Abstract A convenient way to estimate internal body time (BT) is essential for chronotherapy and time-restricted feeding, both of which use body-time information to maximize potency and minimize toxicity during drug administration and feeding, respectively. Previously, we proposed a molecular timetable based on circadian-oscillating substances in multiple mouse organs or blood to estimate internal body time from samples taken at only a few time points. Here we applied this molecular-timetable concept to estimate and evaluate internal body time in humans. We constructed a 1.5-d reference timetable of oscillating metabolites in human blood samples with 2-h sampling frequency while simultaneously controlling for the confounding effects of activity level, light, temperature, sleep, and food intake. By using this metabolite timetable as a reference, we accurately determined internal body time within 3 h from just two anti-phase blood samples. Our minimally invasive, molecular-timetable method with human blood enables highly optimized and personalized medicine.

Many organisms possess a molecular time-keeping mechanism, a circadian clock, which has endogenous, self-sustained oscillations with a period of about 24 h. Circadian regulation of cell activity occurs in diverse biological processes such as electrical activity, gene/protein expression, and concentration of ions and substances (1, 2). In mammals, for example, several clock genes regulate circadian gene expression in central and peripheral clock tissues (3⇓⇓⇓⇓⇓–9), as well as metabolites in the blood (10⇓⇓⇓⇓–15). Reflecting circadian regulation of such processes, the potency and toxicity of administered drugs depends on an individual's body time (BT) (16⇓⇓⇓⇓⇓–22). Drug delivery according to body time improves the outcome of pharmacotherapy by maximizing potency and minimizing toxicity (23), and administrating drugs at an inappropriate body time can result in severe side effects (22). For example, rhythm disturbances were induced by administration of IFN-α during the early active phase in mice, although unaffected during the early rest phase (22); and the time of administration of two anticancer drugs, adriamycin (6:00 AM) and cisplatin (6:00 PM), made a lower toxicity effect than its antiphasic administration (24). However, several reports showed that internal body time varies by 5–6 h in healthy humans (25, 26) and as much as 10–12 h in shift workers without forced entrainments (27, 28). Therefore, for efficient application of body-time drug delivery or “chronotherapy” (16⇓⇓⇓–20) in a clinical setting, a simple and robust method for estimating an individual’s internal body time is needed.

Additionally, the timing of food intake may contribute to weight gain (29) and metabolic disease (30) because energy regulation and circadian rhythms are molecularly and physiologically intertwined (31⇓⇓⇓⇓⇓⇓⇓⇓⇓–41). For example, mice fed a high-fat diet during a 12-h light phase gain significantly more weight than mice fed only during a 12-h dark phase (29). These results suggest that food intake at different body times can alleviate or exacerbate diet-induced obesity. Therefore, an accurate and convenient way to detect internal body time may improve time-restricted diet strategies.

One conventional way to estimate human internal body time is to periodically sample for more than 24 h the level of melatonin or cortisol, which have robust circadian oscillations in the blood (10⇓–12). Although this strategy directly measures internal body time, it requires labor-intensive constant sampling under controlled environmental conditions to reveal metabolite peaks and rhythms. To reduce this burden, we previously developed a molecular-timetable method (12, 42), which was inspired by Linné’s flower clock (Fig. 1A). In Linné’s flower clock, one can estimate the time of day by watching the opening and closing pattern of various flowers. Similarly, by using our molecular timetable method, one can estimate the body time of day by profiling the circadian oscillation patterns of gene expression or metabolites in the body. Compared with the conventional method, the molecular-timetable method requires only one or few time-point samplings. In mice, this method works with the expression activity of clock-controlled genes in different organs (42, 43) and with oscillatory metabolites in blood plasma (12).

