ORIGINAL ARTICLE

Metabolomic analysis of urine and serum in Parkinson’s disease

Andrew W. Michell Æ David Mosedale ÆDavid J. Grainger Æ Roger A. Barker

Received: 1 February 2008 / Accepted: 23 April 2008 / Published online: 8 May 2008

� Springer Science+Business Media, LLC 2008

Abstract Objective To investigate the metabolic profile

of serum and urine samples from 23 female patients with

Parkinson’s disease (PD) and 23 age and sex-matched

controls. Methods We used gas chromatography coupled to

mass spectrometry to detect metabolites (approximately

1,600 in total), then supervised statistical analysis (using

projection to latent structures discriminant analysis) to

study the differences between control and PD samples.

Results Supervised statistical analysis yielded models that

possessed statistically significant predictive value for blind

samples on the basis of the metabolic profile of urine but

not of serum. However, whilst no individual biomarkers

were identified, suggesting that any metabolic disturbance

associated with PD is comparatively minor, a multivariate

metabolic signature associated with PD was identified in

urine. Interpretation There is a relatively subtle, yet

distinct, metabolic signature of PD present in the urine of

patients with early disease. The signature may itself act as a

useful biomarker for PD, although larger studies will be

required to validate our present findings.

Keywords Biomarker � Metabolomics � GC-MS �Parkinson’s disease

1 Introduction

Parkinson’s disease (PD) is a common neurodegenerative

disease, affecting about one million people in the United

States (Olanow and Tatton 1999) and classically presents

with a triad of bradykinesia, tremor and rigidity (Parkinson

1817). In some cases the diagnosis can be difficult to make

given that the disease has a gradual onset after a long

presymptomatic phase (Fearnley and Lees 1991), and that

there are a number of conditions that can present with a

similar clinical phenotype such as progressive supranuclear

palsy (Burn and Lees 2002) and multiple system atrophy

(Poewe and Wenning 2002). Pathological studies have

estimated that in specialist centres the positive predictive

value of the clinical diagnosis of idiopathic PD is 99%,

with a sensitivity of 91% (Hughes et al. 2002), but it is

considerably worse outside the specialist setting (Hughes

et al. 1992). There is therefore great need for a biomarker

to improve diagnosis both in the outpatient clinic and for

clinical trials. Furthermore, a great effort is being made to

identify biomarkers of disease progression that might not

only help demonstrate whether newly developed therapies

are truly neuroprotective, but also aid in the identification

of subgroups within this heterogeneous disease population

(Lewis et al. 2005).

The search for molecular biomarkers that reflect the

underlying pathology in PD has been wide, encompassing a

range of compounds assayed from a variety of different

David J. Grainger and Roger A. Barker are the senior authors of this

manuscript.

A. W. Michell � R. A. Barker (&)

Cambridge Centre for Brain Repair, Forvie Site, Robinson Way,

Cambridge CB2 2PY, UK

e-mail: rab46@cam.ac.uk

A. W. Michell � R. A. Barker

Department of Clinical Neuroscience, Addenbrooke’s Hospital,

University of Cambridge, Hills Road, Cambridge CB2 2QQ, UK

D. Mosedale � D. J. Grainger

Translational Research Unit, Papworth Hospital NHS

Foundation Trust, Papworth Everard, Cambridge CB3 8RE, UK

D. J. Grainger

Department of Medicine, Addenbrooke’s Hospital, University

of Cambridge, Hills Road, Cambridge CB2 2QQ, UK

123

Metabolomics (2008) 4:191–201

DOI 10.1007/s11306-008-0111-9

body fluids and tissues (Sato et al. 2005; Michell et al.

2005; Scherzer et al. 2007; Abdi et al. 2006) reviewed in

(Michell et al. 2004). However, it seems unlikely that any

single test will suffice given that PD pathology extends

beyond nigral dopaminergic cell loss, to affect much of the

brain (Braak et al. 2003), and even the gut (Edwards et al.

1992), including abnormalities in other neurotransmitters

such as noradrenaline and serotonin (Scatton et al. 1983),

acetyl choline (Bohnen et al. 2003), substance P (Mauborgne

et al. 1983) and enkephalins (Taquet et al. 1983). This

complex pathology occurs on a background of marked

heterogeneity of the genetic and environmental aetiologies

of the disease (Vila et al. 2004; Di Monte 2003), such that

although trends and influences can be identified within a

population they are rarely directly causal for an individual.

