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.

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.

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Modern society is characterized by the steadily increasing demand and desire for wakefulness at all hours of the day. Research in the fields of sleep physiology and chronophysiology can offer instruments enabling the identification of people with and without advantageous traits, such as little need for sleep, decreased sleepiness associated with sleep loss, and rapid adaptation to changes in the workrest schedule. These instruments can also help to quantify the severity of sleep-wake disorders (i.e., excessive daytime sleepiness) and the effects of their treatment. However, inter-individual differences in sleep need, vulnerability to sleep loss, and circadian adaptation remain scientifically understudied and are rarely theoretically and practically considered [19]. At present, it remains largely unknown what may underlie and predict sleep-wake related traits, what relationship these traits may have to each other, and what functional significance may be associated with these traits [20].Van Dongen and Dinges [19] emphasized the importance of three independent parameters for any model aimed at predicting reduced alertness and performance in the conditions of sleep loss and sleep disruption. These include (1) the timing and/or rate of circadian adjustment (i.e., circadian phase), (2) the amount of sleep required per day (i.e., sleep need), and (3) the rate of impairment per hour of sleep loss (i.e., vulnerability to sleep deprivation). There is solid evidence for substantial and clearly distinguishable interindividual variation in each of these three parameters. The differences between individuals are often characterized by within-individual stability (i.e., replicability) and robustness (i.e., insensitivity to experimental manipulation). These features suggest that the inter-individual differences represent systematic trait-like variability (see [18,20] for details). Understanding the basis of this variability may yield new insight into sleep-wake regulation and sleep-wake pathology [20].Agreement exists with respect to the markers of the individual circadian phase (i.e., it can be determined by tracing daily variations in core body temperature, secretion of melatonin and cortisol), but, to date, there is no consensus on the biological markers of sleep need and vulnerability to impairment from lack of sleep. However, it is possible to identify these markers by quantification of the response of brain wave activity to sleep deprivation. The analysis of changes in spectral characteristics of the electroencephalogram (EEG) is regarded as the physiological gold standard for identifying shifts along the alertness-drowsiness continuum [1,2,9,17]. It was demonstrated that sleep loss selectively modulates the spectral power densities of the EEG signal recorded either during prolonged wakefulness [3,4] or during subsequent recovery sleep [4,5,12]. Moreover, the results of numerous studies indicate that the changes in oscillatory brain activity within certain frequency ranges can provide reliable indexes of the reduced alertn...

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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.

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.