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While artificial pancreas (AP) systems are expected to improve the quality of life among people with type 1 diabetes mellitus (T1DM), the design of convenient systems that optimize the user experience, especially for those with active lifestyles, such as children and adolescents, still remains an open research question. In this work, we introduce an embeddable design and implementation of model predictive control (MPC) of AP systems for people with T1DM that significantly reduces the weight and on-body footprint of the AP system. The embeddable controller is based on a zone MPC that has been evaluated in multiple clinical studies. The proposed embedded zone MPC features a simpler design of the periodic safe zone in the cost function and the utilization of state-of-the-art alternating minimization algorithms for solving the convex programming problems inherent to MPC with linear models subject to convex constraints. Off-line closed-loop data generated by the FDA-accepted UVA/Padova simulator is used to select an optimization algorithm and corresponding tuning parameters. Through hardware-in-the-loop in silico results on a limited-resource Arduino Zero (Feather M0) platform, we demonstrate the potential of the proposed embedded MPC. In spite of resource limitations, our embedded zone MPC manages to achieve comparable performance of that of the full-version zone MPC implemented in a 64-bit desktop for scenarios with/without meal-disturbance compensations. Metrics for performance comparison included median percent time in the euglycemic ([70, 180] mg/dL range) of 84.3% vs. 83.1% for announced meals, with an equivalence test yielding p = 0.0013 and 66.2% vs. 66.0% for unannounced meals with p = 0.0028.

The suprachiasmatic nucleus (SCN) acts as a master pacemaker driving circadian behavior and physiology. Although the SCN is small, it is composed of many cell types, making it difficult to study the roles of particular cells. Here we develop bioluminescent circadian reporter mice that are Cre dependent, allowing the circadian properties of genetically defined populations of cells to be studied in real time. Using a Color-Switch PER2::LUCIFERASE reporter that switches from red PER2::LUCIFERASE to green PER2::LUCIFERASE upon Cre recombination, we assess circadian rhythms in two of the major classes of peptidergic neurons in the SCN: AVP (arginine vasopressin) and VIP (vasoactive intestinal polypeptide). Surprisingly, we find that circadian function in AVP neurons, not VIP neurons, is essential for autonomous network synchrony of the SCN and stability of circadian rhythmicity., Competing Interests: Declaration of Interests The authors declare no competing interests., (Copyright © 2020 Elsevier Inc. All rights reserved.)

Objective: Limited information is available about glycemic outcomes with a closed-loop control (CLC) system compared with a predictive low-glucose suspend (PLGS) system., Research Design and Methods: After 6 months of use of a CLC system in a randomized trial, 109 participants with type 1 diabetes (age range, 14-72 years; mean HbA 1c , 7.1% [54 mmol/mol]) were randomly assigned to CLC ( N = 54, Control-IQ) or PLGS ( N = 55, Basal-IQ) groups for 3 months. The primary outcome was continuous glucose monitor (CGM)-measured time in range (TIR) for 70-180 mg/dL. Baseline CGM metrics were computed from the last 3 months of the preceding study., Results: All 109 participants completed the study. Mean ± SD TIR was 71.1 ± 11.2% at baseline and 67.6 ± 12.6% using intention-to-treat analysis (69.1 ± 12.2% using per-protocol analysis excluding periods of study-wide suspension of device use) over 13 weeks on CLC vs. 70.0 ± 13.6% and 60.4 ± 17.1% on PLGS (difference = 5.9%; 95% CI 3.6%, 8.3%; P < 0.001). Time >180 mg/dL was lower in the CLC group than PLGS group (difference = -6.0%; 95% CI -8.4%, -3.7%; P < 0.001) while time <54 mg/dL was similar (0.04%; 95% CI -0.05%, 0.13%; P = 0.41). HbA 1c after 13 weeks was lower on CLC than PLGS (7.2% [55 mmol/mol] vs. 7.5% [56 mmol/mol], difference -0.34% [-3.7 mmol/mol]; 95% CI -0.57% [-6.2 mmol/mol], -0.11% [1.2 mmol/mol]; P = 0.0035)., Conclusions: Following 6 months of CLC, switching to PLGS reduced TIR and increased HbA 1c toward their pre-CLC values, while hypoglycemia remained similarly reduced with both CLC and PLGS., (© 2020 by the American Diabetes Association.)

Although glucocorticoid resistance contributes to increased inflammation, individuals with posttraumatic stress disorder (PTSD) exhibit increased glucocorticoid receptor (GR) sensitivity along with increased inflammation. It is not clear how inflammation coexists with a hyperresponsive hypothalamic-pituitary-adrenal (HPA) axis. To understand this better, we developed and analyzed an integrated mathematical model for the HPA axis and the immune system. We performed mathematical simulations for a dexamethasone (DEX) suppression test and IC 50 -dexamethasone for cytokine suppression by varying model parameters. The model analysis suggests that increasing the steepness of the dose-response curve for GR activity may reduce anti-inflammatory effects of GRs at the ambient glucocorticoid levels, thereby increasing proinflammatory response. The adaptive response of proinflammatory cytokine-mediated stimulatory effects on the HPA axis is reduced due to dominance of the GR-mediated negative feedback on the HPA axis. To verify these hypotheses, we analyzed the clinical data on neuroendocrine variables and cytokines obtained from war-zone veterans with and without PTSD. We observed significant group differences for cortisol and ACTH suppression tests, proinflammatory cytokines TNFα and IL6, high-sensitivity C-reactive protein, promoter methylation of GR gene, and IC 50-DEX for lysozyme suppression. Causal inference modeling revealed significant associations between cortisol suppression and post-DEX cortisol decline, promoter methylation of human GR gene exon 1F ( NR3C1-1F ), IC 50-DEX , and proinflammatory cytokines. We noted significant mediation effects of NR3C1-1F promoter methylation on inflammatory cytokines through changes in GR sensitivity. Our findings suggest that increased GR sensitivity may contribute to increased inflammation; therefore, interventions to restore GR sensitivity may normalize inflammation in PTSD.

Background: Traumatic stress can adversely affect physical and mental health through neurobiological stress response systems. We examined the effects of trauma exposure and posttraumatic stress disorder (PTSD) on telomere length, a biomarker of cellular aging, and volume of the amygdala, a key structure of stress regulation, in combat-exposed veterans. In addition, the relationships of psychopathological symptoms and autonomic function with telomere length and amygdala volume were examined., Methods: Male combat veterans were categorized as having PTSD diagnosis (n = 102) or no lifetime PTSD diagnosis (n = 111) based on the Clinician-Administered PTSD Scale. Subjects were assessed for stress-related psychopathology, trauma severity, autonomic function, and amygdala volumes by magnetic resonance imaging., Results: A significant interaction was found between trauma severity and PTSD status for telomere length and amygdala volume after adjusting for multiple confounders. Subjects with PTSD showed shorter telomere length and larger amygdala volume than those without PTSD among veterans exposed to high trauma, while there was no significant group difference in these parameters among those exposed to low trauma. Among veterans exposed to high trauma, greater telomere shortening was significantly correlated with greater norepinephrine, and larger amygdala volume was correlated with more severe psychological symptoms and higher heart rates., Conclusions: These data suggest that the intensity of the index trauma event plays an important role in interacting with PTSD symptomatology and autonomic activity in predicting telomere length and amygdala volume. These results highlight the importance of trauma severity and PTSD status in predicting certain biological outcomes., (Copyright © 2020 Society of Biological Psychiatry. All rights reserved.)

