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Identification of Human Plasma Metabolites Exhibiting Time-of-Day
Variation Using an Untargeted Liquid Chromatography–Mass
Spectrometry Metabolomic Approach
Joo Ern Ang,
1
Victoria Revell,
2
Anuska Mann,
2
Simone Mäntele,
2
Daniella T. Otway,
2
Jonathan D. Johnston,
2
Alfred E. Thumser,
2
Debra J. Skene,
2
and Florence Raynaud
1
1
Cancer Research UK Cancer Therapeutics Unit, Division of Cancer Therapeutics, The Institute of Cancer Research, Sutton,
Surrey, UK,
2
Faculty of Health and Medical Sciences, University of Surrey, Guildford, Surrey, UK
Although daily rhythms regulate multiple aspects of human physiology, rhythmic control of the metabolome remains
poorly understood. The primary objective of this proof-of-concept study was identification of metabolites in human
plasma that exhibit significant 24-h variation. This was assessed via an untargeted metabolomic approach using liquid
chromatography–mass spectrometry (LC-MS). Eight lean, healthy, and unmedicated men, mean age 53.6 (SD ± 6.0) yrs,
maintained a fixed sleep/wake schedule and dietary regime for 1 wk at home prior to an adaptation night and
followed by a 25-h experimental session in the laboratory where the light/dark cycle, sleep/wake, posture, and calorific
intake were strictly controlled. Plasma samples from each individual at selected time points were prepared using
liquid-phase extraction followed by reverse-phase LC coupled to quadrupole time-of-flight MS analysis in positive
ionization mode. Time-of-day variation in the metabolites was screened for using orthogonal partial least square
discrimination between selected time points of 10:00 vs. 22:00 h, 16:00 vs. 04:00 h, and 07:00 (d 1) vs. 16:00 h, as well
as repeated-measures analysis of variance with time as an independent variable. Subsequently, cosinor analysis was
performed on all the sampled time points across the 24-h day to assess for significant daily variation. In this study,
analytical variability, assessed using known internal standards, was low with coefficients of variation <10%. A total of
1069 metabolite features were detected and 203 (19%) showed significant time-of-day variation. Of these, 34
metabolites were identified using a combination of accurate mass, tandem MS, and online database searches. These
metabolites include corticosteroids, bilirubin, amino acids, acylcarnitines, and phospholipids; of note, the magnitude of
the 24-h variation of these identified metabolites was large, with the mean ratio of oscillation range over MESOR (24-h
time series mean) of 65% (95% confidence interval [CI]: 49–81%). Importantly, several of these human plasma
metabolites, including specific acylcarnitines and phospholipids, were hitherto not known to be 24-h variant. These
findings represent an important baseline and will be useful in guiding the design and interpretation of future
metabolite-based studies. (Author correspondence: Jooern.Ang@icr.ac.uk or Florence.Raynaud@icr.ac.uk)
Keywords: Acylcarnitines, Daily variation, Human, Liquid chromatography–mass spectrometry, Metabolomics, Plasma
metabolites
INTRODUCTION
Metabolomics is the study of small molecule (<1 kDa)
metabolic profiles in biological systems, and comp-
lements genomic and proteomic approaches in providing
global views of biological processes. Metabolic profiles
capture endogenous and exogenous influences on a
living organism and may provide better representation
of its functional phenotype than changes in DNA, RNA,
and proteins (Allen et al., 2003; Nicholson et al., 2002).
Hence, metabolic perturbations caused by such disparate
factors as genetic changes, microbes, diseases, food, and
therapeutic interventions may be investigated using a
metabolomic approach (Allen et al., 2003; Nicholson
et al., 2002).
Recently published studies have identified clear and
pervasive circadian influence on the murine hepatic
metabolome (Eckel-Mahan et al., 2012; Fustin et al.,
2012). Plasma metabolites in a mouse model that vary
significantly with time-of-day have crucially been
Address correspondence to Dr. Joo Ern Ang or Dr. Florence Raynaud, Drug Metabolism, Pharmacokinetics & Metabolomics Team, Cancer
Research UK Cancer Therapeutics Unit, Division of Cancer Therapeutics, The Institute of Cancer Research, Sutton, Surrey SM2 5NG, United
Kingdom. Tel.: +44 (0)2087224383; Fax: +44 (0)2087224309; E-mail: Jooern.Ang@icr.ac.uk(Joo Ern Ang) or Florence.Raynaud@icr.ac.uk
(Florence Raynaud)
Submitted January 12, 2012, Returned for revision February 16, 2012, Accepted May 22, 2012
Chronobiology International, 29(7): 868–881, (2012)
Copyright © Informa Healthcare USA, Inc.
ISSN 0742-0528 print/1525-6073 online
DOI: 10.3109/07420528.2012.699122
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identified in a landmark study; 14 oscillating metabolites
were identified by liquid chromatography–mass spec-
trometry (LC-MS) and 28 by capillary electrophoresis–
mass spectrometry (Minami et al., 2009). Direct trans-
lation of these animal data to humans, however, cannot
be made. In addition to the obvious problem of compar-
ing diurnal and nocturnal species, the timing and
amount of feeding, activity, rest, sleep, and posture
were not controlled in these animal experiments.
Although daily rhythms regulate multiple aspects of
human physiology, rhythmic control of the plasma meta-
bolome remains poorly understood. Previous studies
have investigated time-of-day variation in the human me-
tabolome by comparing metabolomic profiles of morning
with early evening urine samples (Slupsky et al., 2007;
Walsh et al., 2006). Although both studies identified sig-
nificant changes in metabolite levels, such as increased
creatinine and dimethylamine in the morning samples,
comparison of samples collected at only two time points
does not provide comprehensive overview of variation
across the 24-h day. Furthermore, these studies did not
control for sleep, activity, medication, alcohol/caffeine
intake, or environmental lighting, and thus, the results
obtained are extremely unlikely to show nonconfounded
time-of-day variation. To overcome this, a recent study
employed a targeted platform to study the human
plasma metabolome under controlled laboratory con-
ditions (Dallmann et al., 2012). The analytical platform,
however, was limited to 281 metabolites, and plasma
samples from all subjects were pooled at each time
point, precluding analyses of intersubject variability and
intrasubject daily variation and potentially reducing
the sensitivity of this methodology. The current study
circumvented these limitations by the use of a global,
untargeted metabolomic approach in the analysis of indi-
vidual plasma samples collected from healthy human vol-
unteers at different time points. The aim of this proof-of-
concept study was characterization of 24-h variation of
the plasma metabolome in human subjects maintained
under highly controlled conditions of food, posture,
light/dark cycle, sleep/wake schedule, and prior exposure
to pharmacologic agents. The technological platform with
LC-MS used in this study has been previously validated by
our group with over a thousand metabolites in plasma
being reproducibly detected (Pandher et al., 2009, 2012).
