Abstract
Establishing circadian and wake-dependent changes in the human
metabolome are critical for understanding and treating human diseases
due to circadian misalignment or extended wake. Here, we assessed
endogenous circadian rhythms and wake-dependent changes in plasma
metabolites in 13 participants (4 females) studied during 40-hours of
wakefulness. Four-hourly plasma samples were analyzed by hydrophilic
interaction liquid chromatography (HILIC)-LC-MS for 1,740 metabolite
signals. Group-averaged (relative to DLMO) and individual participant
metabolite profiles were fitted with a combined cosinor and linear
regression model. In group-level analyses, 22% of metabolites were
rhythmic and 8% were linear, whereas in individual-level analyses, 14%
of profiles were rhythmic and 4% were linear. We observed metabolites
that were significant at the group-level but not significant in a
single individual, and metabolites that were significant in
approximately half of individuals but not group-significant. Of the
group-rhythmic and group-linear metabolites, only 7% and 12% were also
significantly rhythmic or linear, respectively, in ≥50% of
participants. Owing to large inter-individual variation in rhythm
timing and the magnitude and direction of linear change, acrophase and
slope estimates also differed between group- and individual-level
analyses. These preliminary findings have important implications for
biomarker development and understanding of sleep and circadian
regulation of metabolism.
Introduction
Circadian rhythms, endogenously generated cycles of approximately
24 hours, govern many patterns of behavior and physiology including
sleep/wake cycles, cognition, feeding patterns, hormone secretion, gene
expression and cellular processes. Given the circadian system’s control
over so many biological processes, it is unsurprising that disruption
to this endogenous clock and its outputs is associated with adverse
health outcomes. Shift workers, for example, whose circadian rhythms
are often chronically misaligned from their sleep-wake
cycle^[40]1,[41]2, have an increased risk of developing serious
diseases including obesity, diabetes, cardiovascular disease, stroke
and some cancers^[42]3–[43]5. Moreover, experimentally-induced
circadian disruption in controlled laboratory settings shows that
misalignment of circadian and behavioral cycles leads to acute
cardiometabolic dysfunction in humans^[44]6–[45]8. A direct influence
of the circadian system on metabolic homeostasis has been demonstrated
in rodents, whereby knocking out core clock genes significantly alters
metabolism^[46]9–[47]11. Furthermore, studies have demonstrated 24-h
rhythms in the hepatic, serum and plasma metabolomes of
rodents^[48]12–[49]14, prompting investigation of circadian control of
the metabolome in humans.
Metabolomic analysis of human plasma samples collected during a normal
day with either an 8:16 h sleep/wake cycle, or during sleep deprivation
reveals 24-h oscillations in addition to wake-dependent increases or
decreases in metabolites from a wide variety of chemical
classes^[50]15,[51]16. Studies conducted under constant routine
conditions, the gold-standard method for assessing endogenous circadian
rhythms^[52]17, have also described 24-h rhythms and increases or
decreases over time awake in the human metabolome^[53]18–[54]21.
Analysis of individual metabolomic profiles, however, has shown
substantial inter-individual differences in the timing and abundance of
rhythmic lipids and in the magnitude and direction of change in lipids
that increase or decrease with time awake^[55]18,[56]19. Despite this
variability, many of the studies published to date have only conducted
group-level analyses, which given the underlying inter-individual
variation, may not accurately describe circadian and wake-dependent
control of metabolite levels. Furthermore, previous studies have
focused on metabolites that are resolved on reverse phase LC matrixes
(i.e. capturing lipids, fatty acids, acyl carnitines, some amino acids
and carbohydrates) and have not detected changes in more polar
compounds, such as nucleotides, nucleosides, organic acids, amino
acids, and carbohydrates, which are important intermediates in central
carbon metabolism and are reflective of changes in macromolecule
synthesis, the urea cycle, and pathways of energy (i.e. glycolysis and
the Krebs cycle).
Polar metabolites have been identified as biomarkers of
cancers^[57]22,[58]23, diabetes^[59]24, Alzheimer’s disease^[60]25,
myocardial ischemia and infarction^[61]26,[62]27, and
osteoarthritis^[63]28. With single-point assessments of polar
metabolites potentially being used as biomarkers of a variety of
disease states, it is important that the circadian variation and effect
of inadequate sleep on these compounds is well understood. Variation in
a metabolite’s concentration at different times of day or variation
induced by sleep loss has implications for the timing and
interpretation of clinical diagnostic tests and the efficacy of
treatments. Improved understanding of circadian- and wake-dependent
control of metabolism will also contribute to understanding the
etiology of cardiometabolic diseases and may inform future development
of interventions and chronotherapies to treat such disorders. In the
current study, we characterized circadian- and wake-dependent changes
in polar metabolites using HILIC-LC-MS over 40-hours of continuous
wakefulness under highly controlled conditions. Changes to metabolite
levels were subsequently assessed using both group- and
individual-level analyses to observe the degree of concordance between
these analysis approaches.
Results
Circadian and wake-dependent modulation of plasma polar metabolites was
investigated in 13 healthy adults (4 females) aged 20–32 years
(Table [64]1), who underwent a 40-hour constant routine (CR) protocol
(Fig. [65]1). The final dataset of metabolites included 99 metabolites
identified based on their accurate mass and coelution with authentic
metabolite standards, in addition to 1,641 unidentified metabolite
features that constituted the untargeted matrix and were detected in
all participants. Ten of 13 participants had missing data points in the
targeted matrix resulting in 14% (18 samples; total n = 112) missing
data, and 12 of 13 participants had missing data points in the
untargeted matrix resulting in a total of 16% (21 samples; total
n = 109) missing data. Further information on missing samples can be
found in the methods section.
Table 1.
Demographic characteristics of study participants.
Demographics M ± SD or No. (%)
N 13
Age (years) 25.00 ± 4.31
Males 9 (69%)
Body mass index (kg/m^2) 22.00 ± 2.14
Dim light melatonin onset time (decimal time) 20.91 ± 1.47
Wake time (decimal time) 07.19 ± 0.73
Bed time (decimal time) 23.19 ± 0.73
Morningness Eveningness Questionnaire score 37.92 ± 2.66
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Note: Participant excluded from the analysis is not included in this
table.