Objective: Assess the efficacy of inControl AP, a mobile closed-loop control (CLC) system., Research Design and Methods: This protocol, NCT02985866, is a 3-month parallel-group, multicenter, randomized unblinded trial designed to compare mobile CLC with sensor-augmented pump (SAP) therapy. Eligibility criteria were type 1 diabetes for at least 1 year, use of insulin pumps for at least 6 months, age ≥14 years, and baseline HbA 1c <10.5% (91 mmol/mol). The study was designed to assess two coprimary outcomes: superiority of CLC over SAP in continuous glucose monitor (CGM)-measured time below 3.9 mmol/L and noninferiority in CGM-measured time above 10 mmol/L., Results: Between November 2017 and May 2018, 127 participants were randomly assigned 1:1 to CLC ( n = 65) versus SAP ( n = 62); 125 participants completed the study. CGM time below 3.9 mmol/L was 5.0% at baseline and 2.4% during follow-up in the CLC group vs. 4.7% and 4.0%, respectively, in the SAP group (mean difference -1.7% [95% CI -2.4, -1.0]; P < 0.0001 for superiority). CGM time above 10 mmol/L was 40% at baseline and 34% during follow-up in the CLC group vs. 43% and 39%, respectively, in the SAP group (mean difference -3.0% [95% CI -6.1, 0.1]; P < 0.0001 for noninferiority). One severe hypoglycemic event occurred in the CLC group, which was unrelated to the study device., Conclusions: In meeting its coprimary end points, superiority of CLC over SAP in CGM-measured time below 3.9 mmol/L and noninferiority in CGM-measured time above 10 mmol/L, the study has demonstrated that mobile CLC is feasible and could offer certain usability advantages over embedded systems, provided the connectivity between system components is stable., (© 2020 by the American Diabetes Association.)

Posttraumatic stress disorder (PTSD) is associated with neuroendocrine alterations and metabolic abnormalities; however, how metabolism is affected by neuroendocrine disturbances is unclear. The data from combat-exposed veterans with PTSD show increased glycolysis to lactate flux, reduced TCA cycle flux, impaired amino acid and lipid metabolism, insulin resistance, inflammation, and hypersensitive hypothalamic-pituitary-adrenal (HPA) axis. To analyze whether the co-occurrence of multiple metabolic abnormalities is independent or arises from an underlying regulatory defect, we employed a systems biological approach using an integrated mathematical model and multiomic analysis. The models for hepatic metabolism, HPA axis, inflammation, and regulatory signaling were integrated to perform metabolic control analysis (MCA) with respect to the observations from our clinical data. We combined the metabolomics, neuroendocrine, clinical laboratory, and cytokine data from combat-exposed veterans with and without PTSD to characterize the differences in regulatory effects. MCA revealed mechanistic association of the HPA axis and inflammation with metabolic dysfunction consistent with PTSD. This was supported by the data using correlational and causal analysis that revealed significant associations between cortisol suppression, high-sensitivity C-reactive protein, homeostatic model assessment of insulin resistance, γ-glutamyltransferase, hypoxanthine, and several metabolites. Causal mediation analysis indicates that the effects of enhanced glucocorticoid receptor sensitivity (GRS) on glycolytic pathway, gluconeogenic and branched-chain amino acids, triglycerides, and hepatic function are jointly mediated by inflammation, insulin resistance, oxidative stress, and energy deficit. Our analysis suggests that the interventions to normalize GRS and inflammation may help to manage features of metabolic dysfunction in PTSD.

Background: Closed-loop systems that automate insulin delivery may improve glycemic outcomes in patients with type 1 diabetes., Methods: In this 6-month randomized, multicenter trial, patients with type 1 diabetes were assigned in a 2:1 ratio to receive treatment with a closed-loop system (closed-loop group) or a sensor-augmented pump (control group). The primary outcome was the percentage of time that the blood glucose level was within the target range of 70 to 180 mg per deciliter (3.9 to 10.0 mmol per liter), as measured by continuous glucose monitoring., Results: A total of 168 patients underwent randomization; 112 were assigned to the closed-loop group, and 56 were assigned to the control group. The age range of the patients was 14 to 71 years, and the glycated hemoglobin level ranged from 5.4 to 10.6%. All 168 patients completed the trial. The mean (±SD) percentage of time that the glucose level was within the target range increased in the closed-loop group from 61±17% at baseline to 71±12% during the 6 months and remained unchanged at 59±14% in the control group (mean adjusted difference, 11 percentage points; 95% confidence interval [CI], 9 to 14; P<0.001). The results with regard to the main secondary outcomes (percentage of time that the glucose level was >180 mg per deciliter, mean glucose level, glycated hemoglobin level, and percentage of time that the glucose level was <70 mg per deciliter or <54 mg per deciliter [3.0 mmol per liter]) all met the prespecified hierarchical criterion for significance, favoring the closed-loop system. The mean difference (closed loop minus control) in the percentage of time that the blood glucose level was lower than 70 mg per deciliter was -0.88 percentage points (95% CI, -1.19 to -0.57; P<0.001). The mean adjusted difference in glycated hemoglobin level after 6 months was -0.33 percentage points (95% CI, -0.53 to -0.13; P = 0.001). In the closed-loop group, the median percentage of time that the system was in closed-loop mode was 90% over 6 months. No serious hypoglycemic events occurred in either group; one episode of diabetic ketoacidosis occurred in the closed-loop group., Conclusions: In this 6-month trial involving patients with type 1 diabetes, the use of a closed-loop system was associated with a greater percentage of time spent in a target glycemic range than the use of a sensor-augmented insulin pump. (Funded by the National Institute of Diabetes and Digestive and Kidney Diseases; iDCL ClinicalTrials.gov number, NCT03563313.)., (Copyright © 2019 Massachusetts Medical Society.)

The circadian oscillator regulates many critical biological functions; misalignment between the phase of this oscillator and the environment has been linked to adverse health outcomes. Thus, shifting the circadian phase of the oscillator to align with the environment using either light or small molecule pharmaceuticals as control inputs is desired. One challenge to controlling circadian phase is that the magnitude and direction of the phase shift caused by these inputs is dependent on the phase at which the input is delivered. Simulations show that model predictive control (MPC) can successfully shift the phase of the circadian clock using perfect knowledge of the current phase of the system. However, methods to assess circadian phase continuously in real time, as would be needed to implement MPC in vivo , are limited in their accuracy. Here, we explore the impact of imperfect sensing on our ability to control circadian phase. While some pathological patterns of sensor error can make control impossible, we show that by assuming errors in the phase sensor are bounded to be sufficiently small, we can bound the error of our MPC algorithm. We propose using the expected phase response curve to improve control when sensor error is present.

