a type 2 biomarker separates relapsing-remitting from ...oct 21, 2014 · the transition from...

ARTICLES

Alex M Dickens PhDJames R Larkin PhDJulian L Griffin PhDAna CaveyLucy Matthews MRCPMartin R Turner PhDGordon K Wilcock DMBenjamin G Davis PhDTimothy DW Claridge

PhDJacqueline Palace DMDaniel C Anthony PhDNicola R Sibson PhD

Correspondence toDr AnthonyDanielAnthonypharmoxacuk

Editorial page 1488

Supplemental dataat Neurologyorg

A type 2 biomarker separatesrelapsing-remitting from secondaryprogressive multiple sclerosis

ABSTRACT

Objective We tested whether it is possible to differentiate relapsing-remitting (RR) from second-ary progressive (SP) disease stages in patients with multiple sclerosis (MS) using a combinationof nuclear magnetic resonance (NMR) metabolomics and partial least squares discriminant anal-ysis (PLS-DA) of biofluids which makes no assumptions on the underlying mechanisms ofdisease

Methods Serum samples were obtained from patients with primary progressive MS (PPMS)SPMS and RRMS patients with other neurodegenerative conditions and age-matched controlsSamples were analyzed by NMR and PLS-DA models were derived to separate disease groups

Results The PLS-DA models for serum samples from patients with MS enabled reliable differen-tiation between RRMS and SPMS This approach also identified significant differences betweenthe metabolite profiles of each of the MS groups (PP SP and RR) and the healthy controls as wellas predicting disease group membership with high specificity and sensitivity

Conclusions NMR metabolomics analysis of serum is a sensitive and robust method for differen-tiating between different stages of MS yielding diagnostic markers without a priori knowledge ofdisease pathogenesis Critically this study identified and validated a type II biomarker for the RRto SP transition in patients with MS This approach may be of considerable benefit in categorizingpatients for treatment and as an outcome measure in future clinical trials

Classification of evidence This study provides Class II evidence that serum metabolite profiles accu-rately distinguish patients with different subtypes and stages of MS Neurologyreg 2014831492ndash1499

GLOSSARYAD5 Alzheimer disease ALS 5 amyotrophic lateral sclerosis AUC5 area under the curveMS5multiple sclerosis NMR5nuclear magnetic resonance PLS-DA 5 partial least squares discriminant analysis PP 5 primary progressive ROC 5receiver operator characteristic RR 5 relapsing-remitting SP 5 secondary progressive

The transition from relapsing-remitting (RR) to secondary progressive (SP) multiple sclerosis(MS) occurs subtly and is difficult to define clinically1 Identifying biomarkers that can distin-guish between the different clinical phenotypes of MS is an important goal to ensure that theappropriate treatment regimens are adopted in a timely fashion2ndash4 Furthermore such bio-markers may provide new insight into the pathologic basis for the progressive process and leadto the development of effective treatments for disability prevention5

Metabolomic profiling of biofluids with high-resolution proton nuclear magnetic resonance(NMR) spectroscopy and partial least squares discriminant analysis (PLS-DA)6 a multivariatestatistical pattern recognition technique can be used to identify metabolites that vary in acorrelated fashion within individual groups78 This approach enables patterns of metabolitevariation that are characteristic of a specific disease to be defined rather than requiring identi-fication of a unique candidate-led biomarker9 We have previously used this technique to showthat animals with either predominantly macrophage-rich or neutrophil-rich brain pathologies

These authors contributed equally to this work and are listed alphabetically

From the CR-UKMRC Gray Institute for Radiation Oncology and Biology (AMD JRL NRS) Department of Pharmacology (AMDDCA) Department of Chemistry (AMD BGD TDWC) Nuffield Department of Clinical Neurosciences (AC LM MRT) andNuffield Department of Medicine (GKW) University of Oxford and the Department of Biochemistry (JLG JP) University of CambridgeUK

Go to Neurologyorg for full disclosures Funding information and disclosures deemed relevant by the authors if any are provided at the end of the article

