Malaysian Healthy Ageing Society Professor David Ames BA MD - - PowerPoint PPT Presentation

Organised by:

Malaysian Healthy Ageing Society

Co-Sponsored:

Professor David Ames BA MD FRCPsych FRANZCP Director National Ageing Research Institute University of Melbourne Professor of Ageing and Health PO Box 2127, Royal Melbourne Hospital, 3050, Victoria, Australia dames@unimelb.edu.au

Alzheimer’s disease – why it matters to all of us (and what we are doing about it)

Conflict of interest declaration

• I have received honoraria for talks/consultancies,

assistance with conference attendance, and/or financial support of research from Astra-Zeneca, Eisai, Eli Lilly, Forrest, GSK, Janssen-Cilag, Lundbeck, Novartis, Pfizer, Roche, Sandoz, Sanofi-Aventis, Servier, SmithKlineBeecham, Voyager, Wyeth.

• Former Editor-in-Chief International Psychogeriatrics

2003-11

• Member International Psychogeriatric Association,

Geelong Football Club, 2 Wagner societies and unpaid medical advisor to Alzheimer’s Australia and ADI

Australia

• 22 million people
• 13.1% aged over 65 (10.3% 1985), 315,000 85+
• 6 states and 2 territories
• Mixture of national and state government health care

responsibilities

• Mixed private and public system with universal health insurance

(medicare)

• Rapid growth in dementia population
• 100,000 late 1980s, 220,000 now (1% total population),

1,000,000 (3%) by 2050

Dementia

Acquired decline in multiple higher mental functions (intellect, memory and personality) occurring in an alert patient which is usually progressive and irreversible and caused by an organic medical condition. To be diagnosed the cognitive impairment must affect daily function. Dementia is a syndrome with many causes Dementia is an age related disorder whose prevalence doubles every five years between age 60 and 90

Life Expectancy

Ikeda N. et al. The Lancet. 2011; 378:1094-105

Demographic Ageing

• Increasing life expectancy
• Falling fertility rates (declining child mortality,

increasing education, economic development)

• Variable migration patterns
• Rates of demographic ageing in China, India and

Latin America are unprecedented in world history

Growth of numbers of people with dementia

• The World Alzheimer Report

(2009) estimated:

– 35.6 million people living with dementia worldwide in 2010 – Increasing to 65.7 million by 2030 – 115.4 million by 2050

Worldwide cost of dementia

• The societal cost of dementia is

already enormous.

• Dementia is already significantly

affecting every health and social care system in the world.

• The economic impact on families

is insufficiently appreciated.

• The total estimated worldwide

costs of dementia are US$604 billion in 2010.

• These costs are around 1% of

the world’s GDP

0.24% in low income 1.24% in high income

Worldwide costs of dementia

• The World Alzheimer

Report (2010) estimated that: If dementia care were a country, it would be the world’s 18th largest economy

74.84 26.05

10 20 30 40 50 60 70 80

Ratio of working-age to dementia

2000 2010 2020 2030 2040 2050

Year

No of working-age persons per person with dementia 2000-2050

Jorm A et al. 2007 ANZ J Psychiatry

Dementia - the concept

• Dementia is an umbrella term to

describe a syndrome of

– memory loss and – other cognitive impairments – interfering with daily function

• Over 100 causes of dementia
• Alzheimer’s disease is the most

common

Alois Alzheimer (1864-1915)

The Amyloid Plaque

From W Spielmeyer, Histopathologie des Nervensystems. 1922

Tangles – tau protein

Translating dementia research into practice

Beta-amyloid plaque shown with PiB PET

Beta amyloid plaques seen under a microscope in post mortem brain tissue from a patient with Alzheimer’s Disease PiB PET scan showing brain areas containing beta-amyloid plaques (yellow and red areas) in a living person with early Alzheimer’s Disease

Alzheimer’s disease

• Causes 50-80% of all dementias
• Characterised by insidious onset and slow steady progression of

deficits

• Initially new learning is affected, later praxis, language and some

frontal functions will deteriorate

• Pathological hallmarks are plaques (amyloid) between cells and

tangles (tau) within neurons

• Appears related to breakdown of amyloid precursor protein

leading to amyloid production

• Main risk factors are unmodifiable – age, family history, APOE ε4,

female sex, but potentially modifiable may include head injury and vascular risk factors

