Human blood metabolite timetable indicates internal
body time
Takeya Kasukawaa,1, Masahiro Sugimotob,c,1, Akiko Hidad, Yoichi Minamie, Masayo Morib, Sato Honmaf,
Ken-ichi Honmaf, Kazuo Mishimad, Tomoyoshi Sogab,2, and Hiroki R. Uedaa,e,g,h,i,2aFunctional Genomics Unit, and eLaboratory for Systems Biology, RIKEN Center for Developmental Biology, Chuo-ku, Kobe, Hyogo 650-0047, Japan; bInstitutefor Advanced Biosciences, Keio University, Tsuruoka, Yamagata 997-0017, Japan; cLaboratory for Malignancy Control Research, Medical Innovation Center,
Graduate School of Medicine and Faculty of Medicine, Kyoto University, Yoshida-Konoe, Sakyo-ku, Kyoto 606-8501, Japan; dDepartment of Psychophysiology,
National Institute of Mental Health, National Center of Neurology and Psychiatry, Kodaira, Tokyo 187-8553, Japan; fDepartment of Chronomedicine,
Hokkaido University Graduate School of Medicine, Kita-ku, Sapporo 060-8638, Japan; gDepartment of Biological Sciences, Graduate School of Science,
Osaka University, Toyonaka, Osaka 560-0043, Japan; hDepartment of Mathematics, Graduate School of Science, Kyoto University, Kitashirakawa Oiwake-cho,
Sakyo-ku, Kyoto 606-8502, Japan; and iLaboratory for Synthetic Biology, RIKEN Quantitative Biology Center, Chuo-ku, Kobe, Hyogo 650-0047, Japan

Edited by Joseph S. Takahashi, Howard Hughes Medical Institute, University of Texas Southwestern Medical Center, Dallas, TX, and approved July 20,
2012 (received for review May 8, 2012)

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 weapplied 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 theconfounding effects of activity level, light, temperature, sleep, andfood 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, moleculartimetable method with human blood enables highly optimized
and personalized medicine.

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metabolomics circadian rhythm
spectrometry diagnostic tool|

| liquid chromatography massM

any 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 tobody 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 byadministration of IFN-α during the early active phase in mice,although unaffected during the early rest phase (22); and thetime of administration of two anticancer drugs, adriamycin (6:00
AM) and cisplatin (6:00 PM), made a lower toxicity effect thanits 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 ofbody-time drug delivery or “chronotherapy” (16–20) in a clinicalsetting, a simple and robust method for estimating an individual’sinternal body time is needed.
Additionally, the timing of food intake may contribute toweight gain (29) and metabolic disease (30) because energy regulation and circadian rhythms are molecularly and physiologically15036–15041 | PNAS | September 11, 2012 | vol. 109 | no. 37

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 foodintake 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 isto periodically sample for more than 24 h the level of melatoninor cortisol, which have robust circadian oscillations in the blood(10–12). Although this strategy directly measures internal bodytime, it requires labor-intensive constant sampling under controlled environmental conditions to reveal metabolite peaks andrhythms. 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 ofvarious ﬂowers. Similarly, by using our molecular timetable
method, one can estimate the body time of day by proﬁling thecircadian 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) andwith oscillatory metabolites in blood plasma (12).
In this study, we applied this method to detect internal bodytime using blood samples in humans (Fig. 1B). First, we usedliquid chromatography mass spectrometry (LC–MS) (44–46) tomeasure 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, andfood intake. From these data we constructed a molecular timetable of metabolites that oscillate over 24-h, arranged according totheir peak abundance during the day. This reference metabolite
timetable enabled us to accurately detect an individual’s body timewith 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.

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.
Freely available online through the PNAS open access option.
1

T.K. and M.S. contributed equally to this work.

2

To whom correspondence may be addressed. E-mail: uedah-tky@umin.ac.jp or soga@sfc.
keio.ac.jp.

This article contains supporting information online at www.pnas.org/lookup/suppl/doi:10.
1073/pnas.1207768109/-/DCSupplemental.

www.pnas.org/cgi/doi/10.1073/pnas.1207768109

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MethodMolecular Timetable
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Results
Construction of Human Blood Molecular Timetable. First, we aimed

to construct a molecular timetable that includes a list of oscillating metabolites in human blood with information on mean, amplitude, and peak time (PT) of metabolite abundance over 24 h.
We recruited six healthy volunteers (subjects A–F) who stayedin an environmentally controlled sleep laboratory for about 2 wk
and underwent a forced desynchronization (FD) protocol consisting of three activity routines—constant routine one (CR1),
then semiconstant routine (SCR), and ﬁnally constant routine
two (CR2) (Fig. 1 C and D). The FD protocol used in this study
was composed of the following three parts: (i) measurement
under CR1 (47) followed by (ii) a 28-h sleep–wake schedule for
7 d during SCR (48), and (iii) a second measurement under CR2.
The 28-h sleep–wake schedule enforces desynchronization between sleep homeostasis and circadian cycle. Therefore, the circadian clock of each individual runs according to his/her own
period length and hence reveals the variability of its natural
frequency or phase during CR2 after SCR.
Blood samples were collected every hour for 1.5 d in CR1 and
CR2 with a deﬁned caloric amount of food eaten every 2 h. We
measured the time-course abundance of melatonin and cortisol
Kasukawa et al.

Fig. 1. Experimental condition. (A) The concept ofbody-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 bodytime; 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 bodytime detection, we collected blood samples at a few
time points, isolated plasma, measured with LC–MS,
and estimated body time. (C) Image of the constantroutine (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 inthis 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 andmelatonin levels show that some subjects (e.g.,subject E in CR2) have shifted internal body timeagainst sampled time. CR, constant routine; SCR,
semiconstant routine; BT, body time; LC–MS, liquid
chromatography mass spectrometry.

in these blood plasma samples by RIA and found a wide variation in melatonin and cortisol rhythms (Fig. 1E). We used blood
samples from three subjects (subjects A–C) in CR1 to constructa standard metabolic timetable because their body time mostclosely matched each other and previously reported melatoninand cortisol rhythms. We note that constructing a timetable withany combination of subjects did not change the results of ourstudy (SI Results and Discussion).
We used an LC–MS system to obtain the abundance of various
metabolites for each subject at 17 time points (every 2 h) in eachconstant routine (CR1 or CR2) for a total of 204 samples. Using
the three blood sample sets of CR1 from subjects A–C (51samples in total) for molecular timetable construction, 2,541 and
1,796 metabolite peaks were detected in the positive and negative ion modes, respectively. To select circadian-oscillating metabolite peaks from these, we performed cosine ﬁtting to themean peak area of each sample set, which is averaged over three
individuals at the same time point, and tested the signiﬁcance of
circadian rhythmicity by permutation tests (i.e., calculating theprobability by using randomly shufﬂed time-course data of mean
peak area). With the cutoff false discovery rate (FDR; the expected proportions of false positives in the selected set) of ≤0.1,
PNAS | September 11, 2012 | vol. 109 | no. 37 | 15037

PHYSIOLOGY8we obtained 142 and 168 metabolite peaks of positive and negativeions, respectively, as circadian-oscillating metabolite peaks. Thesepeaks include circadian-oscillating metabolite peaks with larger orsmaller amplitudes and, as a result, some of them show variability
among the three individuals. This is because our initial test only
selects for peaks with signiﬁcant circadian rhythmicity, but notsigniﬁcant amplitude. Thus, we further selected circadian-oscillating metabolite peaks with signiﬁcant amplitude, whose circadian amplitude was signiﬁcantly larger than the variations amongindividuals, with the cutoff FDR of ≤0.1 in a one-way ANOVA
analysis. Consequently, the substantial overall cutoff is 0.1 × 0.1 =0.01 because metabolite peaks were ﬁltered by two independent
criteria (FDR ≤ 0.1), and this is equivalent to FDR ≤ 0.01 used in
our previous mouse study (12). As a ﬁnal set of circadian-oscillating metabolites (with signiﬁcant rhythmicity and amplitude), we
obtained 33 and 25 metabolite peaks of positive and negative ions,
respectively (Fig. 2 and Dataset S1). The procedure for the molecular timetable construction is illustrated in Fig. S1. The numberof metabolite peaks (58 peaks) is adequate for body-time estimation because we previously found that 20 metabolites were
sufﬁcient for accurate body-time estimation (12).
Body-Time Estimation with Metabolic Molecular Timetable. We analyzed the remaining nine blood sample sets (three sample setsfrom subjects D–F in CR1 and all six sample sets from subjects
A–F in CR2) to test the accuracy of our molecular timetable
method. To evaluate its accuracy, we determined the “expectedbody time” for each sample set by a conventional method (Fig.
1A) using cosine ﬁtting to time-course data of cortisol abundanceover 1.5 d (Fig. 1E, Lower). Although we used cortisol peak time
for this purpose, we did not detect a signiﬁcant difference when
we determined the expected body time based on dim-light melatonin onset (DLMO) (SI Results and Discussion). Each sample
showed a wide range of expected body time. For example, somesamples showed expected body time that is almost identical to
the sampled time (Fig. 3A, black bars), whereas one showed
expected body time that was almost antiphasic to the sampled
time (Fig. 3A, white bar). Remaining samples showed expectedbody time with an intermediate difference from the sampled
time (Fig. 3A, gray bars). The observed variability did not result
from errors of the conventional method, but reﬂect individual
differences of internal body time because each sample showedsimilar expected body time when calculated from the 1.5-d time-

