Abstract
Many human diseases, including metabolic diseases, are intertwined with
the immune system. The understanding of how the human immune system
interacts with pharmaceutical drugs is still limited, and
epidemiological studies only start to emerge. As the metabolomics
technology matures, both drug metabolites and biological responses can
be measured in the same global profiling data. Therefore, a new
opportunity presents itself to study the interactions between
pharmaceutical drugs and immune system in the high-resolution mass
spectrometry data. We report here a double-blinded pilot study of
seasonal influenza vaccination, where half of the participants received
daily metformin administration. Global metabolomics was measured in the
plasma samples at six timepoints. Metformin signatures were
successfully identified in the metabolomics data. Statistically
significant metabolite features were found both for the vaccination
effect and for the drug-vaccine interactions. This study demonstrates
the concept of using metabolomics to investigate drug interaction with
the immune response in human samples directly at molecular levels.
Subject terms: Biotechnology, Medical research
Introduction
The human immune system is our defense against infectious agents and
malignancy, but also susceptible to many signaling cues^[36]1–[37]6.
The dysfunction of immune system often underlies a large number of
autoimmune, metabolic and inflammatory conditions^[38]1,[39]7–[40]9.
The health burden is significantly greater in the ageing
population^[41]10, which commonly live under pharmaceutical
medications, and respond poorly to many vaccines^[42]11. It is clearly
important to understand how pharmaceutical drugs interact with the
immune system, but it is not an easy task given that human immunology
is still a rapidly evolving science^[43]12,[44]13. In recent years,
much progress in human immunology was achieved via vaccine studies, as
vaccines are an excellent tool to probe human immune system providing
an opportunity to learn about immunological molecular perturbations
over time from days to years^[45]14.
Limited epidemiological studies have reported evidence of small
molecular drugs impacting human immune responses, e.g., administration
of statins has a minor detrimental but significant effect on influenza
vaccination^[46]15–[47]17. Using hospital records, Kidd et al. showed
that a number of small molecular drugs influence the numbers of white
blood cell subsets^[48]18. These hospital records are not easy to match
to immunological studies, and they are not meant to be comprehensive.
Furthermore, direct measurement of concentration of small molecular
drugs and their metabolites, similar to the tests on professional
athletes for prohibited drugs, is the most important information.
Because it reflects the ongoing biological state and varying metabolic
rate among individuals. In this regard, the advancement of mass
spectrometry based metabolomics is potentially a game changer.
Metabolomics is the global measurement of small molecules in a
biological system, which includes biological metabolites, dietary
intake, microbial contributions, environmental pollutants, and often
pharmaceutical drugs^[49]19–[50]22. Liu et al. recently showed
successful identification of nicotine (tobacco), naphthol sulfate
(industrial chemical), omeprazole (medication) and piperine (food) and
their derivatives in routine metabolomics analysis of human
samples^[51]23. With both the environmental factors and biological
responses in the same data, a new paradigm of
gene-metabolome-environment interaction is emerging^[52]24,[53]25.
Thus, in a controlled study of vaccine induced immune responses,
metabolomics offers the opportunity to examine both the drug response
and vaccine response, and test for potential interactions.
The application of metabolomics to vaccinology is still in a nascent
stage. Previous metabolomic analyses showed that systemic metabolites
were perturbed by seasonal influenza vaccine^[54]26 and by herpes
zoster vaccine^[55]27, with broad interactions with cellular and gene
programs. A significant observation in the integrative analysis of
human transcriptome and metabolome in Li et al. (2017)^[56]27 was that
baseline SREBF1 (Sterol regulatory element-binding protein 1) activity
was associated with B cell responses to vaccination, and the finding
was recently confirmed in a detailed mouse study^[57]28.
Here, we have conducted a pilot study of seasonal inactivated influenza
vaccine (IIV) in older adults, where half of the participants received
metformin, a common drug for controlling diabetes, for a total of 20
weeks. The study was double-blinded and placebo-controlled, enrolling
nondiabetic/nonprediabetic men and women over the age of 65 years.
Ultrahigh-resolution metabolomics was performed on the plasma samples
from 15 participants over six timepoints. The study was motivated by
the effects of metformin in improving mitochondrial functions, reducing
chronic pro-inflammatory signaling^[58]29, and targeting multiple
hallmarks of aging^[59]30. Given the prominent role of metformin in
aging studies and its geroprotective potential at molecular
level^[60]31, it becomes an important question if metformin enhances
the immune responses to vaccines. Research in this direction is urgent
because better vaccine efficacy is needed for the older population and
how to optimize their immunity has broad health impacts. In this
report, we focus on how the metabolomics data manifest the biological
responses to metformin, IIV vaccine and their interaction.
