Diazoxide choline extended-release tablet in people with Prader-Willi syndrome: results from long-term open-label study.

OBJECTIVE: This study assessed the effect of 1-year administration of diazoxide choline extended-release tablet (DCCR) on hyperphagia and other complications of Prader-Willi syndrome (PWS). METHODS: The authors studied 125 participants with PWS, age ≥ 4 years, who were enrolled in the DESTINY PWS Phase 3 study and who received DCCR for up to 52 weeks in DESTINY PWS and/or its open-label extension. The primary efficacy endpoint was Hyperphagia Questionnaire for Clinical Trials (HQ-CT) score. Other endpoints included behavioral assessments, body composition, hormonal measures, and safety. RESULTS: DCCR administration resulted in significant improvements in HQ-CT (mean [SE] -9.9 [0.77], p < 0.0001) and greater improvements in those with more severe baseline hyperphagia (HQ-CT > 22). Improvements were seen in aggression, anxiety, and compulsivity (all p < 0.0001). There were reductions in leptin, insulin, and insulin resistance, as well as a significant increase in adiponectin (all p < 0.004). Lean body mass was increased (p < 0.0001). Disease severity was reduced as assessed by clinician and caregiver (both p < 0.0001). Common treatment-emergent adverse events included hypertrichosis, peripheral edema, and hyperglycemia. Adverse events infrequently resulted in discontinuation (7.2%). CONCLUSIONS: DCCR administration to people with PWS was well tolerated and associated with broad-ranging improvements in the syndrome. Sustained administration of DCCR has the potential to reduce disease severity and the burden of care for families.

Emerging Pediatric Obesity Epidemic with the COVID-19 Pandemic as an Influence.

Obesity remains a significant public health issue that leads to serious acute and chronic diseases. The prevalence of childhood obesity is on the rise, especially when taking into consideration the novel coronavirus disease 2019 (COVID-19) pandemic. Pediatricians and primary care providers can help support children at risk for many obesity-related comorbidities by using a family based approach for intervention. In this review, we will provide a brief overview of childhood obesity with COVID-19 pandemic ramifications and guidance for pediatricians to provide needed support and initial treatment. [Pediatr Ann. 2023;52(2):e48-e50.].

Pediatric Obesity: Primary Care Perspective and Prevention.

In 1997, the World Health Organization declared obesity a global epidemic. Despite multiple efforts, obesity rates have been exponentially increasing for the past few decades. In the last few years, obesity rates have reached an alarming number. Multiple factors play a role in pediatric obesity, such as diet, sedentarism, and poor sleep, as well as psychosocial and environmental factors. Pediatricians and primary care providers are key in the management of overweight and obesity. They have the advantage of observing children over a long period of time, having a family centered perspective, and often being seen as a reliable source of information. Studies have shown that not only is obesity underdiagnosed, but there is a lack of knowledge among physicians and available resources regarding pediatric obesity. This article reviews the principles of prevention in a primary care outpatient setting. Additionally, it discusses some of the challenges commonly faced when addressing pediatric obesity. [Pediatr Ann. 2023;52(2):e51-e56.].

Diazoxide Choline Extended-Release Tablet in People With Prader-Willi Syndrome: A Double-Blind, Placebo-Controlled Trial.

