alzheimer and ftd - diagnostic tools

8/8/2019 Alzheimer and FTD - Diagnostic Tools

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Le indagini strumentali nella diagnostica

della malattia di Alzheimer e della

demenza frontotemporale


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Introduction


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Neurodegenerative Brain Diseases Alzheimers Disease (AD)

Vascular Dementia (VaD) Lewy Body Dementia (LBD)-

Parkinsons Disease (PD)

w emen a Frontotemporal Dementia

(FTD)

Others e. g. Creutzfeldt-Jakob disease (CJD),

Huntingtons disease (HD)


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Abnormal Protein Aggregates

Senileplaques

Neurofibrillary

tangles

NeurodegenerationLewyBodies

Pick

BodiesPoly-Q

inclusions

PrPSc

plaques


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Cerebral Proteinopathies

DiseaseAggregated

protein

Pathological

lesionProteopathy

AD

Amyloid Amyloid

PlaquesAmyloidosis

Neurofibrillarau

tanglesauopat y

CAA, HCHWA-D Amyloid Vasculature Amyloidosis

Picks disease Tau Pick bodies Tauopathy

PD/LBD Synuclein Lewy bodies/neurites Synucleinopathy

CJD/GSS Prion Prion plaques Prionopathy


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Alzheimers dementia


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Occipital

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Frontal


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trans-entorhinal cortex


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Entorhinal cortex

likely asymptomatic


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Hippocampus

possible MCI


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13/12/2010 11probable MCI

Temporal pole (BA 38)


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inferior temporal cortex

very probable MCI


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13/12/2010 13Subtle but significant cognitive impairment

mid temporal cortex


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13/12/2010 14First stage of clinical Alzheimers disease

polymodal association cortex


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Broca area(BA 44)

Intermediate stage of AD


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Unimodal areas

13/12/2010 16Moderate to severe AD

Unimodal areas


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All neocortical areas are affected, and

many subcortical areas as well

13/12/2010 17Last stage of AD


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Taupathies


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FTD spectrum


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FTD spectrum


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BloodBrain


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Neuroimaging techniques


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In vivo amyloid imaging

PET ligands with affinity for amyloid plaques

18FFDDNP

11CSB13

s urg compoun orN-methyl-11C2-(4-methylamino-phenyl)-6-hydroxybenzothiazole (Thioflavine T derivative)

PiB PET imaging in AD patients reveals adistribution of binding consistent with the

amyloid pathology (Thal et al, 2002; Klunk et al., 2004)


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PiB PET in AD


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PiB PET in AD


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PiB PET in AD

Confirmation of AD diagnosis

Insight into the role of A in ADpathophysiology and clinical manifestations

o c ange n eve s regar ess oprogression after about 2 years; only FDG-PET

metabolism significantly declined amyloid

deposition plateau hypothesis (Engler et al., 2006)

On autopsy studies, amyloid pathology weakly

correlates with cognition relative to other markers

of disease severity, i.e. NFT burden (Terry et al., 1991)


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PiB PET in AD

Assessment of brain reserve in modifying the

relationship between AD pathology andphenotype

.,

Equivalent loads of AD pathology may be

associated to different degrees of cognitive

dysfunction, being more highly educated patients

less affected (Roe et al., 2007)

PiB uptake in beer educated than lesseducated AD patients matched for disease

severity (Kemmpainen et al., 2008)


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PiB PET in AD


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PiB PET in FTD

Differential diagnosis of FTD spectrum

40% of patients with PNFA had AD pathology atautopsy rather than the spectrum of pathology

chan es associated to FTD Alladi et al. 2007

A minority of FTD paents presents PiBbinding

Amyloid deposition


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MRI in early detection and predicting the

progression of AD


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progression of AD

Alzheimer Disease Neuroimaging Initiative

(ADNI) (Jack et al., 2008; Petersen et al., 2010)multicenter MRI longitudinal study of MCI and AD

with standardized data acquisition and processing

MCI individuals converting to AD present avery similar pattern of atrophic changes to theAD up to 1 year before AD diagnosis (Risacher et

al., 2009) Medial temporal lobe atrophy is the best

indicator of progression to AD (Risacher et al., 2009)


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progression of AD

Voxel-based morphometry (VBM) is reliable indetecting atrophy of specific brain regions in AD

