Abstract
COVID-19, a systemic multi-organ disease resulting from infection with
severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is known
to result in a wide array of disease outcomes, ranging from
asymptomatic to fatal. Despite persistent progress, there is a
continued need for more accurate determinants of disease outcomes,
including post-acute symptoms after COVID-19. In this study, we
characterised the serum metabolomic changes due to hospitalisation and
COVID-19 disease progression by mapping the serum metabolomic
trajectories of 71 newly hospitalised moderate and severe patients in
their first week after hospitalisation. These 71 patients were spread
out over three hospitals in Switzerland, enabling us to meta-analyse
the metabolomic trajectories and filter consistently changing
metabolites. Additionally, we investigated differential
metabolite–metabolite trajectories between fatal, severe, and moderate
disease outcomes to find prognostic markers of disease severity. We
found drastic changes in serum metabolite concentrations for 448 out of
the 901 metabolites. These results included markers of hospitalisation,
such as environmental exposures, dietary changes, and altered drug
administration, but also possible markers of physiological functioning,
including carboxyethyl-GABA and fibrinopeptides, which might be
prognostic for worsening lung injury. Possible markers of disease
progression included altered urea cycle metabolites and metabolites of
the tricarboxylic acid (TCA) cycle, indicating a SARS-CoV-2-induced
reprogramming of the host metabolism. Glycerophosphorylcholine was
identified as a potential marker of disease severity. Taken together,
this study describes the metabolome-wide changes due to hospitalisation
and COVID-19 disease progression. Moreover, we propose a wide range of
novel potential biomarkers for monitoring COVID-19 disease course, both
dependent and independent of the severity.
Keywords: COVID-19, hospitalisation, metabolomics, serum, disease
progression, multi-centre
1. Introduction
COVID-19 is a respiratory disease caused by infection with severe acute
respiratory syndrome coronavirus 2 (SARS-CoV-2) [[44]1]. Although most
COVID-19 cases are mild, severe cases can result in acute lung injury
[[45]2] and acute respiratory distress syndrome [[46]3], leading to
potentially fatal outcomes [[47]4]. COVID-19 has been reported to
affect multiple organs beyond the lungs, including the gastrointestinal
tract [[48]5], the kidney [[49]6], the liver [[50]5,[51]7], and the
brain [[52]8]. Due to increasing evidence on the systemic
extrapulmonary implications of COVID-19 [[53]9], the disease has been
recognised as a multisystem disease [[54]9]. Since the start of the
COVID-19 pandemic, significant efforts have been made to find
determinants of COVID-19 disease outcomes. Phenotypic and
epidemiological determinants of COVID-19 disease outcomes, such as age
[[55]10,[56]11], BMI [[57]11,[58]12,[59]13], sex [[60]14,[61]15], air
quality [[62]16,[63]17], and environmental pollution [[64]14,[65]15],
have been instrumental in understanding which individuals are at risk
of severe COVID-19 outcomes. These epidemiological metrics, however,
are limited as they do not take genetics and molecular phenotypes into
account. To find more accurate and time-dependent predictors of disease
outcomes, omics-based studies, in particular blood metabolomics, have
consistently found new prognostic markers of disease outcomes and
enabled important insights into the pathogenesis of COVID-19
[[66]18,[67]19,[68]20].
Previous blood metabolomic studies have uncovered widespread
physiological and molecular responses upon infection by SARS-CoV-2
[[69]21,[70]22,[71]23,[72]24]. After recognition of the SARS-CoV-2
double-stranded RNA by the host, a series of molecular cascades is
initiated, resulting in the production of cytokines and chemokines
[[73]1]. In severe and critical COVID-19 patients, the production of
cytokines can become excessive, leading to a “cytokine storm”, which
has been proposed as the main driver of COVID-19 severity [[74]25].
IL-6, one of the main cytokines driving COVID-induced hyperinflammation
[[75]25], has been reported to correlate with shifts in tryptophan
metabolism, nitrogen metabolism, and oxidative stress markers, such as
methionine sulfoxide and cysteine. Notably, the depletion of
tryptophan, an essential precursor of several neuroactive metabolites,
such as serotonin and melatonin [[76]26], has also been linked with
persistent long-COVID symptoms.
Besides physiological and molecular changes due to the host response,
SARS-CoV-2 has also been found to hijack the host metabolism to promote
its replication [[77]22,[78]27,[79]28]. SARS-CoV-2 is known to promote
nucleotide production for its replication by increasing glucose-derived
carbon uptake, leading to an increased oxidative tricarboxylic acid
(TCA) cycle activity and nucleotide production [[80]27]. Alterations in
lipid metabolism have also been linked to SARS-CoV-2-induced metabolic
hijacking [[81]29,[82]30,[83]31]. SARS-CoV-2 replication is dependent
on lipid droplets [[84]32], which consist of various phospholipids,
sphingolipids, and cholesterol compounds. Alterations in lipid
metabolism have been confirmed by various metabolomic studies linking
phospholipids and sphingolipids to COVID-19 disease outcomes
[[85]22,[86]33,[87]34].
COVID-19, being a systemic disease, impacts metabolism through
COVID-19-related changes in the gut microbiome, with pathogenic gut
microbes increasing in their abundance while beneficial microbes
decrease in relative abundance [[88]35]. Moreover, several
gut-microbiome-modulated metabolites in the blood have been linked with
COVID-19 outcomes, including short-chain fatty acids and
neurotransmitters [[89]35,[90]36].
Although great strides have already been made to find determinants of
COVID-19 disease outcomes, the interaction between hospitalisation and
disease progression remains understudied. When an individual moves from
the home environment to the hospital environment, the patient is often
subject to a drastically different exposome, an umbrella term of all
dietary, drug, behavioural, and environmental exposures [[91]37]. The
exposome shift from the home environment to the hospital can be
expected to have systemic effects and may, thus, influence COVID-19
disease progression.
To investigate how the hospital exposome, disease progression, and
exposome–disease interactions influence physiological functioning, we
analysed untargeted serum metabolomics data of newly hospitalised
moderate and severe COVID-19 patients. Using a repeated-measure design,
we investigated metabolome-wide shifts in the first week after
hospitalisation of 71 patients in three different locations in
Switzerland, namely Geneva, St. Gallen, and Ticino. Each location was
investigated as a separate study, after which the results were
meta-analysed to find consistently changing metabolites. We found
drastic metabolome-wide changes in metabolism that could be linked to
various aspects of hospitalisation and COVID-19 disease progression.
This list included markers of changing environmental exposures, diet,
drug metabolism, host–gut microbiota crosstalk, physiological
functioning, and COVID-19-induced metabolic reprogramming. Moreover, we
propose carboxyethyl-GABA and fibrinopeptides as potential markers of
COVID-19-driven lung injury. This study comprehensively describes the
shifts in serum metabolite concentrations in newly hospitalised
COVID-19 patients and put these changes into their metabolic context.