Figure 1.
[67]Figure 1
[68]Open in a new tab
Participants completed a 6-day laboratory protocol. The protocol
consisted of (i) two baseline days (8:16 sleep/wake based on average
sleep time two weeks before admit [AD]), (ii) a 40-hour constant
routine, and (iii) two recovery days with up to 12-hour sleep
opportunities before discharge (DC). White bars represent wake episodes
in 100 lux, black bars represent sleep episodes in 0 lux, and grey bars
represent a DLMO assessment on day 2 and the 40-h CR in <3 lux ambient
light. During the CR protocol, black diamonds represent blood samples,
with larger diamonds representing samples used in the current
metabolomics analysis. The protocol is shown in relative clock time
with a relative bedtime of midnight. Study events were scheduled
relative to each individual’s pre-study self-selected wake time.
We assessed the proportion of plasma metabolites in the targeted and
untargeted matrices that changed in a rhythmic, linear or combined
rhythmic and linear fashion over the 40-hours of extended wakefulness.
Results of these analyses and model estimates for all statistical
analyses are shown in SI Figs [69]S1 and [70]S2, and SI
Tables [71]1–[72]4 respectively. Representative examples of metabolites
that exhibited rhythmic, linear and combined rhythmic and linear
changes in plasma levels at the group-level are shown in Fig. [73]2B
for the targeted matrix and in Fig. [74]3B for the untargeted matrix.
Figure 2.
[75]Figure 2
[76]Open in a new tab
(A) Time course of metabolite concentrations (z-score area under the
peak) for all identified metabolites in the targeted matrix.
Significant metabolites are marked by the coloured bar to the right of
the heatmap (red – rhythmic; green – linear; blue – combined rhythmic
and linear). Data are represented relative to DLMO (time 0), the time
at which plasma melatonin reached 5 pg/mL. (B) Examples of significant
profiles are shown for tryptophan (top: night peaking rhythmic, not
linear), sucrose-6-phosphate (middle, upper: day peaking rhythmic, not
linear), L-proline (middle, lower: linear increasing, not rhythmic),
and 2-methylcitrate (bottom: night peaking rhythmic with linear
increase). Data are plotted relative to DLMO, and by relative clock
time, with relative bedtime at midnight. Errors bars represent SEM. The
area between the dashed lines represent the ‘biological night’, defined
as DLMO plus 10 hours, and the blue dashed line represents the
predicted fit of the model.
Figure 3.
[77]Figure 3
[78]Open in a new tab
Group-level analysis of the untargeted metabolite matrix. (A) The time
course of metabolites significantly rhythmic (top left), linear (top
right), and combined rhythmic and linear (bottom) in group-level
analysis. Data are represented relative to DLMO (time 0), the time
plasma melatonin reached 5 pg/mL. (B) Metabolite concentrations showing
rhythmic and linear trends during sleep deprivation from the untargeted
matrix. Examples include day peaking rhythmic, not linear (top left),
night peaking rhythmic, not linear (top right), day peaking rhythmic
with linear increase (middle, upper left), night peaking rhythmic with
linear increase (middle, upper right), day peaking rhythmic with linear
decrease (middle, lower left), night peaking rhythmic with linear
decrease (middle, lower right), linear increase, not rhythmic (bottom
left), and linear decrease, not rhythmic (bottom right). Data are
plotted relative to DLMO, and by relative clock time, with relative
bedtime at midnight. Errors bars represent SEM. The area between the
dashed lines represent the ‘biological night’, defined as DLMO plus
10 hours, and the blue dashed line represents the predicted fit of the
model.
Analysis of metabolites at the group-level
Group-level analysis of the 99 identified metabolites showed that 21
metabolites were significantly rhythmic and nearly all (90%) had a peak
time (acrophase) during the biological night, occurring within 10 hours
after Dim Light Melatonin Onset (DLMO). Furthermore, four metabolites
showed a significant linear change; aconitic acid and uridine increased
whereas phosphoric acid and proline decreased with time awake. In
addition to the 21 rhythmic only and four linear only metabolites,
seven of the 99 metabolites showed both rhythmic and linear changes.
Five metabolites increased (threonine, cysteic acid, phenylalanine,
ornithine and methylcitrate) and two decreased with time awake
(pantothenic acid and lysine) and all had acrophases during the
biological night. The 28 rhythmic metabolites (including combined
rhythmic and linear metabolites), comprised 16 amino acids, 6 organic
acids, 2 nucleotides, 2 carbohydrates and derivatives, and a single
vitamin and peptide (see Table [79]S1 for metabolite identities).
Pathway analysis^[80]29 showed enrichment of the phenylalanine and
tyrosine metabolism pathway (Table [81]2, and SI Fig. [82]S3 and
Table [83]S6). The overall 11 linear metabolites (including combined
rhythmic and linear metabolites), comprised 6 amino acids, 2 organic
acids, and a single vitamin, xenobiotic and nucleoside (see
Table [84]S2 for metabolite identities). While the arginine and proline
metabolism pathway showed enrichment, this was no longer significant
following false discovery rate correction (SI Fig. [85]S4 and
Table [86]S7). The time-course of all metabolites from the targeted
matrix are shown in Fig. [87]2A.
Table 2.
Results of the pathway enrichment analysis showing significant
pathways*.
Analysis Pathway Total metabs in pathway #sig. metabs in pathway Raw p
FDR adjusted p
Group Rhythmic Phenylalanine and tyrosine metabolism 13 4 0.000623
0.0492
Linear — — — — —
Individual Rhythmic Citric acid cycle 23 9 0.000107 0.0085
Urea cycle 20 8 0.000226 0.00892
Linear Citric acid cycle 23 10 2.41E-06 0.00019
Urea cycle 20 8 5.83E-05 0.0023
Malate-aspartate shuttle 8 4 0.00195 0.0337
Beta-alanine metabolism 13 5 0.00207 0.0337
Galactose metabolism 25 7 0.00213 0.0337
[88]Open in a new tab
*Only metabolic pathways that were significant following FDR correction
are shown. The total number of metabolites in the pathway, the number
of significant metabolites in the pathway and the raw and FDR adjusted
p-values are shown for each pathway. The full results of the pathway
enrichment analysis are shown in SI Tables [89]6–9 and Figs [90]3–6.