MATERIALS AND METHODS
All aspects of the study were conducted in accordance
with the Declaration of Helsinki and conformed to
international ethical standards (Portaluppi et al., 2010).
A favorable ethical opinion was obtained from the
Surrey Research Ethics Committee and the University of
Surrey Ethics Committee. Written informed consent
was obtained from all participants.
Eligibility for the study was determined via self-
completed questionnaires, including General Health
Questionnaire, General Sleep Questionnaire, Horne-Östberg
Questionnaire, Pittsburgh Sleep Quality Index, Beck
Depression Inventory, and Epworth Sleepiness Scale,
to assess general health, sleep patterns, and diurnal pre-
ference. Full details of the screening process have been
previously presented (Otway et al., 2011). To be in-
cluded, subjects needed to report a regular sleep sche-
duleofbetween6and8hindurationandnotbe
extreme morning or evening chronotype according to
the Horne-Östberg Questionnaire (Horne & Östberg,
1976). Subjects were excluded if they were taking
regular medication or food supplements known to influ-
ence metabolism, inflammatory markers, endothelial
markers, sleep, or the circadian system, or if they con-
sumed more than four caffeinated beverages per day.
Subjects with a history of any of the following were
also excluded: (a) circadian or sleep disorder; (b)
metabolic, cardiovascular, or chronic infectious/inflam-
matory disease; (c) psychiatric or neurological disease;
and (d) drug and alcohol abuse.
Eight lean, male volunteers were recruited with a
mean age of 53.6 (SD ±6.0) yrs, mean body mass index
(BMI) of 23.2 kg/m
2
(SD ±1.4), and fasting glucose of
4.2 mmol/L (SD ±.7). One participant was a smoker,
but refrained from smoking for 1 wk prior to study. No
participant had undertaken shiftwork within 5 yrs or
crossed any time zones within 1 mo of the study. For 1
wk prior to the laboratory study, volunteers were required
to maintain scheduled daily meal times (monitored by
food diaries) and a fixed sleep/wake schedule (23:00–
07:00 h), confirmed via wrist actigraphy (Actiwatch-L;
Cambridge Neurotechnology, Cambridge, UK), sleep
diaries, and calling a time-stamped voicemail (Otway
et al., 2011). Participants also abstained from eating
fatty or sugary foods and drinking alcohol or caffeine
throughout this baseline week. For the final 3 d of this
week, participants were provided with meals of specific
nutritional content: the daily calorific content was 1.5-
fold the basal metabolic rate (estimated in calories =
11.5 × body weight [kg] + 873), with ∼35% of energy
derived from fat (Schofield, 1985).
In-laboratory Session
The in-laboratory session was conducted at the Surrey
Clinical Research Centre. Following an adaptation night
in the laboratory, subjects were woken at 06:30 h and
commenced a 25-h experimental session throughout
which they maintained a semi-recumbent posture to
minimize the impact of exogenous factors on the
measured parameters. Subjects remained awake in
normal room lighting (range 440–825 lux in the direction
of gaze) between 06:30 and 22:30 h and were allowed to
sleep between 22:30 and 06:30 h in 0 lux. During the
waking period, participants were provided with hourly
nutritional drinks (Fortisip; Nutricia, Schiphol, The
Netherlands) and were allowed to drink water ad
libitum. The hourly consumption of this drink met the
protein, carbohydrate, fat, and fiber requirements of
each participant. Daily energy intake was 1.1-fold basal
Time-of-Day Variation in Human Plasma Metabolites
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metabolic rate spread equally over the waking hours.
Throughout the protocol, including overnight, blood
samples were collected via an indwelling cannula by a
qualified person, and attempts were made to minimize
any disruption of the participants’sleep. Blood samples
were collected at selected time points (.5 mL/time point
for metabolomic analysis) into lithium heparin tubes,
and the plasma fraction was separated by centrifugation
(3000 × g, 10 min, 4°C) and stored at −80°C.
Reagents and Solutions
Water (LC-MS grade), acetonitrile (LC-MS grade), and
formic acid (Aristar grade) were purchased from Fisher
Scientific (Loughborough, UK), and leucine enkephalin
was purchased from Sigma (Poole, UK). External stan-
dards, creatine (CAS number: 57-00-1), and colchicine
(CAS number: 64-86-8), were purchased from Sigma.
Plasma Preparation
Samples from selected time points (07:00, 10:00, 16:00,
22:00, 04:00, and 07:00 h [d 2]) were extracted by
mixing 1 volume of heparinized plasma with 4 volumes
of methanol/ethanol 1:1, followed by centrifugation at
18 000 × gfor 15 min at 4°C.
Assessment of Analytical Variability
Pooled plasma from the eight human subjects from all
the sampled time points (i.e., 48 samples) served as
quality controls and was analyzed throughout the exper-
imental batch to continuously monitor the analytical
variability of the system. These quality-control samples
(1/10 injections) were spiked with 1 mM colchicine and
creatine. Variability of these spiked compounds and
endogenous metabolites, including carnitine, phenyl-
alanine, and lysophosphatidylcholine (lysoPC(16:0)),
were evaluated.
LC-MS
Experiments were carried out on two LC-MS systems,
namely Acquity UPLC coupled to QTOF Premier mass
spectrometer (Waters Corporation, Manchester, UK) and
Agilent 1290 Series UPLC connected to a hybrid quadru-
pole-time-of-flight Agilent 6510 mass spectrometer
(Agilent, Waldbronn, Germany); the first system was used
for full-scan analysis and the second system for MS/MS.