2. Results
2.1. Data Descriptions
To investigate serum metabolome trajectories during COVID-19, we
analysed a total of 71 patients from three independent, longitudinal
cohorts of COVID-19 patients placed in Ticino (n = 20), St. Gallen (n =
22), and Geneva (n = 29). We analysed serum samples using untargeted
mass spectrometry during the first eight days of hospitalisation at two
different time points. The Geneva and St. Gallen samples only consisted
of severe COVID-19 cases, while the cohort in Ticino consisted of nine
severe and eleven moderate COVID-19 cases ([92]Table 1). Moderate cases
were defined as PCR-confirmed SARS-CoV-2-infected patients with
symptoms of pneumonia, fever, and respiratory tract symptoms. Severe
COVID-19 patients had all the symptoms of moderate cases, but also had
a respiratory rate of ≥30 breaths per minute and an oxygen saturation
of ≤93% when breathing ambient air or having a PaO[2]/FiO[2] below 300
mmHg. Patients that did not meet these requirements but needed
ventilator support were also classified as severe COVID-19 patients.
All moderate patients were situated in the hospital ward, whereas the
severe patients were all in the intensive care unit (ICU). Across the
three locations, 49 (37%) patients had one sample taken, 55 (42%)
patients had two samples taken, 17 patients (13%) had three samples
taken, and only nine patients (6%) had a fourth sample taken
([93]Figure 1A).
Table 1.
Summary of COVID-19 patient demographics from three Swiss hospitals.
SD—standard deviation. BMI—body mass index.
Geneva Moderate Severe-Survived Severe-Fatal
Analysed patients 14 15
Mean age (SD) 66.8 (9.6) 66.6 (8.82)
Female/male 5/9 3/12
Mean BMI (SD) 28.6 (6.2) 25.5 (4.2)
Analysed samples 28 30
St. Gallen Moderate Severe-Survived Severe-Fatal
Analysed patients 11 11
Mean age (SD) 59 (10.6) 65.4 (8.98)
Female/male 1/10 2/9
Mean BMI (SD) 31.2 (6.1) 30.4 (3.7)
Analysed samples 22 22
Ticino Moderate Severe-Survived Severe-Fatal
Analysed patients 11 7 2
Mean age (SD) 53.5 (9.11) 60 (8.56) 68.5 (3.54)
Female/male 5/6 2/5 1/1
Mean BMI (SD) 24.1 (4.4) 28.0 (2.0) 29.7 (5.7)
Analysed samples 22 14 4
[94]Open in a new tab
Figure 1.
[95]Figure 1
[96]Open in a new tab
Distributions of serum metabolites by biochemical family. (A) Overview
of the number of significantly and consistently changed metabolites per
pathway in the first eight days after hospitalisation. Each pathway was
coloured and sorted by their biochemical family. The grey bars
represent the total number of metabolites analysed in each pathway.
Panel (B–E) show the distributions of metabolites per biochemical
family, respectively, for all measured metabolites, all removed
metabolites, all analysed metabolites, and all significant and
consistently changed metabolites.
The serum metabolome datasets contained measurements of 1086 different
metabolites, consisting of a wide range of compounds with varied
origins. These compounds included dietary markers, markers of
environmental exposures, microbiome-derived metabolites, as well as a
wide range of endogenously produced metabolites. Before analysing the
metabolome datasets, we excluded all metabolites that were absent in
over 20% of the samples in all three locations, resulting in 901
metabolites included in this study ([97]Table S1). The included
metabolites represented nine global biochemical families, of which
lipids (40% of all compounds) and amino acids and amino acid
derivatives (20% of all compounds) were most numerous in terms of
metabolic species ([98]Figure 1B, [99]Table S2). The removed set of
metabolites consisted mostly of lipids (41%) and unnamed metabolites
(32%, [100]Table S2). Tryptophan was excluded from analysis, since its
plasma levels dropped below the limit of detections in 67% of COVID-19
patients at the day of hospital admission, highlighting tryptophan
depletion during COVID-19 as reported earlier [[101]24].
2.2. The Serum Metabolome Drastically Changed within One Week Hospitalisation
with COVID-19
We first analysed changes in the serum metabolome during the first
eight days in the hospital. Therefore, we performed mixed-effect linear
regression analysis for each sample and each metabolite, with the
log-transformed concentration as the response variable and the time
point as the predictor of interest (days after hospitalisation). Age,
sex, BMI, and death due to COVID-19 were included as covariates with
random intercepts for the individual. Next, we meta-analysed the
regression outcomes to derive a list of consistently changing
metabolites, reducing possible bias due to hospital-specific
environments and treatments. After correction for multiple testing
using the false discovery rate (FDR), 545 metabolites were
significantly changed in the first eight days. After filtering out
metabolites with heterogeneous effects (qFDR > 0.05) across the three
samples ([102]Table S3), 448 metabolites consistently changed in their
serum concentration. Each major biochemical class in the full
metabolome data was included in these results, with lipids (163
compounds), amino acids (96 compounds), and xenobiotics (56 compounds)
as the largest groups. Likewise, each pathway in the list of analysed
metabolites was represented in the list of significant metabolites
([103]Figure 1E), indicating large and widespread changes in
physiological and metabolic functioning. The significantly changed
metabolites point towards a range of physiological and metabolic
processes previously linked with COVID-19 severity, but are also
indicators of host–microbiome interactions, dietary changes, drug
administration, and environmental exposures. Some of our top hits
included carboxyethyl-GABA (increased), pantoate (increased),
phenylacetylcarnitine (increased), and sphingomyelin (d18:1/25:0,
d19:0/24:1, d20:1/23:0, d19:1/24:0) (increased, [104]Figure 2A,B).
Importantly, our results replicated with comparable effect sizes across
the three different cohorts, indicating that these changes were
independent of hospital-specific exposures. We further quantified our
results by performing a pathway enrichment analysis on the 448
significantly and consistently changed metabolites using the
MetaboAnalyst [[105]38] pathway enrichment web service. Out of the 448
significant metabolites, 289 metabolites could be mapped onto the
MetaboAnalyst 5.0 database ([106]Table S4). Pathway enrichment analysis
then was performed against the KEGG homo sapiens reference pathway
library [[107]39], which resulted in eight enriched pathways after
correcting for the false discovery rate. The KEGG arginine biosynthesis
pathway, which included the urea cycle, was the top hit (eight hits,
FDR = 1.11 × 10^−5). The other enriched pathways were, in order of
significance, aminoacyl-tRNA biosynthesis (ten hits, FDR = 0.006),
panthothenate and CoA biosynthesis (six hits, FDR = 0.009),
phenylalanine, tyrosine and tryptophan biosynthesis (three hits, FDR =
0.013), histidine metabolism (five hits, FDR = 0.019), caffeine
metabolism (four hits, FDR = 0.019), beta-alanine metabolism (five
hits, FDR = 0.0463), and sphingolipid metabolism (five hits, FDR =
0.046) ([108]Figure S2 and Table S5).
Figure 2.
[109]Figure 2
[110]Open in a new tab
Overview of the top 20 most significantly changed serum metabolites.
(A) Volcano plot of regression outcomes for all 901 analysed
metabolites with the regression estimate on the x-axis against the
−log10 transformed p-value on the y-axis. The red and blue dots
represent all increased and decreased metabolites, respectively. The
top 20 most-changed metabolites are labelled. (B) Summary of the
regression results for the top 20 metabolites with the lowest FDR
corrected p-values. In addition to the metabolite names, the standard
errors (SE), the regression coefficient estimates (Estimate), and the
95% confidence intervals (CI95) are displayed. The FDR corrected
p-values from the regression models are shown as FDR. The QFDR values
represent the FDR corrected p-values obtained from the Cochran’s Q-test
for quantifying the between-cohort heterogeneity.
