Weekday and weekend sleep times across the human lifespan: a model-based simulation.

PURPOSE: The shifts in the opposite directions, toward later and earlier sleep timing, occur during the transition through adolescence and adulthood, respectively. Such a n-shape of age-associated change in sleep timing does not resemble the inverse relationship of sleep duration with ages. Age-associated variation in the parameters of the mechanisms of circadian and homeostatic regulation of sleep would underlie these different shapes of relationship of sleep times with ages. Here, we searched for a parsimonious explanation of these different shapes by simulating sleep times on weekdays and weekends with one of the variants of the two-process model of sleep regulation. METHODS: Using mean age of a sample with reported sleep times on weekdays and weekends, the whole set of 1404 such samples was subdivided into 15 age subsets. Simulations of sleep times in these subsets were performed with and without the suggestion of age-associated variation in the circadian phase. RESULTS: Simulations showed that the age-associated decay of slow-wave activity can parsimoniously explain not only the parallel decreases in weekend sleep duration and rate of the buildup of sleep pressure during the wake phase of the sleep-wake cycle, but also both the delay and advance of sleep timing during the transition through adolescence and adulthood, respectively. CONCLUSION: The almost functional relationships were revealed between the age-related changes in sleep duration, rate of the buildup of sleep pressure, and slow-wave activity that is a good electrophysiological marker of cortical metabolic rate and synaptic density, strength and efficacy.

Two Old Wild-Type Strains of Drosophila melanogaster Can Serve as an Animal Model of Faster and Slower Aging Processes.

BACKGROUND: Drosophila melanogaster provides a powerful platform to study the physiology and genetics of aging, i.e., the mechanisms underpinnings healthy aging, age-associated disorders, and acceleration of the aging process under adverse environmental conditions. Here, we tested the responses of daily rhythms to age-accelerated factors in two wild-type laboratory-adapted strains, Canton-S and Harwich. METHODS: On the example of the 24 h patterns of locomotor activity and sleep, we documented the responses of these two strains to such factors as aging, high temperature, carbohydrate diet, and diet with different doses of caffeine-benzoate sodium. RESULTS: The strains demonstrated differential responses to these factors. Moreover, compared to Canton-S, Harwich showed a reduced locomotor activity, larger amount of sleep, faster rate of development, smaller body weight, lower concentrations of main sugars, lower fecundity, and shorter lifespan. CONCLUSIONS: It might be recommended to use at least two strains, one with a relatively fast and another with a relatively slow aging process, for the experimental elaboration of relationships between genes, environment, behavior, physiology, and health.

How to quantify sleepiness during an attempt to sleep?

Background. Sleepiness assessment tools were mostly developed for detection of an elevated sleepiness level in the condition of sleep deprivation and several medical conditions. However, sleepiness occurs in various other conditions including the transition from wakefulness to sleep during an everyday attempt to get sleep.Objective. We examined whether objective sleepiness indexes can be implicated in detection of fluctuations in sleepiness level during the polysomnographically-monitored attempt to sleep, i.e. in the absence of self-reports on perceived sleepiness level throughout such an attempt.Approach. The polysomnographic signals were recorded in the afternoon throughout 106 90 min napping attempts of 53 university students (28 females). To calculate two objective sleepiness indexes, the electroencephalographic (EEG) spectra were averaged on 30 s epochs of each record, assigned to one of 5 sleep-wake stages, and scored using either the frequency weighting curve for sleepiness substate of wake state or loadings of each frequency on the 2nd principal component of variation in the EEG spectrum (either sleepiness score or PC2 score, respectively).Main results. We showed that statistically significant fluctuations in these two objective sleepiness indexes during epochs assigned to wake stage can be described in terms of the changes in verbally anchored levels of subjective sleepiness assessed by scoring on the 9-step Karolinska Sleepiness Scale.Significance. The results afford new opportunities to elaborate importance of intermediate substates between wake and sleep states for sleep-wake dynamics in healthy individuals and patients with disturbed sleep.

Menstrual cycles are influenced by sunshine.

INTRODUCTION: The study determined the effect of seasons and meteorological variables on ovarian-menstrual function. METHODS: Women (N=129) living in Novosibirsk (55°N), Russia, provided data on normal menstrual cycles for over 1 year between 1999 and 2008. Of these, 18 together with 20 other healthy women were investigated once in winter and once in summer in 2006-2009. The investigated variables included serum levels of follicle-stimulating hormone (FSH), luteinising hormone (LH) and prolactin on day ∼ 7 of the menstrual cycle, ovary follicle size (by ultrasound) on day ∼ 12 and ovulation occurrence on subsequent days. RESULTS: In summer vs. winter, there was a trend towards increased FSH secretion, significantly larger ovarian follicle size, higher frequency of ovulation (97% vs. 71%) and a shorter menstrual cycle (by 0.9 days). LH and prolactin levels did not change. In all seasons combined, increased sunshine (data derived from local meteorological records) 2-3 days before the presumed ovulation day (calculated from the mean menstrual cycle) led to a shorter cycle length. Air/perceived temperature, atmospheric pressure, moon phase/light were not significant predictors. CONCLUSIONS: Ovarian activity is greater in summer vs. winter in women living in a continental climate at temperate latitudes; sunshine is a factor that influences menstrual cycle.

