1.0

1.5

2.0

2.5

3.0

Neocort

ical S

UV

Rcb

Time (years)

Mean SUVR AD+

(2.33)

19.2 yr (95%CI 17-23 yrs)

Mean SUVR HC-

(1.17) 12.0 yr

(95%CI 10-15 yrs)

2.9%/yr (95%CI 2.5-3.3%/yr)

HC-

MCI+

AD

MCI-

HC+

0 10 20 30 40

AIBL: Aβ deposition over time

-30 -25 -20 -15 -10 -5 0

CDR 1.0

demented non-demented

Time (years)

cut-off

abnormal

normal

Relationship between “abnormality” and CDR of 1.0

Bio

ma

rke

r m

ag

nitu

de

Ab deposition (-17.0 yr)

Hippocampal volume (-4.2 yr)

Episodic memory (-3.3 yr)

Grey matter volume (-2.5 yr)

Non-memory (-1.3 yr)

Epis

odic

mem

ory

Ab burden

HC (n=193)

MCI (n=90)

AD (n=68)

R2 = 0.29 (p < 0.0001) R2 = 0.00 (n.s.)

High Ab Low Ab

R2 = 0.014 (p = 0.1)

Relationship between Ab burden

and episodic memory

High Ab Low Ab High Ab Low Ab

HC- HC+ MCI- MCI+ AD

Biomarker results

Translating dementia research into practice

Partner logo here

Reference: Biological Markers for Alzheimer’s Disease With Special Emphasis on Cerebrospinal Fluid b-Amyloid and Tau - TERO TAPIOLA

Research shows that measurement of pTau : Aβ1-42 Ratio in CSF is the best biomarker to date for Alzheimer’s Disease

Tau: Aβ1-42 Ratio - Sensitivity : 85.7% Specificity : 84.6%

13

13.5

14

14.5

15

15.5

16

MC NMC MCI AD

AP

OE

(m

g/d

l)

ANOVA , F = 14.105, P < 0.001

n = 391 n = 124 n = 199 n = 365

*

Tukey HSD, P < 0.001 vs. MC and NMC, P = 0.016 vs. MCI *

Full AIBL cohort (n=1079)

Mean of ApoE levels within clinical categories

(Gupta and Martins, 2010)

Proteomics: Data-mining McCUSKER

RESEARCH FOUNDATION

INC

ALZHEIMER’S

Biomarker panel

Two panels of biomarkers were selected from a dataset of 224 biomarkers

Set A – panel of 18 biomarkers

Set B – panel of 8 biomarkers

Set A performed with a Sens./Spec. of 85% in the AIBL cohort

Validation in ADNI at 77%

Set B performed with a Sens./Spec. of 83% in the AIBL cohort

Validation in ADNI at 80%

NUTRITION AND LIFESTYLE

FACTORS IN ALZHEIMER’S

DISEASE

High Education

Physical Activity

Healthy Diet

Mental Activity

Social

Engagement

Lifestyle factors in Alzheimer’s

Disease

HEALTHY

AGEING ALZHEIMER’S

DISEASE

Genetic Risk Factors

Socioeconomic

Factors

Hypertension

Depression

Head Trauma

Occupation

Bad Lifestyle Habits Modifiable in Mid-Late Life

AIBL Lifestyle Programme

Aim: Identification of lifestyle and

dietary modifications which prevent or

delay onset of AD

Dietary data

Data Collection

• Cancer Council of Victoria Food Frequency

Questionnaire (CCV FFQ)

• Quantify intake of 74 foods and beverages

• Examine dietary patterns

Baseline 36 month 54 month 72 month Follow-up: 18 month

Current Status

• High intake of fruit and vegetables

• Moderate to high fish intake

• Moderate to high cereal intake

• High unsaturated fatty acids

• Low saturated fatty acids

• Low to moderate dairy product intake

• Low meat and poultry intake

• Regular but moderate alcohol intake

Mediterranean Diet (MeDi)

• Higher MeDi adherence has been associated with lower risk of:

Obesity (Bullo et al., 2011)

Hypertension (Nunez-Cordoba et al., 2009)

Abnormal glucose metabolism (Gouveri et al., 2011)

Diabetes (Salas-Salvado et al., 2011)

Coronary heart disease (Kastorini et al., 2010)

Cognitive decline and AD (Feart et al., 2009)

Health Benefits and MeDi

Adherence

0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

5

5.5

HC MCI AD

Me

an

Me

Di S

core

*** *

Mean ± SEM. *p<0.05; ***p<0.001; multinomial logistic

regression models. Controlling for age, gender, education,

APOE genotype, country of birth, BMI, total caloric intake,

smoking status, history of hypertension, angina, stroke,

diabetes and heart attack.