Improvements in sensor accuracy, greater convenience and ease of use, and expanding reimbursement have led to growing adoption of continuous glucose monitoring (CGM). However, successful utilization of CGM technology in routine clinical practice remains relatively low. This may be due in part to the lack of clear and agreed-upon glycemic targets that both diabetes teams and people with diabetes can work toward. Although unified recommendations for use of key CGM metrics have been established in three separate peer-reviewed articles, formal adoption by diabetes professional organizations and guidance in the practical application of these metrics in clinical practice have been lacking. In February 2019, the Advanced Technologies & Treatments for Diabetes (ATTD) Congress convened an international panel of physicians, researchers, and individuals with diabetes who are expert in CGM technologies to address this issue. This article summarizes the ATTD consensus recommendations for relevant aspects of CGM data utilization and reporting among the various diabetes populations., (© 2019 by the American Diabetes Association.)

Post-traumatic stress disorder (PTSD) is a psychiatric illness with a highly polygenic architecture without large effect-size common single-nucleotide polymorphisms (SNPs). Thus, to capture a substantial portion of the genetic contribution, effects from many variants need to be aggregated. We investigated various aspects of one such approach that has been successfully applied to many traits, polygenic risk score (PRS) for PTSD. Theoretical analyses indicate the potential prediction ability of PRS. We used the latest summary statistics from the largest published genome-wide association study (GWAS) conducted by Psychiatric Genomics Consortium for PTSD (PGC-PTSD). We found that the PRS constructed for a cohort comprising veterans of recent wars (n = 244) explains a considerable proportion of PTSD onset (Nagelkerke R 2  = 4.68%, P = 0.003) and severity (R 2  = 4.35%, P = 0.0008) variances. However, the performance on an African ancestry sub-cohort was minimal. A PRS constructed with schizophrenia GWAS also explained a significant fraction of PTSD diagnosis variance (Nagelkerke R 2  = 2.96%, P = 0.0175), confirming previously reported genetic correlation between the two psychiatric ailments. Overall, these findings demonstrate the important role polygenic analyses of PTSD will play in risk prediction models as well as in elucidating the biology of the disorder.

Current artificial pancreas systems (AP) operate via subcutaneous (SC) glucose sensing and SC insulin delivery. Due to slow diffusion and transport dynamics across the interstitial space, even the most sophisticated control algorithms in on-body AP systems cannot react fast enough to maintain tight glycemic control under the effect of exogenous glucose disturbances caused by ingesting meals or performing physical activity. Recent efforts made towards the development of an implantable AP have explored the utility of insulin infusion in the intraperitoneal (IP) space: a region within the abdominal cavity where the insulin-glucose kinetics are observed to be much more rapid than the SC space. In this paper, a series of canine experiments are used to determine the dynamic association between IP insulin boluses and plasma glucose levels. Data from these experiments are employed to construct a new mathematical model and to formulate a closed-loop control strategy to be deployed on an implantable AP. The potential of the proposed controller is demonstrated via in-silico experiments on an FDA-accepted benchmark cohort: the proposed design significantly outperforms a previous controller designed using artificial data (time in clinically acceptable glucose range: 97.3±1.5% vs. 90.1±5.6%). Furthermore, the robustness of the proposed closed-loop system to delays and noise in the measurement signal (for example, when glucose is sensed subcutaneously) and deleterious glycemic changes (such as sudden glucose decline due to physical activity) is investigated. The proposed model based on experimental canine data leads to the generation of more effective control algorithms and is a promising step towards fully automated and implantable artificial pancreas systems., Competing Interests: Competing Financial Interests PL is CEO of PhysioLogic Devices, Inc. BS and DC are consultants for PhysioLogic Devices, Inc. Other authors declare no competing financial interests.

Posttraumatic stress disorder (PTSD) is associated with impaired major domains of psychology and behavior. Individuals with PTSD also have increased co-morbidity with several serious medical conditions, including autoimmune diseases, cardiovascular disease, and diabetes, raising the possibility that systemic pathology associated with PTSD might be identified by metabolomic analysis of blood. We sought to identify metabolites that are altered in male combat veterans with PTSD. In this case-control study, we compared metabolomic profiles from age-matched male combat trauma-exposed veterans from the Iraq and Afghanistan conflicts with PTSD (n = 52) and without PTSD (n = 51) ('Discovery group'). An additional group of 31 PTSD-positive and 31 PTSD-negative male combat-exposed veterans was used for validation of these findings ('Test group'). Plasma metabolite profiles were measured in all subjects using ultrahigh performance liquid chromatography/tandem mass spectrometry and gas chromatography/mass spectrometry. We identified key differences between PTSD subjects and controls in pathways related to glycolysis and fatty acid uptake and metabolism in the initial 'Discovery group', consistent with mitochondrial alterations or dysfunction, which were also confirmed in the 'Test group'. Other pathways related to urea cycle and amino acid metabolism were different between PTSD subjects and controls in the 'Discovery' but not in the smaller 'Test' group. These metabolic differences were not explained by comorbid major depression, body mass index, blood glucose, hemoglobin A1c, smoking, or use of analgesics, antidepressants, statins, or anti-inflammatories. These data show replicable, wide-ranging changes in the metabolic profile of combat-exposed males with PTSD, with a suggestion of mitochondrial alterations or dysfunction, that may contribute to the behavioral and somatic phenotypes associated with this disease., Competing Interests: The authors have declared that no competing interests exist.

The widespread adoption of closed-loop control in systems biology has resulted from improvements in sensors, computing, actuation, and the discovery of alternative sites of targeted drug delivery. Most control algorithms for circadian phase resetting exploit light inputs. However, recently identified small-molecule pharmaceuticals offer advantages in terms of invasiveness and potency of actuation. Herein, we develop a systematic method to control the phase of biological oscillations motivated by the recently identified small molecule circadian pharmaceutical KL001. The model-based control architecture exploits an infinitesimal parametric phase response curve (ipPRC) that is used to predict the effect of control inputs on future phase trajectories of the oscillator. The continuous time optimal control policy is first derived for phase resetting, based on the ipPRC and Pontryagin's maximum principle. Owing to practical challenges in implementing a continuous time optimal control policy, we investigate the effect of implementing the continuous time policy in a sampled time format. Specifically, we provide bounds on the errors incurred by the physiologically tractable sampled time control law. We use these results to select directions of resetting (i.e. phase advance or delay), sampling intervals, and prediction horizons for a nonlinear model predictive control (MPC) algorithm for phase resetting. The potential of this ipPRC-informed pharmaceutical nonlinear MPC is then demonstrated in silico using real-world scenarios of jet lag or rotating shift work.

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

The long-term use of the artificial pancreas (AP) requires an automated insulin delivery algorithm that can learn and adapt with the growth, development, and lifestyle changes of patients. In this work, we introduce a data-driven AP adaptation method for improved glucose management in a home environment. A two-phase Bayesian optimization assisted parameter learning algorithm is proposed to adapt basal and carbohydrate-ratio profile, and key feedback control parameters. The method is evaluated on the basis of the 111-adult cohort of the FDA-accepted UVA/Padova type 1 diabetes mellitus simulator through three scenarios with lifestyle disturbances and incorrect initial parameters. For all the scenarios, the proposed method is able to robustly adapt AP parameters for improved glycemic regulation performance in terms of percent time in the euglycemic range [70, 180] mg/dl without causing risk of hypoglycemia in terms of percent time below 70 mg/dl.

