Orthogonal partial least squares discriminant analysis in metabolomic for disease characterization

ORTHOGONAL PARTIAL LEAST SQUARES
DISCRIMINANT ANALYSIS IN METABOLOMICS
FOR KIDNEY AND CATARACT DISEASE
CHARACTERIZATION

CHEW AI PING
(B.Sc. (Hons.), NUS)

A THESIS SUBMITTED
FOR THE DEGREE OF MASTER OF SCIENCE

DEPARTMENT OF CHEMISTRY
NATIONAL UNIVERSITY OF SINGAPORE
2012



Declaration

I hereby declare that this thesis is my original work and
it has been written by me in its entirety.
I have duly acknowledged all the sources of information
which have been used in the thesis.

This thesis has also not been submitted for any degree
in any university previously.

__________________________
Chew Ai Ping
25 June 2012

i


Acknowledgements
It is my honour to thank the following who have made this thesis possible.

Firstly, I thank Professor Sam Li, my main supervisor, for the support and
patient guidance these few years, from the start of the project to the end of
the write-up for this thesis.

I also thank Dr. Ong Eng Shi for being the co-supervisor for this project, and
for starting me on this project with the kind and thoughtful help in obtaining
and running the samples.

I also thank Professor Ong Choon Nam, NUS, for kindly agreeing to release
the samples, and for his prompt replies to my questions and offering
assistance in any way possible.

I thank my lab mates for their support in my studies, research, and also for
giving valuable advice where needed. They are Drs. Lau Hiu Fung, Law Wai
Siang, Tok Junie, Zuo Xinbing, Wu Huanan, Liu Feng, Grace Birungi, Jiang
Zhangjian, Ms Elaine Tay, Ms Fang Guihua, Ms Gan Peipei, Ms Lü Min, Ms
Huang Yan, Mr Jon Ashley, Mr Chen Baisheng, and Mr Lin Junyu. I also
thank Mr Ting Aik Leong, whose help in running the samples has also made
this thesis possible.

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I thank the National University of Singapore for giving me the financial support

and the chance to take up this degree under the Research Scholarship
programme.

I sincerely thank the pastors, full-time staff, elders, leaders, and brothers and
sisters of the Tabernacle Church and Missions, Singapore, for loving,
teaching, guiding, and spurring me towards completing my thesis.

I sincerely thank my family for their unfailing support and love given these few
years while I undertook studies for my Master’s degree.

Finally, all thanks and glory be to God, who has made all things possible
through Him and in Him.

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Table of Contents
Declaration ...................................................................................................... i
Acknowledgements....................................................................................... ii
Table of Contents ......................................................................................... iv
Summary ...................................................................................................... vii
List of Tables .............................................................................................. viii
List of Figures............................................................................................... ix
List of Abbreviations ................................................................................... xii
List of Symbols........................................................................................... xiii
Chapter 1 Introduction ................................................................................. 1
1.1 Metabolomics ....................................................................................... 1
1.1.1 Overview...................................................................................... 1
1.1.2 Metabolomics in Disease Diagnosis ............................................ 2
1.1.3 Non-targeted and Targeted Approaches in Metabolomics........... 4

1.1.4 Using Urine for Metabolomic Analysis ......................................... 6
1.2 Analytical and Separation Techniques in Metabolomics ...................... 8
1.2.1 Nuclear Magnetic Resonance...................................................... 8
1.2.2 Mass Spectrometric Techniques in Metabolomics ..................... 10
1.2.3 Separation Techniques in Metabolomics ................................... 12
1.2.3.1 Overview............................................................................ 12
1.2.3.2 Gas Chromatography ........................................................ 12
1.2.3.3 High Performance Liquid Chromatography........................ 14
1.3 Chemometrics in Metabolomics ......................................................... 16
1.3.1 Overview.................................................................................... 16
1.3.2 Principal Component Analysis ................................................... 18

