Plasma metabolomics reveals biomarkers of the atherosclerosis

Research Article

Plasma metabolomics reveals biomarkers ofthe atherosclerosis

Atherosclerotic cardiovascular disease remains the leading cause of morbidity and

mortality in industrialized societies. The lack of metabolite biomarkers has impeded the

clinical diagnosis of atherosclerosis so far. In this study, stable atherosclerosis patients

(n 5 16) and age- and sex-matched non-atherosclerosis healthy subjects (n 5 28) were

recruited from the local community (Harbin, P. R. China). The plasma was collected from

each study subject and was subjected to metabolomics analysis by GC/MS. Pattern

recognition analyses (principal components analysis, orthogonal partial least-squares

discriminate analysis, and hierarchical clustering analysis) commonly demonstrated

plasma metabolome, which was significantly different from atherosclerotic and non-

atherosclerotic subjects. The development of atherosclerosis-induced metabolic pertur-

bations of fatty acids, such as palmitate, stearate, and 1-monolinoleoylglycerol, was

confirmed consistent with previous publication, showing that palmitate significantly

contributes to atherosclerosis development via targeting apoptosis and inflammation

pathways. Altogether, this study demonstrated that the development of atherosclerosis

directly perturbed fatty acid metabolism, especially that of palmitate, which was confirmed

as a phenotypic biomarker for clinical diagnosis of atherosclerosis.

Keywords: Atherosclerosis / Biomarkers / GC/MS / Plasma metabolomics /PalmitateDOI 10.1002/jssc.201000395

1 Introduction

Atherosclerotic cardiovascular disease remains the leading

cause of morbidity and mortality in industrialized societies

[1]. Atherosclerosis (also known as arteriosclerotic vascular

disease) is a condition characterized by thickening of the

arterial wall resulting from build-up of fatty materials such

as cholesterol. It is a syndrome affecting arterial blood

vessels, a chronic inflammatory response in the walls of

arteries, in large part due to the accumulation of macro-

phage white blood cells, promoted by low-density lipopro-

teins. The increased vascular wall thickness in

atherosclerosis is also due to the accumulation of the cells

and extracellular matrix between the endothelium and the

smooth muscle cell wall [2]. Epidemiological evidence

reveals a cause and relationship between chronic inflamma-

tion and atherosclerosis [3]. Microvascular barrier dysfunc-

tion is implicated in the initiation and progress of

inflammation in atherosclerosis [4], leading to chronic

infection and inflammation associated with the develop-

ment of atherosclerosis [5].

An important connection between inflammation and

disease may be attributed to the secretion of highly reactive

oxygen and nitrogen species by macrophages and neutro-

phils, including hypohalous acids, nitrous anhydride, and

nitrosoperoxycarbonate [4].

The lipid fraction of blood plasma has a profound effect

on the development of cardiovascular disease, an evidence

that the aqueous metabolites in vivo also have a role in

following the effects of myocardial infarction and monitor-

ing the development of atherosclerosis (reviewed by

Waterman et al. [6]).

Metabolites reveal the dynamic processes underlying

cellular homeostasis. Recent advances in analytical chem-

istry and molecular biology have set the stage for metabolite

profiling to help us understand complex molecular proces-

ses and physiology [7]. Metabolomics is the comparative

analysis of metabolite flux and how it relates to biological

phenotypes [7]. As an intermediate phenotype, metabolite

signatures capture a unique aspect of cellular dynamics that

is not typically interrogated, providing a distinct perspective

on cellular homeostasis [7].

Xi Chen1�

Lian Liu2�

Gustavo Palacios2

Jie Gao3

Ning Zhang4

Guang Li5

Juan Lu1

Ting Song1

Yingzhi Zhang1

Haitao Lv2

1Institute of Medicinal PlantDevelopment, Chinese Academyof Medical Sciences, PekingUnion Medical College, Beijing,P. R. China

2Department of Medicine, AlbertEinstein College of Medicine,New York, USA

3The First Hospital, HeilongjiangUniversity of Chinese Medicine,Harbin, P. R. China

4College of Jiamusi, HeilongjiangUniversity of Chinese Medicine,Jiamusi, P. R. China

5Department of Pharmacognosy,Heilongjiang University ofChinese Medicine, Harbin,P. R. China

Received June 4, 2010Revised July 11, 2010Accepted July 11, 2010

Abbreviations: FFA, free fatty acid; HCA, hierarchicalclustering analysis; OPLS-DA, orthogonal partial least-squares discriminate analysis; PCA, principal componentanalysis; SFA, saturated fatty acids �These authors contributed equally to this article.

