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In early childhood, consolidation of sleep from a biphasic to a monophasic sleep-wake pattern, that is, the transition from sleeping during an afternoon nap and at night to sleeping only during the night, represents a major developmental milestone. Reduced napping behavior is associated with an advance in the timing of the circadian system; however, it is unknown if this advance represents a standard response of the circadian clock to altered patterns of light exposure or if it additionally reflects features of the developing circadian system. Using a mathematical model of the human circadian pacemaker, we investigated the impact of napping and non-napping patterns of light exposure on entrained circadian phases. Simulated light schedules were based on published data from 20 children (34.2 ± 2.0 months) with habitual napping or non-napping sleep patterns (15 nappers). We found the model predicted different circadian phases for napping and non-napping light patterns: both the decrease in afternoon light during the nap and the increase in evening light associated with napping toddlers' later bedtimes contributed to the observed circadian phase difference produced between napping and non-napping light schedules. We systematically quantified the effects on phase shifting of nap duration, timing, and light intensity, finding larger phase delays occurred for longer and earlier naps. In addition, we simulated phase response curves to a 1-h light pulse and 1-h dark pulse to predict phase and intensity dependence of these changes in light exposure. We found the light pulse produced larger shifts compared with the dark pulse, and we analyzed the model dynamics to identify the features contributing to this asymmetry. These findings suggest that napping status affects circadian timing due to altered patterns of light exposure, with the dynamics of the circadian clock and light processing mediating the effects of the dark pulse associated with a daytime nap. [ABSTRACT FROM AUTHOR]

Summary: In adults, recent evidence demonstrates that sleep and circadian physiology change across lunar phases, including findings that endogenous melatonin levels are lower near the full moon compared to the new moon. Here, we extend these results to early childhood by examining circalunar fluctuations in children's evening melatonin levels. We analysed extant data on young children's circadian rhythms (n = 46, aged 3.0–5.9 years, 59% female). After following a strict sleep schedule for 5–7 days, children completed an in‐home, dim‐light circadian assessment (<10 lux). Salivary melatonin was assessed at regular 20‐ to 30‐min intervals until 1 h past each child's scheduled bedtime. Melatonin levels varied significantly across lunar phases, such that melatonin was lower in participants assessed near the full moon as compared to near the new moon. Significant differences were observed at 50 min (meanfull = 2.5 pg/ml; meannew = 5.4 pg/ml) and 10 min (meanfull = 7.3 pg/ml; meannew = 15.8 pg/ml) before children's scheduled bedtime, as well as at 20 min (meanfull = 15.5 pg/ml; meannew = 26.1 pg/ml) and 50 min (meanfull = 19.9 pg/ml; meannew = 34.3 pg/ml) after bedtime. To our knowledge, these are the first data demonstrating that melatonin secretion, a process regulated by the human circadian system, is sensitive to changes in lunar phase at an early age. Future research is needed to understand the mechanisms underlying this association (e.g., an endogenous circalunar rhythm) and its potential influence on children's sleep and circadian health. [ABSTRACT FROM AUTHOR]

Late sleep timing is prevalent in early childhood and a risk factor for poor behavioral and health outcomes. Sleep timing is influenced by the phase of the circadian clock, with later circadian timing linked to delayed sleep onset in young children. Light is the strongest zeitgeber of circadian timing and, in adults, evening light produces circadian phase delay in an intensity-dependent manner. The intensity-dependent circadian phase-shifting response to evening light in children, however, is currently unknown. In the present study, 33 healthy, good-sleeping children aged 3.0 to 4.9 years (M = 4.14 years, 39% male) completed a 10-day between-subjects protocol. Following 7 days of a stable sleep schedule, an in-home dim-light circadian assessment was performed. Children remained in dim-light across 3 days (55 h), with salivary melatonin collected in regular intervals throughout each evening. Phase-shifting effects of light exposure were determined via changes in the timing of the dim-light melatonin onset (DLMO) prior to (Day 8) and following (Day 10) a light exposure stimulus. On Day 9, children were exposed to a 1 h light stimulus in the hour before their habitual bedtime. Each child was randomly assigned to one intensity between 5 and 5000 lux (4.5-3276 melanopic EDI). Across light intensities, children showed significant circadian phase delays, with an average phase delay of 56.1 min (SD = 33.6 min), and large inter-individual variability. No relationship between light intensity and magnitude of the phase shift was observed. However, a greater percentage of melatonin suppression during the light exposure was associated with a greater phase delay (r = −0.73, p < 0.01). These findings demonstrate that some young children may be highly sensitive to light exposure in the hour before bedtime and suggest that the home lighting environment and its impact on circadian timing should be considered a possible contributor to behavioral sleep difficulties. [ABSTRACT FROM AUTHOR]

