Relationship between Optimum Mini-doses of Glucagon and Insulin Levels when Treating Mild Hypoglycaemia in Patients with Type 1 Diabetes - A Simulation Study.

Hypoglycaemia remains the main limiting factor in type 1 diabetes management. We developed an insulin-dependent glucagon dosing regimen for treatment of mild hypoglycaemia based on simulations. A validated glucose-insulin-glucagon model was used to describe seven virtual patients with insulin pump-treated type 1 diabetes. In each simulation, one of ten different and individualized subcutaneous insulin boluses was administered to decrease plasma glucose (PG) from 7.0 to ≤3.9 mmol/l. Insulin levels were estimated as ratio of actual to baseline serum insulin concentration (se/ba-insulin), insulin on board (IOB) or percentage of IOB to total daily insulin dose (IOB/TDD). Insulin bolus sizes were chosen to provide pre-defined insulin levels when PG reached 3.9 mmol/l, where one of 17 subcutaneous glucagon boluses was administered. Optimum glucagon bolus to treat mild hypoglycaemia at varying insulin levels was the lowest dose that in most patients caused PG peak between 5.0 and 10.0 mmol/l and sustained PG ≥ 3.9 mmol/l for 2 hr after the bolus. PG response to glucagon declined with increasing insulin levels. The glucagon dose to optimally treat mild hypoglycaemia depended exponentially on insulin levels, regardless of how insulin was estimated. A 125-µg glucagon dose was needed to optimally treat mild hypoglycaemia when insulin levels were equal to baseline levels. In contrast, glucagon doses >500 µg were needed when se/ba-insulin >2.5, IOB >2.0 U or IOB/TDD >6%. Although the proposed model-based glucagon regimen needs confirmation in clinical trials, this is the first attempt to develop an insulin-dependent glucagon dosing regimen for treatment of insulin-induced mild hypoglycaemia in patients with type 1 diabetes.

Cross-Validation of a Glucose-Insulin-Glucagon Pharmacodynamics Model for Simulation Using Data From Patients With Type 1 Diabetes.

BACKGROUND: Currently, no consensus exists on a model describing endogenous glucose production (EGP) as a function of glucagon concentrations. Reliable simulations to determine the glucagon dose preventing or treating hypoglycemia or to tune a dual-hormone artificial pancreas control algorithm need a validated glucoregulatory model including the effect of glucagon. METHODS: Eight type 1 diabetes (T1D) patients each received a subcutaneous (SC) bolus of insulin on four study days to induce mild hypoglycemia followed by a SC bolus of saline or 100, 200, or 300 µg of glucagon. Blood samples were analyzed for concentrations of glucagon, insulin, and glucose. We fitted pharmacokinetic (PK) models to insulin and glucagon data using maximum likelihood and maximum a posteriori estimation methods. Similarly, we fitted a pharmacodynamic (PD) model to glucose data. The PD model included multiplicative effects of insulin and glucagon on EGP. Bias and precision of PD model test fits were assessed by mean predictive error (MPE) and mean absolute predictive error (MAPE). RESULTS: Assuming constant variables in a subject across nonoutlier visits and using thresholds of ±15% MPE and 20% MAPE, we accepted at least one and at most three PD model test fits in each of the seven subjects. Thus, we successfully validated the PD model by leave-one-out cross-validation in seven out of eight T1D patients. CONCLUSIONS: The PD model accurately simulates glucose excursions based on plasma insulin and glucagon concentrations. The reported PK/PD model including equations and fitted parameters allows for in silico experiments that may help improve diabetes treatment involving glucagon for prevention of hypoglycemia.

Comparison of three nonlinear filters for fault detection in continuous glucose monitors.

The purpose of this study is to compare the performance of three nonlinear filters in online drift detection of continuous glucose monitors. The nonlinear filters are the extended Kalman filter (EKF), the unscented Kalman filter (UKF), and the particle filter (PF). They are all based on a nonlinear model of the glucose-insulin dynamics in people with type 1 diabetes. Drift is modelled by a Gaussian random walk and is detected based on the statistical tests of the 90-min prediction residuals of the filters. The unscented Kalman filter had the highest average F score of 85.9%, and the smallest average detection delay of 84.1%, with the average detection sensitivity of 82.6%, and average specificity of 91.0%.

Inter-expert and intra-expert reliability in sleep spindle scoring.

OBJECTIVES: To measure the inter-expert and intra-expert agreement in sleep spindle scoring, and to quantify how many experts are needed to build a reliable dataset of sleep spindle scorings. METHODS: The EEG dataset was comprised of 400 randomly selected 115s segments of stage 2 sleep from 110 sleeping subjects in the general population (57±8, range: 42-72 years). To assess expert agreement, a total of 24 Registered Polysomnographic Technologists (RPSGTs) scored spindles in a subset of the EEG dataset at a single electrode location (C3-M2). Intra-expert and inter-expert agreements were calculated as F1-scores, Cohen's kappa (κ), and intra-class correlation coefficient (ICC). RESULTS: We found an average intra-expert F1-score agreement of 72±7% (κ: 0.66±0.07). The average inter-expert agreement was 61±6% (κ: 0.52±0.07). Amplitude and frequency of discrete spindles were calculated with higher reliability than the estimation of spindle duration. Reliability of sleep spindle scoring can be improved by using qualitative confidence scores, rather than a dichotomous yes/no scoring system. CONCLUSIONS: We estimate that 2-3 experts are needed to build a spindle scoring dataset with 'substantial' reliability (κ: 0.61-0.8), and 4 or more experts are needed to build a dataset with 'almost perfect' reliability (κ: 0.81-1). SIGNIFICANCE: Spindle scoring is a critical part of sleep staging, and spindles are believed to play an important role in development, aging, and diseases of the nervous system.