Group-level analysis of the untargeted data showed a similar proportion
of metabolites (~22%) to those in the targeted matrix were
significantly rhythmic. A wide range in acrophase times were observed
with many of the metabolites peaking during the daytime (60%).
Group-level analysis of the untargeted matrix also showed that ~8% of
metabolites were significantly linear, of which most (66%) of these
decreased with time awake. Approximately 3% of metabolites showed a
combined rhythmic and linear pattern of change. Just over half (51%) of
these metabolites decreased over time and the majority (67%) of these
peaked during the day. Of the metabolites showing combined rhythmic and
linear trends that increased, the majority (72%) had an acrophase
during the night. The time-course of metabolites from the untargeted
matrix that were significantly rhythmic, linear or combined rhythmic
and linear are shown in Fig. [91]3A.
Analysis of metabolites at the individual-level
Following group-level analysis, we next analyzed individual participant
metabolite profiles (i.e. single metabolite profiles over time for each
participant), including the 1,287 (99 × 13 participants) targeted
profiles and 21,333 (1,641 × 13 participants) profiles from the
untargeted matrix. Results of these analyses are shown in SI
Fig. [92]2. Of the profiles from the targeted matrix, ~10% were
significantly rhythmic and over half (64%) of these rhythmic profiles
peaked during the day. Profiles that were significantly linear
accounted for ~5% of all analyzed profiles within the targeted matrix,
and most (63%) of these showed an increase with time awake.
Approximately 3% of individual profiles in the targeted matrix showed a
combined linear and rhythmic pattern of change. Of these, over half
(56%) increased with time awake, and 70% had acrophases during the
night. In metabolites that decreased, however, there was an even spread
of acrophases throughout the day and night.
The identified rhythmic compounds detected at the individual-level
comprised mainly amino (29%) and organic acids (22%), although a number
of nucleotides and nucleosides (16%) and carbohydrates and derivatives
(14%) were also rhythmic. Amino acids had acrophases mainly during the
evening and throughout the night. Similarly, organic acids
predominantly peaked during the biological night, whereas carbohydrates
and their derivatives had acrophases throughout the day and night.
Nucleotides and nucleosides, however, tended to peak during the morning
hours, in the first half of the day. Pathway analysis of these rhythmic
compounds showed that the urea and Krebs cycle pathways were
significantly enriched (see Table [93]2, and SI Fig. [94]S5 and
Table [95]S8). Similar to the rhythmic metabolites, linearly changing
metabolites comprised mainly amino acids (29%), organic acids (29%),
carbohydrates (16%), and nucleotides and nucleosides (9%). The amino
and carboxylic acids showed both increases (amino: 55%; carboxylic:
52%) and decreases (amino: 45%; carboxylic: 48%), whereas most of the
carbohydrates increased (69%) and all nucleosides and nucleotides
increased with time awake. Significantly enriched pathways for the
linear metabolites included the Krebs and urea cycles, malate-aspartate
shuttle, beta-alanine metabolism and galactose metabolism (see
Table [96]2, and SI Fig. [97]S6 and Table [98]S9).
Of the 21,333 individual untargeted profiles, ~14% were significantly
rhythmic and ~4% showed a linear change with time awake. Similar to the
group-rhythmic metabolites, most (62%) of the significant
individual-level rhythmic profiles peaked during the day. There was a
near-even split in the direction of linear change, with just over half
(54%) of metabolites decreasing with time awake. Of all the significant
individual profiles, ~4% were combined rhythmic and linear. These
profiles tended to show an increase (56%) with time awake and peaked
mostly during the day (79% of those increasing, and 56% of those
decreasing).
Comparison of group- and individual-level analyses
We next examined the level of concordance in group- and
individual-level analyses. As seen in Fig. [99]4A, the proportion of
significant (rhythmic, linear or both rhythmic and linear) metabolites
was decreased overall in the individual-level analysis (22%) compared
to the group-level analysis (32%). This decrease appeared to be driven
mainly by a reduction in significantly rhythmic metabolites; however,
the proportion of linear metabolites also decreased slightly in the
individual-level analysis of untargeted profiles. Figure [100]4 also
shows the percentage of metabolites showing rhythmic (Fig. [101]4B,
including combined rhythmic and linear metabolites) and linear
(Fig. [102]4C, including combined rhythmic and linear metabolites)
changes for each individual participant. The proportion of overall
significant metabolites differed between participants, with some
participants having less than 10% of metabolites showing rhythmic and
linear changes during prolonged wakefulness (Fig. [103]4A).
Figure 4.
[104]Figure 4
[105]Open in a new tab
Comparison of group- versus individual-level analyses. (A) The
percentage of metabolites that were significantly rhythmic (black),
linear (grey), or combined rhythmic and linear (blue) at the
group-level (Gr.), overall for the individual-level analysis (Ind.
total), and for each individual participant (A–M). (B) Acrophase and
(C) slope values are shown for significant metabolites (including
combined rhythmic and linear metabolites) at the group-level (Gr.) and
in individual participants (A–M). Metabolites are ordered based on the
number of significant cosinor or linear fits across participants, with
group-significant metabolites shown first. Participants are ordered
from left to right based on the greatest number of significantly
rhythmic or linear metabolites at the individual-level. (D) Metabolites
that were significantly rhythmic (left) or linear (right) in group- and
individual-level analyses. (E) Metabolites significantly rhythmic
(left) or linear (right) in almost half of individuals but were not
group-significant. (F) Metabolites that were significantly rhythmic
(left) or linear (right) in group-level, but not individual-level
analyses (non-significance denoted by broken lines in individual-level
plots). Individual participant profiles are shown in colour and the
group mean (±SEM) for that metabolite is shown below in black.