In the following paragraphs, we will further highlight and
contextualise the 448 measured serum biochemical changes. Please note
that due to the large number of results, it was not possible to
highlight and contextualise all results. Nevertheless, we tried to give
a varied and broad overview of the measured biochemical changes with
special attention to the effects of hospitalisation and COVID-19
disease progression ([111]Table S3).
2.3. Serum Metabolome Trajectories Reflect Changing Environmental Exposures
after Hospitalisation
Our results contained several markers of changing environmental
exposures ([112]Figure 3, [113]Table S3). For example, one marker was
perfluorooctanoate (PFOA, decreased), which is an industrial surfactant
and forever chemical used in the textile industry as a water and oil
repellent coating [[114]40]. Other markers of environmental exposure
included propyl-4-hydroxybenzoate sulphate (decreased) and
methyl-4-hydroxybenzoate sulphate (increased). Propyl-4-hydroxybenzoate
and methyl-4-hydroxybenzoate are known as propylparaben and
methylparaben, respectively, and both are widely used in cosmetics and
body care [[115]41], indicating changes in products used for body care
in the hospital environment in comparison to the items utilised outside
the hospital. 2,4-di-tert-butylphenol, an antioxidant with wide
applications in industry [[116]42], was also increased in the hospital
setting. While the clinical meaning of those results is not clear, they
suggest that changed environmental exposures due to hospitalisation
affect the serum metabolome.
Figure 3.
[117]Figure 3
[118]Open in a new tab
Meta-analysed regression outcomes of serum metabolites linked to
environmental exposures. Forest plot of meta-analysed compounds linked
to environmental exposures. The estimates, or regression coefficients,
represent the pooled change in concentration over time in the three
cohorts (see [119]Section 4 for details). Negative estimates indicate
decreased serum concentrations, while positive estimates indicate
increased serum concentrations during hospitalisation. The displayed
metabolites all changed consistently and homogenously between cohorts.
All metabolites remained significantly changed after correction for the
false discovery rate. The 95% confidence interval is given by the
protruding lines from the metabolite estimate.
2.4. Acetaminophen Metabolism Favoured Degradation to Glucuronide Conjugates
in Lieu of Sulphate Conjugates
The metabolomics analyses also revealed changes in drug metabolites
([120]Figure 4, [121]Table S3). Changes were found in both aspirin and
paracetamol metabolism; both are wide-spread analgesics used in
hospital and home settings. Salicylate, a downstream metabolite of
acetyl-salicylic acid (aspirin), increased along with glucuronide
conjugates of acetaminophen (paracetamol). The sulphate conjugates of
acetaminophen contrastingly decreased in their concentrations. In
conclusion, untargeted metabolomics identified metabolites of relevant
drugs, indicating that the corresponding degradation pathways may be
influenced by either the disease or changes in the environment due to
hospitalisation.
Figure 4.
[122]Figure 4
[123]Open in a new tab
Meta-analysed regression outcomes of serum metabolites associated with
drug metabolism. Forest plot of meta-analysed compounds associated with
drug metabolism. The estimates, or regression coefficients, represent
the pooled change in concentration over time in the three cohorts (see
[124]Section 4 for details). Negative estimates indicate decreased
serum concentrations, while positive estimates indicate increased serum
concentrations during hospitalisation. The displayed metabolites all
changed consistently and homogenously between cohorts. Metabolites with
red-coloured estimates remained significantly changed after correction
for the false discovery rate. Black-coloured estimates indicate no
significant change after multiple testing correction. The 95%
confidence interval is given by the protruding lines from the
metabolite estimate.
2.5. Dietary Metabolites Indicate Changes in Diet in Hospitalised COVID-19
Cases
The changed serum metabolome trajectories also reflected changes in
diet due to hospitalisation ([125]Figure 5, [126]Table S3). For
example, S-methyl cysteine sulfoxide, a dietary metabolite found in
several vegetables, including cabbages, leeks, garlic, and onions
[[127]43], was strongly decreased in the first eight days. Other
markers of dietary changes included carotene diols (decreasing), which
is naturally sourced from peppers [[128]44], and several increased
flavouring agents, such as erythritol, maltol sulphate, and vanillic
alcohol sulphate. Other flavouring agents, such as catechol sulphate
and derivatives, showed both increases and decreases over time
(catechol sulphate decreasing, 4-methylcatechol sulphate increasing).
Benzoate, a widely used antimicrobial agent and food preservative
[[129]45], and its downstream metabolite hippurate were also increased
over time. Notably, the increase in hippurate (Beta = 0.048, FDR =
0.009) was much weaker compared to benzoate (Beta = 0.091, FDR = 2.95 ×
10^−12, [130]Figure 5).
Figure 5.
[131]Figure 5
[132]Open in a new tab
Meta-analysed regression outcomes of serum metabolites related to
dietary behaviour. Forest plot of meta-analysed compounds related to
dietary behaviour. The estimates, or regression coefficients, represent
the pooled change in concentration over time in the three cohorts (see
[133]Section 4 for details). Negative estimates indicate decreased
serum concentrations, while positive estimates indicate increased serum
concentrations during hospitalisation. The displayed metabolites all
changed consistently and homogenously between cohorts. All metabolites
remained significantly changed after correction for the false discovery
rate. The 95% confidence interval is given by the protruding lines from
the metabolite estimate.
2.6. COVID-19 Related Hospitalisation Impacts Host–Microbiome Co-Metabolism
Beyond the metabolomic changes including markers of environmental
exposure, drug metabolism, and dietary metabolites, metabolites related
to host–microbiome interactions were prominently placed among the
significant metabolites ([134]Figure 6, [135]Table S3). Several
secondary bile acids, which are synthesised from primary bile acids via
microbial conjugations, were accumulating in the serum within the first
eight days of hospitalisation. All major secondary bile acid forms were
increased, including deoxycholate, lithocholate, ursodeoxycholate acid,
and their glycated/taurinated derivatives. Notably, several primary
bile acids were also increasing, namely chenodeoxycholate conjugates.
Cholate and its conjugates did not significantly alter in their
concentrations. These results suggest an altered bile acid
host–microbiome co-metabolism. Tryptophan host–microbiome metabolism
was also affected. Although tryptophan concentration changes could not
be analysed in the regression analyses due to tryptophan being depleted
in more than 20% of all samples, tryptophan depletion could be inferred
from its downstream metabolites, such as the microbially produced
3-formylindole [[136]46] (decreased) and the increase of the
microbiome-mediated tryptophan degradation products [[137]47],
including indolelactate and indoleacetate. Other human degradation
products of tryptophan, namely anthranilate and methoxykynurenate, were
also increased, indicating, together with the found overall tryptophan
depletion, a higher turnover of tryptophan. In summary, we observed
changes in serum concentrations of bile acids and tryptophan
degradation products in newly hospitalised COVID-19 patients,
indicating changing interactions between the host and gut-microbiome
during hospitalisation.
Figure 6.