Correspondence: Dr. Haitao Lv, Albert Einstein College ofMedicine, 1301 Morris Park Avenue, Price Center Room 368,and New York 10461, USAE-mail: [email protected]

& 2010 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim www.jss-journal.com

J. Sep. Sci. 2010, 33, 2776–27832776

Atherosclerosis is a complicated and multifactorial

disease, induced not only by genotype, but also, even more

importantly, by environmental factors. The study on the

metabolic perturbation of endogenous compounds may

offer a deeper insight into the development of athero-

sclerosis [8]. As for this point, the metabolomics provides

the promise to improve our understanding of the complex

process of atherosclerosis development [9]. A body of data

demonstrated that metabolomics technology was applied to

study metabolites profiling modification and phenotype

development of atherosclerosis-based human and animal

models. The analytical techniques adopted from mass

spectrometry (LC/MS or GC/MS) to NMR, and main plas-

ma or urine were applied in these studies. Further, these

studies revealed the metabolic perturbations of phenylala-

nine, tryptophan, bile acids, amino acids, fatty acids, etc.involved in the development and progress of atherosclerosis

[10–12]. However, these studies mostly focused on clarifying

variations in the metabolites pool and the fingerprint of

urine and plasma, to phenotype metabolic syndrome during

the development of atherosclerosis. Low specific attention

was paid to the discovery of metabolite biomarkers used for

the clinical diagnosis of atherosclerosis.

The application of metabolomics in the discovery of

diagnostic disease-specific biomarkers has made some

progress [13–15]. The main purpose of this study was to

discover specific biomarkers for use in the clinical diagnosis

of atherosclerosis. We adopted a GC/MS-based approach of

analyzing the plasma metabolites from healthy subjects and

patients with stable atherosclerosis.

2 Materials and methods

2.1 Chemicals and reagents

Acetonitrile (HPLC grade) was purchased from Sigma

(USA); methoxyamine hydrochloride and triethylamine

(in heptanes) were purchased from Sigma. High-purity

water (LC-MS grade) was purchased from Fisher

Scientific (USA).

2.2 Study subjects and sampling

Stable atherosclerosis patients (n 5 16) and age- and sex-

matched non-atherosclerosis healthy subjects (n 5 28) were

recruited from the local community (Harbin, P. R. China).

Subjects provided written informed consent approved by the

Clinic Institutional Review of the Heilongjiang University of

Chinese Medicine Board and in accordance with the

Declaration of Helsinki. All study subjects were screened

with detailed medical history, physical examination, and

hematological and biochemical profile. A standard dinner

was given to each study subjects throughout seven days

before sampling blood. Five milliliters of arterialized venous

blood was obtained from a catheterized hand vein before

breakfast on the eighth day. Plasma was isolated and kept in

liquid nitrogen until GC/MS analysis.

2.3 Sample preparation

In this study, plasma samples were prepared by using a

simple protocol recorded in [16]. Briefly, the thawed plasma

samples were vortex-mixed with acetonitrile. The super-

natant was separated and dried out under a stream of N2

gas. Finally, the precipitation was derivatized by methox-

imation and trimethylsilylation for GC/MS analysis.

2.4 Plasma metabolic profiling

The GC/EI MS system consisted of an Agilent 6890/5973

GC/MS system, G1701DA workstation, NIST02 MS library,

and an SE54 capillary column (30 m� 0.25 mm� 0.25 mm)

(Agilent, USA). The injection port temperature was 2601C.