iv


1.3.3 Partial Least Squares/ Projection to Latent Structures............... 19
1.3.4 Orthogonal Partial Least Squares Discriminant Analysis ........... 20
1.3.5 Pre-treatment of Data for Chemometric Analysis....................... 21
1.4 Chronic Kidney Disease ..................................................................... 24
1.4.1 Overview of Chronic Kidney Disease......................................... 24
1.4.2 Diagnosis of Chronic Kidney Disease ........................................ 27
1.4.3 Metabolomics and Chemometrics for Chronic Kidney Disease . 30
1.5 Cataract Disease................................................................................ 31
1.5.1 Overview of Cataract Disease ................................................... 31
1.5.2 Diagnosis of Cataract Disease................................................... 32
1.5.3 Metabolomics and Chemometrics for Cataract Disease ............ 33
1.6 Approach and Scope of Study............................................................ 34
Chapter 2 Materials and Methods ............................................................. 36
2.1 Materials............................................................................................. 36
2.2 Urine Sample Collection..................................................................... 36

2.3 Equipment and Procedure for HPLC-MS/MS ..................................... 36
2.4 Extraction and Normalization of Chromatogram Peak Areas ............. 37
2.5 Chemometric Analysis........................................................................ 38
2.6 Statistical Analysis.............................................................................. 39
Chapter 3 Results and Discussion for Chronic Kidney Disease ............ 40
3.1 Results for Chronic Kidney Disease ................................................... 40
3.1.1 Results for Control vs. Chronic Kidney Disease ESI+ Dataset .. 40
3.1.2 Results for Control vs. Chronic Kidney Disease ESI- Dataset ... 51
3.1.3 Results for Combined ESI+ and ESI- Dataset ........................... 60
3.2 Discussion for Chronic Kidney Disease.............................................. 66

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3.3 Summary............................................................................................ 74
Chapter 4 Results and Discussion for Cataract Disease ........................ 76
4.1 Results for Cataract Disease.............................................................. 76
4.1.1 Results for Control vs. Cataract Disease ESI+ Dataset ............. 76
4.1.2 Results for Control vs. Cataract Disease ESI- Dataset .............. 83
4.1.3 Results for Combined ESI+ and ESI- Dataset ........................... 90
4.2 Discussion for Cataract Disease ........................................................ 95
4.3 Summary............................................................................................ 98
Chapter 5 Conclusion and Future Work ................................................... 99
References ................................................................................................. 103

vi


Summary
This thesis shows how metabolomics and multivariate statistical methods

such as Orthogonal Partial Least Squares Discriminant Analysis (OPLS-DA)
can be used to study and enhance understanding of two diseases.

The study utilizes univariate and multivariate statistical techniques to
determine the differences in a targeted set of metabolites for healthy controls
and two groups of diseased persons. Urine samples were collected from
healthy controls and patients suffering from chronic kidney disease (CKD).
High performance liquid chromatography-tandem mass spectrometry analysis
was

performed

on

each

sample,

and

chromatographic

and

mass

spectrometric data were obtained. After pre-treatment of the data through
normalization and scaling, principal component analysis and OPLS-DA were
used to visualize the differences in these two classes. Further statistical
analysis was employed to determine fluctuations in target metabolites to

understand disease pathology, and also to identify potential biomarker
candidates for CKD. This same method was also employed for a separate
group of patients suffering from cataract disease for further validation.

The thesis is then concluded with a summary of the main findings, a
discussion on the challenges faced, and suggestions for future work in
metabolomic studies of CKD and cataract disease.