[138]Figure 6
[139]Open in a new tab
Meta-analysed regression outcomes of serum metabolites related to
crosstalk between the host and gut-microbiome. Forest plot of
meta-analysed compounds related to crosstalk between the host and
gut-microbiome. The estimates, or regression coefficients, represent
the pooled change in concentration over time in the three cohorts (see
[140]Section 4 for details). Negative estimates indicate decreased
serum concentrations, while positive estimates indicate increased serum
concentrations during hospitalisation. The displayed metabolites all
changed consistently and homogenously between cohorts. Metabolites with
red-coloured estimates remained significantly changed after correction
for the false discovery rate. Black-coloured estimates indicate no
significant change after multiple testing correction. The 95%
confidence interval is given by the protruding lines from the
metabolite estimate.
2.7. Indicators of Changed Physiological Functioning Are Reflected in the
Serum Metabolome Trajectories
The serum metabolome also contained several markers related to
physiological function in progressing COVID disease trajectories
([141]Figure 7, [142]Table S3). For example, the top hit that
consistently changed in this study was carboxyethyl-GABA (increased),
which is a GABA derivative detected in human cerebrospinal fluid
[[143]48] and a faecal metabolite [[144]49]. Other top hits in our
study included a stark increase of fibrinopeptide A and B, which are
components of fibrin [[145]50], a major component in the coagulation
cascade, which stops bleeding after vessel trauma [[146]51].
Cholesterol sulphate was another increased top hit with known
coagulation-inducing properties [[147]52]. Additionally, several
bilirubin degradation products were increasing, hinting at accelerated
heme degradation. Although no conclusions can be made on how useful
these compounds are in predicting thrombosis, these results do warrant
further investigations into the clinical relevance of fibrinopeptides,
cholesterol sulphate, and bilirubin degradation products in predicting
thrombosis. Another compound of potential clinical interest was
3-methylglutaconate (increased), which is a known marker of metabolic
acidosis [[148]53]. Metabolic acidosis is a potential complication of
severe COVID-19 [[149]54], as the lung is one of the key regulators of
blood pH value. The first week of hospitalisation also saw alterations
in vitamin status with pyridoxal (vitamin B6) and retinol (vitamin A)
decreasing, while tocopherols (vitamin E) displayed an inconsistent
pattern (alpha-tocopherol increasing, beta/gamma-tocopherol
decreasing). Of relevance in this context, 2-methyl-ascorbic acid, a
vitamin C metabolite, was strongly increasing. While it is unclear
whether these changes were caused by COVID-19 or changed nutrition
during hospitalisation, it shows that the vitamin status was affected
in hospitalised COVID-19 cases in a replicable pattern across three
independent hospitals. In summary, the metabolomic changes point
towards broad alterations in disease-relevant physiological processes,
although, given the design of the study, it remains unclear whether
these results were caused by COVID-19 or by the hospitalisation.
Figure 7.
[150]Figure 7
[151]Open in a new tab
Meta-analysed regression outcomes of serum metabolites related to
physiological functioning. Forest plot of meta-analysed compounds
related to physiological functioning. The estimates, or regression
coefficients, represent the pooled change in concentration over time in
the three cohorts (see [152]Section 4 for details). Negative estimates
indicate decreased serum concentrations, while positive estimates
indicate increased serum concentrations during hospitalisation. The
displayed metabolites all changed consistently and homogenously between
cohorts. Metabolites with red-coloured estimates remained significantly
changed after correction for the false discovery rate. Black-coloured
estimates indicate no significant change after multiple testing
correction. The 95% confidence interval is given by the protruding
lines from the metabolite estimate.
2.8. Metabolomic Results Reveal Potential Markers of Metabolic Reprogramming
in Hospitalised COVID-19 Cases
The metabolomic analyses also revealed evidence for SARS-CoV-2-induced
metabolic reprogramming ([153]Figure 8 and [154]Figure 9, [155]Table
S3). Various lipid metabolites were changed after one week of
hospitalisation, including several sphingomyelins,
glycerophosphorylcholine, sphinganine-1-phosphate, and
phosphatidylethanolamine ([156]Figure 8, [157]Table S3). Although no
consistent trend was found in metabolites in any of these classes,
these changes in lipid metabolism were consistent with previous
findings that SARS-CoV-2 rewires lipid metabolism to promote its
replication spread [[158]55]. Other possible markers of metabolic
reprogramming were altered concentrations of amino acids ([159]Figure 8
and [160]Figure 9, [161]Table S3), such as valine (decreasing),
arginine (decreasing), lysine (decreasing), histidine (decreasing),
glycine (increasing), and serine (increasing).
Figure 8.
[162]Figure 8
[163]Open in a new tab
Meta-analysed regression outcomes of serum metabolites linked to
SARS-CoV-2-induced metabolic reprogramming. Forest plot of
meta-analysed compounds that are linked to SARS-CoV-2 induced metabolic
reprogramming. The estimates, or regression coefficients, represent the
pooled change in concentration over time in the three cohorts (see
[164]Section 4 for details). Negative estimates indicate decreased
serum concentrations, while positive estimates indicate increased serum
concentrations during hospitalisation. The displayed metabolites all
changed consistently and homogenously between cohorts. All metabolites
remained significantly changed after correction for the false discovery
rate. The 95% confidence interval is given by the protruding lines from
the metabolite estimate.
Figure 9.
[165]Figure 9
[166]Open in a new tab
Metabolic reprogramming in the urea cycle and the TCA cycle. (A) Serum
metabolic changes of the urea cycle metabolites and TCA cycle
metabolites. Metabolites in red were increased in the serum after one
week, whereas metabolites in blue were decreased. The yellow star
indicates if the change was significant (FDR < 0.05) over time. All
displayed metabolites were consistently changed across the three
locations. (B) Forest plot of meta-analysed regression outcomes of the
visualised urea cycle and TCA cycle metabolites in [167]Figure 2A. The
estimates, or regression coefficients, represent the pooled change in
concentration over time in the three cohorts (see [168]Section 4 for
details). Negative estimates indicate decreased serum concentrations,
while positive estimates indicate increased serum concentrations during
hospitalisation. The displayed metabolites all changed consistently and
homogenously between cohorts. Metabolites with red-coloured estimates
remained significantly changed after correction for the false discovery
rate. Black-coloured estimates indicate no significant change after
multiple testing correction. The 95% confidence interval is given by
the protruding lines from the metabolite estimate.
We found indications of SARS-CoV-2-induced metabolic reprogramming in
the urea and TCA cycle. The measured substrates of the urea cycle,
including citrulline and ornithine, decreased over time, whereas the
products of the urea cycle, namely fumarate and urea, increased
([169]Figure 9A,B). This result could be interpreted as higher fluxes
through the urea cycle after one week of hospitalisation. Similar to
the urea cycle, several TCA cycle metabolites decreased, including
citrate, isocitrate, and aspartate ([170]Figure 9A,B, [171]Table S3).
Interestingly, glucose also decreased during hospitalisation
([172]Table S3). In conclusion, these results revealed metabolomic
patterns, which may relate to processes of metabolic programming over
the course of a viral infection. While these results cannot be directly
linked to disease progression, they do suggest a distinct set of
SARS-CoV2-influenced metabolite trajectories in the serum of newly
hospitalised COVID-19 patients.
