Oral O1-06: Clinical Diagnosis and MCI IS106

amyloid-forming proteins, suggest a model where these proteins can be re-

leased from affected cells in the form of amyloid seeds, and then re-enter

other cells and aid in the spread of the disease. How are these aggregates re-

leased from the cells? Where does the aggregation take place? Once re-

leased, how do they form plaques and propagate in the aqueous

extracellular space to gain access to their host counterparts?Methods: Exo-

somes are produced from either cell culture conditioned media or from su-

pernatants of cortical neurons from transgenic APP mice. Results: We

provide evidence that amyloids involved in neurodegeneration such as am-

yloid beta are released via exosomes and that exosome-associated amyloids

act as seeds for plaque formation.Conclusions:We propose that exosomes,

endocytically derived nanovesicles, are a major way to shuttle amyloids out

of the cell and aid in the plaque formation.

SUNDAY, JULY 17, 2011

ORAL O1-06

CLINICAL DIAGNOSIS AND MCI I

O1-06-01 SOLUBLE AMYLOID PRECURSOR PROTEIN IN

CEREBROSPINAL FLUID: CLINICAL AND

GENETIC ASSOCIATIONS IN ALZHEIMER’S

DISEASE

Robert Perneczky, Alexander Kurz, Panagiotis Alexopoulos,

Timo Grimmer, Janine Diehl-Schmid, Liang-Hao Guo, Technische

Universit€at M€unchen, Munich, Germany.

Background: Mild cognitive impairment (MCI) patients progress to clini-

cally diagnosable Alzheimer’s disease (AD) at a rate of 15% yearly. Some

patients, however, may never progress or may revert to normal. Given this

variable prognosis and in view of the development of disease-modifying

strategies, which will probably show the strongest impact if applied early,

biomarkers capable of identifying pre-dementia AD are of interest. The sol-

uble amyloid precursor proteins (sAPP) a and ß mirror fundamental early

events of AD pathogenesis, and CSF concentration changes have been re-

ported in AD. We therefore explored whether sAPPa/sAPPß improved the

accuracy of the detection of incipient AD inMCI compared with established

biomarkers. We also aimed to explore the association between the CSF bio-

markers and genetic variants of the AD risk gene SORL1, which seems to

play an important role in amyloidogenesis. Methods: Follow-up assess-

ments of 58 MCI patients with baseline CSF sampling were conducted;

21 patients had progressed to probable AD (MCI-AD) 27 were still suffering

from MCI; eight had reverted to normal (MCI-NAD); two frontotemporal

dementia (FTD) patients were excluded. 16 additional FTD patients were

included to explore the specificity of the CSF markers. CSF concentrations

of sAPPa/sAPPß/tau/Aß42 were measured. Associations between diagnos-

tic status, CSF protein concentrations and other patient characteristics in-

cluding genetic variants SORL1 were explored. Results: The MCI-AD

group had significantly higher sAPPß concentrations than the MCI-NAD

and the FTD groups. A combination of sAPPß/tau/age differentiated the

MCI-AD and the MCI-NAD groups with a sensitivity of 80.0% and a spec-

ificity of 81.0%. The best model for the differentiation of the MCI-AD and

the FTD groups included sAPPß/tau and showed a sensitivity of 95.2% and

a specificity of 81.2%. Aß42and sAPPa did not significantly contribute to

the models. Although genetic variants within the SORL1 gene were not as-

sociated with sAPPa/sAPPß, several single nucleotid polymorphisms andha-

plotypes within SORL1 were correlated with tau/Aß42. Conclusions: Our

findings suggest that sAPPß may be clinically useful, and superior to

Aß42, in the early and differential diagnosis of incipient AD. The associa-

tions between SORL1 genetic variants and CSF biomarkers underline the

relevance of SORL1 for AD pathogenesis.

O1-06-02 A PREDICTION RULE OF CSF BIOMARKERS

FOR ALZHEIMER’S DISEASE IN CLINICAL

PRACTICE

Petra Spies1, Philip Scheltens2, Wiesje Van der Flier2, Marcel Verbeek1,

Marcel Olde Rikkert1, Jurgen Claassen1, Charlotte Teunissen2,

Marinus Blankenstein2, Petronella Peer1, 1Radboud University Nijmegen

Medical Centre; 2VU University Medical Center, Nijmegen, The

Netherlands, Amsterdam, the Netherlands.

