Nadia Magdalinou Clinical Research Fellow 27.02.14 Cerebrospinal Fluid Studies in MSA Nadia...

Cerebrospinal Fluid Studies

in MSA

Nadia Magdalinou

Clinical Research Fellow

27.02.14

Pathophysiology

Protein misfolding and pathological aggregation are common threads in neurodegeneration

α-Syn deposition in MSA Tau deposition in PSP

Courtesy of Dr Janice Holton

Overlapping Pathologies & Phenotypes in “Proteinopathies”

Adapted from Constantinescu and Mondello 2013

Biomarker

“a characteristic that is objectively measured and evaluated as an indicator of normal biological processes, pathogenic processes or pharmacologic response to a therapeutic intervention” (Biomarkers Definitions Working 2001)

‘Ideal’ biomarker: • sensitive• reproducible• closely associated with the disease process • non-invasive and inexpensive

Cerebrospinal fluid

• proximity to brain structures undergoing degeneration

• Proteins/peptides directly reflective of disease pathology would most likely diffuse into the CSF than any other fluid

• can be tested serially; assessing evolving pathology throughout the disease course

CSF studies in Parkinsonism

Main focus to investigate a priori defined compounds (hypothesis-driven)

• in patients and in healthy controls • looking for differences, patterns and associations; α-Syn, tau

Recently, trend towards hypothesis-generating, “omics” techniques

• unbiased and sensitive approach • identifying markers unexpectedly involved in

neurodegeneration

Even though several promising candidates exist there is still no reliable biomarker

Putative pathogenic pathways underlying CSF Biomarkers in PD

Parnetti, L. et al. Nat Rev.Neurol. 9, 131-140 (2013)

Total-α-Syn (Magdalinou, Lees, Zetterberg, JNPP 2014 in press)

Research Groups Participants Technique Main Findings

Van Dijk et al 2013

PD n=53, HC n=50 TR-FRET Decrease in both t- -Syn + t- -Syn:t-protein ratio levels in α αPD vs HC

Kang et al 2013

PD n=39 (drug naïve patients);HC n=63PPMI cohort

ELISA Decrease in PD vs HC

Wennstrom et al 2013

PD n=38, PDD n=22, DLB n=33, AD n=46, HC n=52

ELISA Decrease in PDD>PD>DLB vs AD + HC

Mollenhauer et al 2013

PD n=78 (de novo, drug-naive patients), HC n=48

ELISA (3rd generation) Decrease in de novo PD patients vs HC

Hall et al 2012

PD n=90, PDD n=33, DLB n=70, PSP n=45, CBD n=12, MSA n=48, AD n=48, Controls n=107

Bead-based multi-analyte assay (Luminex)

Modest decrease in AD>DLB+PDD>PD + MSA vs Controls , AD and PSP

Aerts et al 2012

PD n=58, MSA n=47, DLB n=3, VaP n=22, PSP n=10, CBD n=2

ELISA No difference between groups

Tatenoet al 2012

PD n=11, DLB n=6, MSA n=11, AD n=9, Controls n=11

ELISA • t- -Syn decrease in PD, DLB, MSA vs AD + Controlsα• No difference among PD, DLB, MSA


Training cohort: PD n=51, DLB n=55, MSA n=29, AD n=62, Controls n=76 Validation cohort: PD n=273, DLB n=66, PSP n=8, MSA n=15, NPH n=22, Controls n=23

ELISA (1st and 2nd generation) • Decrease in PD, DLB, MSA vs AD, NPH, PSP and controls• High degree of concordance in t-a-Syn levels between PD

+ MSA

Parnetti et al 2011

PD n=38, DLB n=32 , AD n=48, FTD n=31Controls n=32

ELISA • t- -Syn decrease in all diseased groups (esp DLB/FTD)α• ratio: decrease in PD vs all other diseased groups

Shi et al 2011

Discovery cohort: PD n=126, MSA n=32AD n=50, Controls n=137Validation Cohort :PD n=83