vii


List of Tables
Table 1 Description of stages of chronic kidney disease (adapted from [50,
117, 128], originally from [131]) .................................................................... 28
Table 2 Metabolites identified in human urine samples for Controls and CKD
patients in ESI+ mode ................................................................................... 48
Table 3 Metabolites identified in human urine samples for Controls and CKD
patients in ESI- mode .................................................................................... 58
Table 4 Metabolites identified in human urine samples for Controls and
cataract disease patients in ESI+ mode ........................................................ 81
Table 5 Metabolites identified in human urine samples for Controls and
cataract disease patients in ESI- mode ......................................................... 88

viii


List of Figures
Figure 1 Representative TICs (ESI+) of (A) Control, and (B) Patient with CKD.
...................................................................................................................... 40
Figure 2 (A) PCA scores plot for Control ESI+ data; (B) DModX scores plot

for Control ESI+ data; (C) PCA scores plot for CKD ESI+ data. .................... 42
Figure 3 PCA scores plot for Control and CKD ESI+ dataset ....................... 43
Figure 4 OPLS-DA scores plot for Control against CKD ESI+ dataset ......... 44
Figure 5 Cross-validation scores plot for Control and CKD ESI+ dataset ..... 45
Figure 6 Random permutation test scores plot for Control and CKD ESI+
dataset........................................................................................................... 46
Figure 7 (A) VIP and (B) Loadings plot for Control-CKD ESI+ dataset. Interval
bars denote the jack-knife confidence intervals for each metabolite.............. 50
Figure 8 Representative TICs (ESI-) of (A) Control, and (B) Patient with
Chronic Kidney Disease. ............................................................................... 51
Figure 9 (A) PCA scores plot for Control ESI- data; (B) DModX scores plot for
Control ESI- data; (C) PCA scores plot for CKD ESI- data............................ 53
Figure 10 PCA scores plot for Control and CKD ESI- dataset ...................... 54
Figure 11 OPLS-DA scores plot for Control against CKD ESI- dataset ........ 55
Figure 12 Cross-validation scores plot for Control and CKD ESI- dataset.... 55
Figure 13 Random permutation test scores plot for Control and CKD ESIdataset........................................................................................................... 56
Figure 14 (A) VIP and (B) Loadings plot for Control-CKD ESI- dataset.
Interval bars denote the jack-knife confidence intervals for each metabolite. 59
Figure 15 (A) PCA scores plot for Control combined ESI+/ESI- data; (B)
DModX scores plot for Control combined ESI+/ESI- data; (C) PCA scores plot
for CKD combined ESI+/ESI- data ................................................................ 61
Figure 16 PCA scores plot for Control and CKD combined dataset.............. 61
Figure 17 OPLS-DA scores plot for Control against CKD combined dataset 62
Figure 18 Cross-validation scores plot for Control and CKD combined dataset
...................................................................................................................... 63
Figure 19 Random permutation test scores plot for Control and Cataract
Disease combined dataset ............................................................................ 64

ix

Figure 20 (A) VIP and (B) Loadings plot for Control and CKD combined
dataset. Interval bars denote the jack-knife confidence intervals for each
metabolite. ..................................................................................................... 65
Figure 21 Representative TICs (ESI+) of (A) Healthy Control, and (B) Patient
with Cataract Disease.................................................................................... 76
Figure 22 (A) PCA scores plot and (B) DModX scores plot for Cataract
Disease ESI+ data......................................................................................... 77
Figure 23 PCA scores plot for Control and Cataract Disease ESI+ dataset . 78
Figure 24 OPLS-DA scores plot for Control against Cataract Disease ESI+
dataset........................................................................................................... 79
Figure 25 Cross-validation scores plot for Control and Cataract Disease ESI+
dataset........................................................................................................... 79
Figure 26 Random permutation test scores plot for Control and Cataract
Disease ESI+ dataset .................................................................................... 80
Figure 27 (A) VIP and (B) Loadings plot for Control-Cataract Disease ESI+
dataset. Interval bars denote the jack-knife confidence intervals for each
metabolite. ..................................................................................................... 82
Figure 28 Representative TICs (ESI-) of (A) Healthy control, and (B) Patient
with Cataract Disease.................................................................................... 83
Figure 29 (A) PCA scores plot for Cataract Disease ESI- data; (B) DModX
scores plot for Cataract Disease ESI- dataset ............................................... 84
Figure 30 PCA scores plot for Control and Cataract Disease ESI- dataset .. 85
Figure 31 OPLS-DA scores plot for Control against Cataract Disease ESIdataset........................................................................................................... 86
Figure 32 Cross-validation scores plot for Control and Cataract Disease ESIdataset........................................................................................................... 86
Figure 33 Random permutation test scores plot for Control and Cataract
Disease ESI- dataset..................................................................................... 87
Figure 34 (A) VIP and (B) Loadings plot for Control-Cataract Disease ESIdataset. Interval bars denote the jack-knife confidence intervals for each
metabolite. ..................................................................................................... 89
Figure 35 (A) PCA scores plot and (B) DModX scores plot for Cataract