The circadian clock orchestrates 24-h rhythms in physiology in most living organisms. At the molecular level, the dogma is that circadian oscillations are based on a negative transcriptional feedback loop. Recent studies found the NAD + -dependent histone deacetylase, SIRT1, directly regulates acetylation status of clock components and influences circadian amplitude in cells. While Nakahata et al. [Nakahata Y, Kaluzova M (2008) Cell 134:329-340] reported that loss of SIRT1 increases amplitude through BMAL1 acetylation, Asher et al. [Asher G, Gatfield D (2008) Cell 134:317-328] reported that loss of SIRT1 decreases amplitude through an increase in acetylated PER2. To address this SIRT1 paradox, we developed a circadian enzymatic model. Predictions from this model and experimental validation strongly align with the findings of Asher et al., with PER2 as the primary target of SIRT1. Further, the model suggested SIRT1 influences BMAL1 expression through actions on PGC1α. We validated this finding experimentally. Thus, our computational and experimental approaches suggest SIRT1 positively regulates clock function through actions on PER2 and PGC1α., Competing Interests: The authors declare no conflict of interest.

The mammalian suprachiasmatic nucleus (SCN) functions as a master circadian pacemaker, integrating environmental input to align physiological and behavioral rhythms to local time cues. Approximately 10% of SCN neurons express vasoactive intestinal polypeptide (VIP); however, it is unknown how firing activity of VIP neurons releases VIP to entrain circadian rhythms. To identify physiologically relevant firing patterns, we optically tagged VIP neurons and characterized spontaneous firing over 3 days. VIP neurons had circadian rhythms in firing rate and exhibited two classes of instantaneous firing activity. We next tested whether physiologically relevant firing affected circadian rhythms through VIP release. We found that VIP neuron stimulation with high, but not low, frequencies shifted gene expression rhythms in vitro through VIP signaling. In vivo, high-frequency VIP neuron activation rapidly entrained circadian locomotor rhythms. Thus, increases in VIP neuronal firing frequency release VIP and entrain molecular and behavioral circadian rhythms. VIDEO ABSTRACT., (Copyright © 2018 Elsevier Inc. All rights reserved.)

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

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.

A novel Model Predictive Control (MPC) law for the closed-loop operation of an Artificial Pancreas (AP) to treat type 1 diabetes is proposed. The contribution of this paper is to simultaneously enhance both the safety and performance of an AP, by reducing the incidence of controller-induced hypoglycemia, and by promoting assertive hyperglycemia correction. This is achieved by integrating two MPC features separately introduced by the authors previously to independently improve the control performance with respect to these two coupled issues. Velocity-weighting MPC reduces the occurrence of controller-induced hypoglycemia. Velocity-penalty MPC yields more effective hyperglycemia correction. Benefits of the proposed MPC law over the MPC strategy deployed in the authors' previous clinical trial campaign are demonstrated via a comprehensive in-silico analysis. The proposed MPC law was deployed in four distinct US Food & Drug Administration approved clinical trial campaigns, the most extensive of which involved 29 subjects each spending three months in closed-loop. The paper includes implementation details, an explanation of the state-dependent cost functions required for velocity-weighting and penalties, a discussion of the resulting nonlinear optimization problem, a description of the four clinical trial campaigns, and control-related trial highlights.

In mammals, the suprachiasmatic nucleus (SCN) of the hypothalamus coordinates daily rhythms including sleep-wake, hormone release, and gene expression. The cells of the SCN must synchronize to each other to drive these circadian rhythms in the rest of the body. The ontogeny of circadian cycling and intercellular coupling in the SCN remains poorly understood. Recent in vitro studies have recorded circadian rhythms from the whole embryonic SCN. Here, we tracked the onset and precision of rhythms in PERIOD2 (PER2), a clock protein, within the SCN isolated from embryonic and postnatal mice of undetermined sex. We found that a few SCN cells developed circadian periodicity in PER2 by 14.5 d after mating (E14.5) with no evidence for daily cycling on E13.5. On E15.5, the fraction of competent oscillators increased dramatically corresponding with stabilization of their circadian periods. The cells of the SCN harvested at E15.5 expressed sustained, synchronous daily rhythms. By postnatal day 2 (P2), SCN oscillators displayed the daily, dorsal-ventral phase wave in clock gene expression typical of the adult SCN. Strikingly, vasoactive intestinal polypeptide (VIP), a neuropeptide critical for synchrony in the adult SCN, and its receptor, VPAC2R, reached detectable levels after birth and after the onset of circadian synchrony. Antagonists of GABA or VIP signaling or action potentials did not disrupt circadian synchrony in the E15.5 SCN. We conclude that endogenous daily rhythms in the fetal SCN begin with few noisy oscillators on E14.5, followed by widespread oscillations that rapidly synchronize on E15.5 by an unknown mechanism. SIGNIFICANCE STATEMENT We recorded the onset of PER2 circadian oscillations during embryonic development in the mouse SCN. When isolated at E13.5, the anlagen of the SCN expresses high, arrhythmic PER2. In contrast, a few cells show noisy circadian rhythms in the isolated E14.5 SCN and most show reliable, self-sustained, synchronized rhythms in the E15.5 SCN. Strikingly, this synchrony at E15.5 appears before expression of VIP or its receptor and persists in the presence of blockers of VIP, GABA or neuronal firing. Finally, the dorsal-ventral phase wave of PER2 typical of the adult SCN appears ∼P2, indicating that multiple signals may mediate circadian synchrony during the ontogeny of the SCN., (Copyright © 2018 the authors 0270-6474/18/381326-09$15.00/0.)

Aims: To compare intraperitoneal (IP) to subcutaneous (SC) insulin delivery in an artificial pancreas (AP)., Research Design and Methods: Ten adults with type 1 diabetes participated in a non-randomized, non-blinded sequential AP study using the same SC glucose sensing and Zone Model Predictive Control (ZMPC) algorithm adjusted for insulin clearance. On first admission, subjects underwent closed-loop control with SC delivery of a fast-acting insulin analogue for 24 hours. Following implantation of a DiaPort IP insulin delivery system, the identical 24-hour trial was performed with IP regular insulin delivery. The clinical protocol included 3 unannounced meals with 70, 40 and 70 g carbohydrate, respectively. Primary endpoint was time spent with blood glucose (BG) in the range of 80 to 140 mg/dL (4.4-7.7 mmol/L)., Results: Percent of time spent within the 80 to 140 mg/dL range was significantly higher for IP delivery than for SC delivery: 39.8 ± 7.6 vs 25.6 ± 13.1 ( P = .03). Mean BG (mg/dL) and percent of time spent within the broader 70 to 180 mg/dL range were also significantly better for IP insulin: 151.0 ± 11.0 vs 190.0 ± 31.0 ( P = .004) and 65.7 ± 9.2 vs 43.9 ± 14.7 ( P = .001), respectively. Superiority of glucose control with IP insulin came from the reduced time spent in hyperglycaemia (>180 mg/dL: 32.4 ± 8.9 vs 53.5 ± 17.4, P = .014; >250 mg/dL: 5.9 ± 5.6 vs 23.0 ± 11.3, P = .0004). Higher daily doses of insulin (IU) were delivered with the IP route (43.7 ± 0.1 vs 32.3 ± 0.1, P < .001) with no increased percent time spent <70 mg/dL (IP: 2.5 ± 2.9 vs SC: 4.1 ± 5.3, P = .42)., Conclusions: Glycaemic regulation with fully-automated AP delivering IP insulin was superior to that with SC insulin delivery. This pilot study provides proof-of-concept for an AP system combining a ZMPC algorithm with IP insulin delivery., (© 2017 John Wiley & Sons Ltd.)

