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Metabolomic-Driven Elucidation of Serum Disturbances Associated with Alzheimer’s Disease and Mild Cognitive Impairment

  • Instituto de Investigación e Innovación Biomédica de Cádiz (INiBICA)

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Numerous efforts have been made in the last years to discover potential biomarkers of Alzheimer's disease and its progression from mild cognitive impairment, considered as an intermediate phase in the development of Alzheimer's disease from normal aging. However, there is still a considerable lack of understanding about pathological mechanisms underlying to disease. In the present study, serum metabolomics based on ultra-high-performance liquid chromatography-mass spectrometry was applied to investigate metabolic differences between subjects with Alzheimer's disease and mild cognitive impairment, as well as healthy controls. The most important findings can be associated with impaired metabolism of phospholipids and sphingolipids leading to membrane breakdown, wherein the nature of the fatty acids contained in the structure in terms of acyl chain length and degree of unsaturation appears to play a crucial role. Furthermore, several discriminant metabolites were found for the first time in relation to known pathological processes associated with Alzheimer's disease, such as the accumulation of acylcarnitines in relation to mitochondrial dysfunction, decreased levels of oleamide and monoglycerides as a result of defects in endocannabinoid system, or increased serum phenylacetylglutamine, which could reveal alterations in glutamine homeostasis. Therefore, these results represent a suitable approximation to understand the pathogenesis and progression of the disease. .
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Metabolomic-Driven Elucidation of Serum Disturbances Associated with
Alzheimer’s Disease and Mild Cognitive Impairment
Raúl González-Domíngueza,b,c, Francisco Javier Rupérezd, Tamara García-Barreraa,b,c,
Coral Barbasd, and José Luis Gómez-Arizaa,b,c,*
aDepartment of Chemistry and CC.MM. Faculty of Experimental S cience. University of Huelva. Cam-
pus de El Carmen. 21007 Huelva. SPAIN; bCampus of Excellence International ceiA3. University of
Huelva. SPAIN; cResearch Center of Health and Environment (CYSMA). University of Huelva. Cam-
pus de El Carmen. 21007 Huelva. SPAIN; dCenter for Metabolom ics and Bioanalysis (CEMBIO),
Pharmacy Faculty, Campus Monteprincipe, Universidad San Pablo-CEU, 28668 Boadilla del Monte,
Madrid, SPAIN
Abstract: Numerous efforts have been made in the last years to discover potential biomarkers of Alz-
heimer’s disease and its progression from mild cognitive impairment, considered as an intermediate
phase in the development of Alzheimer’s disease from normal aging. However, there is still a consid-
erable lack of understanding about pathological mechanisms underlying to disease. In the present study, serum me-
tabolomics based on ultra-high-performance liquid chromatography-mass spectrometry was applied to investigate meta-
bolic differences between subjects with Alzheimer’s disease and mild cognitive impairment, as well as healthy controls.
The most important findings can be associated with impaired metabolism of phospholipids and sphingolipids leading to
membrane breakdown, wherein the nature of the fatty acids contained in the structure in terms of acyl chain length and
degree of unsaturation appears to play a crucial role. Furthermore, several discriminant metabolites were found for the
first time in relation to known pathological processes associated with Alzheimer’s disease, such as the accumulation of
acylcarnitines in relation to mitochondrial dysfunction, decreased levels of oleamide and monoglycerides as a result of de-
fects in endocannabinoid system, or increased serum phenylacetylglutamine, which could reveal alterations in glutamine
homeostasis. Therefore, these results represent a suitable approximation to understand the pathogenesis and progression of
the disease.
Keywords. Alzheimer’s disease, disease progression, membrane breakdown, metabolomics, mild cognitive impairment, patho-
logical mechanisms.
Alzheimer’s disease (AD) is the most prevalent neurode-
generative disorder worldwide, principally among older peo-
ple, characterized by a complex etiology in which multiple
pathological processes are involved. A great challenge in AD
research is early diagnosis, given that currently it can be only
detected at advanced stages of disease by exclusion of other
pathologies based on clinical criteria defined by the
NINCDS-ADRDA [1]. Despite these criteria have been re-
cently revised, and the use of molecular biomarker is ex-
pected to enhance the pathophysiological specificity of diag-
nosis of AD, clinical criteria are still the main tools for diag-
nosis in clinical practice [2]. Therefore, new diagnostic tools
are required for predicting the development of dementia
from older people with very mild symptoms of cognitive
dysfunction. This pre-dementia phase, known as mild cogni-
tive impairment (MCI), is considered an intermediate stage
in the development of Alzheimer’s disease from normal ag-
*Address correspondence to this author at the Department of Chemistry and
CC.MM, Faculty of Experimental Science, University of Huelva, Campus
de El Carmen, 21007 Huelva, Spain; Tel: +34959219968;
Fax: +34 959 219942; E-mail:
ing. However, MCI is a heterogeneous syndrome with sev-
eral possible outcomes, so that although up to 80% of pa-
tients develop AD, other subjects show a benign form of
MCI as part of the normal aging course [3]. Thus, numerous
efforts have been made to discover biomarkers of Alzheimer
and its progression, in order to differentiate between AD,
MCI and healthy control subjects. Conventional markers of
AD such as cerebrospinal fluid levels of Aβ peptides and tau
protein have proven useful in the study of MCI [4], and dif-
ferent neuroimaging techniques have been also extensively
applied for detection of brain abnormalities in AD and MCI
patients [5]. Alternatively, it is recognized that metabolomics
plays nowadays an important role in health survey and bio-
markers discovery, because changes in the metabolome level
may be representative of pathological situations. Further-
more, the non-targeted character of this approach allows a
broader investigation of disease and better understanding of
underlying pathological mechanisms. In this way, several
authors have previously described the use of metabolite pro-
filing for monitoring progression of AD from MCI. On the
one hand, it has been demonstrated the occurrence of early
metabolic changes associated with the onset of dementia in
MCI patients compared to age-matched controls. 1H NMR
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2 Current Alzheimer Research, 2016, Vol. 13, No. 4 González-Domínguez et al.
spectroscopy of serum samples underlined the association
between MCI and altered lipid metabolism, particularly with
low relative amount of ω3 fatty acids and metabolic syn-
drome [6]. Moreover, Zheng et al. developed a metabolomic
approach based on liquid chromatography-mass spectrome-
try combined with an isotope dansylation labeling method
for the analysis of human salivary metabolites, whose appli-
cation to MCI patients revealed the implication of taurine in
its pathogenesis [7]. On the other hand, other authors fo-
cused on determining altered profiles associated with the
progression of dementia by means of comparative studies
between MCI and AD patients. Greenberg et al. found a con-
siderable increase of three bile acids in serum from AD and
MCI patients [8]. In other prospective cohort study, AD pa-
tients presented diminished levels of different classes of lip-
ids, and 2,4-dihydroxybutanoic acid, dipalmitoyl-
phosphatidylcholine and an unidentified organic acid were
found as predictive markers of progression to AD in the fol-
low-up [9]. More recently, Armirotti et al. developed a po-
larity-driven sample preparation protocol coupled to or-
thogonal hydrophobic-hydrophilic liquid chromatography for
“shotgun” plasma metabolomics, which has been success-
fully applied to the discovery of metabolomic alterations that
accompany disorders of human cognition, such as MCI and
AD [10]. Similarly, Wang et al. described two biomarker
panels consisting in several plasma metabolites able to dif-
ferentiate between AD, MCI and normal controls using an
integrated analytical platform based on gas and liquid chro-
matography coupled to mass spectrometry [11]. Further-
more, we optimized in a previous work a serum metabolomic
approach based on ultrafiltration and analysis by capillary
electrophoresis for obtaining representative fingerprints of
the polar metabolome in order to distinguish between pa-
tients with Alzheimer’s disease, mild cognitive impairment
and healthy controls [12]. Alternatively, different analytical
platforms have been also applied to examine metabolic dif-
ferences in CSF from patients with different cognitive de-
cline related to AD progression, including capillary electro-
phoresis-mass spectrometry [13], ultra-high performance
liquid chromatography-mass spectrometry [14] and liquid
chromatography-electrochemical coulometric array detection
[15]. Finally, integrated analysis of plasma and CSF samples
has been also performed in order to evaluate in a more com-
prehensive manner the early disease mechanisms shared in
progression from normal aging to MCI and AD, which al-
lowed the discovery of numerous canonical pathways sig-
nificantly disturbed [16]. Nevertheless, despite the consider-
able amount of literature on this subject, there is still a con-
siderable lack of understanding about pathological mecha-
nisms underlying to this neurodegenerative disorder.