Disease combined ESI+/ESI- data ................................................................ 90
Figure 36 PCA scores plot for Control and Cataract Disease combined
ESI+/ESI- dataset.......................................................................................... 91

x


Figure 37 OPLS-DA scores plot for Control and Cataract Disease combined
dataset........................................................................................................... 92
Figure 38 Cross-validation scores plot for Control and Cataract Disease
combined dataset .......................................................................................... 92
Figure 39 Cross-validation scores plot for Control and Cataract Disease
combined dataset .......................................................................................... 93
Figure 40 (A) VIP and (B) Loadings plot for Control and Cataract disease
combined datasets. Interval bars denote the jack-knife confidence intervals for
each metabolite. ............................................................................................ 94

xi


List of Abbreviations
ANN

Artificial neural network

CKD

Chronic kidney disease

CV

Cross validation

DA

Discriminant analysis

EIC

Extracted ion chromatogram

ESI+/-

Electrospray ionization (positive/ negative mode)

GC

Gas chromatography

GFR

Glomerular filtration rate

HPLC

High performance liquid chromatography

LC

Liquid chromatography

MS

Mass spectrometry

MS/MS

Tandem mass spectrometry

NMR

Nuclear magnetic resonance

OPLS

Orthogonal projections to latent structures/ orthogonal partial
least squares

PCA

Principal component analysis

PLS

Partial least squares/ Projection to latent structures

RT

Retention time

SIMCA

Soft independent modelling of class analogy

SPE

Solid-phase extraction

TIC

Total ion chromatogram

UPLC

Ultra Performance Liquid Chromatography

VIP

Variable importance plot/ Variable influence on projection

xii


List of Symbols
D-crit

Critical distance

DModX

Distance to the model in X-space

m/z

Mass-to-charge ratio

Q2X(cum)

Cross-validation parameter representing the predictability of
the model

Q2Y(cum)

Cross-validation parameter showing the cumulative predicted
variation in the Y matrix, representing the predictive ability of
the model

R2X(cum)

Cumulative modelled variation in the X matrix, representing
the total explained variance in the model

R2Y(cum)

Coefficient of determination of OPLS-DA model, showing the
cumulative modelled variation in the Y matrix, and
representing the goodness of fit of the model in explaining the
variation by the components in the model

t[a]

X-score of component a in the model

tcv

Cross-validated X-score of component a in the model

to

Orthogonal X-scores of (uncorrelated) component in the
OPLS-DA model, also representing within class variation

tp

Predictive component in the OPLS-DA model, also
representing between-class variation

w[a]

Loading vector of component a

X

Matrix of predictor variables

Y

Matrix of response variables

xiii

Chapter 1 Introduction
1.1 Metabolomics
1.1.1 Overview
Metabolomics is the area of study that is concerned with the metabolome,
which comprises small molecule components (of size less than 1 kDa [1])
associated with the biochemical processes of a given organism [2]. Examples
of such small molecules include simple sugars, fatty acid amides, and amino
acids. The presence of and quantity of these metabolites are a reflection of
what goes on within and outside the cell. The goal of metabolomics is not only
to determine the disease pathology, the role of metabolites in the biochemistry
of the organism, and potential biomarkers, but also ultimately to determine the
molecular structure of these biomarkers [3]. Overall, metabolomic studies
greatly aid our understanding of the biology of an organism at a systems level.