Objective: Adolescents with polycystic ovary syndrome (PCOS) and obesity can have insulin resistance, dysglycemia, and hepatic steatosis. Excess pancreatic fat may disturb insulin secretion and relate to hepatic fat. Associations between pancreatic fat fraction (PFF) and metabolic measures in PCOS were unknown.Methods: This secondary analysis included 113 sedentary, nondiabetic adolescent girls (age = 15.4 [1.9] years), with or without PCOS and BMI ≥ 90th percentile. Participants underwent fasting labs, oral glucose tolerance tests, and magnetic resonance imaging for hepatic fat fraction (HFF) and PFF. Groups were categorized by PFF (above or below the median of 2.18%) and compared.Results: Visceral fat and HFF were elevated in individuals with PCOS versus control individuals, but PFF was similar. PFF did not correlate with serum androgens. Higher and lower PFF groups had similar HFF, with no correlation between PFF and HFF, although hepatic steatosis was more common in those with higher PFF (≥5.0% HFF; 60% vs. 36%; p = 0.014). The higher PFF group had higher fasting insulin (p = 0.026), fasting insulin resistance (homeostatic model assessment of insulin resistance, p = 0.032; 1/fasting insulin, p = 0.028), free fatty acids (p = 0.034), and triglycerides (p = 0.004) compared with those with lower PFF. β-Cell function and insulin sensitivity were similar between groups.Conclusions: Neither PCOS status nor androgens related to PFF. However, fasting insulin and postprandial lipids were worse with higher PFF. [ABSTRACT FROM AUTHOR]

Rates of overweight/obesity and insufficient/delayed sleep are high among adolescents and are also unique risk factors for mood/behavior difficulties. This study aimed to evaluate relationships between sleep/circadian health and mood/behavior in a cohort of adolescents with overweight/obesity. Twenty-two adolescents (16.4 ± 1.1 years) with overweight/obesity attending high school completed in the study. Participants completed one week of home sleep monitoring (actigraphy), questionnaires assessing chronotype (diurnal preference; Morningness/Eveningness Scale for Children) and mood/behavior (Strengths & Difficulties Questionnaire), and had in-laboratory salivary melatonin sampling on a Thursday or Friday during the academic year. Linear regressions revealed later weekday bedtime and shorter weekday time in bed and sleep duration were associated with worse mood/behavior scores. Shorter duration of melatonin secretion and greater "eveningness" were also associated with worse mood/behavior scores. Short and late sleep, shorter melatonin secretion, and eveningness chronotype are associated with worse mood/behavior symptoms in a cohort of adolescents with overweight/obesity. Clinicians should assess for both sleep and mood/behavior symptoms and further research is needed to evaluate the impact of improved sleep on mood/behavior in adolescents with overweight/obesity. [ABSTRACT FROM AUTHOR]

The human circadian pacemaker entrains to the 24-h day, but interindividual differences in properties of the pacemaker, such as intrinsic period, affect chronotype and mediate responses to challenges to the circadian system, such as shift work and jet lag, and the efficacy of therapeutic interventions such as light therapy. Robust characterization of circadian properties requires desynchronization of the circadian system from the rest-activity cycle, and these forced desynchrony protocols are very time and resource intensive. However, circadian protocols designed to derive the relationship between light intensity and phase shift, which is inherently affected by intrinsic period, may be applied more broadly. To exploit this relationship, we applied a mathematical model of the human circadian pacemaker with a Markov-Chain Monte Carlo parameter estimation algorithm to estimate the representative group intrinsic period for a group of participants using their collective illuminance-response curve data. We first validated this methodology using simulated illuminance-response curve data in which the intrinsic period was known. Over a physiological range of intrinsic periods, this method accurately estimated the representative intrinsic period of the group. We also applied the method to previously published experimental data describing the illuminance-response curve for a group of healthy adult participants. We estimated the study participants' representative group intrinsic period to be 24.26 and 24.27 h using uniform and normal priors, respectively, consistent with estimates of the average intrinsic period of healthy adults determined using forced desynchrony protocols. Our results establish an approach to estimate a population's representative intrinsic period from illuminance-response curve data, thereby facilitating the characterization of intrinsic period across a broader range of participant populations than could be studied using forced desynchrony protocols. Future applications of this approach may improve the understanding of demographic differences in the intrinsic circadian period. [ABSTRACT FROM AUTHOR]

Stable isotope tracers have been used to gain an understanding of integrative animal and human physiology. More commonly studied organ systems include hepatic glucose metabolism, lipolysis from adipose tissue, and whole body protein metabolism. Recent improvements in isotope methodology have included the use of novel physiologic methods/models and mathematical modeling of data during different physiologic states. Here we review some of the latest advancements in this field and highlight future research needs. First we discuss the use of an oral [U-13C3]-glycerol tracer to determine the relative contribution of glycerol carbons to hepatic glucose production after first cycling through the tricarboxylic acid cycle, entry of glycerol into the pentose phosphate pathway or direct conversion of glycerol into the glucose. Second, we describe an adaptation of the established oral minimal model used to define postprandial glucose dynamics to include glycerol dynamics in an oral glucose tolerance test with a [2H5]-glycerol tracer to determine dynamic changes in lipolysis. Simulation results were optimized when parameters describing glycerol flux were determined with a hybrid approach using both tracer-based calculations and constrained parameter optimization. Both of these methodologies can be used to expand our knowledge of not only human physiology, but also the effects of various nutritional strategies and medications on metabolism. [ABSTRACT FROM AUTHOR]