These myriad factors conspire to give a varied clinical

phenotype encompassing affective and cognitive abnor-

malities (Mayeux et al. 1990), autonomic defects (Visser

et al. 2004) and other symptoms in addition to the classical

triad described above. Biomarkers of PD must ultimately

reflect this clinical and pathological heterogeneity, but

given the complexity of the disease it is extremely difficult

to select the best candidates. It is therefore logical to adopt

non-hypothesis driven systems biology approaches to

identify candidate patterns of biomarkers that together

provide a fingerprint that is robustly linked to diagnosis and

disease progression. Some preliminary promise has been

shown by systems biology RNA and proteomic approaches

(Scherzer et al. 2007; Abdi et al. 2006).

Metabolomics, the study of the entire complement of

metabolites in a tissue or biofluid, provides an overview of

the dynamic metabolic status of the organism, reflecting

the complex interaction of genes, proteins and environ-

ment. The metabolic profile at a point in time reflects the

complex hierarchical network of metabolic interactions

(Ravasz et al. 2002) and is affected by internal influences

such as gut flora (Nicholson and Wilson 2003), as well as

external factors such as the environment, medication and so

on. Metabolomics, therefore, represents an attractive

approach to biomarker identification.

There is no single way of performing a metabolomic

screen—nuclear magnetic resonance spectroscopy, mass

spectrometry, high-performance liquid chromatography,

optical spectroscopic analyses or a combination of tech-

niques, each with their own advantages and pitfalls, can all

be applied (Nicholson et al. 2002; Dunn et al. 2005; Griffin

2003). The output is a complex n-dimensional metabolic

fingerprint from which patterns are sought using mega-

variate analytical approaches (see methods).

Many metabolomic techniques are sensitive to meta-

bolic abnormalities, and have been used to detect the

relatively minor metabolic differences resulting from

genetic strain differences in mice (Gavaghan et al. 2000),

from otherwise silent genetic mutations (Raamsdonk et al.

2001), or due to diet and cultural differences (Lenz et al.

2004). Metabolomic analysis of serum has also been used

to detect the presence of various diseased states, including

the presence and severity of coronary heart disease (Brindle

et al. 2002), although detection of lipid abnormalities may

be more difficult in patients taking lipid-lowering medi-

cations (Kirschenlohr et al. 2006). An association between

metabolic profiles and blood pressure has also been

reported (Brindle et al. 2003), as well as an association

with epithelial ovarian cancer (Odunsi et al. 2005). Meta-

bolomic analyses of other body fluids have also shown

encouraging results, including the use of urine to diagnose

interstitial cystitis and bacterial cystitis (Van et al. 2003)

and CSF to distinguish patients with Alzheimer’s disease

from controls (Ghauri et al. 1993) and to confirm the

presence and predict outcome of aneurysmal subarachnoid

haemorrhage (Dunne et al. 2005).

Metabolic profiling by our group has already yielded an

interesting insight into the neurodegenerative condition of

Huntington’s disease (HD) (Underwood et al. 2006).

Comparison of metabolic profiles from human HD patients

and mice expressing a mutant huntingtin gene product

identified a common pro-catabolic phenotype, consistent

with idea that disturbed energy expenditure is a contributor

to the pathogenesis of HD. This study provided a number

of candidate biomarkers (such as serum malonate and

valine levels) which may be useful for monitoring HD

progression, although further larger studies will be required

to validate these markers. Finally, this study provided the

first application of metabolomics to validate an animal

model of a human condition, confirming that the system-

wide metabolic perturbations in the transgenic mouse were

very similar to those in HD patients.

Here, we have applied the same approach to investigate

differences between subjects with PD and controls, analy-

sing both serum and urine metabolite profiles. As for the

HD studies, our aim was to identify candidate biomarkers

to assist in the clinical diagnosis and monitoring of PD, and

also to gain insight into the molecular pathogenesis of the

disease.

2 Methods

2.1 Subjects and samples

Subjects were recruited from the PD research clinic at the

Cambridge Centre for Brain Repair, and all met the UK

PDS brain bank criteria for idiopathic PD (Gibb and Lees

1988) (Table 1). To minimise metabolic variability single

sex subjects were chosen, in this case female, and subjects

were all in the relatively early stages of disease, without

192 A. W. Michell et al.

123

overt dementia or motor complications such as dyskinesias

or on-off fluctuations. Age and sex-matched controls were

female partners or carers of patients attending the clinic.

The study was approved by the Local Regional Ethics

Committee, with written information provided and

informed consent obtained from all subjects. Prior to

sample collection all subjects completed a questionnaire

encompassing a wide range of social, environmental and

medical factors that might affect the metabolic profile

(Table 2). Details of clinical assessment from their most

recent research clinic visit were recorded.

A 10 ml venous blood sample was taken from the

antecubital fossa of all subjects using a 19-gauge needle. It

was transferred into a polypropylene tube and allowed to

clot for between 2 and 3 h at room temperature, the exact

duration being noted. After clotting the tube was centri-

fuged (3,650 g, 5 min), following which the clot was

loosened from the wall of the tube and spun again using the

same protocol. The serum supernatant was carefully

removed into a fresh polypropylene tube and spun once

more using the same settings. Serum from the top third of

this final supernatant was carefully removed and a 1 ml

aliquot stored at -80�C.