In this work we present a metabolomic study of Alz-
heimer’s disease pathogenesis and its progression from mild
cognitive impairment in serum samples. For this, a large
population sample has been enrolled, comprising AD and
MCI patients, and aged-control subjects (n=137). Me-
tabolomic screening of serum samples was performed by
ultra-high performance liquid chromatography-MS, which
has become the main workhorse in this field due to its high
resolution and sensitivity, short analysis time and great po-
tential for biomarker identification [17]. Thus, numerous
compounds were identified as potential markers of AD and
MCI, which may contribute to deepen into underlying patho-
logical mechanisms related to neurodegenerative processes.
2.1. Chemicals
Methanol, ethanol and acetonitrile (HPLC-grade), as well
as formic acid (MS-grade) were purchased from Sigma-
Aldrich (Steinheim, Germany), and water was purified with
a Milli-Q Plus 185 system (Millipore, Bedford, USA).
Purine and HP921 (standard reference solutions) were ob-
tained from Agilent Technologies (USA).
2.2. Blood Collection
Blood samples were collected from 137 subjects re-
cruited by the Neurological Service of Hospital Juan Ramón
Jiménez (Huelva, Spain), including healthy controls (HC),
mild cognitive impairment (MCI) and Alzheimer’s disease
(AD) patients. Blood was extracted by venipuncture of the
antecubital region after 8 hours of fasting, and collected in
BD Vacutainer SST II tubes with gel separator and Advance
vacuum system, previously cooled in a refrigerator. It should
be noted that all these samples were collected in the morning
in order to avoid the influence of circadian rhythm. Samples
were immediately cooled and protected from light for 30
minutes to allow clot retraction, and then centrifuged at 3500
rpm for 10 minutes. Serum was aliquoted in Eppendorf tubes
and frozen at -80°C until analysis. Alzheimer’s disease pa-
tients (n=75, 33 male and 42 female, medium age 79.9±5.0
y) were newly diagnosed of sporadic AD according to the
criteria of the NINCDS-ADRDA [1], and only subjects that
had not yet received any type of medication were included in
the study. Mild cognitive impairment patients (n=17, 10
male and 7 female, medium age 76.1±5.5 y) reported cogni-
tive decline and impairment on objective cognitive tasks, but
they were not demented and did not meet the NINCDS-
ADRDA requirements for a possible or probable diagnosis
of Alzheimer [3]. Finally, matched healthy controls in sex
and age (n=45, 18 male and 27 female, medium age 72.4±5.5
y) were enrolled after examination by neurologists to con-
firm the absence of neurological disorders, whom had not
more than two reported cases of Alzheimer’s disease in their
families. Demographic characteristics of subjects enrolled in
this study, including age, gender, comorbidities, medication
and family history of AD, are listed in Table S1, available as
Supplementary Material. All subjects gave informed consent
for the extraction of peripheral venous blood, and the study
was performed in accordance with the principles contained
in the Declaration of Helsinki and approved by the Ethical
Committee of University of Huelva.
2.3. Metabolomic Fingerprinting
Metabolomic analysis of serum samples was performed
following a procedure previously optimized [18]. For protein
precipitation and metabolite extraction, 50µL of serum were
mixed with 150 µL of a cold mixture of methanol:ethanol
(1:1). Then, samples were briefly vortexed and maintained
for 5 min in an ice bath. Finally, protein precipitate was re-
moved by centrifugation at 13000 rpm for 20 min at 4ºC, and
the supernatant was filtered through a 0.22 µm nylon filter.
Metabolomic Study of Alzheimer’s Disease Progression Current Alzheimer Research, 2016, Vol. 13, No. 4 3
Samples were subsequently fingerprinted by ultra-high per-
formance liquid chromatography (Agilent 1290) coupled to a
quadrupole-time-of-flight mass spectrometry system
equipped with electrospray source (Agilent 6550). Separa-
tion was performed in a reversed-phase column (Zorbax Ex-
tend C18, 2.1x50 mm, 1.8µm) thermostated at 60ºC, with an
injection volume of 0.5 µL. Solvents were delivered at a
flow rate 0.6 mL/min, using water with 0.1% formic acid
(solvent A) and acetonitrile with 0.1% formic acid (solvent
B). The gradient program was as follows: initial conditions
were 5% B for 1 min, followed by a gradual increase to 80%
B in 6 min and finally 100% B in other 4.5 min. Then, sys-
tem returns to initial conditions in 0.5 min, and column is
equilibrated for 1 min with 5% B. Therefore, total analysis
time was 15 min. MS operated in positive and negative po-
larities in separated runs, acquiring full scan spectra in the
m/z range 50-1000. The capillary voltage was set to 3000 V,
with 1000 V of nozzle voltage, 175 V of fragmentor voltage
and 65 V of skimmer voltage. Nitrogen was used as drying
and nebulizer gas, whose temperature was fixed at 250ºC.
Drying gas was supplied at 12 L/min, while nebulizer gas
pressure was 52 psi.
2.4. Data Processing
Raw data was preprocessed using the Molecular Feature
Extraction (MFE) tool in MassHunter Qualitative Analysis
Software (Agilent Technologies) in order to cleaning back-
ground noise and unrelated ions (elimination of spurious
signals from the mass spectra). The MFE algorithm uses the
accuracy of the measurements for grouping related ions by
charge state envelope, isotopic distribution, and/or the pres-
ence of adducts and dimmers, and then creates a list of all
possible components (or features) described by mass, reten-
tion time and abundance [19]. Thus, processing was per-
formed by applying an abundance cutoff of 200 counts and
enabling the search of different ion species (M+H+, M+Na+,
M+K+, M+NH4+, M-H2O in positive mode; M-H+,
M+HCOO-, M+Cl- in negative mode). In addition, for iso-
tope grouping, the peak spacing tolerance was set at 0.0025
m/z, and the charge states were limited to 2. Then, alignment
and filtering were conducted using the Mass Profiler Profes-
sional software (Agilent Technologies). For this, data was
filtered by selecting features into the range 0.05-11.5 min,
and then, peaks were aligned applying a retention time win-
dow of 0.15 minutes and a mass window of 20 ppm.
2.5. Data Analysis
First of all, data was filtered in Mass Profiler Profes-
sional to remove non reproducible signals before to perform
any statistical analysis. For this purpose, features were fil-
tered by choosing masses present in at least 75% of samples
in one of the compared groups, and then features were again
filtered on sample variability, selecting only variables with a
coefficient of variation less than 50% within each group.
Then, data were processed by partial least squares discrimi-
nant analysis (PLS-DA) in SIMCA-P™ software (version
11.5, Umetrics AB, Umeå, Sweden), in order to find differ-
ences between the groups of study. For this, data was sub-
mitted to Pareto scaling, for reducing the relative importance
of larger values, and logarithmic transformation, in order to
approximate a normal distribution [20]. In addition, quality
of the model was assessed by the R2 and Q2 values, supplied
by the software, which provide information about the class
separation and predictive power of the model, respectively.
These parameters are ranged between 0 and 1, and they indi-
cate the variance explained by the model for all the data ana-
lyzed (R2) and this variance in a test set by cross-validation
(Q2). Finally, potential biomarkers of disease and its progres-
sion were found by two-class comparisons, AD vs. HC, MCI
vs. HC (markers of advanced and early dementia, respec-
tively), and AD vs. MCI (markers of dementia progression).
For this purpose, univariate statistical analyses with Bonfer-
roni correction for multiple testing (t-test, p0.05) were per-
formed, and loadings plots from PLS-DA were inspected to
select altered metabolites according to the Variable Impor-
tance in the Projection, or VIP (a weighted sum of squares of
the PLS weight, which indicates the importance of the vari-
able in the model), considering only variables with VIP val-
ues higher than 2, indicative of significant differences among
2.6. Identification of Metabolites
Identification of significant compounds was made match-
ing the experimental accurate mass and tandem mass spectra
(MS/MS) with those available in metabolomic databases
(HMDB, METLIN, KEGG and LIPIDMAPS), using a mass
accuracy of 20 ppm. In addition, the identity of lipids was
confirmed based on characteristic fragmentation patterns
described in literature. Phospholipids presented characteristic
fragments in positive ionization mode of 184, 104, and 86
m/z for phosphatidylcholines (PCs) and lyso-phosphati-
dylcholines (lysoPCs), and [M+H-141]+ for phosphatidyl-
ethanolamines (PEs) and lyso-phosphatidylethanolamines
(lysoPEs), while in negative mode these distinctive signals
are found at 168 and 196, respectively. Furthermore, frag-
mentation in the glycerol backbone by losses of the fatty acyl
substituents enables the identification of individual species
of phospholipids [21]. For sphingolipids, typical product
ions appear at m/z 264 and 282 due to the fragmentation in
the sphingosine moiety, and in the particular case of sphin-
gomyelins, cleavage of phosphocholine headgroup generates
characteristic fragments of 184 and 168 m/z, in positive and
negative modes respectively [22]. Finally, acylcarnitines
were confirmed based on characteristic fragments of 60 and
85 m/z [23].