The human circadian system regulates internal 24-h rhythmicity and plays an important role in many aspects of human health and behavior. To investigate properties of the human circadian pacemaker such as intrinsic period and light sensitivity, experimental researchers have developed forced desynchrony (FD) protocols in which manipulations of the light-dark (LD) cycle are used to desynchronize the intrinsic circadian rhythm from the rest-activity cycle. FD protocols have typically been based on exposure to long LD cycles, but recently, ultradian FD protocols with short LD cycles have been proposed as a new methodology for assessing intrinsic circadian period. However, the effects of ultradian FD protocol design, including light intensity or study duration, on estimates of intrinsic circadian period have not, to our knowledge, been systematically studied. To address this gap, we applied a light-sensitive, dynamic mathematical model of the human circadian pacemaker to simulate ultradian FD protocols and analyze the effects of protocol design on estimates of intrinsic circadian period. We found that optimal estimates were obtained using protocols with low light intensities, at least 10 d of exposure to ultradian cycling, and a 7-h LD cycle duration that facilitated uniform light exposure across all circadian phases. Our results establish a theoretical framework for ultradian FD protocols that can be used to provide insights into data obtained under existing protocols and to optimize protocols for future experiments. [ABSTRACT FROM AUTHOR]

The timing of human sleep is strongly modulated by the 24 h circadian rhythm and desynchronization of sleep-wake cycles from the circadian rhythm can negatively impact health. To investigate the dynamics of circadian modulation of sleep patterns and of entrainment of the sleep-wake cycle with the circadian rhythm, we developed a one-dimensional map for a physiologically based, sleep-wake regulatory network model for human sleep. The map dictates the phase of the circadian cycle at which sleep onset occurs on day n + 1 as a function of the circadian phase of sleep onset on day n. We numerically compute the map for a reduced, though still high-dimensional, version of the sleep-wake network model that incorporates recent measurements of the time constants of the homeostatic sleep drive in humans. The map is piecewise continuous with discontinuities caused by circadian modulation of the duration of sleep and wake episodes and the occurrence of rapid eye movement (REM) sleep episodes. We analyze the map as model parameters are varied to affect the occurrence of REM sleep within the sleep period and we determine that the structure of the map changes with different REM sleep patterning. Specically, variations in the discontinuities in the map correspond to changes in the number of REM bouts during sleep episodes. Using fast-slow decomposition, we exploit the underlying bifurcation structure of the model to reveal a reduced dimensional manifold, represented by the map, on which the model trajectory travels during entrainment of sleep-wake cycles with the circadian rhythm. Analysis of map structure reveals changes in sleep patterning, including REM sleep behavior, as sleep occurs over different circadian phases. Thus, the map provides a portrait of the circadian modulation of sleep-wake behavior and its effects on REM sleep patterning. [ABSTRACT FROM AUTHOR]

This article provides a summary of the ideas discussed at the 2015 Annual Meeting of the Society for Integrative and Comparative Biology society-wide symposium on Leading Students and Faculty to Quantitative Biology through Active Learning. It also includes a brief review of the recent advancements in incorporating active learning approaches into quantitative biology classrooms. We begin with an overview of recent literature that shows that active learning can improve students' outcomes in Science, Technology, Engineering and Math Education disciplines. We then discuss how this approach can be particularly useful when teaching topics in quantitative biology. Next, we describe some of the recent initiatives to develop hands-on activities in quantitative biology at both the graduate and the undergraduate levels. Throughout the article we provide resources for educators who wish to integrate active learning and technology into their classrooms. [ABSTRACT FROM AUTHOR]

The article examines molecular pathways that underlie the intense activity cycles of hibernator Brown Adipose Tissue (BAT). Topics discussed include periodic arousal from torpor driven by BAT through heat generation and the application of digital transcriptome analysis to precisely timed samples for identifying the molecular pathways. Increased transcripts due to enhanced stabilization linked to maintenance and or extension of long poly(A) tails is mentioned.

A physiologically based mathematical model of a putative sleep-wake regulatory network is used to investigate the transition from typical human sleep patterns to spontaneous internal desynchrony behavior observed under temporal isolation conditions. The model sleep-wake regulatory network describes the neurotransmitter-mediated interactions among brainstem and hypothalamic neuronal populations that participate in the transitions between wake, rapid eye movement (REM) sleep, and non-REM (NREM) sleep. Physiologically based interactions among these sleep-wake centers and the suprachiasmatic nucleus (SCN), whose activity is driven by an established circadian oscillator model, mediate circadian modulation of sleep-wake behavior. When the sleep-wake and circadian rhythms are synchronized, the model simulates stereotypically normal human sleep-wake behavior within the limits of individual variation, including typical NREM-REM cycling across the night. When effects of temporal isolation are simulated by increasing the period of the sleep-wake cycle, the model replicates spontaneous internal desynchrony with the appropriate dependence of multiple features of REM sleep on circadian phase. In temporal isolation experiments, subjects have exhibited different desynchronized sleep-wake behaviors. Our model can generate similar ranges of desynchronized behaviors by variations in the period of the sleep-wake cycle and the strength of interactions between the SCN and the sleep-wake centers. Analysis of the model suggests that similar mechanisms underlie several different desynchronized behaviors and that the phenomenon of phase trapping may be dependent on SCN modulation of REM sleep-promoting centers. These results provide predictions for physiologically plausible mechanisms underlying interindividual variations in sleep-wake behavior observed during temporal isolation experiments. [ABSTRACT FROM PUBLISHER]