Results
Fifteen volunteers were recruited at the University of Connecticut
Health Center, and randomly double-blinded assigned into two groups for
either placebo (n = 7) or metformin (n = 8) treatment. The cohort was
between 67 to 89 years old, with 8 males and 7 females and no
significant differences in basic characteristics at baseline (placebo:
74.71 ± 2.45 years old, 3 males, BMI: 27.31 ± 1.68; metformin:
74.13 ± 2.42 years old, 5 males, BMI: 26.43 ± 1.47). The metformin
group received Metformin Hydrochloride Extended-Release, 1500 mg/day
(three 500 mg ER tablets once a day, starting at 500 mg ER/day and
progressed per current recommendations). High dose trivalent
inactivated influenza vaccine (Fluzone, Sanofi Pasteur Inc) was
administered via intramuscular injection to all participants at
approximately day 70 (Fig. [61]1).
Fig. 1. A double blinded clinical study of metformin in influenza vaccination
in the elderly.
Fig. 1
[62]Open in a new tab
A total of 15 study participants over the age of 65 years were randomly
assigned to metformin or placebo treatment for 20 weeks. All
participants were vaccinated with high-dose trivalent inactivated
influenza vaccine after 10 weeks of treatment of metformin or placebo.
Blood samples were collected over six timepoints, on days 0, 35, 70,
77, 105 and 140 approximately. Metformin administration started on day
0, and vaccine was administered on day 70. Our statistical analysis
used two models to focus on the metformin effect (Model 1 using first
three timepoints), and on the vaccine effect and interaction (Model 2
using two timepoints before and after vaccination).
The antibody and T cell responses in this cohort are published
elsewhere^[63]32. Briefly, similar increase of antibody titers was
observed post vaccination in both the metformin and control groups.
Decreased CD57 expression was observed in CD4 T cells but not in CD8 T
cells. Overall, the immunological data showed some trending
improvements with metformin for flu vaccine responses, including
circulating T follicular helper cells, but the cohort was underpowered
for full conclusions. However, the adaptive responses are only part of
our highly complex immune system. Therefore, in order to gain in-depth
molecular insights, we analyzed the metabolomic profiles in this
cohort.
Untargeted metabolomics measured metformin and its abundance in the plasma of
study participants
The plasma samples collected from the participants were analyzed by
ultrahigh-resolution metabolomics. We applied four untargeted LC-MS
(liquid chromatography-mass spectrometry) methods to increase the
coverage of assays: hydrophilic interaction chromatography (HILIC) with
positive electrospray ionization (ESI + ) and with negative
electrospray ionization (ESI-), reversed phase (RP) chromatography with
ESI+ and ESI−. The numbers of metabolite features in each method are
reported in Table [64]1, after filtering of background peaks and by
signal-to-noise ratio (SNR).
Table 1.
Summary of significant metabolite features in statistical models.
HILIC ESI+ HILIC ESI− RP ESI+ RP ESI−
Total # Features 5987 4245 3284 8546
Model1 Drug Response 58 179 83 176
Model2 Vaccine Response 19 07 46 46
Drug Response 06 02 03 02
Drug Response * Vaccine Response 02 0 0 01
[65]Open in a new tab
Significance is defined by FDR < 5% and absolute fold change response
>1.5 in both post-metformin visits compared to baseline in Model 1 or
after and before vaccination in Model 2. See Method for detail of the
statistical models.
As a first step, the global metabolomics data enabled us to investigate
the metabolic impact by metformin administration. This was analyzed by
the time course in the metformin treated participants, using the three
timepoints prior to vaccination (days 0, 35 and 70 as depicted in Fig.
[66]1). Since metabolomics not only measures biological metabolites but
small molecules in general, it is not surprising to find metformin
itself in the metabolomics data. Indeed, the most significant two
features were metformin and its ^13C isotopologue (Fig. [67]2a). The
isotopologue was from the naturally occurring stable ^13C carbon atoms
and eluted at the same time as the more abundant ^12C form in
chromatography. The LC-MS spectra of metformin and its identification
via MS/MS are shown in Fig. [68]2b. The feature intensity values (peak
area in LC-MS) in metabolomics are a proxy of the concentration in
biological samples. With metformin identified, its abundance in the
study participants was plotted in Fig. [69]2c, where the metformin
group show a persistent level of metformin through the course of this
study and the placebo group have no detected level. Individual
variation is also seen in Fig. [70]2c, which reflects the
heterogenicity of human populations, including the different metabolic
rates among individuals. It cannot be ruled out that individuals might
have different compliance to the study regimen. These data prove that
valuable pharmacological information can be directly obtained from
metabolomics without clinical records.