Decrease in PD vs controls and AD

Hong et al 2010

PD n=117, AD n=50, HC n=132 Bead-based multi-analyte assay (Luminex)

Decrease in PD vs AD and controls (after omitting samples with high haemoglobin concentration)

Nogutsi et al 2009

DLB n=16, AD n=21 ELISA No difference

Spies et al 2009

DLB n=40, AD n=131, VaD n=28, FTD n=39 ELISA No difference

Ohrfelt et al 2009

PD n=15, DLB n=15, AD n=66, Controls n= 55 ELISA Decrease in AD, no difference in parkinsonian groups


PD n=8, DLB n=38, AD n=13, CJD n=8, Controls n=13

ELISA (1st and 2nd generation) Marginal decrease in LBD and PD vs all other groups

• Inconsistent initial data• Consensus emerging: decreased in DLB, PDD, PD

and MSA, but not in PSP and CBD• Can differentiate synucleinopathies from

tauopathies and controls• Cannot discriminate between synucleinopathy

groups

Phosphorylated (p- -Syn α ) and Oligomeric α-syn (o- -Syn α )

Research Groups

Participants Analytes Technique Main Findings

Comments

Wang et al 2012

Discovery cohort:PD n=83, MSA n=14, PSP n=30, AD n=25, HC n=51Validation cohort:PD n=109, MSA n=20, PSP n=22, AD n=50, HC n=71

t- -Syn αp- -Synαp- -Syn:t- -Syn α αratio


• t- -Syn decrease αin PD+MSA vs controls

• Increase -Syn αratio in MSA vs PSP

• Increase -Syn αratio in PD vs controls and PSP

Positive correlation with UPDRS in PD, no correlation with H&Y score

Fouldset al 2012

PD n=39, DLB n=17, PSP n=12, MSA n=8, Controls n=26

Post-mortem samples

t- -Syn αp- -Synαo- -Syn αo-p- -Syn α

Modified Sandwich ELISAs

• o-p- -Syn α can differentiate pts with MSA from all other groups

No correlation with disease severity/duration

• Wang: p- -Syn:t- -Syn ratio could discriminated MSA from PSPα α• Foulds: o-p- -Syn can differentiate MSA from other synucleinopathies and α

tauopathies• No correlation with age/disease duration/cognitive function

Total & Phosphorylated Tau

Research Groups

Participants Technique Main Findings

Kang et al 2013

PD n=39 (drug naïve patients), HC n=63PPMI cohort


Decrease in t-tau + p-tau in PD vs controls

Hall et al 2012



Increased t-and p-tau in AD vs DLB +PDD

Bech et al 2012

PD n=22, PDD n=3, DLB n=11, MSA n=10, PSP n=20, CBD n=3

ELISA No difference between parkinsonian groups

Anderson et al 2011

DLB n=47, PDD n=17, AD n=150 ELISA Increased t-tau in DLB vs PDD

Shi et al 2011

Discovery cohort:PD n=126, MSA n=32, AD n=50, Controls n=137Validation Cohort: PD n=83


• Decrease in PD vs to controls• Decrease in PD + MSA vs AD

Parnetti et al 2011

PD n=38, DLB n=32 , AD n=48, FTD n=31Controls n=32

ELISA • Increase in AD>FTD>DLB vs PD and controls• No difference between PD and controls

Montine et al 2010

PD n=41, PDD n=11, AD n=49, HC n=150 Bead-based multi-analyte assay (Luminex)

• t-tau: no difference between parkinsonian groups• p-tau: reduced in PD vs HC

Süssmuthet al 2010

PSP-RS n=20, PSP-P n=7, MSA-P n=11, MSA-C n=14, PD n=23, Controls n=20

ELISA p-tau:t-tau ratio lower in PSP and MSA vs PD

Alves et al 2010

PD n=109, AD n=20, HC n=36

ELISA No difference between PD and controls

Ohrfeltet al 2009

PD n=15, DLB n=15, AD n=66, Controls n= 55

ELISA No difference between parkinsonian groups

Compta et al 2009

PD n=20, PDD n=20, HC n=15 ELISA t- and p- tau: increase in PDD vs PD and controls