1492 copy 2014 American Academy of Neurology

can be differentiated from each other solely onthe basis of their urine metabolite composi-tion10 Using a similar approach characteris-tic metabolite patterns were identified inurine samples in a marmoset model of MSas well as in samples from patients with MScompared with either healthy volunteers orother neurologic controls11 These findingssupport the concept that biofluids analyzedby NMRPLS-DA may be highly sensitivenot just to the presence of pathology withinthe brain but to differences and changes inpathologic processes In this study we usedNMRPLS-DA analysis of serum metabolitecomposition to determine whether it is possi-ble to objectively differentiate between MSclinical subtypes on the basis of metabolomicbiofluid analysis

METHODS Biofluid samples We obtained serum samples

from 3 independent cohorts (A B and C) of patients with MS

(RR SP and primary progressive [PP]) as well as age- and sex-

matched control volunteers recruited from the John Radcliffe

Hospital Oxford UK (see STARD flowchart of patient

recruitment exclusion and methodology in figure 1) We

diagnosed patients with MS using the gold standard 2001 and

2005 McDonald criteria with at least 2 clinical attacks for RR

phenotypes1213 and continuous worsening of neurologic

impairment over at least 12 months not explained by

incomplete recovery from relapses for those defined as SPMS

Of the 58 patients with SPMS in the study 53 did not have a

relapse in the 2 years before and after sampling 3 had one relapse

and data on the remaining 2 are not available No patients with

PPMS had any recorded relapses A consultant neurologist

performed the clinical assessments of all patients with MS (JP)

Overall patient demographics are shown in table 1 and a more

detailed breakdown together with a list of therapies is provided in

table e-1 on the Neurologyreg Web site at Neurologyorg Blood

samples were collected and allowed to clot for 30 minutes at room

temperature in Vacutainers containing clot-activator and gel (BD

UK) Samples were centrifuged (3000 gn 10 minutes) and the

serum aliquoted and stored at 280degC Samples were excluded

Figure 1 STARD flowchart of multiple sclerosis patient recruitment and general methodology

MS 5 multiple sclerosis NMR 5 nuclear magnetic resonance PPMS 5 primary progressive multiple sclerosis ROC 5 receiver operator characteristicRRMS 5 relapsing-remitting multiple sclerosis SPMS 5 secondary progressive multiple sclerosis

Neurology 83 October 21 2014 1493

from analysis if patients were found to have another major disease

(eg diabetes mellitus [n5 1]) if the spectral quality was poor or

grossly distorted (n5 15) or if the prepared sample was found to

have a lipid content greater than 2 3 SD from the mean of the

group (n 5 13 appendix e-1) For comparison to other

neurologic diseases serum was obtained from patients with

either amyotrophic lateral sclerosis (ALS n 5 20 diagnosed

by MRT) or Alzheimer disease (AD n 5 10 diagnosed by

GKW) and compared with age-matched samples from healthy

volunteers at the respective disease clinics (n 5 14 and 20

respectively)

Standard protocol approvals registrations and patientconsents MS samples were collected and analyzed with ethical

approval between 1995 and 2012 under project 08H0604

155 ALS and AD patient samples were obtained from tissue

banks and analyzed as part of the Oxford Study for Biomarkers

in Motor Neurone Disease (BioMOx approved by the South

Oxfordshire Research Ethics Committee 08H060585) or the

OPTIMA and Challenge cohorts with prior approval from the

local research ethics committee (COREC 1656 and 96243

respectively)

NMR spectroscopy A 1D proton NMR spectrum was ob-

tained for each sample using a Carr-Purcell-Meiboom-Gill

pulse sequence as previously described14 with some slight

modifications Prior to spectroscopy samples were defrosted at

4degC then further centrifuged (100000 3 g 30 minutes 4degC)

Serum (150 mL) was mixed with 024 M sodium phosphate

NMR buffer prepared in D2O (pH 74) to a final volume of

600 mL Mixed samples were transferred to a 5-mm NMR

tube and spectra acquired using a 700-MHz spectrometer

operating at 164T (Bruker Avance III Billerica MA) One

sample from each batch was analyzed by 2D spectroscopy to

aid metabolite identification More detailed NMR methods are

provided in appendix e-1

Data preprocessing Spectra were imported into MATLAB

(Mathworks Natick MA) and scaled using the internal standard

to ensure correct amplitude Further preprocessing corrected for

subtle shifts in peak position arising from pH differences between

samples15 The remaining processing was carried out as previously

described14 with slight modifications Briefly the spectra were

subdivided into 002-ppm buckets from 020 to 960 ppm (d 5

midpoint of integral region) In each spectrum buckets in the

430ndash500 ppm region which is highly variable owing to

imperfect water suppression were excluded In addition

buckets containing gross contaminants (eg organic solvent)