3.1. Metabolite Fingerprinting for Samples Classification
Metabolomic fingerprints obtained from serum samples
in both positive and negative ionization modes (Fig. 1) were
submitted to multivariate statistical analysis to check the
potential of the methodology to discriminate between AD,
MCI and healthy control subjects. For data collected in posi-
tive mode, the PLS-DA model showed a clear classification
of the three groups under investigation (Fig. 2A), whose
quality was appropriated in terms of variance explained
(R2=0.84) and variance predicted (Q2=0.263). On the other
hand, negative data provided worse statistical models
(R2=0.844, Q2=0.032), which leads to less robust separation
between the three groups (Fig. 2B). Finally, to obtain more
4 Current Alzheimer Research, 2016, Vol. 13, No. 4 González-Domínguez et al.
Fig. (1). Metabolomic fingerprints from serum samples obtained by UHPLC-ESI-QTOFMS in positive (A) and negative (B) ion mode.
detailed information about the variables involved in dis-
crimination according the stage of dementia, groups were
compared by pairs: AD vs. HC, MCI vs. HC and AD vs.
MCI. Scores plots demonstrated a total separation between
the different groups (Fig. 2C-E), but quality parameters were
again lower for statistical models built with data collected in
negative mode.
3.2. Selection of Potential Biomarkers
Statistically significant compounds identified are summa-
rized in Tables 1-3, including their retention times and ex-
perimental accurate masses, the mass error, the ionization
mode used for detection, the percentage of change observed
in each comparison and the p-value. All these metabolites
were significantly altered in AD patients respect to healthy
controls (p0.05), but in addition, some of them were also
perturbed in MCI. Thus, changes in the comparison MCI/HC
can be associated with failures in the early development of
this disorder, while altered metabolites between AD and
MCI patients would be related to the advance of cognitive
dysfunction. Furthermore, column plots representing the
mean values with standard deviation bars for these selected
metabolites showed that inter-individual variability within
each group is less important than differences between groups
(Fig. 3), thus corroborating that metabolic alterations de-
tected can be attributed to disease state, as previously dem-
onstrated [24].
3.3. Biological Meaning
The use of blood samples in AD research has been tradi-
tionally relegated to the background due to the difficulty to
interpret the association between blood-based measures and
brain processes. However, recent works have demonstrated
close similarities in metabolomic abnormalities observed in
serum [25-26] and brain samples [27-28] from transgenic
models of AD. Furthermore, there is growing evidence that
Alzheimer’s disease might be a systemic disorder [29], thus
demonstrating the utility of peripheral samples in the inves-
tigation of pathological mechanisms associated with AD. In
this study, numerous metabolites were found significantly
altered in serum from AD and MCI patients, which show the
deep impact that this disorder causes on multiple essential
pathways in the organism. One of the most important
changes was observed in phospholipids and related com-
pounds, listed in Table 1. Alzheimer’s disease has been pre-
viously associated with increased degradation of brain phos-
pholipids due to the over-activation of phospholipases [30],
leading to the breakdown of cellular membranes, although it
could be also related to changes in the fatty acid composition
of these lipids. Thereby, the loss of unsaturated fatty acids
due to oxidative stress, such as docosahexaenoic and
araquidonic acid that are highly enriched in neurons, seems
to contribute to AD pathogenesis via membrane damage, in
line with previous studies reporting reduced levels of phos-
pholipids containing PUFAs in brain and blood of patients
Metabolomic Study of Alzheimer’s Disease Progression Current Alzheimer Research, 2016, Vol. 13, No. 4 5
Fig. (2). PLS-DA scores plots from UHPLC-MS data. (A) Scores plot for all samples analyzed in positive ion mode (3 components, R2=0.84,
Q2=0.267); (B) Scores plot for all samples analyzed in negative ion mode (3 components, R2=0.844, Q2=0.032); (C) Scores plot for two-class
comparison, AD vs. HC (positive mode: 4 components, R2=0.996, Q2=0.44; negative mode: 2 components, R2=0.915, Q2=0.125); (D) Scores
plot for two-class comparison, MCI vs. HC (positive mode: 4 components, R2=0.999, Q2=0.654; negative mode: 3 components, R2=0.998,
Q2=0.085); (E) Scores plot for two-class comparison, AD vs. MCI (positive mode: 4 components, R2=0.995, Q2=0.496; negative mode: it was
not possible to build any model). HC: open triangles (n=45); MCI: open diamonds (n=17); AD: black dots (n=75).
with Alzheimer’s disease [31-32]. On the other hand, satu-
rated fatty acids (SFA) have received much less attention,
and most authors found no differences in their distribution
among phospholipids. Nevertheless, Söderberg et al. showed
that the decrease in PUFA-containing phosphatidylethano-
lamines in AD brain is paralleled by an increase in the rela-
tive amounts of the saturated myristic, palmitic and stearic
acids [33], and more recently, it has been demonstrated that
these imbalances in the ratio saturated/polyunsaturated fatty
acids are also present in serum phosphatidylcholines [21,
34]. In the present work, this rationale was corroborated by
increased levels of SFA-containing phosphocholines and
phosphoethanolamines, together with a significant decrease
in several unsaturated phosphocholines, containing linoleic,
arachidonic and docosahexaenoic acid (Table 1). Moreover,
several plasmalogen species were also down-regulated dur-
ing the development of Alzheimer. Previous studies have
shown that ethanolamine plasmalogen deficiency is closely
related to AD [31], but also plasmenylcholines appear to be
involved in neurodegenerative processes [34]. However,
based on our experimental results, we can conclude that
these pathological processes occur at different stages of the
disease, which has not been previously described to our
knowledge. Choline plasmalogen levels were significantly
decreased in serum from AD and MCI patients compared to
healthy controls, which supports that its metabolism is trig-
gered in the onset of neurodegeneration. On the other hand,
MCI and healthy controls were indistinguishable in terms of
ethanolamine plasmalogens, indicating that this impairment
must occur at more advanced stages of disease. Besides the
already described changes in serum levels of phospholipids,
lyso-phospholipids were also involved in pathogenesis of
AD. In previous studies, total lyso-phosphocholines concen-
tration tended to be lower in blood of AD patients [21, 35],
reflecting alterations in the metabolism of choline-containing
phospholipids that may be attributed to impairments in the
6 Current Alzheimer Research, 2016, Vol. 13, No. 4 González-Domínguez et al.
Table 1. Discriminant lyso-phospholipids and phospholipids identified in serum from AD and MCI patients.
change (%)
mass (D a)
RT (min)
p value
LPE (16:0)
LPE (18:2)
LPC (16:1)
LPC (16:0)
LPC (O-18:0)
LPC (20:5)
LPC (22:6)
LPC (22:5)
Abbreviations. LPE: lyso-phosphoethanolamine; LPC: lyso-phosphocholine; PE: phosphoethanolamine; PC: phosphocholine; PPC: choline-plasmalogen;
PPE: ethanolamine-plasmalogen; NS: non significant
deacylation-reacylation cycle of phospholipids. However, it
is noteworthy that lyso-phospholipids presented a similar
distribution to that of their precursors in our analysis, with
increased levels of saturated compounds respect to unsatu-
rated ones in both ethanolamine and choline species (Table
1). Therefore, it can be concluded that the imbalance ob-
served in phospholipids is finally reflected in their degrada-
tion products, which makes them useful markers of destabi-
lization of neuronal membranes. Furthermore, the compari-
son of the three study groups lead us to believe in a temporal
evolution of the processes leading to phospholipids degrada-
tion given that, while unsaturated lysophospholipids are
down-expressed in both AD and MCI patients, the increase
of saturated species is only observed in AD. Thus, it could
be hypothesized that, in the membrane destabilization proc-
ess, the first step supposes the degradation of PUFA-
Metabolomic Study of Alzheimer’s Disease Progression Current Alzheimer Research, 2016, Vol. 13, No. 4 7
Table 2. Discriminant sphingolipids identified in serum from AD and MCI patients.