2.9. Metabolite Trajectories during Hospitalisation Were Dependent on Disease
Severity
The results described above refer to changes in metabolite
concentration regardless of the severity of COVID-19. In the next step,
however, we investigated whether the changes in the serum metabolomes
during hospitalisation were dependent on disease severity. Note that
this analysis was confined to the Ticino cohort, as this was the only
hospital having samples for severe and moderate COVID-19 cases. We
performed mixed-effect regressions as above, introducing, however, the
severity of COVID-19 (binary: moderate vs. severe) as a predictor of
interest as well as an interaction term between the time variable and
the COVID-19 severity. While only three annotated metabolites
(caprylate, methylsuccinate, and xanthurenate) had significantly
different serum concentrations between the two conditions ([173]Table
S6) across all time points after correction for multiple testing by
correcting for the false discovery rate, 17 metabolites had a
significantly different time course during the first eight days in
hospital dependent on the severity of COVID-19 ([174]Figure 10,
[175]Table S7). It is noteworthy that glycerophosphorylcholine, a
glycophospholipid degradation product increasing generally in serum
during the first week of hospitalisation ([176]Table S3), was
increasing in severe COVID-19 cases and decreasing in moderate cases
([177]Figure 10), making it a potential marker for monitoring disease
progression. Cysteine-S-sulphate, another top hit, was decreasing much
more steeply in moderate cases compared to severe cases ([178]Figure
10). Interestingly, cysteine-S-sulphate is a metabolite known to be a
biomarker of sulphite oxidase insufficiency [[179]56], a rare disease
leading to severe neurological dysfunction. Importantly,
cysteine-S-sulphate is a very potent N-methyl-D-aspartate receptor
agonist [[180]57]. Some of the metabolites with severity-dependent
trajectories were also found to change due to hospitalisation
([181]Figure S3). Notably, several bilirubin degradation products were
also among the metabolites with altered trajectory in severe COVID-19,
as well glycochenodeoxycholate glucuronide. Taken together, we found
various compounds that may serve as biomarkers for disease progression.
Figure 10.
[182]Figure 10
[183]Open in a new tab
Overview of serum concentrations of metabolites with disease-dependent
trajectories in the Ticino cohort. Boxplots of the log-transformed
concentrations of metabolites with different serum trajectories in
moderate and severe patients in Ticino. In each tile, comparisons are
made between the first (salmon red) and second (turquoise) timepoint
for the moderate cases (left two boxplots) and severe cases (right two
boxplots). The black dots represent the individual concentration
values.
In a further step, we analysed whether trajectories of metabolites were
altered in cases that died of COVID-19 and thus could serve as early
biomarkers for COVID-19 mortality. Here, we combined the samples from
Geneva and St. Gallen, since the number of deaths in the Ticino
cohortwas not sufficient for statistical analysis. Once again, we
performed mixed-effect regressions, introducing this time death by
COVID-19 (binary: died of COVID-19 vs. survived) as a predictor of
interest as well as an interaction term between the time variable and
COVID-19 survival. However, the analysis did not reveal any biomarker
after correction for multiple testing, hinting at missing statistical
power ([184]Table S8). Taken together, while the analyses could
identify biomarkers of severe COVID-19 with severity-dependent time
trajectories, we could not identify biomarkers related to survival in
this string of analysis.
2.10. Metabolite–Metabolite Relations Are Affected by Disease Severity and
Disease Outcome
Next, we analysed the effect of disease severity on the bivariate
distributions of all pairs of metabolites in the Ticino cohort. To this
end, we calculated mixed-effect linear regressions as before,
including, however, each metabolite and a metabolite-severity
interaction term into the regressions as predictors. We then tested the
interaction term on significance. This resulted in 901 × 901 = 811,801
tests, and we corrected p-values accordingly to account for multiple
testing via Bonferroni correction. This analysis tests whether the
statistical relation between two metabolites is influenced by the
severity of the disease. We identified 14 metabolite–metabolite pairs
where the statistical relation was significantly influenced by disease
severity after correction for multiple testing ([185]Figure 11A). For
example, the correlation between N-acetyl-glutamate and
cinnamoylglycine was clearly positive in severe COVID-19 cases, while
being negative in moderate cases. For pyruvate and thymolsulfate, no
correlation was found in moderate cases, while a positive association
was detected in severe cases. The widespread alterations in bivariate
metabolite–metabolite distributions depending on COVID-19 severity
highlight the systemic changes due to COVID-19.
Figure 11.
[186]Figure 11
[187]Open in a new tab
Altered bivariate metabolite distributions. (A) Altered bivariate
distributions of metabolite–metabolite pairs in moderate (red) and
severe (blue) COVID cases from Ticino. All shown metabolite–metabolite
pairs differed significantly between moderate and severe COVID
patients. (B) Significantly altered bivariate distributions of
metabolite–metabolite pairs in severe COVID patients in Geneva and St.
Gallen. Bivariate metabolite distributions of patients that survived
COVID are shown in red, while bivariate metabolite distributions of
patients that later died are shown in blue.
Finally, we tested whether people who died had different
metabolite–metabolite dependencies, pooling the samples from St. Gallen
and Geneva and following the same procedure as above with the
difference that the interaction term was now defined as a
metabolite–death interaction term. We found two altered
metabolite–metabolite relations after correction for multiple testing
([188]Figure 11B). Interestingly, the correlation between sulphate and
1-methyl-myristoylglycerol was far stronger in severe COVID-19 cases
that later died compared to severe, surviving COVID-19 cases. As we
found many sulphated metabolites to be altered due to hospitalisation,
these results together may hint at sulphation processes being of
relevance in COVID-19. In conclusion, while individual bivariate
metabolite–metabolite distributions are difficult to interpret, our
analyses provided ample evidence of altered metabolite–metabolite
distributions in connection to disease severity and disease outcome.
3. Discussion
In this study, we described how the serum metabolomes of COVID-19
patients changed in the first week after hospitalisation in three
independent Swiss hospitals. The repeated measurement design allowed us
to assess the trajectory of the serum metabolome in 71 patients and
revealed wide-spread metabolomic alterations during hospitalisation in
the first eight days. In total, after meta-analysing the results, we
found 448 metabolites consistently changing over time out of the 901
analysed compounds. This list covered all measured biochemical classes
and showed surprising consistency in the detected profiles across the
three hospitals. To the best of our knowledge, this is the first study
integrating metabolome data from three independent hospitals in the
context of hospitalisation.
In the following, we will highlight several aspects of our results that
could deserve further investigation for their potential clinical
importance. In particular, we will discuss (1) metabolomic changes in
relation to the hospital exposome, including dietary behaviour and drug
metabolism, (2) metabolomic changes related to host–microbiome
interactions, (3) potential markers of COVID-19-related
pathophysiology, and (4) metabolomic changes that deliver potential
markers of viral reprogramming of the host metabolism. Together, these
results reveal important patterns that, while not being directly
translatable in terms of clinical outcomes, can point towards the
processes that are responsible for adverse COVID-19 trajectories or
physiological resilience to COVID-19.