Waking and sleep states are regulated by the coordinated activity of a number of neuronal populations in the brainstem and hypothalamus whose synaptic interactions compose a sleep-wake regulatory network. Physiologically based mathematical models of the sleep-wake regulatory network contain mechanisms operating on multiple time scales including relatively fast synaptic-based interactions between neuronal populations, and much slower homeostatic and circadian processes that modulate sleep-wake temporal patterning. In this study, we exploit the naturally arising slow time scale of the homeostatic sleep drive in a reduced sleep-wake regulatory network model to utilize fast-slow analysis to investigate the dynamics of rapid eye movement (REM) sleep regulation. The network model consists of a reduced number of wake-, non-REM (NREM) sleep-, and REM sleep-promoting neuronal populations with synaptic interactions reflecting the mutually inhibitory flip-flop conceptual model for sleep-wake regulation and the reciprocal interaction model for REM sleep regulation. Network dynamics regularly alternate between wake and sleep states as governed by the slow homeostatic sleep drive. By varying a parameter associated with the activation of the REM-promoting population, we cause REM dynamics during sleep episodes to vary from suppression to single activations to regular REM-NREM cycling, corresponding to changes in REM patterning induced by circadian modulation and observed in different mammalian species. We also utilize fast-slow analysis to explain complex effects on sleep-wake patterning of simulated experiments in which agonists and antagonists of different neurotransmitters are microinjected into specific neuronal populations participating in the sleep-wake regulatory network. [ABSTRACT FROM AUTHOR]

Orexin (also known as hypocretin) neurons play a key role in regulating sleep-wake behavior, but the links between orexin neuron electrophysiology and function have not been explored. Orexin neurons are wake-active, and spiking activity in orexin neurons may anticipate transitions to wakefulness by several seconds. However, it is suggested that while the orexin system is necessary to maintain sustained wake bouts, orexin has little effect on brief wake bouts. In vitro experiments investigating the actions of orexin and dynorphin, a colocalized neuropeptide, on orexin neurons indicate that the dynamics of desensitization to dynorphin may represent a mechanism for modulating local network activity and resolving the apparent discrepancy between the onset of firing in orexin neurons and the onset of functional orexin effects. To investigate the role of dynorphin on orexin neuron activity, a Hodgkin-Huxley—type model orexin neuron was developed in which baseline electrophysiology, orexin/dynorphin action, and dynorphin desensitization were closely tied to experimental data. In this model framework, model orexin neuron responses to orexin/dynorphin action were calibrated by simulating the physiologic effects of static orexin and dynorphin bath application on orexin neurons. Then behavior in a small network of model orexin neurons was simulated with pure orexin, pure dynorphin, or combined orexin and dynorphin coupling based on the mechanisms established in the static case. It was found that the dynamics of desensitization to dynorphin can mediate a clear shift from a network in which firing is suppressed by dynorphin-mediated inhibition to a network of neurons with high firing rates sustained by orexin-mediated excitation. The findings suggest that dynamic interactions between orexin and dynorphin at transitions from sleep to wake may delay onset of functional orexin effects. [ABSTRACT FROM PUBLISHER]

• Habitual sleep duration modeled by tolerance to homeostatic sleep drive. • Parameter ensembles replicated variability in sleep deprivation recovery data. • Model results predict short sleepers are more resilient to acute sleep deprivation. • Longer sleepers show more variability and long-lasting effects from sleep deprivation. Adult humans exhibit high interindividual variation in habitual sleep durations, with short sleepers typically sleeping less than 6 h per night and long sleepers typically sleeping more than 9 h per night. Analysis of the time course of homeostatic sleep drive in habitual short and long sleepers has not identified differences between these groups, leading to the hypothesis that habitual short sleep results from increased tolerance to high levels of homeostatic sleep drive. Using a physiologically-based mathematical model of the sleep-wake regulatory network, we investigate responses to acute sleep deprivation in simulated populations of habitual long, regular and short sleepers that differ in daily levels of homeostatic sleep drive. The model predicts timing and durations of wake, rapid eye movement (REM), and non-REM (NREM) sleep episodes as modulated by the homeostatic sleep drive and the circadian rhythm, which is entrained to an external light cycle. Model parameters are fit to experimental measures of baseline sleep durations to construct simulated populations of individuals of each sleeper type. The simulated populations are validated against data for responses to specific acute sleep deprivation protocols. We use the model to predict responses to a wide range of sleep deprivation durations for each sleeper type. Model results predict that all sleeper types exhibit shorter sleep durations during recovery sleep that occurs in the morning, but, for recovery sleep times occurring later in the day, long and regular sleepers show longer and more variable sleep durations, and can suffer longer lasting disruption of daily sleep patterns compared to short sleepers. Additionally, short sleepers showed more resilience to sleep deprivation with longer durations of waking episodes following recovery sleep. These results support the hypothesis that differential responses to sleep deprivation between short and long sleepers result from differences in the tolerance for homeostatic sleep pressure. [ABSTRACT FROM AUTHOR]

Introduction: During sleep periods, most mammals alternate multiple times between rapid-eye-movement (REM) sleep and non-REM (NREM) sleep. A common theory proposes that these transitions are governed by an "hourglass-like" homeostatic need to enter REM sleep that accumulates during the inter-REM interval and partially discharges during REM sleep. However, markers or mechanisms for REM homeostatic pressure remain undetermined. Recently, an analysis of sleep in mice demonstrated that the cumulative distribution function (CDF) of the amount of NREM sleep between REM bouts correlates with REM bout duration, suggesting that time in NREM sleep influences REM sleep need. Here, we build on those results and construct a predictive measure for the propensity to enter REM sleep as a function of time in NREM sleep since the previous REM episode., Methods: The REM propensity measure is precisely defined as the probability to enter REM sleep before the accumulation of an additional pre-specified amount of NREM sleep., Results: Analyzing spontaneous sleep in mice, we find that, as NREM sleep accumulates between REM bouts, the REM propensity exhibits a peak value and then decays to zero with further NREM accumulation. We show that the REM propensity at REM onset predicts features of the subsequent REM bout under certain conditions. Specifically, during the light phase and for REM propensities occurring before the peak in propensity, the REM propensity at REM onset is correlated with REM bout duration, and with the probability of the occurrence of a short REM cycle called a sequential REM cycle. Further, we also find that proportionally more REM sleep occurs during sequential REM cycles, supporting a correlation between high values of our REM propensity measure and high REM sleep need., Discussion: These results support the theory that a homeostatic need to enter REM sleep accrues during NREM sleep, but only for a limited range of NREM sleep accumulation., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Ginsberg, Cruz, Weber, Booth and Diniz Behn.)