Parnetti et al 2008


ELISA • t-tau: DLB>PDD>controls• p-tau: no difference between parkinsonian groups

• Inconsistent data• Can discriminate PD from AD• No difference in parkinsonian conditions• Age, not diagnosis, strongest factor affecting t-tau

levels

Neurofilament-light chainResearch Groups

Participants Technique Main Findings Comments

Hall et al 2012



NF-L differentiates PD from atypical parkinsonism

Higher levels of NF-L correlate with disease severity in PD, AD and PD

Bech et al 2012

PD n=22, PDD n=3, DLB n=11, MSA n=10, PSP n=20, CBD n=3

ELISA Higher NF-L levels in atypical patkinsonian disorders vs PD

Lower levels NF-L in PD despite significantly longer disease duration compared with atypical parkinsonian disorders

Constantinescu et al 2010

PD n=10, MSA n=21, PSP n=14, CBD n=11, HC n=59(x2 consecutive samples available in all diseased groups, other than CBD)

ELISA • NF-L: normal levels in PD, elevated in MSA, PSP+CBD

• No statistical significance overtime

NF-L remain stable despite disease progression

• NF-L normal in PD and increased in MSA, PSP and CBD vs controls

• No difference in atypical parkinsonism• Levels remain stable despite disease progression in a

longitudinal study

Oxidative Stress MarkersResearch Groups Participants Analytes Technique Main Findings Comment

Herbert et al 2013

PD n=43, MSA n=23, Controls n=30

DJ-1 ELISA • Increase in MSA>PD• Significant difference

in MSA vs PD, MSA vs Controls and PD vs Controls

Diagnostic accuracy for discriminating MSA from PD improved by combining DJ-1 with t-tau + p-tau

Salvesen et al 2012

PD n=30, DLB n=17, MSA n=14, PSP n=19

DJ-1 ELISA No difference among groups

Shi et al 2011

Discovery cohortPD n=126, MSA n=32AD n=50, Controls n=137Validation CohortPD n=83

DJ-1


Decrease in MSA + PD vs controls + AD

Hong et al 2010

PD n=117, AD n=50, HC n=132

DJ-1 Bead-based multi-analyte assay (Luminex)

• Decreased levels in PD vs Controls and AD

• No difference between AD + Controls

Correlation with age (esp in HC), but not with disease severity


PD n=6, MSA n=13, PSP n=18, CBD n=6, HC n=18

Urate Enzymatic method on a modular system

No difference

MaetzlerEt al 2011

PD n=55, PDD n=20, DLB n=20, Controls n=76

Uric acid ADVIA analyser + photometric methods

Increase in PD vs DLB Positive correlation with Aβ42 in HC but not in DLB

Gmitterová et al 2009

PD n=27, PSS n=21, LBD n=18, AD n=18 Controls n=13

8-OHdgG ELISA Increase in PD and PDD vs controls, but only significant difference between non demented PD + controls

Increase in 8-OHdG levels with lower MMSE score in PDD

• DJ1: inconsistent results; ? Increase in MSA could differentiate MSA from PD• Urate: inconsistent results

Inflammatory Markers

Research Groups

Participants Analytes Technique Main Findings Comment

Wennstrom et al 2013


Neurosin ELISA • Lowest levels in DLB, but no difference between synucleinopathies

• When pooled, synucleinopathies decrease levels vs AD + HC

• Decreased CSF neurosin levels significantly associated with decreased t- -Syn levels in HC, α PD + PDD, but not in AD + DLB

• Correlation with age, esp in HC

Shi et al 2011

Discovery cohortPD n=126, MSA n=32AD n=50, Controls n=137Validation CohortPD n=83

