BACKGROUND. The circadian clock is a fundamental and pervasive biological program that coordinates 24-hour rhythms in physiology, metabolism, and behavior, and it is essential to health. Whereas therapy adapted to time of day is increasingly reported to be highly successful, it needs to be personalized, since internal circadian time is different for each individual. In addition, internal time is not a stable trait, but is influenced by many factors, including genetic predisposition, age, sex, environmental light levels, and season. An easy and convenient diagnostic tool is currently missing. METHODS. To establish a validated test, we followed a 3-stage biomarker development strategy: (a) using circadian transcriptomics of blood monocytes from 12 individuals in a constant routine protocol combined with machine learning approaches, we identified biomarkers for internal time; and these biomarkers (b) were migrated to a clinically relevant gene expression profiling platform (NanoString) and (c) were externally validated using an independent study with 28 early or late chronotypes. RESULTS. We developed a highly accurate and simple assay (BodyTime) to estimate the internal circadian time in humans from a single blood sample. Our assay needs only a small set of blood-based transcript biomarkers and is as accurate as the current gold standard method, dim-light melatonin onset, at smaller monetary, time, and sample-number cost. CONCLUSION. The BodyTime assay provides a new diagnostic tool for personalization of health care according to the patient’s circadian clock. FUNDING. This study was supported by the Bundesministerium für Bildung und Forschung, Germany (FKZ: 13N13160 and 13N13162) and Intellux GmbH, Germany.
Nicole Wittenbrink, Bharath Ananthasubramaniam, Mirjam Münch, Barbara Koller, Bert Maier, Charlotte Weschke, Frederik Bes, Jan de Zeeuw, Claudia Nowozin, Amely Wahnschaffe, Sophia Wisniewski, Mandy Zaleska, Osnat Bartok, Reut Ashwal-Fluss, Hedwig Lammert, Hanspeter Herzel, Michael Hummel, Sebastian Kadener, Dieter Kunz, Achim Kramer
Best-performance internal cross-validation predictors built on the BOTI RNA-Seq or NanoString data sets