The robustness, reproducibility, and cross-platform vali-
dation of the two systems in studying the exo-metabolome,
including plasma, have been previously published
(Pandher et al., 2009, 2012). To minimize systematic
analytical drift from use of large sample numbers and as
the analytical reproducibility of the system is high, one
analytical replicate from each individual per time point
was used. An electrospray ionization source in positive
mode was used for both LC-MS setups in this proof-of-
concept study, as the ions detected in the positive mode
are known to represent a large proportion of the exo-meta-
bolome in our analytical system (Pandher et al., 2009).
Briefly, chromatographic separation was performed
on a Waters Acquity HSS T3 C18 (100 × 2.1 mm, internal
diameter [I.D.] 1.8 µm) column. Mobile phase A was LC-
MS-grade water containing .1% formic acid and mobile
phase B was LC-MS-grade acetonitrile containing .1%
formic acid. The column and the autosampler were
maintained at a temperature of 50°C and 4°C, respect-
ively. A 13-min linear gradient elution was performed
as follows: 100% mobile phase A for the first .5 min, chan-
ging to 100% B over 7.5 min, holding at 100% B up to 9.5
min, and finally back to 100% A at 10 min and holding for
3 min. The flow rate was .6 mL/min, with an injection
volume of 10 µL. The MS instrument and data acquisition
parameters were as previously described (Pandher et al.,
2009, 2012).
Data Handling and Statistical Considerations
Raw data were detected, aligned, and processed using
MarkerLynx application manager software (version 4.1;
Waters, Milford, MA, USA), with parameters documented
previously (Pandher et al., 2009). Each metabolite
feature was characterized by a unique combination of
mass/charge ratio and retention time. The data matrix
obtained was subsequently subjected to multivariate stat-
istical analysis using (i) SIMCA-P v11.0 software (Ume-
trics AB, Umeå, Sweden): metabolite features that were
differentially expressed in one or more of three chosen
sets of selected time points—10:00 vs. 22:00 h, 16:00 h
vs. 04:00 h, and 07:00 h (d 1) vs. 16:00 h—were identified
using orthogonal partial least squares-discriminant
analysis (OPLS-DA) with a low threshold of |p(corr)| > .5
on the OPLS-DA S-plot (Wiklund et al., 2008); and (ii)
repeated-measures analysis of variance (ANOVA) with
time as an independent variable; statistical significance
was deemed to be achieved at p< .05. Extracted ion chro-
matograms (EICs) of the selected metabolite features
were then generated using QuanLynx application
manager software (version 4.1; Waters). Finally, cosinor
analysis using the mean peak height of EICs of all metab-
olite features of interest at each time point by the method
of least squares (period of 24 h) was carried out to derive
estimates by the cosine curve approximation of
MESOR (24-h time series mean), amplitude (one-half
peak-to-trough variation), acrophase (peak) time, and
pvalue for test of the null hypothesis that the amplitude
of the fitted curve was 0; (Nelson et al., 1979); rhythm
detection was considered statistically significant when
p< .05 for the zero-amplitude test.
Metabolite Identification
The accurate mass and tandem MS fragmentation
pattern of each metabolite feature of interest was
ascertained and identification performed by database
searching (including Human Metabolome Database,
Lipid maps, and Metlin) and/or comparison with pure
commercial standards. MS/MS was performed on the
Agilent system with a default iso-width (width half-
maximum of the quadrupole mass bandpass used
J. E. Ang et al.
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during MS/MS precursor isolation) of 4 m/zusing a fixed
collision energy of 15V and data acquired in the range of
50 to 800 Da.
RESULTS
Analytical Reproducibility
Within the pooled plasma quality-control samples, coef-
ficients of variation for endogenous metabolites (carni-
tine, phenylalanine, and lysoPC(16:0)) were 1.7%, 3.0%,
and 8%, respectively, whereas that of spiked exogenous
compounds (creatine and colchicine) were 3.6% and
7.7%, respectively.
Details of Workflow
Figure 1 summarizes our data analysis workflow. In the
present study, a total of 1069 metabolite features were
detected across all analyzed plasma samples. Of these,
318 features passed the OPLS-DA filter using three pair-
wise comparisons of 10:00 vs. 22:00 h, 16:00 vs. 04:00 h,
and 07:00 vs. 16:00 h. Subsequently, 167 features were
confirmed to be significantly 24-h variant using cosinor
analysis of EIC data ( p< .05). In parallel, using repeated-
measures ANOVA, 254 putative features were detected,
and 203 were confirmed to exhibit 24-h variation. The
203 confirmed features detected by repeated-measures
ANOVA represented the sum total of all the temporally
variant features detected in this study (19% of all detected
features in this study) and included all 167 features ident-
ified by OPLS-DA and 36 features additionally identified
by repeated-measures ANOVA.
Of a total of 203 24-h variant features, the levels of 110
(54%) were significantly different between 16:00 and
04:00 h, whereas those of 68 (33%) and 65 (32%) were sig-
nificantly different between 10:00 and 22:00 h, and 07:00
and 16:00 h, respectively. Repeated-measures ANOVA
detected 36 features unique to those obtained from the
stated paired comparisons. These results are summar-
ized in Supplementary Figure 1 and Supplementary
Table 1. Using a combination of accurate mass, tandem
MS, and online database searches, the identities of 34
metabolites were determined from the 203 rhythmic
features. Fragmentation patterns and properties of
these compounds on our LC-MS system are summarized
in Table 1.
Variation of Human Plasma Metabolite Levels Across
Time-of-Day
Metabolites showing significant 24-h variation were from
a variety of chemical classes and included acylcarnitines,
lysophospholipids, bilirubin, corticosteroids, and amino
acids. For these identified compounds, the mean ratio
of oscillation range relative to the MESOR was 65%
(95% confidence interval [CI]: 49–81%). Biological varia-
bility was consistently greater than analytical variability;
the mean analytical coefficient of variation of these ions
was 8% (95% CI: 6–10%).