Nicholson et al. in their landmark paper have defined metabolomics as the
study of the “dynamic multiparametric metabolic response of living systems to
pathophysiological stimuli or genetic modification” [4], helping us to
understand how living systems actually work. Specifically, when organisms
are under a state of stress as a result of disease (“pathophysiological stimuli”)
or perturbations to the genetic content of the cells (“genetic modification”),
metabolomics as a discipline becomes useful [5]. Knowledge of the cellular
response under such conditions helps researchers identify potential
therapeutic targets. In this manner, therapy does not end with just
symptomatic treatment to just address the metabolic flux but indeed has the
end-goal of a total cure in mind.

1

Metabolomics, or metabonomics, has been gaining new ground in the field of
systems-level “omics” research [6], i.e. genomics, transcriptomics, and
proteomics. It is a relatively new area of study compared to its sister
disciplines [7] and complements these fields [8]. The advantage of
metabolomics over its counterparts is that the metabolome is much more
closely correlated to the actual cellular response than the genome or
proteome [3, 7, 8]. Also, the amount of data generated is less due to the lower
number of metabolites compared to the number of genes or proteins [2, 7]. In
addition, each metabolite may be involved in one or several pathways,
contributing to the complex expressed phenotype of the organism. It is this set
of downstream biochemical pathways, and not just a single pathway, that
metabolomics aims to map out [2, 8]. This allows us to obtain a more timely
and accurate understanding of cellular and systemic processes.

1.1.2 Metabolomics in Disease Diagnosis
Metabolomics is increasingly becoming a valuable tool to study disease
pathology, and to screen, diagnose and determine the effect of treatment on
diseases as well. A wide variety of diseases is being studied, with the
analytical methods used varying with the disease and the aim of the study.
For example, Jung et al. have successfully used proton NMR (1H-NMR) and
targeted metabolic profiling with multivariate analysis to distinguish patients
with cerebral infarctions from healthy controls by analysis of urine and plasma
[9]. Also, Kim et al. have combined toxicology with metabolomics to determine
urinary biomarkers for human gastric cancer using a mouse model [10]. In a

2

recent study, Bao et al. have also devised a novel method of measuring the
systemic effects of various drug treatments on type 2 diabetes mellitus (T2DM)
instead of just obtaining the conventional glucose measurement for T2DM [11].
Further, in an attempt to obtain a comprehensive understanding of and to
diagnose renal cell carcinoma, Kind et al. have successfully utilized various
separation techniques coupled with mass spectrometry and subsequent
multivariate analysis to analyze and discriminate patient urine from healthy
controls in a small pilot study [3]. Given the increasing number of parameters
in metabolomic analysis, there is an even greater need for reliable and
informative multivariate techniques to analyse this data.

The combination of multivariate statistical tools with metabolomics has been
shown to be powerful for disease screening involving non-targeted
determinations. One such study of interest is that by Michell et al. In their
metabolomic analysis of Parkinson’s disease patient serum and urine
samples, they were able to separate female Parkinson’s patients from their
age-matched controls using partial least squares discriminant analysis (PLSDA) based on the urine data, despite not finding strong individual biomarkers
responsible for this separation. They surmise that there is a unique metabolic
pattern of Parkinson’s disease contributed by certain metabolites [12]. Also, in
a separate study by Kemperman et al., they observe that while multivariate
statistical analysis was able to show discriminatory peptide peaks, univariate
analysis failed to show these as discriminatory due to “a very large biological
variation among the proteinuric patient group” [13]. These studies show the

3


necessity of multivariate techniques in view of the nature of samples and data
obtained.