Efficient and accurate methods to estimate insulin sensitivity ( S I ) and β-cell function (BCF) are of great importance for studying the pathogenesis and treatment effectiveness of type 2 diabetes (T2D). Existing methods range in sensitivity, input data, and technical requirements. Oral glucose tolerance tests (OGTTs) are preferred because they are simpler and more physiological than intravenous methods. However, current analytical methods for OGTT-derived S I and BCF also range in complexity; the oral minimal models require mathematical expertise for deconvolution and fitting differential equations, and simple algebraic surrogate indices (e.g., Matsuda index, insulinogenic index) may produce unphysiological values. We developed a new insulin secretion and sensitivity (ISS) model for clinical research that provides precise and accurate estimates of SI and BCF from a standard OGTT, focusing on effectiveness, ease of implementation, and pragmatism. This model was developed by fitting a pair of differential equations to glucose and insulin without need of deconvolution or C-peptide data. This model is derived from a published model for longitudinal simulation of T2D progression that represents glucose-insulin homeostasis, including postchallenge suppression of hepatic glucose production and first- and second-phase insulin secretion. The ISS model was evaluated in three diverse cohorts across the lifespan. The new model had a strong correlation with gold-standard estimates from intravenous glucose tolerance tests and insulin clamps. The ISS model has broad applicability among diverse populations because it balances performance, fidelity, and complexity to provide a reliable phenotype of T2D risk. NEW & NOTEWORTHY The pathogenesis of type 2 diabetes (T2D) is determined by a balance between insulin sensitivity ( S I ) and β-cell function (BCF), which can be determined by gold standard direct measurements or estimated by fitting differential equation models to oral glucose tolerance tests (OGTTs). We propose and validate a new differential equation model that is simpler to use than current models and requires less data while maintaining good correlation and agreement with gold standards. Matlab and Python code is freely available.

Under healthy conditions, the pancreas responds to a glucose challenge by releasing insulin. Insulin suppresses lipolysis in adipose tissue, thereby decreasing plasma glycerol concentration, and it regulates plasma glucose concentration through action in muscle and liver. Insulin resistance (IR) occurs when more insulin is required to achieve the same effects, and IR may be tissue-specific. IR emerges during puberty as a result of high concentrations of growth hormone and is worsened by youth-onset obesity. Adipose, liver, and muscle tissue exhibit distinct dose-dependent responses to insulin in multi-phase hyperinsulinemic-euglycemic (HE) clamps, but the HE clamp protocol does not address potential differences in the dynamics of tissue-specific insulin responses. Changes to the dynamics of insulin responses would alter glycemic control in response to a glucose challenge. To investigate the dynamics of insulin acting on adipose tissue, we developed a novel differential-equations based model that describes the coupled dynamics of glycerol concentrations and insulin action during an oral glucose tolerance test in female adolescents with obesity and IR. We compared these dynamics to the dynamics of insulin acting on muscle and liver as assessed with the oral minimal model applied to glucose and insulin data collected under the same protocol. We found that the action of insulin on glycerol peaks approximately 67 min earlier ( p < 0.001) and follows the dynamics of plasma insulin more closely compared to insulin action on glucose as assessed by the parameters representing the time constants for insulin action on glucose and glycerol ( p < 0.001). These findings suggest that the dynamics of insulin action show tissue-specific differences in our IR adolescent population, with adipose tissue responding to insulin more quickly compared to muscle and liver. Improved understanding of the tissue-specific dynamics of insulin action may provide novel insights into the progression of metabolic disease in patient populations with diverse metabolic phenotypes., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Hampton, Bartlette, Nadeau, Cree-Green and Diniz Behn.)

Study Objectives: We determine if young people with narcolepsy type 1 (NT1), narcolepsy type 2 (NT2), and idiopathic hypersomnia (IH) have distinct nocturnal sleep stability phenotypes compared to subjectively sleepy controls., Methods: Participants were 5- to 21-year old and drug-naïve or drug free: NT1 (n = 46), NT2 (n = 12), IH (n = 18), and subjectively sleepy controls (n = 48). We compared the following sleep stability measures from polysomnogram recording between each hypersomnolence disorder to subjectively sleepy controls: number of wake and sleep stage bouts, Kaplan-Meier survival curves for wake and sleep stages, and median bout durations., Results: Compared to the subjectively sleepy control group, NT1 participants had more bouts of wake and all sleep stages (p ≤ .005) except stage N3. NT1 participants had worse survival of nocturnal wake, stage N2, and rapid eye movement (REM) bouts (p < .005). In the first 8 hours of sleep, NT1 participants had longer stage N1 bouts but shorter REM (all ps < .004). IH participants had a similar number of bouts but better survival of stage N2 bouts (p = .001), and shorter stage N3 bouts in the first 8 hours of sleep (p = .003). In contrast, NT2 participants showed better stage N1 bout survival (p = .006) and longer stage N1 bouts (p = .02)., Conclusions: NT1, NT2, and IH have unique sleep physiology compared to subjectively sleepy controls, with only NT1 demonstrating clear nocturnal wake and sleep instability. Overall, sleep stability measures may aid in diagnoses and management of these central nervous system disorders of hypersomnolence., (© Sleep Research Society 2021. Published by Oxford University Press on behalf of the Sleep Research Society. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