CONTEXT: Prader-Willi syndrome (PWS) is a rare neurobehavioral-metabolic disease caused by the lack of paternally expressed genes in the chromosome 15q11-q13 region, characterized by hypotonia, neurocognitive problems, behavioral difficulties, endocrinopathies, and hyperphagia resulting in severe obesity if not controlled. OBJECTIVE: The primary end point was change from baseline in hyperphagia using the Hyperphagia Questionnaire for Clinical Trials (HQ-CT). Other end points included Global Impression Scores, and changes in body composition, behaviors, and hormones. METHODS: In DESTINY PWS, a 13-week, randomized, double-blind, placebo-controlled, phase 3 trial, 127 participants with PWS aged 4 years and older with hyperphagia were randomly assigned 2:1 to diazoxide choline extended-release tablet (DCCR) or placebo. RESULTS: DCCR did not significantly improve hyperphagia (HQ-CT least-square mean (LSmean) [SE] -5.94 [0.879] vs -4.27 [1.145]; P = .198), but did so in participants with severe hyperphagia (LSmean [SE] -9.67 [1.429] vs -4.26 [1.896]; P = .012). Two of 3 secondary end points were improved (Clinical Global Impression of Improvement [CGI-I]; P = .029; fat mass; P = .023). In an analysis of results generated pre-COVID, the primary (HQ-CT; P = .037) and secondary end points were all improved (CGI-I; P = .015; Caregiver Global Impression of Change; P = .031; fat mass; P = .003). In general, DCCR was well tolerated with 83.3% in the DCCR group experiencing a treatment-emergent adverse event and 73.8% in the placebo group (not significant). CONCLUSION: DCCR did not significantly improve hyperphagia in the primary analysis but did in participants with severe baseline hyperphagia and in the pre-COVID analysis. DCCR treatment was associated with significant improvements in body composition and clinician-reported outcomes.

Online Glucose Prediction Using Computationally Efficient Sparse Kernel Filtering Algorithms in Type-1 Diabetes.

Streaming data from continuous glucose monitoring (CGM) systems enable the recursive identification of models to improve estimation accuracy for effective predictive glycemic control in patients with type-1 diabetes. A drawback of conventional recursive identification techniques is the increase in computational requirements, which is a concern for online and real-time applications such as the artificial pancreas systems implemented on handheld devices and smartphones where computational resources and memory are limited. To improve predictions in such computationally constrained hardware settings, efficient adaptive kernel filtering algorithms are developed in this paper to characterize the nonlinear glycemic variability by employing a sparsification criterion based on the information theory to reduce the computation time and complexity of the kernel filters without adversely deteriorating the predictive performance. Furthermore, the adaptive kernel filtering algorithms are designed to be insensitive to abnormal CGM measurements, thus compensating for measurement noise and disturbances. As such, the sparsification-based real-time model update framework can adapt the prediction models to accurately characterize the time-varying and nonlinear dynamics of glycemic measurements. The proposed recursive kernel filtering algorithms leveraging sparsity for improved computational efficiency are applied to both in-silico and clinical subjects, and the results demonstrate the effectiveness of the proposed methods.

Multi-Model Sensor Fault Detection and Data Reconciliation: A Case Study with Glucose Concentration Sensors for Diabetes.

Erroneous information from sensors affect process monitoring and control. An algorithm with multiple model identification methods will improve the sensitivity and accuracy of sensor fault detection and data reconciliation (SFD&DR). A novel SFD&DR algorithm with four types of models including outlier robust Kalman filter, locally weighted partial least squares, predictor-based subspace identification, and approximate linear dependency-based kernel recursive least squares is proposed. The residuals are further analyzed by artificial neural networks and a voting algorithm. The performance of the SFD&DR algorithm is illustrated by clinical data from artificial pancreas experiments with people with diabetes. The glucose-insulin metabolism has time-varying parameters and nonlinearities, providing a challenging system for fault detection and data reconciliation. Data from 17 clinical experiments collected over 896 hours were analyzed; the results indicate that the proposed SFD&DR algorithm is capable of detecting and diagnosing sensor faults and reconciling the erroneous sensor signals with better model-estimated values.

Multi-level Supervision and Modification of Artificial Pancreas Control System.