Background: Cerebrospinal fluid (CSF) biomarkers amyloid ß42 (Aß42),

phosphorylated tau181 (p-tau) and total tau (t-tau) are increasingly used to

aid the clinical diagnosis of Alzheimer’s disease. A guideline for their in-

terpretation is, however, still lacking. We aimed to advance the use of CSF

biomarkers in clinical practice by developing an objective prediction rule

for Alzheimer’s disease (AD). This prediction rule should translate the

biomarker results into a single outcome that is easy to interpret and re-

flects the probability that dementia is due to Alzheimer’s disease.

Methods: From the Radboud University NijmegenMedical Centre memory

clinic database, all patients were selected with a presumptive diagnosis of

dementia and in whom the CSF biomarkers had been analysed. The clinical

diagnosis was AD (n¼ 272) or non-AD (n¼ 289). The non-AD group con-

sisted of patients in whomAD had been part of the differential diagnosis, but

whowere eventually diagnosed with another type of dementia or a psychiat-

ric disorder. The biomarkers were log transformed to reduce strong influ-

ence of outliers. The prediction rule was developed with logistic

regression analysis, using group membership (AD or non-AD) as dependent

variable. Different combinations of the three log transformed CSF bio-

markers, sex and age were analysed. The prediction rule was validated in

an independent dataset containing clinical data and CSF data of 334 AD

and 157 non-AD patients from the VU University Medical Center memory

clinic. Prevalence of AD was 48% in the development dataset and 68% in

the validation dataset. To adjust the prediction rule for the difference in prev-

alence between the memory clinics, a correction factor was estimated as fol-

lows: the original model (see table 1) without the intercept was used to

calculate individual scores. A logistic regressionmodel was fitted on the val-

idation dataset with these individual scores as linear predictor and the out-

come as dependent variable. The slope of this model was fixed at 1. The

intercept that was obtained is the correction factor, which was added to

the intercept of the original model. The ability of the prediction rule to dis-

criminate between AD and non-AD was quantified by the area under the

receiver operating characteristic curve (AUC). The agreement between

the probability of AD estimated by the prediction rule and the actual fre-

quency of AD diagnoses in the validation dataset was assessed with a cali-

bration plot. Classes of predicted probabilities were plotted against the

actual frequency of AD within these classes. Results: The prediction rule

that we developed is shown in table 1. CSFAß42, p-tau and female gender

were significant predictors of an AD diagnosis. Due to a strong correlation

with p-tau, the addition of t-tau was no longer significant. The prediction rule

was adjusted to take into account the difference in prevalence of AD between

the memory clinics (table 1). The discriminative ability of the adjusted predic-

tion rule proved to be very good with anAUC of 0.85. The agreement between

the probability of AD estimated by the prediction rule and the actual frequency

of AD diagnoses was very good based on the calibration plot (figure 1) and chi

square tests per class of predicted probabilities.Conclusions:This study pres-

ents a novel and robust prediction rule that is useful in clinical practice and that

optimizes and facilitates the interpretation of CSF biomarker results. The pre-

diction rule combines results of CSF biomarker analyses into a single outcome

that represents the probability that a patient has AD. In contrast to previous

studies, this prediction rule is based on a representative clinical sample of

memory clinic patients. Previous models described CSF biomarker profiles

that separate AD from normal controls, but these models do not provide the

clinician with a tool to Interpret individual CSF biomacker results in order

to predict a patient’s risk of having AD. Our validated prediction rule is easily

applicable and accurately reflects the probability of AD inmemory clinic pop-

ulation with a suspected diagnosis of dementia.

O1-06-03 CSFAND MRI BIOMARKERS AS

PREDICTORS FOR ALZHEIMER’S-TYPE

DEMENTIA IN SUBJECTS WITH MILD

COGNITIVE IMPAIRMENT

Stephanie Vos1, Hilkka Soininen2, Pieter Jelle Visser3, Kaj Blennow4,

Frans Verhey5, Leah Burns6, Harald Hampel7, Lars-Olof Wahlund8,

Robin Wolz9, Philip Scheltens10, Wiesje Van der Flier11, Magda Tsolaki12,