As seen in Fig. [106]4B and 4C, there were differences between
participants in which metabolites were significant, and there were no
metabolites that were significantly rhythmic or linear for all
participants. While not significant in all participants, there were
metabolites that were relatively consistent across some participants
(i.e. significant in at least n = 6), although, acrophase and slope
estimates often differed substantially between participants for many of
these metabolites. Due to this inter-individual variability, we
observed a number of metabolite features, ~21% (6/28) and ~17% (4/23)
for rhythmic and linear metabolites (including combined rhythmic and
linear metabolites) that were significant in almost half of the
individual participants profiles (n = 6/13), but not significant at the
group-level. The observed inter-individual variability in acrophase
also contributed to some group acrophase estimates not being
representative of the timing of rhythms in individual participants,
even when these metabolites were significant at both the group- and
individual-level (Fig. [107]4B). For linear metabolites, however, group
estimates of the direction of change generally reflected changes at the
individual-level, although the magnitude of change was often decreased
in the group estimate relative to individual participant profiles
(Fig. [108]4C). Overall metabolites with consistent profiles between
individuals were more likely to be significant at the group-level
(Fig. [109]4D), and metabolites that had either a wide range in
acrophases for rhythmic metabolites, or opposing slope directions for
linear metabolites, were not significant at the group-level, despite
being significant in approximately half of the participants
(Fig. [110]4E). Surprisingly, we also observed a group of metabolites
that were significant at the group-level but were not significant in a
single individual, as seen in the examples shown in Fig. [111]4F.
Approximately 6% (25/428) of group-rhythmic metabolite features and 15%
(27/185) of group-linear metabolite features were not significant in a
single participant. This proportion increased to ~65% for rhythmic and
~68% for linear metabolite features when all metabolites significant in
less than a third of participants (n = 3/13) were included.
Discussion
Our study presents the first evidence of circadian- and wake-dependent
modulation of polar metabolites over the course of 40-hours of extended
wakefulness. We describe rhythmic and linear changes in plasma
metabolites at both the group- and individual- level. Due to large
inter-individual differences observed in both circadian- and
wake-dependent metabolites, our findings highlight the importance of
data being examined at both the group-and individual-level for
biomarker discovery work. For a biomarker discovery program, aiming to
identify biomarkers of an abnormal state, the biomarker should ideally
have utility at the individual-level. Targeting or rejecting a
metabolite based on group-level data may therefore lead to inconclusive
results or a missed signal of interest. With this in mind, while our
data support earlier findings demonstrating rhythmic and/or linear
changes in the human plasma metabolome during sleep
deprivation^[112]15,[113]16,[114]18–[115]21, they also suggest caution
when interpreting results from analyses of grouped data.
Previous studies using reverse phase-LC-MS to detect changes in plasma
metabolite levels, indicated that ~13–40% of the lipids and apolar
metabolites preferentially detected by this platform are under
circadian control^[116]15,[117]19–[118]21. We show that a similar
proportion of polar metabolites detected using HILIC-LC-MS exhibited
circadian rhythmicity in our group-level analyses (~22% or ~26%
including metabolites that were combined rhythmic and linear).
Furthermore, the timing of the peak of metabolite rhythms in our
targeted matrix was consistent with previous reports that have shown
amino acids to peak predominantly in the evening and during the
biological night^[119]15,[120]20,[121]21. Ten (of 28, including those
showing a combined rhythmic and linear pattern) metabolites found to be
rhythmic in our targeted group of compounds were rhythmic in at least
one other study^[122]15,[123]16,[124]20,[125]21. These included
leucine, lysine, methionine, valine, 4-hydroxyphenylpyruvate,
isoleucine, tyrosine, ornithine, phenylalanine, and tryptophan.
Furthermore, 4 metabolites (citrulline, arginine, citric acid and
pantothenic acid) found to be rhythmic in the current study did not
show rhythmicity in previous studies^[126]15,[127]16,[128]20 and 4
metabolites (proline, glutamate, lactate and glycerol) shown to be
rhythmic in at least one previous study^[129]16,[130]20 were not
rhythmic in our dataset in group-level analyses but were rhythmic
(except glutamate) in at least one participant. Differences in
methodologies, for example CR versus non-CR conditions, meal timing and
pooling samples, may account for the differences in results. The
findings of the current study also confirmed lack of rhythmicity in 13
metabolites previously shown not to be rhythmic in at least one other
study^[131]16,[132]20 (e.g. taurine, uridine, serine, oxalate, pyruvate
and AMP). Overall, our analyses using HILIC-LC-MS detected 12
previously unreported rhythmic metabolites, which included organic
acids (e.g. methylcitric acid, fumaric acid, glucuronic acid, isocitric
acid, and cysteic acid) and nucleotides (e.g. deoxyuridine
monophosphate and inosinic acid).
Following our group-level analysis, we also analyzed individual
participant profiles. These analyses showed that ~14% (~18% including
combined rhythmic and linear metabolites) of individual participant
metabolite profiles were rhythmic. Similarly, despite the difference in
the classes of measured metabolites, Chua et al.^[133]19 reported that
~18% of lipid metabolite profiles were rhythmic, suggesting that a
similar proportion of lipid and polar metabolites are under circadian
control. Based on the identified metabolites from the targeted matrix,
the rhythmic metabolites in the current study were predominantly amino
and organic acids, such that pathways involving these classes of
metabolites, including the Krebs and urea cycles, showed enrichment.
While some metabolites involved in the urea and Krebs cycles had
acrophases during the biological night, most had acrophases during the
day, consistent with the diurnal peak in urea
concentration^[134]30,[135]31 and energy expenditure reported in the
literature^[136]32. Furthermore, the timing of amino acid rhythms in
the current study is broadly consistent with previous research showing
that transcripts associated with gene expression and RNA metabolism
tend to peak during the night^[137]33–[138]35, such that that the
timing of amino acids observed in the current study coincides with the
timing of protein synthesis.
As has been reported previously for plasma lipids^[139]19, none of the
polar metabolites were consistently rhythmic across all individuals and
we observed a large degree of inter-individual variation in the timing
of rhythms between participants. As seen in Fig. [140]3B, some
participants appeared to have a similar timing of rhythms across most
metabolites (e.g. acrophase estimates for participant K were mostly
during the evening hours), suggesting that some individuals may have a
particular phase predominance in their metabolic profile. A similar
finding was observed in plasma lipids, whereby participants could be
clustered into morning and evening phenotypes based on the peak times
of lipid rhythms^[141]19. Further characterization of the range of
inter-individual variability in metabolites within and between
individuals is necessary if these are to be used as potential
biomarkers. The wide range of individual phases observed in metabolites
is not surprising, however, when the inter-individual variation in
well-established circadian markers is taken into consideration.