3.1. Metabolomic Changes Related to the Hospital Exposome
We found clear indications of changes in environmental exposure due to
hospitalisation, covering so-called forever-chemicals, including
perfluorooctanoate (decreased). Perfluorooctanoate has an estimated
half-life between 0.5 and 1.5 years in the blood [[189]58]. However,
unaccounted background exposures have been known to result in widely
varied perfluorooctanoate half-life estimates [[190]59]. Although the
timeframe of this study is only one week, our results similarly seem to
show more rapid decreases in PFOA concentrations compared to what would
be expected based on the half-life estimates, with average decreasing
PFOA concentrations in one week of 16%, 26%, and 37% in Geneva, St.
Gallen, and Ticino, respectively. These results seem to suggest a
faster decrease in serum PFOA when changing environments. Future
studies on this topic, however, would need to test this hypothesis. Our
results also showed indicators of decreased usage of cosmetic and body
care products, highlighting the breadth of metabolic changes that can
be detected via untargeted metabolomics. Importantly, none of the
markers of environmental exposure showed evidence of a
disease-dependent trajectory in blood. As all severe patients in our
cohorts were situated in the ICU and all moderate patients were
situated in the hospital ward, this result also suggests that we did
not find environmental exposures specific to the hospital ward or the
ICU that influenced disease severity. Nevertheless, we believe that the
topic of environment-disease interactions in the hospital deserves
further considerations in future studies dealing with hospitalised
patients.
Although no data on medication usage were integrated in this work, the
serum metabolome provided information, on which drugs were administered
and how these drugs were degraded. For example, changes in
acetaminophen metabolism were reflected in an increased concentration
of glucuronide conjugated degradation products of acetaminophen while
sulphate conjugates decreased. This decrease might be explained by the
longer half-life of glucuronide conjugates [[191]60]. An alternative
explanation of this result is that the increase of glucuronide
conjugates was due to patients in all locations having an overweight
BMI (BMI > 25), as a higher BMI has been associated with an enhanced
glucuronide conjugation in overweight individuals [[192]61].
Our results also included several markers of changed dietary behaviour,
in particular hinting at decreased intake of compounds commonly found
in vegetables and an increased intake of food additives and food
preservatives. These changes in dietary markers hint at a reduced
nutrient content during the hospital stay, which would be in line with
previous findings that reported 66.7% of patients in the ICU and 23.7%
of the moderate patients in the hospital ward to be malnourished
[[193]62]. It should be noted that the measured changes in these
dietary outcomes are likely driven by patients in the ICU, given the
fact that ICU patients might have needed enteric or intravenous
nutrition. The statistical power to test differences in dietary changes
in moderate and severe patients, however, was not enough to derive
clear inferences due to the low number of moderately ill patients.
Future studies on this subject should investigate how differences in
ICU nutrition and the hospital ward diet may influence the patient’s
metabolism and physiology.
In conclusion, although hospital exposome-related compounds could not
be linked with disease severity or mortality, we believe that the
measured exposome-related serum changes are a relevant step towards a
better and more holistic understanding on exposome–disease
interactions.
3.2. Metabolomic Changes Related to Host–Microbiome Interactions
We also found markers of diet–microbiome interactions in changes in
benzoate metabolism. The human gut-microbiome is a known modulator of
benzoate degradation [[194]63] and has evolved pathways to protect
against the normally antimicrobial properties of benzoate [[195]64].
The benzoate degradation product hippurate was also increased, but much
less then benzoate. One possible explanation might be that changed
microbiome compositions, which are known to occur in COVID-19
[[196]65], reduced the importance of benzoate degradation via
hippurate. While unclear in their clinical importance, these results
indicate that hospitalisation has strong effects on the human serum
metabolome due to changes in diet and environment. Furthermore, we also
found markers of changing host–microbiome co-metabolism, as primary
bile acids had an increased turnover rate and secondary bile acids were
accumulating in the serum. These findings are in line with earlier
findings on gut-microbiome compositions, as both in-hospital disease
progression and disease severity have been found to correlate with
higher abundances of pathogenic and opportunistic bacteria and lower
abundances of beneficial bacteria [[197]65,[198]66].
3.3. Potential Markers of COVID-19 Related Pathophysiology
Besides markers of changing host–gut microbiota interactions, we found
various other domains of metabolism that may be related to
physiological changes and disease progression. For example, the top hit
in this study, carboxyethyl-GABA (increased), is known to promote cell
proliferation and migration in mouse fibroblasts [[199]67], which are
cells that function as support in the structural integrity of the
intercellular matrix and play an important role in wound healing. The
increased carboxyethyl-GABA concentrations over time led us to
hypothesise that carboxyethyl-GABA could be a possible marker of
interstitial pulmonary fibrosis, a group of diseases marked by the
pathological healing of lung tissue and pathological fibroblast
behaviour. Interstitial pulmonary fibrosis is associated with lung
injury and is common in severe COVID-19 disease trajectories [[200]68].
We also found indicators of increased coagulation activity ([201]Figure
7, [202]Table S3). Fibrinopeptide A and B increased during
hospitalisation. These compounds are released in the coagulation
cascade from fibrinogen in the formation of fibrin [[203]69], which
forms a matrix around vessel lesions and captures platelets to produce
blood clots [[204]51,[205]70]. Another indicator of increased
coagulation was the higher level of cholesterol sulphate during
hospitalisation. Cholesterol sulphate is present in platelet membranes
and promotes platelet adhesion and clotting [[206]71]. Serum
fibrinopeptide and cholesterol sulphate concentrations could be
potential markers to assess the risk of a patient developing
intravenous thrombosis, which is a common complication in severe
COVID-19 patients [[207]72]. Anticoagulation drugs, such as low
molecular weight heparins [[208]73], could be given to patients at high
risk of developing intravenous thrombosis.
Our results also showed increased concentrations of several bilirubin
degradation products during hospitalisation ([209]Figure 7, [210]Table
S3). Bilirubin is a waste product from the breakdown of red blood cells
and is metabolised in the liver and subsequently degraded by microbes
in the colon [[211]74]. Previous findings have linked increases in
serum bilirubin and its degradation products in COVID-19 patients to
decreased liver function [[212]33], but increased bilirubin degradation
could also be explained by SARS-CoV-2-induced breakdown of red blood
cells [[213]75]. 3-methylglutaconate was proposed as a potential marker
of severe COVID-19 outcomes due to its properties related to the
prediction of metabolic acidosis [[214]76]. Although no conclusions can
be made on how useful these compounds are in predicting thrombosis,
these results do warrant further investigations into the clinical
relevance of these compounds in predicting lung injuries and
thrombosis.
3.4. Potential Markers of Viral Reprogramming of Host Metabolism
Our results also included possible signals of SARS-CoV-2-induced
metabolic programming of the host. Notably, the urea cycle was found to
be dysregulated, with urea cycle substrates arginine, citrulline, and
ornithine decreasing over time while its products accumulated.
Interestingly, these results confirm metabolic modelling efforts on
SARS-CoV-2-driven metabolic reprogramming of the host, which also found
decreased fluxes of citrulline and ornithine and increased fumarate
fluxes when comparing mild and severe COVID-19 patients against
non-infected individuals [[215]28]. Besides a higher flux through the
urea cycle, we observed that citrulline decreased more over time than
ornithine ([216]Figure 9). Although these differences in the regression
slopes could be explained by processes not captured in this study, this
observation could also indicate that the increased urea production
([217]Figure 9) from arginine resulted in a decreased production of
nitric oxide from arginine. However, as nitric oxide was not measured
in our data, no conclusions on SARS-CoV-2 influences on nitric oxide
productions can be made. SARS-CoV-2 has been hypothesised before to
benefit from a decrease in nitric oxide production, as nitric oxide is
known to inhibit the early steps in the replication of the original
SARS-CoV virus [[218]77]. At the same, an increased production of
ornithine could promote viral replication as it is an important
precursor of several polyamines, which play important roles in viral
replication [[219]78]. These dysregulations hint at a worsening
COVID-19 progression as urea cycle dysregulation and reduced importance
of nitric oxide production from arginine have also been found when
comparing healthy individuals with moderate and severe COVID-19
patients [[220]79].