All subjects provided a urine sample in a polypropylene

container. This was kept on ice for between 2 and 3 h, the

exact duration being noted. 10 ml from the top third of the

Table 1 Clinical features of PD patients at most recent clinical assessment prior to sample collection

Code Age, years Disease

duration,

months

UPDRS

motor

Hoehn

and Yahr

Timed walking

test, seconds

MMSE Beck

depression

inventory

Daily PD medication

Summary of PD group

68.6 ± 7.2 33.2 ± 20.7 30.8 ± 14.7 1.7 ± 0.8 10.7 ± 3.7 28.7 ± 1.2 8.9 ± 5.7 23 PD patients in total 8 not on

PD medication

Individual scores

ND246 68 80 30.5 2 12 29 3 Co-careldopa 3 9 62.5 mg

ND226 71 12 46.5 2.5 11 29 7 Cabergoline 1 9 4 mg

ND216 69 39 46 2.5 14 28 20 Co-beneldopa 4 9 125 mg,

1 9 62.5 mg, 1 9 125 mg CR

ND219 75 13 43 3 12 25 5 Co-beneldopa 1 9 125 mg CR

ND211 83 36 60.5 3 17 27 6 Co-careldopa 2 9 125 mg

ND214 71 29 36.5 3 12 30 2 Ropinirole 3 9 4 mg

ND222 63 14 11.5 1 9 29 9 None

ND237 75 40 22 1 14 28 9 None

ND227 63 34 38 1 11 28 22 None

ND231 65 20 33 2.5 11 29 13 Cabergoline 1 9 3 mg

ND215 68 20 15 1 10 30 15 Co-beneldopa 3 9 125 mg CR

ND213 65 69 22.5 1 8 30 n/a Co-beneldopa 3 9 62.5 mg

ND228 76 28 18.5 1.5 9 29 5 Co-beneldopa 3 9 125 mg

ND204 66 22 15 1 7 29 4 None

ND220 61 30 16.5 1 7 30 3 Cabergoline 1 9 4 mg

ND212 67 47 13.5 1 0 27 4 Co-beneldopa 3 9 125, Ropinirole

3 9 1 mg

ND229 56 26 29.5 1 9 30 4 None

ND235 57 19 23.5 1 10 30 8 Cabergoline 1 9 4 mg

ND210 63 19 33 2 12 29 14 Ropinirole 3 9 5 mg

ND242 71 86 26.5 1 11 29 12 None for 6 years

ND203 72 18 31.5 1.5 12 29 8 None

ND240 84 30 65 3 18 28 14 Co-beneldopa 2 9 250 mg,

1 9 125 mg

ND241 53 1 n/a n/a n/a n/a n/a None

Group means ± SD are presented. Code numbers ‘ND’ refer to figures that follow. UPDRS, Unified Parkinson’s Disease Rating Scale

All subjects were female

All fulfilled the UK Brain Bank diagnostic criteria for PD

All were Caucasian, and all except five described their origin as British

Metabolomic analysis of urine and serum in PD 193

123

sample was removed into a polypropylene tube and

centrifuged (3,650 g, 5 min). 1 ml from the top third of this

sample was removed and stored at -80�C.

2.1.1 Metabolite profiling

Sample preparation for GC-MS was performed exactly as

previously described (Underwood et al. 2006). Briefly,

175 ll of each serum sample was spiked with 20 ll

internal standard solution (1.53 mg/ml succinic d4 acid,

2.34 mg/ml malonic d2 acid, 1.59 mg/ml glycine d5;

Sigma-Aldrich, Gillingham, UK), then deproteinised by

addition of 450 ll of acetonitrile followed by centrifuga-

tion (13,385 g, 15 min) and freeze drying.

Urine samples were treated in a similar way. 175 ll of

each sample was spiked with 20 ll internal standard

solution as above. Because of the very high urea content of

urine, 50 ll urease solution (10 mg/ml) was added and the

mixture warmed for 20 min at 30�C. 400 ll acetonitrile

was added followed by centrifugation and freeze drying.

Urine and serum residues were chemically derivatised

to increase their volatility and thermal stability, using

70 ll O-methylhydroxylamine (20 mg/ml in pyridine)

at 40�C for 90 min to enhance oxime formation, then

70 ll MSTFA (N-acetyl-N-(trimethylsilyl)-trifluoroaceta-

mide, 20 mg/ml in pyridine) at 40�C for 90 min to

enhance trimethylsilylation. The final solution was spiked

with 20 ll retention index solution (6 mg/ml n-decane,

n-dodecane, n-pentadecane, n-nonadecane, n-docosane

dissolved in hexane).