Study Objectives: Disrupted nighttime sleep (DNS) is a core narcolepsy symptom of unconsolidated sleep resulting from hypocretin neuron loss. In this study, we define a DNS objective measure and evaluate its diagnostic utility for pediatric narcolepsy type 1 (NT1)., Methods: This was a retrospective, multisite, cross-sectional study of polysomnograms (PSGs) in 316 patients, ages 6-18 years (n = 150 NT1, n = 22 narcolepsy type 2, n = 27 idiopathic hypersomnia, and n = 117 subjectively sleepy subjects). We assessed sleep continuity PSG measures for (1) their associations with subjective and objective daytime sleepiness, daytime sleep onset REM periods (SOREMPs), self-reported disrupted nocturnal sleep and CSF hypocretin levels and (2) their predictive value for NT1 diagnosis. We then combined the best performing DNS measure with nocturnal SOREMP (nSOREMP) to assess the added value to the logistic regression model and the predictive accuracy for NT1 compared with nSOREMP alone., Results: The Wake/N1 Index (the number of transitions from any sleep stage to wake or NREM stage 1 normalized by total sleep time) was associated with objective daytime sleepiness, daytime SOREMPs, self-reported disrupted sleep, and CSF hypocretin levels (p's < 0.003) and held highest area under the receiver operator characteristic curves (AUC) for NT1 diagnosis. When combined with nSOREMP, the DNS index had greater accuracy for diagnosing NT1 (AUC = 0.91 [0.02]) than nSOREMP alone (AUC = 0.84 [0.02], likelihood ratio [LR] test p < 0.0001)., Conclusions: The Wake/N1 Index is an objective DNS measure that can quantify DNS severity in pediatric NT1. The Wake/N1 Index in combination with or without nSOREMP is a useful sleep biomarker that improves recognition of pediatric NT1 using only the nocturnal PSG., (© Sleep Research Society 2020. Published by Oxford University Press on behalf of the Sleep Research Society. All rights reserved. For permissions, please e-mail journals.permissions@oup.com.)

Stable isotope tracers have been used to gain an understanding of integrative animal and human physiology. More commonly studied organ systems include hepatic glucose metabolism, lipolysis from adipose tissue, and whole body protein metabolism. Recent improvements in isotope methodology have included the use of novel physiologic methods/models and mathematical modeling of data during different physiologic states. Here we review some of the latest advancements in this field and highlight future research needs. First we discuss the use of an oral [U- 13 C 3 ]-glycerol tracer to determine the relative contribution of glycerol carbons to hepatic glucose production after first cycling through the tricarboxylic acid cycle, entry of glycerol into the pentose phosphate pathway or direct conversion of glycerol into the glucose. Second, we describe an adaptation of the established oral minimal model used to define postprandial glucose dynamics to include glycerol dynamics in an oral glucose tolerance test with a [ 2 H 5 ]-glycerol tracer to determine dynamic changes in lipolysis. Simulation results were optimized when parameters describing glycerol flux were determined with a hybrid approach using both tracer-based calculations and constrained parameter optimization. Both of these methodologies can be used to expand our knowledge of not only human physiology, but also the effects of various nutritional strategies and medications on metabolism., Competing Interests: Competing interests: Yes, there are competing interests for one or more authors and I have provided a Competing Interests statement in my manuscript., (© American Federation for Medical Research 2020. No commercial re-use. See rights and permissions. Published by BMJ.)

Context: To our knowledge, circadian rhythms have not been examined in girls with polycystic ovarian syndrome (PCOS), despite the typical delayed circadian timing of adolescence, which is an emerging link between circadian health and insulin sensitivity (SI), and decreased SI in PCOS., Objective: To examine differences in the circadian melatonin rhythm between obese adolescent girls with PCOS and control subjects, and evaluate relationships between circadian variables and SI., Design: Cross-sectional study., Participants: Obese adolescent girls with PCOS (n = 59) or without PCOS (n = 33)., Outcome Measures: Estimated sleep duration and timing from home actigraphy monitoring, in-laboratory hourly sampled dim-light, salivary-melatonin and fasting hormone analysis., Results: All participants obtained insufficient sleep. Girls with PCOS had later clock-hour of melatonin offset, later melatonin offset relative to sleep timing, and longer duration of melatonin secretion than control subjects. A later melatonin offset after wake time (i.e., morning wakefulness occurring during the biological night) was associated with higher serum free testosterone levels and worse SI regardless of group. Analyses remained significant after controlling for daytime sleepiness and sleep-disordered breathing., Conclusion: Circadian misalignment in girls with PCOS is characterized by later melatonin offset relative to clock time and sleep timing. Morning circadian misalignment was associated with metabolic dysregulation in girls with PCOS and obesity. Clinical care of girls with PCOS and obesity would benefit from assessment of sleep and circadian health. Additional research is needed to understand mechanisms underlying the relationship between morning circadian misalignment and SI in this population., (Copyright © 2019 Endocrine Society.)

