Abstract
The temporal organization of molecular and physiological processes is driven by environmental and behavioral cycles as well as by self-sustained molecular circadian oscillators. Quantification of phase, amplitude, period, and disruption of circadian oscillators is essential for understanding their contribution to sleep-wake disorders, social jet lag, interindividual differences in entrainment, and the development of chrono-therapeutics. Traditionally, assessment of the human circadian system, and the output of the SCN in particular, has required collection of long time series of univariate markers such as melatonin or core body temperature. Data were collected in specialized laboratory protocols designed to control for environmental and behavioral influences on rhythmicity. These protocols are time-consuming, expensive, and not practical for assessing circadian status in patients or in participants in epidemiologic studies. Novel approaches for assessment of circadian parameters of the SCN or peripheral oscillators have been developed. They are based on machine learning or mathematical model-informed analyses of features extracted from 1 or a few samples of high-dimensional data, such as transcriptomes, metabolomes, long-term simultaneous recording of activity, light exposure, skin temperature, and heart rate or in vitro approaches. Here, we review whether these approaches successfully quantify parameters of central and peripheral circadian oscillators as indexed by gold standard markers. Although several approaches perform well under entrained conditions when sleep occurs at night, the methods either perform worse in other conditions such as shift work or they have not been assessed under any conditions other than entrainment and thus we do not yet know how robust they are. Novel approaches for the assessment of circadian parameters hold promise for circadian medicine, chrono-therapeutics, and chrono-epidemiology. There remains a need to validate these approaches against gold standard markers, in individuals of all sexes and ages, in patient populations, and, in particular, under conditions in which behavioral cycles are displaced.
Highlights
Assessing the phase, period, and amplitude of circadian oscillators is central to the study of circadian rhythms, be it in cyanobacteria, mice, or humans (Kuhlman et al, 2018)
Those studies have revealed that 24-h environmental and behavioral cycles contribute to 24-h rhythmicity in many aspects of physiology
This distinction relates to the concept of behavioral masking and implies that assessment of the endogenous circadian component of a rhythm requires that components driven by environmental cycles are removed and that components driven by behavioral cycles must be controlled (Rietveld et al, 1993)
Summary
Period, and amplitude of circadian oscillators is central to the study of circadian rhythms, be it in cyanobacteria, mice, or humans (Kuhlman et al, 2018). The sleep-wake cycle is almost always associated with cycles of dark-light and fasting-feeding, and these latter cycles affect physiological, endocrine, and molecular rhythms These peripheral markers (melatonin, cortisol, core temperature) can be used to assess SCN status only when the masking effects of environmental and behavioral cycles are adequately controlled. In this study, the model was tested in participants on a normal sleep-wake schedule and in shift workers, and the gold standard proxy for SCN phase was either DLMO or 6-sulphatoxy melatonin Whereas in this independent validation, the model performed well under baseline conditions, the performance deteriorated dramatically for assessments in night-shift workers, such that the error was more than 2 h in approximately half of the assessments The performance of these models appears impressive, it should be noted that the range of DLMO was limited, and this model has not been tested under conditions of displaced sleep
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