A

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12

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+2.5SD

Fig. 2. Heat maps of circadian-oscillating metabolites in the human timetable. Circadian-oscillating metabolite peaks in the plasma samples [positive
ions (A); negative ions (B)] are shown. Some metabolites in this heat map
were identiﬁed and shown in Fig. 4. On the heat maps, magenta tiles indicate a high quantity of substances and green tiles indicate a low quantity
in plasma. Metabolites are sorted according to their molecular peak time.

15038 | www.pnas.org/cgi/doi/10.1073/pnas.1207768109

course abundance data of melatonin (Fig. S2A). Accordingly, wecategorized nine sample sets into three groups on the basis of themagnitude of the differences between expected body time basedon a cortisol rhythm and sampled times: small shift group (CR1of subject F; CR2 of subjects B and C), moderate shift group(CR1 of subjects D and E; CR2 of subjects A, D, and F), andlarge shift group (CR2 of subject E).
Next, we calculated the “estimated body time” for each sample
set (Fig. 3 B–E and Table S1 and Figs. S2 B and C) by ourmolecular timetable method using the constructed reference
metabolite timetable of human blood (Fig. 2). For body-time
estimation, we used only two samples of blood drawn 12 h apart.
We derived 11 estimated body-time values for each sample set
consisting of 17 samples (Fig. 3E) and then compared the estimated body time with the predetermined expected body time(via conventional method) to evaluate the accuracy of our molecular timetable method. In the small shift group, the differences between estimated and expected body time were within 2 h(Fig. 3 B and E). The result was comparable to the previous
study using mouse blood samples (12). In the moderate shiftgroup, the differences were within 3 h (Fig. 3 C and E) and in thelarge shift group, the differences were about 3 h (Fig. 3 D and E).
In these two groups, the differences were larger than the small
shift group. However, in the moderate shift groups, only 3 time
points (out of 55) differed by more than 2 h. The time differenceof this method (<3 h) is less than the range of the internal bodytime of shift workers (27, 28). These results suggest that ourmolecular timetable method can correctly detect shifts of internal body time.
Identiﬁcation of Metabolites with Circadian Oscillation. Identiﬁcation of the circadian-oscillating metabolites is important forconnecting our current metabolite timetable of human blood topreviously reported circadian biology in humans. Identiﬁcation is
also important for further development of our method because
once a metabolite is identiﬁed, we can update the circadianmetabolite reference list in our human blood timetable, which
will improve the sensitivity and speciﬁcity of the molecular
timetable (12, 42). Therefore, we attempted to identify several
circadian-oscillating metabolites in constructing our metabolite
timetable and found that a large fraction belong to the steroid
hormone metabolism pathway (Fig. 4 and Fig. S3 A and B).
These include cortisol (Fig. 4A), cortisone (Fig. 4B), urocortisone-3-glucronide (Fig. 4C), pregnanolone sulfate-like metabolite (Fig. 4D), and urocoritsol-3-glucronide (Fig. 4E) as well as
the amino acids, phenylalanine (Fig. 4F), tryptophan (Fig. 4G),
and leucine (Fig. 4H). Cortisol is a well-known circadian-oscillating metabolite in the blood (11), and cortisone is a direct
metabolite of the cortisol also found in the blood (49). Urocortisone-3-glucuronide and urocortisol-3-glucronide are glucuronide forms of urocortisone and urocortisol, respectively, which
are downstream metabolites of cortisol and cortisone. A pregnanolone isomer and its precursor (progesterone) were also
found as circadian-oscillating metabolites in the brain and blood(50). The circadian oscillations in the human blood of cortisoland cortisone were also detected in the recent report about thehuman circadian metabolome (13). The peak times (PT) of thecortisol, cortisone, and urocortisone-3-glucronide were aroundthe afternoon (PT12–PT15; we deﬁned PT12 as noon), and peaktimes were aligned catabolically according to the steroid metabolism pathway from upstream to downstream (Fig. S3E). The peaktime of the corticosterone in the mouse plasma samples was
CT11.7 (corresponding to PT17.7 in this human study, because
both CT6 and PT12 indicate noon) in our previous study (12), andis ∼3–6 h later than human peak time. The amino acids, phenylalanine, tryptophan, and leucine were also identiﬁed as oscillating
metabolites in our mouse plasma study (12), and peak times inmouse and human plasma were ZT18.7–18.9 (corresponding to
Kasukawa et al.

CR1

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PT0.7–0.9 in this human study because both ZT6 and PT12 indicate noon), and PT2.2–3.1, respectively, which might reﬂect
the similarity in the phase of the central clock between diurnal
and nocturnal animals (51–56).
We also found several lipids among oscillating metabolites,
although these metabolites had weak amplitude and/or highvariability among individuals (Fig. S3 A–D). These lipids include
PG(18:1(9Z)/0:0), a glycerophospholipid (Fig. S3C), and LysoPC(16:0), a lysophospholipid (Fig. S3D). Peak times of these metabolites differed: PT12.0 [PG(18:1(9Z)/0:0)], and PT3.2 [LysoPC(16:0)]. Lysophospholipids also showed circadian oscillation inmouse plasma (12), but their peak time was almost antiphasic(ZT8 in mice corresponding to PT14 in this study), whichmight reﬂect the differences between human diurnality andmurine nocturnality.
The comparison of our identiﬁed circadian-oscillating metabolites to recent studies (13, 14) is explained in SI Results andDiscussion.
Discussion
In this study, we constructed a human blood metabolite timetablefrom three individuals, and applied it to these individuals and threeother 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
Kasukawa et al.

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E 10

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Peak time (h)Fig. 3. Estimation of human body time. (A) Difference between expected body time and sampled
time. Nine sample sets for body-time estimation
were classiﬁed into three groups by the magnitudeof the difference: small (black), moderate (gray),and large (white). (B–D) Body-time estimation at
12 h on the ﬁrst day for nine sample sets in small (B),moderate (C), and large (D) body-time differencegroups. Colors of the dots indicate the molecularpeak times of each metabolite. Peak time of the redcosine curves indicates estimated body time andpeak time of the blue indicates the time of the
expected body time. The smaller the distance between red and blue curves, the greater the accuracyof the measurement. Dashed vertical lines show theestimated body time (red), expected body time
(blue), or the time the sample was taken (black). In
all subjects, expected body time (based on cortisol
rhythm) and estimated body time (based on molecular timetable) were similar, indicated by theclose proximity of the blue and red dashed lines. (E)Summary of body-time estimation. Difference between expected and estimated body time for all
estimated samples is shown. The leftmost threesamples were ones in the small difference group,
the middle ﬁve samples were in the moderate difference group, and the rightmost sample was in thelarge difference group. All samples estimated internal body time within or around 3-h differences
between expected and estimated body time. BT,
body time.

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 inconstant routine 2 after they experienced a semiconstant routine ofenforced 28-h sleep–wake cycles, in comparison with those inconstant routine 1 (Fig. 3A). Importantly, these delays after
enforced sleep–wake cycles were successfully detected by using ourmetabolite timetable method (Fig. 3 C and D). These results suggest that the molecular timetable method has potential to bea 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, andother irregular lifestyles, cause changes in the internal body timeof 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 topotentially detect the circadian rhythm disorders caused by genetic differences (61, 62). They collected skin samples fromhuman subjects, cultured these cells, and transfected them witha clock-controlled reporter to characterize the features of themolecular clock in these tissues. They found that the circadianrhythmicity in isolated cells is correlated with the chronotypes ofthe subjects, implying that the method could detect circadianrhythm disorders caused by genetic differences. Our molecular
PNAS | September 11, 2012 | vol. 109 | no. 37 | 15039

PHYSIOLOGY

A

A

B

C

D

E

F

G

H

In this study, we used metabolites in blood to detect the internal
body time. We note that a molecular timetable concept can beapplied not only for circadian-oscillating metabolites, but also forother 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 andskin 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, ourmethod 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, themetabolite 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.