• No perfect diagnostic test in living patient but clinical diagnosis

correlates 80-90% with autopsy findings in experienced hands

• Typical course 7-10 years from onset to death

Compelling evidence for the A beta theory of AD

• A beta is the major macromolecule in the AD plaque
• Mutations in APP and gamma-secretase (PS1, PS2)

cause AD

• Correlations of AD with PiB-PET and CSF A beta42 ()
• Pathologic lesions of AD occur in DS (APP triplication)
• Zn / Cu interactions with A beta explain the selective

topography of AD in the excitatory glutamatergic system

• ApoE polymorphism – the only major risk factor - may act

through A beta clearance pathway

• Preliminary evidence of 1-2% loss of clearance capacity of

A beta in sporadic AD (Bateman)

• Preliminary evidence that therapeutic targeting of A beta is

effective

• Alternate theories not developed

What has Australia done to help?

• 1985 Masters and Beyreuther – molecular structure of Aβ amyloid

protein

• 1987 Jorm, Korten and Henderson – meta-analysis of published

studies showed that dementia prevalence doubled with every 5.1 years of age from 60-90

• 1989 Brodaty and Gresham – showed that support and education
• f 100 spouse caregivers decreased caregiver stress and delayed

institutionalisation of people with dementia

• From early 1990s Bush, Masters et al – role of metals in amyloid

toxicity

• From 2003, LoGiudice, Smith et al. - dementia detection in

remote community tribal aborigines

• From 2004 Rowe et al. - amyloid imaging
• From 2006 AIBL study
• 2008 Lautenschlager et al. – exercise appears protective for

cognitive function (JAMA)

Partner logo here

Research funding per $1 of 2023 direct care costs

The Australian Imaging, Biomarkers and Lifestyle Flagship Study of Ageing – an example of Australian research on Alzheimer’s disease

PERTH MELBOURNE

Sites

.

Major Sponsor

Translating dementia research into practice

Partner logo here Clinical/cognitive data 80ml blood Lifestyle information PET & MRI scans (250 volunteers) Clinical/cognitive data 80ml blood PET & MRI scans

Baseline Follow-up (every 18 months)

 Large scale cohort study: 1112 participants  Patients with AD, MCI and healthy volunteers  Multi-disciplinary approach, 4 research streams cognitive, imaging, biomarkers and lifestyle

 A$3+ million study launched Nov 14th 2006

largest study of its kind in Australia

 3-year prospective longitudinal study

The Cohort

Study is conducted between Perth (40%) and Melbourne (60%)

• CSIRO P-Health*
• University of Melbourne*
• Neurosciences Australia Ltd (NSA)*
• Edith Cowan University (ECU)*
• Mental Health Research Institute (MHRI)*
• National Ageing Research Institute (NARI)
• Austin Health
• University of WA (UWA)
• CogState Ltd.
• Charles Gairdner Hospital radiology and nuclear medicine
• Alzheimer’s Australia
• Macquarie University

*denotes signatories to the AIBL study contract

AIBL collaborators

Study aims

1. To improve the understanding of the pathogenesis and diagnosis of Alzheimer’s disease using neuropsychological, neuroimaging and biomarker techniques, with a focus on early diagnosis of AD 2. To examine lifestyle and diet factors that may be involved in the pathogenesis of AD, towards future lifestyle intervention

Why AIBL? Why would we want pre-symptomatic detection?

– To enable research into causes – To identify at risk individuals for lifestyle research – To identify at risk individuals for putative drug therapies – Ultimately, to identify people who can have the onset of AD delayed by intervention – Essential arm of a twin track strategy (early detection and effective intervention)

Assessments

• BP, HR, weight, height, abdominal girth
• 80 ml blood
• 2 hours neuropsychological testing
• HADS and GDS
• Medication list
• Diet and lifestyle questionnaires
• PiB PET scan and MRI for ¼
• Diagnostic panel evaluation
• DA file review
• Repeat every 18 months

Progress

AIBL 1 – initial phase of the study

• Recruit cohort of 1000+ people (1112 by August

2008)