Melatonin, the gold standard marker of circadian phase, when measured
under dim light conditions exhibits an ~5-h range of phase (5.85-h in
the current sample) and phase angles (DLMO time relative to sleep) in
young healthy individuals (e.g.^[142]36,[143]37). Even within
individuals, there is also variation in internal phase relationships,
for example, between melatonin and temperature^[144]38, not only
because of methodological variance but also likely intrinsic
differences in internal circadian organization. It is therefore
important to interpret potential new circadian markers with similar
expectations, i.e., that substantial inter-individual variation will
exist, and biomarkers are not likely to exhibit identical timing
between, or even within individuals, but that does not preclude their
use as circadian biomarkers. A long-term goal of this work is to better
understand the inter-individual variation in biomarker profiles to
inform the eventual development of single- or dual-timepoint markers of
circadian phase for clinical and operational use, as has already been
attempted using both the human metabolome^[145]21 and
transcriptome^[146]39,[147]40.
Given large inter-individual differences in metabolites, we sought to
compare the results of our group- and individual-level analyses.
Overall, metabolites that had consistent profiles between participants
tended to be significant at the group-level, while those with a large
spread of acrophases were typically not significant at the group-level.
Furthermore, we observed that 25 of 428 metabolites were significant at
the group-level despite not being significant in a single individual.
These findings are important given the widespread use of group-level
analyses in previous studies assessing circadian rhythms in the
metabolome^[148]15,[149]16,[150]20,[151]41.
In addition to identifying rhythmic metabolites, we also identified
approximately 8% (11% including combined rhythmic and linear
metabolites) of metabolites that showed a linear increase or decrease
with time awake in group-level analyses. The proportion of linearly
changing metabolites in the current study is similar to the proportion
of metabolites that showed an increase or decrease in response to acute
sleep deprivation (~12%^[152]20), and sleep restriction to 5.5-h
time-in-bed for 8 nights (~4%^[153]42). Despite similarity in the
proportion of metabolites increasing or decreasing, metabolites that
changed linearly in the current study are not consistent with the
metabolites that changed in response to chronic (5–8 nights) sleep
restriction^[154]42,[155]43, such that a number of metabolites that
were altered by sleep restriction did not show a wake-dependent change
in the present study (e.g methionine, tryptophan, oxalic acid, gluconic
acid, malic acid and glucose). Furthermore, 3 metabolites showing a
linear change in the current study did not change in response to
chronic sleep restriction^[156]42,[157]43 (cis-aconitic acid, lysine
and threonine). Phenylalanine, however, which showed an increase with
extended wakefulness in the current study also showed an increase
following 5 nights of 4-h time-in-bed^[158]43 but did not change
following 8-nights of 5.5-h time-in-bed^[159]42. The difference
suggests that biomarkers signaling sleep loss due to acute sleep
deprivation may not be the same as those sensitive to chronic sleep
deficiency, which is consistent with that reported using a
transcriptomic approach^[160]44. Another possible explanation is that
the 24-hour rhythm of some metabolites was shifted due to the sleep
restriction protocol (as seen for melatonin^[161]45,[162]46), such that
the change attributed to sleep restriction may represent measurement at
a different phase of the rhythm. This may be the case for some of the
metabolites identified as markers of sleep restriction, for example
tryptophan, phenylalanine, and isoleucine, as these metabolites have
been shown to be rhythmic, both in the current study and in previous
research^[163]15,[164]16,[165]21. Further investigation is required to
determine whether the metabolites that show wake-dependent increases or
decreases in response to sleep deprivation are also altered by sleep
restriction, or whether there are different mechanisms resulting in a
different set of metabolites showing change in response to the sleep
deprivation versus sleep restriction.
Our data examining wake-dependent changes in metabolites during acute
sleep deprivation largely confirm those reported
previously^[166]16,[167]20 with respect to the metabolites which did
not change in response to sleep loss. For example, 23 metabolites that
showed no linear change in the current study also did not change in
response to sleep deprivation in previous studies^[168]16,[169]20. We
did, however, detect a linear change in 8 metabolites which were found
not to change in response to sleep deprivation in previous research
(phenylalanine, pantothenic acid, ornithine, uridine, threonine,
proline, lysine and cis-aconitic acid). Furthermore, our results
differed to previous studies showing increases in lactid acid^[170]20,
taurine and tryptophan^[171]16 (though tryptophan did not change in one
previous study^[172]20) as we did not show a linear change with time
awake in group-level analyses for these metabolites. With the use of
HILIC-LC-MS in our study however we were able to detect linear changes
in 3 metabolites (phosphoric acid, cysteic acid, 2-methylcitric acid)
not previously captured in other studies.
In our analysis of individual participant metabolite profiles, we found
that 4% (~8% including combined rhythmic and linear metabolites) of
metabolite profiles changed linearly. Based on the targeted analyses,
these were mainly amino and organic acids, as well as a smaller number
of carbohydrates. Enrichment analysis showed that the linearly changing
metabolites were related to energy metabolism in the glycolysis and
Krebs cycle pathways. While some of the metabolites in these pathways
decreased, the majority increased with time awake and this is
consistent with the reported increase in energy expenditure during
sleep deprivation^[173]32,[174]47. The urea cycle pathway also showed
enrichment, with majority of the metabolites involved in this pathway
showing an increase with time awake, which is consistent with the
increase in urea in response to sleep loss^[175]30,[176]48.