Artificial pancreas (AP) systems provide automated regulation of blood glucose concentration (BGC) for people with type 1 diabetes (T1D). An AP includes three components: a continuous glucose monitoring (CGM) sensor, a controller calculating insulin infusion rate based on the CGM signal, and a pump delivering the insulin amount calculated by the controller to the patient. The performance of the AP system depends on successful operation of these three components. Many APs use model predictive controllers that rely on models to predict BGC and to calculate the optimal insulin infusion rate. The performance of model-based controllers depends on the accuracy of the models that is affected by large dynamic changes in glucose-insulin metabolism or equipment performance that may move the operating conditions away from those used in developing the models and designing the control system. Sensor errors and missing signals will cause calculation of erroneous insulin infusion rates. And the performance of the controller may vary at each sampling step and each period (meal, exercise, and sleep), and from day to day. Here we describe a multi-level supervision and controller modification (ML-SCM) module is developed to supervise the performance of the AP system and retune the controller. It supervises AP performance in 3 time windows: sample level, period level, and day level. At sample level, an online controller performance assessment sub-module will generate controller performance indexes to evaluate various components of the AP system and conservatively modify the controller. A sensor error detection and signal reconciliation module will detect sensor error and reconcile the CGM sensor signal at each sample. At period level, the controller performance is evaluated with information collected during a certain time period and the controller is tuned more aggressively. At the day level, the daily CGM ranges are further analyzed to determine the adjustable range of controller parameters used for sample level and period level. Thirty subjects in the UVa/Padova metabolic simulator were used to evaluate the performance of the ML-SCM module and one clinical experiment is used to illustrate its performance in a clinical environment. The results indicate that the AP system with an ML-SCM module has a safer range of glucose concentration distribution and more appropriate insulin infusion rate suggestions than an AP system without the ML-SCM module.

Multivariable Artificial Pancreas for Various Exercise Types and Intensities.

BACKGROUND: Exercise challenges people with type 1 diabetes in controlling their glucose concentration (GC). A multivariable adaptive artificial pancreas (MAAP) may lessen the burden. METHODS: The MAAP operates without any user input and computes insulin based on continuous glucose monitor and physical activity signals. To analyze performance, 18 60-h closed-loop experiments with 96 exercise sessions with three different protocols were completed. Each day, the subjects completed one resistance and one treadmill exercise (moderate continuous training [MCT] or high-intensity interval training [HIIT]). The primary outcome is time spent in each glycemic range during the exercise + recovery period. Secondary measures include average GC and average change in GC during each exercise modality. RESULTS: The GC during exercise + recovery periods were within the euglycemic range (70-180 mg/dL) for 69.9% of the time and within a safe glycemic range for exercise (70-250 mg/dL) for 93.0% of the time. The exercise sessions are defined to begin 30 min before the start of exercise and end 2 h after start of exercise. The GC were within the severe hypoglycemia (<55 mg/dL), moderate hypoglycemia (55-70 mg/dL), moderate hyperglycemia (180-250 mg/dL), and severe hyperglycemia (>250 mg/dL) for 0.9%, 1.3%, 23.1%, and 4.8% of the time, respectively. The average GC decline during exercise differed with exercise type (P = 0.0097) with a significant difference between the MCT and resistance (P = 0.0075). To prevent large GC decreases leading to hypoglycemia, MAAP recommended carbohydrates in 59% of MCT, 50% of HIIT, and 39% of resistance sessions. CONCLUSIONS: A consistent GC decline occurred in exercise and recovery periods, which differed with exercise type. The average GC at the start of exercise was above target (185.5 ± 56.6 mg/dL for MCT, 166.9 ± 61.9 mg/dL for resistance training, and 171.7 ± 41.4 mg/dL HIIT), making a small decrease desirable. Hypoglycemic events occurred in 14.6% of exercise sessions and represented only 2.22% of the exercise and recovery period.

Incorporating Unannounced Meals and Exercise in Adaptive Learning of Personalized Models for Multivariable Artificial Pancreas Systems.