Injection into the GC-MS system was performed in the

pulsed splitless mode with a pulse pressure of 207 kPa and a

pulse time of 1.5 min. The GC oven was programmed with

an initial temperature of 1201C, which was held for 2 min

and then increased to 2701C at 51C/min and held at the final

temperature for 2 min. High-purity helium (99.999%) was

used for the carrier gas at a flow rate of 1 mL/min. The GC-

MS interface temperature was set at 2801C. The EI source

temperature was set at 2301C. The energy was 70 eV. The

split ratio was 1:20. The selected mass range was 30–650 Da

and the selected scan speed was 3.2 scans per second. The

pretreated plasma samples were profiled based on the above

GC/MS method. Identification of GC/MS detected peaks

was performed by comparing their mass spectra and

retention index in the NIST02 MS library. For overlapping

peaks, an AMDIS of deconvolution software was adopted to

identify all the peaks in the chromatogram before NIST02

MS library searching [17].

2.5 Data processing

By metabolic profiling analysis of Section 2.4, the raw data

of TIC metabolic profiling of the plasma samples from the

healthy subjects and the patients with stable atherosclerosis

will be achieved. The Agilent Mass Hunter Workstation

software (Demo Version) (Agilent) was used to generate a

3D data matrix constructed with the sample ID as

observations, the metabolites ID as the response variables,

and peak area of metabolites as abundance. Then, the 3D

data matrix was imported into Ezinfo software incorporated

in MakerLynxV4.1 SCN714 for pattern recognition analysis

(Waters Company, USA). By pattern recognition analysis of

raw data, the metabolic profiling modification will be

clarified. In this study, we adopted a pattern recognition

analysis that included principal components analysis (PCA),

orthogonal partial least-squares discriminate analysis

J. Sep. Sci. 2010, 33, 2776–2783 Gas Chromotagraphy 2777


(OPLS-DA) and hierarchical clustering analysis (HCA) [13,

18–21].

3 Results and discussion

Efforts to reduce cardiovascular events in ‘‘high-risk’’

individuals or recurrent events in patients with established

atherosclerotic cardiovascular disease emphasize broad-

based implementation of guideline-directed therapeutic

lifestyle changes and pharmacotherapy. Despite successful

implementation of these evidence-based strategies, many

individuals are not properly identified before their first event

or they continue to experience cardiovascular events despite

‘‘optimal’’ levels of biomarkers [9]. The key cause is the lack

of clinical metabolite biomarkers that could be used for the

clinical diagnosis of atherosclerosis. In this study, we mainly

focused on discovering a plasma biomarker for athero-

sclerosis, which can be validated to distinguish patients and

healthy subjects using a GC/MS-based metabolomics

analysis.

The typical MS TIC plots of the plasma samples are

shown in Fig. 1, which only indicated some macro-differ-

ence of the fingerprint regions between the healthy subjects

and the patients with stable atherosclerosis. The direct

observation of the metabolic profiling of TIC MS does not

characterize and clarify the subtle and essential metabolic

modifications of small molecular metabolites in plasma

A

B

4.00 6.00 8.00 10.00 12.00 14.00 16.00 18.00 20.00 22.00 24.00 26.00 28.00

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50000

100000

150000

200000

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4.00 6.00 8.00 10.00 12.00 14.00 16.00 18.00 20.00 22.00 24.00 26.00 28.00

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Figure 1. Typical MS TIC plots of plasma analysis from (A) healthy subject and (B) patient with atherosclerosis.

J. Sep. Sci. 2010, 33, 2776–27832778 X. Chen et al.


between the healthy subjects and the patients with stable

atherosclerosis.

These complex and subtle modifications could signifi-

cantly contribute to profound biochemical interpretation of

the pathophysiological events in biological body [13, 15].

This subtle and profound metabolome modification can

only be achieved by the pattern recognition analysis of raw

data of metabolic profiles. This is achieved by importing raw

data or pre-processed data into pattern recognition related

software.

In this study, a total of 240 metabolites were detected

and profiled by analysis of plasma samples of healthy

subjects and patients with stable atherosclerosis using the

Agilent Mass Hunter Workstation Software (Fig. 2), the

mass range of most of the metabolites is below 200 Da, and

almost concentrated, distributed within 30 min of running

time. Meanwhile, Agilent Mass Hunter Workstation Soft-

ware constructed the 3D data matrix with the sample IDs as

observations, the metabolites ID as the response variables,

and the peak area of metabolites as abundance. Then, the

Ezinfo software-based PCA was performed based on this 3D

data matrix. Obvious groups classification was characterized

by scores plot resulting from PCA, suggesting that the

plasma metabolites profiles of the patients with athero-

sclerosis were significantly different from those of the

healthy subjects. These are in accordance with the previous

studies [10–12], which confirm that the development of

atherosclerosis results in the obvious metabolic modifica-

tion of metabolites pool in human plasma due to modifi-

cations and perturbations of related metabolic pathways.