Comparable to the rhythmic metabolites, there was also inter-individual
variation in the patterns of change of linear metabolites, such that
the magnitude, and in some cases the direction of change, differed
between participants (Fig. [177]4C). While these different responses
between participants may indicate differential vulnerability to the
metabolic consequences of sleep loss, confirmation requires further
investigation to determine whether these inter-individual responses to
sleep loss are stable and trait-like. Our finding of inter-individual
variation in linearly changing metabolites is consistent with the large
inter-individual differences reported in lipids showing wake-dependent
changes^[178]18, although our results suggest that polar metabolites
are less likely to change with time awake than lipid species (27% lipid
vs 8% polar). As with our analysis of rhythmic metabolites, we observed
differences in which metabolites showed significant linear changes
depending on whether the data were analysed at the group- or
individual-level. For example, we observed that 29 of the 188
metabolites detected as significantly linear at the group-level were
not significantly linear in a single participant. This discrepancy
highlights the importance of using both group- and individual-level
analyses in biomarker discovery, as had we only conducted a group-level
analysis, significant resources may have been used in trying to
identify and validate group-significant metabolites that lack utility
as a biomarker at the individual-level. There were a small number of
unidentified linear metabolites from the untargeted matrix, however,
that showed strikingly consistent changes across majority of the
participants (e.g. right panel of Fig. [179]4D). Metabolites such as
these may be useful as biomarkers of sleep pressure, however, further
work to identify these metabolites and to validate our results is
required.
Our study has three main strengths. First, our data are novel in that
our analytical approaches allowed detection of a broad range of polar
and non-polar metabolites, extending the range of metabolites that had
previously been detected. While circadian- and wake-dependent changes
have been previously described in moderately polar metabolites under CR
conditions^[180]20,[181]21, our findings, showing large
inter-individual differences in the circadian phase of polar
metabolites, suggest that these prior data were potentially confounded
by the pooling of samples from multiple participants at the same clock
time. Second, our study is the first to employ both group- and
individual-level analyses to examine 24-hour rhythms and wake-dependent
changes in polar metabolites. Differences between group- and
individual-level analyses have only been investigated in plasma
lipids^[182]18,[183]19, while other studies that have measured both
moderately and non-polar compounds have reported data at the
group-level^[184]15,[185]16,[186]20,[187]42,[188]43. Finally, our
sample includes female participants, which have not been included in
some previous publications (e.g.^[189]15,[190]16,[191]18–[192]21).
While the small number of females precludes extensive analyses of sex
differences, we did observe that four metabolite features were
significantly rhythmic in 3 of 4 women and no men, and a further four
were significantly linear in 3 of 4 women and no men. While these
preliminary findings suggest there may be sex differences in the
expression of some metabolites, further research with larger numbers of
men and women, and equal group sizes is required to further elucidate
possible sex differences in circadian and wake-dependent changes in
plasma metabolites.
This study comprises the first step within a larger biomarker discovery
program, where the current study was designed to produce
proof-of-concept data within a small, but highly controlled study. The
small sample size or the frequency of sampling, however, means that the
current study may have been underpowered to detect some rhythmic or
linear changes at the group-level, particularly those with low
amplitudes or shallow slopes. Despite this, our sample size and
sampling frequency is commensurate to previous metabolomics
studies^[193]15,[194]16,[195]20. Future validation studies should be
conducted on larger populations with more frequent sampling (e.g.
2-hourly). Furthermore, as with previous studies, participants in the
current study were all young and extremely healthy, and the laboratory
conditions were highly controlled during the CR protocol. While at this
stage in the biomarker development process it is important to first
identify the presence of any circadian and wake-dependent changes in an
homogenous sample under highly controlled conditions, these findings
will need to be validated in other populations and in less controlled,
applied settings including circadian misalignment, sleep restriction,
and in field settings. To this end, future inclusion of a control group
with a normal sleep/wake schedule, ambulatory activity and typical food
intake would aid in determining whether any linear changes observed
were due to external factors. Finally, while we identified metabolites
that changed linearly during sleep deprivation it is difficult to
ascertain whether these metabolites are directly under the control of
the sleep homeostat and are truly wake-dependent, or perhaps represent
something else, for example, a build-up of certain metabolites from the
hourly meals given during the CR. Similarly, the use of a CR protocol
makes it difficult to uncouple the contribution of the circadian system
and the sleep homeostat. Future studies might employ a forced
desynchrony protocol to allow for a more comprehensive investigation of
the individual contribution of the homeostatic and circadian processes
on the abundance of specific metabolites.
To our knowledge this is the first study to characterize circadian- and
wake-dependent changes in polar plasma metabolites. Our results
describe circadian- and wake-dependent control of the polar metabolome
and highlight the importance of analyzing these types of data at both
the group- and individual-level. We showed that analysis at the
group-level resulted in inaccurate measures of the abundance and
time-course of both rhythmic and linearly changing metabolites.
Underlying inter-individual differences in circadian- and
wake-dependent modulation of the metabolome will also likely be an
important consideration for future biomarker development programs using
metabolomics.
Methods
Participants
Fourteen healthy adults (13 following exclusion of n = 1, 4 females,
24.74 ± 4.09 years; Table [196]1) completed a 6-day in-laboratory
study. Participants were free from medical, psychiatric or sleep
disorders, had not engaged in night- and/or shift-work in the past
three years, or travelled across more than one time zone in the past
three months. Two weeks prior to the laboratory study, participants
maintained a self-selected 8:16 sleep-wake schedule, which was
confirmed by wrist actigraphy (Actiwatch Spectrum, MiniMitter Inc,
Bend, OR) and sleep diaries. The use of prescription and
over-the-counter medications, supplements, recreational drugs (also
exclusionary if consumed in the previous month based on self-report),
nicotine, caffeine, and alcohol were not permitted from 3 weeks prior
to admission until completion of the study. Urine drug screening, and a
pregnancy test for women, was conducted prior to laboratory admission.
All participants provided informed consent and the study was approved
by the Monash University Human Research Ethics Committee (CF14/2790 –
2014001546). The experiments were conducted in accordance with the
Declaration of Helsinki.
Study Protocol
Participants were continuously monitored for 6-days in an environment
free of time cues. There was no access to windows, clocks, live TV, or
newspapers, and participants were supervised by technicians trained not
to reveal time of day. Women were studied during their follicular
phase, with admit occurring immediately after their last menses, to
minimize differences between women due to menstrual phase. The study
started with two baseline nights with sleep scheduled at the same time
as participants’ self-selected sleep in the two weeks prior to
admission. Full polysomnography was recorded on the first night to
confirm no presence of sleep disorders, including restless legs
syndrome, periodic limb movements, and sleep disordered breathing.