Indications of SARS-CoV-2-induced metabolic programming were also found
in decreased concentrations of several TCA cycle metabolites, including
aspartate, citrate, and isocitrate, and in a decrease of serum glucose
concentrations. Although no causal inferences can be made from our
results, it is likely that SARS-CoV-2 played a role in these metabolic
changes as SARS-CoV-2 is known to increase TCA cycle activity by
promoting the cellular uptake of glucose [[221]27]. The dysregulation
of TCA cycle metabolites in our cohorts agrees with previous results
where healthy individuals were compared to mild and severe COVID-19
patients, which might again suggest a worsening of COVID-19 disease
progression. To summarise, we found drastic changes in urea and TCA
cycle metabolite concentrations in the first week after
hospitalisation. These results agreed with previous findings that
associated urea and TCA cycle metabolites with COVID-19 disease
severity [[222]79]. Therefore, we hypothesise that urea cycle and TCA
cycle metabolites could be possible prognostic markers of disease
progression.
We also found hints of metabolic reprogramming by testing bivariate
metabolite–metabolite trajectories against disease severity and disease
outcome. Although our results could be confounded by the altered
prevalence of disease-severity-associated comorbidities, the altered
metabolite trajectories still present potential biomarkers for disease
progression and disease monitoring. The top hit was
glycerophosphorylcholine, a phospholipid degradation product, which had
a far steeper increase in the serum in severe cases in comparison to
moderate cases. Glycerophosphorylcholine is a known marker of COVID-19
disease severity [[223]22,[224]23,[225]80]. Interestingly,
phospholipids in general are known to be beneficial for viral
replication, as they are important constituents of lipid droplets and
lipid rafts, which play major roles in the viral lifecycle
[[226]81,[227]82]. These results align with previous insights into
SARS-CoV-2 rewiring of phospholipid metabolism
[[228]22,[229]23,[230]80], thus making serum glycerophosphorylcholine
trajectory a potential marker for disease severity.
3.5. Strengths and Limitations
The main strength of the study is the possibility of meta-analysing the
results of three independent cohorts from three different hospitals
with repeated metabolome measurements. This study design enabled
finding consistent metabolomic changes across the three cohorts, while
the use of the untargeted metabolomics approach made it possible to
access a wide range of serum metabolites. Thus, we could analyse not
only the main players in the human metabolism, such as amino acids or
lipids, but also markers of environmental exposure, shedding a light on
the complex and intertwined changes in the metabolome during
hospitalisation. The study, however, has some important limitations. It
is of observational design, and as such causal inferences are generally
impossible, due to unmeasured confounding factors, making
interpretations of individual results difficult. As a consequence, our
findings cannot be attributed to COVID-19 since hospitalisation also
leads to changes in environmental exposure and behavioural changes,
e.g., physical activity, medication, and diet, which all influence
human metabolism. Moreover, the limited sample size in the cohorts,
while being enough to detect the vast and drastic changes in the
trajectories, may not have been sufficient to detect all of the
relevant effects present. Part of the analyses included utilising
interaction terms, where it is known that large sample sizes are needed
for adequate statistical power. Therefore, it is conceivable that when
it comes to metabolite–metabolite relations, many true effects were not
detected, delivering an incomplete picture. A further limitation is
that we did not analyse the effect of comorbidities on metabolomics
trajectories, as this was beyond the scope of this work.
3.6. Conclusions
In conclusion, our work revealed the drastic effects of COVID-19
hospitalisation on the human serum metabolome. We found markers related
to hospitalisation, including environmental, dietary, and drug
metabolism. Our results also included possible markers of changed lung
injury, including carboxyethyl-GABA and fibrinopeptides. Furthermore,
we proposed urea cycle metabolites, TCA cycle metabolites, and
glycerophosphorylcholine as potential markers of COVID-19-induced
metabolic reprogramming and disease progression. We hope that the
reported results prove to be fruitful and helpful for the
interpretation of metabolome data sampled within hospitals in general.
4. Methods
4.1. Study Cohorts
In order to analyse serum metabolome trajectories in COVID-19 patients
during hospitalisation, three prospective cohorts were selected across
different locations across Switzerland. A total of 71 patients were
recruited in total, with 29 patients from Geneva, 22 patients from St.
Gallen, and 20 patients from Ticino ([231]Table 1). The Geneva cohort
originally included 30 patients; however, one patient was dropped as
they did not have information on mortality and sex. Patient recruitment
started in August 2020. The selection criteria included that the study
participants were adults ≥18 years and were admitted to the hospital
ward or ICU due to PCR-confirmed SARS-CoV-2 infection. Finally,
participants were only included if they or their representatives had
signed an informed consent form. This study was approved by a local
ethics committee (EKOS 20/058). The final cohorts included moderate and
severe cases in Ticino and only severe cases in Geneva and St. Gallen.
Moderate cases were defined as PCR-confirmed SARS-CoV-2-infected
patients with symptoms of pneumonia, fever, and respiratory tract
problems. Severe COVID-19 patients had all the symptoms of moderate
cases, but also needed to have a respiratory rate of ≥30 breaths per
minute and an oxygen saturation of ≤93% when breathing ambient air or
having a PaO[2]/FiO[2] below 300 mmHg. Patients that did not meet these
requirements but needed ventilator support were also classified as
severe COVID-19 patients. Moderately ill patients were all staying in a
hospital ward, whereas severe patients were all situated in the
intensive care unit.
4.2. Sample Collection and Treatment
All patients that were willing to partake in this study and passed the
inclusion criteria had a first blood sample collected within 24–48 h
after hospitalisation. Subsequent samples were generally taken every
week after hospitalisation ([232]Figure S2). Collected samples were
immediately stored at −80 °C until processing. Sample preparation was
performed by Metabolon^TM using the automated MicroLab STAR^® system
from Hamilton Company^TM (Reno, NV, USA). Proteins were removed by
dissociating small molecules bound to protein or trapped in the
precipitated protein matrix. Chemically diverse metabolites were then
recovered by precipitating proteins with methanol under vigorous
shaking for 2 min (GenoGrinder 2000^®, Glen Mills Inc., Clifton, NJ,
USA), followed by centrifugation. The samples were briefly placed on a
TurboVap^® (Zymark Corp, Portland, OR, USA) to remove the organic
solvent, after which they were stored overnight under nitrogen before
preparation for analysis.