Fig. 2. Identification of metformin and measured kinetics in study cohort.
[71]Fig. 2
[72]Open in a new tab
a Metabolite features that are different between the metformin group
and placebo group, analyzed using Model 1, a mixed effect model where
visit was modeled as fixed effect and participants were modeled as
random effect. Significance is shown as -log[10](adjusted p-value) on
Y-axis. The two most significant features correspond to metformin and
its ^13C isotopologue. Features with false discovery rate (FDR) under
0.05 are colored in red. b Metformin is identified by accurate mass and
fragmentation in MS/MS. Reference MS/MS spectrum of metformin is from
MassBank (id: [73]EA255011; red color), precursor ion m/z 130.1089. c
Kinetics of metformin in all study participants. No metformin is
detected in the placebo group (red). Each participant in the metformin
group is plotted in light blue, and their mean values are in dark blue.
All data in this figure are based on ESI+ mass spectrometry coupled
with a HILIC column.
Metformin induced broad metabolomic changes, including fatty acid
biosynthesis
The metabolomic analysis of participants after metformin administration
revealed a number of significantly altered features (58 in HILIC ESI+
in Fig. [74]2a; 179 in HILIC ESI−, 83 in RP ESI+, 176 in RP ESI−, Table
[75]1), with a stringent threshold of false discovery rate (FDR) < 0.05
and fold change >1.5. The group average of the HILIC ESI+ features is
shown in Fig. [76]3a as a heatmap. Among them, 37 metabolite features
were increased and 21 decreased consistently in post metformin visits.
Examples of individual metabolites from Fig. [77]3a are shown as box
and whisker plots in Fig. [78]3b. These include urea cycle metabolites
citruline and N-acetyl arginine, and bile acids, such as
glycochenodeoxycholic acid and chenodeoxycholic acid. Significant
pathways impacted by metformin are summarized in Fig. [79]3c. Several
metabolites in de novo fatty acid biosynthesis showed consistent
decrease in plasma post metformin treatment (e.g. linolenic acid in
Fig. [80]3b). Decrease of N-acetyl arginine, citrulline and several
short-chain and long-chain carnitines (Supplementary Fig. [81]S1) is
similar to the observations made by previous metabolomic studies of
metformin^[82]33,[83]34.
Fig. 3. Metabolomic response to metformin in study cohort.
[84]Fig. 3
[85]Open in a new tab
a Metabolite features significantly different after metformin
administration in the plasma samples of participants. Heatmap shows
group mean values, for 58 features with FDR < 0.05 and absolute fold
change response >1.5 in both post-metformin visits (i.e. day 70 and day
77). b Selected significant features, all significant as in (a) but
also marked by paired t-test p-values (*p < 0.05, **p < 0.01). The
annotation of 2-hydroxypyridine sulfate was based on MS1 and MS2
spectra matches (level 2). The other metabolites were identified with
authentic standards (level 1). All of the box plots show the median
(center line), first and third quantiles (box limits), and max
1.5 × interquartile range (IQR) from box limits in each direction
(upper and lower whiskers). c Pathway enrichment of top metabolite
features using mummichog software (across all modes). Only top ten
pathways enriched at p < 0.05 and >3 overlapping empirical compounds
are shown.
Metabolomic impacts by the seasonal trivalent inactivated influenza vaccine
To analyze the effect of IIV vaccine administration, we focused on the
two timepoints before and after vaccination (days 70 and 77 as depicted
in Fig. [86]1), as IIV induces a recall immune response that peaks
around one week after vaccination^[87]35. The day 70 served as
vaccination baseline. This was fitted to a mixed effect statistical
model with metformin status as a covariate and considering metformin
and vaccine interaction (Model 2, described in Methods). With
FDR < 0.05 and fold change >1.5, the numbers of significant features
are shown in Table [88]1 for all four LC-MS methods. Of note, Model 2
identified fewer significant features associated with metformin
response, because the data points here are cross-sectional comparison
with the placebo group, while Model 1 was able to use three time points
before vaccination that were matched to the same individuals.