The fitted peak times of the identified 24-h varying
plasma metabolites were spread across the day, but
appeared to be clustered around early morning, after-
noon, and evening (Table 1). Levels of long-chain un-
saturated acylcarnitines (C14:1, C14:2, and C18:1)
peaked before the long-chain saturated acylcarnitines
(C10, C12, C14, C16, and C18) in the morning,
whereas the acrophases of short-chain acylcarnitines
were observed at radically different times across the
day (C2 at 4.9 h, C6 at 5.4 h, C3 at 14.3 h, and C4 at
17.7 h). Bilirubin and cortisol peaked after the start
of the light phase, whereas the levels of detected
amino acids, such as methionine, tyrosine, proline,
lysine, phenylalanine, and leucine, were highest from
mid- to late afternoon. By contrast, levels of carnitine,
alanine, arginine, tryptophan, and valine (Supplemen-
tary Table 1) did not vary significantly over the 24 h.
Of the detected phospholipids, lysophosphatidyletha-
nolamines (lysoPEs) peaked in the late afternoon
and early evening, followed by the phosphatidyl-
cholines (PCs), which peaked later in the evening.
In contrast, two other phosphocholines (lysoPC(16:0)
and lysoPC(18:1)) (Supplementary Table 2) detected
in our analytical system had levels that did not
change significantly over the day.
The peak height versus time profiles of four selected
metabolites are presented in Figure 2, whereas Figure 3
additionally shows the time profile of one of the metabolites
of interest, acetylcarnitine, in each of the eight subjects.
DISCUSSION
In this study, an untargeted, global metabolomic
approach was employed to discover novel 24-h rhythmic
FIGURE 1. Flowchart summarizing data analysis workflow.
Time-of-Day Variation in Human Plasma Metabolites
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metabolites in human plasma obtained under highly
controlled conditions. In this context, a UPLC-QTOF
MS platform was chosen for its high reproducibility, sen-
sitivity, and mass accuracy. It is stressed that rigorous
conditions of sample collection are necessary, as the
plasma metabolome is known to be exquisitely sensitive
to a broad range of intrinsic and extrinsic factors (Lawton
et al., 2008; Lewis et al., 2010; Psychogios et al., 2011).
A panel of 24-h rhythmic metabolites from a range
of chemical classes were identified, including bilirubin,
corticosteroids, amino acids, acylcarnitines, and phos-
pholipids. Nineteen percent of the metabolite features
detected using our analytical platform exhibited signifi-
cant daily variation, with the mean oscillation range
over MESOR ratio of 65% (95% CI: 49–81%) in the ident-
ified metabolites, pointing to the presence of clinically
significant time-of-day biological variation.
In order to identify daily rhythms in metabolites, two
sets of time points 12-h apart were selected for compari-
son, the rationale being they have a periodicity of ∼24 h
and typically oscillate in a manner that approximates a
cosine curve with the peak and nadir occurring ∼12 h
out of phase. To identify such rhythms in metabolites,
sets of time points 9–12 h apart were selected for
comparison to provide a good probability of detecting
something that approximates to the maximum temporal
difference. Repeated-measures ANOVA was additionally
used to capitalize on the paired nature of the data,
TABLE 1. LC-MS characteristics of identified, 24-h variant plasma metabolites and key parameters characterizing temporal variation
Identification
Mass
(Da)
Retention
time
(min)
Analytical
CV (%)
Elemental
composition Fragments
Oscillation
range/
MESOR (%)
Peak
time
(dec·h)
p
value
Octenoylcarnitine (C8:1) 285.20 3.2 4.9 C
15
H
27
NO
4
286.202, 227.1197, 85.0229, 60.077 7.5 1.4 .020
Acetylcarnitine (C2:0) 203.12 .4 4.2 C
9
H
17
NO
4
204.124, 85.010, 60.064 94.4 4.9 <.001
Oleoylcarnitine (C18:1) 425.36 6.3 9.7 C
25
H
47
NO
4
426.358, 85.032, 60.084 43.6 5.2 .039
Hexanoylcarnitine (C6:0) 259.18 2.8 15.1 C
13
H
25
NO
4
260.181, 201.115,
183.010, 85.029, 60.080
98.8 5.4 <.001
Tetradenenoylcarnitine
(C14:1)
369.30 5.2 6.9 C
21
H
39
NO
4
370.296, 85.029, 60.082 70.8 5.5 <.001
Tetradecadienoylcarnitine
(C14:2)
367.28 4.9 11.2 C
21
H
37
NO
4
368.280, 85.023, 60.076 63.8 6.1 .002
Palmitoylcarnitine (C16:0) 399.34 6.1 17.8 C
23
H
45
NO
4
400.343, 341.268, 85.029, 60.081 109.8 6.4 <.001
Stearoylcarnitine (C18:0) 427.37 6.7 8.5 C
25
H
49
NO
4
428.375, 369.304, 85.030, 60.0811 45.6 6.5 .019
Tetradecanoylcarnitine
(C14:0)
371.31 5.6 12.8 C
21
H
41
NO
4
372.309, 313.235, 85.028, 60.081 146.0 6.6 <.001
Dodecanoylcarnitine
(C12:0)
343.28 5.0 10.3 C
19
H
37
NO
4
344.276, 285.201, 133.085, 85.027,
60.079
122.6 7.1 <.001
Bilirubin 584.27 8.2 5.0 C
33
H
36
N
4
O
6
585.274, 568.243, 299.139, 285.123 51.2 7.1 <.001
Octanoylcarnitine (C8:0) 287.21 3.6 7.4 C
15
H
29
NO
4
288.213, 85.027, 60.079 68.6 7.4 .003
Decanoylcarnitine (C10:0) 315.25 4.3 9.1 C
17
H
33
NO
4
316.249, 85.029, 60.080 77.2 7.8 <.001
Cortisol 362.22 3.7 8.1 C
21
H
30
O
5
363.209, 327.194, 121.063 194.6 8.4 <.001
Cortisone 360.19 3.8 19.5 C
21
H
28
O
5
361.201, 301.118, 163.071, 105.068 143.0 8.6 <.001
LysoPC(18:4) 515.31 5.5 3.0 C
25
H
46
NO
7