4.3. Metabolomics
Untargeted metabolomics data were generated from patient serum samples
using the Metabolon^TM using the HD4 platform. Waters ACQUITY
ultra-performance liquid chromatography (UPLC) was performed with a
Thermo Scientific (Waltham, MA, USA) Q-Exactive
high-resolution/accurate mass spectrometer interfaced with a heated
electrospray ionization (HESI-II) source and Orbitrap mass analyser,
which operated at a mass resolution of 35,000. Before analysis, the
serum sample extract was dried and reconstituted in four separate
solvents compatible with each of the four used methods. The first
aliquot was analysed in acidic positive ion conditions, which were
chromatographically optimised for hydrophilic compounds. The second
aliquot was analysed in acidic positive ion conditions, which were
chromatographically optimised for more hydrophobic compounds. The third
aliquot was analysed using a separate dedicated C18 column in basic
negative ion optimised conditions. Analysis of the fourth aliquot was
conducted via negative ionization after elution from a HILIC column
(Waters UPLC BEH Amide 2.1 × 150 mm, 1.7 µm). A gradient was used
consisting of water and acetonitrile with 10 mM ammonium formate and a
pH of 10.8. The mass spectrometry (MS) analysis was performed in an
alternating manner between MS and data-dependent MS^n scans using
dynamic exclusion. There were slight variations between the methods,
but the scan range covered 70–1000 m/z.
The serum samples were analysed by the Metabolon platform in two
batches. Batch effects were mitigated by running 12 anchor samples from
healthy volunteers in both Metabolon runs. The second batch was
rescaled to be on an identical scale as the first batch. First, the
anchor sample metabolite ratios were computed as follows:
[MATH:
ratiox,y=m
etabolit
ex,ybatc
h 1/metabolitex,ybatch 2 :MATH]
, where
[MATH: x :MATH]
corresponds to the anchor sample and
[MATH: y :MATH]
corresponds to the metabolite area under the peak value. The second
batch was then rescaled by multiplying the metabolite values with the
median of twelve anchor sample ratios. If a metabolite was not measured
in more than half of the anchor datasets, scaling was not performed for
that metabolite and the metabolite was excluded from our analysis.
After batch scaling, missing values were imputed with the minimum value
measured for a metabolite value.
Metabolites that were not present in at least 20% of all samples in the
three cohorts were removed. Data processing and sample removal were
performed using the Tidyverse [[233]83] software suite in the R
programming language version 4.2.2.
4.4. Analyses of Differential Metabolite Trajectories over Time
Metabolite trajectories and metabolite depletion trajectories were
analysed by performing linear and logistic mixed-effect regressions
with, respectively, the log-transformed metabolite concentrations as
the outcome. Both regression models used the number of days after
hospitalisation as the predictor of interest and included age, sex,
BMI, and death due to COVID-19 as control variables. The individual
patient was used as the random intercept. Both regression models were
performed on each of the 901 selected metabolites and for each
location.
Next, we meta-analysed the metabolite and metabolite depletion
regressions for each of the 901 metabolites. We treated the regression
coefficients
[MATH: β^i, i=1,…, 3 :MATH]
of the standardised metabolite concentrations for each of the three
studies as the individual effect estimates we wanted to summarise.
Given the sample estimates
[MATH: σ^i2 :MATH]
of Var(
[MATH: β^i) :MATH]
and assuming that the individual effect estimates were fixed outcomes
of the three studies, we calculated the overall effect estimates
[MATH: β¯
:MATH]
using a fixed-effects model [[234]84] and the inverse variance method
for each metabolite. We quantified heterogeneity using Cochran’s Q-test
for each of the 901 metabolites. After FDR correction of the p-values
of the overall effect estimates, for 545 metabolites, the overall
effect remained significant (FDR < 0.05). After that, we filtered out
the metabolites of the total 901 that were significant (FDR < 0.05)
according to Cochran’s Q-test, resulting in 448 metabolites.
Additionally, we also investigated time-dependent trajectories in the
number of samples where a metabolite was depleted, i.e., metabolites
with concentrations below the detection limit in a sample. We ran
logistic mixed-effect regressions for each location with the binary
metabolite detected/not detected as the outcome and the days after
hospitalisation as the predictor of interest. We again controlled for
age, sex, and BMI, with random intercepts for the individuals, and
meta-analysed the results as we did with the metabolite concentration
trajectories. However, due to most metabolites having too few depleted
samples in one or more locations, meta-analysed results could only be
obtained for 22 metabolites. Of these 22 metabolites, five were
nominally significant, which were all already identified in the
regressions on the metabolite concentration trajectories. Due to the
uninformative results from this analysis, it was dropped from the
reported results.
The regression analyses and meta-analyses were performed using the
following software packages in R (v.4.2.2). Metabolite trajectories
were calculated using the PLM package [[235]85]. The lme4 package
[[236]86] was used to perform the logistic mixed-effect regressions on
the metabolite depletions and to calculate the 95% confidence
intervals. All calculations for the meta-analysis were performed using
the metafor package [[237]84].
4.5. Analyses of Time-Dependent Metabolite Trajectories against Disease
Severity and Outcome
Potential biomarkers that could differentiate between moderate and
severe cases were investigated by performing mixed-effect regressions
on the log-transformed metabolite concentrations (response) against the
disease severity (binary: moderate vs. severe) as the predictor of
interest, while controlling for age and sex and the interaction between
disease severity and the number of days after hospitalisation. The
p-values of the regression coefficients were corrected for the false
discovery rate with an alpha value of 0.05. All samples between the
first and ninth day after hospitalisation were included. This analysis
was only conducted for Ticino, as it was the only cohort with both
moderate and severe patients. In a second analysis, we again
investigated potential biomarkers, but now between severe surviving and
fatal cases in a combined cohort from St. Gallen and Geneva. Ticino was
excluded as it did not include enough fatal cases (two fatal cases) for
any useful statistical analyses. Mixed-effect regressions were
performed on the log-transformed metabolite concentrations (response)
against the binary: died of COVID-19 vs. survived as the predictor of
interest. This time, we controlled not only for age and sex, but also
for the location and the interaction between time and COVID-19
mortality. We again corrected for the false discovery rate with an
alpha value of 0.05.
4.6. Pathway Enrichment Analyses
Pathway enrichment analysis was performed on MetaboAnalyst 5.0
[[238]38]. The HMDB IDs of the list of significant metabolites were
uploaded to the MetaboAnalyst website, after which the metabolites were
mapped onto the MetaboAnalyst database. A total of 289 metabolites
could be mapped. Enrichment analysis was then performed against the
KEGG Homo sapiens reference pathway library [[239]39]. The enrichment
impact and p-values were calculated with the Fisher exact test
(hypergeometric test) using a relative-betweenness centrality.
4.7. Disease-Severity-Dependent Metabolite–Metabolite Interactions
Associations between bivariate metabolite–metabolite distributions and
disease severity were investigated by performing mixed-effect linear
regressions for all 901 × 901 = 811,801 metabolite pairs with one
metabolite as response and predictors for a second metabolite and the
metabolite-severity interaction term. Control variables were added for
age and sex. The resulting outcomes were corrected via Bonferroni
correction to account for multiple testing. The relationship between
metabolite–metabolites distributions and disease severity were only
tested in Ticino as this was the only location with both moderate and
severe COVID-19 patients. Bivariate metabolite–metabolite distributions
were associated with COVID-19 mortality in the same manner as outlined
earlier, with the difference that an interaction term for metabolite
concentration and disease mortality was used as the predictor of
interest.
Acknowledgments