Fig. 4. Identiﬁed circadian-oscillating metabolites. Abundance is shown ofcortisol (A), cortisone (B), urocortisone-3-glucronide (C), pregnanolone sulfate-like metabolite (D), (allo)urocortisol-3-glucronide (E), L-phenylalanine(F), tryptophan (G), and L-leucine (H) in the blood sampled from three subjects (subject A, subject B, and subject C) whose CR1 samples were used in themetabolite timetable construction. X axes show the sampled time from 12 hon day 2 to 20 h on day 3 in CR1, and y axes show area of the corresponding
peak in the LC–MS analysis.

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 notacquired 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 theconventional 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 detectedby 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 suchabnormal environments or genetic differences.
15040 | www.pnas.org/cgi/doi/10.1073/pnas.1207768109

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 andassociated 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 forthe 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 duringCR2) 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 theInstitute 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

Kasukawa et al.

and T.S.), a grant-in-aid for the Strategic Research Program for Brain Sciencesfrom the Ministry of Education, Culture, Sports, Science and Technology ofJapan (to K.M.), an Intramural Research Grant (No. 23-3) for Neurologicaland 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.).

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PNAS | September 11, 2012 | vol. 109 | no. 37 | 15041

PHYSIOLOGYCDB for helpful discussion and for proofreading of the manuscript; andTakuro Ito at IAB, Keio University for providing materials used in theidentiﬁcation 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.,

Supporting Information
Kasukawa et al. 10.1073/pnas.1207768109
SI Results and Discussion

expected body time. However, as DLMO is frequently used todetermine human internal body times, we also evaluated our
body-time estimation with the expected body time based on
DLMO. We ﬁrst determined DLMO for each constant routine
and each subject. The threshold to determine the DLMO for eachmelatonin proﬁle was set to the average of the ﬁve lowest raw data
points plus 25% of the average of the ﬁve highest raw data points.
DLMO was calculated by a linear interpolation between data
points just above and below this threshold. Next the expectedbody times were determined on the basis of the DLMO with thesame method for CPT (Fig. S4A). With this expected body time
based on DLMO, we reevaluated our body-time estimation (Fig.
S4B). Although there were some additional data points at which
the time differences were more than 3 h (one time point in CPT
and three time points in DLMO), the time differences based on
DLMO were not signiﬁcantly different from CPT because the Pvalue of the Wilcox rank sum test between time differences
based on CPT and DLMO was 0.125 (>0.05).

Effect of Food Intake in Body-Time Estimation. Although we did not
directly evaluate the effect of food intake, we minimized the effectof food intake as much as possible. In the molecular timetable
construction, we used blood samples drawn from subjects underconstant routine, in which foods were taken every 2 h during
sampling periods, and blood samples were collected just before
food intake every time to keep diet effects as low as possible. This
allowed us to exclude metabolites whose circadian oscillations
were driven by the dietary cycle. Furthermore, we excluded circadian oscillating metabolite peaks with variability among individuals. This enabled us to remove some metabolites that wereaffected by food intake. For example, we could detect several
lipids among 310 circadian oscillating peaks (142 positive and 168
negative ion peaks) such as SM(d18:1/18:1), PC(18:2/18:2), and
LysoPC(16:0), but ultimately all were excluded except one lipid
Cer(d18:2/16:0) based on individual variability. The reason forsuch variation in the lipid proﬁles may be that the subjects tookdifferent foods in the constant routines, which may weaken theeffects of speciﬁc foods and ingredients. Thus, we believe that theeffect of food intake in our body-time estimation was minimized
to some extent.

Evaluation of Body-Time Estimation with Single Time Point and ThreeTime Points. We used blood samples at two antiphasic time points

Comparison of Identiﬁed Circadian-Oscillating Metabolites in This
Study to Other Studies. Two recent studies identiﬁed circadian-for the body-time estimation in this study. We also evaluatedother conditions of the body-time estimation with a sample at one
time point and samples at three time points separated by 8 h (oneat the target time point, one 8 h later, and one 16 h later). We
estimated the body times from 1200 hours to 2200 hours on day 1
and from 0000 hours to 0600 hours on day 2 in CR1 (subjects D–F) and CR2 (subjects A–F) for every condition. The result issummarized in Fig. S4C. With one time-point sampling, the timedifferences were larger than in two time-point sampling. With
three time-point sampling, the time differences were similar to
two time-point sampling. This result suggests that one time-point
sampling is difﬁcult to use for body-time estimation with thecurrent method and technology, but that both two and three
time-point samplings are practical. The accuracy in the one timepoint method could be improved if the quantitativeness and reproducibility of the MS measurements is improved in the future.

oscillating metabolites in humans (1) and in mice (2). In the
Dallmann et al. study (1), they identiﬁed 41 metabolites in human plasma and 29 metabolites in human saliva. There are a few
common plasma circadian metabolites that were identiﬁed bothin their study and ours, for example, cortisol and cortisone.
Meanwhile, 33 of 41 plasma circadian metabolites were lipids intheir study but only one metabolite in our timetable was a lipid.
The difference may be caused by our timetable construction
method to exclude metabolite peaks exhibiting variability amongindividuals. We did ﬁnd several lipids, but they were excluded on
the basis of variation between individuals (Dataset S1). In EckelMahan’s study (2), they identiﬁed 60 circadian metabolites inmouse liver. Although some circadian-oscillating amino acids
were identiﬁed in both their study and ours, there were nocommon metabolites. This may be because the targeted organs
were different (plasma and liver), different conditions were usedin the mass-spectrometry analysis, or because many of our circadian-oscillating metabolites remain unidentiﬁed.
In this study, the number of metabolite peaks (58 peaks) withcircadian ﬂuctuations was smaller and the false discovery rate(FDR) cutoff (0.1) was higher than our previous mouse study (3).
This is because our changed method for choosing circadian
metabolite peaks in the human molecular timetable is different.
In the mouse study, we ﬁtted cosine curves to abundances oftime-course metabolite peak areas and chose ones with FDR≤0.01 using shape information only. This was possible because
the duration of our mouse study was 96 h (light-dark condition
48 h and dark-dark condition 48 h) compared with 34 h in thisstudy. This probably decreases the sensitivity of selecting circadian metabolites. To overcome this issue, we chose metabolite
peaks for the human metabolite molecular timetable using two
criteria—shapes and amplitude—with relaxed cutoff scores(FDR ≤ 0.1). Although each cutoff is higher (0.1) than the oneused in the mouse study, the overall cutoff is 0.1 × 0.1 = 0.01
because metabolite peaks were ﬁltered by two independent criteria (FDR ≤ 0.1), which is equivalent to FDR ≤ 0.01 used in themouse study.

Evaluation of Body-Time Estimation with Dim-Light Melatonin Onset(DLMO). We used cortisol peak times (CPT) for determining theBias in Selection of Subjects for the Molecular Timetable Construction.