P 516.306, 184.976, 104.107, 86.095 9.8 14.1 .020
Propionylcarnitine (C3:0) 217.14 .5 3.3 C
10
H
19
NO
4
218.132, 100.986, 85.014, 60.076 28.3 14.3 .009
Methionine 149.06 .5 6.5 C
5
H
11
NO
2
S 150.057, 133.031, 104.054, 61.010,
56.050
20.2 15.3 <.001
Tyrosine 181.08 .5 10.3 C
9
H
11
NO
3
182.086, 165.051, 136.075,
123.042, 119.050, 91.054
28.4 15.5 <.001
LysoPE(16:0) 453.29 5.9 2.3 C
21
H
44
NO
7
P 454.290, 313.295, 62.058 27.8 16.5 .003
Proline 115.07 .4 3.1 C
5
H
9
NO
2
116.067, 70.056 47.8 16.6 <.001
LysoPE(18:3) 475.28 5.9 11.2 C
23
H
42
NO
7
P 476.276, 335.269 35.0 16.9 .023
Butyrylcarnitine (C4:0) 231.16 1.9 9.9 C
11
H
21
NO
4
232.151, 85.026, 60.082 53.4 17.7 <.001
LysoPE(20:4) 501.30 6.1 17.7 C
25
H
44
NO
7
P 502.294, 484.283, 361.274, 62.059 113.2 18.3 <.001
LysoPE(18:2) 477.29 5.7 13.2 C
23
H
44
NO
7
P 478.292, 337.270 110.4 18.8 <.001
LysoPE(18:1) 479.31 6.1 2.2 C
23
H
46
NO
7
P 480.309, 339.289, 62.060 132.0 19.1 <.001
Lysine 146.11 6.8 .1 C
6
H
14
N
2
O
2
147.113, 130.088, 84.082 7.8 19.1 .014
Phenylalanine 165.09 1.8 2.6 C
9
H
11
NO
2
166.085, 120.081 18.6 19.3 <.001
LysoPC(18:2) 519.34 5.7 2.5 C
26
H
50
NO
7
P 520.340, 184.073, 104.107, 86.096 44.6 19.6 .001
LysoPC(20:5) 541.32 5.7 2.3 C
28
H
48
NO
7
P 542.324, 524.313, 184.073,
104.107, 86.097
23.7 19.8 .04
LysoPC(20:3) 545.69 6.0 18.6 C
28
H
52
NO
7
P 546.354, 184.072, 104.107, 86.096 57.1 20.0 .02
LysoPC(18:3) 517.32 5.4 5.9 C
26
H
48
NO
7
P 518.324, 184.071, 86.096 67.2 20.5 .002
Leucine 131.10 .5 3.9 C
6
H
13
NO
2
132.102, 86.098, 44.051 15.6 21.5 .048
LysoPC(20:1) or PC
(18:1/2:0)
549.39 6.7 8.9 C
28
H
56
NO
7
P 550.385, 184.070, 104.105, 86.098 26.0 23.1 .020
J. E. Ang et al.
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FIGURE 2. Time profiles of four plasma metabolites with different acrophases: (a) acetylcarnitine, (b) LysoPE(18:1), (c) proline, and (d)
cortisol. On the horizontal axis, black bar indicates lights-off (0 lux) and white bar lights-on (440–825 lux). Internal standard (IS) shows the
analytical variation of each ion in the pooled, replicate samples analyzed throughout the LC-MS run.
FIGURE 3. Plasma acetylcarnitine profiles for eight individuals maintained under controlled light/dark, sleep/wake, posture, and calorific
intake conditions. Black bar indicates lights-off (0 lux) and white bar lights-on (440–825 lux).
Time-of-Day Variation in Human Plasma Metabolites
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i.e., plasma was collected from eight individuals across
time. This latter method was the most sensitive and
yielded 36 features in addition to OPLS-DA comparisons
(Supplementary Figure 1). Not only did this validate
results already obtained by OPLS-DA, but it also demon-
strated the effect of intersubject variability as well as the
relatively higher sensitivity of this approach (Supplemen-
tary Figure 1). To illustrate this point, the 24-h temporal
profile of a metabolite feature is presented in Sup-
plementary Figure 2, which highlights the significant
intersubject variation in this ion compared to intrasub-
ject time-of-day changes. In principle, other multivariate
tools, such as the multilevel partial least square projec-
tion to latent structures (PLS), could be used instead of
OPLS-DA for each pairwise comparison in the screening
step, as it offers greater statistical power (Westerhuis
et al., 2010). In this study, multilevel PLS was not
carried out; instead, repeated-measures ANOVA was per-
formed, making use of all data across all time points.
Our data extend previous findings that an unbiased
metabolomic platform may be used to identify 24-h
variant metabolites (Dallmann et al., 2012; Eckel-
Mahan et al., 2012; Fustin et al., 2012; Minami et al.,
2009; Slupsky et al., 2007; Walsh et al., 2006); some of
the 24-h variant plasma metabolites detected in this
study are already known to show such variation, includ-
ing bilirubin, cortisol, and several amino acids (Eriksson
et al., 1989; Feigin et al., 1967, 1968; Larsson et al., 2009;
Selmaoui & Touitou, 2003; Wurtman et al., 1968), and
this provides further validation of this approach. It is
also noteworthy that numerous metabolites linked to
major metabolic pathways were identified in this study;
the 24-h variant nature of these metabolites has not
been previously demonstrated.
Rhythms in human plasma levels of amino acids have
been previously reported (Eriksson et al., 1989; Feigin
et al., 1967, 1968; Wurtman et al., 1968). Typically,
maximal concentrations were observed in the afternoon/
evening, and minimal concentrations in the early hours
of the morning before waking. Our results are in keeping
with these findings: of the detected amino acids, lysine,
proline, leucine, methionine, phenylalanine, and tyrosine
exhibited significant day/night variation with similar
peak/trough temporal changes. The mechanisms regulat-
ing this 24-h rhythmicity remain obscure, and they may
be related to the periodicity of many other metabolic pro-
cesses (Eckel-Mahan et al., 2012; Feigin et al., 1971). For in-
stance, the tricarboxylic acid cycle, gluconeogenesis, and
lipogenesis utilize amino acid carbon backbones and
may be important in this context.