The significant metabolite features associated with vaccine response in
the HILIC ESI+ data are shown in Fig. [89]4a. One of those is glyceric
acid (Fig. [90]4b), a common intermediate of multiple pathways,
especially in energy metabolism. It was previously reported to be
elevated in autoimmune diseases^[91]36,[92]37. It’s increase here
correlates with the timing of major expansion of antibody secreting
cells. Energy metabolism, such as fructose and mannose metabolism and
TCA cycle, is indeed enriched in our pathway analysis (Fig. [93]4c).
Pathway analysis also revealed multiple pathways on inflammatory lipid
mediators in response to IIV, including leukotriene, arachidonic acids
and glycosphingolipids (Fig. [94]4c). The remaining metabolites in Fig.
[95]4b have a lower-confidence annotation, which may get updated when
further information is obtained on these compounds. But they have
relatively high abundance in these plasma samples, and their LC-MS
peaks are easily verifiable (Supplementary Fig. [96]S2). Therefore, our
data indicate that they are true chemical compounds that were elevated
after vaccination. Using the same statistical criteria, no significant
metabolite was found at days 105 and 140 in comparison to day 70. This
was not surprising because most immunological events after IIV occur
within the first two weeks^[97]26,[98]38–[99]41.
Fig. 4. Metabolomic response to seasonal influenza vaccine in study cohort.
[100]Fig. 4
[101]Open in a new tab
a Metabolite features different after IIV vaccination in the plasma
samples of participants, shown in volcano plot with significance on
Y-axis and magnitude on X-axis. Significant metabolite features were
determined by Model 2 (FDR < 0.05 and absolute fold change response
>1.5). b Selected significant features, all significant as in (a) but
also marked by paired t-test p-values (*p < 0.05, **p < 0.01). Glyceric
acid was identified with MSI level 1 annotation and others
(1-Methylinosine [283.1037@103.62], Thymidine glycol [275.0833@110.96],
Bissulfine [117.0041@45.67]) with MSI level 4 annotation. All of the
box plots show the median (center line), first and third quantiles (box
limits), and max 1.5 × interquartile range (IQR) from box limits in
each direction (upper and lower whiskers). c Pathway enrichment of top
metabolite features using mummichog software (across all modes). Only
top ten pathways enriched at p < 0.05 and >3 overlapping compounds are
shown.
Statistically significant interaction between metformin and IIV was found in
metabolomic features
The above analyses showed that specific metabolic features were
impacted by the drug metformin or by the IIV vaccine. To understand if
a drug has a positive or negative effect on the vaccination, it is
important to know if metabolites are impacted by both. This global
metabolomics dataset provides the opportunity to test the statistical
interaction between metformin and IIV. This was included as an
interaction term in our Model 2. Among four LC-MS methods, 2 in HILIC
ESI+ and 1 feature in RP ESI− were found to be significant, using a
stringent FDR < 0.05 (Table [102]1). As shown in Fig. [103]5, the
vaccine responses of these three features are clearly different between
the metformin and placebo groups. The m/z values of these features
match to a large number of compounds in metabolite databases. The
feature 512.1714@164.48 (ESI−) also showed a proper isotopologue
pattern. Unfortunately, we failed to identify these three metabolites,
but believe they are real compounds because they all passed our filter
of background peaks and SNR, and their LC-MS peaks are distinct
(Supplementary Fig. [104]S3). Metabolite identification is a common
challenge in metabolomics today. Alternatively, functional insight of
an unknown metabolite can be gained from metabolome-wide association
studies (MWAS)^[105]42,[106]43.
Fig. 5. Metabolite features found with significant statistical interaction
between metformin and vaccine.
[107]Fig. 5
[108]Open in a new tab
a Metabolites with FDR < 0.05 in Model 2 show different responses to
vaccination based on the metformin treatment. Their m/z@retention time
is shown on top. All of the box plots show the median (center line),
first and third quantiles (box limits), and max 1.5 × interquartile
range (IQR) from box limits in each direction (upper and lower
whiskers). b Metabolite features associated with [147.0847@65.03] in
this study, HILIC ESI+ data. c The metabolite features associated with
[147.0843@354.85] in the Broad dataset. The p-value on Y-axis in (b, c)
is based on Spearman rank correlation. FDR values are in similar range.
d Retention times between the two studies are comparable after
realignment, based on common known metabolites in both datasets. Both
are HILIC ESI+ data. e Pathway enrichment of metabolites significantly
associated with the 147.0843 features in both datasets, as in (b) and
(c). All pathways with p < 0.01 are shown.