BACKGROUND: Despite the recent advancements in the modeling of glycemic dynamics for type 1 diabetes mellitus, automatically considering unannounced meals and exercise without manual user inputs remains challenging. METHOD: An adaptive model identification technique that incorporates exercise information and estimates of the effects of unannounced meals obtained automatically without user input is proposed in this work. The effects of the unknown consumed carbohydrates are estimated using an individualized unscented Kalman filtering algorithm employing an augmented glucose-insulin dynamic model, and exercise information is acquired from noninvasive physiological measurements. The additional information on meals and exercise is incorporated with personalized estimates of plasma insulin concentration and glucose measurement data in an adaptive model identification algorithm. RESULTS: The efficacy of the proposed personalized and adaptive modeling algorithm is demonstrated using clinical data involving closed-loop experiments of the artificial pancreas system, and the results demonstrate accurate glycemic modeling with the average root-mean-square error (mean absolute error) of 25.50 mg/dL (18.18 mg/dL) for six-step (30 minutes ahead) predictions. CONCLUSIONS: The approach presented is able to identify reliable time-varying individualized glucose-insulin models.

Adaptive and Personalized Plasma Insulin Concentration Estimation for Artificial Pancreas Systems.

BACKGROUND: The artificial pancreas (AP) system, a technology that automatically administers exogenous insulin in people with type 1 diabetes mellitus (T1DM) to regulate their blood glucose concentrations, necessitates the estimation of the amount of active insulin already present in the body to avoid overdosing. METHOD: An adaptive and personalized plasma insulin concentration (PIC) estimator is designed in this work to accurately quantify the insulin present in the bloodstream. The proposed PIC estimation approach incorporates Hovorka's glucose-insulin model with the unscented Kalman filtering algorithm. Methods for the personalized initialization of the time-varying model parameters to individual patients for improved estimator convergence are developed. Data from 20 three-days-long closed-loop clinical experiments conducted involving subjects with T1DM are used to evaluate the proposed PIC estimation approach. RESULTS: The proposed methods are applied to the clinical data containing significant disturbances, such as unannounced meals and exercise, and the results demonstrate the accurate real-time estimation of the PIC with the root mean square error of 7.15 and 9.25 mU/L for the optimization-based fitted parameters and partial least squares regression-based testing parameters, respectively. CONCLUSIONS: The accurate real-time estimation of PIC will benefit the AP systems by preventing overdelivery of insulin when significant insulin is present in the bloodstream.

Automatic Detection and Estimation of Unannounced Meals for Multivariable Artificial Pancreas System.

BACKGROUND: Automatically attenuating the postprandial rise in the blood glucose concentration without manual meal announcement is a significant challenge for artificial pancreas (AP) systems. In this study, a meal module is proposed to detect the consumption of a meal and to estimate the amount of carbohydrate (CHO) intake. METHODS: The meals are detected based on qualitative variables describing variation of continuous glucose monitoring (CGM) readings. The CHO content of the meals/snacks is estimated by a fuzzy system using CGM and subcutaneous insulin delivery data. The meal bolus amount is computed according to the patient's insulin to CHO ratio. Integration of the meal module into a multivariable AP system allows revision of estimated CHO based on knowledge about physical activity, sleep, and the risk of hypoglycemia before the final decision for a meal bolus is made. RESULTS: The algorithm is evaluated by using 117 meals/snacks in retrospective data from 11 subjects with type 1 diabetes. Sensitivity, defined as the percentage of correctly detected meals and snacks, is 93.5% for meals and 68.0% for snacks. The percentage of false positives, defined as the proportion of false detections relative to the total number of detected meals and snacks, is 20.8%. CONCLUSIONS: Integration of a meal detection module in an AP system is a further step toward an automated AP without manual entries. Detection of a consumed meal/snack and infusion of insulin boluses using an estimate of CHO enables the AP system to automatically prevent postprandial hyperglycemia.

Model-Fusion-Based Online Glucose Concentration Predictions in People with Type 1 Diabetes.