In our timetable construction, we chose subjects (A–C) whosebody time most closely matched each other and previously reported melatonin and cortisol rhythms. However, there is a possibility of inherent bias in selecting the “best” three subjects.
Consequently, we constructed molecular timetables from all
combinations of three subjects (A–F) and performed body-time
estimation of the other samples (that is, 99 time points in total)that were not used in molecular timetable construction. Fig. S4Dshows the result of this evaluation. Although there were someoutlier estimations, 75% of all body-time estimations showed lessthan 2-h differences in almost all combinations of subjects, andno strong bias in choosing subjects A, B, and C for the timetable
construction seems to be detected. In the combinations of subjects (A, D, and E), (B, D, and E), and (C, D, and E), the estimation results were a bit worse than the other combinations.
This may reﬂect the fact that subjects D and E in CR1 had more
shifts than the other four subjects. These results suggest that the
selection of the subjects for the molecular timetable constructionin this study is appropriate.
Kasukawa et al. www.pnas.org/cgi/content/short/1207768109

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SI Materials and Methods
Sampling. All subjects participated in a 13-d forced desynchronization (FD) protocol in a temporal isolation laboratory freefrom external time cues. Subjects entered individual rooms in thesleep laboratory (5.9 m2 polysomnographic bedroom, 67.9 m2
living room, restroom, and bathroom) at 1700 hours on day 1.
After having a meal and taking a bath, they turned the lights off
and went to bed at 2400 hours and slept for 9 h 20 min on a bed
in an individual room. The FD protocol used in this study was
conducted entirely in the laboratory and was composed of thefollowing three studies: (i) measurement under a constant routine condition (CR1) (4) followed by (ii) a 28-h sleep–wakeschedule for 7 d (5), and (iii) a second measurement under CR2.
The 28-h sleep–wake schedule included 9 h 20 min of sleep(promoting sleep/bedrest with lights off) and 18 h 40 min of
wakefulness (prohibiting sleep). During the FD protocol, subjects
stayed in the laboratory in isolation from external time cues suchas clock, radio, newspaper, cell phone, and Internet access. They
were asked to maintain wakefulness under a low-intensity light
condition (<15 lx) during the scheduled wake period and to sleepin the bedroom with the lights off (0 lx) during the sleep period.
Throughout the study, subjects were under constant surveillanceby seven researchers and were verbally awakened when they
unintentionally took a nap during the wake period. During thewake period, however, they were allowed to move freely aroundin the laboratory, read and write, enjoy music and videos, playvideogames, and engage in conversation with a researcher. Ambient temperature and humidity in the laboratory were maintained at 25 ± 0.5 °C and 50 ± 5% relative humidity (RH),
respectively. During the constant routine conditions, subjects
were required to lie on a reclining chair in a supine position andstay awake for 38 h 40 min. Blood was collected every 2 h using
an i.v. catheter placed in a forearm vein of the subject. Water wasavailable at all times and a 200-kcal meal was provided every 2 h.
The protocol was approved by the institutional ethics committeeof the National Center of Neurology and Psychiatry and was
conducted in accordance with the Declaration of Helsinki.
Measuring Cortisol and Melatonin in the Blood. Plasma was isolated
from whole blood by centrifugation. Plasma melatonin andcortisol levels at each time point were measured using RIA(Bühlmann Lab).
LC–MS Measurement. To 30 μL of the plasma samples, 270 μL of

isopropyl alcohol containing an internal standard (2.2 μmol/L ofcamphor-10-sulfonic acid) was added with shaking. The mixture
was centrifuged at 20,400 × g for 10 min at 4 °C, and the supernatant was then transferred to another tube and vacuum driedat 35 °C. The samples were mixed with Milli-Q water and chloroform in a volume ratio of 2:4:1 containing 20 μmol/L of 3,5-ditert-butyl-4-hydroxyhydrocinnamate, centrifuged at 20,400 × g,
for 5 min at 4 °C, and 25 μL of the supernatant was used for
LC–TOF/MS analysis.
The LC system was an Agilent 1290 inﬁnity HPLC (Agilent
Technologies). The Acquity ultra performance liquid chromatography BEH C18 (1.7 μm, 2.1 mm × 50 mm) column waspurchased from Waters, and was maintained at 50 °C. The mobile phase consisted of 0.5% acetic acid/water as eluent A andisopropanol as eluent B. The gradient was linearly changed fromsolvent (A/B = 99/1, vol/vol) to solvent (A/B = 1/99, vol/vol) for
12 min and held for 5 min. The ﬂow rate was 0.3 mL/min and theinjection volume was 1 μL. MS data were acquired on a 6530
Accurate-Mass Q-TOF LC/MS using the dual spray ESI of G3251A (Agilent). Samples were analyzed by both positive andnegative ion electrospray mass spectrometry. The MS conditions
used were as follows: gas temperature 350 °C, drying gas 10 L/
min, nebulizer 30 psig, fragmentor 200 V, skimmer 90 V, OCT1
RF Vpp 250 V, scan range m/z 100–1,600 and nozzle voltage

Kasukawa et al. www.pnas.org/cgi/content/short/1207768109

1,000 V. The capillary voltages were 3.5 and 4.0 kV for negativeand positive mode, respectively.
Peak Association. We used two peak association algorithms for
intraseries peaks and for interseries peaks. The algorithm for
intraseries peaks was used for associating peaks obtained from
17 samples taken from each subject in any of two constantroutines (CR1 or CR2). For the intraseries peak association, weused the KEIO MasterHands software (6), in which both automated algorithm and manual curation can be performed. For
the interseries peak association, we ﬁrst corrected retention
time (RT) according to the reported method (7), then we associated the peaks in two series with the smallest values of:(difference of m/z between 2 series/X)2 + (difference of RT
between 2 series/Y)2 in peaks with (difference of m/z) < X and(difference of RT) < Y in both series. Parameters X and Y were0.02 and 0.25, respectively.
Normalization. Each peak area was ﬁrst normalized by the area of
the internal standard (camphor-10-sulfonic acid) spiked into eachsample. After this normalization, we also performed a second
normalization as follows:
In the second normalization process (normalization of area
distribution), we ﬁrst selected one (centroid) sample from all
samples (this centroid sample had the largest mean of Pearson’s
correlations with the other samples). Centroid samples wereindependently chosen for positive and negative ion data. Then,for each sample, linear regression to the function Y = aX +b was performed with all log areas of the target (Y) and centroid(X) samples, and areas of the target samples normalized according to the ﬁtted linear function by subtracting the ﬁtted
value b and divided by a from the target areas (Y), resulting in
the normalized target area (Y′ = (Y − b)/a).
Construction of Molecular Timetable. We chose peaks that weredetected and associated at nine or more time points in all threesubjects (subjects A–C) in CR1. For each chosen peak, we performed two statistical tests for selecting candidates for thetimetable. First, we searched for the maximum Pearson’s correlation of a cosine curve of a 24-h period to determine thephase using a Fourier transformation-based method (8) to mean
area values of three subjects by averaging the area values at thesame sampled time. We then estimated P values and FDRs by
permutation tests as shown in the previous study (3). Next, we
tested signiﬁcant diurnal changes against the individual difference using one-way ANOVA, in which we grouped samples ofthree subjects with the same sampled time. We then calculated P
values and FDRs. Finally, we selected peaks that have FDR ≤0.1
in both tests as indicators in the human timetable. We focusedon cosine-wave–like metabolites, because the estimation of peaktime of circadian metabolites of cosine-wave form is not affectedby the time points in the construction of metabolite timetable.
Expected Body Time. First, the mean cortisol peak time of the threesample sets that were used in the timetable construction (subjects
A–C during CR1) was calculated and deﬁned by standard cortisol peak time (PT11.68). Differences of cortisol peak times forthe other nine samples to the standard cortisol peak time were
calculated and determined as differences between expected bodytime and sampled time.
MS Identiﬁcation of Oscillating Metabolites. LC MS/MS analysis was
performed for unknown peak identiﬁcation based on the method
described in the previous report (3) with slight modiﬁcation. The
LC system was an Agilent 1290 inﬁnity HPLC (Agilent Technologies) and the mobile phase consisted of 0.5% acetic acid/water as eluent A and isopropanol as eluent B. MS data wereacquired on a 6530 Accurate-Mass Q-TOF LC/MS using the
2 of 6

dual spray ESI of G-3251A (Agilent Technologies). The collisionenergy was set at a range of 20–60 V. For fragmentation products, the mass range was set between m/z 50 and m/z 1,000. The
peaks at m/z 539.255, 395.194, 509.2852, and 541.2705 wereidentiﬁed by comparing their fragmented spectrum. First MS(Q1) selected their ions and the second MS (Q2 or collision cell)
observed their fragmented spectrums, and these patterns werecompared with those of steroid with glucuronic acid or sulfate
and phospholipid. The other peaks at m/z 421.2268, 329.1783,
1. Dallmann R, Viola AU, Tarokh L, Cajochen C, Brown SA (2012) The human circadian
metabolome. Proc Natl Acad Sci USA 109:2625–2629.
2. Eckel-Mahan KL, et al. (2012) Coordination of the transcriptome and metabolome by
the circadian clock. Proc Natl Acad Sci USA 109:5541–5546.
3. Minami Y, et al. (2009) Measurement of internal body time by blood metabolomics.
Proc Natl Acad Sci USA 106:9890–9895.
4. Mills JN, Minors DS, Waterhouse JM (1978) Adaptation to abrupt time shifts of theoscillator(s) controlling human circadian rhythms. J Physiol 285:455–470.
5. Czeisler CA, et al. (1999) Stability, precision, and near-24-hour period of the humancircadian pacemaker. Science 284:2177–2181.