change (%)
mass (D a)
mass error
RT (min)
ion mode
p value
Sphingoi d bases
Abbreviations. S1P: sphingosine-1-phosphate; CER: ceramide; SM: sphingomyelin; Hex-CER: hexosyl-ceramide; SULF: sulfatide; Lac-CER: lactosyl-
ceramide; NS: non significant
Table 3. Other discriminant metabolites identified in serum from AD and MCI patients.
change (%)
mass (D a)
mass error
RT (min)
ion mode
p value
8 Current Alzheimer Research, 2016, Vol. 13, No. 4 González-Domínguez et al.
(Table 3) contd….
change (%)
mass (D a)
mass error
RT (min)
ion mode
p value
Abbreviations. MG(16:0): monopalmitin; MG(18:0): monostearin; CAR(16:0): palmitoyl-carnitine; CAR(18:2): linoleyl-carnitine; CAR(18:1): oleyl-
carnitine; CAR(18:0): stearoyl-carnitine; PAG: phenyl-acetyl-glutamine; PREGS: pregnenolone sulfate; NS: non significant.
containing phospholipids (probably due to oxidative stress),
and secondly, their replacement by saturated ones. Among
these lysophospholipids, one deserves special attention:
LPC(O-18:0), an alkyl ether lysophospholipid involved in
the synthesis of platelet activating factor (PAF) by the re-
modeling pathway. In this context, Ryan et al. found three
PAF species significantly elevated in AD cortex: C16:0
PAF, its precursor C16:0 lyso-PAF, and C18:1 lyso-PAF
[36]. Thus, the increase observed in serum C18:0 lyso-PAF
(Table 1) could reveal a selective disruption of stearoyl-PAF
biosynthesis, complementary to findings by Ryan et al.,
which evidences that these impairments in lipid metabolism
are finally reflected in peripheral fluids.
The involvement of perturbed sphingolipid metabolism
also emerges as a pivotal event in membrane degeneration in
AD, as can be observed in Table 2. Defects in sphingolipid
metabolism have been previously associated with Alz-
heimer’s disease, with up-regulated activities of different
enzymes such as ceramide synthases [37] and acid sphingo-
myelinase [38], which suggests a shift in metabolism to-
wards the accumulation of ceramides. In this sense, high
levels of total ceramides have been previously found in brain
[37-39] and blood [40-41] of AD patients, which is in
agreement with our findings in serum (Table 2). On the other
hand, studies of sphingomyelin (SM) levels in AD are less
clear. Results from post-mortem brain analyses are contra-
dictory, showing increased [42] or decreased [38] total con-
centrations of SMs, while plasma levels appear to be de-
creased [37]. The reasons for these discrepancies are not
understood to date, but they could be related to differences in
the acyl chain composition. It has been demonstrated that the
increase of ceramides in AD brain is more pronounced for
species containing very long chain fatty acids [43], which is
consequent with the elevated expression of long-chain cera-
mide synthase [37]. Similarly, when individual SM species
are considered, most authors noted a decrease in very long
chain sphingomyelins [40, 43], while other species are usu-
ally over-expressed [42]. In this work, this hypothesis was
corroborated by the increased serum levels of several me-
dium- and long-chain sphingomyelins (MCFA-SM and
LCFA-SM) in AD and MCI patients, from 12 to 18 carbon
atoms (Table 2), while very long chain species remained
unchanged. Alternatively, levels of SM(d18:1/18:2) were
decreased in diseased subjects, suggesting a possible impli-
cation of oxidative stress in its degradation, as occurred with
analogous phospholipids (Table 1). Furthermore, besides the
described differences according the fatty acid contained in
the structure, it is remarkable the trend observed in sphingo-
myelin levels between the three groups of study. For most of
these compounds, the percentage of change was slightly
more pronounced in the comparison MCI vs. HC than in AD
vs. HC, which suggests that alterations in metabolism of
sphingomyelins probably contribute to early pathological
mechanisms. In this sense, Kosicek et al. found significantly
increased SM levels in the CSF from patients with prodro-
mal AD compared to healthy controls, mild and moderate
AD [44], and Mielke et al. have demonstrated that blood
sphingomyelins and ceramides vary along the progression of
memory impairment [45]. Therefore, peripheral levels of
these sphingolipids could be good predictors of memory im-
pairment and markers of progression. Sphingosine-1-
phosphate (S1P) is a signaling molecule produced via degra-
dation of ceramides by the enzyme ceramidase and subse-
quent phosphorylation by sphingosine kinase. Previous stud-
ies reported that acid ceramidase is up-regulated in AD
brains versus normal controls [38], as well as the activity of
sphingosine kinase 2 [46], which supports the increased lev-
els of S1P found in serum samples (Table 2). Finally,
changes in hexosylceramides, lactosylceramides and sulfa-
Metabolomic Study of Alzheimer’s Disease Progression Current Alzheimer Research, 2016, Vol. 13, No. 4 9
tides (Table 2) also support the involvement of glycosphin-
golipids in pathogenesis of AD. On the one hand, a substan-
tial reduction of sulfatide levels was observed in serum sam-
ples from AD patients, as previously reported in brain [36].
On the other hand, the increase of different species of lacto-
sylceramides (Table 2) could be indicative of an impaired
metabolism of gangliosides. Like sulfatides, earlier studies in
AD showed alterations of ganglioside metabolism resulting
in reduced levels in the majority of brain regions [47], but
increased serum lactosylceramide has been associated with
risk of AD only once [48]. Complementarily, hexosylcera-
mides, which comprise isomeric glucosyl and galactosyl
species indistinguishable by MS, were also increased in se-
rum samples. Glucosyl- and galactosyl-ceramides are poten-
tial degradation products of glycosphingolipids, so their
over-expression support the depletion observed in gangli-
osides and sulfatides, respectively. Finally, it should be
pointed out that the increase of hexosyl- and lactosyl-
ceramides was sharper in MCI than in AD, which suggests
that depletion of glycosphingolipids occurs in the onset of
disease, as previously mentioned for sphingomyelins. There-
fore, it can be concluded that lipid homeostasis might be a
primary target in pathogenesis of Alzheimer’s disease, in-
volving both phospholipids and sphingolipids forming part
of cellular membranes and/or lipoproteins. This abnormal
metabolism of phospholipids and sphingolipids observed
during the progression of disease resulted in important bio-
chemical changes in serum samples, listed in Tables 1 and 2.
Thus, membrane breakdown could be considered as a source
of potential markers of Alzheimer, as summarized in Fig.
Fig. (3). Column plots with standard deviation bars for the discriminant metabolites identified in serum from AD and MCI patients. HC:
black columns (n=45); MCI: light gray columns (n=17); AD: dark gray columns (n=75).
10 Current Alzheimer Research, 2016, Vol. 13, No . 4 González-Domínguez et al.
Aside from changes related to membrane defects, the de-
crease observed in monoglycerides (monopalmitin and
monosterain) and oleamide (Table 3) could point to distur-
bances in endocannabinoid (EC) system. This system plays a
role in numerous physiological processes, principally in the
central nervous system where acts as neuromodulator. How-
ever, accumulating data show an imbalance in key elements
of the EC system associated with AD and other neurodegen-
erative disorders. In this sense, the expression levels of can-
nabinoid receptors (CB) have been found altered in AD
brains, including a decrease of CB1 receptors and a comple-
mentary up-regulation of CB2 receptors in the hippocampus
[49]. In addition, the activities of enzymes fatty acid amide
hydrolase (FAAH) and monoacylglycerol lipase (MAGL),
which are involved in the metabolism of the major endocan-
nabinoids, anandamide and 2-arachidonoyl-glycerol respec-
tively, are affected in AD. Benito et al. reported that the ac-
tivity of FAAH seems to be elevated in the plaques and sur-
rounding areas of AD brains [49], which could be responsi-
ble for the decrease of oleamide found in serum of AD and
MCI patients. Oleamide is a bioactive fatty acid amide
catabolically related to anandamide, which may be also de-
graded by FAAH thereby serving as indicator of its over-
expression. On the other hand, there is also evidence for the
involvement of monoglyceride lipase in AD, given that it has
been found that microglia expressing MAGL accumulates
around senile plaques [50]. This lipase is the major enzyme
inactivating 2-arachidonoyl glycerol, but other monoglyc-
erides are also potential substrates. In this way, reductions of
circulating levels of monopalmitin and monostearin could be
considered as indicators of MAGL over-expression.
