPC as a means for enabling optimized complex interventions in behavioral medicine, and the benefits of a HMPC framework in contrast to conventional interventions relying on "IF-THEN" decision rules.

Outpatient Randomized Crossover Automated Insulin Delivery Versus Conventional Therapy with Induced Stress Challenges.

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.

Review of Automated Insulin Delivery Systems for Individuals with Type 1 Diabetes: Tailored Solutions for Subpopulations.

Automated insulin delivery (AID) systems have proven safe and effective in improving glycemic outcomes in individuals with type 1 diabetes (T1D). Clinical evaluation of this technology has progressed to large randomized, controlled outpatient studies and recent commercial approval of AID systems for children and adults. However, several challenges remain in improving these systems for different subpopulations (e.g., young children, athletes, pregnant women, seniors and those with hypoglycemia unawareness). In this review, we highlight the requirements and challenges in AID design for selected subpopulations, and discuss current advances from recent clinical studies.

Zone-MPC Automated Insulin Delivery Algorithm Tuned for Pregnancy Complicated by Type 1 Diabetes.

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.

Using Iterative Learning for Insulin Dosage Optimization in Multiple-Daily-Injections Therapy for People With Type 1 Diabetes.

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 µ = [50, 75, 75] grams and standard deviation σ = [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.

Randomized Crossover Comparison of Automated Insulin Delivery Versus Conventional Therapy Using an Unlocked Smartphone with Scheduled Pasta and Rice Meal Challenges in the Outpatient Setting.

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.

The Effect of Two Types of Pasta Versus White Rice on Postprandial Blood Glucose Levels in Adults with Type 1 Diabetes: A Randomized Crossover Trial.

Background: Food choices are essential to successful glycemic control for people with diabetes. We compared the impact of three carbohydrate-rich meals on the postprandial glycemic response in adults with type 1 diabetes (T1D). Methods: We performed a randomized crossover study in 12 adults with T1D (age 58.7 ± 14.2 years, baseline hemoglobin A1c 7.5% ± 1.3%) comparing the postprandial glycemic response to three meals using continuous glucose monitoring: (1) "higher protein" pasta containing 10 g protein/serving, (2) regular pasta with 7 g protein/serving, and (3) extra-long grain white rice. All meals contained 42 g carbohydrate; were served with homemade tomato sauce, green salad, and balsamic dressing; and were repeated twice in random order. After their insulin bolus, subjects were observed in clinic for 5 h. Linear mixed effects models were used to assess the glycemic response. Results: Compared with white rice, peak glucose levels were significantly lower for higher protein pasta (-32.6 mg/dL; 95% CI -48.4 to -17.2; P < 0.001) and regular pasta (-43.2 mg/dL, 95% CI -58.7 to -27.7; P < 0.001). The difference between the two types of pastas did not reach statistical significance (-11 mg/dL; 95% CI -24.1 to 3.4; P = 0.17). Total glucose area under the curve was also significantly higher for white rice compared with both pastas (P < 0.001 for both comparisons). Conclusions: This exploratory study concluded that different food types of similar macronutrient content (e.g., rice and pasta) generate significantly different postprandial glycemic responses in persons with T1D. These results provide useful insights into the impact of food choices on and optimization of glucose control. Clinical Trial Registry: clinicaltrials.gov NCT03362151.

Design and Clinical Evaluation of the Interoperable Artificial Pancreas System (iAPS) Smartphone App: Interoperable Components with Modular Design for Progressive Artificial Pancreas Research and Development.

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

Real-Time Detection of Infusion Site Failures in a Closed-Loop Artificial Pancreas.

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

Design and Clinical Evaluation of a Novel Low-Glucose Prediction Algorithm with Mini-Dose Stable Glucagon Delivery in Post-Bariatric Hypoglycemia.

BACKGROUND: Postbariatric hypoglycemia (PBH) is a complication of bariatric surgery with limited therapeutic options. We developed an event-based system to predict and detect hypoglycemia based on continuous glucose monitor (CGM) data and recommend delivery of minidose liquid glucagon. METHODS: We performed an iterative development clinical study employing a novel glucagon delivery system: a Dexcom CGM connected to a Windows tablet running a hypoglycemia prediction algorithm and an Omnipod pump filled with an investigational stable liquid glucagon formulation. Meal tolerance testing was performed in seven participants with PBH and history of neuroglycopenia. Glucagon was administered when hypoglycemia was predicted. Primary outcome measures included the safety and feasibility of this system to predict and prevent severe hypoglycemia. Secondary outcomes included hypoglycemia prediction by the prediction algorithm, minimization of time below hypoglycemia threshold using glucagon, and prevention of rebound hyperglycemia. RESULTS: The hypoglycemia prediction algorithm alerted for impending hypoglycemia in the postmeal state, prompting delivery of glucagon (150 µg). After observations of initial incomplete efficacy to prevent hypoglycemia in the first two participants, system modifications were implemented: addition of PBH-specific detection algorithm, increased glucagon dose (300 µg), and a second glucagon dose if needed. These modifications, together with rescue carbohydrates provided to some participants, contributed to progressive improvements in glucose time above the hypoglycemia threshold (75 mg/dL). CONCLUSIONS: Preliminary results indicate that our event-based automatic monitoring algorithm successfully predicted likely hypoglycemia. Minidose glucagon therapy was well tolerated, without prolonged or severe hypoglycemia, and without rebound hyperglycemia.