Fig. 1. Experimental condition. (A) The concept of body-time detection with conventional and molecular timetable methods illustrated by Linné’s flower clock. In the conventional method a single indicator monitored over a few days detects internal body time; in the molecular timetable method, multiple metabolic “flowers” are simultaneously measured at a few time points, which reduce efforts in sampling. (B) Metabolite timetable construction. We collected time-course blood samples, isolated plasma, measured with LC–MS, and selected circadian-oscillating metabolites as indicators. In body-time detection, we collected blood samples at a few time points, isolated plasma, measured with LC–MS, and estimated body time. (C) Image of the constant routine (CR) experiments. During CR, subjects stayed in chairs while various measurements were performed. Note: the man in this picture is demonstrating the set-up and is not an actual subject in this study. (D) Sampling schedule for each subject. Black circles indicate the time points when blood samples were taken and when subjects ate during CR. White boxes indicate the time when subjects were awake, and black boxes indicate when they were asleep. The blood samples of three subjects during CR1 were used for timetable construction, and other samples were used for body-time estimation. (E) Measured melatonin (Upper) and cortisol (Lower) rhythms in the collected blood samples during CR1 (Left) and CR2 (Right). The cortisol and melatonin levels show that some subjects (e.g., subject E in CR2) have shifted internal body time against sampled time. CR, constant routine; SCR, semiconstant routine; BT, body time; LC–MS, liquid chromatography mass spectrometry.

In this study, we applied this method to detect internal body time using blood samples in humans (Fig. 1B). First, we used liquid chromatography mass spectrometry (LC–MS) (44⇓–46) to measure the abundance of various metabolites in blood plasma samples from several healthy human subjects over 1.5 d while controlling for activity level, environmental changes, sleep, and food intake. From these data we constructed a molecular timetable of metabolites that oscillate over 24-h, arranged according to their peak abundance during the day. This reference metabolite timetable enabled us to accurately detect an individual’s body time with only two samples of blood drawn 12 h apart. Our study demonstrates that human internal body time can be detected using limited time-point sampling and a reference metabolite timetable.

Discussion In this study, we constructed a human blood metabolite timetable from three individuals, and applied it to these individuals and three other individuals after a forced desynchronization protocol. We successfully detected internal body time in all individuals, even in the different experimental conditions (Fig. S4), which suggests the robustness of our metabolite-timetable method against individual genetic differences. Interestingly, some individuals such as subjects E and F showed 6.8-h and 3.5-h delayed internal body time in constant routine 2 after they experienced a semiconstant routine of enforced 28-h sleep–wake cycles, in comparison with those in constant routine 1 (Fig. 3A). Importantly, these delays after enforced sleep–wake cycles were successfully detected by using our metabolite timetable method (Fig. 3 C and D). These results suggest that the molecular timetable method has potential to be a body-time estimation tool for humans in both normal and abnormal environments like enforced sleep–wake cycles. Abnormal environments such as shift working, jet lag, and other irregular lifestyles, cause changes in the internal body time of individuals (57). In addition to abnormal environments, genetic differences such as familial advanced sleep phase syndrome (FASPS) also cause changes in the internal body time of individuals (58⇓–60). Brown et al. recently developed a method to potentially detect the circadian rhythm disorders caused by genetic differences (61, 62). They collected skin samples from human subjects, cultured these cells, and transfected them with a clock-controlled reporter to characterize the features of the molecular clock in these tissues. They found that the circadian rhythmicity in isolated cells is correlated with the chronotypes of the subjects, implying that the method could detect circadian rhythm disorders caused by genetic differences. Our molecular timetable method can detect both genetic and environmental circadian rhythm disorders, but cannot distinguish between them, whereas Brown et al.’s method can detect genetic but not acquired disorders. These two methods are therefore complementary for detecting circadian rhythm disorders. Our metabolite timetable method may provide a good platform for the initial screening of body-time abnormality caused by either abnormal environments or genetic differences, because our method is minimally invasive and less labor intensive compared with the conventional method or biopsy adopted in Brown et al. (61, 62). Moreover, the metabolite timetable method can track the transient dynamics of internal body time, which cannot be detected by conventional time-course sampling method. It will be interesting in the future to determine whether our timetable method can detect internal body time differences caused by such abnormal environments or genetic differences. In this study, we used metabolites in blood to detect the internal body time. We note that a molecular timetable concept can be applied not only for circadian-oscillating metabolites, but also for other circadian-oscillating substances (12, 42) and in other tissues like human hair follicle cells from the head or chin (63). Comparing molecular timetable methods with different reference molecules such as clock-controlled RNAs in humans will be of interest to reveal internal synchronization (or desynchronization) between different peripheral clocks such as blood clocks and skin clocks. In summary, our study shows the possibility that a metabolic molecular timetable can be a convenient diagnostic tool. A minimally invasive internal body-time detection method is essential for facilitating the development of chronotherapy to realize tailored medication regimens as well as for efficient time-restricted feeding to avoid or cure obesity. To measure the abundance of metabolites for body-time estimation, we used LC–MS analysis, which is rarely implemented in current clinical hospitals. However, bench-top MS machines are beginning to appear, and they might be available at hospitals in the future as a routine diagnostic tool; additionally, it may be possible to develop a simpler diagnosis kit using antibodies against a subset of selected circadian metabolites. In any case, our method is more pragmatic than serial time-course sampling because only a few samples are required for body-time estimation and because blood is an easy tissue to sample and analyze. Moreover, our timetable is robust against personal differences because we selected metabolites that oscillated independently of the individual, food intake, sleep, light, and temperature. Thus, the metabolite timetable proposed in this study will provide a convenient way to estimate human body time and may lead to highly optimized and personalized medicine in the future.