1.1.3 Non-targeted and Targeted Approaches in Metabolomics
There are two general approaches towards metabolomic studies – nontargeted and targeted. Non-targeted or global profiling approaches in
metabolomics aim to capture as many features of an organism’s metabolic
profile as possible. This approach allows researchers to obtain a holistic
picture of the types and concentrations (relative or absolute) of the
metabolites, so that comparisons can be made between study groups in order
to determine patterns of changes which are useful for diagnosis [14].

Non-targeted approaches as that in metabolic fingerprinting may not identify
the specific metabolites involved in disease pathology, but consider the total
combination of analytes and their concentrations in totality [15]. This approach
allows for the “simultaneous analysis of multiple end products”, allowing for a
“more powerful and robust means by which to stratify disease severity,
progression and to assess drug efficacy than the analysis of any single
marker over a patient population” [16]. For example, Vallejo et al. have used
capillary electrophoresis coupled with ultraviolet detection and subsequent
metabolic fingerprinting to distinguish between normal rats and diabetic rats
on antioxidant treatment [17]. Issaq et al. have also successfully utilized
metabolomic profiling with high performance liquid chromatography-mass
spectrometry (HPLC-MS) to detect bladder cancer using urine samples in
their proof-of-concept study. Their study does not use the traditional

4


techniques which are less sensitive towards low-grade tumours (i.e. through
urine cytology) or more invasive in terms of methodology (i.e. cystoscopy) [5].
Novel biomarkers may also be identified in non-targeted approaches, e.g. by
structural studies through NMR or tandem mass spectrometry.

Given the knowledge of metabolites and their interactions in specific
biochemical pathways, one can also capitalise on targeted approaches to
study specific metabolites or groups of metabolites [18] using reference
spectra for analysis [19]. The duration for post-acquisition data processing
and identification of metabolites are shorter as well [19]. Metabolite and
pathway databases and search engines such as the Human Metabolome
Database [20], Kyoto Encyclopaedia of Genes and Genomes database [21,
22] and the METLIN Metabolite Database [23] are useful resources in this
area of pathway analysis. Researchers can also make use of targeted
analysis to determine how the concentrations of particular metabolites in a
system vary with concentration changes of other metabolites. For example,
Grison et al. have successfully used targeted profiling to determine a
metabolic signature for chronic caesium exposure [24]. Also, Wu et al. have
compared the metabolite profiles of salt-tolerant and salt-intolerant soybean
plants, and through multivariate analysis, have found that secondary
metabolites such as isoflavones and saponins distinguished these two
varieties [25]. One limitation of this targeted approach is that since only known
metabolites can be identified and quantified, it is not possible to discover
novel compounds as biomarkers through this approach [18]. Yet, the

5


numerous successes using this approach show that there is a need and use
for such targeted studies.

1.1.4 Using Urine for Metabolomic Analysis
While many types of body fluids (biofluids) have been used for metabolomic
studies, the choice of biofluid is highly dependent on the disease being
studied. The choices of biofluid include blood serum [12, 26-29], plasma [27,

30-32], cerebrospinal fluid [33], urine [3, 5, 7, 12, 29, 34-43], saliva [44, 45],
tears [46], and even vitreous humour [15]. Urine has an advantage of being
easily obtained in large enough volumes for multiple analyses [1, 18, 47]. It is
also one of the least invasive body fluids to collect from patients [10], allowing
for multiple collections at different times [18], and at the minimal level of
discomfort to study subjects [1]. Furthermore, urine is the biofluid through
which the majority of metabolic waste products are excreted from most organ
systems in the body and therefore can provide much information about the
body’s biochemical processes as a whole system [3], since it is not subject to
strict homeostatic regulation as is serum [48]. In addition, obtaining or
preparing urine samples is usually more straightforward than for other
biofluids such as blood [49], serum [18], plasma [1], or tears.

In addition, the concentrations of metabolites are often higher in urine [47],
which makes it easier for determination and detection. It has also been found
that in the study of renal diseases, measurements of kidney function are
generally more accurate when using urine measurements than plasma,
provided sufficient and accurate volumes of urine samples can be obtained

6


[50]. Metabolic changes that take place at the cellular level are easily reflected
in the urine, as, other than blood, it is the biofluid which most of the kidney is
exposed to [2, 3].