were excluded from analysis Each remaining bucket was

integrated yielding 420 independent variables for each sample

Prior to multivariate statistical analysis data were scaled using the

Pareto variance to suppress noise More detailed methodology is

available in appendix e-1

Multivariate statistical analysis For each comparison we

used SIMCA 13 (Umetrics Sweden) to construct a PLS-DA

model that best explained the differences between the variables

for the groups being studied Each model was assessed for

predictivity by calculating the q2 value A q2 0 is predictive

and it is generally accepted that q2 04 is the threshold for

significance in biological modeling14 Models were further

validated by a Monte Carlo method where 100 models were

built using random group assignments Only models where the

genuine q2 was higher than 95 of the randomly generated q2

values were considered significant More detailed statistical

methods are included in appendix e-1

Buckets driving separations were identified using variable

importance and contribution plots for each model Metabolites

within buckets were identified using 2D spectroscopy literature

values and reference to the human metabolome database16ndash18

To confirm identity of singlet peaks predicted metabolites were

spiked into samples as authentic references

Independent validation Two sets of patients A and B were

used to build independent predictive models Each modelrsquos load-

ings and contributions were compared to ensure similarity in

variables driving separation In addition a third independent

set of samples C was used as a testing set A researcher blinded

to all clinical information tested the ability of the Set B model to

predict group membership of the unknown Set C samples The

results were expressed as a 2 3 2 contingency table and receiver

operator characteristic (ROC) curves were constructed Model

membership probability thresholds derived from the model-

building set were used to determine sensitivity and specificity

in the prediction set (Set C)

RESULTS Initial MS sample set (Set A) Spectra wereobtained from the Set A serum samples and processedas described above to give integral values for each

Table 1 Patient demographic information

PPMS RRMS SPMS Control

Median (range) age y 58 (43ndash63)a 40 (22ndash62) 51 (28ndash65)b 54 (18ndash75)

Female n () 6 (46) 15 (58) 43 (74) 16 (67)

Male n () 7 (54) 11 (42) 15 (26) 8 (33)

Median (IQR) EDSS 6 (44ndash64) 2 (1ndash54) 6 (575ndash65)c 0 (0ndash0)d

Median (range) TSR mo NA 20 (1ndash161) NA NA

Median (range) DD mo 73 (17ndash333) 72 (6ndash318) 160 (65ndash430)c 0 (0ndash0)d

Abbreviations DD 5 disease duration EDSS 5 Expanded Disability Status Scale IQR 5 interquartile range NA 5 notapplicable PPMS 5 primary progressive multiple sclerosis RRMS 5 relapsing-remitting multiple sclerosis SPMS 5 sec-ondary progressive multiple sclerosis TSR 5 time since relapseMore detailed breakdown of demographics and therapies received is given in table e-1ap 0001 vs RRMSbp 005 vs RRMScp 0001 with respect to RRMSdp 0001 vs PPMS RRMS and SPMS

1494 Neurology 83 October 21 2014

independent bucket Example spectra from a patientwith RRMS and from a patient with SPMS are shownin figure e-1 Models built with data from Set Aserum samples showed a significant separationbetween RR and SP groups (q2 5 045 figure 2A)Examination of the loadings revealed that fatty acids(dx-y 5 088 130 535) phosphocholine (dx-y 5

323) an N-acetyl species (dx-y 5 203) and glucose

(dx-y 5 325 375 391) were decreased in SPMSwith respect to RRMS whereas other fatty acids andb-hydroxybutyrate (dx-y 5 119) were increased Forother intra-MS comparisons the model comparingthe PP and RR groups appeared to differentiate 2clear groups (figure e-2) However a small numberof overlapping patients in each group resulted in anonpredictive q2 value (2011) The PP vs SPmodel also returned a nonpredictive q2 value(2002) with marked overlap (figure e-2)