Sleep-spindle detection: crowdsourcing and evaluating performance of experts, non-experts and automated methods.

Sleep spindles are discrete, intermittent patterns of brain activity observed in human electroencephalographic data. Increasingly, these oscillations are of biological and clinical interest because of their role in development, learning and neurological disorders. We used an Internet interface to crowdsource spindle identification by human experts and non-experts, and we compared their performance with that of automated detection algorithms in data from middle- to older-aged subjects from the general population. We also refined methods for forming group consensus and evaluating the performance of event detectors in physiological data such as electroencephalographic recordings from polysomnography. Compared to the expert group consensus gold standard, the highest performance was by individual experts and the non-expert group consensus, followed by automated spindle detectors. This analysis showed that crowdsourcing the scoring of sleep data is an efficient method to collect large data sets, even for difficult tasks such as spindle identification. Further refinements to spindle detection algorithms are needed for middle- to older-aged subjects.

Validation of a novel automatic sleep spindle detector with high performance during sleep in middle aged subjects.

Many of the automatic sleep spindle detectors currently used to analyze sleep EEG are either validated on young subjects or not validated thoroughly. The purpose of this study is to develop and validate a fast and reliable sleep spindle detector with high performance in middle aged subjects. An automatic sleep spindle detector using a bandpass filtering approach and a time varying threshold was developed. The validation was done on sleep epochs from EEG recordings with manually scored sleep spindles from 13 healthy subjects with a mean age of 57.9 ± 9.7 years. The sleep spindle detector reached a mean sensitivity of 84.6 % and a mean specificity of 95.3 %. The sleep spindle detector can be used to obtain measures of spindle count and density together with quantitative measures such as the mean spindle frequency, mean spindle amplitude, and mean spindle duration.

A circadian clock in Antarctic krill: an endogenous timing system governs metabolic output rhythms in the euphausid species Euphausia superba.

Antarctic krill, Euphausia superba, shapes the structure of the Southern Ocean ecosystem. Its central position in the food web, the ongoing environmental changes due to climatic warming, and increasing commercial interest on this species emphasize the urgency of understanding the adaptability of krill to its environment. Krill has evolved rhythmic physiological and behavioral functions which are synchronized with the daily and seasonal cycles of the complex Southern Ocean ecosystem. The mechanisms, however, leading to these rhythms are essentially unknown. Here, we show that krill possesses an endogenous circadian clock that governs metabolic and physiological output rhythms. We found that expression of the canonical clock gene cry2 was highly rhythmic both in a light-dark cycle and in constant darkness. We detected a remarkable short circadian period, which we interpret as a special feature of the krill's circadian clock that helps to entrain the circadian system to the extreme range of photoperiods krill is exposed to throughout the year. Furthermore, we found that important key metabolic enzymes of krill showed bimodal circadian oscillations (∼9-12 h period) in transcript abundance and enzymatic activity. Oxygen consumption of krill showed ∼9-12 h oscillations that correlated with the temporal activity profile of key enzymes of aerobic energy metabolism. Our results demonstrate the first report of an endogenous circadian timing system in Antarctic krill and its likely link to metabolic key processes. Krill's circadian clock may not only be critical for synchronization to the solar day but also for the control of seasonal events. This study provides a powerful basis for the investigation into the mechanisms of temporal synchronization in this marine key species and will also lead to the first comprehensive analyses of the circadian clock of a polar marine organism through the entire photoperiodic cycle.

Protein phosphatase 1 (PP1) is a post-translational regulator of the mammalian circadian clock.

Circadian clocks coordinate the timing of important biological processes. Interconnected transcriptional and post-translational feedback loops based on a set of clock genes generate and maintain these rhythms with a period of about 24 hours. Many clock proteins undergo circadian cycles of post-translational modifications. Among these modifications, protein phosphorylation plays an important role in regulating activity, stability and intracellular localization of clock components. Several protein kinases were characterized as regulators of the circadian clock. However, the function of protein phosphatases, which balance phosphorylation events, in the mammalian clock mechanism is less well understood. Here, we identify protein phosphatase 1 (PP1) as regulator of period and light-induced resetting of the mammalian circadian clock. Down-regulation of PP1 activity in cells by RNA interference and in vivo by expression of a specific inhibitor in the brain of mice tended to lengthen circadian period. Moreover, reduction of PP1 activity in the brain altered light-mediated clock resetting behavior in mice, enhancing the phase shifts in either direction. At the molecular level, diminished PP1 activity increased nuclear accumulation of the clock component PER2 in neurons. Hence, PP1, may reduce PER2 phosphorylation thereby influencing nuclear localization of this protein. This may at least partially influence period and phase shifting properties of the mammalian circadian clock.

A large-scale functional RNAi screen reveals a role for CK2 in the mammalian circadian clock.

Post-translational processes are essential for the generation and dynamics of mammalian circadian rhythms. In particular, phosphorylation of the key circadian protein PER2 precisely controls the period and phase of circadian oscillations. However, the mechanisms underlying that control are poorly understood. Here, we identified in a high-throughput RNAi-based genetic screen casein kinase 2 (CK2) as a PER2-phosphorylating kinase and novel component of the mammalian circadian clock. When CK2 subunits are silenced by RNAi or when CK2 activity is inhibited pharmacologically, circadian rhythms are disrupted. CK2 binds to PER2 in vivo, phosphorylates PER2 specifically at N-terminal residues in vitro, and supports normal nuclear PER2 accumulation. Mutation of CK2 phosphorylation sites decreases PER2 stability and copies CK2 inhibition regarding oscillation dynamics. We propose a new concept of how PER2 phosphorylation and stabilization can set the clock speed in opposite directions, dependent on the phase of action.