Samples were vaporised and analysed using an Agilent

6890N gas chromatograph and 7683 autosampler (Agilent

Technologies, Stockport, UK) coupled to a LECO Pegasus

III electron impact time-of-flight mass spectrometer

(LECO Corporation, St Joseph, USA). Optimised instru-

mental conditions for serum have been described elsewhere

(O’Hagan et al. 2005). Initial processing of raw data was

undertaken using LECO ChromaTof v2.12 software to

construct a data matrix (metabolite peak versus sample

number) using response ratios (peak area metabolite/peak

area succinic-d4 internal chromatographic standard) to give

the relative amount of each metabolite in each sample,

and this table was used for all subsequent chemometric

modelling. Approximately 900 unique mass spectral sig-

natures were distinguished in the urine and 700 (some of

which may be due to the same metabolites as in urine) in

the serum for each of the 46 individuals (a total of almost

81,000 data points).

2.1.2 Chemometric modelling

There are a number of approaches to the megavariate data

output from GC-MS (see, for example Valafar 2002). Here,

we used unsupervised principal components analysis

(PCA) to look for clustering and remove extreme outliers.

Further supervised methods were then employed to search

for signatures associated with disease status, using a blind

hold-out set for validation.

Following removal of extreme outliers, projection to

latent structures discriminant analysis (PLS-DA) was

applied. Since supervised methods are well known to

overfit models, the robustness of the models obtained were

checked in two ways. Firstly, as an initial screen for

overfitting, the cross-validation parameter Q2 for the model

was compared with Q2 for 20 models built with a randomly

permuted Y-vector (with predictive models generally

showing an intercept on this test which is close to 0).

Secondly, and more importantly, the model based on the 31

Table 2 There was no

significant difference in lifestyle

or sample preparation between

PD patients (n = 23) and

controls (n = 23)

Mean ± SD. Not all

questionnaire parameters are

displayed. Statistical

comparisons were by Mann–

Whitney U test, M–W U

Parameter Parkinson’s disease Control M–W U P

Lifestyle

BMI 27.1 ± 4.5 27.8 ± 5.3 255.0 0.84

Age, years 67.9 ± 7.8 64.6 ± 10.0 232.0 0.48

Lived in country, years 28.3 ± 21.6 32.5 ± 25.8 201.5 0.82

Units alcohol/week 7.9 ± 9.3 8.0 ± 9.0 248.0 0.71

Cups coffee/day 1.8 ± 0.3 1.9 ± 0.3 254.0 0.81

Cups tea/day 2.9 ± 2.2 3.3 ± 2.1 241.0 0.60

Hours exercise/week 2.7 ± 3.1 2.8 ± 3.0 242.0 0.61

Hours sleep/night 6.4 ± 1.3 6.7 ± 1.0 225.0 0.38

Smoking, pack, years 13.3 ± 4.3 8.5 ± 3.5 242.0 0.59

Smoking now, cigs/day 1.8 ± 1.3 0 ± 0 230.0 0.14

Sample preparation

Serum clot time, min 164.0 ± 41.5 150.1 ± 22.8 228.5 0.43

Urine time on ice, min 152.0 ± 61.2 140.4 ± 35.3 261.5 0.95

194 A. W. Michell et al.

123

individuals was used to predict the disease status of a blind

external hold-out set of 15 individuals, with the statistical

significance of that prediction tested using the v2 statistic.

Only this second test, with prediction of the blind hold-out

set, was used to infer validity of the model. Finally,

significant loadings associated with disease status were

reported, and, where possible, identified with reference to

library searches of mass spectra (MPI/Golm and NIST/

EPA libraries, reported provided s [ 600).

Supervised models were constructed using PLS-DA for

urine, serum and the combined data set. Further models

were constructed to check the effect of medication on the

observed results, including the effect of L-dopa, dopamine

agonists, diuretics, statins, antidepressants and thyroxine.

PCA and PLS-DA was performed using Simca version 10

(Umetrics AB, Umea, Sweden). Analysis was performed on

log transformed data (primarily to eliminate zeros, where a

given mass spectral signature was undetectable in a partic-

ular sample), and default selections were made to exclude

variables with low variance in one or both groups. All

models were constructed on unscaled data, without center-

ing, to preserve weighting in accordance with the inter-

person variance in each metabolite, unless indicated

otherwise.

3 Results

Samples of serum and urine were collected from a total of

23 female PD patients and 23 female controls. The clinical

characteristics of the patients are shown in Table 1, and

lifestyle parameters, as well as details on the preparation of

samples are shown in Table 2. The control and PD groups

did not differ significantly on any lifestyle parameter

measured.

Analysis of urine metabolites alone is shown in Fig. 1.