Obese adolescent girls are at increased risk for type 2 diabetes, characterized by defects in insulin secretion and action. We sought to determine if later glucose peak timing (>30 minutes), 1-hour glucose >155 mg/dl, or monophasic pattern of glucose excursion during an oral glucose tolerance test (OGTT) reflect a worse cardiometabolic risk profile. Post-pubertal overweight/obese adolescent girls without diabetes were studied (N = 88; age, 15.2 ± 0.2 years; body mass index percentile, 97.7 ± 0.5). All participants completed an OGTT and body composition measures. Thirty-two girls had a four-phase hyperinsulinemic euglycemic clamp with isotope tracers, vascular imaging, and muscle mitochondrial assessments. Participants were categorized by glucose peak timing (≤30 min = early; >30 min = late), 1-hour glucose concentration (±155 mg/dL) and glucose pattern (monophasic, biphasic). Girls with a late (N = 54) vs earlier peak (n = 34) timing had higher peak glucose ( P < 0.001) and insulin ( P = 0.023), HbA1c ( P = 0.021); prevalence of hepatic steatosis (62% vs 26%; P = 0.003) and lower oral disposition index ( P < 0.001) and glucagon-like peptide-1 response ( P = 0.037). When classified by 1-hour glucose, group differences were similar to peak timing, but minimal when classified by glucose pattern. In the >155 mg/dL group only, peripheral insulin sensitivity and fasting free fatty acids were worse. A later glucose peak or >155 mg/dL 1-hour glucose predicts metabolic disease risk in obese adolescent girls. This may defect incretin effects and first phase insulin response, and muscle and adipose insulin resistance.

Nocturnal human sleep is composed of cycles between rapid eye movement (REM) sleep and non-REM (NREM) sleep. In adults, the structure of ultradian cycles between NREM and REM sleep is well characterized; however, less is known about the developmental trajectories of ultradian sleep cycles across early childhood. Cross-sectional studies indicate that the rapid ultradian cycling of active-quiet sleep in infancy shifts to a more adult-like pattern of NREM-REM sleep cycling by the school-age years, yet longitudinal studies elucidating the details of this transition are scarce. To address this gap, we examined ultradian cycling during nocturnal sleep following 13 h of prior wakefulness in 8 healthy children at 3 longitudinal points: 2Y (2.5-3.0 years of age), 3Y (3.5-4.0 years of age), and 5Y (5.5-6.0 years of age). We found that the length of ultradian cycles increased with age as a result of increased NREM sleep episode duration. In addition, we observed a significant decrease in the number of NREM sleep episodes as well as a nonsignificant trend for a decrease in the number of cycles with increasing age. Together, these findings suggest a concurrent change in which cycle duration increases and the number of cycles decreases across development. We also found that, consistent with data from adolescents and adults, the duration of NREM sleep episodes decreased with time since lights-off whereas the duration of REM sleep episodes increased over this time period. These results indicate the presence of circadian modulation of nocturnal sleep in preschool children. In addition to characterizing changes in ultradian cycling in healthy children ages 2 to 5 years, this work describes a developmental model that may provide insights into the emergence of normal adult REM sleep regulatory circuitry as well as potential trajectories of dysregulated ultradian cycles such as those associated with affective disorders.

Study Objectives: Narcolepsy is caused by loss of the orexin (also known as hypocretin) neurons. In addition to the orexin peptides, these neurons release additional neurotransmitters, which may produce complex effects on sleep/wake behavior. Currently, it remains unknown whether the orexin neurons promote the initiation as well as the maintenance of wakefulness, and whether the orexin neurons influence initiation or maintenance of sleep. To determine the effects of the orexin neurons on the dynamics of sleep/wake behavior, we analyzed sleep/wake architecture in a novel mouse model of acute orexin neuron loss., Methods: We used survival analysis and other statistical methods to analyze sleep/wake architecture in orexin-tTA ; TetO diphtheria toxin A mice at different stages of orexin neuron degeneration., Results: Progressive loss of the orexin neurons dramatically reduced survival of long wake bouts, but it also improved survival of brief wake bouts. In addition, with loss of the orexin neurons, mice were more likely to wake during the first 30 sec of nonrapid eye movement sleep and then less likely to return to sleep during the first 60 sec of wakefulness., Conclusions: These findings help explain the sleepiness and fragmented sleep that are characteristic of narcolepsy. Orexin neuron loss impairs survival of long wake bouts resulting in poor maintenance of wakefulness, but this neuronal loss also fragments sleep by increasing the risk of awakening at the beginning of sleep and then reducing the likelihood of quickly returning to sleep., (© 2016 Associated Professional Sleep Societies, LLC.)