Urine as a biofluid for analysis, however, also has its disadvantages. There
may be large variations in terms of volume and therefore the degree of
dilution of metabolites [17], resulting in a very wide dynamic range [1] and
concentration differences of five-thousand fold or more [17, 51]. These

differences represent natural variation, and may be exacerbated under
conditions of disease [1]. In addition, as with other body fluids, the
concentrations of metabolites may not correspond to their importance in
disease pathology [17]. Also, xenobiotics may be present [1], and these may
or may not be directly related to the organism’s core metabolism; if so, they
may provide valuable information on the varied interactions of the organism
with its environment. The analysis method chosen must therefore be able to
deal with these problems associated with metabolic studies involving urine, in
addition to being reliable and reproducible [14].

Despite these limitations in terms of variation of urine volume, metabolite
concentration differences, and the presence of xenobiotics, urine has been
one of the choice candidates for metabolomics studies. This is because the
advantages of using urine for this current study far outweigh the
disadvantages, as will be discussed further in the foregoing sections. and is
therefore the choice of body fluid for this study of chronic kidney disease
(CKD).

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1.2 Analytical and Separation Techniques in Metabolomics
1.2.1 Nuclear Magnetic Resonance
The expanding area of research in metabolomics can also be attributed to the
improvement in technologies that allow for sensitive, specific, and
reproducible studies to be carried out. Traditionally, NMR was, and still is, a
major analysis technique employed for the purposes of mapping the
metabolome [18, 52]. The main advantage that NMR affords is its
reproducibility [53] over different runs and across different instruments [2] and
its ability to detect a wide range of metabolites [19], allowing for the building of

compound libraries [46]. Furthermore, sample preparation is usually minimal
[2, 19] and non-destructive [8], and analysis times are short as well [8]. NMR
is also able to analyse intact tissue through high-resolution magic-angle
spinning [54].

In addition, NMR allows for the molecular structures of biomarkers to be
discerned in two-dimensional structural studies [55]. It allows researchers to
determine metabolite profile patterns through metabolic fingerprinting to
classify groups of subjects without actual identification of the molecules
involved [53]. For example, Brindle et al. have used 1H-NMR to successfully
profile human serum for the accurate diagnosis of coronary heart disease [56],
while Keun et al. have successfully used

13

C-NMR to investigate urine in

metabolomic studies [57]. Further, Kang et al. have also successfully used
NMR with orthogonal partial least squares discriminant analysis (OPLS-DA) –
a multivariate statistical tool – to discriminate between Korean and Chinese
herbal medicines [58].As the number of variables being analysed increases, it

8


is apparent that multivariate tools become necessary in order to obtain a more
complete understanding of the systems being studied. These multivariate
tools must also allow for a logical and systematic way of handling the
information obtained. It is with this thought in mind that multivariate statistical
techniques feature in our study, and which will be further reviewed in this

chapter.

However, a main drawback of NMR is its inherent lack of analytical sensitivity
[2, 8, 46], which results in the inability to detect metabolites which have a
concentration lower than 5 µM [18]. Spin-spin coupling also causes
complications in data interpretation [19]. Several recent advances in NMR
technology include microprobes and miniature probe coils for smaller volumes
of sample [53] and cryoprobes for better sensitivity and shorter acquisition
times [53, 59]. However, high-throughput profiling does not seem possible if
the issues of complicated spectra and difficult compound identification are to
be resolved [19]. In addition, the high cost and space requirements of
equipment [48] may also mean that not all laboratories will be appropriately
equipped. Therefore, while NMR is a very useful and powerful technology for
metabolite profiling, it does not allow for high-throughput studies on
metabolites of very low concentrations. In view of these considerations, other
analytical methods such as mass spectrometry and chromatographic
separation techniques need to be considered.

9