The scores plot resulting from OPLS-DA and the hier-

archical clustering plot furthermore confirmed this result

(Fig. 3B and C). The similarity value is only 0.10 in HCA

analysis; two separated clustering groups were validated,

created, and except few overlaid samples, suggesting that

highly significant difference of plasma metabolomes

between the healthy subjects and the patients with stable

atherosclerosis can be attributed to effect of atherosclerosis

on metabolites pool of human body [10–12].

By observing S-plot resulting from OPLS-DA, Fig. 4A,

three metabolites were found that contributed the most,

toward groups’ classification of plasma metabolome of

healthy subjects and patients with stable atherosclerosis,

which was characterized by the score plots resulting

from PCA and OPLS-DA. The chemical structure of

these three metabolites was identified by combining targe-

ted peak deconvolution of AMDIS software and NIST02

MS library searching as palmitate, stearate, and 1-mono-

linoleoylglycerol (Fig. 4B–D), which are all fatty acids. The

palmitate and stearate are long-chain saturated fatty acids

(SFAs). It has been shown earlier that the presence of the

long-chain SFAs in parenteral formulations may have

harmful effects on the vascular system [22].

Inflammation and insulin resistance associated with

visceral obesity are important risk factors for the develop-

ment of atherosclerosis, and the metabolic syndrome [23].

The dyslipidemia of insulin resistance, with high levels of

albumin-bound fatty acids, is a strong cardiovascular disease

risk [24]. However, overproduction of plasma fatty acid is

one of the main causes inducing inflammation and insulin

resistance [25–27]. The urokinase plasminogen activator

system with its receptor uPAR contributes to the migratory

potential of macrophages, a key event in atherosclerosis; the

free fatty acid (FFA) is proved to differentially stimulate the

uPAR expression in human monocytes/macrophages [28].

Especially, the long-chain SFA induce proinflammatory

cytokines in human macrophages via pathways involving denovo ceramide synthesis, contributing to the activation of

macrophages in atherosclerotic plaques [29].

Our results from this study revealed that the metabolic

disorder of fatty acids is the most obvious phenotype in the

development of atherosclerosis indicating that fatty acids

may be used as biomarkers for clinical diagnosis of ather-

osclerosis.

Palmitate is the most abundant fatty acid in vivo,

accounting for approximately 26% of the total plasma fatty

acids [30]. Palmitate can induce apoptosis via a p38 MAPK-

dependent pathway and a reduction in IB in hCAECs

independent of p38 MAPK activity [31]. Though apoptosis is

directly involved in the development and progression of

atherosclerotic lesions, the protein kinase C signalling is of

importance in atherosclerosis as well as apoptosis. Protein

kinase C iota is activated by saturated fatty acids and

mediates lipid-induced apoptosis of HCAEC [31]. This

suggests that palmitate induces apoptosis and inflammation

in hCAECs via distinctly different mechanisms and p38

MAPK may have exerted a key role in the FFA-induced

coronary endothelial injury and atherosclerosis [31]. Because

inflammatory cytokines are the potent activators of p38

MAPK, which plays a very important role in modulating

inflammation, most likely p38 MAPK is also involved in the

FFA-mediated inflammation in the vascular endothelium

[31]. In this study, our results showed that palmitate was

markedly increased by 8-fold in the plasma of patients with

600

5

RT (minutes)

Mas

s

10 15 20 25 30

500

400

300

200

100

Figure 2. Coefficient plot of retention time, mass and abundanceof peak area, and distribution. This plot can phenotype thenumber of metabolites after deconvolution of peaks.



stable atherosclerosis, when compared to the healthy

subjects (Fig. 5). This suggests that the development of

atherosclerosis can induce overproduction of palmitate. The

palmitate accelerates atherosclerosis development by indu-

cing apoptosis and inflammation via special p38 MAPK

pathways.