During baseline days, participants were fed three main meals and three
snacks per day.
Upon waking on Day 3, participants commenced a 40-h constant routine
(CR). During the CR, participants remained awake under constant
supervision in dim light conditions (<3 lux), in a semi-recumbent
posture (head of bed at 45°), and received identical hourly snacks
(quarter sandwich, 60 ml water and 40 ml apple juice). The calorie
content of hourly snacks was ~1.1 x the average resting energy
expenditure (REE) for all participants (1796 ± 236 cal/day) and the
macronutrient content adhered to the recommendations of the Australian
Dietary Guidelines 2013^[197]49. REE for each participant was
calculated as^[198]50:
[MATH:
REEMa<
/mi>le=9.99∗
weight(kg)+6.25∗he
ight(cm)−4.29∗ag
e+5 :MATH]
[MATH:
REEFe<
/mi>male=9.99∗weigh<
mi>t(kg)+6.25∗he
ight(cm)−4.29∗ag
e−161 :MATH]
Participants had the choice of four sandwich options that were
approximately equivalent in calorie (1804 ± 99 cal/day) and
macronutrient content (~20% protein, ~33% fat, ~46% carbohydrate). Each
participant received only one of the sandwich options for the duration
of the CR.
Lighting
During baseline and recovery days, maximum ambient light during wake
episodes was ~100.9 ± 18.2 lux when measured in the horizontal plane
and ~44.0 ± 13.9 lux when measured in the vertical plane at the height
of 182 cm. On baseline night 2 and during CR, lights were dimmed to
~2.8 ± 0.5 lux in the horizontal plane and ~1.2 ± 0.3 lux in the
vertical plane when measured at 182 cm. During scheduled sleep
episodes, ambient lighting was turned off. The room lighting was
generated from ceiling-mounted 4100 K fluorescent lamps (Master TL5 HE
28W/840 cool lights, Philips Lighting, Amsterdam, Netherlands) that
were covered with neutral density filters (3-stop LEE Filters,
Lightmoves, Noble Park, Australia). Illuminance measures (J17 Lumacolor
photometer, Tektronix, Beavertown, USA) were taken daily in four
locations around the room, positioned directly under light panels.
Blood sample collection and processing
Plasma samples were collected during the CR via an indwelling
intravenous cannula, inserted into the forearm or antecubital vein
approximately 1-hour after wake. Blood was collected hourly for plasma
melatonin assay, and additional blood was collected every 2-hours for
metabolomics analysis starting 2-hours post wake. At each collection,
whole blood was collected in a syringe and aliquoted into a blood tube
spray coated with K2EDTA. Samples were immediately centrifuged at 4 °C,
or stored in a fridge at 4 °C for up to ~30 minutes until processing.
Samples were spun at 1,300 × g for 10 minutes and plasma was aliquoted
into 500 µL fractions and temporally stored on dry ice before transfer
to permanent storage at −80 °C within 4–12 hours.
Of the total 546 scheduled blood collections (39 samples × 14
participants), 529 were collected successfully (3.11% missing samples)
and assayed for melatonin. For the metabolomics analysis, up to ten
4-hourly samples per participant were analyzed at times 2, 6, 10, 14,
18, 22, 26, 30, 34 and 38 hours post-wake. Of the 140 possible samples,
18 (13.6%) were missing due to either a missed collection (n = 1
sample) or had moderate to severe haemolysis (orange to pink in colour;
n = 17 samples). To be included in the metabolomics analysis,
participants could not have more than 70% missing blood samples, and no
more than two consecutive missing samples. To avoid excluding two
individuals, a 4-hourly sample was replaced with a successful
collection occurring 2 hours before or after the sample that required
replacement— for example, a sample collected at 32 hours was used to
replace a missing sample at 34 hours. With these replacements, a total
of 124 samples were included in the final metabolomics analysis from 14
participants.
Of the 124 plasma samples analysed using LC-MS, five samples were lost
in both the targeted and untargeted matrices post-analysis due to
mis-injection into the LC-MS (n = 119 samples). Retention time drifts
(>2 mins) resulted in the exclusion of an additional three samples from
the untargeted matrix following XCMS analysis (n = 116), but not from
the targeted matrix which was manually integrated such that the
retention time window could be widened to incorporate these metabolites
(n = 119). One male participant was excluded entirely from further
analysis, as this additional loss of samples resulted in three
consecutive missing time-points in the middle of their data series
making it difficult to interpret the model fits. Following removal of
this participant, 10 of 13 participants had missing data points in the
targeted matrix resulting in 14% (18 samples; total n = 112) missing
data, and 12 of 13 participants had missing data points in the
untargeted matrix resulting in a total of 16% (21 samples; total
n = 109) missing data. Demographic information of the 13 participants
included in the final analysis are shown in Table [199]1.
Marker of the circadian clock
Total blood plasma melatonin was determined at the Adelaide Research
Assay Facility (ARAF; University of South Australia, Adelaide,
Australia) by reverse-phase C-18 column extraction of 500 µl plasma,
followed by double antibody radioimmunoassay using standards and
reagents supplied by Buhlmann Laboratories (RKMEL-2, Buhlmann
Laboratories AG, Schönenbuch, Switzerland). The sensitivity of the
assay using 500 µl of extracted plasma was 1.0 pg/ml. Samples were
assayed in duplicate and the intra-assay coefficient of variation of
the assays was 7.61%. The inter-assay coefficient of variation of the
low concentration quality control was 11.03%, and the inter-assay
coefficient of variation of the high concentration quality control was
13.08%.
To determine circadian phase, Dim Light Melatonin Onset (DLMO) was
defined as the time at which plasma melatonin levels reached 5 pg/ml in
the first cycle of the CR, calculated by interpolating between two
adjacent samples^[200]51. For two participants, DLMO was calculated
from the second cycle due to missing samples in the first 24-h cycle.
The biological night was defined as DLMO plus 10 hours (DLMO + 10) and
split according to first half (first 5 hours) and second half (second
5 hours) of the night. The biological day was defined hours the
14 hours between DLMO + 10 and DLMO. The biological day was further
broken down into two equal 7-h halves.