The MWAS profile of the feature 147.0847@65.03 is shown in Fig.
[109]5b, showing strikingly significant associations to a cluster of
compounds that are eluted at 161 s. To validate this in an independent
cohort, we retrieved a large dataset of 1172 samples that were analyzed
using a similar platform at Broad Institute (Orbitrap mass spectrometer
with HILIC ESI+,^[110]44). Our feature 147.0847@65.03 was matched to
147.0843@354.85 in the Broad study, which has a highly significant
association pattern to a cluster of compounds at 556 s (Fig. [111]5c).
Between the two studies, the liquid chromatography had different
length, but the retention times were comparable after realignment using
known compounds (Fig. [112]5d). The trendline in Fig. [113]5d indicates
that 65 and 161 s in our study are matched to 354 and 555 s in the
Broad study. Therefore, these results indicate that the MWAS pattern of
147.0847@65.03 in our study is reproduced in the Broad data. Indeed,
the cluster at 161 s in Fig. [114]5b and the cluster at 555 s in Fig.
[115]5c share at least five same m/z values. Both clusters, however,
have few matches in HMDB, suggesting that the compounds are probably
part of the exposome; the feature at 147.0847 is likely to be part of
their biological response. These two clusters do not contribute to
pathway enrichment tests statistically, because they do not match to
known pathways. Yet, the pathway patterns underlying the two overall
MWAS results share the same top two pathways (Fig. [116]5e). Taken
together, our feature 147.0847@65.03 showed significant association
with urea cycle and aspartate and asparagine metabolism (Fig. [117]5e);
it correlates intriguingly but reproducibly with a group of unknown
compounds that warrant future investigation. The other feature
160.0801@23.09 eluted too early to be found in the Broad data (while
other major peaks of this m/z match perfectly between two studies).
Discussion
The metabolic responses to metformin here are consistent with previous
reports. The glucose lowering potential of metformin has been largely
attributed to its ability to suppress hepatic gluconeogenesis through
both AMPK dependent and independent pathways (reviewed in^[118]45).
Interest are also growing in its anti-aging, anti-inflammatory and
anti-proliferative roles^[119]46–[120]51. Metformin reduces
pro-inflammatory cytokines and inhibits NF-κB
signaling^[121]29,[122]52,[123]53, both contributing to increased basal
inflammation with aging. Overall, metformin regulates several aspects
of nutrient sensing and energy homeostasis in various metabolically
active organs leading to improved blood glucose and lipid
profiles^[124]54,[125]55. Our data revealed that several lipids and
amino acids related pathways were altered by metformin administration.
De novo fatty acid biosynthesis was the most significantly altered
pathway (Fig. [126]3b) upon metformin administration in our data. AMPK
is one of the key targets of metformin under pharmacological
concentrations in liver and have been reported for its role in improved
blood glucose and lipid profiles^[127]56,[128]57. AMPK is also a master
regulator of whole-body energy homeostasis and maintains the balance
between nutrients supply and energy demand. Liver mediated AMPK
phosphorylation of SREBP1 (master regulator of lipogenesis) and ACC1/2
(a rate-limiting enzyme for fatty acid synthesis) inhibits hepatic
de-novo lipogenesis^[129]45,[130]58–[131]60. Moreover, ACC1/2
phosphorylation leads to a decreased production of malonyl-CoA (which
is an inhibitor of mitochondrial carnitine palmitoyl transferase 1
(CPT1)) and subsequent enhanced hepatic fatty acid oxidation^[132]45.
Our results showed a decrease of abundance in several metabolites in
pathways responsible for synthesis of lipids and their derivatives
including de novo fatty acid biosynthesis pathway, arachadonic acid
metabolism, glycerophosopholipid metabolism and prostaglandin formation
from arachidonate. These results are in line with the previous reports
where many lipids and lipid derivatives including poly unsaturated
fatty acids (PUFAs), eiconsaids, glycerophospholipds were observed to
show decreased plasma abundance upon metformin administration in
healthy volunteers^[133]33,[134]34, and alterations in de-novo fatty
acids synthesis and inflammatory lipid derivatives in different
pathological conditions^[135]61–[136]66.