Kasukawa et al. www.pnas.org/cgi/content/short/1207768109

397.2109, 367.1630, 554.3534, 351.1363, 203.0856, and 130.0889
were identiﬁed by comparing retention time and fragment patternwith authentic specimen for cortisol, cortisone, pregnenolone-3sulfate, dehydroepiandrosterone sulfate, lysophosphatidylcholine,
L-phenylalanine, L-tryptophan, and L-leucine, respectively.
Image of the Steroid Hormone Pathway. The image of the steroid
hormone pathway was generated through the use of IPA (Ingenuity Systems).
6. Sugimoto M, Wong DT, Hirayama A, Soga T, Tomita M (2010) Capillary electrophoresis
mass spectrometry-based saliva metabolomics identiﬁed oral, breast and pancreaticcancer-speciﬁc proﬁles. Metabolomics 6:78–95.
7. Reijeng JC, Martens JH, Giuliani A, Chiari M (2002) Pherogram normalization incapillary electrophoresis and micellar electrokinetic chromatography analyses in casesof sample matrix-induced migration time shifts. J Chromatogr B Analyt Technol
Biomed Life Sci 770:45–51.
8. Chatﬁeld C (1996) The Analysis of Time Series: An Introduction (Chapman & Hall/CRC,
London), 5th Ed, p 304.

3 of 6

Table S1. Summary of human body-time estimation
Target sample
Subject D/CR1/day1-1200 h
Subject D/CR1/day1-1400 h
Subject D/CR1/day1-1600 h
Subject D/CR1/day1-1800 h
Subject D/CR1/day1-2000 h
Subject D/CR1/day1-2200 h
Subject D/CR1/day2-0000 h
Subject D/CR1/day2-0200 h
Subject D/CR1/day2-0400 h
Subject D/CR1/day2-0600 h
Subject D/CR1/day2-0800 h
Subject E/CR1/day1-1200 h
Subject E/CR1/day1-1400 h
Subject E/CR1/day1-1600 h
Subject E/CR1/day1-1800 h
Subject E/CR1/day1-2000 h
Subject E/CR1/day1-2200 h
Subject E/CR1/day2-0000 h
Subject E/CR1/day2-0200 h
Subject E/CR1/day2-0400 h
Subject E/CR1/day2-0600 h
Subject E/CR1/day2-0800 h
Subject F/CR1/day1-1200 h
Subject F/CR1/day1-1400 h
Subject F/CR1/day1-1600 h
Subject F/CR1/day1-1800 h
Subject F/CR1/day1-2000 h
Subject F/CR1/day1-2200 h
Subject F/CR1/day2-0000 h
Subject F/CR1/day2-0200 h
Subject F/CR1/day2-0400 h
Subject F/CR1/day2-0600 h
Subject F/CR1/day2-0800 h
Subject A/CR2/day1-1200 h
Subject A/CR2/day1-1400 h
Subject A/CR2/day1-1600 h
Subject A/CR2/day1-1800 h
Subject A/CR2/day1-2000 h
Subject A/CR2/day1-2200 h
Subject A/CR2/day2-0000 h
Subject A/CR2/day2-0200 h
Subject A/CR2/day2-0400 h
Subject A/CR2/day2-0600 h
Subject A/CR2/day2-0800 h
Subject B/CR2/day1-1200 h
Subject B/CR2/day1-1400 h
Subject B/CR2/day1-1600 h
Subject B/CR2/day1-1800 h
Subject B/CR2/day1-2000 h
Subject B/CR2/day1-2200 h
Subject B/CR2/day2-0000 h
Subject B/CR2/day2-0200 h
Subject B/CR2/day2-0400 h
Subject B/CR2/day2-0600 h
Subject B/CR2/day2-0800 h
Subject C/CR2/day1-1200 h
Subject C/CR2/day1-1400 h
Subject C/CR2/day1-1600 h
Subject C/CR2/day1-1800 h
Subject C/CR2/day1-2000 h
Subject C/CR2/day1-2200 h
Subject C/CR2/day2-0000 h
Subject C/CR2/day2-0200 h

Antiphasic sample

Shift group

Used peaks

Expected phase

Estimated phase

Difference

Correlation

Subject D/CR1/day2-0000 h
Subject D/CR1/day2-0200 h
Subject D/CR1/day2-0400 h
Subject D/CR1/day2-0600 h
Subject D/CR1/day2-0800 h
Subject D/CR1/day2-1000 h
Subject D/CR1/day2-1200 h
Subject D/CR1/day2-1400 h
Subject D/CR1/day2-1600 h
Subject D/CR1/day2-1800 h
Subject D/CR1/day2-2000 h
Subject E/CR1/day2-0000 h
Subject E/CR1/day2-0200 h
Subject E/CR1/day2-0400 h
Subject E/CR1/day2-0600 h
Subject E/CR1/day2-0800 h
Subject E/CR1/day2-1000 h
Subject E/CR1/day2-1200 h
Subject E/CR1/day2-1400 h
Subject E/CR1/day2-1600 h
Subject E/CR1/day2-1800 h
Subject E/CR1/day2-2000 h
Subject F/CR1/day2-0000 h
Subject F/CR1/day2-0200 h
Subject F/CR1/day2-0400 h
Subject F/CR1/day2-0600 h
Subject F/CR1/day2-0800 h
Subject F/CR1/day2-1000 h
Subject F/CR1/day2-1200 h
Subject F/CR1/day2-1400 h
Subject F/CR1/day2-1600 h
Subject F/CR1/day2-1800 h
Subject F/CR1/day2-2000 h
Subject A/CR2/day2-0000 h
Subject A/CR2/day2-0200 h
Subject A/CR2/day2-0400 h
Subject A/CR2/day2-0600 h
Subject A/CR2/day2-0800 h
Subject A/CR2/day2-1000 h
Subject A/CR2/day2-1200 h
Subject A/CR2/day2-1400 h
Subject A/CR2/day2-1600 h
Subject A/CR2/day2-1800 h
Subject A/CR2/day2-2000 h
Subject B/CR2/day2-0000 h
Subject B/CR2/day2-0200 h
Subject B/CR2/day2-0400 h
Subject B/CR2/day2-0600 h
Subject B/CR2/day2-0800 h
Subject B/CR2/day2-1000 h
Subject B/CR2/day2-1200 h
Subject B/CR2/day2-1400 h
Subject B/CR2/day2-1600 h
Subject B/CR2/day2-1800 h
Subject B/CR2/day2-2000 h
Subject C/CR2/day2-0000 h
Subject C/CR2/day2-0200 h
Subject C/CR2/day2-0400 h
Subject C/CR2/day2-0600 h
Subject C/CR2/day2-0800 h
Subject C/CR2/day2-1000 h
Subject C/CR2/day2-1200 h
Subject C/CR2/day2-1400 hModerate
Moderate
Moderate
Moderate
Moderate
Moderate
Moderate
Moderate
Moderate
Moderate
Moderate
Moderate
Moderate
Moderate
Moderate
Moderate
Moderate
Moderate
Moderate
Moderate
Moderate
Moderate
Small
Small
Small
Small
Small
Small
Small
Small
Small
Small
Small
Moderate
Moderate
Moderate
Moderate
Moderate
Moderate
Moderate
Moderate
Moderate
Moderate
Moderate
Small
Small
Small
Small
Small
Small
Small
Small
Small
Small
Small
Small
Small
Small
Small
Small
Small
Small
Small

54
54
53
51
55
55
54
54
53
51
55
54
53
51
53
54
54
54
53
50
53
54
51
49
50
47
50
51
51
50
48
50
53
57
57
58
57
56
56
57
58
55
57
54
52
54
55
55
55
55
53
54
55
55
56
45
54
53
53
53
52
40
55

10.0238
12.0238
14.0238
16.0238
18.0238
20.0238
22.0238
0.02382.02384.02386.02388.707110.7071
12.7071
14.7071
16.7071
18.7071
20.7071
22.70710.70712.70714.707111.653913.653915.653917.653919.653921.653923.65391.65393.65395.65397.65399.326511.3265
13.3265
15.3265
17.3265
19.3265
21.3265
23.3265
1.32653.32655.3265
11.7863
13.7863
15.7863
17.7863
19.7863
21.7863
23.78631.78633.78635.7863
7.786312.31614.31616.31618.31620.31622.316
0.316
2.316