Measurement of glycated hemoglobin (HbA 1c ) has been the traditional method for assessing glycemic control. However, it does not reflect intra- and interday glycemic excursions that may lead to acute events (such as hypoglycemia) or postprandial hyperglycemia, which have been linked to both microvascular and macrovascular complications. Continuous glucose monitoring (CGM), either from real-time use (rtCGM) or intermittently viewed (iCGM), addresses many of the limitations inherent in HbA 1c testing and self-monitoring of blood glucose. Although both provide the means to move beyond the HbA 1c measurement as the sole marker of glycemic control, standardized metrics for analyzing CGM data are lacking. Moreover, clear criteria for matching people with diabetes to the most appropriate glucose monitoring methodologies, as well as standardized advice about how best to use the new information they provide, have yet to be established. In February 2017, the Advanced Technologies & Treatments for Diabetes (ATTD) Congress convened an international panel of physicians, researchers, and individuals with diabetes who are expert in CGM technologies to address these issues. This article summarizes the ATTD consensus recommendations and represents the current understanding of how CGM results can affect outcomes., (© 2017 by the American Diabetes Association.)

Objective: Artificial pancreas (AP) systems are best positioned for optimal treatment of type 1 diabetes (T1D) and are currently being tested in outpatient clinical trials. Our consortium developed and tested a novel adaptive AP in an outpatient, single-arm, uncontrolled multicenter clinical trial lasting 12 weeks., Research Design and Methods: Thirty adults with T1D completed a continuous glucose monitor (CGM)-augmented 1-week sensor-augmented pump (SAP) period. After the AP was started, basal insulin delivery settings used by the AP for initialization were adapted weekly, and carbohydrate ratios were adapted every 4 weeks by an algorithm running on a cloud-based server, with automatic data upload from devices. Adaptations were reviewed by expert study clinicians and patients. The primary end point was change in hemoglobin A 1c (HbA 1c ). Outcomes are reported adhering to consensus recommendations on reporting of AP trials., Results: Twenty-nine patients completed the trial. HbA 1c , 7.0 ± 0.8% at the start of AP use, improved to 6.7 ± 0.6% after 12 weeks (-0.3, 95% CI -0.5 to -0.2, P < 0.001). Compared with the SAP run-in, CGM time spent in the hypoglycemic range improved during the day from 5.0 to 1.9% (-3.1, 95% CI -4.1 to -2.1, P < 0.001) and overnight from 4.1 to 1.1% (-3.1, 95% CI -4.2 to -1.9, P < 0.001). Whereas carbohydrate ratios were adapted to a larger extent initially with minimal changes thereafter, basal insulin was adapted throughout. Approximately 10% of adaptation recommendations were manually overridden. There were no protocol-related serious adverse events., Conclusions: Use of our novel adaptive AP yielded significant reductions in HbA 1c and hypoglycemia., (© 2017 by the American Diabetes Association.)

Background: Continuous glucose monitoring (CGM) systems are increasingly becoming essential components in type 1 diabetes mellitus (T1DM) management. Current CGM technology requires frequent calibration to ensure accurate sensor performance. The accuracy of these systems is of great importance since medical decisions are made based on monitored glucose values and trends., Methods: In this work, we introduce a calibration strategy that is augmented with a weekly updating feature. During the life cycle of the sensor, the calibration mechanism periodically estimates the parameters of a calibration model to fit self-monitoring blood glucose (SMBG) measurements. At the end of each week of use, an optimization problem that minimizes the sum of squared residuals between past reference and predicted blood glucose values is solved remotely to identify personalized calibration parameters. The newly identified parameters are used to initialize the calibration mechanism of the following week., Results: The proposed method was evaluated using two sets of clinical data both consisting of 6 weeks of Dexcom G4 Platinum CGM data on 10 adults with T1DM (over 10 000 hours of CGM use), with seven SMBG data points per day measured by each subject in an unsupervised outpatient setting. Updating the calibration parameters using the history of calibration data indicated a positive trend of improving CGM performance., Conclusions: Although not statistically significant, the updating framework showed a relative improvement of CGM accuracy compared to the non-updating, static calibration method. The use of information collected for longer periods is expected to improve the performance of the sensor over time.

Recent in vitro studies have identified small-molecule pharmaceuticals effecting dose-dependent changes in the mammalian circadian clock, providing a novel avenue for control. Most studies employ light for clock control, however, pharmaceuticals are advantageous for clock manipulation through reduced invasiveness. In this paper, we employ a mechanistic model to predict the phase dynamics of the mammalian circadian oscillator under the effect of the pharmaceutical under investigation. These predictions are used to inform a constrained model predictive controller (MPC) to compute appropriate dosing for clock re-entrainment. Constraints in the formulation of the MPC problem arise from variation in the phase response curves (PRCs) describing drug effects, and are in many cases non-intuitive owing to the nonlinearity of oscillator phase response effects. We demonstrate through in-silico experiments that it is imperative to tune the MPC parameters based on the drug-specific PRC for optimal phase manipulation.

Background: Model predictive control (MPC) performance depends on the accuracy of the prediction model implemented by the controller. Complex physiology and modeling limitations often prevent the ability to provide long and accurate glucose predictions, which results in the need to account for prediction errors., Method: Optimal insulin dosage by Zone-MPC is calculated by solving an optimization problem in which a scalar index is minimized by penalizing relative input deviations and glucose predictions out of the reference zone. The controller's tuning parameters are the penalties on the input variable (insulin). Positive and negative relative inputs are penalized differently. A dynamic adaptation of the tuning parameters based on the accuracy of the model in recent history is implemented in this article and compared in silico to aggressive and conservative tunings of the same controller structure., Results: Similar average glucose and time in the safe glucose range (70-180 mg/dL) are achieved for the adaptive design and traditional controller configurations. However, percentage time under 70 mg/dL is significantly reduced, both for announced meals using bolus compensation and unannounced meals with a meal detection algorithm triggered bolus. No differences in the average insulin delivered were observed between the adaptive design and the conservative or aggressive tuning for the bolus strategy, and the adaptive controller delivered less insulin in the other scenario considered., Conclusions: The adaptive strategy provides safe and effective glucose management as well as significant reduction of hypoglycemia events. No abnormal insulin delivery profiles were observed upon the application of the adaptive strategy.