Metformin has been reported to alter the composition of gut
microbiota^[137]67–[138]69, which is expected to change the profile of
metabolites of microbial origin. Dahabiyeh et al. showed two microbial
metabolites following a similar abundance pattern to metformin
administration in healthy volunteers^[139]34. Our data revealed an
increased trend of the plasma metabolites related to bile acids over
post metformin visits (Fig. [140]3c). This is consistent with the
report by Hao et al. on an overall increase of plsama bile acids
(total, primary, secondary, and unconjugated), along with altered
microbiota composition in metformin administered treatment-naïve
recently diagnosed diabetic partcipants^[141]67.
So far, metabolomics has been applied to only few vaccine
studies^[142]26,[143]27,[144]39,[145]70. Thus, the current knowledge of
vaccine induced responses was mainly learnt from serological, cellular
and transcriptional data^[146]27,[147]38,[148]71,[149]72. Previous
studies of seasonal influenza vaccination in humans revealed common
gene signatures of type 1 interferons (between days 1–3) and plasma
cells (between days 7–11) corresponding to the induction of innate and
adaptive responses, respectively. Many of these studies have
highlighted the age dependent differences in vaccine immunogenicity,
and our cohort is considered as older adults. Our data revealed several
perturbed pathways on carbohydrate and amino acid metabolism 1 week
post vaccination. They are in line with previous studies where serine
metabolism was shown to be associated with vaccine
response^[150]38,[151]39. Mitochondrial biogenesis and oxidative
phosphorylation processes were observed to be impacted by immune
responses induced by influenza vaccine in ours and previous
reports^[152]40,[153]41.
In this study, we have focused on day 7 after vaccination. In future
opportunities, it will be informative to analyze more time points,
including the early ones. The field of immunometabolomics is in its
infancy, and it will take time to gain fuller understanding of the
vaccine responses in terms of metabolic phenotypes. More and more
immunological and vaccine studies take a systems or holistic approach,
by collection high throughput multi-omics data, which shall contribute
to useful insights on how small molecules, biological or abiological,
interact with the immune system. Advantages of metabolomics also
include that it’s easy to use biobanked materials, and that it can
become very economical in the near future.
We reported the annotation confidence according to the MSI
standards^[154]73. Metabolite identification, however, is still
challenging in metabolomics, especially for low-abundance or less
common metabolites. This is similar to the early days of genomics, when
genes were deposited as unknown sequence but annotation improved over
time. The ultrahigh mass resolution of our data also means that people
can reuse the data from public repository, and the unknown metabolites,
like unknown genes, can gain annotation in the future. This is already
demonstrated in our reuse of the Broad dataset^[155]44: the MWAS of our
feature 147.0847@65.03, significant in the interaction between IIV and
metformin, is reproduced in the larger study in Fig. [156]5.
This pilot study was designed to test the interaction between metformin
and IIV in a small cohort. Nonetheless, with highly stringent
statistical analysis, ultrahigh-resolution metabolomics prove to be
powerful to identify (i) metformin and its metabolic signatures in
untargeted metabolomics data, (ii) significant metabolic responses to
IIV, and (iii) significant metabolites as a result of the interaction
between metformin and vaccination. The proof-of-principle is important,
demonstrating the feasibility of studying the interaction of drugs and
immune responses in human populations.
Methods
Clinical study design
This pilot study is a double-blinded placebo-controlled trial in men
and women over the age of 65 years. Subjects were screened rigorously
for eligibility. Study exclusion criteria included the following: any
unstable medical conditions or severe co-morbidities (severe COPD,
severe congestive heart failure, advanced neurological disorders, etc),
contraindications for metformin (severe renal or liver impairment),
contraindication for flu vaccine (history of Guillain-Barre syndrome
post vaccination or allergic to component of vaccine),
immunosuppressive disorders, immunosuppressive medications, and active
cancer. Importantly, participants were excluded if they were
prediabetic or diabetic (HbA1c ≥ 5.7%) to avoid any confounding impact
of metformin on diabetes status. Eligible participants were randomized
to metformin (final dose 1500 mg extended release (ER)/day) or placebo
treatment. To limit gastrointestinal issues per current metformin label
recommendations, participants started with 1 tablet a day for week 1
(500 mg metformin ER/day or placebo), then 2 tablets a day for week 2
(1000 mg metformin ER or placebo), and finally 3 tablets a day for week
3 until the completion of the study (1500 mg metformin ER or placebo).
Fifteen subjects (n = 8 metformin, n = 7 placebo) were randomized and
completed the study on treatment with no differences in basic
characteristics at baseline (placebo: 74.71 ± 2.45 years old, 3 males,
BMI: 27.31 ± 1.68; metformin: 74.13 ± 2.42 years old, 5 males, BMI:
26.43 ± 1.47).