PCA revealed no clustering, but two borderline outliers

(Fig. 1a), although inclusion or exclusion of these points

made little difference to subsequent supervised analysis

with PLS-DA, so only the analysis with them retained is

shown here. A PLS-DA supervised model clearly separated

control (red) from PD (blue) samples (Fig. 1b). This model

was then validated against overfitting (encouragingly, the

intercept of the Q2 parameter in the Y-vector permutation

test was 0.015 for a model Q2 of 0.277). Nine of the

external hold out set were clearly predicted by the model,

although the remaining six individuals fell in the uncertain

region between the two groups and no prediction was made

for these individuals (Fig. 1c). All nine predictions were

correct (P \ 0.01; v2 test). We conclude that there is

potentially a multiparametric signature within the urine

metabolite profile which distinguishes PD sufferers from

controls.

Examination of the loadings of this PLS-DA model

(Fig. 1d) illustrates that no individual metabolites dominate

the model—the loadings plot has the characteristic

‘‘snowball’’ appearance typical of a model which only

weakly distinguishes the modelled classes through the

contribution of very many metabolites, each of which

contributes only a small amount of discriminatory power.

Importantly, in contrast (for example) to our study of HD

(Underwood et al. 2006), no candidate biomarkers were

identified although the multivariate signature may ulti-

mately have clinical utility in diagnosing PD if our findings

are replicated in larger studies.

The PLS-DA model used to separate the control and

PD urine specimens was optimised (on the basis of the

cross validation parameter Q2) with five components.

Rather than plot the first two components (Fig. 1b), if

the first three were plotted in three dimensions then a

subdivision of the PD group became apparent (Fig. 2). In

this case the first component performed most of the

separation of PD from control, with the second and third

subgroups tending to subdivide the classes. The cluster-

ing effect was not drug-related, for example ND227

received no drugs of any kind, and ND237 received no

medication for their PD.

A similar analysis of the serum metabolites alone

is shown in Fig. 3. In this case PCA revealed three

outliers (Fig. 3a): two blind (ND232, ND209) and one PD

(ND211) sample, which were removed from further

analysis. The PLS-DA model (Q2 = 0.35, maximal with

three components) once again was apparently able to

separate control from PD samples (Fig. 3b). However, this

time the model was clearly over-fitted, a common problem

with this type of analysis, since only 2/15 of the external

hold-out set could be correctly classified (P = 0.67; v2

test). This strongly suggests that the serum metabolic

profiles were more similar between control and PD than

the urine profiles, and that any metabolic disturbance

associated with PD is less marked than, for example, in

the related neurodegenerative condition HD (Underwood

et al. 2006).

The urine and serum metabolic profile data were com-

bined (by concatenation of the data vectors) and analysed

simultaneously (data not shown). PCA revealed a total of five

outliers: three blind (ND232, ND209, ND221), one control

(ND238) and one PD (ND204), which were excluded from

further models. PLS-DA modelling (Q2 = 0.21, maximal

with two components) separated the control and PD samples

with similar predictive power to the model built on the urine

profile alone (P = 0.05; v2 test for blind external predic-

tions). Once again, the loadings plot revealed no candidate

biomarkers.

The strongest loadings from the two externally predic-

tive models (built on the urine only data and the serum plus

Metabolomic analysis of urine and serum in PD 195

123

urine data) are shown in Table 3. For some of these

metabolite peaks, a molecular structure could be assigned

based on comparison of its mass spectrum with a library

of more than 80,000 such spectra held at MPI/Golm

(a process very similar to homology searches with gene

sequences). However, it is important to stress that none of

these individual metabolites was significantly different

between PD and control groups in univariate analysis, nor

was any combination of them discriminatory, once again

underlining the subtle nature of any metabolic disturbance

in PD. It is interesting that, despite urease treatment of

urine samples, urea remained one of the more important

metabolites contributing to the distinction of PD versus

control in PLS-DA models.

Since a significant fraction (9/23) of the PD patients in

the study had been receiving L-dopa medication around the

time that samples were taken, it was possible that this (or

the co-administered peripheral dopa-decarboxylase inhibi-

tors benserazide or carbidopa) might have contributed to

the altered urine profiles in the PD group. To test this

possibility, a PLS model was built attempting to describe

the L-dopa dosage on the basis of metabolic profile data

(Fig. 4; Q2 = 0.36, maximal with two components).

However, both the response vector permutation intercept

(0.322) and the failure to predict L-dopa dosage among the

blind validation set demonstrate that the model is overfitted.