Metabolomics analysis
Metabolomics analysis was performed on plasma samples collected at
4-hourly intervals starting 2-hours post wake (Metabolomics Australia,
Bio21 Molecular Science and Biotechnology Institute, University of
Melbourne, Parkville, Australia). Samples were thawed on ice and 20 µL
of plasma was aliquoted for analysis by LC-MS. An additional 20 µL from
each sample was pooled to generate a plasma quality control (PQC)
sample, from which aliquots were taken in preparation for extraction
with plasma samples. Plasma samples and PQCs were extracted using
180 µL acetonitrile/methanol (1:1 v/v) solution containing 2 µM
^13C-sorbitol, 2 µM ^13C^15N-AMP, and 2 µM ^13C^15N-UMP as internal
standards. Samples were vortexed for 30 seconds, sonicated for
5 minutes at 4 °C, then incubated for 10 minutes at 4 °C (in an
Eppendorf Thermomixer). Sample (prepared in batches of 24, with a PQC
every 10 samples) were centrifuged (4,500 × g, 10 minutes, 4 °C) and
180uL of the supernatant was transferred into a glass vial. An aliquot
of each sample of the extracts (10 µL) was pooled to create a pooled
biological quality control (PBQC) sample.
Samples (10 µL) were resolved on a ZIC®-pHILIC column (5 µm particle
size, 150 × 4.6 mm, Merck SeQuant®) connected to an Agilent 1260 (Santa
Clara, CA, USA) HPLC system running a 29.5-minute gradient with mobile
phases 20 mM ammonium carbonate (pH 9.0; Sigma-Aldrich; Solvent A) and
100% acetonitrile (solvent B) at a constant flow rate of 300 µL/min.
The elution gradient started at a composition of 80% solvent B and
decreased to 30% solvent B in 18.5-minutes for 6.5-minutes. Extracted
plasma volumes of 7 µL were injected onto the column (maintained at
25 °C). Metabolites were detected by electrospray ionization using an
Agilent 6545 Q-ToF MS system (Santa Clara, CA, USA) in negative
ionization mode. The instrument was cleaned and calibrated weekly to
ensure a mass accuracy of ±0.2 ppm. Detailed Q-Tof MS parameters can be
found in Stewart, et al.^[201]52. Samples were analysed in the same
analytical batch and randomized by participant and time, with a QC
every 5 samples. PQCs were run every tenth sample to monitor any batch
preparation effects and PBQCs were run every tenth sample to monitor
instrument performance during the run. Solvent blanks were analysed
every 24 samples to monitor background. Five mixtures of authentic
standards (234 metabolites) were also run to generate a library for the
targeted analysis.
Metabolite identification for the targeted analysis (targeted matrix)
was based on accurate mass, retention time and MS/MS fragmentation
patterns for metabolites in the standard mixtures. Relative abundances
based on area under the metabolite peak were obtained using MassHunter
Quantitative Analysis B 0.7.00 (Agilent). Metabolites with low quality
chromatographic peaks (<10,000 area count) and peaks not reliably
detected across samples were excluded resulting in the detection of 99
(of 234) metabolites [Level 1 confidence according to the Metabolomics
Standard Initiative^[202]53]. The untargeted matrix containing 1,641
metabolite features, which include metabolite features already
represented in the targeted matrix, was generated by XCMS centWave
algorithm^[203]54 to detect molecular features in the raw files and the
features list was further refined in CAMERA^[204]55 to group related
features by annotating isotope and adduct peaks.
Statistical analysis
To reduce biological variability between participants and timepoints,
raw area count data were normalized relative to the median metabolite
abundance for each individual sample. Given differences in the relative
concentration of metabolites between individuals, the median normalized
data were z-scored in order to scale the data prior to analysis. Data
were z-scored relative to the mean and standard deviation of each
participants’ scores for a single metabolite. Each time-point was then
expressed relative to DLMO, where DLMO was defined as time 0. Grouped
data were averaged across 4-hour phase bins to align the data points
relative to each participant’s internal circadian time. Data were
fitted with a non-linear regression model^[205]18 that was comprised of
a cosinor function with a linear component:
[MATH: y=Acos{2π(t−φτ)}+Ct+D :MATH]
In the model, A is the amplitude of the sinusoid, τ is the period set
at 24-h, t is time, Ø is the acrophase of the sinusoid, and C and D are
the slope and y-intercept of the linear component, respectively.
Fitting of the model was conducted in SAS 9.4 using the proc nlin
procedure. The model was fitted to the 99 and 1,641 metabolite profiles
averaged within phase bins from the targeted and untargeted matrix
(group-level analysis), and to each individual participants’ metabolite
profiles from the targeted and untargeted matrix (individual-level
analysis). The cosinor and linear components of the regression were
considered significant if the amplitude and slope, respectively, were
significantly different from 0. Where the regression model detected a
significant nadir, acrophase was calculated as the peak 12-hours later.
Given the exploratory nature of the study, p-values were set at 0.05.
The model estimates for all analyses, including amplitude, acrophase
and slope estimates are shown in SI Tables 1–4.
Pathway enrichment analysis was conducted in MetaboAnalyst 3.0
([206]http://www.metaboanalyst.ca) using the Enrichment Analysis
module. This analysis provides a p-value for the overall likelihood
that a metabolite set or pathway is involved based on the metabolites
entered, and also indicates the degree of enrichment (fold enrichment),
which is representative of how many metabolites within a specific
pathway are present in the metabolite set entered into the analysis.
For example, if two out of four metabolites in a pathway are present
then that pathway will show a greater fold enrichment than a pathway
that has two out of 10 metabolites that are present. Pathway enrichment
analysis was conducted separately for linear and rhythmic metabolites
at the group and individual level. Metabolites that were combined
rhythmic and linear were included in both analyses. The results of the
pathway enrichment analyses generated in MetaboAnalyst 3.0 are shown in
SI Figs 3[207]–6 and SI Tables [208]6–[209]9.
Supplementary information
[210]Supplemental information^ (654.6KB, pdf)
[211]Supplemental dataset 1^ (8.3MB, xlsx)
Acknowledgements