9.6
12.2
14.3
15.7
18.6
18.1
21.3
0.9
2.2
3.4
6.5
10.1
11.1
14.7
16.7
16.3
19.3
22.8
22.1
2.1
3.8
4.2
11.9
13.3
15.4
19.5
20.6
22.5
0.3
1
5.2
5.3
7.8
9.6
12.5
14.5
17.1
17.2
17.4
20.8
22.6
0
2.8
5.7
11.8
13.1
15.6
17.7
20
21.3
23.3
23.8
2
4
6.9
12.3
13.9
17
17.7
19.5
22.1
0.9
1.6

0.42380.17620.27620.32380.5762
1.92380.72380.87620.17620.62380.4762
1.39290.39291.99291.99290.40710.59292.09290.6071
1.39291.09290.50710.24610.35390.25391.84610.94610.84610.64610.65391.54610.35390.14610.27351.17351.17351.77350.1265
1.92650.52650.72651.32650.52650.37350.01370.68630.18630.08630.2137
0.4863
0.4863
1.9863
1.7863
1.78630.8863
0.016
0.416
0.684
0.616
0.816
0.216
0.584
0.716

0.733
0.89890.88250.909
0.66990.74310.75310.85780.82110.78080.67530.66930.90890.91480.8366
0.91370.7496
0.72680.64790.83490.86840.86450.90280.93210.94550.67820.76470.70250.88180.92140.83680.899
0.76760.86540.8876
0.94250.84190.73390.86320.79020.86530.8591
0.79260.57
0.9166
0.91310.93190.91990.82650.8616
0.89260.85880.87640.89750.72290.89650.9167
0.78620.78910.79090.83980.82370.9187

Kasukawa et al. www.pnas.org/cgi/content/short/1207768109

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Table S1. Cont.
Target sample
Subject C/CR2/day2-0400 h
Subject C/CR2/day2-0600 h
Subject C/CR2/day2-0800 h
Subject D/CR2/day1-1200 h
Subject D/CR2/day1-1400 h
Subject D/CR2/day1-1600 h
Subject D/CR2/day1-1800 h
Subject D/CR2/day1-2000 h
Subject D/CR2/day1-2200 h
Subject D/CR2/day2-0000 h
Subject D/CR2/day2-0200 h
Subject D/CR2/day2-0400 h
Subject D/CR2/day2-0600 h
Subject D/CR2/day2-0800 h
Subject E/CR2/day1-1200 h
Subject E/CR2/day1-1400 h
Subject E/CR2/day1-1600 h
Subject E/CR2/day1-1800 h
Subject E/CR2/day1-2000 h
Subject E/CR2/day1-2200 h
Subject E/CR2/day2-0000 h
Subject E/CR2/day2-0200 h
Subject E/CR2/day2-0400 h
Subject E/CR2/day2-0600 h
Subject E/CR2/day2-0800 h
Subject F/CR2/day1-1200 h
Subject F/CR2/day1-1400 h
Subject F/CR2/day1-1600 h
Subject F/CR2/day1-1800 h
Subject F/CR2/day1-2000 h
Subject F/CR2/day1-2200 h
Subject F/CR2/day2-0000 h
Subject F/CR2/day2-0200 h
Subject F/CR2/day2-0400 h
Subject F/CR2/day2-0600 h
Subject F/CR2/day2-0800 h

Antiphasic sample

Shift group

Used peaks

Expected phase

Estimated phase

Difference

Correlation

Subject C/CR2/day2-1600 h
Subject C/CR2/day2-1800 h
Subject C/CR2/day2-2000 h
Subject D/CR2/day2-0000 h
Subject D/CR2/day2-0200 h
Subject D/CR2/day2-0400 h
Subject D/CR2/day2-0600 h
Subject D/CR2/day2-0800 h
Subject D/CR2/day2-1000 h
Subject D/CR2/day2-1200 h
Subject D/CR2/day2-1400 h
Subject D/CR2/day2-1600 h
Subject D/CR2/day2-1800 h
Subject D/CR2/day2-2000 h
Subject E/CR2/day2-0000 h
Subject E/CR2/day2-0200 h
Subject E/CR2/day2-0400 h
Subject E/CR2/day2-0600 h
Subject E/CR2/day2-0800 h
Subject E/CR2/day2-1000 h
Subject E/CR2/day2-1200 h
Subject E/CR2/day2-1400 h
Subject E/CR2/day2-1600 h
Subject E/CR2/day2-1800 h
Subject E/CR2/day2-2000 h
Subject F/CR2/day2-0000 h
Subject F/CR2/day2-0200 h
Subject F/CR2/day2-0400 h
Subject F/CR2/day2-0600 h
Subject F/CR2/day2-0800 h
Subject F/CR2/day2-1000 h
Subject F/CR2/day2-1200 h
Subject F/CR2/day2-1400 h
Subject F/CR2/day2-1600 h
Subject F/CR2/day2-1800 h
Subject F/CR2/day2-2000 h

Small
Small
Small
Moderate
Moderate
Moderate
Moderate
Moderate
Moderate
Moderate
Moderate
Moderate
Moderate
Moderate
Large
Large
Large
Large
Large
Large
Large
Large
Large
Large
Large
Moderate
Moderate
Moderate
Moderate
Moderate
Moderate
Moderate
Moderate
Moderate
Moderate
Moderate

54
54
55
54
51
50
54
55
51
52
53
54
54
53
32
36
36
37
40
34
25
34
31
37
42
53
49
51
50
47
47
48
49
52
51
50

4.316
6.316
8.316
9.5643
11.5643
13.5643
15.5643
17.5643
19.5643
21.5643
23.56431.56433.56435.56431.87483.87485.87487.87489.874811.8748
13.8748
15.8748
17.8748
19.8748
21.87488.148410.1484
12.1484
14.1484
16.1484
18.1484
20.1484
22.1484
0.14842.14844.1484

4.6
4.8
7
10.1
12.8
14.4
17
18.9
18.5
21.9
23.8
0.5
4
8
4.6
6.5
7.2
10.4
12.1
14
17.1
17.5
18.2
21.4
22.6
8.6
9.8
11.8
14
16.7
15.4
18.6
20.5
23.1
0.3
3.8

0.284
1.516
1.316
0.53571.23570.83571.43571.33571.06430.33570.23571.06430.43572.43572.72522.6252
1.32522.52522.22522.12523.2252
1.62520.3252
1.52520.72520.4516
0.34840.34840.14840.55162.7484
1.5484
1.6484
1.0484
1.84840.34840.767
0.68540.51880.81220.87190.93390.88060.90250.82510.82810.89280.90080.9171
0.79370.6901
0.58850.73020.8376
0.79480.8367
0.63170.81550.78950.76890.83890.61770.7872
0.94490.938
0.895
0.88860.806
0.71340.89660.92220.8857

The format of the blood samples is subject name/condition (CR1 or CR2)/day (1 or 2)-time of day (0000–2200 h).

Fig. S1. Procedure to select metabolite peaks for our human metabolic molecular timetable construction.

Fig. S1

Fig. S2. Estimation of human body time. (A) Correlation between cortisol peak time and melatonin peak time. Expected body time is calculated by conventional method using cortisol time-course abundance in this study. Expected body time calculated by melatonin time-course abundance also provides
a similar expected body time because there is strong correlation between cortisol and melatonin peak times. (B) Summary of expected body time and estimated body time. X axes show sampling time (h) and y axes show estimated or expected body time (h). Red dot indicates estimated body time; blue and black
dashed lines indicate expected body time and sampled time, respectively. Expected body time (based on cortisol rhythm) and estimated body time (based onmolecular timetable) were similar in all samples, which is indicated by the observation that the red dots are near blue dashed lines. (C) Details of body-time
estimation results. Results of all body-time estimation performed in this study are shown. Colors of the dots indicate the molecular peak times of each metabolite. Peak time of the red cosine curves indicates estimated body time; peak time of the blue cosine curves indicates expected body time. Dashed verticallines show the estimated body time (red) or expected body time (blue). Header of each graph indicates the blood sample in the format subject name/condition(CR1 or CR2)/day (1 or 2)-time (0000–2200 h).