Accurate predictions of glucose concentrations are necessary to develop an artificial pancreas (AP) system for people with type 1 diabetes (T1D). In this work, a novel glucose forecasting paradigm based on a model fusion strategy is developed to accurately characterize the variability and transient dynamics of glycemic measurements. To this end, four different adaptive filters and a fusion mechanism are proposed for use in the online prediction of future glucose trajectories. The filter fusion mechanism is developed based on various prediction performance indexes to guide the overall output of the forecasting paradigm. The efficiency of the proposed model fusion based forecasting method is evaluated using simulated and clinical datasets, and the results demonstrate the capability and prediction accuracy of the data-based fusion filters, especially in the case of limited data availability. The model fusion framework may be used in the development of an AP system for glucose regulation in patients with T1D.

The Effect of the Testis on the Ovary: Structure-Function Relationships in a Neonate with a Unilateral Ovotestis (Ovotesticular Disorder of Sex Development)
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AIMS: To evaluate gonadal function in a newborn with suspected ovotesticular disorder of sex development (DSD). METHODS: Gonadal function was evaluated at baseline and after gonadotropin-releasing hormone agonist (GnRHag) stimulation testing. RESULTS: A full-term 46,XX neonate with genital ambiguity produced serum testosterone and anti-Müllerian hormone (AMH) levels appropriate for males within days, while serum estradiol remained prepubertal, both spontaneously and in response to GnRHag stimulation testing. Ovotesticular DSD was diagnosed at laparoscopy: the left gonad was an ovotestis and the right gonad an ovary arrested at the primordial follicle stage of development. Mosaicism for an isochromosome of the Y short arm in 6-18% of gonadal cells was demonstrated. After ovotestis removal at 3 weeks of age, serum AMH became low within a month, but the elevated testosterone was slow to resolve, apparently from ovarian androgenic hyperfunction coincident with ovarian estrogenic hyperfunction and an adult degree of ovarian development. Ovarian morphology and function gradually normalized as neonatal minipuberty waned. CONCLUSIONS: In a neonate with genital ambiguity due to ovotesticular DSD, testicular AMH and testosterone production respectively appear to account for the initial arrest of ovarian development and subsequent rapid hyperfunction of the contralateral ovary after ovotestis removal.
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Hybrid online sensor error detection and functional redundancy for systems with time-varying parameters.

Supervision and control systems rely on signals from sensors to receive information to monitor the operation of a system and adjust manipulated variables to achieve the control objective. However, sensor performance is often limited by their working conditions and sensors may also be subjected to interference by other devices. Many different types of sensor errors such as outliers, missing values, drifts and corruption with noise may occur during process operation. A hybrid online sensor error detection and functional redundancy system is developed to detect errors in online signals, and replace erroneous or missing values detected with model-based estimates. The proposed hybrid system relies on two techniques, an outlier-robust Kalman filter (ORKF) and a locally-weighted partial least squares (LW-PLS) regression model, which leverage the advantages of automatic measurement error elimination with ORKF and data-driven prediction with LW-PLS. The system includes a nominal angle analysis (NAA) method to distinguish between signal faults and large changes in sensor values caused by real dynamic changes in process operation. The performance of the system is illustrated with clinical data continuous glucose monitoring (CGM) sensors from people with type 1 diabetes. More than 50,000 CGM sensor errors were added to original CGM signals from 25 clinical experiments, then the performance of error detection and functional redundancy algorithms were analyzed. The results indicate that the proposed system can successfully detect most of the erroneous signals and substitute them with reasonable estimated values computed by functional redundancy system.

Hypoglycemia Detection and Carbohydrate Suggestion in an Artificial Pancreas.