Long chain acylcarnitines were also increased in serum
of AD and MCI patients, which may indicate an incomplete
fatty acid β-oxidation in the onset of disease. Elevation of
palmitoyl-, stearoyl-, oleyl- and linoleyl-carnitines is the
characteristic feature of carnitine palmitoyltransferase II de-
ficiency, an inherited metabolic impairment that prevents
mitochondrial oxidation of long chain fatty acids [51]. In this
sense, mitochondrial dysfunction appears to be one of the
primary events in the course of AD, which can perturb cellu-
lar bioenergetics [52]. Therefore, this impaired metabolism
of lipids could be involved in neurodegenerative energetic
failures as a supplementary pathway to those previously de-
scribed, such as deregulated tricarboxilic acid cycle or oxida-
tive phosphorylation system [53].
Histidine is an amino acid with antioxidant and anti-
inflammatory properties, like other imidazole-containing
compounds. Thus, oxidative stress may account for the de-
crease of this amino acid (Table 3), as previously reported
[31, 34]. Pregenenolone sulfate (PREGS) is a neurosteroid
precursor of estrogens, progestins and androgens, which
plays important roles in the aging nervous system. Weill-
Engerer et al. found a trend toward lower levels of PREGS
and other steroids in brain from AD patients, and a negative
correlation between PREGS and β amyloid levels [54]. In the
present study, pregnenolone sulfate was decreased in serum
from AD patients compared to MCI and controls, corroborat-
ing the potential of neurosteroids as diagnostic markers of
Alzheimer. Finally, increased serum levels of phenylacetyl-
glutamine (PAG) were found in AD and MCI patients, which
could be associated with elevated levels of glutamine, given
that PAG can be also synthesized in the liver by condensa-
Fig. (4). Metabolomic changes observed in phospholipids and sphingolipids due to membrane breakdown. () increased compounds, ()
decreased compounds in AD and/or MCI respect to healthy controls (individual changes are shown in Tables 1-2). Abbreviations: PL: phos-
pholipid; PUFA: polyunsaturated fatty acid; SFA: saturated fatty acid; Pls: plasmalogen; lyso-PL: lyso-phospholipid; SM: sphingomyelin;
MCFA: medium chain fatty acid; LCFA: long chain fatty acid; Cer: ceramide; ; S1P: sphingosine-1-phosphate; Glc-Cer: glucosyl-ceramide;
Lac-Cer: lactosyl-ceramide; Gal-Cer: galactosyl-ceramide; Hex-Cer: hexosyl-ceramide.
Metabolomic Study of Alzheimer’s Disease Progression Current Alzheimer Research, 2016, Vol. 13, No. 4 11
tion of glutamine with phenylacetyl-CoA. Therefore, the
increase observed in PAG could be considered a novel
marker of impaired glutamate-glutamine homeostasis in rela-
tion to the regulation of ammonia levels in the organism
Metabolomic fingerprinting of serum samples by
UHPLC-ESI-QTOFMS has been demonstrated as a suitable
approach to distinguish between patients with Alzheimer’s
disease, mild cognitive impairment and healthy controls.
Multiple metabolites, principally lipids, were identified as
potential markers for diagnosis, which indicated the in-
volvement of different pathological processes in the devel-
opment of neurodegeneration. Thus, it was observed a deep
dysregulation in metabolism of phospholipids and sphin-
golipids in terms of altered levels of saturated/unsaturated
fatty acids contained in the structure, but also due to differ-
ences in the acyl chain length. In addition, these imbalances
were accompanied by significant changes in related com-
pounds (ceramides, lyso-phospholipids) that pointed to
membrane breakdown. On the other hand, novel potential
biomarkers not previously described were found for well-
known pathological situations associated with AD: defects in
energy metabolism (long chain acylcarnitines) and endocan-
nabinoid dysfunction (monoglycerides and oleamide). Fi-
nally, reductions in histidine and PREGS were related to
oxidative stress and altered neurosteroid metabolism, respec-
tively, while increase of PAG could reveal impaired glu-
tamine homeostasis in pathogenesis of AD. Furthermore, the
comparative study between AD and MCI patients allowed
exploring these altered pathways in more detail for better
understanding of pathogenesis and progression of disease
from pre-clinical stages of dementia. As future plan, it would
be interesting to extend this study to other types of dementia
in order to assess the specificity of these potential markers
against other neurodegenerative disorders.
AD = Alzheimer’s disease
MCI = mild cognitive impairment
HC = healthy control
UHPLC-MS = ultra-high performance liquid chromatog-
raphy mass spectrometry
PLS-DA = partial least squares discriminant analysis
VIP = variable importance in the projection
LPE = lyso-phosphoethanolamine
LPC = lyso-phosphocholine
PE = phosphoethanolamine
PC = phosphocholine
PPC = choline-plasmalogen
PPE = ethanolamine-plasmalogen
S1P = sphingosine-1-phosphate
CER = ceramide
SM = sphingomyelin
Hex-CER = hexosyl-ceramide
SULF = sulfatide
Lac-CER = lactosyl-ceramide
MG = monoglyceride
CAR = carnitine
PAG = phenyl-acetyl-glutamine
PREGS = pregnenolone sulfate
PUFA = polyunsaturated fatty acid
SFA = saturated fatty acid
The author(s) confirm that this article content has no con-
flicts of interest.
This work was supported by the projects CTM2012-
38720-C03-01 from the Ministerio de Ciencia e Innovación
and P008 FQM-3554 and P009-FQM-4659 from the Conse-
jería de Innovación, Ciencia y Empresa (Junta de An-
dalucía). Raúl González Domínguez thanks the Ministerio de
Educación for a predoctoral scholarship. The authors also
thank to Dr. Alberto Blanco and Carlos Salgado from Hospi-
tal Juan Ramon Jimenez for providing serum samples.
Table S1. Demographic characteristics of subjects en-
rolled in the study.
Supplementary material is available on the publisher’s
web site along with the published article.
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Received: ???????????????? Revised: ???????????????? Accepted: ????????????????
... ↑ plasma [90,100] ↑ serum [82] ↓ baseline plasma levels in MCI/AD converters than ↓ serum levels progressively decreased from CN > SMC > MCI > AD [84] ↑ serum levels in MCI and then decreased slightly in AD (CN < AD < MCI) [104] ↑ plasma [86] Octadecenoylcarnitine (C18:1) ...
... ↑ plasma [81,90] ↑ serum [82,99] ↓ serum levels progressively decreased from CN > SMC > MCI > AD [84] ↑ serum levels in MCI and then decreased in AD (CN < AD < MCI) [104] ↑ plasma [86,89] Octadecadienylcarnitine (C18:2) ↑ plasma [81] ↓ serum levels progressively decreased from CN > SMC > MCI > AD [84] ↑ serum levels in MCI and then decreased in AD (CN < AD < MCI) [104] ↑ plasma [86] Ketone bodies ↓ levels associated with increased risk of cognitive decline [112] ↓ plasma levels decreased from CN > MCI then returned to normal levels in AD (CN = AD > MCI) [113] ↓ serum levels in MCI > CN [110] ↓ serum levels in MCI > CN [122] ↓ baseline levels in AD were associated with a higher risk of cognitive decline [123] ↑ plasma [86,88] ↑ serum [93] ↑ blood levels associated with lower risk of AD and dementia [124] ↓ levels associated with declines in memory and executive function [125] ↑ CSF levels progressively increased from CN < MCI < ...
... ↑ plasma [81,90] ↑ serum [82,99] ↓ serum levels progressively decreased from CN > SMC > MCI > AD [84] ↑ serum levels in MCI and then decreased in AD (CN < AD < MCI) [104] ↑ plasma [86,89] Octadecadienylcarnitine (C18:2) ↑ plasma [81] ↓ serum levels progressively decreased from CN > SMC > MCI > AD [84] ↑ serum levels in MCI and then decreased in AD (CN < AD < MCI) [104] ↑ plasma [86] Ketone bodies ↓ levels associated with increased risk of cognitive decline [112] ↓ plasma levels decreased from CN > MCI then returned to normal levels in AD (CN = AD > MCI) [113] ↓ serum levels in MCI > CN [110] ↓ serum levels in MCI > CN [122] ↓ baseline levels in AD were associated with a higher risk of cognitive decline [123] ↑ plasma [86,88] ↑ serum [93] ↑ blood levels associated with lower risk of AD and dementia [124] ↓ levels associated with declines in memory and executive function [125] ↑ CSF levels progressively increased from CN < MCI < ...