Background: In the past few years, the artificial pancreas-the commonly accepted term for closed-loop control (CLC) of blood glucose in diabetes-has become a hot topic in research and technology development. In the summer of 2014, we initiated a 6-month trial evaluating the safety of 24/7 CLC during free-living conditions., Research Design and Methods: Following an initial 1-month Phase 1, 14 individuals (10 males/4 females) with type 1 diabetes at three clinical centers in the United States and one in Italy continued with a 5-month Phase 2, which included 24/7 CLC using the wireless portable Diabetes Assistant (DiAs) developed at the University of Virginia Center for Diabetes Technology. Median subject characteristics were age 45 years, duration of diabetes 27 years, total daily insulin 0.53 U/kg/day, and baseline HbA1c 7.2% (55 mmol/mol)., Results: Compared with the baseline observation period, the frequency of hypoglycemia below 3.9 mmol/L during the last 3 months of CLC was lower: 4.1% versus 1.3%, P < 0.001. This was accompanied by a downward trend in HbA1c from 7.2% (55 mmol/mol) to 7.0% (53 mmol/mol) at 6 months. HbA1c improvement was correlated with system use (Spearman r = 0.55). The user experience was favorable with identified benefit particularly at night and overall trust in the system. There were no serious adverse events, severe hypoglycemia, or diabetic ketoacidosis., Conclusion: We conclude that CLC technology has matured and is safe for prolonged use in patients' natural environment. Based on these promising results, a large randomized trial is warranted to assess long-term CLC efficacy and safety.

The mammalian circadian clock is a complex multi-scale, multivariable biological control system. In the past two decades, methods from systems engineering have led to numerous insights into the architecture and functionality of this system. In this review, we examine the mammalian circadian system through a process systems lens. We present a mathematical framework for examining the cellular circadian oscillator, and show recent extensions for understanding population-scale dynamics. We provide an overview of the routes by which the circadian system can be systemically manipulated, and present in silico proof of concept results for phase resetting of the clock via model predictive control.

Development of an effective artificial pancreas (AP) controller to deliver insulin autonomously to people with type 1 diabetes mellitus is a difficult task. In this paper, three enhancements to a clinically validated AP model predictive controller (MPC) are proposed that address major challenges facing automated blood glucose control, and are then evaluated by both in silico tests and clinical trials. First, the core model of insulin-blood glucose dynamics utilized in the MPC is expanded with a medically inspired personalization scheme to improve controller responses in the face of inter- and intra-individual variations in insulin sensitivity. Next, the asymmetric nature of the short-term consequences of hypoglycemia versus hyperglycemia is incorporated in an asymmetric weighting of the MPC cost function. Finally, an enhanced dynamic insulin-on-board algorithm is proposed to minimize the likelihood of controller-induced hypoglycemia following a rapid rise of blood glucose due to rescue carbohydrate load with accompanying insulin suspension. Each advancement is evaluated separately and in unison through in silico trials based on a new clinical protocol, which incorporates induced hyper- and hypoglycemia to test robustness. The advancements are also evaluated in an advisory mode (simulated) testing of clinical data. The combination of the three proposed advancements show statistically significantly improved performance over the nonpersonalized controller without any enhancements across all metrics, displaying increased time in the 70-180 mg/dL safe glycemic range (76.9 versus 68.8%) and the 80-140 mg/dL euglycemic range (48.1 versus 44.5%), without a statistically significant increase in instances of hypoglycemia. The proposed advancements provide safe control action for AP applications, personalizing and improving controller performance without the need for extensive model identification processes., Competing Interests: The authors declare no competing financial interest.

Background: Insulin infusion set failure resulting in prolonged hyperglycemia or diabetic ketoacidosis can occur with pump therapy in type 1 diabetes. Set failures are frequently characterized by variable and unpredictable patterns of increasing glucose values despite increased insulin infusion. Early detection may minimize the risk of prolonged hyperglycemia, an important consideration for automated insulin delivery and closed-loop applications., Methods: A novel algorithm designed to alert the patient to the onset of infusion set failure was developed based upon continuous glucose sensor values and insulin delivered from an insulin pump. The method was calibrated on 12 weeks of infusion set wear without failures recorded by 4 patients in ambulatory conditions and prospectively validated on 18 weeks of infusion set wear with and without failures belonging to 9 other subjects in ambulatory conditions., Results: The algorithm, evaluated retrospectively, identified a failure 2.52 ± 1.91 days ahead of the actual event as recorded by the clinical team, corresponding to 50% sensitivity, 66% specificity and 55% accuracy. If set failure alarms had been activated in real time, the average time >180 mg/dl would be reduced from 82.7 ± 40.9 hours/week/subject (without alarm) to 58.8 ± 31.1 hours/week/subject (with alarm), corresponding to a potential 29% reduction in time spent >180mg/dl., Conclusion: The proposed method for early detection of infusion set failure based on glucose sensor and insulin data demonstrated favorable results on retrospective data and may be implemented as an additional safeguard in a future fully automated closed-loop system., Competing Interests: The author(s) declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: MC: None. DJD: None. TTL: None. DMM: Insulet Corporation, Dexcom, Inc, Medtronic, Inc. LHM: Medtronic, Inc. BAB: Medtronic, Inc, Animas Corporation, BD Medical Diabetes Care, Tandem Diabetes Care, Inc, Sanofi US, Novo Nordisk Inc, Hylenex, Dexcom, Inc. FJD: Insulet Corporation. ED: Animas Corporation, Insulet Corporation., (© 2016 Diabetes Technology Society.)

A novel Model Predictive Control (MPC) law for an Artificial Pancreas (AP) to automatically deliver insulin to people with type 1 diabetes is proposed. The MPC law is an enhancement of the authors' zone-MPC approach that has successfully been trialled in-clinic, and targets the safe outpatient deployment of an AP. The MPC law controls blood-glucose levels to a diurnally time-dependent zone, and enforces diurnal, hard input constraints. The main algorithmic novelty is the use of asymmetric input costs in the MPC problem's objective function. This improves safety by facilitating the independent design of the controller's responses to hyperglycemia and hypoglycemia. The proposed controller performs predictive pump-suspension in the face of impending hypoglycemia, and subsequent predictive pump-resumption, based only on clinical needs and feedback. The proposed MPC strategy's benefits are demonstrated by in-silico studies as well as highlights from a US Food and Drug Administration approved clinical trial in which 32 subjects each completed two 25 hour closed-loop sessions employing the proposed MPC law.

Research on and commercial development of the artificial pancreas (AP) continue to progress rapidly, and the AP promises to become a part of clinical care. In this report, members of the JDRF Artificial Pancreas Project Consortium in collaboration with the wider AP community 1) advocate for the use of continuous glucose monitoring glucose metrics as outcome measures in AP trials, in addition to HbA1c, and 2) identify a short set of basic, easily interpreted outcome measures to be reported in AP studies whenever feasible. Consensus on a broader range of measures remains challenging; therefore, reporting of additional metrics is encouraged as appropriate for individual AP studies or study groups. Greater consistency in reporting of basic outcome measures may facilitate the interpretation of study results by investigators, regulatory bodies, health care providers, payers, and patients themselves, thereby accelerating the widespread adoption of AP technology to improve the lives of people with type 1 diabetes., (© 2016 by the American Diabetes Association. Readers may use this article as long as the work is properly cited, the use is educational and not for profit, and the work is not altered.)