• Conduct thorough assessment at baseline
• Repeat assessment at 18-months

18-month assessments completed in mid-2010 (90%) Commitment from the AIBL partners and CSIRO SIEF fund to continue the study for at least 2 more timepoints (36-months and 54-months) while replenishing cohort (AIBL expansion – at least 200) 3 year assessments completed end 2011 Sponsorship to enable additional imaging AIBL active intervention study

AIBL: Longitudinal cohort

Baseline

(1,112)

18M

(968)

36M

(824) 318 NMC 374 SMC 81 MCI 196 AD 301 NMC 310 SMC 58 MCI 154 AD

(33) (29) (51) (30) (97) (114) (7 ) (14) (4) (1) (3 ) (32) (39) (40) (61) (23) Psychometrics Bloods MRI/PET Lifestyle Genotype Psychometrics Bloods MRI/PET Psychometrics Bloods MRI/PET (1) (79) (64) (5) (4) (14) (1) (16) (1 ) (2 ) (5 ) (11)

396 SMC 133 MCI 211 AD 372 NMC

(220) (253) (159) (211) (240) (63) (37) (133) Non-Return: 115 Deceased: NMC 2 SMC 4 MCI 5 AD 17

4 Non-AD Dementia

(2)VaD (1)VaD

November 2011 (NMC) Non-Memory Complainer, (SMC) Subjective-Memory Complainer, (MCI) Mild Cognitive Impairment, (AD) Alzheimer’s disease, PDD (Parkinson’s Disease Dementia), VaD (Vascular Dementia).

Non-Return: 120 Deceased: NMC 3 SMC 3 MCI 4 AD 33 Returned at 36 months 19

1 Non-AD Dementia

(1)PDD (1 PDD)

Imaging results

• Imaging collaboration led by Chris Rowe and Victor

Villemagne at Austin Health and by Nat Lenzo, Roger Price and Peter Robins in WA with strong input from CSIRO via Olivier Salvado et al.

The binding of PIB matches the histopathology of Abeta

A B C

Braak Stages (1997)

Imaging Cohort Baseline demographics (n=288)

HC* MCI AD

57 53 Age 73.6 ± 7.6 77.4 ± 7.5* 74.0 ± 8.7 MMSE 28.8 ± 1.2 27.1 ± 2.3* 20.5 ± 4.9* %ApoE e4 43% 54% 71%* 178

*Significantly different from HC, p <0.05

*enriched with ApoE e4

PiB neocortical SUVR in AIBL+

2.50 1.00

HC

1.40±0.4 (n = 195)

MCI

1.91±0.6 (n = 92)

AD

2.30±0.4 (n = 79)

Neocortical SUVR

1.50 2.00 3.00 (n = 366)

31% 99% 68%

20 40 60 80 100

ApoE e4-ve ApoE e4+ve 79% PiB-ve 51% PiB-ve

Influence of ApoE e4 status on PiB+ in Healthy Controls

21% PiB+ve 49% PiB+ve

% healthy persons with +ve amyloid scan

10 20 30 40 50 60 70 80 90 60-69 70-79 80+ e4- e4+

YEARS OF AGE

PiB+ vs Age in Healthy Controls

(AIBL ApoE e4 prevalence corrected data)

Prevalence

• f AD

(Tobias, 2008) 10 20 30 40 50 60 30 40 50 60 70 80 90 100

Prevalence (%) Age (years) Prevalence of plaques in HC

(Davies, 1988, n=110) (Braak, 1996, n=551) (Sugihara, 1995, n=123)

e4 corrected AIBL data 12% 32% 52%

Neocortical SUVR Age (years)

* PiB+/PiB- SUVR cut-off = 1.5

1.0 1.3 1.5 1.8 2.0 2.3 2.5 2.8 3.0 3.3 3.5 55 60 65 70 75 80 85 90 95

HC

(n=104)

Progression to aMCI Progression to naMCI Progression to AD

Longitudinal PiB PET follow-up

2.6 % increase/year

Neocortical SUVR Age (years)

* PiB+/PiB- SUVR cut-off = 1.5

1.0 1.3 1.5 1.8 2.0 2.3 2.5 2.8 3.0 3.3 3.5 55 60 65 70 75 80 85 90 95

MCI

(n=48)