This analysis suggests that no valid model can be generated

predicting L-dopa use from the metabolic profile of the

-20

0

20

40

-20 -10 0 10

-20 -10 0 10

20 30

ND206ND209ND210ND213 ND215ND216 ND221ND223 ND226

ND229 ND230ND232ND235ND236

ND241ND243 ND201

ND202

ND205ND207ND208ND217

ND218ND224ND225

ND233ND234 ND238

ND239ND244 ND245ND203

ND204

ND211 ND212

ND214

ND219ND220ND222

ND227ND228ND231

ND237ND240ND242

ND246

(a) PCA urine

-10

0

10

100-10

ND201

ND202ND205

ND207ND208 ND217ND218 ND224ND225

ND233

ND234

ND238ND239

ND244

ND245

ND203ND204

ND211

ND212ND214

ND219

ND220ND222

ND227

ND228

ND231

ND237

ND240ND242

ND246

-20

-10

0

10

20

30

ND210

ND213 ND215ND216

ND221ND223ND226

ND229

ND230ND232 ND235

ND236

ND241

ND243

(b) PLS-DA urine

(c) PLS-DA predictions

-0.100

-0.050

0.000

0.050

0.100

0.100.00-0.10

U999U998 U994 U992

U991

U990

U99U988

U987

U982

U981

U98

U979

U978

U976U974

U972

U970

U968U964U962

U960U959

U958

U955

U953

U952

U951U95

U949

U948

U947

U944

U943U942

U940

U939

U938 U937U936

U935U934

U933

U930U929

U928U927

U925

U922U921U920

U92U918

U913

U911

U909

U906

U904 U903

U901

U900

U9

U899U898

U897

U895

U893

U891U890U89

U888

U887

U886

U885

U883U882

U881

U88

U879

U876

U874

U873

U872

U87

U869

U867

U866 U865 U863U862

U860

U858U857U856

U853U852 U851

U850

U849

U848

U847U846U843

U842

U841

U840

U84

U838U837

U836U835

U834U832

U830

U83

U828U825U823

U822

U821

U819

U817

U816

U813

U812U811U810U809

U807U806

U805U802 U801

U8

U799

U798U797 U796

U793

U792

U791U790

U789

U786

U785 U783U782

U781

U78

U776 U775

U773

U772U770

U77U769

U768U764

U763

U762

U761

U760

U76

U759U758U757 U756U755

U754

U753

U752

U751

U750

U75

U748U747

U746U745

U744

U743

U742

U741

U740

U739U738

U737 U736

U735

U733

U732U730 U73

U728

U726U725

U723U720U718

U717U716

U714

U712

U711U710

U709

U707U705

U704

U703U701

U700

U70

U699U696

U695U693U692

U69

U689

U687

U686

U685

U684

U682

U681U680

U68U679

U678 U677U676

U675

U674U672

U671

U670

U67

U669

U667U666

U663

U662

U66

U657

U655

U654

U653

U652U651

U650

U647

U643

U641

U640

U64

U639

U638

U636U635U634U632U631U630

U629

U628

U627

U626

U625

U624

U622

U621

U620

U62

U619

U616

U614

U613

U612

U61

U609

U608

U606

U605

U604

U600

U60U598U597U596 U595U594

U593

U592

U591

U590

U59U589

U588

U587 U586

U585

U584

U583

U582

U581

U580

U58U579U578

U576

U574

U573U572

U570U57

U569

U564

U563

U562

U561U559

U558

U557

U556

U555

U554

U553

U552

U551

U550

U55

U549

U547

U546

U545U543

U542

U540

U54

U539

U537

U536

U534 U533

U532U531

U53

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U527

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$M6.DA20

$M6.DA30

(d) loadings of the PLS-DA model

t[1]

t[1]

t[2]

t[

2]

tPS[

2]

t[1]

w*c[1]

w*c

[2]

Fig 1 Analysis of urine (a)

Principal component analysis

(k = 995, n = 46,

R2X = 0.133, A = 2; first two

principal components shown.

Blue = PD, red = control,

green = blind). ND202 and

ND204 are borderline outliers

(the elipse represents Hoteling’s

T2 at 95% confidence limit), so

PLS-DA models were built with

and without them, but with little

effect on the results (so only the

full models are shown). (b)

PLS-DA model of disease status

(k = 878, n = 30,

R2Y = 0.990, R2X = 0.227,

Q2 = 0.277 intercept 0.015,

A = 5) showing the first two

principal components

(red = control, blue = PD). (c)

Predictions of the blind

validation set: the model

significantly predicts disease

status, v2; P \ 0.01 (d)

Loadings of the PLS-DA model

shown in (c). Each metabolite is

indicated by its arbitrary peak

number (black), Y vector

loadings are shown in red

196 A. W. Michell et al.

123

subjects in this study, which in turn indicates that L-dopa

use is not a major contributor to the metabolic profile. A

similar analysis (not shown) clearly demonstrated that none

of the other drugs taken by the subjects contributed

significantly to the separation of PD from control on the

basis of the urinary metabolic profile (including the D2

agonists ropinirole and cabergoline, antidepressants,

diuretics, statins or thyroxine).