Objective: To evaluate the efficacy of a portable, wearable, wireless artificial pancreas system (the Diabetes Assistant [DiAs] running the Unified Safety System) on glucose control at home in overnight-only and 24/7 closed-loop control (CLC) modes in patients with type 1 diabetes., Research Design and Methods: At six clinical centers in four countries, 30 participants 18-66 years old with type 1 diabetes (43% female, 96% non-Hispanic white, median type 1 diabetes duration 19 years, median A1C 7.3%) completed the study. The protocol included a 2-week baseline sensor-augmented pump (SAP) period followed by 2 weeks of overnight-only CLC and 2 weeks of 24/7 CLC at home. Glucose control during CLC was compared with the baseline SAP., Results: Glycemic control parameters for overnight-only CLC were improved during the nighttime period compared with baseline for hypoglycemia (time <70 mg/dL, primary end point median 1.1% vs. 3.0%; P < 0.001), time in target (70-180 mg/dL: 75% vs. 61%; P < 0.001), and glucose variability (coefficient of variation: 30% vs. 36%; P < 0.001). Similar improvements for day/night combined were observed with 24/7 CLC compared with baseline: 1.7% vs. 4.1%, P < 0.001; 73% vs. 65%, P < 0.001; and 34% vs. 38%, P < 0.001, respectively., Conclusions: CLC running on a smartphone (DiAs) in the home environment was safe and effective. Overnight-only CLC reduced hypoglycemia and increased time in range overnight and increased time in range during the day; 24/7 CLC reduced hypoglycemia and increased time in range both overnight and during the day. Compared with overnight-only CLC, 24/7 CLC provided additional hypoglycemia protection during the day., (© 2016 by the American Diabetes Association. Readers may use this article as long as the work is properly cited, the use is educational and not for profit, and the work is not altered.)

Background: Time-varying dynamics is one of the main issues for achieving safe blood glucose control in type 1 diabetes mellitus (T1DM) patients. In addition, the typical disturbances considered for controller design are meals, which increase the glucose level, and physical activity (PA), which increases the subject's sensitivity to insulin. In previous works the authors have applied a linear parameter-varying (LPV) control technique to manage unannounced meals., Methods: A switched LPV controller that switches between 3 LPV controllers, each with a different level of aggressiveness, is designed to further cope with both unannounced meals and postprandial PA. Thus, the proposed control strategy has a "standard" mode, an "aggressive" mode, and a "conservative" mode. The "standard" mode is designed to be applied most of the time, while the "aggressive" mode is designed to deal only with hyperglycemia situations. On the other hand, the "conservative" mode is focused on postprandial PA control., Results: An ad hoc simulator has been developed to test the proposed controller. This simulator is based on the distribution version of the UVA/Padova model and includes the effect of PA based on Schiavon.(1) The test results obtained when using this simulator indicate that the proposed control law substantially reduces the risk of hypoglycemia with the conservative strategy, while the risk of hyperglycemia is scarcely affected., Conclusions: It is demonstrated that the announcement, or anticipation, of exercise is indispensable for letting a mono-hormonal artificial pancreas deal with the consequences of postprandial PA. In view of this the proposed controller allows switching into a conservative mode when notified of PA by the user., Competing Interests: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article., (© 2016 Diabetes Technology Society.)

Background: The relationship between daily psychological stress and BG fluctuations in type 1 diabetes (T1DM) is unclear. More research is needed to determine if stress-related BG changes should be considered in glucose control algorithms. This study in the usual free-living environment examined relationships among routine daily stressors and BG profile measures generated from CGM readings., Methods: A total of 33 participants with T1DM on insulin pumps wore a CGM device for 1 week and recorded daily ratings of psychological stress, carbohydrates, and insulin boluses., Results: Within-subjects ANCOVAs found a significant relationship between daily stress and indices of BG variability/instability (r = .172 to .185, P = .011 to .018, r(2) = 2.97% to 3.43%), increased % time in hypoglycemia (r = .153, P = .036, r(2) = 2.33%) and decreased carbohydrate consumption (r = -.157, P = .031, r(2) = 2.47%). Models accounted for more variance for individuals reporting the highest daily stress. There was no relationship between stress and mean daily glucose or low/high glucose risk indices., Conclusions: These preliminary findings suggest that naturally occurring daily stressors can be associated with increased glucose instability and hypoglycemia, as well as decreased food consumption. In addition, findings support the hypothesis that some individuals are more metabolically reactive to stress. More rigorous studies using CGM technology are needed to understand whether the impact of daily stress on BG is clinically meaningful and if it is a behavioral factor that should be considered in glucose control systems for some individuals., Competing Interests: The author(s) declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: LAGF has been a consultant for, received research funding/support from, and/or served on advisory boards for Abbot Laboratories, AstraZeneca plc, Dexcom Inc, Johnson & Johnson Services Inc, and/or Merck & Co Inc. BK, SAB, and CC have received research support/funding from Dexcom Inc and Roche Diagnostics. BK and SP report patents/patent applications related to glycemic control in diabetes managed by the University of Virginia Licensing and Ventures group and holding shares in TypeZero Technologies. ED and FJD report grants from JDRF and NIH during conduct of the study; algorithms licensing by Animas Corporation and mod AGC. BK reports additional research support/funding from Sanofi-Aventis, BD, and Tandem Diabetes Care; advisory panel/consulting for AstraZeneca plc, BD, and Inspark Technologies. SAB has received additional research support/funding from LifeScan, Animas, and ConAgra. SP has received research funding/support from Eli Lilly. HCZ is currently an employee of Insulet Corporation., (© 2016 Diabetes Technology Society.)

In the mammalian suprachiasmatic nucleus (SCN), noisy cellular oscillators communicate within a neuronal network to generate precise system-wide circadian rhythms. Although the intracellular genetic oscillator and intercellular biochemical coupling mechanisms have been examined previously, the network topology driving synchronization of the SCN has not been elucidated. This network has been particularly challenging to probe, due to its oscillatory components and slow coupling timescale. In this work, we investigated the SCN network at a single-cell resolution through a chemically induced desynchronization. We then inferred functional connections in the SCN by applying the maximal information coefficient statistic to bioluminescence reporter data from individual neurons while they resynchronized their circadian cycling. Our results demonstrate that the functional network of circadian cells associated with resynchronization has small-world characteristics, with a node degree distribution that is exponential. We show that hubs of this small-world network are preferentially located in the central SCN, with sparsely connected shells surrounding these cores. Finally, we used two computational models of circadian neurons to validate our predictions of network structure.

In this paper, the dynamic response of blood glucose concentration in response to physical activity of people with Type 1 Diabetes Mellitus (T1DM) is captured by subspace identification methods. Activity (input) and subcutaneous blood glucose measurements (output) are employed to construct a personalized prediction model through semi-definite programming. The model is calibrated and subsequently validated with non-overlapping data sets from 15 T1DM subjects. This preliminary clinical evaluation reveals the underlying linear dynamics between blood glucose concentration and physical activity. These types of models can enhance our capabilities of achieving tighter blood glucose control and early detection of hypoglycemia for people with T1DM.