Stearate is also a type of saturated fatty acid (C18; 0), the

structure of which is very similar to palmitate (C16:0).

Stearate’s proinflammatory response was confirmed by the

phosphorylation of IkappaB-alpha and NF-kappaB in a dose-

dependent manner [25], and stearate has also been shown to

inhibit invasion and proliferation and induce apoptosis in

various human cell types [32, 33]. In this study, stearate was

significantly increased by 3-fold in plasma of patients with

stable atherosclerosis compared with healthy subjects,

suggesting that stearate also contributes to the development

of atherosclerosis via inducing apoptosis and inflammation

pathways. However, 1-monolinoleoylglycerol is a type of

unsaturated fatty acid (C18; 2) with two double bonds. The

expression levels were also significantly increased by 3-fold

in the plasma of patients with stable atherosclerosis

compared with the healthy subjects.

To date there is no data to support 1-mono-

linoleoylglycerol’s mechanisms of action on atherosclerosis

development. Therefore, it is questionable to select

1-monolinoleoylglycerol as biomarker to diagnose athero-

sclerosis in clinical practice. It requires further confirmation

and support from biochemical data to make such a

conclusion.

In summary, we confirm that fatty acids are the

most direct-targeted metabolites pool during the develop-

ment of atherosclerosis. Though the published data

[25, 32, 33] may support the potential effect relationship

between stearate and atherosclerosis development, the

proofs are obviously inferior to palmitate. The palmitate is

the ideal candidate of plasma biomarker for the clinical

healthy subjects

patients with atherosclerosis

A B

0.00.20.40.60.81.0

PSA

HSHSHSHSHSHSHSHSHSHSHSHSHSHSHSHSHSHSHSHSHSHSHSPSAPSAHSHSPSAPSAPSAHSPSAPSAPSAPSAPSAPSAPSAPSAPSAPSA

C

Figure 3. Scores plots from (A) PCA and (B)OPLS-DA and (C) hierarchical plot from HCA,the distance of similarity is only 0.1, suggest-ing dramatic inter-group differences of plas-ma metabolites. HS, Healthy subjects; PSA,patients with stable atherosclerosis.



diagnosis of atherosclerosis [22–31], and supported by the

data from our study. It also supports the concept that a

single biomarker is efficient to diagnose specific diseases

rather than a pool of metabolites, as previously

shown sarcosine was selected to diagnose prostate cancer

progression [13].

C

B

D

m/z

% a

bund

ance

% a

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ance

% a

bund

ance

m/z

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p(co

rr)[

1]P

(C

orre

latio

n)

CoeffCS[2](OBSID(1)) (X Effects)

1 - monolinoleoylglycerol

Palmitate

Stearate

A

Figure 4. Characterization of key metabolitescontributing to plasma metabolome separa-tion between healthy subjects and patientswith stable atherosclerosis. (A) S-plot result-ing from OPLS-DA; (B) derived structure andits product mass spectra of palmitate; (C)derived structure and its product massspectra of stearate; (D) derived structureand its product mass spectra of stearate1-monolinoleoylglycerol.



4 Concluding remarks

In this study, the biomarkers discovery-based metabolomics

technology was applied to characterize plasma metabolic

profiling modification during the atherosclerosis develop-

ment by comparing with healthy subjects by GC/MS.

Subsequently, multiple tools of pattern recognition analyses

(PCA, OPLS-DA, and HCA) were introduced to characterize

plasma metabolome change between the healthy subjects

and the patients with atherosclerosis, which revealed that

key metabolites significantly contributed to the above

plasma metabolomes classification. Finally, three fatty acids:

palmitate, stearate, and 1-monolinoleoylglycerol were iden-

tified as probable potential biomarkers used for the clinical

diagnosis of atherosclerosis. Moreover, by carefully review-

ing the previously published data, and the results from our

study, palmitate was evidenced as a biomarker for the

clinical diagnosis of atherosclerosis.

This study was funded by the National Innovation PlatformProject of Key New Drug of China (Grant No. 2009ZX09502).

The authors have declared no conflict of interest.

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0

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Nor

mal

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leve

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Plasma metabolomics reveals biomarkers of the atherosclerosis

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