The present study identified numerous plasma acyl-
carnitines that demonstrated significant time-of-day vari-
ation; only a subset of these has thus far been reported
(Dallmann et al., 2012). Acylcarnitines are key intermedi-
ates in the β-oxidation of fatty acids in mitochondria
(Kompare & Rizzo, 2008), and abnormal levels of these
metabolites have been linked to errors of metabolism
involving fatty acid oxidation and carnitine cycle
(Pasquali et al., 2006). Costa et al. (1999) reported pre-
ferential increase in levels of specific mono-unsaturated
acylcarnitines in fasting individuals, who generally
display increased levels of blood unesterified fatty
acids and hepatic β-oxidation. Fasting alone, however,
is unlikely to account for the changes observed in the
current study, as levels of many of these metabolites
peaked and started to decrease in the morning before
lights were switched on and feeding of hourly caloric
drinks recommenced. Critical components of the bio-
logical system regulating the levels of acylcarnitines
also demonstrate 24-h variation. For instance, using
gene expression profiling data of liver enzymes across
the time-of-day in a mouse model, mRNA levels of key
transporters of long-chain acylcarnitines, including car-
nitine palmitoyltransferase (CPT) 1a and 2, were shown
to exhibit clear day/night oscillation (Hughes et al.,
2009).
It is interesting to note that the time-of-day pattern of
variation differs between short-chain, unsaturated long-
chain, and saturated long-chain acylcarnitines. Levels of
long-chain unsaturated acylcarnitines (C18:1, C14:1, and
C14:2) peaked early in the morning before the long-
chain saturated acylcarnitines (C10, C12, C14, C16, and
C18), whereas the acrophases of short-chain acylcarni-
tines were observed at radically different times, i.e., C2 at
4.9 h, C6 at 5.4 h, C3 at 14.3 h, and C4 at 17.7 h. Abnorm-
alities in particular enzyme and membrane transporter
systems have already been associated with pathognomo-
nic changes in acylcarnitine subtypes in well-defined sub-
types of inborn errors of metabolism (Santra & Hendriksz
2010). Hence, it is plausible that short- and long-chain acyl
dehydrogenases and transporters are regulated differently
across time. Indeed, preliminary evidence suggests the
presence of such a specific regulatory process. Whereas
mRNA levels of CPT1a are rhythmic with an acrophase
just before lights-off in the mouse—end of the inactivity
span, levels of long-chain acylcarnitines transported by
this mitochondrial membrane enzyme peak at the end
of the dark phase in human subjects—also the end of
inactivity span (selected profiles are juxtaposed in time
relative to the activity/rest rhythm of the respective
species in Figure 4). Given that humans and mice are
diurnal and nocturnal species, respectively, agreement
of these patterns of temporal change suggest that (CPT)
1a may be responsible for the 24-h variation observed in
long-chain acylcarnitines. By contrast, expression profiles
of (CPT) 1b and (CPT) 1c did not show significant time-of-
day difference in the mouse, lending additional support
for the hypothesis that (CPT) 1a may be the specific
rhythmic driver of long-chain acylcarnitines (Hughes
et al., 2009). It is important to point out that these cross-
species associations, although interesting, are tentative
and require further biological validation.
In this study, acrophases of specific C16, C18, and C20
lysoPCs and lysoPEs clustered in the afternoon and
evening; no clear exogenous factor related to this could
be identified, including food consumption. Two other
J. E. Ang et al.
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endogenously expressed plasma phospholipids were
assessed and showed no significant time-of-day
change, suggesting possible specificity of the rhythmic
driver for particular subtypes of lysophospholipids. Of
note, temporal variation in plasma lysophospholipid
levels has also been detected in mouse plasma
(Minami et al., 2009). Enzyme systems currently under-
stood to be mainly responsible for production of these
metabolites are the lecithin:cholesterolacyltransferase
(Lcat), secreted phospholipase A
2
(Pla2), and endothelial
lipase (Lipg) systems (Yamamoto et al., 2011); in a mouse
model, Lcat,Pla2g12a, and Lipg are rhythmically
expressed in the liver (Hughes et al., 2009).
The current results will be of use in a number of areas,
for example, in the evaluation of pharmacodynamic bio-
markers of new drug therapies (Sarker & Workman,
2007). Evaluation of pharmacodynamic biomarkers is
made by drawing intrasubject comparisons across time
with the patient pre-dose used as a control. This
approach, however, may be confounded by the patient’s
internal circadian and homeostatic systems and external
influences, such as dietary intake. The approach taken
in the current study typifies a way to validate such
pharmacodynamic studies by defining metabolites that
are rhythmic and those that are not.
The findingsof our study have implications on the use of
plasma metabolites in clinical testing. At least 19% of the
metabolite features in our study exhibited significant 24-
h variation, with the mean oscillation range over MESOR
ratio of 65% (95% CI: 49–81%) in the identified metabolites.
To improve the predictive utility of clinical biomarkers
used in diagnosis, prognosis, and follow-up,determination
of 24-h variability should become part of the clinical
validation process, especially in the ascertainment of the
degree of 24-h variation relative to the factor(s) in question.
A limitation of the present study is that although a
global, untargeted approach was adopted, only a pro-
portion of the entire plasma metabolome was monitored
using our analytical method. Despite this, the primary ob-
jective of identifying and characterizing novel 24-h variant
plasma metabolites wasmet in this proof-of-concept study.
Indeed, the present study identified all the 24-h variant
metabolites detected by LC-MS in positive ionization
mode in the Dallmann et al. (2012) study that was con-
ducted under “constant routine”conditions of constant
dim lighting, continuous hourly food supplement, and
no sleep, thereby providing external validation of their
data. Significantly, additional 24-h variant plasma metab-
olites were identified in the present study, suggesting that
these metabolites may be affected by lighting, sleep, and/
or feeding condition. Alternatively, as the samples in the
Dallmann et al. (2012) study were pooled for each
studied time point, the nondetection of these additional
metabolites could be due to lower sensitivity of their ap-
proach. Although only eight subjects were studied with
six time points across a 24-h day, multiple factors that
could confound the rhythms were strictly controlled for.