Full-text available
One of the most recognisable features of ageing is a decline in brain health and cognitive dysfunction, which is associated with perturbations to regular lipid homeostasis. Although ageing is the largest risk factor for several neurodegenerative diseases such as dementia, a loss in cognitive function is commonly observed in adults over the age of 65. Despite the prevalence of normal age-related cognitive decline, there is a lack of effective methods to improve the health of the ageing brain. In light of this, exercise has shown promise for positively influencing neurocognitive health and associated lipid profiles. This review summarises age-related changes in several lipid classes that are found in the brain, including fatty acyls, glycerolipids, phospholipids, sphingolipids and sterols, and explores the consequences of age-associated pathological cognitive decline on these li-pid classes. Evidence of the positive effects of exercise on the affected lipid profiles are also discussed to highlight the potential for exercise to be used therapeutically to mitigate age-related changes to lipid metabolism and prevent cognitive decline in later life.
... Decreased oleamide plasma levels were reported in FMR1 premutation carriers and proposed as a potential biomarker for the diagnosis of FXTAS [21], showing a negative correlation with CGG expansion [4], which was also identified in our analysis. A lower abundance of oleamide in plasma has also been postulated as a marker for AD [62]. ...
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The course of pathophysiological mechanisms involved in fragile X-associated tremor/ataxia syndrome (FXTAS) remains largely unknown. Previous proteomics and metabolomics studies conducted in blood samples collected from FMR1 premutation carriers with FXTAS reported abnormalities in energy metabolism, and precursors of gluconeogenesis showed significant changes in plasma expression levels in FMR1 premutation carriers who developed FXTAS. We conducted an analysis of postmortem human brain tissues from 44 donors, 25 brains with FXTAS, and 19 matched controls. We quantified the metabolite relative abundance in the inferior temporal gyrus and the cerebellum using untargeted mass spectrometry (MS)-based metabolomics. We investigated how the metabolite type and abundance relate to the number of cytosine-guanine-guanine (CGG) repeats, to markers of neurodegeneration, and to the symptoms of FXTAS. A metabolomic analysis identified 191 primary metabolites, the data were log-transformed and normalized prior to the analysis, and the relative abundance was compared between the groups. The changes in the relative abundance of a set of metabolites were region-specific with some overlapping results; 22 metabolites showed alterations in the inferior temporal gyrus, while 21 showed differences in the cerebellum. The relative abundance of cytidine was decreased in the inferior temporal gyrus, and a lower abundance was found in the cases with larger CGG expansions; oleamide was significantly decreased in the cerebellum. The abundance of 11 metabolites was influenced by changes in the CGG repeat number. A histological evaluation found an association between the presence of microhemorrhages in the inferior temporal gyrus and a lower abundance of 2,5-dihydroxypyrazine. Our study identified alterations in the metabolites involved in the oxidative-stress response and bioenergetics in the brains of individuals with FXTAS. Significant changes in the abundance of cytidine and oleamide suggest their potential as biomarkers and therapeutic targets for FXTAS.
... Differently, Nho et al. (2021) reported higher levels of seven PC associated with increased Aβ deposition as well as changes in memory and executive functioning [39]. Similarly, Gonzalez-Dominguez et al. (2014,2016) found that PC levels increased in serum samples of patients with AD [43,44]. Indeed, the discrepancies in these findings and conclusions may rise from differences in the population selected for the study or by technicalities related to the procedure used for the analysis. ...
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Recently, measurable peripheral biomarkers in the plasma of patients with Alzheimer's disease (AD) have gained considerable clinical interest. Several studies have identified one or more blood signatures that may facilitate the development of novel diagnostic and therapeutic strategies. For instance, changes in peripheral amyloid β42 (Aβ42) levels have been largely investigated in patients with AD and correlated with the progression of the pathology, although with controversial results. In addition, tumor necrosis factor α (TNFα) has been identified as an inflammatory biomarker strongly associated with AD, and several studies have consistently suggested the pharmacological targeting of TNFα to reduce systemic inflammation and prevent neurotoxicity in AD. Moreover, alterations in plasma metabolite levels appear to predict the progression of systemic processes relevant to brain functions. In this study, we analyzed the changes in the levels of Aβ42, TNFα, and plasma metabolites in subjects with AD and compared the results with those in healthy elderly (HE) subjects. Differences in plasma metabolites of patients with AD were analyzed with respect to Aβ42, TNFα, and the Mini-Mental State Examination (MMSE) score, searching for plasma signatures that changed simultaneously. In addition, the phosphorylation levels of the Tyr682 residue of the amyloid precursor protein (APP), which we previously proposed as a biomarker of AD, were measured in five HE and five AD patients, in whom the levels of Aβ42, TNFα, and two plasma lipid me-tabolites increased simultaneously. Overall, this study highlights the potential of combining different plasma signatures to define specific clinical phenotypes of patient subgroups, thus paving the way for the stratification of patients with AD and development of personalized approaches.
... In particular, the most relevant perturbations were associated with a reduction in the levels of circulating phospholipids containing PUFAs, which was accompanied by a parallel increase of lipid species composed of saturated fatty acids (SFAs), which could provoke membrane destabilization processes (González-Domínguez et al., 2014b, 2014c. Next, the same authors investigated serum samples from a cohort of AD, MCI, and HC participants, using UPLC-QTOF-MS, and observed compromised metabolism of phospholipids and sphingolipids, resulting in membrane disruption, where the biology of the fatty acids integrated in the lipids structure as acyl chain length and extent of unsaturation seems to be critical (González-Domínguez et al., 2016). An untargeted plasma lipidomic analysis, using UPLC-MS, revealed a signature of 10 metabolites distinguishing AD patients from HC with high accuracy. ...
Alzheimer's disease (AD) is determined by various pathophysiological mechanisms starting 10 to 25 years before the onset of clinical symptoms. As multiple functionally interconnected molecular/cellular pathways appear disrupted in AD, the exploitation of high-throughput unbiased omics sciences is critical to elucidating the precise pathogenesis of AD. Among different omics, metabolomics is a fast-growing discipline allowing for the simultaneous detection and quantification of hundreds/thousands of perturbed metabolites in tissues or biofluids, reproducing the fluctuations of multiple networks affected by a disease. Here, we seek to critically depict the main metabolomics methodologies with the aim of identifying new potential AD biomarkers and further elucidating AD pathophysiological mechanisms. From a systems biology perspective, as metabolic alterations can occur before the development of clinical signs, metabolomics - coupled with existing accessible biomarkers used for AD screening and diagnosis - can support early disease diagnosis and help develop individualized treatment plans. Presently, the majority of metabolomic analyses emphasized that lipid metabolism is the most consistently altered pathway in AD pathogenesis. The possibility that metabolomics may reveal crucial steps in AD pathogenesis is undermined by the difficulty in discriminating between the causal or epiphenomenal or compensatory nature of metabolic findings.
... previously associated with AD(González-Domínguez et al., 2016).The levels of hippuric acid positively correlate with the aging process in our work. Other authors obtained the same results and have also related this metabolite to cognitive decline(De Simone et al., 2021), and have been also related to cognitive decline by other authors(De Simone et al., 2021). ...
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Aging biology entails a cell/tissue deregulated metabolism that affects all levels of biological organization. Therefore, the application of "omic" techniques that are closer to phenotype, such as metabolomics, to the study of the aging process should be a turning point in the definition of cellular processes involved. The main objective of the present study was to describe the changes in plasma metabolome associated with biological aging and the role of sex in the metabolic regulation during aging. A high-throughput untargeted metabolomic analysis was applied in plasma samples to detect hub metabolites and biomarkers of aging incorporating a sex/gender perspective. A cohort of 1030 healthy human adults (45.9% females, and 54.1% males) from 50 to 98 years of age was used. Results were validated using two independent cohorts (1: n = 146, 53% females, 30-100 years old; 2: n = 68, 70% females, 19-107 years old). Metabolites related to lipid and aromatic amino acid (AAA) metabolisms arose as the main metabolic pathways affected by age, with a high influence of sex. Globally, we describe changes in bioenergetic pathways that point to a decrease in mitochondrial β-oxidation and an accumulation of unsaturated fatty acids and acylcarnitines that could be responsible for the increment of oxidative damage and inflammation characteristic of this physiological process. Furthermore, we describe for the first time the importance of gut-derived AAA catabolites in the aging process describing novel biomarkers that could contribute to better understand this physiological process but also age-related diseases.