Stochastic noise at the cellular level has been shown to play a fundamental role in circadian oscillations, influencing how groups of cells entrain to external cues and likely serving as the mechanism by which cell-autonomous rhythms are generated. Despite this importance, few studies have investigated how clock perturbations affect stochastic noise-even as increasing numbers of high-throughput screens categorize how gene knockdowns or small molecules can change clock period and amplitude. This absence is likely due to the difficulty associated with measuring cell-autonomous stochastic noise directly, which currently requires the careful collection and processing of single-cell data. In this study, we show that the damping rate of population-level bioluminescence recordings can serve as an accurate measure of overall stochastic noise, and one that can be applied to future and existing high-throughput circadian screens. Using cell-autonomous fibroblast data, we first show directly that higher noise at the single-cell results in faster damping at the population level. Next, we show that the damping rate of cultured cells can be changed in a dose-dependent fashion by small molecule modulators, and confirm that such a change can be explained by single-cell noise using a mathematical model. We further demonstrate the insights that can be gained by applying our method to a genome-wide siRNA screen, revealing that stochastic noise is altered independently from period, amplitude, and phase. Finally, we hypothesize that the unperturbed clock is highly optimized for robust rhythms, as very few gene perturbations are capable of simultaneously increasing amplitude and lowering stochastic noise. Ultimately, this study demonstrates the importance of considering the effect of circadian perturbations on stochastic noise, particularly with regard to the development of small-molecule circadian therapeutics.

Treatment of type 1 diabetes mellitus could be greatly improved by applying a closed-loop control strategy to insulin delivery, also known as an artificial pancreas (AP). In this work, we outline the design of a fully implantable AP using intraperitoneal (IP) insulin delivery and glucose sensing. The design process utilizes the rapid glucose sensing and insulin action offered by the IP space to tune a PID controller with insulin feedback to provide safe and effective insulin delivery. The controller was tuned to meet robust performance and stability specifications. An anti-reset windup strategy was introduced to prevent dangerous undershoot toward hypoglycemia after a large meal disturbance. The final controller design achieved 78% of time within the tight glycemic range of 80-140 mg/dL, with no time spent in hypoglycemia. The next step is to test this controller design in an animal model to evaluate the in vivo performance.

Context: Closed-loop control (CLC) relies on an individual's open-loop insulin pump settings to initialize the system. Optimizing open-loop settings before using CLC usually requires significant time and effort., Objective: The objective was to investigate the effects of a one-time algorithmic adjustment of basal rate and insulin to carbohydrate ratio open-loop settings on the performance of CLC., Design: This study reports a multicenter, outpatient, randomized, crossover clinical trial., Patients: Thirty-seven adults with type 1 diabetes were enrolled at three clinical sites., Interventions: Each subject's insulin pump settings were subject to a one-time algorithmic adjustment based on 1 week of open-loop (i.e., home care) data collection. Subjects then underwent two 27-hour periods of CLC in random order with either unchanged (control) or algorithmic adjusted basal rate and carbohydrate ratio settings (adjusted) used to initialize the zone-model predictive control artificial pancreas controller. Subject's followed their usual meal-plan and had an unannounced exercise session., Main Outcomes and Measures: Time in the glucose range was 80-140 mg/dL, compared between both arms., Results: Thirty-two subjects completed the protocol. Median time in CLC was 25.3 hours. The median time in the 80-140 mg/dl range was similar in both groups (39.7% control, 44.2% adjusted). Subjects in both arms of CLC showed minimal time spent less than 70 mg/dl (median 1.34% and 1.37%, respectively). There were no significant differences more than 140 mg/dL., Conclusions: A one-time algorithmic adjustment of open-loop settings did not alter glucose control in a relatively short duration outpatient closed-loop study. The CLC system proved very robust and adaptable, with minimal (<2%) time spent in the hypoglycemic range in either arm.

The design of a Model Predictive Control (MPC) strategy for the closed-loop operation of an Artificial Pancreas (AP) to treat type 1 diabetes mellitus is considered. The contribution of this paper is to propose a velocity-weighting mechanism, within an MPC problem's cost function, that facilitates penalizing predicted hyperglycemic blood-glucose excursions based on the predicted blood-glucose levels' rates of change. The method provides the control designer some freedom for independently shaping the AP's uphill versus downhill responses to hyperglycemic excursions; of particular emphasis in this paper is the downhill response. The proposal aims to tackle the dangerous issue of controller-induced hypoglycemia following large hyperglycemic excursions, e.g., after meals, that results in part due to the large delays of subcutaneous glucose sensing and subcutaneous insulin infusion - the case considered here. The efficacy of the proposed approach is demonstrated using the University of Virginia/Padova metabolic simulator with both unannounced and announced meal scenarios.

Background: Early detection of exercise in individuals with type 1 diabetes mellitus (T1DM) may allow changes in therapy to prevent hypoglycemia. Currently there is limited experience with automated methods that detect the onset and end of exercise in this population. We sought to develop a novel method to quickly and reliably detect the onset and end of exercise in these individuals before significant changes in blood glucose (BG) occur., Methods: Sixteen adults with T1DM were studied as outpatients using a diary, accelerometer, heart rate monitor, and continuous glucose monitor for 2 days. These data were used to develop a principal component analysis based exercise detection method. Subjects also performed 60 and 30 minute exercise sessions at 30% and 50% predicted heart rate reserve (HRR), respectively. The detection method was applied to the exercise sessions to determine how quickly the detection of start and end of exercise occurred relative to change in BG., Results: Mild 30% HRR and moderate 50% HRR exercise onset was identified in 6 ± 3 and 5 ± 2 (mean ± SD) minutes, while completion was detected in 3 ± 8 and 6 ± 5 minutes, respectively. BG change from start of exercise to detection time was 1 ± 6 and -1 ± 3 mg/dL, and, from the end of exercise to detection time was 6 ± 4 and -17 ± 13 mg/dL, respectively, for the 2 exercise sessions. False positive and negative ratios were 4 ± 2% and 21 ± 22%., Conclusions: The novel method for exercise detection identified the onset and end of exercise in approximately 5 minutes, with an average BG change of only -6 mg/dL., (© 2015 Diabetes Technology Society.)

Background: The Predictive Hypoglycemia Minimizer System ("Hypo Minimizer"), consisting of a zone model predictive controller (the "controller") and a safety supervision module (the "safety module"), aims to mitigate hypoglycemia by preemptively modulating insulin delivery based on continuous glucose monitor (CGM) measurements. The "aggressiveness factor," a pivotal variable in the system, governs the speed and magnitude of the controller's insulin dosing characteristics in response to changes in CGM levels., Methods: Twelve adults with type 1 diabetes were studied in closed-loop in a clinical research center for approximately 24 hours. This analysis focused primarily on the effect of the aggressiveness factor on the automated insulin-delivery characteristics of the controller, and secondarily on the glucose control results., Results: As aggressiveness increased from "conservative" to "medium" to "aggressive," the controller recommended less insulin (-3.3% vs -14.4% vs -19.5% relative to basal) with a higher frequency (5.3% vs 14.4% vs 20.3%) during the critical times when the CGM was reading 90-120 mg/dl and decreasing. Blood glucose analyses indicated that the most aggressive setting resulted in the most desirable combination of the least time spent <70 mg/dl and the most time spent 70-180 mg/dl, particularly in the overnight period. Hyperglycemia, diabetic ketoacidosis, or severe hypoglycemia did not occur with any of the aggressiveness values., Conclusion: The Hypo Minimizer's controller took preemptive action to prevent hypoglycemia based on predicted changes in CGM glucose levels. The most aggressive setting was quickest to take action to reduce insulin delivery below basal and achieved the best glucose metrics., (© 2015 Diabetes Technology Society.)