Fractalkine


Decrease in MSA vs PD, AD + controls

• Fractalkine alone could differentiate PD from MSA

• Fractalkine: Aβ42 ratio: positive correlation with disease severity + progression in PD

Wang et al 2011

PD n=86, MSA n=20, AD n=38 HC n=91

Complement C3/ factor H (FH)


• C3: decrease in MSA vs PD + HC; increase in AD vs all other groups

• FH: increase in AD vs PD + HC

• C3:FH ratio: decreas in MSA vs all other groups

• C3: Aβ42 ratio + FH: Aβ42 ratio correlated with PD severity + presence of cognitive impairment

• C3 + FH levels correlated with disease severity in AD (MMSE scores)

Maetzleret al 2010

PD n=38, PDD n=20, DLB n=21m Controls n=23

Neprilysin Fluorometric assay

Decrease in DLB + PDD vs PD + Controls

• Negative correlation with dementia duration

• Positive correlation with Aβ42 levels

MSA and PD could be differentiated by the CSF Fractalkine and not by α-Syn

Summary of CSF Markers in MSA

Biomarker Conclusion

t-α-Syn • most promising marker• can differentiate synucleinopathies from tauopathies and

controls, but not between synucleinopathy groups

o-p-α-Syn • ? can discriminate MSA from other synucleinopathies• larger cohorts and ante mortem CSF studies required

t-tau/p-tau • no disease specific pattern in parkinsonian disorders

NF-L • can discriminate PD from atypical parkinsonian conditions

DJ-1 • ? could help differentiate MSA from PD

Oxidative stress/Inflammatory

• promising results requiring further studies

Challenges/Limitations

1. Most studies are retrospective and do not have pathological confirmation

2. Lack of standardisation of pre-analytical (sampling collection, handling and storage)

and analytical (analysis execution/sample processing) factors

3. Lack of assay standardisation; different assays can give different absolute concentrations of the protein, making it almost impossible to use global reference limits and diagnostic cut-off points

4. Heterogeneous neurodegenerative groups: in terms of age, disease duration and disease severity

5. Heterogeneous controls groups: including healthy controls, patients with non-neurodegenerative neurological conditions or patients with possible neurodegenerative conditions like mild cognitive impairment and normal pressure hydrocephalus

6. Lack of combination of different biomarker modalities- imaging and CSF markers

TARGETED CEREBROSPINAL FLUID MARKERS IN PARKINSONISM

Methods

Standardised protocol for the collection and storage of CSF (as recommended by the Alzheimer’s

Association QC Program for AD) and sample processing

Prospective, cohort study of patients with parkinsonian conditions, healthy and dementia controls recruited from NHNN

• Patients monitored periodically for at least two years to maximise accuracy of clinical diagnosis

• Dx according to current consensus criteria• Healthy controls with no history of

neurological/psychiatric disease

A subgroup of participants underwent brain imaging to assess whether the combination of multiple modalities improves diagnostic accuracy

≈50% of participants have signed up for brain donation and we have already pathological confirmation in 10 patients

Hypothesis

Parkinsonian syndromes can be differentiated using a combination of targeted cerebrospinal fluid markers

CSF analysis

NHNN Clinical Lab

Routine Ix:Total protein

WCC

RCC

‘Dementia’ markerst-/p- tau

Aβ42

Gothenburg Lab

Proteomic patterns

Gothenburg Lab

NFL

α-Syn

MCP-1

YKL-40

APPα

APPβ

MCP-1 and YKL-40

• Monocyte Chemoattractant Protein-1: a small cytokine

• YKL-40: a secreted glycoprotein named after its three terminal amino acids

• involved in neuroinflammatory processes associated with neurodegeneration in AD

• Decreased levels of YKL-40 in synucleinopathies compared with tauopathies and healthy controls (Olsson et al 2013)

sAPPα and sAPPβ

• 2 soluble metabolites resulting from proteolytic processing of Amyloid Precursor Protein (APP)