Specifically, study subjects were lean, healthy, and unme-
dicated and who maintained a fixed sleep/wake schedule
and dietary regime for 1 wk at home prior to an adaptation
night, followed bya 25-h session in the laboratory when the
light/dark and sleep/wake cycle, posture, and calorific
intake were rigorously controlled. In this context, it would
be of interest to disentangle the impact of food, sleep,
and darkness, and prospective clinical studies are
ongoing to addressthis. In addition, the study participants
were all middle-aged men, and the applicability of these
findings to females and other age groups is unclear.
In summary, we have successfully applied the use of
an untargeted metabolomic approach to identify and
characterize 24-h variant metabolites from a range of
chemical classes. These findings represent an important
baseline and will be useful in guiding the proper design
and interpretation of future metabolite-based studies.
Declaration of Interest: The authors report no conflicts
of interest. The authors alone are responsible for the
content and writing of the paper.
This study was funded by Diabetes-UK (grant 08/
0003607), Biotechnology and Biological Sciences Research
FIGURE 4. (Top panel) The temporal profile of palmitoylcarnitine
in human plasma. (Bottom panel) CPT1a gene expression in a
mouse model from an independent study (Hughes et al., 2009).
Black/gray bars indicate lights-off and the white bar lights-on.
Time-of-Day Variation in Human Plasma Metabolites
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Council (grants BB/D526853/1 and BB/I019405/1) and
Cancer Research UK (grants C309/A8274 and C309/
A2187). J.E.A. receives funding support from the Wellcome
Trust (grant 090952/Z/09/Z) and D.J.S. is a Royal Society
Wolfson Research Merit Award holder. The funders had
no role in study design, data collection and analysis,
decision to publish, or preparation of the manuscript.
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SUPPLEMENTARY FIGURE 1. Venn diagram showing overlapping sets of metabolite features detected by OPLS-DA of paired time points
and repeated-measures ANOVA across all time points.
SUPPLEMENTARY FIGURE 2. Plasma profiles of metabolite feature with mass/charge 455.19 Da and retention time 5.7 min for eight indi-
viduals detected by repeated-measures ANOVA but not pairwise OPLS-DA comparisons, illustrating higher intersubject variability relative
to intrasubject time-of-day variation. All participants were maintained under controlled light/dark, sleep/wake, posture, and calorific intake
conditions. Black bar indicates lights-off (0 lux) and white bar lights-on (440–825 lux).
Time-of-Day Variation in Human Plasma Metabolites
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SUPPLEMENTARY TABLE 1. List of metabolite features detected in the screening process and considered for tandem MS
Metabolite feature Comparison made
Retention time (min) Mass/charge (Da) 16h vs. 04h 10h vs. 22h 07h vs. 16h Repeated-measures ANOVA