Fear of hypoglycemia is a major concern for many patients with type 1 diabetes and affects patient decisions for use of an artificial pancreas system. We propose an alternative way for prevention of hypoglycemia by issuing predictive hypoglycemia alarms and encouraging patients to consume carbohydrates in a timely manner. The algorithm has been tested on 6 subjects (3 males and 3 females, age 24.2 ± 4.5 years, weight 79.2 ± 16.2 kg, height 172.7 ± 9.4 cm, HbA1C 7.3 ± 0.48%, duration of diabetes 209.2 ± 87.9 months) over 3-day closed-loop clinical experiments as part of a multivariable artificial pancreas control system. Over 6 three-day clinical experiments, there were only 5 real hypoglycemia episodes, of which only 1 hypoglycemia episode occurred due to being missed by the proposed algorithm. The average hypoglycemia alarms per day and per subject was 3. Average glucose value when the first alarms were triggered was recorded to be 117 ± 30.6 mg/dl. Average carbohydrate consumption per alarm was 14 ± 7.8 grams. Our results have shown that most low glucose concentrations can be predicted in advance and the glucose levels can be raised back to the desired levels by consuming an appropriate amount of carbohydrate. The proposed algorithm is able to prevent most hypoglycemic events by suggesting appropriate levels of carbohydrate consumption before the actual occurrence of hypoglycemia.

Meal Detection in Patients With Type 1 Diabetes: A New Module for the Multivariable Adaptive Artificial Pancreas Control System.

A novel meal-detection algorithm is developed based on continuous glucose measurements. Bergman's minimal model is modified and used in an unscented Kalman filter for state estimations. The estimated rate of appearance of glucose is used for meal detection. Data from nine subjects are used to assess the performance of the algorithm. The results indicate that the proposed algorithm works successfully with high accuracy. The average change in glucose levels between the meals and the detection points is 16(±9.42) [mg/dl] for 61 successfully detected meals and snacks. The algorithm is developed as a new module of an integrated multivariable adaptive artificial pancreas control system. Meal detection with the proposed method is used to administer insulin boluses and prevent most of postprandial hyperglycemia without any manual meal announcements. A novel meal bolus calculation method is proposed and tested with the UVA/Padova simulator. The results indicate significant reduction in hyperglycemia.

Adolescent polycystic ovary syndrome due to functional ovarian hyperandrogenism persists into adulthood.

BACKGROUND: Menstrual irregularity and above-average testosterone levels in adolescence may presage polycystic ovary syndrome (PCOS) in adulthood but persist in only a minority. Prolonged anovulatory cycles in normal adolescents are associated with increased testosterone levels. Thus, questions have been raised about the accuracy of PCOS diagnosed in adolescents. OBJECTIVE: The purpose of this study was to follow-up hyperandrogenic adolescents with features of PCOS to test the hypothesis that adolescent functional ovarian hyperandrogenism (FOH) persists into adulthood. STUDY SUBJECTS: A series of adults previously reported to have adolescent PCOS, with most documented to have FOH by GnRH agonist or dexamethasone androgen-suppression test criteria, were recalled. METHODS: Recall occurred >3 years after the initial diagnosis and at the age of >18.0 years. Respondents underwent examination, baseline androgen evaluation, and an oral glucose tolerance test after discontinuing oral contraceptive therapy. RESULTS: Of the adolescent hyperandrogenic patients, 68% (15 of 22) were traceable, and 60% of those traced returned for follow-up, including half (n = 8) of the original FOH group. The baseline characteristics of respondents and nonrespondents were not significantly different. Patients with FOH were reevaluated when their mean age was 23.0 years (range, 18.4-29.4 years), gynecologic age was 10.7 years (range, 5.5-18.4 years), and body mass index was 42.3 kg/m(2) (range, 28.3-52.1 kg/m(2); P = .02 vs adolescence). Serum free testosterone was 24 pg/mL (range, 10-38 pg/mL, normal, 3-9 pg/mL; not significant vs adolescence); all were oligomenorrheic. Whereas 3 of 8 had impaired glucose tolerance as adolescents, at follow-up 6 of 8 had developed abnormal glucose tolerance (2 with type 2 diabetes mellitus). CONCLUSIONS: Adolescents with FOH, which underlies most PCOS, uniformly have persistent hyperandrogenism, and glucose tolerance tends to deteriorate. Testing ovarian androgenic function in hyperandrogenic adolescents may be of prognostic value.