4 Discussion

These studies clearly demonstrate that any metabolic dis-

turbance associated with PD is relatively subtle, compared

with the related neurodegenerative disorder HD. We found

no evidence for a similar pro-catabolic phenotype which

characterised HD subjects even before overt clinical

symptoms were present (Underwood et al. 2006). We did,

however, find evidence for a subtle difference in the

urinary metabolite profile, although this signature was

ND205ND202

ND217

ND246ND237ND219

ND227ND214ND204

t[1]

t[2]

t[3]

Fig. 2 Three component plot of the PLS-DA model of disease status

based on the urine metabolic profile. Demonstrates the clear

distinction between control and PD, but shows clustering within the

PD group unrelated to medication (blue = PD, red = control). See

Table 1 for individual patient clinical features

-20

0

20

-30 -20 -10 0 10 20 30 40 50 60

ND206

ND209ND210ND213

ND215ND216ND221ND223

ND226ND229

ND230

ND232ND235 ND236ND241

ND243ND201ND202ND205

ND207

ND208ND217ND218ND224ND225

ND233ND234

ND238

ND239ND244

ND245

ND203

ND204

ND211

ND212ND214ND219ND220ND222

ND227

ND228ND231ND237

ND240ND242ND246

(a) PCA serum

-10

0

10

100-10

ND201ND202ND205

ND207

ND208

ND217ND218

ND224ND225

ND233

ND234

ND238

ND239

ND244

ND245ND203

ND204

ND212

ND214ND219

ND220

ND222ND227

ND228

ND231 ND237

ND240

ND242ND246

(b) PLS-DA serum

-10

0

10

100-10

ND210ND213

ND215ND216

ND221ND223

ND226

ND229ND230ND235

ND236

ND241

ND243

ND206

(c) PLS-DA predictions

t[1]

t[1]

tPS[1]

t[2]

t[

2]

t[2]

Fig. 3 Analysis of serum (a)

Principal component analysis

(k = 805, n = 46,

R2X = 0.206, A = 2; first two

principal components shown.

Green = blind, red = control,

blue = PD). ND209, ND211

and ND232 are outliers, so were

excluded for PLS-DA

modelling. (b) PLS-DA model

of disease status (k = 738,

n = 29, R2Y = 0.967,

R2X = 0.190, Q2 = 0.353,

intercept = 0.171, A = 5)

showing the first two principal

components (red = control,

blue = PD). (c) Predictions of

the blind validation set:

controls = ND206;

PD = ND243, ND216. The

model does not significantly

predict the blind validation set,

v2; P = 0.67. Note that the

loadings of this model are not

shown because it is overfitted

Metabolomic analysis of urine and serum in PD 197

123

highly multivariate (with many metabolites each making a

small, and individually insignificant, contribution to

discrimination), rather than yielding specific biomarkers.

While identification of specific biomarkers has a num-

ber of advantages (simplifying the testing methodology

required, as well as providing insight into the molecular

pathogenesis of the disease), a multivariate signature in the

urinary metabolite profile could still be clinically useful in

the diagnosis of PD. Indeed, in the absence of simple

biomarkers, there may be no practicable alternative to a

profiling diagnostic test (a ‘pronostic’ test). However, a

considerably larger study than this will be required to

validate our findings and estimate the diagnostic power of

such a pronostic test.

Interestingly, in this early study we have found tentative

evidence for subgroups within the PD population, on the

basis of their urine metabolic profile in the predictive

model. A larger study would allow us to investigate the

metabolic variation underlying the clinical heterogeneity

that is increasingly recognised in PD (Lewis et al. 2005;

Foltynie et al. 2002). We do not know the underlying

molecular causes or consequences of such clinical hetero-

geneity, thus a non-hypothesis driven approach that is able

to reflect the integrated effect of disease-related genes,

environment, toxins, dopaminergic pathology, behaviour

and so on would seem an ideal and powerful place to start.

Thus, rather than a tool to look for individual biochemical

markers, it is possible that the overall metabolic profile of

body fluids may represent a powerful biomarker itself.

In addition, a longitudinal study is required to follow the

metabolic trajectory of the disease and correlate this to

clinical progression. Such information might in due course

help determine whether potential disease modifying treat-

ments really work to disrupt this trajectory, and will help

determine the sequence of progression of pathology (dis-

cussed elsewhere, Nicholson et al. 2002; Van der and

McBurney 2005).