We subsequently investigated the separation of pa-tients with MS from an age- and sex-matched controlcohort A model differentiating all patients with MSfrom the control cohort was significantly predictive(q2 5 041) Furthermore the comparisons betweenindividual stages of MS and control groups were alsosignificant (figure 2 B and C) All models were suc-cessfully validated using the cross-validation methoddescribed above Interestingly when comparing eachof the individual stages of MS against the controls 2metabolites were consistent across the groups glucose(dx-y5 325 375 391) and phosphocholine (dx-y5323) were both reduced Further examination ofboth the RR vs control and SP vs control modelloadings showed lactate (dx-y 5 132) a broadsinglet-like resonance tentatively assigned to N-acetylspecies (dx-y 5 203 previously assigned as N-acetylglycoproteins10) and some fatty acids were decreasedin the patients with RRMS or patients with SPMSrelative to controls A separate subset of fatty acidswas found to be elevated in the patients with RRMSand patients with SPMS relative to controls Exami-nation of the PP vs control model loadings showedthat in contrast to RRMS and SPMS lactateN-acetyl species and some fatty acids were increasedin patients with PPMS relative to controls Detailedmetabolite findings are presented in table e-2

Independent MS sample set analysis (Sets B and C) Sam-ples from independent Set B were used in the samemanner as the samples from Set A and 3 new modelswere generated ControlB vs RRMSB ControlB vsSPMSB and RRMSB vs SPMSB In each case themodels from Set B were significantly predictive(q2 04) and were all validated in the same manneras the models from Set A (ControlB vs RRMSB q2 5062 ControlB vs SPMSB q2 5 077 and RRMSB vsSPMSB q2 5 048) Moreover upon examination ofthe loadings the same metabolites were found to beresponsible for the separations observed betweengroups in this secondary dataset with the exceptionof glucose which had a lower variable importance inseparating patients with SPMS from patients withRRMS in Set B

Each new model generated from Set B was testedfor its predictive ability using Set C the independent

Figure 2 1H nuclear magnetic resonance and metabolomics on serum samplescan differentiate relapsing-remitting from secondary progressivemultiple sclerosis

Serum analysis (A) Partial least squares discriminant analysis plot of serum samples com-paring patients with relapsing-remitting multiple sclerosis (RRMS) (orange triangles) and pa-tients with secondary progressive multiple sclerosis (SPMS) (blue squares) (B) Table to showthe q2 values of the models from Set A serum samples np 5 not predictive (ie q2 0) (C)Patients with RRMS (orange diamonds) and control volunteers (green circles) PPMS 5 pri-mary progressive multiple sclerosis


dataset that had not been used in building models AllSet B models were both sensitive and specific at pre-dicting group membership for patients RRMSB vsSPMSB sensitivity 5 09 and specificity 5 08RRMSB or SPMSB vs ControlB sensitivity and speci-ficity 5 10 Graphical representations of models andprediction results (figure 3 AndashC) and contingency

tables (figure 3 DndashF) demonstrate sensitivity andspecificity of the models (Fisher exact test 2-tailed)ROC plots (figure 3G) yielded areas under the curve(AUC) significantly greater than 05 indicating goodpredictive power (control vs RRMS AUC 5 100p 5 00045 control vs SPMS AUC 5 100 p 5

00006 SPMS vs RRMS AUC5 094 p5 00071)

Figure 3 Partial least squares discriminant analysis models and receiver operator characteristic curves demonstrate the selectivity andspecificity of the model separating relapsing-remitting and secondary progressive multiple sclerosis

Set B partial least squares discriminant analysis plots (darker hue) overlaid with Set C prediction results (lighter hue) misclassified patients are shown withopen symbols (A) Relapsing-remitting multiple sclerosis (RRMS) (orange triangles) vs control (green circles) (B) secondary progressive multiple sclerosis(SPMS) (blue squares) vs control (green circles) and (C) RRMS (orange triangles) vs SPMS (blue squares) Receiver operator characteristic (ROC) curvesand contingency tables (DndashF) 2 3 2 contingency tables for serum model prediction results summarizing correct and incorrect identifications In each caseFisher exact 2-tailed p values are less than 005 (ControlB vs RRMSB p5 00013 ControlB vs SPMSB p 00001 SPMSB vs RRMSB p5 0017) (G) ROCcurves constructed for each of the 3 serum models validated with a testing set of patients ControlB vs SPMSB is black squares and black line ControlB vsRRMSB is gray triangles and gray line SPMSB vs RRMSB is open circles and black line (ControlB vs RRMSB area under the curve [AUC] 5 100 p5 00045ControlB vs SPMSB AUC 5 100 p 00001 SPMSB vs RRMSB AUC 5 094 p 5 0007)