.31 198.94 0 1 0 1
.31 200.97 1 0 0 1
.33 288.92 0 1 0 1
.34 327.05 1 0 0 1
.34 566.89 0 1 0 1
.34 634.88 1 0 0 1
.34 498.90 1 0 0 1
.38 200.86 0 0 0 1
.38 134.02 1 0 0 1
.39 164.03 0 1 0 1
.41 103.01 1 0 0 1
.41 116.07 1 0 0 1
.41 117.08 0 0 0 1
.41 219.03 0 0 0 1
.41 221.01 0 0 0 1
.41 365.11 1 0 1 1
.41 430.70 1 0 0 1
.41 204.12 1 0 1 1
.43 218.14 1 0 0 1
.43 229.16 1 0 1 1
.43 335.92 0 0 1 1
.44 133.00 1 0 0 1
.44 194.02 1 0 0 1
.46 135.00 1 0 0 1
.46 150.06 1 0 0 1
.47 182.08 1 0 0 1
.49 149.02 1 0 1 1
.49 132.10 0 1 0 1
.50 337.02 1 0 0 1
.55 226.05 1 0 0 1
.57 254.16 0 0 1 1
.62 168.99 1 0 0 1
1.66 254.16 0 1 1 1
1.81 103.05 1 0 0 1
1.81 107.05 1 0 0 1
1.81 120.08 0 1 0 1
1.81 131.05 1 1 0 1
1.81 149.06 0 1 0 1
1.81 166.09 1 1 0 1
1.93 232.16 1 0 0 1
2.57 257.17 1 1 1 1
2.58 235.18 1 1 1 1
2.82 260.18 1 0 0 1
2.85 251.18 1 0 1 1
3.12 185.12 1 0 0 1
3.23 286.20 1 0 0 1
3.59 310.20 0 1 0 1
3.62 288.22 0 0 1 1
3.66 363.22 0 1 1 1
3.80 361.20 0 1 1 1
4.17 585.27 0 1 1 1
4.20 243.63 0 0 0 1
4.21 464.28 0 0 0 1
4.24 252.63 0 0 0 1
4.24 412.28 1 0 0 1
4.24 430.30 1 0 0 1
4.24 448.31 0 0 0 1
4.24 488.30 1 0 0 1
4.24 504.27 0 0 0 1
4.24 510.28 0 0 0 1
4.32 316.25 0 0 1 1
4.46 416.32 1 0 0 1
4.46 552.24 0 0 0 1
4.56 470.29 0 0 0 1
J. E. Ang et al.
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4.75 583.26 0 0 1 1
4.79 244.27 0 0 0 1
4.81 207.62 0 0 0 1
4.81 226.63 1 0 0 1
4.81 235.63 0 0 0 1
4.81 244.63 1 0 0 1
4.81 414.30 1 0 0 1
4.81 432.31 1 0 0 1
4.81 450.32 0 0 0 1
4.81 472.30 0 0 0 1
4.81 488.27 1 0 0 1
4.81 494.29 1 0 0 1
4.81 504.24 1 0 0 1
4.82 368.28 0 0 1 1
4.84 344.28 0 0 1 1
4.88 251.12 0 0 0 1
4.92 450.32 1 0 0 1
4.92 472.30 1 0 0 1
4.98 415.21 1 1 0 1
4.99 286.14 1 0 0 1
4.99 437.19 1 0 0 1
4.99 453.17 1 1 0 1
5.16 387.19 0 0 1 1
5.16 409.18 0 0 1 1
5.16 425.15 0 0 0 1
5.18 370.29 0 0 1 1
5.22 432.24 1 1 0 1
5.24 107.09 0 1 0 1
5.24 119.09 1 1 0 1
5.24 135.08 1 1 0 1
5.24 160.04 1 1 0 1
5.24 227.08 1 1 0 1
5.24 233.06 0 1 0 1
5.24 281.14 1 1 0 1
5.24 295.12 1 1 0 1
5.24 300.11 1 1 0 1
5.24 367.14 1 1 0 1
5.24 380.25 0 0 0 1
5.24 397.20 1 0 0 1
5.24 415.21 1 1 0 1
5.24 434.18 1 1 0 1
5.24 437.19 1 1 0 1
5.24 453.17 1 0 0 1
5.24 499.17 1 1 0 1
5.24 129.05 1 0 0 1
5.29 253.63 1 0 0 1
5.29 468.31 0 0 0 1
5.29 490.29 0 0 0 1
5.30 299.14 1 1 1 1
5.30 585.27 1 0 1 1
5.36 476.28 1 1 1 1
5.39 278.64 0 1 0 1
5.39 500.28 1 0 0 1
5.39 518.32 0 1 0 1
5.39 540.31 0 0 0 1
5.42 290.64 1 0 0 1
5.42 564.31 1 0 0 1
5.48 299.14 0 0 1 1
5.48 585.27 0 0 1 1
5.49 494.32 0 1 0 1
5.49 516.31 1 0 0 1
5.49 513.29 1 0 0 1
5.52 643.28 0 1 0 1
5.52 603.30 0 0 1 1
5.55 372.31 1 0 1 1
5.57 337.27 0 0 0 1
5.59 475.79 1 0 0 1
Continued
Time-of-Day Variation in Human Plasma Metabolites
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SUPPLEMENTARY TABLE 1. Continued
Metabolite feature Comparison made
Retention time (min) Mass/charge (Da) 16h vs. 04h 10h vs. 22h 07h vs. 16h Repeated-measures ANOVA
5.61 184.07 0 1 0 1
5.61 385.28 0 0 0 1
5.65 398.33 0 0 0 1
5.67 284.23 0 0 0 1
5.67 516.24 1 1 0 1
5.67 497.24 1 1 1 1
5.68 455.19 0 0 0 1
5.68 460.28 1 1 1 1
5.68 473.20 1 1 1 1
5.68 478.30 1 1 1 1
5.68 500.28 1 1 1 1
5.68 526.30 0 0 0 1
5.68 471.16 0 0 1 1
5.69 548.28 0 0 0 1
5.71 520.34 1 1 1 1
5.71 542.32 0 0 1 1
5.71 590.33 1 0 1 1
5.71 798.97 0 1 0 1
5.71 799.47 0 0 0 1
5.71 279.64 1 0 0 1
5.77 580.30 0 0 1 1
5.85 318.24 0 0 1 1
5.88 313.27 0 0 0 1
5.88 436.28 1 0 0 1
5.88 454.29 1 0 0 1
5.88 476.28 1 0 0 1
5.95 546.36 0 0 0 1
5.96 339.29 1 1 1 1
5.98 400.34 1 1 1 1
6.07 457.20 1 0 1 1
6.07 462.30 1 1 1 1
6.07 475.22 1 1 1 1
6.07 480.32 1 1 1 1
6.07 502.30 1 1 1 1
6.08 520.30 0 0 1 1
6.23 405.26 1 0 0 1
6.27 426.35 1 0 1 1
6.47 428.37 0 0 1 1
6.51 459.32 1 0 0 1
6.54 504.31 0 0 0 1
6.56 273.67 0 0 1 1
6.64 419.28 1 0 0 1
6.71 294.67 0 1 0 1
6.72 550.39 0 1 0 1
6.72 572.37 0 1 0 1
6.82 147.11 0 0 1 1
6.83 650.44 1 1 0 1
6.87 672.42 1 1 0 1
6.93 268.26 1 0 0 1
6.98 279.23 1 1 0 1
7.17 329.25 1 0 1 1
7.27 285.93 0 1 0 1
7.29 350.25 0 1 1 1
7.40 563.55 0 0 0 1
7.54 163.15 0 1 0 1
7.59 455.34 0 0 1 1
7.59 354.20 1 0 1 1
7.59 395.22 0 0 1 1
7.59 561.41 0 0 1 1
7.69 339.18 1 0 1 1
7.70 669.50 1 0 0 1
7.74 284.30 1 0 0 1
7.84 381.30 0 0 1 1
7.92 298.31 0 0 0 1
J. E. Ang et al.
Chronobiology International
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8.08 780.55 1 0 0 1
8.20 299.14 1 1 1 1
8.20 584.26 0 1 1 1
8.20 585.27 0 1 1 1
8.20 583.26 0 0 1 1
8.21 581.25 0 0 1 1
8.25 389.25 0 1 0 1
8.37 312.33 0 1 1 1
When the feature is significant in a comparison, it is denoted “1”and when it is not, it is denoted “0”.
SUPPLEMENTARY TABLE 2. LC-MS features of metabolites of interest that did not exhibit significant 24-h variation ( p> .05)
Identification Retention time (min) Mass (Da) Elemental composition Fragments
Arginine .39 174.112 C
6
H
14
N
4
O
2
175.120, 158.070, 116.050, 70.100
Valine .41 117.079 C
5
H
11
NO
2
118.087, 72.070, 55.040
Carnitine .42 161.105 C
7
H
15
NO
3
162.113, 103.040, 85.030, 60.080
Alanine .42 89.048 C
3
H
7
NO
2
90.056, 44.050
Tryptophan 2.03 204.090 C
11
H
12
N
2
O
2
205.098, 188.069, 146.059, 118.064
LysoPC(16:0) 5.90 495.333 C
24
H
50
NO
7
P 496.340, 184.074, 104.105, 86.097
LysoPC(18:1) 6.09 521.348 C
26
H
52
NO
7
P 522.356, 184.073, 104.107, 86.095
Time-of-Day Variation in Human Plasma Metabolites
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