Mediotemporal lobes and lateral temporal andparietal association areas in AD and MCI (Baron etal. 2001 Pennanen et al. 2005

Patterns of atrophy in MCI at risk for conversionresemble those of AD patients (Chetelat et al., 2005)

Atrophy of mediotemporal, laterotemporal, and

parietal association areas may be present ingenetically predisposed individuals prior to theonset of cognitive dysfucntion (Teipel et al., 2004)


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progression of AD

Some studies suggest that MRI is not superior toCSF biomarkers in predicting AD conversion(Bouwman et al., 2007)

Other studies, which employ automated scorings stems, indicate MRI as a more sensitive

predictor of cognitive decline with respect to CSFbiomarkers (Vemuri et al., 2009)

Utility of MRI and CSF biomarkers together inidentifying those individuals with very mild (CDR0.5) or mild (CDR 1) AD at higher risk ofprogression to advanced disease (Hampel et al., 2008)


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MRI as an indicator of AD severity

Cognitive dysfunction correlates with cortical

atrophy on MRI (Ridha et al., 2008; Jack et al., 2005; Stoub et al.,2005)

MRI is a ood surro ate of neuro atholo ic

findings (Bobinski et al., 2000; Zarow et al., 2005)

Rates of change on MRI, but not CSF tau,correlate with change in MMSE scores (Sluimer etal., 2010)

NFT pathology precedes neuronal loss in AD affected areas

MRI variations reflect the final outcome of the numerous intricatepathological processes that characterize AD (Vemuri et al., 2009)


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FDG-PET in AD

Metabolic rate for glucose (CMRglc)

posterior cingulate, parietal, temporal, andprefrontal cortex in patients with AD or mild

.,

Correlation with severity and progression(Alexander et al., 2002; Minoshima et al., 1995)

Prediction of the rate of (1) cognitivedecline in individuals with very mild

dementia and of (2) AD pathology (Silverman etal., 2001)


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FDG-PET in AD

Hippocampal glucose metabolism predict

decline to MCI in normal individuals andconversion to AD in MCI subjects (de Leon et al.,2001; Drzez a et al., 2003

Glucose metabolism in enthorhinal cortexpredict decline to MCI with a sensitivity of

83% and a specificity of 85% (de Leon et al., 2007)

FDG-PET correlates with CSF A42 andcognitive dysfunction (Jagust et al., 2009)


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FDG-PET in AD


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FDG-PET as a research tool in AD

Detection of AD changes (bi-parietal and

temporal hypometabolism) in individuals withgenetic susceptibility to AD and with no sign

.

Correlation with AD progression (differentlyfrom CSF biomarkers)

Secondary outcome measures in clinical trials Lack of neuropathologic correlations and costs


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Functional MRI in AD

Measures changes in blood flow associated brainactivation during cognitive tasks

Alterations in neuronal function likely precedeneuronal loss detectable with volumetric MRI

terat ons n unct on ur ng cogn t ve tas s may emore sensitive then resting state function - Stresstest

Provide a link between the pathology of AD and the

early clinical symptomatology and perhaps lack ofsymptoms in occult amyloid pathology


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The default network: brain regions activated

during several internally focused tasks such asremembering past events or imagining future

,

Medial temporal lobe and hippocampus, medialfrontal association area, posterior cingulate,

retrosplenial, inferior parietal cortex and lateral

temporal lobe (Buckner et al., 2005)


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The default network is disrupted in

subjects with amyloid deposition, even in thepreclinical period (Sheline et al., 2010; Sperling et al., 2009)


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Imaging in FTD

Up to 90% sensitivity of detecting FTD with

SPECT or PET (Mendez et al.,2007)

Differentiation of FTD from AD

Automate met o s or quanti ying regionaatrophy

Functional studies (metabolites, regional

cerebral blood flow, glucose metabolism)


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Structural MRI in FTD

Typical (?) MRI findings in FTD

Frontal and temporal lobes atrophy (oftenasymmetric)

Caudate atrophy

Typical (?) MRI findings in AD

mesial temporal lobe and hippocampal atrophy


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Hippocampal/mesial temporal atrophy alone

does not distinguish AD from FTLD (Bocti et al.,2006; Galton et al.,2001)

A severe or asymmetrical pattern of amygdalaatrophy (along with hippocampal atrophy) (Barneset al.,2006)

An atrophy pattern involving the frontal lobes andanterior temporal regions (Bocti et al.,2006)


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Distribution of cerebral atrophy in a patient with

AD (A) and frontotemporal dementia (FTD) (B)

Both patients had similar interscan intervals (1 1 months). Areas of

volume loss are highlighted in red.


