Estimating insulin sensitivity and ß-cell function from the oral glucose tolerance test: validation of a new insulin sensitivity and secretion (ISS) model.

Efficient and accurate methods to estimate insulin sensitivity (SI) and ß-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 SI 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 (SI) and ß-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.

Data-driven mathematical modeling of sleep consolidation in early childhood.

Across early childhood development, sleep behavior transitions from a biphasic pattern (a daytime nap and nighttime sleep) to a monophasic pattern (only nighttime sleep). The transition to consolidated nighttime sleep, which occurs in most children between 2- and 5-years-old, is a major developmental milestone and reflects interactions between the developing homeostatic sleep drive and circadian system. Using a physiologically-based mathematical model of the sleep-wake regulatory network constrained by observational and experimental data from preschool-aged participants, we analyze how developmentally-mediated changes in the homeostatic sleep drive may contribute to the transition from napping to non-napping sleep patterns. We establish baseline behavior by identifying parameter sets that model typical 2-year-old napping behavior and 5-year-old non-napping behavior. Then we vary six model parameters associated with the dynamics of and sensitivity to the homeostatic sleep drive between the 2-year-old and 5-year-old parameter values to induce the transition from biphasic to monophasic sleep. We analyze the individual contributions of these parameters to sleep patterning by independently varying their age-dependent developmental trajectories. Parameters vary according to distinct evolution curves and produce bifurcation sequences representing various ages of transition onset, transition durations, and transitional sleep patterns. Finally, we consider the ability of napping and non-napping light schedules to reinforce napping or promote a transition to consolidated sleep, respectively. These modeling results provide insight into the role of the homeostatic sleep drive in promoting interindividual variability in developmentally-mediated transitions in sleep behavior and lay foundations for the identification of light- or behavior-based interventions that promote healthy sleep consolidation in early childhood.

Evidence of circalunar rhythmicity in young children's evening melatonin levels.

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.

Habitual sleep duration affects recovery from acute sleep deprivation: A modeling study.

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.

Sleep & Circadian Health are Associated with Mood & Behavior in Adolescents with Overweight/Obesity.

OBJECTIVE/BACKGROUND: 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. PARTICIPANTS: Twenty-two adolescents (16.4 ± 1.1 years) with overweight/obesity attending high school completed in the study. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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.

Modeling the Effects of Napping and Non-napping Patterns of Light Exposure on the Human Circadian Oscillator.

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.

NREM-REM alternation complicates transitions from napping to non-napping behavior in a three-state model of sleep-wake regulation.

The temporal structure of human sleep changes across development as it consolidates from the polyphasic sleep of infants to the single nighttime sleep episode typical in adults. Experimental studies have shown that changes in the dynamics of sleep need may mediate this developmental transition in sleep patterning, however, it is unknown how sleep architecture interacts with these changes. We employ a physiologically-based mathematical model that generates wake, rapid eye movement (REM) and non-REM (NREM) sleep states to investigate how NREM-REM alternation affects the transition in sleep patterns as the dynamics of the homeostatic sleep drive are varied. To study the mechanisms producing these transitions, we analyze the bifurcations of numerically-computed circle maps that represent key dynamics of the full sleep-wake network model by tracking the evolution of sleep onsets across different circadian (∼ 24 h) phases. The maps are non-monotonic and discontinuous, being composed of branches that correspond to sleep-wake cycles containing distinct numbers of REM bouts. As the rates of accumulation and decay of the homeostatic sleep drive are varied, we identify the bifurcations that disrupt a period-adding-like behavior of sleep patterns in the transition between biphasic and monophasic sleep. These bifurcations include border collision and saddle-node bifurcations that initiate new sleep patterns, period-doubling bifurcations leading to higher-order patterns of NREM-REM alternation, and intervals of bistability of sleep patterns with different NREM-REM alternations. Furthermore, patterns of NREM-REM alternation exhibit variable behaviors in different regimes of constant sleep-wake patterns. Overall, the sequence of sleep-wake behaviors, and underlying bifurcations, in the transition from biphasic to monophasic sleep in this three-state model is more complex than behavior observed in models of sleep-wake regulation that do not consider the dynamics of NREM-REM alternation. These results suggest that interactions between the dynamics of the homeostatic sleep drive and the dynamics of NREM-REM alternation may contribute to the wide interindividual variation observed when young children transition from napping to non-napping behavior.

Evening Light Intensity and Phase Delay of the Circadian Clock in Early Childhood.

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.

Mathematical modeling reveals differential dynamics of insulin action models on glycerol and glucose in adolescent girls with obesity.

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.

Pancreatic fat relates to fasting insulin and postprandial lipids but not polycystic ovary syndrome in adolescents with obesity.

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. ß-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.

Stability of nocturnal wake and sleep stages defines central nervous system disorders of hypersomnolence.

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.

Oral minimal model-based estimates of insulin sensitivity in obese youth depend on oral glucose tolerance test protocol duration.

INTRODUCTION: The Oral Minimal Model (OMM), a differential-equations based mathematical model of glucose-insulin dynamics, utilizes data from a frequently sampled oral glucose tolerance test (OGTT) to quantify insulin sensitivity ( S I ). OMM-based estimates of S I can detect differences in insulin resistance (IR) across population groups and quantify effects of clinical or behavioral interventions. These estimates of S I have been validated in healthy adults using data from OGTTs with durations from 2 to 7 h. However, data demonstrating how protocol duration affects S I estimates in highly IR populations such as adolescents with obesity are limited. METHODS: A 6-h frequently sampled OGTT was performed in adolescent females with obesity. Two, 3-, and 4- hour implementations of OMM assuming an exponentially-decaying rate of glucose appearance beyond measured glucose concentrations were compared to the 6-h implementation. A 4- hour OMM implementation with truncated data (4h Tr) was also considered. RESULTS: Data from 68 participants were included (age 15.8 ± 1.2 years, BMI 35.4 ± 5.6 kg/m2). Although S I values were highly correlated for all implementations, they varied with protocol duration (2h: 2.86 ± 3.31, 3h: 2.55 ± 2.62, 4h: 2.81 ± 2.59, 4h tr: 3.13 ± 3.14, 6h: 3.06 ± 2.85 x 10-4 dl/kg/min per U/ml). S I estimates based on 2 or 3 h of data underestimated S I values, whereas 4-h S I estimates more closely approximated 6-h S I values. DISCUSSION: These results suggest that OGTT protocol duration should be considered when implementing OMM to estimate S I in adolescents with obesity and other IR populations.

Simulating microinjection experiments in a novel model of the rat sleep-wake regulatory network.

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.

Abnormal sleep/wake dynamics in orexin knockout mice.

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.

Estimating Representative Group Intrinsic Circadian Period from Illuminance-Response Curve Data.

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.

Advances in stable isotope tracer methodology part 1: hepatic metabolism via isotopomer analysis and postprandial lipolysis modeling.

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.

Defining disrupted nighttime sleep and assessing its diagnostic utility for pediatric narcolepsy type 1.

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.

Morning Circadian Misalignment Is Associated With Insulin Resistance in Girls With Obesity and Polycystic Ovarian Syndrome.

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.

Neuronal models for sleep-wake regulation and synaptic reorganization in the sleeping hippocampus.

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.

Oral Glucose Tolerance Test Glucose Peak Time Is Most Predictive of Prediabetes and Hepatic Steatosis in Obese Girls.

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.