... Previous studies have pointed out that HCC may be associated with cognitive disorders of the brain such as hepatic encephalopathy, but the specific mechanisms by which HCC induces VCI are unclear (45). Metabolomic studies have shown that the pathogenesis of VCI may involve various mechanisms such as impaired myelin synthesis caused by glucolipid metabolism disorders and related metabolites leading to bloodbrain barrier disruption and vascular endothelial damage (46)(47)(48). HCC may affect metabolic disorders promoting the development of VCI. According to the current studies, the possible mechanisms of cognitive impairment due to HCC include impaired blood ammonia and bile acid metabolism, oxidative stress injury and inflammatory response, impaired blood-brain barrier, neurotransmission disorders, neurotoxic accumulation, and disturbance of cerebral energy metabolism (49-51). ...
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Introduction Screening for metabolically relevant differentially expressed genes (DEGs) shared by hepatocellular carcinoma (HCC) and vascular cognitive impairment (VCI) to explore the possible mechanisms of HCC-induced VCI. Methods Based on metabolomic and gene expression data for HCC and VCI, 14 genes were identified as being associated with changes in HCC metabolites, and 71 genes were associated with changes in VCI metabolites. Multi-omics analysis was used to screen 360 DEGs associated with HCC metabolism and 63 DEGs associated with VCI metabolism. Results According to the Cancer Genome Atlas (TCGA) database, 882 HCC-associated DEGs were identified and 343 VCI-associated DEGs were identified. Eight genes were found at the intersection of these two gene sets: NNMT, PHGDH, NR1I2, CYP2J2, PON1, APOC2, CCL2, and SOCS3. The HCC metabolomics prognostic model was constructed and proved to have a good prognostic effect. The HCC metabolomics prognostic model was constructed and proved to have a good prognostic effect. Following principal component analyses (PCA), functional enrichment analyses, immune function analyses, and TMB analyses, these eight DEGs were identified as possibly affecting HCC-induced VCI and the immune microenvironment. As well as gene expression and gene set enrichment analyses (GSEA), a potential drug screen was conducted to investigate the possible mechanisms involved in HCC-induced VCI. The drug screening revealed the potential clinical efficacy of A-443654, A-770041, AP-24534, BI-2536, BMS- 509744, CGP-60474, and CGP-082996. Conclusion HCC-associated metabolic DEGs may influence the development of VCI in HCC patients.
The discovery of new biomarkers that can distinguish Alzheimer's disease (AD) from mild cognitive impairment (MCI) in the early stages will help to provide new diagnostic and therapeutic strategies and slow the transition from MCI to AD. Patients with AD may present with a concomitant metabolic disorder, such as diabetes, obesity, and dyslipidemia, as a risk factor for AD that may be involved in the onset of both AD pathology and cognitive impairment. Therefore, metabolite profiling, or metabolomics, can be very useful in diagnosing AD, developing new therapeutic targets, and evaluating both the course of treatment and the clinical course of the disease. In addition, studying the relationship between nutritional behavior and AD requires investigation of the role of conditions such as obesity, hypertension, dyslipidemia, and elevated glucose level. Based on this literature review, nutritional recommendations, including weight loss by reducing calorie and cholesterol intake and omega-3 fatty acid supplementation can prevent cognitive decline and dementia in the elderly. The underlying metabolic causes of the pathology and cognitive decline caused by AD and MCI are not well understood. In this review article, metabolomics biomarkers for diagnosis of AD and MCI and metabolic risk factors for cognitive decline in AD were evaluated.
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Alzheimer’s disease (AD) is an aging-related neurodegenerative disease, leading to the progressive loss of memory and other cognitive functions. As there is still no cure for AD, the growth in the number of susceptible individuals represents a major emerging threat to public health. Currently, the pathogenesis and etiology of AD remain poorly understood, while no efficient treatments are available to slow down the degenerative effects of AD. Metabolomics allows the study of biochemical alterations in pathological processes which may be involved in AD progression and to discover new therapeutic targets. In this review, we summarized and analyzed the results from studies on metabolomics analysis performed in biological samples of AD subjects and AD animal models. Then this information was analyzed by using MetaboAnalyst to find the disturbed pathways among different sample types in human and animal models at different disease stages. We discuss the underlying biochemical mechanisms involved, and the extent to which they could impact the specific hallmarks of AD. Then we identify gaps and challenges and provide recommendations for future metabolomics approaches to better understand AD pathogenesis.
Recent technical advances in mass spectrometry, as applied to the analytical chemistry of lipid molecules, enable the simultaneous detection of the multiplicity of lipid complex species present in the human brain. This, in combination with quantitative studies carried out in plasma samples, helps to identify disease biomarkers including for Alzheimer’s disease (AD). Mass spectrometry imaging (MSI) is particularly powerful for the anatomical localization of lipids in brain slices, identifying lipid modifications in postmortem frozen samples from AD patients.Human brain tissues are sectioned in a cryostat and then covered with a chemical matrix, such as mercaptobenzothiazole (MBT) or α-cyano-4-hydroxycinnamic acid (CHCA), to ionize the lipid molecules either by sublimation or by spraying. We describe the use of matrix-assisted laser desorption ionization (MALDI) in an LTQ–Orbitrap–XL mass spectrometer to scan brain tissue slices with high spatial resolution, analyzing 50 μm cell layers. The lipid spectra obtained for each pixel are transformed to color-coded intensity maps of hundreds of lipid species included those within a single tissue slice.Key wordsLipidMALDI-MSI UHPLC-MS Biomarkers Alzheimer’s diseaseNeurolipid Mass spectroscopy
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Alzheimer's Disease (AD) currently affects more than 5 million Americans, with numbers expected to grow dramatically as the population ages. The pathophysiological changes in AD patients begin decades before the onset of dementia, highlighting the urgent need for the development of early diagnostic methods. Compelling data demonstrate that increased levels of amyloid-beta compromise multiple cellular pathways; thus, the investigation of changes in various cellular networks is essential to advance our understanding of early disease mechanisms and to identify novel therapeutic targets. We applied a liquid chromatography/mass spectrometry-based non-targeted metabolomics approach to determine global metabolic changes in plasma and cerebrospinal fluid (CSF) from the same individuals with different AD severity. Metabolic profiling detected a total of significantly altered 342 plasma and 351 CSF metabolites, of which 22% were identified. Based on the changes of .150 metabolites, we found 23 altered canonical pathways in plasma and 20 in CSF in mild cognitive impairment (MCI) vs. cognitively normal (CN) individuals with a false discovery rate ,0.05. The number of affected pathways increased with disease severity in both fluids. Lysine metabolism in plasma and the Krebs cycle in CSF were significantly affected in MCI vs. CN. Cholesterol and sphingolipids transport was altered in both CSF and plasma of AD vs. CN. Other 30 canonical pathways significantly disturbed in MCI and AD patients included energy metabolism, Krebs cycle, mitochondrial function, neurotransmitter and amino acid metabolism, and lipid biosynthesis. Pathways in plasma that discriminated between all groups included polyamine, lysine, tryptophan metabolism, and aminoacyl-tRNA biosynthesis; and in CSF involved cortisone and prostaglandin 2 biosynthesis and metabolism. Our data suggest metabolomics could advance our understanding of the early disease mechanisms shared in progression from CN to MCI and to AD.
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Although Alzheimer's Disease (AD) is the most common neurodegenerative disease, the etiology of AD is not well understood. In some cases, genetic factors explain AD risk, but a high percentage of late-onset AD is unexplained. The fact that AD is associated with a number of physical and systemic manifestations suggests that AD is a multifactorial disease that affects both the CNS and periphery. Interestingly, a common feature of many systemic processes linked to AD is involvement in energy metabolism. The goals of this review are to 1) explore the evidence that peripheral processes contribute to AD risk, 2) explore ways that AD modulates whole-body changes, and 3) discuss the role of genetics, mitochondria, and vascular mechanisms as underlying factors that could mediate both central and peripheral manifestations of AD. Despite efforts to strictly define AD as a homogeneous CNS disease, there may be no single etiologic pathway leading to the syndrome of AD dementia. Rather, the neurodegenerative process may involve some degree of baseline genetic risk that is modified by external risk factors. Continued research into the diverse but related processes linked to AD risk is necessary for successful development of disease -modifying therapies.