Comparison with other neurologic diseases The controlvs RRMS model (figure 2C) was compared withmodels separating ALS and AD from age-matchedcontrol samples (figure 4 A and B respectively)Neither of the latter 2 models was predictive with astrong overlap of groups in both cases

Effect of therapeutic agents age and sex No significantmodels could be generated when modeling patientswithin our study randomized to either active therapyor placebo arms of the Cupid trial (tetrahydrocannab-inol q2 52008) or BioMS trial (Dirucotide MBP-8298 q2 5 2016) Thus the presence of thesetherapeutic agents did not contribute to theseparations observed Full details of therapiesreceived are given in table e-1 Furthermore whenconsidering age as a factor within the controlpopulation a model between older ($46) and

younger volunteers (46) was not predictive (q2 52019) The cutoff of 46 was chosen as it lay halfwaybetween the mean ages of the RRMS and SPMScohorts Likewise when the sex of patients washighlighted on existing models (eg the Set ASPMS vs RRMS model) no discernible patternswere evident with both sexes being distributedevenly Thus none of these variables (presence oftherapeutic agent age or sex) contributed to thesignificant separations observed in our MS models

DISCUSSION Using PLS-DA metabolomicsanalysis of serum NMR spectra it was possible togenerate validated predictive models thatdistinguished RRMS from SPMS as well asseparating each of the MS patient groups (RRMSSPMS or PPMS) from healthy control cohortsThis is Class II evidence The majority of serummetabolites that underlay the model separationswere identified The sensitivity of the models tospecific metabolites varied between each of the MSgroups when compared with the control cohortand most notably between PPMS and either of theother 2 groups indicating sensitivity to thedifferent disease states The findings in otherdiseases (ALS and AD) served as a negative controland confirmed that the predictive models generatedin the MS cohorts were based on disease-specificmetabolite changes and did not reflect commonneurodegenerative processes per se

It is of particular interest that our serum modelsgenerated a positive and significant separationbetween RR and SP patient groups This separationwas highly robust with 2 independent sample sets(A and B) producing equivalent predictive modelsand identifying the same underlying metabolitesThe only difference in the metabolites identifiedwas glucose which had a lesser influence in drivingthe models derived from Set B than Set A This dif-ference is likely to have arisen from variations in thetiming of sample collection in the clinic and theperiod after the last meal Critically models createdfrom Set B accurately predicted group membershipof samples from the third independent set (Set C)These findings strongly support the concept thatNMRPLS-DA analysis of serum samples can reliablydifferentiate between the RR and SP stages of MS dis-ease progression Based on the positive outcome ofthese relatively small cross-sectional studies furtherlarger cohort multicenter longitudinal trials are war-ranted to assess the potential of this approach both asa means to monitor transitioning from RRMS toSPMS and as a surrogate marker of time to SP diseasein treatment trials

Our models were not able to separate the progress-ive groups of MS from each other This supports the

Figure 4 Models comparing different neurologic diseases to their respectivecontrols

Models to probe separation between 2 different neurologic diseases and control volunteersfrom their respective disease clinics (A) Patients with amyotrophic lateral sclerosis (ALS)(purple hexagons) and control volunteers (gray stars) model is nonpredictive (q2 5 2021)(B) Patients with Alzheimer disease (AD) (turquoise diamonds) and control volunteers (graystars) model is nonpredictive (q2 5 2021)


view that a similar pathologic mechanism underliesthese phases19 However the lack of a predictivemodel between the PP and RR patients appeared tobe disrupted by just 4 patients (3 RR and 1 PP) lyingin the incorrect model region suggesting that theunderlying pathologic mechanisms of RRMS andPPMS are distinct Although it is easy to distinguishRRMS from PPMS in the clinical setting under-standing the underlying pathogenic basis for PP dis-ease is important Thus these observations warrantinvestigation and validation in larger cohorts Fur-ther a study to assess the potential of this approachin predicting conversion of clinically isolated syn-dromes to RRMS would be valuable and might helpidentify those patients for whom early treatmentwould be beneficial