Progression to FTD Progression to VaD Progression to AD

Longitudinal PiB PET follow-up

2.0% increase/ year

* PiB+/PiB- SUVR cut-off = 1.5

Neocortical SUVR Age (years)

1.0 1.3 1.5 1.8 2.0 2.3 2.5 2.8 3.0 3.3 3.5 55 60 65 70 75 80 85 90 95

AD

(n=33)

Longitudinal PiB PET follow-up

1.1% increase / year

5-year follow-up

1.0 1.5 2.0 2.5 20 38 56

Neocortical SUVR

Time (months)

HC+ (n=5) AD+ (n=3) HC- (n=17)

+6.7% +0.0% +0.4% +1.0% +3.9% +5.7%

* PiB+/PiB- SUVR cut-off = 1.5

Neocortical SUVR

AD MCI HC

Time (months) 2.6% 1.1% 2.0%

1.0 1.5 2.0 2.5

20 38 20 38 20 38

Longitudinal PiB PET follow-up

Average rate of atrophy over one year in HC PiB- vs PiB+.

• 6.0%
• 5.0%
• 4.0%
• 3.0%
• 2.0%
• 1.0%

0.0%

AIBL follow-up

Hippocampal volume

† Significantly different from HC (p<0.05)

* Significantly different from baseline (p<0.003)

Decrease in hippocampal volume

HC MCI AD

†* †*

*

AIBL+ Prediction of Progression: HC to MCI/AD

36 months n=195

PiB-ve Subjects: 129 Converters to MCI/AD 7% PiB+ve Subjects: 66 Converters to MCI/AD 19%

AIBL+ Prediction of Progression: MCI to Dementia

36 Months n=92 PiB -ve : 34

Converters to AD 10% Other dementia 17% No dementia 73%

PiB +ve : 58

Converters to AD 56% No dementia 44%

HC- HC+

58% decliners* 15% decliners

100%

* Significantly different from HC-, p <0.05

Change in memory vs Baseline PiB:

Decline >0.5 SD in HC with a 3 year follow-up

(n=60)

BASELINE Ab burden correlates with memory decline over 3 years in HC

0.9 0.6 0.3 0.0

• 0.3
• 0.6
• 0.9

1.0 1.5 2.0 2.5 3.0 r = 0.38 (p= 0.0005)

Neocortical SUVR Episodic memory decline

• Biomarkers 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%

SUVR Ab40 Ab42 Ab42/40

Beta-Amyloid

R=-0.077 R=-0.166** R=--0.151*

50 100 150 200 250 300 1 2 3 4 20 40 60 80 1 2 3 4 0.2 0.4 0.6 0.8 1 1.2 1 2 3 4

Measuring beta amyloid in blood: Correlations with brain beta amyloid

Spearman’s rho, *p<0.05, **p<0.001

Beta-Amyloid levels and PIB-PET

Lui et al 2009

13 13.5 14 14.5 15 15.5 16 MC NMC MCI AD APOE (mg/dl)

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%

• Diet and lifestyle

• AIBL sub-set - n=227 (healthy controls only)
• Worn for seven consecutive days on front of hip
• Output includes

–Total counts (average activity over 7 days) –Peak counts (average highest intensity reached

• ver 7 days)

Physical Activity Monitoring - Actigraphy

www.theactigraph.com

1000 2000 3000 4000 5000 6000 7000 Activity TIME Light Moderate Hard

Total Activity (Total Counts)

Intensity (Peak Counts)

Actigraph Output

Translating dementia research into practice

Partner logo here

9 9.5 10 10.5 11 11.5 12 12.5 13 Q1 Q2 Q3 Q4 CVLT Delayed Recall Score

p = .04

9 9.5 10 10.5 11 11.5 12 12.5 13 Q1 Q2 Q3 Q4 CVLT Retention Score 40 42 44 46 48 50 52 54 56 58 60 Q1 Q2 Q3 Q4 CVLT Learning Score

p = .006

One-Way ANOVA Post-hoc Analysis Tukeys; α <0.05

F=3.642 p=.014 F=1.405 p=.242 F=2.671 p=.048 n=57 57 56 57

Values expressed as mean ± standard error

Translating dementia research into practice

Partner logo here

0.185 0.19 0.195 0.2 0.205 0.21 0.215 0.22 0.225 0.23 1 2 3 4

Aβ42/Aβ40 Ratio

IPAQ Quartile Groups

1 - Lowest level of physical activity 4 -Highest level of Physical activity

p=0.013

Aβ42/ Aβ40 Ratio within IPAQ Quartile Groups

• Both total physical activity and higher intensity

physical activity is associated with;