Metabolomic analysis of brain tissue from transgenic mouse models of Alzheimer's disease has demonstrated a great potential for the study of pathological mechanisms and the development of new therapies and biomarkers for diagnosis. However, in order to translate these investigations to the clinical practice it is necessary to corroborate these findings in peripheral samples. To this end, this work considers the application of a novel metabolomic platform based on the combination of a two-steps extraction procedure with complementary analysis by direct infusion electrospray mass spectrometry and flow infusion atmospheric pressure photoionization mass spectrometry for a holistic investigation of metabolic abnormalities in serum samples from APP/PS1 mice. A number of metabolites were found to be perturbed in this mouse model, including increased levels of di- and tri-acylglycerols, eicosanoids, inosine, choline and glycerophosphoethanolamine; reduced content of cholesteryl esters, free fatty acids, lysophosphocholines, amino acids, energy-related metabolites, phosphoethanolamine and urea, as well as abnormal distribution of phosphocholines depending on the fatty acid linked to the molecular moiety. This allowed the elucidation of possible pathways disturbed underlying to disease (abnormal homeostasis of phospholipids leading to membrane breakdown, energy-related failures, hyperammonemia and hyperlipidemia, among others), thus demonstrating the utility of peripheral samples to investigate pathology in the APP/PS1 model. Copyright © 2015 Elsevier B.V. All rights reserved.
The transgenic mouse APP/PS1 is widely employed by neuroscientists because reproduces well some of the neuropathological and cognitive deficits observed in human Alzheimer's disease. In this study, serum samples from APP/PS1 mice (n = 30) and wild-type controls (n = 30) were analyzed using a metabolomic multiplatform based on the combination of gas chromatography-mass spectrometry and ultra-high performance liquid chromatography-mass spectrometry, in order to obtain wide information about serum metabolome. Metabolic profiles showed significant differences between the groups of study, and numerous metabolites were identified as potential players in the development of Alzheimer-type disorders in this transgenic model. Pathway analysis revealed the involvement of multiple metabolic networks in the underlying pathology, such as deficiencies in energy metabolism, altered amino acid homeostasis, abnormal membrane lipid metabolism, and other impairments related to the integrity of the central nervous system. It is noteworthy that some of these metabolomic markers are in accordance with pathological alterations observed in human Alzheimer's disease, while others have not been previously described. Therefore, these results demonstrate the potential of metabolomics and the use of transgenic animal models to understand the pathogenesis of Alzheimer's disease. Copyright © 2015 Elsevier B.V. and Sociétéfrançaise de biochimie et biologie Moléculaire (SFBBM). All rights reserved.
The identification of pathological mechanisms underlying to Alzheimer's disease is of great importance for the discovery of potential markers for diagnosis and disease monitoring. In this study, we investigated regional metabolic alterations in brain from the APP/PS1 mice, a transgenic model that reproduces well some of the neuropathological and cognitive deficits observed in human Alzheimer's disease. For this purpose, hippocampus, cortex, cerebellum and olfactory bulbs were analyzed using a high-throughput metabolomic approach based on direct infusion mass spectrometry. Metabolic fingerprints showed significant differences between transgenic and wild-type mice in all brain tissues, being hippocampus and cortex the most affected regions. Alterations in numerous metabolites were detected including phospholipids, fatty acids, purine and pyrimidine metabolites, acylcarnitines, sterols and amino acids, among others. Furthermore, metabolic pathway analysis revealed important alterations in homeostasis of lipids, energy management, and metabolism of amino acids and nucleotides. Therefore, these findings demonstrate the potential of metabolomic screening and the use of transgenic models for understanding pathogenesis of Alzheimer's disease. Copyright © 2014 Elsevier B.V. All rights reserved.
The use of atmospheric pressure photoionization is not widespread in metabolomics, despite its considerable potential for the simultaneous analysis of compounds with diverse polarities. This work considers the development of a novel analytical approach based on flow injection analysis and atmospheric pressure photoionization mass spectrometry for rapid metabolic screening of serum samples. Several experimental parameters were optimized, such as type of dopant, flow injection solvent, and their flows, given that a careful selection of these variables is mandatory for a comprehensive analysis of metabolites. Toluene and methanol were the most suitable dopant and flow injection solvent, respectively. Moreover, analysis in negative mode required higher solvent and dopant flows (100µlmin(-1) and 40µlmin(-1), respectively) compared to positive mode (50µlmin(-1) and 20µlmin(-1)). Then, the optimized approach was used to elucidate metabolic alterations associated with Alzheimer׳s disease. Thereby, results confirm the increase of diacylglycerols, ceramides, ceramide-1-phosphate and free fatty acids, indicating membrane destabilization processes, and reduction of fatty acid amides and several neurotransmitters related to impairments in neuronal transmission, among others. Therefore, it could be concluded that this metabolomic tool presents a great potential for analysis of biological samples, considering its high-throughput screening capability, fast analysis and comprehensive metabolite coverage.
Alzheimer's disease (AD) is the most common neurodegenerative disorder worldwide, but its etiology is still not completely understood. The identification of underlying pathological mechanisms is becoming increasingly important for the discovery of biomarkers and therapies, for which metabolomics presents a great potential. In this work, we studied metabolic alterations in different brain regions of the APP/PS1 mice by using a high-throughput metabolomic approach based on the combination of gas chromatography-mass spectrometry and ultra-high performance liquid chromatography-mass spectrometry. Multivariate statistics showed that metabolomic perturbations are widespread, affecting mainly to hippocampus and cortex, but also present in regions not primarily associated with AD such as striatum, cerebellum and olfactory bulbs. Multiple metabolic pathways could be linked to the development of AD-type disorders in this mouse model, including abnormal purine metabolism, bioenergetic failures, dyshomeostasis of amino acids and disturbances in membrane lipids, among others. Interestingly, region-specific alterations were observed for some of the potential markers identified, associated with abnormal fatty acid composition of phospholipids and sphingomyelins, or differential regulation of neurotransmitter amino acids (e.g. glutamate, glycine, serine, N-acetyl-aspartate), not previously described to our knowledge. Therefore, these findings could provide a new insight into brain pathology in Alzheimer's disease.
Currently, there is no cure for Alzheimer’s disease and early diagnosis is very difficult, since no biomarkers have been established with the necessary reliability and specificity. For the discovery of new biomarkers, the application of omics is emerging, especially metabolomics based on the use of mass spectrometry. In this work, an analytical approach based on direct infusion electrospray mass spectrometry was applied for the first time to blood serum samples in order to elucidate discriminant metabolites. Complementary methodologies of extraction and mass spectrometry analysis were employed for comprehensive metabolic fingerprinting. Finally, the application of multivariate statistical tools allowed us to discriminate Alzheimer patients and healthy controls, and identify some compounds as potential markers of disease. This approach provided a global vision of disease, given that some important metabolic pathways could be studied, such as membrane destabilization processes, oxidative stress, hypometabolism, or neurotransmission alterations. Most remarkable results are the high levels of phospholipids containing saturated fatty acids, respectively, polyunsaturated ones and the high concentration of whole free fatty acids in Alzheimer’s serum samples. Thus, these results represent an interesting approximation to understand the pathogenesis of disease and the identification of potential biomarkers. Graphical Abstract ᅟ
There is high interest in the discovery of early diagnostic biomarkers of Alzheimer's disease, for which metabolomics exhibits a great potential. In this work, a metabolomic approach based on ultrafiltration and analysis by capillary electrophoresis mass spectrometry has been used to obtain representative fingerprints of polar metabolites from serum samples in order to distinguish between patients with Alzheimer's disease, mild cognitive impairment and healthy controls. By the use of partial least squares discriminant analysis it was possible to classify patients according to the disease stage and then identify potential markers. Significant increase was observed with progression of disease in levels of choline, creatinine, asymmetric dimethyl-arginine, homocysteine-cysteine disulfide, phenylalanyl-phenylalanine, and different medium chain acylcarnitines. On the other hand, asparagine, methionine, histidine, carnitine, acetyl-spermidine and C5-carnitine were reduced in these serum samples. In this way, multiple essential pathways were found implicated in the underlying pathology, such as oxidative stress or defects in energy metabolism. However, the most interesting results are related to the association of several vascular risk factors with Alzheimer's disease.This article is protected by copyright. All rights reserved
In this study, we demonstrated the potential of direct infusion mass spectrometry for the lipidomic characterization of Alzheimer's disease. Serum samples were extracted for lipids recovery, and directly analyzed using an electrospray source. Metabolomic fingerprints were subjected to multivariate analysis in order to discriminate between groups of patients and healthy controls, and then some key-compounds were identified as possible markers of Alzheimer's disease. Major differences were found in lipids, although some low molecular weight metabolites also showed significant changes. Thus, important metabolic pathways involved in neurodegeneration could be studied on the basis of these perturbations, such as membrane breakdown (phospholipids and diacylglycerols), oxidative stress (prostaglandins, imidazole and histidine), alterations in neurotransmission systems (oleamide and putrescine) and hyperammonaemia (guanidine and arginine). Moreover, it is noteworthy that some of these potential biomarkers have not been previously described for Alzheimer's disease.