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.

分子和生理过程的时间组织由环境和行为周期以及自我维持的分子昼夜节律振荡器驱动。对昼夜节律振荡器的相位、幅度、周期和中断进行量化对于了解它们对睡眠-觉醒障碍、社交时差、夹带的个体间差异以及计时疗法的发展的贡献至关重要。传统上，人类昼夜节律系统的评估，特别是 SCN 的输出，需要收集长时间序列的单变量标记，如褪黑激素或核心体温。数据是在专门的实验室协议中收集的，旨在控制环境和行为对节律性的影响。这些协议耗时、昂贵、并且对于评估患者或流行病学研究参与者的昼夜节律状态不切实际。已经开发了用于评估 SCN 或外围振荡器的昼夜节律参数的新方法。它们基于机器学习或数学模型对从 1 个或几个高维数据样本中提取的特征进行分析，例如转录组、代谢组、活动的长期同步记录、光照、皮肤温度和心率或者体外方法。在这里，我们回顾了这些方法是否成功地量化了由金标准标记索引的中央和外围昼夜节律振荡器的参数。尽管有几种方法在夜间睡眠时在夹带条件下表现良好，但这些方法要么在其他条件下表现更差，例如轮班工作，要么在夹带以外的任何条件下都没有进行过评估，因此我们还不知道它们的鲁棒性如何。评估昼夜节律参数的新方法为昼夜节律医学、计时疗法和计时流行病学带来了希望。在所有性别和年龄的个体中，在患者群体中，特别是在行为周期被取代的条件下，仍然需要根据金标准标记来验证这些方法。