All participants were vaccinated with Fluzone high-dose trivalent flu
vaccine (Sanofi Pasteur Inc., Swiftwater, PA) after ~70 days of
treatment. Blood was drawn via standard venipuncture into EDTA-treated
vacutainers prior to treatment (Day 0), prior to vaccination (~day 35
and ~day 70), and 7, ~35, and ~70 days post vaccination. The study
protocol was approved by the Institutional Review Board at the
University of Connecticut Health Center (UCHC) and registered at
ClinicalTrials.gov ([157]NCT03996538). All study participants provided
written informed consent to participate in the study.
Plasma sample collection and preparation
EDTA-treated whole blood was immediately centrifuged and the resultant
plasma was stored at −80 °C until analyses. Plasma metabolites
extraction was carried out by protein precipitation technique using
extraction solvent, acetonitrile:methanol (8:1, v/v) containing 0.1%
formic acid and isotope labelled [Trimethyl-13C3]-caffeine,
[13C5]-L-glutamic acid, [15 N2]-Uracil, [15 N,13C5]-L-methionine,
[13C6]-D-glucose and [15N]-L-tyrosine as spike-in controls. 30 μl of
plasma was taken and 60 μl of extraction solvent was added. Extraction
blanks were also prepared to remove features of non-biological origins.
All samples were vortexed and incubated with shaking at 1000 rpm for
10 min at 4 °C followed by centrifugation at 4 °C for 15 min at
20,817 × g. The supernatant was transferred into mass spec vials and
2 μl injected into UHPLC-MS.
LC-MS metabolomics and LC-MS/MS analysis
The chromatographic separations were performed using Thermo
Scientific^TM Transcend^TM Duo LX-2 UHPLC system interfaced with high
resolution Thermo Scientific^TM Orbitrap ID-X^TM Tribid^TM mass
spectrometer with a HESI ionization source, using positive and negative
ionization modes. All samples were maintained at 4 °C in the
autosampler. Data were acquired using hydrophilic interaction liquid
chromatography (HILIC) and reversed phase (RP) column in parallel both
in positive and negative polarities in full scan mode with mass
resolution of 120,000. An Accucore^TM−150-Amide HILIC column (2.6 μm,
2.1 mm × 50 mm) and a Hypersil GOLD^TM RP column (3 μm, 2.1 mm × 50 mm)
maintained at 45 °C were used for chromatographic separation. 10 mM
ammonium acetate in acetonitrile:water (95:5, v/v) with 0.1% acetic
acid as mobile phase A and 10 mM ammonium acetate in acetonitrile:water
(50:50, v/v) with 0.1% acetic acid as mobile phase B were used for
HILIC method. 0.1% formic acid in water and 0.1% formic acid in
acetonitrile were used as mobile phase A and B respectively for RP
acquisition. For HILIC acquisition, following gradient was applied at a
flow rate of 0.55 ml/min: 0–0.1 min: 0% B, 0.10–5.0 min: 98% B, and
5 min for cleaning and equilibration of column. For RP column,
following gradient was applied at a flow rate of 0.4 ml/min: 0–0.1 min:
0% B, 0.10–1.9 min: 60% B, 1.9–5.0 min: 98% B, and 5 min cleaning and
column equilibration. This way the mass spec data for each sample was
collected consecutively, carrying only one (either HILIC or RP) eluent
to the MS for 5 min, while the other eluent was directed to the waste
during washing and re-equilibration.
Mass spectrometry data were collected with the following MS settings:
mass range, 80–1000 m/z; spray voltage, 3500 V (ESI + ), 2800 V (ESI−);
sheath gas, 45 Arb; auxiliary gas, 20 Arb; sweep gas, 1 Arb; ion
transfer tube temperature, 325 °C; vaporizer temperature, 325 °C; full
scan mass resolution, 120,000 (MS1); normalized AGC target (%), 25;
maximum injection time, 100 ms. Data dependent fragmentation (dd-MS/MS)
parameters for each polarity as follows: isolation window (m/z), 1.2;
stepped HCD collision energy (%), 20,40,80; dd-MS/MS resolution,
30,000; normalized AGC target (%), 20; maximum injection time (ms), 54;
microscan, 1; cycle time (sec), 1.2. A full scan data-dependent MS2
(ddMS2) method was utilized to collect MS2 spectra for identification
of compounds.
