Chronobiology unit, Groningen Institute of Evolutionary Life Sciences, University of Groningen, 9700 CC Groningen, the Netherlands.
Chronobiology, Faculty of Health and Medical Sciences, University of Surrey, Guildford GU2 7XH, United Kingdom.
Proc Natl Acad Sci U S A. 2023 May 2;120(18):e2212685120. doi: 10.1073/pnas.2212685120. Epub 2023 Apr 24.
Circadian rhythms influence physiology, metabolism, and molecular processes in the human body. Estimation of individual body time (circadian phase) is therefore highly relevant for individual optimization of behavior (sleep, meals, sports), diagnostic sampling, medical treatment, and for treatment of circadian rhythm disorders. Here, we provide a partial least squares regression (PLSR) machine learning approach that uses plasma-derived metabolomics data in one or more samples to estimate dim light melatonin onset (DLMO) as a proxy for circadian phase of the human body. For this purpose, our protocol was aimed to stay close to real-life conditions. We found that a metabolomics approach optimized for either women or men under entrained conditions performed equally well or better than existing approaches using more labor-intensive RNA sequencing-based methods. Although estimation of circadian body time using blood-targeted metabolomics requires further validation in shift work and other real-world conditions, it currently may offer a robust, feasible technique with relatively high accuracy to aid personalized optimization of behavior and clinical treatment after appropriate validation in patient populations.
昼夜节律会影响人体的生理、代谢和分子过程。因此，估计个体的生物钟（昼夜节律相位）对于个体行为（睡眠、进餐、运动）的优化、诊断采样、医疗和治疗昼夜节律障碍都非常重要。在这里，我们提供了一种偏最小二乘回归（PLSR）机器学习方法，该方法使用一个或多个样本中的血浆衍生代谢组学数据来估计褪黑素起始时间（DLMO），作为人体昼夜节律相位的替代指标。为此，我们的方案旨在尽可能接近实际情况。我们发现，在有规律的条件下，针对女性或男性优化的代谢组学方法的表现与使用更耗时的基于 RNA 测序的方法的现有方法一样好，甚至更好。虽然使用针对血液的靶向代谢组学来估计生物钟时间需要在轮班工作和其他真实环境中进一步验证，但它目前可能是一种可靠、可行的技术，具有相对较高的准确性，可以在适当验证后帮助患者人群实现行为和临床治疗的个性化优化。