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MR Spectroscopy in FTD

In vivo noninvasive estimation of brain

metabolitesN-acetyl aspartate (NAA) neuronal acvity

Significant MI and NAA and glutamate inthe frontal region in FTD vs AD

MI prior to NAA in temporal regions(glial proliferation in early stages of FTD)(Ernst et al.,1997)


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FDG-PET in FTD

Pattern of hypometabolic regions in

differential diagnosis of dementias (Foster et al.,2007; Ibach et al., 2004; Silverman et al., 2002)

The metabolic decline occurs uite earl in

FTD courseRecently, the Centers of Medicaid and Medicare

approved reimbursement of FDG-PET for the

indication of distinguishing AD from FTD


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FDG-PET in FTD

Frontal and anterior temporal cortices in FTD

vs posterior cingulate and parietotemporalcortices in AD (Diehl-Schmid et al., 2007; Ishii et al., 1998; Jeonget al., 2005a; Mosconi et al., 2008)

Also other areas i.e. anterior cingulate, uncus,insula, and subcortical regions including basal

ganglia are involved in FTD (Jeong et al., 2005)

Hemispheric metabolic asymmetry is common(Garraux et al., 1999; Jeong et al., 2005)


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SPECT in FTD

Uptake of Tc99m lipid-soluble radionucleotide

(perfusion marker) Correct diagnosis in 100% of FTD and 90% of

C arpentier et a ., 2000

Sensitivity of 87.5% and specificity of 76.8% inFTD vs AD diagnosis (Sjogren et al., 2000)

Hypoperfusion of posterior cingulate cortex inAD vs FTD (posterior cingulate sign) (Bonte et al.,2004)


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Biomarkers


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Dementia biomarkers

Biomarker discovery research

GenomicsProteomics

(Hu et al., 2007; Ray et al., 2007)

Inflammation, oxidative stress,apolipoproteins, neurodegeneration markers(Maes et al., 2007)


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Holistic approach(genome, proteome, transcriptome,

metabolome)

Pathophysiologic approach

(candidate marker)


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Pl bi k


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Plasma biomarkers

Baseline plasma levels of A1-42 are higher

in patients with AD The Plasma A40/42 ratio predicts a high risk

normal individuals(Mayeux et al., 2003; van Oijen et al., 2006)

ProblemsReproducibilityStandardization

Short half-life

Peripheral production

Biomarkers and neurodegenerative


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g

diseases

Diagnosis Classification Early diagnosis

Prognosis Therapy

Differential diagnosis

Limiti della diagnosi clinica


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Limiti della diagnosi clinica

Il caso dellAD

Sensibilit elevata (93%) ma specificit

relativamente bassa (55% in una casisticaneuropatologica)

Corrispondenza tra clinica e patologia nel 80-90%in centri specializzati

Difficolt sia di diagnosi precoce sia diagnosi

differenziale con forme atipiche o conmanifestazioni non usuali

Mayeux, Neurobiol Aging 1998

Caratteristiche di un biomarker ideale


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Caratteristiche di un biomarker ideale

Base razionale

Convalida su casi definiti

Sensibilit e specificit > 80%

Riproducibilit, accessibilit, facilit di analisi,convenienza

PrecocitConsensus Report of the Working Group on Molecular and

Biochemical Markers of Alzheimers Disease, Neurobiol Aging 1998


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N ADControlli FTD

a

Amyloid Precursor Protein (APP)


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Amyloid Precursor Protein (APP)

Dosaggio A42 nel CSF


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Dosaggio A42 nel CSF

Moderata riduzione di A42 nellAD (50%)

Correlata al deposito della proteina nelle

lacche

Correlazione con la patologia a livello

dellippocampo

CSF A42 nellAD


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CSF A42 nell AD

CSF A42 and A40


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CSF A42 and A40

Strong evidence across many cross-sectional

studies that CSF A42 levels are reduced byabout 50% in AD compared to controls, even in.,