From: Gardener, Rainey-Smith et al., 2012. Translational

Psychiatry.

Higher Adherence to MeDi in Healthy

Controls compared to MCI and AD

MeDi Score

Pe

rce

nta

ge

of

He

alt

hy C

on

tro

ls

0

5

10

15

20

25

0 1 2 3 4 5 6 7 8 9

Percentage of Healthy Controls

with each MeDi Score

0

5

10

15

20

25

0 1 2 3 4 5 6 7 8

100%

100%

38%

50%

57%

70%

52%

67%

42%

55%

50%

66%

38%

62%

100%

50%

100%

100%

MeDi Score

Perc

en

tag

e o

f A

Ds

% past smokers % APOE ε4 positive

Percentage of ADs with each MeDi

Score

• Prudent diet - ‘healthy’

• Heavily loaded with vegetables, fruits and nuts

• Western diet - ‘unhealthy’

• Heavily loaded with red and processed meats,

high fat dairy products, chips, refined grains,

potatoes, sweets and condiments.

Additional Dietary Patterns Analysed

Prudent and Western Diet vs Cognition

over 36 months

MeDi Score Prudent Score Western Score

Change in Cognitive

Domain

Adjusted

β Value

Adjusted

P-Value

Adjusted

β Value

Adjusted

P-Value

Adjusted

β Value

Adjusted

p-Value

Episodic Memory 0.0167 0.365 -0.0001 0.604 0.0000 0.980

Verbal Memory 0.0157 0.377 0.0001 0.620 -0.0002 0.424

Visual Memory -0.0085 0.643 -0.0000 0.859 -0.0002 0.538

Executive Function 0.0261 0.044* 0.0000 0.973 -0.0000 0.970

Language 0.0013 0.946 0.0002 0.528 -0.0002 0.510

Attention 0.0089 0.565 -0.0000 0.909 -0.0002 0.257

Visuospatial Function -0.0267 0.082 0.0003 0.100 -0.0005 0.022*

Dietary patterns and their association with

change in cognitive scores over 36 months

All analyses were controlled for age, gender, years of education, Apolipoprotein E (APOE) ε4

allele carriage, country of birth, body mass index, energy intake, past smoking status, and history

of hypertension, angina, stroke, heart attack and diabetes to provide adjusted models.

Linear mixed models (LMM) analyses: *p < 0.05, ** p < 0.01.

Physical activity data

Methods

• International physical activity questionnaire - Answers used to calculated metabolic equivalent score

(METs∙min/wk-1)

• Actigraph - Total counts (volume of activity)

- Peak counts (intensity of activity)

• To date all analyses have been on cognitively healthy controls only

0

1000

2000

3000

4000

5000

6000

7000

Acti

vit

y

TIME

Light

Moderate

Hard

Total Activity (Total

Counts)

Intensity

(Peak Counts)

Actigraph Output

Physical Activity and AD

• Physical activity has previously been associated with:

- Reduced cognitive decline and AD risk

- Enhanced cognitive functioning

• Most mechanistic studies have been animal studies

• AIBL array of biomarkers and comprehensive neuropsychological battery

- Potential to investigate association of physical activity with a number of AD-related factors in one cohort.

Cross-sectional analyses

Peak counts and cognitive

function

Raw and adjusted (for age, gender and YOE) of cognitive test score means (+

standard error) for each actigraphy intensity (peak count) tertile. 1RCFT, Rey figure

complex test.

Brown et al (2012), Translational Psychiatry.

Actigraphy Intensity Tertiles

Actigraphy Intensity Tertiles

Cross-sectional analyses

Physical activity (IPAQ) and PiB

SUVR

Adjusted means ± standard error (corrected for age, gender and years of education) of PiB

SUVR for each self-report IPAQ tertile; after stratification by APOE ε4 allele carriage.

From: Brown et al 2012, Molecular Psychiatry.

Cross-sectional analyses

Physical activity (IPAQ) and blood

biomarkers

Adjusted means ± standard error (corrected for age and gender) of blood

biomarkers across each self-report IPAQ tertile

From: Brown et al 2012, Molecular Psychiatry.