• sAPPα and sAPPβ unaltered in AD, but not investigated in other neurodegenerative conditions

Final number of subjects included in the analysis

Total number eligible for study

n=221

Total recruited n=177

Total included in analysis

n=169

Total Number eligible for study

n= 42

Total recruited n=30

Total included in analysis

n=30

8 patients with mild cognitive

impairment, excluded from

analysis

DISEASED SUBJECTS HEALTHY CONTROLS

Demographic and Clinical Characteristics HC

n=30

PSP

n=40

CBS

n=17

MSA

n=31

PD

n=31

AD

n=26

FTD

n=16

“Unclass’

n=8

No (%) of

men

15

(50)

24

(60)

5

(29.4)

16

(51.6)

20

(64.5)

9

(34.6)

11

(68.8)

4

(50)

Age (yrs) 63.5 a

(50-67)

69.5 b

(66-72.5)

71

(63-75)

64

(60-67)

67

(61-74)

63

(58-68)

63.5

(57-71.5)

74.5 c

(69-79.5)

DisDur

(yrs)

N/A 5

(3-8)

4

(2-5)

4

(3-6)

8 d

(5-15)

3

(2-4)

2.5

(2-4.5)

3

(2-6)

H&Y score N/A 4 e

(3-5)

3

(3-4)

3

(2.5-4)

2.5

(2-4)

N/A N/A 3

(2.5-3)

UPDRS N/A 36

(28-43)

41.5

(35-48)

N/A 28

(24-30)

N/A N/A ND

MMSE 30

(30-30)

27

(25-28.5)

26

(26-28)

N/A 28 f

(24-30)

ND ND ND

• no significant age difference in parkinsonian syndromes • significant difference in disease duration between the PD group and the rest• significant difference in H&Y score between PSP and MSA, PD and ‘unclassifiable’ • no significant difference in UPDRS • significant difference in MMSE scores between PD and controls

‘Dementia’ Markers

HC

n=30

PSP

n=40

CBS

n=17

MSA

n=31

PD

n=31

AD

n=26

FTD

n=16

“Unclass’

n=8

t-tau

(ng/mL)

303.5

(189-402)

260.5

(234-369)

275

(217-377)

277

(210-341)

339

(226-444)

806 a

(469-1140)

241.5

(219-361.5)

358.5

(234-388.5)

p-tau

(ng/mL)

38

(29-54)

34

(31-44.5)

36

(30-43)

34

(28-38)

39

(31-54)

81.5 b

(57-94)

35

(30-44.5)

36

(33-41)

Aβ42

(ng/mL)

953

(771-1199)

659

(539-838.5)

716

(547-975)

775

(520-911)

770

(584-1044)

363.5 c

(264-511)

878

(695-1140.5)

689

(478.5-858)

t-tau/Aβ42

(ng/mL)

0.3

(0.22-0.36)

0.415

(0.3-0.53)

0.35

(0.23-0.53)

0.43

(0.28-0.53)

0.38

(0.3-0.61)

2.17 d

(1.495-3.475)

0.335

(0.22-0.465)

0.48

(0.37-0.66)

Aβ42, t-tau and p-tau showed a significant difference between AD and all other groups, but did not discriminate between parkinsonian syndromes

Targeted Markers

• Significant reduction in MSA compared with healthy controls and AD• No significant difference in PD

Targeted Markers

• There was a significant increase in all studied groups compared with healthy controls

• PSP, CBS and MSA pts had higher levels compared with PD, AD, FTD and Unclassifiable pts

Targeted Markers

• There was a significant difference between healthy controls and all studied groups

• In PSP there were significant higher levels compared with FTD

Targeted Markers

• Healthy controls had significantly lower levels compared with PSP, MSA and Unclassifiable pts

• There were higher levels in CBS compared with PD and Unclassifiable pts

Targeted Markers

• Healthy controls had significantly higher levels compared with PSP, MSA and CBS