Application of Zone Model Predictive Control Artificial Pancreas During Extended Use of Infusion Set and Sensor: A Randomized Crossover-Controlled Home-Use Trial.

OBJECTIVE: As artificial pancreas (AP) becomes standard of care, consideration of extended use of insulin infusion sets (IIS) and continuous glucose monitors (CGMs) becomes vital. We conducted an outpatient randomized crossover study to test the safety and efficacy of a zone model predictive control (zone-MPC)-based AP system versus sensor augmented pump (SAP) therapy in which IIS and CGM failures were provoked via extended wear to 7 and 21 days, respectively. RESEARCH DESIGN AND METHODS: A smartphone-based AP system was used by 19 adults (median age 23 years [IQR 10], mean 8.0 ± 1.7% HbA1c) over 2 weeks and compared with SAP therapy for 2 weeks in a crossover, unblinded outpatient study with remote monitoring in both study arms. RESULTS: AP improved percent time 70-140 mg/dL (48.1 vs. 39.2%; P = 0.016) and time 70-180 mg/dL (71.6 vs. 65.2%; P = 0.008) and decreased median glucose (141 vs. 153 mg/dL; P = 0.036) and glycemic variability (SD 52 vs. 55 mg/dL; P = 0.044) while decreasing percent time <70 mg/dL (1.3 vs. 2.7%; P = 0.001). AP also improved overnight control, as measured by mean glucose at 0600 h (140 vs. 158 mg/dL; P = 0.02). IIS failures (1.26 ± 1.44 vs. 0.78 ± 0.78 events; P = 0.13) and sensor failures (0.84 ± 0.6 vs. 1.1 ± 0.73 events; P = 0.25) were similar between AP and SAP arms. Higher percent time in closed loop was associated with better glycemic outcomes. CONCLUSIONS: Zone-MPC significantly and safely improved glycemic control in a home-use environment despite prolonged CGM and IIS wear. This project represents the first home-use AP study attempting to provoke and detect component failure while successfully maintaining safety and effective glucose control.

A genome-wide approach to screen for genetic variants in broilers (Gallus gallus) with divergent feed conversion ratio.

Feed conversion ratio (FCR) is an economically important trait in broilers and feed accounts for a significant proportion of the costs involved in broiler production. To explore the contribution of functional variants to FCR trait, we analyzed coding and non-coding single-nucleotide variants (SNVs) across the genome by exome sequencing in seven pairs of full-sibs broilers with divergent FCR and with a sequence coverage at an average depth of fourfold. We identified 192,119 high-quality SNVs, including 30,380 coding SNVs (cSNVs) in the experimental population. We discovered missense SNVs in PGM2, NOX4, TGFBR3, and TMX4, and synonymous SNVs in TSNAX, ITA, HSP90B1, and COL18A1 associated with FCR. Haplotype analyses of genome-wide significant SNVs in PGM2, PHKG1, DGKZ, and SOD2 were also observed with suggestive evidence of haplotype association with FCR. Single-variant and FCR QTL-related genes-based association analyses of SNVs identified newly associated genes for FCR in the regions subjected to targeted exome sequencing. The top seven SNVs were next evaluated in independent replication data sets where SNV chr. 3: 13,990,160 (c. 961G>C) at TMX4 was replicated (p < 0.05). Collectively, we have detected SNVs associated with FCR in broiler as well as identification of SNVs in known FCR QTL region. These findings should facilitate the discovery of causative variants for FCR and contribute to marker-assisted selection.

Optimized Treatment of Fibromyalgia Using System Identification and Hybrid Model Predictive Control.

The term adaptive intervention is used in behavioral health to describe individually-tailored strategies for preventing and treating chronic, relapsing disorders. This paper describes a system identification approach for developing dynamical models from clinical data, and subsequently, a hybrid model predictive control scheme for assigning dosages of naltrexone as treatment for fibromyalgia, a chronic pain condition. A simulation study that includes conditions of significant plant-model mismatch demonstrates the benefits of hybrid predictive control as a decision framework for optimized adaptive interventions. This work provides insights on the design of novel personalized interventions for chronic pain and related conditions in behavioral health.