Previous studies of biomarkers in neurologic dis-eases including MS have investigated either individ-ual or small numbers of indicators20ndash23 Howeverdespite differences identified at a group level predic-tive value for an individual has been low24 Whereas itis possible to separate patients with MS from controlsusing techniques such as MRI121325 or using ametabolomics-based approach on CSF samples26 toour knowledge no studies have been reported inwhich patients with RRMS and SPMS have beenseparated from each other on the basis of serumNMR As an anecdotal example of the potentialstrength of this approach one patient with RRMSwas found persistently to lie in the SPMS portionof the model As this patient had been categorizedsome years previously her neurologist was consultedto obtain her current status The sample was collectedin 2008 and at follow-up 1 year later the patientreported progressing over that year By 2010 therewas no doubt that the patient was progressing Thesefindings suggest that the metabolomic profile of thepatientrsquos serum in 2008 was an early indicator ofentering the progressive stage of disease

It is important to note that owing to its class-based learning approach PLS-DA analysis reducesthe impact of underlying conditions such as therapeu-tic status or diet-based effects since these are likely tobe randomly distributed throughout our sample pop-ulation If any variance in a population does notexplain the difference between the user-defined clas-ses it is considered less important This was demon-strated in our data as some patients were on blindedclinical trials The models could discriminate betweenindividuals with RRMS or SPMS in a manner thatwas independent of treatment regimen suggestingthat the metabolite changes caused by the disease pro-gression were more consistent than those associatedwith the metabolism of any drugs present Thisadvantage of the PLS-DA approach is essential if itis to be useful clinically as it must be able to

discriminate between people with the specific diseasestates despite these nonspecific population variations

The PLS-DA models developed here could beused to mark the change from RRMS to SPMS inclinical studies and thus enable onset of secondaryprogression to be used as an outcome measure in ther-apeutic trials27 This is likely to be a more importantoutcome than relapse activity and better than short-term (3ndash6 months) disability change which includesrelapse-associated disability as well as progressionMoreover this approach may identify key metabolitesinvolved in the pathogenesis underlying progressionof MS28 and identify targets for the development ofnovel therapies

AUTHOR CONTRIBUTIONSAM Dickens contributed to the design of the principal experiments