• PSP, CBS and MSA had significantly lower levels compared with PD and AD

Targeted Markers

• Healthy controls had significantly higher levels compared with atypical parkinsonian groups

• Atypical parkinsonian groups had lower levels compared with PD, but there were no differences between them

• PSP had lower levels compared with AD

SummaryBiomarker Findingst-α-Syn Most promising marker: can differentiate synucleinopathies from

tauopathies and controls, but not between synucleinopathy groups• In our cohort, we did not confirm above findings; α-Syn was

significantly decreased in MSA and not in PD

t-tau/p-tau No disease specific pattern in parkinsonian disorders• Confirmed findings in our cohort

NF-L Can discriminate PD from atypical parkinsonian conditions, but no significant difference between PSP, MSA, CBS• Confirmed findings in our cohort

YKL-40 Decreased levels in synucleinopathies compared with tauopathies and controls• Good sensitivity, but poor specificity to differentiate

neurodegenerative diseases from healthy controls

MCP-1 • Significant difference in MSA and PSP compared with healthy controls

• Could differentiate CBS from PD

APPαAPPβ

• Could discriminate atypical parkinsonian groups from PD and healthy controls, but there was no significant difference between PSP, MSA and CBS

Summary

Conclusion

• Unpublished data• Preliminary analysis only• Promising early results: reproduced other

published data• Unlikely that a single biomarker will hold the

answer: combination of markers may be required

CEREBROSPINAL FLUID PROTEOMICS IN PARKINSONISM

Proteomics

• protein content (proteome) of a sample is characterised

• proteomes between patients and controls are compared and differences are identified

Technology:

1. separation of proteins

2. analysing proteins through mass spectrometry

3. quantifying and identifying proteins through advanced data processing

Proteomics StudiesResearch Groups

Technique Main Findings


SELDI-TOF MS • 4 proteins: ubiquitin, β2-microglobulin and two secretographin 1 fragments

• Differentiated PD + HC from atypical parkinsonism with an AUC of 0.8

Ishigami et al 2012

MALDI-TOF MS • Using the proteomic pattern (combined set of many protein peaks)

• Able to differentiate PD from MSA, even at early stages

Limitations:1. inherently biased towards identification of abundant proteins2. blood contamination in CSF major effect on protein concentration3. not standardised sample preparation/implementation and processing

technologies between research groups difficult to validate and replicate results

Proteomics

Hypothesis

Cerebrospinal fluid proteomic patterns can discriminate between parkinsonian syndromes

Discovery Cohort Results

• Immunoglobulinsuperfamilymember8Fragment• Amyloidlikeprotein1OSHomosapiensGNAPLP1PE4SV• NeurosecretoryproteinVGFOSHomosapiensGNVGFPE1• EndothelinBreceptorlikeprotein2OSHomosapiensGN• Scrapieresponsiveprotein1OSHomosapiensGNSCRG1P• Lymphocyteantigen6HOSHomosapiensGNLY6HPE2SV1• Isoform2ofCalsyntenin1OSHomosapiensGNCLSTN1• HaptoglobinOSHomosapiensGNHPPE1SV1HPT_HUMA• Alpha1antichymotrypsinOSHomosapiensGNSERPINA3PE• ComplementC3OSHomosapiensGNC3PE1SV2CO3_HU• Iggamma4chainCregionOSHomosapiensGNIGHG4PE1• Collagenalpha1IchainOSHomosapiensGNCOL1A1PE• ApolipoproteinEOSHomosapiensGNAPOEPE1SV1A• Isoform2ofFibrinogenalphachainOSHomosapiensG• IsoformGammaAofFibrinogengammachainOSHomosap• ProteinAMBPOSHomosapiensGNAMBPPE1SV1AMBP• Isoform2ofMajorprionproteinOSHomosapiensGNP• Alpha1BglycoproteinOSHomosapiensGNA1BGPE1SV4• IgkappachainVIIIregionVGFragmentOSHomosapie• Secretogranin1OSHomosapiensGNCHGBPE1SV2S• Heparincofactor2OSHomosapiensGNSERPIND1PE1SV• Isoform2ofGelsolinOSHomosapiensGNGSNGELS• MonocytedifferentiationantigenCD14OSHomosapiensG• ComplementcomponentC7OSHomosapiensGNC7PE1SV2• ChromograninAOSHomosapiensGNCHGAPE1SV7CMG• Secretogranin2OSHomosapiensGNSCG2PE1SV2SC• Insulinlikegrowthfactorbindingprotein2OSHomosa• Fibulin1OSHomosapiensGNFBLN1PE1SV4FBLN1_H