The separation of PD and control groups has been

possible despite the myriad factors that can affect the

metabolic profile (reviewed by Nicholson and Wilson

2003). The separation, however, is very subtle and there are

a number of explanations for the subjects who were poorly

classified. In this study our gold standard for diagnosis was

careful clinical assessment and follow-up of patients in a

specialist clinic, which has been shown to give a positive

predictive value of about 99% for the diagnosis of idio-

pathic PD (Hughes et al. 2002). Ideally this would be

confirmed by pathological analysis at postmortem,

although in the interim a more practical alternative might

be to seek confirmatory changes on functional imaging

such as 18F-dopa PET or DAT scanning. Furthermore, it is

Table 3 Metabolites robustly distinguishing PD from control in

PLS-DA models with different randomly selected internal hold-out

sets are shown

Higher in controls

S540 2-mercapto-4,6-diaminopyridine

U962 Octenoic acid

U299 Urea

Higher in PD

S560-570 Various monosaccharides

U1250 Sugar alcohol

U737 Suberic acid

U1198 Bioactive amine? (plus at least10 peaks currently unidentified)

Metabolite identifications were made by matching mass spectra to

library databases (see methods, s [ 600), and the best match reported.

Note that GC-MS poorly distinguishes isomers (such as mono-

saccharides), and that some species may have two independent peaks

in the profile resulting from the chemical derivatisation of the sample.

(U, metabolite in urine; S, in serum)

PLS-DA of L-dopa use

-30

-20

-10

0

10

20

30

-30 -20 -10 0 10 20 30

ND203 ND211ND212

ND214

ND219ND220

ND222

ND227

ND228

ND231

ND237ND240

ND242

ND246 500mg 300mg

300mg 200mg

150mg

100mg

t[1]

t[2]

Fig. 4 PLS-DA model of L-dopa use among PD subjects. The dosage of

L-dopa taken by the PD subjects (excluding the blind set) was modelled

by PLS on the combined serum and urine metabolic profiles (k = 1,368,

n = 14, R2X = 0.178, R2Y = 0.994, A = 2). Evidenced by the response

vector permutation parameters (Q2 = 0.364, intercept = 0.322) and the

lack of prediction of L-dopa usage in the blind set, this model has no value

and confirms the lack of a signature in the metabolic profile related to L-

dopa use. Note that the loadings for this model are not shown because it is

overfitted

198 A. W. Michell et al.

123

possible that some of our control group have presymp-

tomatic disease since incidental Lewy body pathology

occurs in approximately 10% of the elderly population

(Mikolaenko et al. 2005; Tsuboi et al. 2001), and up to

50% of the nigral dopaminergic neurons need to be lost

before any motor symptoms of PD develop (Fearnley and

Lees 1991).

There is of course variation inherent in the technique,

both in the reproducibility of GC-MS analysis, but more

importantly in the samples themselves. For example, an

early morning urine sample will provide a different meta-

bolic profile to one taken later in the day, and both blood

and urine will be affected by diet, exercise and so on. The

standardisation of such factors in the experimental protocol

might reduce variability. Any biomarker must be shown to

be reproducible: the same sample needs to be re-run on the

same machine and on different machines with different

operators, a repeat sample should be compared, and the

whole study needs replication in a different population.

Such reproducibility issues are already being investigated

for nuclear magnetic resonance (1H NMR) spectroscopic

analysis of metabolites (Lindon et al. 2003), and as we

learn more about these emerging techniques it should be

possible to adopt protocols that reduce experimental vari-

ation considerably.

This study deliberately examined only a relatively small

number of subjects since, by restricting the size of the

study, we were able to perform a more comprehensive

phenotypic and biochemical characterisation of each indi-

vidual. This reflects a trade-off between sample size and

the amount of data collected on each individual for a given

resource commitment. Additionally, since the discrimina-

tory power of multivariate statistical modelling increases

very slowly with increasing sample size (DJG, unpublished

observations) in many cases biochemical markers likely to

be of clinical relevance will be identified from studies with

20–40 individuals per group. On this basis, we conclude

that the degree of metabolic perturbation seen in PD is

close to the limits for detection by this metabolomic

approach.

In summary, our findings strongly suggest that the pro-

catabolic metabolic state we identified among HD patients

is not present among PD sufferers. Any metabolic distur-

bances associated with PD are relatively subtle, frustrating

biomarker discovery through a metabolomic approach

(consistent with the lack of success identifying metabolite

biomarkers through a conventional candidate approach

over many years). Changes in urine composition may be

more promising, yielding both a candidate multiparametric

metabolic signature of PD as well as the possibility of

defining PD subtypes but substantially larger studies

(possibly employing additional metabolomic approaches)

are required.

Acknowledgements We thank Dave Broadhurst, Rick Dunn, Dave

Ellis and Douglas Kell for assistance with the analyses. Funding: DJG

is a British Heart Foundation Senior Research Fellow. AWM held a

Parkinson’s Disease Society studentship and Sackler award.

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