performed the sample preprocessing for the NMR experiments assisted

with running the NMR experiments performed the multivariate analysis

contributed to writing and proofing of the manuscript JR Larkin con-

tributed to the design of the principal experiments performed the sample

preprocessing for the NMR experiments assisted with running the NMR

experiments performed the multivariate analysis contributed to writing

and proofing of the manuscript JL Griffin designed the NMR sequen-

ces used in this study and assisted with the multivariate analysis contrib-

uted to writing and proofing of the manuscript A Cavey collected the

MS samples used in this study and clinical data required for the samples

contributed to writing and proofing of the manuscript L Matthews col-

lected the MS samples used in this study and clinical data required for the

samples contributed to writing and proofing of the manuscript MR

Turner collected the ALS samples used in this study contributed to writ-

ing and proofing of the manuscript GK Wilcock collected the Alz-

heimer samples used in this study contributed to writing and proofing

of the manuscript BG Davis assisted with the interpretation of the

NMR spectra and multivariate analysis contributed to writing and proof-

ing of the manuscript TDW Claridge ran the NMR experiments in

this study and assisted with metabolite identification from the NMR spec-

tra contributed to writing and proofing of the manuscript J Palace con-

tributed to the design of the principal experiments provided the samples

from the patients with MS and aided interpretation of the results contrib-

uted to writing and proofing of the manuscript DC Anthony contrib-

uted to the design of the principal experiments and interpretation of the

results contributed to writing and proofing of the manuscript NR

Sibson contributed to the design of the principal experiments supervised

all work and helped with the analysis and interpretation of the results

contributed to writing and proofing of the manuscript

STUDY FUNDINGSupported by the Medical Research Council research grant G0401438

(NRS) G0901996 (LM) and Capacity Building Studentship to fund

AD (NRS DCA BGD) NRS and JRL were partly funded by

Cancer Research UK (grant C5255A12678) BGD is a Royal Society

Wolfson Research Merit Award recipient GKW was partly funded by

the NIHR Biomedical Research Centre Program Oxford

DISCLOSUREA Dickens was funded by the Medical Research Council UK under a

Capacity Building Studentship J Larkin was partly funded by Cancer

Research UK grant C5255A12678 J Griffin and A Cavey report

no disclosures L Matthews is funded by the Medical Research Council

UK grant G0901996 M Turner reports no disclosures G Wilcock is

partly funded by the NIHR Biomedical Research Centre Program

Oxford B Davis is a Royal Society Wolfson Research Merit Award

and received research support from the Medical Research Council UK

in the form of a Capacity Building Studentship (to fund AD) T Clar-

idge and J Palace report no disclosures D Anthony received funding


from the Medical Research Council UK in the form of a Capacity

Building Studentship (to fund AD) N Sibson received partial funding

for this work from the Medical Research Council UK under grant

G0401438 and a Capacity Building Studentship (to fund AD) In addi-

tion Dr Sibson was partly funded by Cancer Research UK grant

C5255A12678 Go to Neurologyorg for full disclosures

Received September 20 2013 Accepted in final form June 4 2014

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3 Rieckmann P Smith KJ Multiple sclerosis more than

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24435ndash437

4 Jones JL Coles AJ New treatment strategies in multiple

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5 Hauser SL Chan JR Oksenberg JR Multiple sclerosis

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6 Holmes E Foxall PJ Nicholson JK et al Automatic data

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7 Lindon J Holmes E Nicholson J Metabonomics in phar-

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8 Salek RM Xia J Innes A et al A metabolomic study of

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9 Lindon JC Holmes E Nicholson JK Pattern recognition

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10 Griffin JL Anthony DC Campbell SJ et al Study of

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49ndash54

11 rsquot Hart BA Vogels JT Spijksma G Brok HP Polman C

van der Greef J H-1-NMR spectroscopy combined with

pattern recognition analysis reveals characteristic chemical

patterns in urines of MS patients and non-human primates

with MS-like disease J Neurol Sci 200321221ndash30

12 McDonald W Compston A Edan G et al Recommen-

ded diagnostic criteria for multiple sclerosis guidelines

from the International Panel on the diagnosis of multiple

sclerosis Ann Neurol 200150121ndash127

13 Polman CH Reingold SC Edan G et al Diagnostic criteria

for multiple sclerosis 2005 revisions to the ldquoMcDonald

Criteriardquo Ann Neurol 200558840ndash846

14 Waterman C Currie R Cottrell L et al An integrated

functional genomic study of acute phenobarbital exposure

in the rat BMC Genomics 2010119

15 Tomasi G van den Berg F Andersson C Correlation

optimized warping and dynamic time warping as prepro-

cessing methods for chromatographic data J Chemom

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16 Fan WMT Metabolite profiling by one- and two-

dimensional NMR analysis of complex mixtures Prog

Nucl Magn Reson Spectrosc 199628161ndash219

17 Wishart DS Tzur D Knox C et al HMDB the

human metabolome database Nucleic Acids Res 2007

35D521ndashD526

18 Wishart DS Knox C Guo AC et al HMDB a knowl-

edgebase for the human metabolome Nucleic Acids Res

200937D603

19 Scalfari A Neuhaus A Degenhardt A et al The natural

history of multiple sclerosis a geographically based study

10 relapses and long-term disability Brain 2010133

1914ndash1929

20 Whitaker JN Kachelhofer RD Bradley EL et al Urinary

myelin basic protein-like material as a correlate of the

progression of multiple sclerosis Ann Neurol 199538

625ndash632

21 Whitaker JN McKeehan A Freeman DW Monoclonal

and polyclonal antibody responses to the myelin basic pro-

tein epitope present in human urine J Neuroimmunol

19945253ndash60

22 Ott M Demisch L Engelhardt W Fischer PA Interleukin-2

soluble interleukin-2-receptor neopterin L-tryptophan and

beta 2-microglobulin levels in CSF and serum of patients

with relapsing remitting or chronic progressive multiple scle-

rosis J Neurol 1993241108ndash114

23 Giovannoni G Lai M Kidd D et al Daily urinary neop-

terin excretion as an immunological marker of disease

activity in multiple sclerosis Brain 19971201ndash13

24 Kuhle J Leppert D Petzold A et al Neurofilament heavy

chain in CSF correlates with relapses and disability in

multiple sclerosis Neurology 2011761206ndash1213

25 Polman CH Reingold SC Banwell B et al Diagnostic

criteria for multiple sclerosis 2010 Revisions to the

McDonald criteria Ann Neurol 201169292ndash302

26 Tumani H Hartung H-P Hemmer B et al Cerebrospi-

nal fluid biomarkers in multiple sclerosis Neurobiol

Disease 200935117ndash127

27 Lesko LJ Atkinson A Jr Use of biomarkers and surrogate

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macol Toxicol 200141347ndash366