The Risk of Type 2 Diabetes Mellitus in a Russian Population Cohort According to Data from the HAPIEE Project.

The aim of this study is to investigate the 14-year risk of type 2 diabetes mellitus (T2DM) and develop a risk score for T2DM in the Siberian cohort. A random population sample (males/females, 45-69 years old) was examined at baseline in 2003-2005 (Health, Alcohol, and Psychosocial Factors in Eastern Europe (HAPIEE) project, n = 9360, Novosibirsk) and re-examined in 2006-2008 and 2015-2017. After excluding those with baseline T2DM, the final analysis included 7739 participants. The risk of incident T2DM during a 14-year follow-up was analysed using Cox regression. In age-adjusted models, male and female hazard ratios (HR) of incident T2DM were 5.02 (95% CI 3.62; 6.96) and 5.13 (95% CI 3.56; 7.37) for BMI ≥ 25 kg/m2; 4.38 (3.37; 5.69) and 4.70 (0.27; 6.75) for abdominal obesity (AO); 3.31 (2.65; 4.14) and 3.61 (3.06; 4.27) for fasting hyperglycaemia (FHG); 2.34 (1.58; 3.49) and 3.27 (2.50; 4.26) for high triglyceride (TG); 2.25 (1.74; 2.91) and 2.82 (2.27; 3.49) for hypertension (HT); and 1.57 (1.14; 2.16) and 1.69 (1.38; 2.07) for family history of diabetes mellitus (DM). In addition, secondary education, low physical activity (PA), and history of cardiovascular disease (CVD) were also significantly associated with T2DM in females. A simple T2DM risk calculator was generated based on non-laboratory parameters. A scale with the best quality included waist circumference >95 cm, HT history, and family history of T2DM (area under the curve (AUC) = 0.71). The proposed 10-year risk score of T2DM represents a simple, non-invasive, and reliable tool for identifying individuals at a high risk of future T2DM.

Can we feel like being neither alert nor sleepy? The electroencephalographic signature of this subjective sub-state of wake state yields an accurate measure of objective sleepiness level.

Accurate measurement of objective level of sleepiness can have important implications for experimental and field studies of sleep deprived individuals. We proposed to accurately quantify changes in sleepiness level with single electroencephalographic (EEG) measures obtained from EEG spectra consisting of 16 spectral powers within the frequency interval from 1 to 16 Hz. The EEG signal was recorded every other hour from 19:00 of Friday to 19:00 of Sunday in 48 study participants. The differential spectra were calculated for the 1st minute with eyes closed as the differences between EEG spectra for pairs of distinct subjective sub-states (alert, neither alert nor sleepy, sleepy, and very sleepy sub-states scored on the Karolinska Sleepiness Scale as 3, 5, 7, and 9, respectively).The differential spectra were calculated for the sub-samples of those participants who succeeded and failed to succeed in completing all 25 EEG recording sessions (n = 25 and 23, respectively) and for the addition sample of 130 participants deprived from sleep for only one night. Single spectral EEG measures were then calculated by summation of 16 spectral powers weighted by a differential spectrum. The strongest correlation coefficients (0.981, 0.987, and 0.985) were attained between the time courses of subjective and objective measures when data on 130, 23 and 25 participants, respectively, were used for calculation of frequency waiting curve differentiating alert sub-sate either from sleepy sub-state or from neither alert nor sleepy sub-state. We recommended implementation of the proposed objective measure into experimental procedures requiring accurate estimation of objective sleepiness level.

Simulation of the Ontogeny of Social Jet Lag: A Shift in Just One of the Parameters of a Model of Sleep-Wake Regulating Process Accounts for the Delay of Sleep Phase Across Adolescence.