The dynamics of sleep and wake are strongly linked to the circadian clock. Many models have accurately predicted behaviour resulting from dynamic interactions between these two systems without specifying physiological substrates for these interactions. By contrast, recent experimental work has identified much of the relevant physiology for circadian and sleep-wake regulation, but interaction dynamics are difficult to study experimentally. To bridge these approaches, we developed a neuronal population model for the dynamic, bidirectional, neurotransmitter-mediated interactions of the sleep-wake and circadian regulatory systems in nocturnal rats. This model proposes that the central circadian pacemaker, located within the suprachiasmatic nucleus (SCN) of the hypothalamus, promotes sleep through single neurotransmitter-mediated signalling to sleep-wake regulatory populations. Feedback projections from these populations to the SCN alter SCN firing patterns and fine-tune this modulation. Although this model reproduced circadian variation in sleep-wake dynamics in nocturnal rats, it failed to describe the sleep-wake dynamics observed in SCN-lesioned rats. We thus propose two alternative, physiologically based models in which neurotransmitter- and neuropeptide-mediated signalling from the SCN to sleep-wake populations introduces mechanisms to account for the behaviour of both the intact and SCN-lesioned rat. These models generate testable predictions and offer a new framework for modelling sleep-wake and circadian interactions.

The fine architecture of sleep-wake behavior shows a distinct dynamic structure with distributions of rat sleep and wake bout durations displaying qualitatively different profiles. Wake bout durations follow a power-law relation whereas sleep bout durations are exponentially distributed. We show that a physiologically-based sleep-wake regulatory network model with an underlying deterministic structure governing neuronal interactions can generate realistic rat sleep-wake behavior as assessed by both standard summary statistics and survival analysis of bout distributions. Obtaining appropriate bout duration distributions depended on stochastic elements included in the model, the existence of multiple mechanisms for state transitions, and specific relationships among time constants governing state maintenance. This model provides a novel framework for exploring the disruptions of sleep-wake architecture associated with pharmacological, genetic, and disease states.

This study presents a novel mathematical modeling framework that is uniquely suited to investigating the structure and dynamics of the sleep-wake regulatory network in the brain stem and hypothalamus. It is based on a population firing rate model formalism that is modified to explicitly include concentration levels of neurotransmitters released to postsynaptic populations. Using this framework, interactions among primary brain stem and hypothalamic neuronal nuclei involved in rat sleep-wake regulation are modeled. The model network captures realistic rat polyphasic sleep-wake behavior consisting of wake, rapid eye movement (REM) sleep, and non-REM (NREM) sleep states. Network dynamics include a cyclic pattern of NREM sleep, REM sleep, and wake states that is disrupted by simulated variability of neurotransmitter release and external noise to the network. Explicit modeling of neurotransmitter concentrations allows for simulations of microinjections of neurotransmitter agonists and antagonists into a key wake-promoting population, the locus coeruleus (LC). Effects of these simulated microinjections on sleep-wake states are tracked and compared with experimental observations. Agonist/antagonist pairs, which are presumed to have opposing effects on LC activity, do not generally induce opposing effects on sleep-wake patterning because of multiple mechanisms for LC activation in the network. Also, different agents, which are presumed to have parallel effects on LC activity, do not induce parallel effects on sleep-wake patterning because of differences in the state dependence or independence of agonist and antagonist action. These simulation results highlight the utility of formal mathematical modeling for constraining conceptual models of the sleep-wake regulatory network.

Study Objectives: Narcolepsy with cataplexy is caused by a loss of orexin (hypocretin) signaling, but the physiologic mechanisms that result in poor maintenance of wakefulness and fragmented sleep remain unknown. Conventional scoring of sleep cannot reveal much about the process of transitioning between states or the variations within states. We developed an EEG spectral analysis technique to determine whether the state instability in a mouse model of narcolepsy reflects abnormal sleep or wake states, faster movements between states, or abnormal transitions between states., Design: We analyzed sleep recordings in orexin knockout (OXKO) mice and wild type (WT) littermates using a state space analysis technique. This non-categorical approach allows quantitative and unbiased examination of sleep/wake states and state transitions., Measurements and Results: OXKO mice spent less time in deep, delta-rich NREM sleep and in active, theta-rich wake and instead spent more time near the transition zones between states. In addition, while in the midst of what should be stable wake, OXKO mice initiated rapid changes into NREM sleep with high velocities normally seen only in transition regions. Consequently, state transitions were much more frequent and rapid even though the EEG progressions during state transitions were normal., Conclusions: State space analysis enables visualization of the boundaries between sleep and wake and shows that narcoleptic mice have less distinct and more labile states of sleep and wakefulness. These observations provide new perspectives on the abnormal state dynamics resulting from disrupted orexin signaling and highlight the usefulness of state space analysis in understanding narcolepsy and other sleep disorders.

In this article, we discuss mathematical models that address the control of sleep-wake behavior in the infant and adult rodent and a model that addresses changes in single-cell firing patterns in the hippocampus across wake and rapid eye movement (REM) sleep states. Each of the models describes the dynamics of experimentally identified neuronal components--either the firing activity of wake-and sleep-promoting neuronal populations or the spiking activity of hippocampal pyramidal neurons. Our discussion of each model illustrates how a mathematical model that describes the temporal dynamics of the modeled neuronal components can reveal specifics about proposed neuronal mechanisms that underlie sleep-wake regulation or sleep-specific firing patterns. For example, the dynamics of the models developed for sleep-wake regulation in the infant rodent lend insight into the involved brain-stem neuronal populations and the evolution of the network during maturation. The results of the model for sleep-wake regulation in the adult rodent suggest distinct properties of the involved neuronal populations and their interactions that account for long-lasting and brief waking bouts. The dynamics of the model for sleep-specific hippocampal neural activity proposes neural mechanisms to account for observed activity changes that can invoke synaptic reorganization associated with learning and memory consolidation.