• IsoformCofFibulin1OSHomosapiensGNFBLN1FB• PeptidylprolylcistransisomeraseBOSHomosapiensG• Insulinlikegrowthfactorbindingprotein6OSHomosa• Zincalpha2glycoproteinOSHomosapiensGNAZGP1PE1• Phosphatidylethanolaminebindingprotein1OSHomosapi• SerumamyloidA4proteinOSHomosapiensGNSAA4PE1• Chitinase3likeprotein1OSHomosapiensGNCHI3L1PE• ProstaglandinH2DisomeraseOSHomosapiensGNPTGDSP• CellsurfaceglycoproteinMUC18OSHomosapiensGNMCAM• LumicanOSHomosapiensGNLUMPE1SV2LUM_HUMAN• LysozymeCOSHomosapiensGNLYZPE1SV1LYSC_HUM• Isoform2ofEGFcontainingfibulinlikeextracellula• ProcollagenCendopeptidaseenhancer1OSHomosapiens• Extracellularmatrixprotein1OSHomosapiensGNT• Metalloproteinaseinhibitor1OSHomosapiensGNT• InteralphaGlobulininhibitorH2OSHomosapiensGN• Vsetandtransmembranedomaincontainingprotein2AOS• Secretogranin3OSHomosapiensGNSCG3PE1SV3SC• ProteinFAM3COSHomosapiensGNFAM3CPE1SV1FAM• Neuralproliferationdifferentiationandcontrolprotei

Second Stage of Proteomics Project

Validation Cohort n=67• P

SP n=18, CBS n=7, MSA n=14, PD n=13, HC n=15

Proteins identified• d

ata filtered by removing proteins not identified in <50% of subjects/not statistically significant

Match statistically significant proteins found in both Discovery & Validation cohorts

Study proteins identified using immunoassays or targeted spectrometry assays

• Early and accurate diagnosis in MSA is very important, esp with emergence of disease modifying drugs

• Clinical diagnosis is inaccurate, particularly in the early stages

• ‘Holy grail’- accurate diagnostic test

• Remains elusive; on-going work with established, hypothesis testing biomarkers and hypothesis generating markers from proteomics studies

• Combination of markers may be required

Conclusion

THANK YOU

Acknowledgements

RLWIAndrew Lees

Tom Warner

John Hardy

Rohan de Silva

Janice Holton

Helen Ling

Atbin Djamshidian

Alastair Noyce

Karen Doherty Geshanti Honhamuni

Connie Luk

Iliyana Komsiyska

Karen Shaw

DRCJason Warren

Nick Fox

Cath Mummery

Jon Schott

Martin Rossor

Ross Paterson

Jamie Toombs

Katie Judd

NHNNHenry Houlden

Nick Wood

Kailash Bhatia

Patricia Limousin

Tom Foltynie

Simon Farmer

Paul Jarman

Paola Giunti

Chris Mathias

Gordon Ingle

Lucia Schottlaender

Mike Lunn

Miles Chapman

Funding: PSP association, RLWI, Wolfson foundation award

GothenburgHenrik Zetterberg

Johan Gobom

Max Petzold

Nadia Magdalinou Clinical Research Fellow 27.02.14 Cerebrospinal Fluid Studies in MSA Nadia...

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