Metabolomics data processing
All samples were analyzed in a single batch after randomization. Raw
LC-MS data was converted to mzML format using
ThermoRawFileParser^[158]74. Asari (version 1.9.2), an open source
Python software was used for m/z and retention time (rt) alignment,
peak detection, feature quantification, and empirical compound based
putative identification (level 4 annotation using HMDB)^[159]75 using
the default parameters. Level 1 annotation of compounds were obtained
by matching retention times and accurate masses from in-house authentic
compound libraries. Level 2 annotation of compounds were obtained by
matching acquired MS^2 spectra of accurate precursor masses from pooled
plasma sample against public spectral databases (MassBank,
MoNA)^[160]73,[161]76 using R package Spectra with >=0.7 cosine
similarity score. Matching of MS1 features with precursor ion’s
accurate masses and retention times was performed using within 10 ppm
tolerance and 10 s, respectively. All the metabolite annotation levels
adhere to Metabolomics Standard Initiative (MSI) guidelines^[162]73.
Features were filtered using two criteria. First, the features with
three times greater intensity in biological samples than in blanks
samples were retained. Second, the features with signal to noise ratio
(SNR) greater than 100 were retained (noise in asari is defined by the
mean of all non-peak data points in an extracted ion chromatogram). Two
different quality control samples (a commercial pooled plasma sample
and pooled study samples) were used to verify the chromatography and
signal reproducibility. Visual inspection of outliers through PCA plots
and Total ion count (TIC) was conducted. Data were log2 transformed and
mean normalized using top ten percent of high abundance features.
Features below detection limit were imputed using half of the minimum
intensity value. After QC filtering, 5987, 4245, 3284, 8546 features
were retained for downstream data analysis, in each HILIC ESI+, HILIC
ESI−, RP ESI+ and RP ESI− modes respectively.
Statistical analysis
We constructed two linear mixed effect models using the lme4^[163]77
package in R to assess the metformin response on plasma metabolome,
inactivated influenza vaccine response on plasma metabolome and the
interaction responses of metformin and vaccine in concert on plasma
metabolome.
Model 1: For each metabolite feature, temporal variation due to
metformin administration was assessed using linear mixed effects model:
[MATH: Metabolite Feature~day+(1|participant) :MATH]
1
The variable ‘day‘ is a categorical variable that indicates discrete
time points, including day 0, day 35, and day 70, where day 0 serves as
the baseline for metformin administration and day 35 and day 70
represent time points after treatment with metformin. The term
(1|participant) controls for repeated measurements on the same
participant. The significance of timepoints was assessed with ANOVA and
p-values were adjusted for multiple-testing based on Storey FDR
method^[164]78,[165]79.
Model 2: In order to quantify the vaccine response and the interaction
of metformin and vaccine, we constructed a mixed effect model utilizing
a pre-vaccination time point (day 70, vaccination baseline) and a
post-vaccination time point (day 77) as follows:
[MATH: MetaboliteFeature~day+metformin+day*metformin+(1∣participant)
:MATH]
2
The ‘day‘ and ‘metformin‘ are used as proxy for vaccine treatment and
drug treatment, respectively and modeled as fixed effects. We assessed
the differentially abundant metabolite features between pre- (day 70)
and post-vaccination (day 77) visits using the p value of coefficient
term of the variable ‘day‘. The interaction term day*metformin was used
to quantify statistically the response as a function of both vaccine
and drug administration. The term (1|participant) controls for repeated
measurements on the same participant. P values assessed using ANOVA
were adjusted for multiple-testing based on Storey FDR
method^[166]78,[167]79.
All the above statistical analyses were performed using R version
4.2.0. Pathway enrichment analysis was performed using mummichog
(version 2.6.1)^[168]80 using top metabolite features with p
value < 0.05.
Metabolome wide association analysis
Spearman correlation was performed between the features of interest and
the remaining features in the same dataset, generating p-values for
MWAS. FDR was calculated using the Benjamini-Hochberg method. The
sample number in this study was 90, in the Broad study 1172. The Broad
dataset was retrieved from Metabolomics Workbench (accession number
ST001237). The m/z and retention time of identified compounds in the
Broad study were obtained from the authors. Known compounds from both
studies were compared, then the uniquely matched compounds (same name
and m/z within 5 ppm) were used for realigning the retention time.
Reporting summary
Further information on research design is available in the [169]Nature
Research Reporting Summary linked to this article.
Supplementary information
[170]Supplementary Figures^ (1MB, pdf)
[171]REPORTING SUMMARY^ (1.5MB, pdf)
Acknowledgements