CSF A42 appears to precede amyloidretention as detected by amyloid imaging (PIB):

first evidence of AD pathology in cognitively

normal individuals (?) (Fagan et al., 2006, 2009)

CSF A42 and A40


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CSF A42 and A40

A42 alone is less useful in differentiating AD

from other dementias, since low levels havealso been documented in FTD and in DLB

A42 levels do not correlate well with diseaseduration or severity

PIB-PET

Neuropathology (Braak & Braak)

CSF A42 and A40


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CSF A42 and A40

Limitations

lack of standardization for among differentlaboratories and assays

studies on normal individualsInfluences of normal aging on CSF turnover and

clearance

Normal hour-to-hour or day-to-day variability

(Bateman et al., 2007)

Total tau


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Total tau

Tau


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Tau

Proteina associata ai microtubuli

Legame con la tubulina

Favorisce lassemblaggio dei microtubuli

Oltre 30 siti di fosforilazione

Tau iperfosforilata nelle lesioni caratteristiche

dellAD (neurofibrillary tangles)


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CSF Tau


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tau elevaon may be observed also in other

diseases, potentially limiting the utility of tau alonein the differential diagnosis of dementia (Arai et al.,

1997b

Tau levels seem to remain relatively stable

throughout the disease process and do not

correlate with dementia severity (Sunderland et al., 1999)

Age effect (de Leon et al., 2007)

Phosphorilated tau (pTau)


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p (p )


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CSF Tau nellAD


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CSF pTau nellAD


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Tau e A42 nellAD


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Combinazione dei parametri liquorali


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nella diagnosi di AD

TauTauTauTau AAAA42424242 pTaupTaupTaupTau Sens.Sens.Sens.Sens. Spec.Spec.Spec.Spec.

81% 91%86% 89%

81% 91%

89% 90%

86% 97%


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444 pg/mL

195 pg/mL

Sensibilit 92%

Specificit 89%

A4217 studi

AD group: n=849control group: n=427

Tau34 studiAD group: n=2284

control group: n=1054 JAMA (2003) 289: 2094-2103


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Lancet Neurology, 2007


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Neurology 2002;58:16221628


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Tau 193 pg/ml (FTD vs CTR) sens 86% spec 41%A42 315 pg/ml (FTD vs AD) sens 86% spec 59%

Con Tau tra 193 e 908 pg/ml e A42 >315 pg/ml siidentificano solo il 60% dei FTD


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34 FTLD, 76 AD, 93 CTRL

FTLD vs CTRL

Tau/A 42 ratio Spec. 86.7%, sens. 80.6%

Tau (FTLD vs CTRL) Spec. 95.7%, sens.

64.75%

FTLD from AD

Tau/A42 sens. 64.5%, spec. 90.3%

p-Tau Spec. 85.7%, sens. 68.4%

CSF biomarkers in predicting dementia


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progression CSF A42 is present in asymptomatic elderly

at higher risk to develop AD (Blennow, 2004)

Lower CSF A42/40 ratios seem to indicate

very mild dementia (CDR0.5) (Brys et al., 2009)

CSF tau in MCI who later progressed to AD(Brys et al., 2009)


i


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progression RR of progression from MCI to AD in

paents with baseline tau, p-tau, and

A42 (90% sens.; 100% spec.) (Arai et al., 1997)

of non-converter) at 18 months (Riemenschneider etal., 2002)

tau + A42/P-tau181 may predict

progression of MCI into more advanced AD at4-6 years (Hansson et al., 2006)


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progression Utility of AD CSF profile - tau & A42 - in

predicting normal-MCI progression

70% of those with a high ratio, compared to only

,

normal to MCI over a 3 year period (Fagan et al., 2007)All MCI converters had elevated tau/A42 ratios,

while no conversions occurred in the normal ratio

group over a 42 month period (Li et al., 2007)


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Prediction of 1 year cortical change from baseline CSF biomarker levels. One

year change in cortical volume in MCI was predicted from baseline CSF

biomarker values point by point across the cortical surface, with age and sex

used as covariates. The top shows uncorrected p values, and the bottom

shows the p maps thresholded at FDR < 0.05.


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