Fig. S2

Kasukawa et al. www.pnas.org/cgi/content/short/1207768109

5 of 6

Fig. S3. Identiﬁed circadian-oscillating metabolites. (A–D) Abundance is shown of pregnenolone 3-sulfate (A), dehydroepiandrosterone sulfate (B), PG(18:1(9Z)/0:0) (C), and LysoPC(16:0) (D) in the blood taken from three subjects (subjects A–C). X axes show the sampled time (h); y axes show normalized area of thecorresponding peak in LC–MS analysis. We note that pregnenolone 3-sulfate in Fig. S3A and pregnanolone sulfate-like metabolite are different metabolites. (E)Identiﬁed circadian-oscillating metabolites in the steroid hormone pathway. Identiﬁed circadian-oscillating metabolites are indicated in red letters and arrows.
Numbers in parentheses show the peak time of each metabolite.

Fig. S3

Fig. S4. Evaluation of methods for the timetable construction and body-time estimation. (A and B) Evaluation of body-time estimation based on dim-lightmelatonin onset (DLMO). (A) Difference between the expected body time based on DLMO and the sampled time. Nine sample sets for body-time estimation
were classiﬁed into three groups by the magnitude of the difference: small (black), moderate (gray), and large (white). (B) Summary of body-time estimation.
Difference between expected and estimated body time for all estimated samples is shown, in which expected body time was determined on the basis of DLMO,as shown in Fig. 3E. (C) Evaluation of body-time estimation using a single time point and three time points separated by 8 h. X axis shows the number of time
points used in the body-time estimation and the y axis shows the time difference. Each whisker represents the range of time differences; each red box
represents the range of 0.25 and 0.75 quartiles of the time differences, and the horizontal line in each red box is the median of the time differences. (D) Bias inselection of subjects for the molecular timetable construction. The x axis shows the combination of subjects whose samples were used in the timetable
construction and the y axis shows the time difference as shown in C.

Fig. S4

Dataset S1.

List of identiﬁed circadian-oscillating metabolites

Dataset S1
The list contains two sheets, one for a list of circadian-oscillating metabolite detected in the positive-ion mode, and the other is in the negative-ion mode.
Each sheet lists the unique number, m/z, retention time (RT), a mark (*) whether the metabolite is used in the human blood metabolite timetable, the name of
the metabolite if experimentally identiﬁed, the names of computationally predicted metabolites, mean of peak areas, SD of peak areas, peak time (phase),
maximum correlation to cosine curves, P value of the cosine ﬁtting, FDR of the cosine ﬁtting, P value of the ANOVA test, FDR of the ANOVA test, and peakareas for all blood samples.

Kasukawa et al. www.pnas.org/cgi/content/short/1207768109

6 of 6

LC-MS analysis

Peak association among samples
normalization of peak areas

4,679 positive; 4,016 negative ions

Filtering out peaks
detected in less than 9 time points

2,541 positive; 1,796 negative ions

Cosine fitting to detect circadian oscillation
Calculation of P-value with permutation test
Correction of multiple comparisons

Selection of peaks with FDR≤0.1

Circadian-oscillating metabolite peaks
142 positive; 168 negative ions

One-way ANOVA to detect significant amplitudes
Correction of multiple comparisons

Selection of peaks with FDR≤0.1

Fig. S1

Human molecular timetable peaks
33 positive; 25 negative ions

A

Function: 1.01x 7.51

Fig. S2

Melatonin peak time h

20
15
10
5
0

0

5

10

15

Sampled time
Expected BT(Cortisol rhythm)
Estimated BT(Molecular timetable)

20

Cortisol peak time hSubj. D CR1

105
12 16 20

0

4

15

15

10

0

8

5
12 16 20

0

4

0

8

0

4

Sampled time hSubj. A CR2

Subj. B CR2

Subj. C CR2

20

15

15

10

10

0

4

0

8

15
10
5

5
12 16 20

12 16 20

0

4

0

8

12 16 20

04

Sampled time hSampled time hSampled time hSubj. D CR2

Subj. E CR2

Subj. F CR2

15

15

15

Time h20

Time h

20

10

10
5

5
12 16 20

04

Sampled time h

8

0

8

20

20

0

12 16 20

Sampled time h

20

0

10

Sampled time hTime hTime h20

55Time h20
Time h15Time hTime h200Subj. F CR1

Subj. E CR1Time hB8105

12 16 20

04

Sampled time h8

0

12 16 20

04

Sampled time h

8

10

15

20

2
1
0
1
2
0

5

10

15

20

2
1
0
1
2
0

5

10

15

20

Peak time hPeak time h

Subject DCR1Day120h

Subject DCR1Day122h

Subject DCR1Day200h

2
1
0
1
2
0

5

10

15

20

Standard deviation

Peak time hStandard deviation

2
1
0
1
2
0

5

10

15

20

2
1
0
1
2
0

5

10

15

20

Peak time hPeak time h

Subject DCR1Day202h

Subject DCR1Day204h

Subject DCR1Day206h

Standard deviation

Peak time h

2
1
0
1
2
0

5

10

15

20

Peak time hStandard deviation

Standard deviation

5

Subject DCR1Day118h
Standard deviation

2
1
0
1
2
0

Standard deviation

Subject DCR1Day116h
Standard deviation

Standard deviation

Subject DCR1Day114h

2
1
0
1
2
0

5

10

15

Peak time h

20

2
1
0
1
2
0

5

10

15

20

Peak time h

Subject DCR1Day208h
Standard deviation

C

2
1
0
1
2
0

Sampled time
Expected BT(Cortisol rhythm)
Estimated BT(Molecular timetable)

5

10

15

Peak time hFig. S2

20

10

15

20

2
1
0
1
2
0

5

10

15

20

2
1
0
1
2
0

5

10

15

20

Peak time hPeak time h

Subject ECR1Day120h

Subject ECR1Day122h

Subject ECR1Day200h

2
1
0
1
2
0

5

10

15

20

Standard deviation

Peak time hStandard deviation

2
1
0
1
2
0

5

10

15

20

2
1
0
1
2
0

5

10

15

20

Peak time hPeak time h

Subject ECR1Day202h

Subject ECR1Day204h

Subject ECR1Day206h

Standard deviation

Peak time h

2
1
0
1
2
0

5

10

15

20

Peak time hStandard deviation

Standard deviation

5

Subject ECR1Day118h
Standard deviation

2
1
0
1
2
0

Standard deviation

Subject ECR1Day116h
Standard deviation

Standard deviation

Subject ECR1Day114h

2
1
0
1
2
0

5

10

15

Peak time h

20

2
1
0
1
2
0

5

10

15

20

Peak time hStandard deviation

Subject ECR1Day208h

2
1
0
1
2
0

Sampled time
Expected BT(Cortisol rhythm)
Estimated BT(Molecular timetable)

5

10

15

Peak time hFig. S2

20

10

15

20

2
1
0
1
2
0

5

10

15

20

2
1
0
1
2
0

5

10

15

20

Peak time hPeak time h

Subject FCR1Day120h

Subject FCR1Day122h

Subject FCR1Day200h

2
1
0
1
2
0

5

10

15

20

Standard deviation

Peak time hStandard deviation

2
1
0
1
2
0

5

10

15

20

2
1
0
1
2
0

5

10

15

20

Peak time hPeak time h

Subject FCR1Day202h

Subject FCR1Day204h

Subject FCR1Day206h

Standard deviation

Peak time h

2
1
0
1
2
0

5

10

15

20

Peak time hStandard deviation

Standard deviation

5

Subject FCR1Day118h
Standard deviation

2
1
0
1
2
0

Standard deviation

Subject FCR1Day116h
Standard deviation

Standard deviation

Subject FCR1Day114h

2
1
0
1
2
0

5

10

15

Peak time h

20

2
1
0
1
2
0

5

10

15

20

Peak time hStandard deviation

Subject FCR1Day208h

2
1
0
1
2
0

Sampled time
Expected BT(Cortisol rhythm)
Estimated BT(Molecular timetable)

5

10

15

Peak time hFig. S2

20

10

15

20

2
1
0
1
2
0

5

10

15

20

2
1
0
1
2
0

5

10

15

20

Peak time hPeak time h

Subject ACR2Day120h

Subject ACR2Day122h

Subject ACR2Day200h

2
1
0
1
2
0

5

10

15

20

Standard deviation

Peak time hStandard deviation

2
1
0
1
2
0

5

10

15

20

2
1
0
1
2
0

5

10

15

20

Peak time hPeak time h

Subject ACR2Day202h

Subject ACR2Day204h

Subject ACR2Day206h

Standard deviation

Peak time h

2
1
0
1
2
0

5

10

15

20

Peak time hStandard deviation

Standard deviation

5

Subject ACR2Day118h
Standard deviation

2
1
0
1
2
0

Standard deviation

Subject ACR2Day116h
Standard deviation

Standard deviation

Subject ACR2Day114h

2
1
0
1
2
0

5

10

15

Peak time h

20

2
1
0
1
2
0

5

10

15

20

Peak time hStandard deviation

Subject ACR2Day208h

2
1
0
1
2
0

Sampled time
Expected BT(Cortisol rhythm)
Estimated BT(Molecular timetable)