Materials and Methods Subjects. Six healthy, male volunteers, aged 20–23 y (mean age 21.5 y) participated in this study. Subjects had regular sleep–wake patterns, normal hematology, and urinalysis data, and no history of psychiatric disease (assessed by the Mini-International Neuropsychiatric Interview) or severe physical disease. Written informed consent was obtained from each subject. See SI Materials and Methods for details. Measurement of Metabolites. The LC system used was an Agilent 1290 infinity HPLC (Agilent Technologies) with the KEIO MasterHands software (64). See SI Materials and Methods for details about the sample preparation, measurements, and data preprocessing. Construction of Molecular Timetable. We chose peaks that were detected and associated at nine or more time points in all three subjects (subjects A–C) in CR1. For each chosen peak, we performed two statistical tests for selecting candidates for the timetable. See SI Materials and Methods for details. Body-Time Estimation. Body-time estimation was performed as described for the previous mouse blood study (12). Expected body time of nine sample sets for body-time estimation (subjects D–F during CR1, and all six subjects during CR2) were determined on the basis of peak times of the cortisol rhythms (Fig. 1D, Lower). See SI Materials and Methods for details. MS Identification of Oscillating Metabolites. LC MS/MS analysis was performed for unknown peak identification based on the method described previously (12) with slight modification. See SI Materials and Methods for details. Ethics. The study was approved by RIKEN Kobe Institute Research Ethics Committee (KOBE-IRB-09-07) and the ethics committee of the National Center of Neurology and Psychiatry (22–1-4). All participants have given written consent for the use of material for this research purpose.

Acknowledgments We thank Shinobu Abe and Yasuko Sakaguchi at the Institute for Advanced Biosciences (IAB) for metabolomic analysis; Masahiro Hayashi, Hiroshi Fujishima, and Tomoyo Hashiguchi at RIKEN Center for Developmental Biology (CDB) for handling samples; Hiroyuki Tamiya and Ryohei Narumi at RIKEN CDB for helpful discussion; Arthur Millius at RIKEN CDB for helpful discussion and for proofreading of the manuscript; and Takuro Ito at IAB, Keio University for providing materials used in the identification of oscillating metabolites. This research was supported by an intramural grant-in-aid from the RIKEN CDB (to H.R.U.), research funds from New Energy Developing Organization (to H.R.U.), research funds from the Yamagata Prefectural Government and the city of Tsuruoka (to M.S., M.M., and T.S.), a grant-in-aid for the Strategic Research Program for Brain Sciences from the Ministry of Education, Culture, Sports, Science and Technology of Japan (to K.M.), an Intramural Research Grant (No. 23-3) for Neurological and Psychiatric Disorders of National Center of Neurology and Psychiatry, and a Grant-in Aid for Scientific Research (No. 21390335) from Japan Society for the Promotion of Science (to K.M. and A.H.).

Footnotes Author contributions: T.K., M.S., A.H., Y.M., M.M., S.H., K.H., K.M., T.S., and H.R.U. designed research; T.K., M.S., A.H., Y.M., M.M., S.H., K.M., and H.R.U. performed research; M.S. contributed new reagents/analytic tools; T.K., M.S., A.H., Y.M., and M.M. analyzed data; and T.K., M.S., A.H., M.M., and H.R.U. wrote the paper.

The authors declare no conflict of interest.

This article is a PNAS Direct Submission.

This article contains supporting information online at www.pnas.org/lookup/suppl/doi:10.1073/pnas.1207768109/-/DCSupplemental.