The term "social jet lag" was introduced for defining the conflict between social and biological clocks due to the general practice of shifting weekday risetime on early morning hours. The phase delay of the sleep-wake cycle during adolescence is one of the most remarkable features of the ontogenesis of sleep that is incompatible with early school start times. It was previously proposed that the process of accumulation of sleep pressure during wakefulness is slowing down in post-pubertal teens to allow them to stay awake for a longer period of time thus causing the delay of their bedtime. In order to examine this proposition, we traced the ontogeny of social jet lag using sleep times reported for 160 samples of study participants of different ages as an input to a model of sleep-wake regulatory process. The simulations suggested that a gradual change in just one of the model's parameters, the time constant of wakefulness phase of the sleep-wake regulatory process, might explain the association of the transition between childhood and adulthood with the prolongation of time staying awake, delay of sleep time, and reduction of sleep duration. We concluded that the implication of the sleep-wake regulating model would be of help for understanding precisely how social jet lag varies with age and what are the chronophysiological causes of this variation.

Generalizability of Frequency Weighting Curve for Extraction of Spectral Drowsy Component From the EEG Signals Recorded in Eyes-Closed Condition.

One of the critical barriers to reducing the threats of sleep loss to public health, safety, and productivity is a lack of practical tools for quick identification of objective level of sleepiness. We examined a novel sleepiness measure named "spectral drowsy component score" to provide evidence for generalizability of a frequency weighting curve required for calculation of this measure. Each spectral drowsy component score is a sum of 16 weighted ln-transformed single-Hz power densities (1-16 Hz) obtained by the fast Fourier transformation of an electroencephalographic signal recorded during the first minute after closing the eyes. A set of 16 weights (frequency weighting curve) is derived empirically. One type of such curve is a correlation spectrum. It consists of 16 coefficients of correlation of a group-averaged experimental time course of sleepiness with16 time courses of single-Hz power densities. Sleepiness is determined either subjectively (by self-scoring on the Karolinska Sleepiness Scale) or objectively (as sleep latency). Another type is a differential spectrum reflecting difference between 2 sets of 16 power densities obtained for either distant phases of sleep deprivation experiment or distinct alertness-sleepiness substates. Analysis of 3 datasets collected in sleep deprivation experiments with, in total, 160 participants showed that, despite differences in the protocols of these experiments and ages of their participants, the forms of frequency weighting curves always resembled one another. Such resemblance led to practical identity of scoring results. We concluded that spectral drowsy component scoring might be implemented into quick, simple, direct, transparent, and objective test of sleepiness.

The hockey-stick method to estimate evening dim light melatonin onset (DLMO) in humans.

The onset of melatonin secretion in the evening is the most reliable and most widely used index of circadian timing in humans. Saliva (or plasma) is usually sampled every 0.5-1 hours under dim-light conditions in the evening 5-6 hours before usual bedtime to assess the dim-light melatonin onset (DLMO). For many years, attempts have been made to find a reliable objective determination of melatonin onset time either by fixed or dynamic threshold approaches. The here-developed hockey-stick algorithm, used as an interactive computer-based approach, fits the evening melatonin profile by a piecewise linear-parabolic function represented as a straight line switching to the branch of a parabola. The switch point is considered to reliably estimate melatonin rise time. We applied the hockey-stick method to 109 half-hourly melatonin profiles to assess the DLMOs and compared these estimates to visual ratings from three experts in the field. The DLMOs of 103 profiles were considered to be clearly quantifiable. The hockey-stick DLMO estimates were on average 4 minutes earlier than the experts' estimates, with a range of -27 to +13 minutes; in 47% of the cases the difference fell within ±5 minutes, in 98% within -20 to +13 minutes. The raters' and hockey-stick estimates showed poor accordance with DLMOs defined by threshold methods. Thus, the hockey-stick algorithm is a reliable objective method to estimate melatonin rise time, which does not depend on a threshold value and is free from errors arising from differences in subjective circadian phase estimates. The method is available as a computerized program that can be easily used in research settings and clinical practice either for salivary or plasma melatonin values.

Quantification of sleepiness through principal component analysis of the electroencephalographic spectrum.

Although circadian and sleep research has made extraordinary progress in the recent years, one remaining challenge is the objective quantification of sleepiness in individuals suffering from sleep deprivation, sleep restriction, and excessive somnolence. The major goal of the present study was to apply principal component analysis to the wake electroencephalographic (EEG) spectrum in order to establish an objective measure of sleepiness. The present analysis was led by the hypothesis that in sleep-deprived individuals, the time course of self-rated sleepiness correlates with the time course score on the 2nd principal component of the EEG spectrum. The resting EEG of 15 young subjects was recorded at 2-h intervals for 32-50 h. Principal component analysis was performed on the sets of 16 single-Hz log-transformed EEG powers (1-16 Hz frequency range). The time course of self-perceived sleepiness correlated strongly with the time course of the 2nd principal component score, irrespective of derivation (frontal or occipital) and of analyzed section of the 7-min EEG record (2-min section with eyes open or any of the five 1-min sections with eyes closed). This result indicates the possibility of deriving an objective index of physiological sleepiness by applying principal component analysis to the wake EEG spectrum.