Hippocampal Volume and

Physical Activity

Those undertaking higher physical activity have larger

hippocampal volumes

Cognitive stream results

Anticholinergics

• Anticholinergic drugs associated with

modest cognitive difficulty – Sittirannorit et

al. 2011

Predictors of rapid decline in AD

• Alessandro Sona, Ping Zhang et al. (in press at Int Psychogeriatrics)

• 211 AD at baseline – 156 followed at 18 months

• 33% (51) rapid decliners (lost 6+ MMSE points in 18 months)

• Higher CDR and CDR box score plus baseline prescription of a CheI predicted faster decline (OR 3.4 univariate)

Subjective Memory Complaints –

Rachel Buckley

• AIM: Does affect, memory or AD

biomarkers predict subjective memory

complaints in healthy elderly controls or

patients with MCI?

• Dependent variable: Memory Assessment

Clinics Self-Rating Questionnaire (MAC-Q)

Predictors of memory complaint

severity

Significant predictor (p < 0.003)

Sub-threshold predictor (p > 0.05)

Impact of AIBL

• 130+ publications to date

• Enabled training of 18 PhD students and 13

postdoctoral fellows

• Furthered international research efforts in AD

through data being made accessible worldwide

via EOI and uploading a subset of AIBL data

(ADNI compatible data) onto LONI websites.

• AIBL data accessed over 429 times via LONI

website, facilitating international collaboration

and co-operation for understanding AD.

• Established an IP portfolio of 3 patents and

licensable software.

• If the initial investment is to be worthwhile

the cohort should be followed for 10-20

years

• Only over this kind of time period can the

predictors of decline in initially healthy

subjects be reliably determined

* AIBL management committee

Prof. David Ames

Prof. Lynne Cobiac

Prof. Ralph Martins

Prof. Colin Masters

Prof. Christopher Rowe

Dr. Lance Macauley

Dr Kevin Taddei

Dr Alan Rembach

Dr Stephanie Rainey-Smith

Dr Ian Cooke

Dr Larry Ward

* The AIBL study team comprises 80+ scientists (see www.aibl.csiro.au)

and over 1400 Australian research volunteers

The AIBL management team

• CSIRO (AUS)

• National Health and Medical Research Council (NHMRC) (AUS)

• Alzheimer’s Association (USA)

• Alzheimer’s Drug Discovery Foundation (USA)

• An Anonymous Foundation (USA)

• Pfizer

• GE Healthcare

• Astra Zeneca

• Science and Industry Endowment Fund

Financial Supporters

AIBL study participants, their families, and the AIBL study team

Acknowledgements and

thanks

Osca Acosta

David Ames

Jennifer Ames

Manoj Agarwal

David Baxendale

Justin Bedo

Carlita Bevage

Lindsay Bevege

Pierrick Bourgeat

Belinda Brown

Rachel Buckley

Samantha Burnham

Ashley Bush

Tiffany Cowie

Kathleen Crowley

Andrew Currie

David Darby

Daniela De Fazio

Kim Lucy Do

James Doecke

Harriet Downing

Denise El- Sheikh

Kathryn Ellis

Kerryn Dickinson

Noel Faux

Jonathan Foster

Jurgen Fripp

Christopher Fowler

Samantha Gardener

Veer Gupta

Gareth Jones

Adrian Kamer

Jane Khoo

Asawari Killedar

Neil Killeen

Tae Wan Kim

Adam Kowalczyk

Eleftheria Kotsopoulos

Gobhathai Kunarak

Rebecca Lachovitski

Simon Laws

Nat Lenzo

Qiao-Xin Li

Xiao Liang

Kathleen Lucas

James Lui

Georgia Martins

Ralph Martins

Paul Maruff

Colin Masters

Yumiko Matsumoto

Sabine Matthaes

Simon McBride

Andrew Milner

Claire Montague

Lynette Moore

Audrey Muir

Christopher O’Halloran

Graeme O'Keefe

Anita Panayiotou

Athena Paton

Jacqui Paton

Jeremiah Peiffer

Svetlana Pejoska

Kelly Pertile

Kerryn Pike

Lorien Porter

Roger Price

Parnesh Raniga

Alan Rembach

Carolina Restrepo

Miroslava Rimajova

Jo Robertson

Elizabeth Ronsisvalle

Rebecca Rumble

Mark Rodrigues

Christopher Rowe

Stephanie Rainey-Smith

Olivier Salvado

Jack Sach

Greg Savage

Cassandra Szoeke

Kevin Taddei

Tania Taddei

Brett Trounson

Marinos Tsikkos

Victor Villemagne

Stacey Walker

Vanessa Ward

Michael Weinborn

Andrea Wilson

Bill Wilson

Michael Woodward

Olga Yastrubetskaya

Ping Zhang

AIBL is a large collaborative study and a complete list of contributors can be

found at www.aibl.csiro.au AIBL is funded in part by a grant from the Science

and Industry Endowment Fund. We thank all who took part in the study.