5

10

15

Peak time hFig. S2

20

10

15

20

2
1
0
1
2
0

5

10

15

20

2
1
0
1
2
0

5

10

15

20

Peak time hPeak time h

Subject BCR2Day120h

Subject BCR2Day122h

Subject BCR2Day200h

2
1
0
1
2
0

5

10

15

20

Standard deviation

Peak time hStandard deviation

2
1
0
1
2
0

5

10

15

20

2
1
0
1
2
0

5

10

15

20

Peak time hPeak time h

Subject BCR2Day202h

Subject BCR2Day204h

Subject BCR2Day206h

Standard deviation

Peak time h

2
1
0
1
2
0

5

10

15

20

Peak time hStandard deviation

Standard deviation

5

Subject BCR2Day118h
Standard deviation

2
1
0
1
2
0

Standard deviation

Subject BCR2Day116h
Standard deviation

Standard deviation

Subject BCR2Day114h

2
1
0
1
2
0

5

10

15

Peak time h

20

2
1
0
1
2
0

5

10

15

20

Peak time hStandard deviation

Subject BCR2Day208h

2
1
0
1
2
0

Sampled time
Expected BT(Cortisol rhythm)
Estimated BT(Molecular timetable)

5

10

15

Peak time hFig. S2

20

10

15

20

2
1
0
1
2
0

5

10

15

20

2
1
0
1
2
0

5

10

15

20

Peak time hPeak time h

Subject CCR2Day120h

Subject CCR2Day122h

Subject CCR2Day200h

2
1
0
1
2
0

5

10

15

20

Standard deviation

Peak time hStandard deviation

2
1
0
1
2
0

5

10

15

20

2
1
0
1
2
0

5

10

15

20

Peak time hPeak time h

Subject CCR2Day202h

Subject CCR2Day204h

Subject CCR2Day206h

Standard deviation

Peak time h

2
1
0
1
2
0

5

10

15

20

Peak time hStandard deviation

Standard deviation

5

Subject CCR2Day118h
Standard deviation

2
1
0
1
2
0

Standard deviation

Subject CCR2Day116h
Standard deviation

Standard deviation

Subject CCR2Day114h

2
1
0
1
2
0

5

10

15

Peak time h

20

2
1
0
1
2
0

5

10

15

20

Peak time hStandard deviation

Subject CCR2Day208h

2
1
0
1
2
0

Sampled time
Expected BT(Cortisol rhythm)
Estimated BT(Molecular timetable)

5

10

15

Peak time hFig. S2

20

10

15

20

2
1
0
1
2
0

5

10

15

20

2
1
0
1
2
0

5

10

15

20

Peak time hPeak time h

Subject DCR2Day120h

Subject DCR2Day122h

Subject DCR2Day200h

2
1
0
1
2
0

5

10

15

20

Standard deviation

Peak time hStandard deviation

2
1
0
1
2
0

5

10

15

20

2
1
0
1
2
0

5

10

15

20

Peak time hPeak time h

Subject DCR2Day202h

Subject DCR2Day204h

Subject DCR2Day206h

Standard deviation

Peak time h

2
1
0
1
2
0

5

10

15

20

Peak time hStandard deviation

Standard deviation

5

Subject DCR2Day118h
Standard deviation

2
1
0
1
2
0

Standard deviation

Subject DCR2Day116h
Standard deviation

Standard deviation

Subject DCR2Day114h

2
1
0
1
2
0

5

10

15

Peak time h

20

2
1
0
1
2
0

5

10

15

20

Peak time hStandard deviation

Subject DCR2Day208h

2
1
0
1
2
0

Sampled time
Expected BT(Cortisol rhythm)
Estimated BT(Molecular timetable)

5

10

15

Peak time hFig. S2

20

10

15

20

2
1
0
1
2
0

5

10

15

20

2
1
0
1
2
0

5

10

15

20

Peak time hPeak time h

Subject ECR2Day120h

Subject ECR2Day122h

Subject ECR2Day200h

2
1
0
1
2
0

5

10

15

20

Standard deviation

Peak time hStandard deviation

2
1
0
1
2
0

5

10

15

20

2
1
0
1
2
0

5

10

15

20

Peak time hPeak time h

Subject ECR2Day202h

Subject ECR2Day204h

Subject ECR2Day206h

Standard deviation

Peak time h

2
1
0
1
2
0

5

10

15

20

Peak time hStandard deviation

Standard deviation

5

Subject ECR2Day118h
Standard deviation

2
1
0
1
2
0

Standard deviation

Subject ECR2Day116h
Standard deviation

Standard deviation

Subject ECR2Day114h

2
1
0
1
2
0

5

10

15

Peak time h

20

2
1
0
1
2
0

5

10

15

20

Peak time hStandard deviation

Subject ECR2Day208h

2
1
0
1
2
0

Sampled time
Expected BT(Cortisol rhythm)
Estimated BT(Molecular timetable)

5

10

15

Peak time hFig. S2

20

10

15

20

2
1
0
1
2
0

5

10

15

20

2
1
0
1
2
0

5

10

15

20

Peak time hPeak time h

Subject FCR2Day120h

Subject FCR2Day122h

Subject FCR2Day200h

2
1
0
1
2
0

5

10

15

20

Standard deviation

Peak time hStandard deviation

2
1
0
1
2
0

5

10

15

20

2
1
0
1
2
0

5

10

15

20

Peak time hPeak time h

Subject FCR2Day202h

Subject FCR2Day204h

Subject FCR2Day206h

Standard deviation

Peak time h

2
1
0
1
2
0

5

10

15

20

Peak time hStandard deviation

Standard deviation

5

Subject FCR2Day118h
Standard deviation

2
1
0
1
2
0

Standard deviation

Subject FCR2Day116h
Standard deviation

Standard deviation

Subject FCR2Day114h

2
1
0
1
2
0

5

10

15

Peak time h

20

2
1
0
1
2
0

5

10

15

20

Peak time hStandard deviation

Subject FCR2Day208h

2
1
0
1
2
0

Sampled time
Expected BT(Cortisol rhythm)
Estimated BT(Molecular timetable)

5

10

15

Peak time hFig. S2

20

Normalized quantity

A

Pregnenolone 3sulfate
0.030
0.025
0.020
0.015

Dehydroepiandrosterone sulfate

C

0.016

0.011

0.014

0.010

0.012

0.009

0.010

0.008

0.008

0.007

PG18:19Z0:0

0.010
12 16 20 0

4

8 12 16 20

Time hD
Normalized quantity

B

12 16 20 0

4

8 12 16 20

12 16 20 0

4

8 12 16 20

Time hTime h

LysoPC16:0

Subj. A
Subj. B
Subj. C

2.0
1.9
1.8
1.7
1.6
1.5
12 16 20 0

4

8 12 16 20

Time hE

Dehydroepiandrosterone
sulfate 17.0

12.6(-3-glucronide) 14.8

Fig. S3(-3-sulfate) 13.9

11.5(-3-glucronide or-21-glucronide) 15.4(-sulfate like) 15.1

A

CR1

CR2

Subj. D
Subj. E
Subj. F
Subj. A
Subj. B
Subj. C
Subj. D
Subj. E
Subj. F

10

5

Fig. S40

5

10

Difference between sampled and expected time (h)B
Difference h

10
5
0
5
10

C

Subj. F Subj. B Subj. C Subj. D Subj. E Subj. A Subj. D Subj. F Subj. E(CR1) (CR2) (CR2) (CR1) (CR1) (CR2) (CR2) (CR2) (CR2)12Time difference h

10
8
0.75

4

0.5

2

0.25

0

D

1

6

0
1 time point

2 time points

3 time points

12

8

1

6

0.75

4

0.5

2

0.25

0

0
ABC
ABD
ABE
ABF
ACD
ACE
ACF
ADE
ADF
AEF
BCD
BCE
BCF
BDE
BDF
BEF
CDE
CDF
CEF
DEF

Time difference h10