28 Frank R Hargreaves R Clinical biomarkers in drug dis-

covery and development Nat Rev Drug Discov 20032

566ndash580


DOI 101212WNL00000000000009052014831492-1499 Published Online before print September 24 2014Neurology Alex M Dickens James R Larkin Julian L Griffin et al

sclerosisA type 2 biomarker separates relapsing-remitting from secondary progressive multiple

This information is current as of September 24 2014

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rights reserved Print ISSN 0028-3878 Online ISSN 1526-632X1951 it is now a weekly with 48 issues per year Copyright copy 2014 American Academy of Neurology All

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can be differentiated from each other solely onthe basis of their urine metabolite composi-tion10 Using a similar approach characteris-tic metabolite patterns were identified inurine samples in a marmoset model of MSas well as in samples from patients with MScompared with either healthy volunteers orother neurologic controls11 These findingssupport the concept that biofluids analyzedby NMRPLS-DA may be highly sensitivenot just to the presence of pathology withinthe brain but to differences and changes inpathologic processes In this study we usedNMRPLS-DA analysis of serum metabolitecomposition to determine whether it is possi-ble to objectively differentiate between MSclinical subtypes on the basis of metabolomicbiofluid analysis

METHODS Biofluid samples We obtained serum samples

from 3 independent cohorts (A B and C) of patients with MS

(RR SP and primary progressive [PP]) as well as age- and sex-

matched control volunteers recruited from the John Radcliffe

Hospital Oxford UK (see STARD flowchart of patient

recruitment exclusion and methodology in figure 1) We

diagnosed patients with MS using the gold standard 2001 and

2005 McDonald criteria with at least 2 clinical attacks for RR

phenotypes1213 and continuous worsening of neurologic

impairment over at least 12 months not explained by

incomplete recovery from relapses for those defined as SPMS

Of the 58 patients with SPMS in the study 53 did not have a

relapse in the 2 years before and after sampling 3 had one relapse

and data on the remaining 2 are not available No patients with

PPMS had any recorded relapses A consultant neurologist

performed the clinical assessments of all patients with MS (JP)

Overall patient demographics are shown in table 1 and a more

detailed breakdown together with a list of therapies is provided in

table e-1 on the Neurologyreg Web site at Neurologyorg Blood

samples were collected and allowed to clot for 30 minutes at room

temperature in Vacutainers containing clot-activator and gel (BD

UK) Samples were centrifuged (3000 gn 10 minutes) and the

serum aliquoted and stored at 280degC Samples were excluded

Figure 1 STARD flowchart of multiple sclerosis patient recruitment and general methodology

MS 5 multiple sclerosis NMR 5 nuclear magnetic resonance PPMS 5 primary progressive multiple sclerosis ROC 5 receiver operator characteristicRRMS 5 relapsing-remitting multiple sclerosis SPMS 5 secondary progressive multiple sclerosis












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Female n () 6 (46) 15 (58) 43 (74) 16 (67)

Male n () 7 (54) 11 (42) 15 (26) 8 (33)

















00006 SPMS vs RRMS AUC5 094 p5 00071)

















































































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respectively)









respectively)
































































Female n () 6 (46) 15 (58) 43 (74) 16 (67)

Male n () 7 (54) 11 (42) 15 (26) 8 (33)

















00006 SPMS vs RRMS AUC5 094 p5 00071)

















































































24435ndash437








1994220284ndash296












49ndash54



















200418231ndash241






35D521ndashD526



200937D603




1914ndash1929




625ndash632




19945253ndash60
























566ndash580




















Reprints















00006 SPMS vs RRMS AUC5 094 p5 00071)

















































































24435ndash437








1994220284ndash296












49ndash54



















200418231ndash241






35D521ndashD526



200937D603




1914ndash1929




625ndash632




19945253ndash60
























566ndash580




















Reprints

















































































24435ndash437








1994220284ndash296












49ndash54



















200418231ndash241






35D521ndashD526



200937D603




1914ndash1929




625ndash632




19945253ndash60
























566ndash580




















Reprints






















Reprints




a type 2 biomarker separates relapsing-remitting from ...oct 21, 2014 · the transition from...

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