– Lower insulin (Regensteiner, 1991) – Lower triglycerides (Lehtonen, 2009) – Higher levels of HDL (Lehtonen, 2009)

• Higher levels of intense physical activity is

associated with better performance in assessments targeting;

– Working memory – Attention – Verbal & Spatial Learning and Recall – Executive Functioning Direction of association?

Summary

Cognitive stream results

• Establishment of Australian norms of a

wide range of neuropsychological tests on 768 healthy elderly people

• Paper by Koftopoulos et al. now in

preparation.

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)

Continuation, add-on and complimentary studies

36 month follow ups complete and now continuing at 54 months too while allowing replenishment of cohort ADNI data uploads Cerebro-spinal fluid (25 to date) Australian Brain Bank Network AIBL Rate of Change Substudy (2/9 cogstate variables) 3 NHMRC grants for 2011 (blood work, imaging and intervention) Initial carer strain study now completed AIBL active underway

AIBL – the next 3 years

• Perform 54 month follow-ups (underway)
• Add 200 new subjective memory complainers

and MCI subjects

• Amyloid imaging in all participants (1000)
• “AIBL Active” – exercise intervention in 150

HC/MCI

• Attempt to obtain funding for 6 and 7.5 year

follow-ups

• 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

Summary

• A highly motivated and well-characterized cohort who

represent a unique resource for the study of AD in Australia

• Cross-sectional analysis of the AIBL dataset have already

demonstrated links between cognition, brain beta-amyloid burden and blood biomarkers

• 36-month follow-up data review is complete and 54 month

data collection has commenced

• Follow-up of this cohort will allow the significance of candidate

risk factors associated with cognitive decline and early diagnostic indicators of AD to be examined.

The AIBL Study Team

Osca Acosta David Ames Jennifer Ames Manoj Agarwal David Baxendale Kiara Bechta-Metti Carlita Bevage Lindsay Bevege Pierrick Bourgeat Belinda Brown Rachel Buckley Ashley Bush Tiffany Cowie Kathleen Crowley Andrew Currie David Darby Daniela De Fazio Harriet Downing Denise El- Sheikh Kathryn Ellis Kerryn Dickinson Noel Faux Jonathan Foster Jurgen Fripp Christopher Fowler Veer Gupta Karra Harrington Gareth Jones Adrian Kamer Jane Khoo Asawari Killedar Neil Killeen Tae Wan Kim Eleftheria Kotsopoulos Gobhathai Kunarak Rebecca Lachovitski Nat Lenzo Qiao-Xin Li Xiao Liang Kathleen Lucas James Lui Georgia Martins Ralph Martins Paul Maruff Colin Masters Sabine Matthaes 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 Stephanie Rainey-Smith Parnesh Raniga Alan Rembach Miroslava Rimajova Jo Robertson Mark Rodrigues Elizabeth Ronsisvalle Rebecca Rumble Christopher Rowe Olivier Salvado Jack Sach Greg Savage Cassandra Szoeke Kevin Taddei Tania Taddei Brett Trounson Marinos Tsikkos Victor Villemagne Stacey Walker Vanessa Ward Michael Woodward Olga Yastrubetskaya

• 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 management committee

• Prof. David Ames

Prof Ashley Bush Dr Ian Cooke

• Prof. Richard Head
• Dr. Kathryn Ellis
• Prof. Ralph Martins
• Prof. Colin Masters
• Dr. Andrew Milner
• Prof. Christopher Rowe
• Dr. Cassandra Szoeke
• Dr. Lance Macauley
• Dr. Tim O’Meara

* The AIBL study team comprises 80+ scientists (see www.aibl.csiro.au) and 1112 Australian research volunteers

The AIBL management team