DIET, NEUROIMAGING BIOMARKERS AND ALZHEIMER’S DISEASE: DATA FROM THE AUSTRALIAN IMAGING, - PowerPoint Presentation

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DIET, NEUROIMAGING BIOMARKERS AND ALZHEIMER’S DISEASE: DATA FROM THE AUSTRALIAN IMAGING, BIOMARKERS AND LIFESTYLE STUDY OF AGEING

Ralph N Martins, PhD

Launched in November 2006; prospective longitudinal

study

Aims to improve understanding of the causes and diagnosis of AD, and help develop preventative strategies

The AIBL Study

Baseline

36 month

54 month

72 month

Follow-up: 18 month

Current Status

The Cohort

40% Perth-based, 60% Melbourne-based

~1000 Participants

Healthy Controls

Alzheimer’s Disease (AD)

Mild Cognitive Impairment (MCI)

4 research streams:

A Multidisciplinary Study

Cognitive

Imaging

Biomarkers

Lifestyle

Cancer Council of Victoria Food Frequency Questionnaire (CCV FFQ)

Previously validated in multiple epidemiological studies

(Keogh et al., 2010)

Quantifies intake of 74 foods and beverages

Completions at baseline:

Lifestyle - Diet

Healthy Controls

723

MCI

98

AD

149

Total:

970

HC > AD*

Fortified Wine

Capsicum

White Wine

Lettuce

Red Wine

Avocado

Light Beer

Spinach

Other Spirits

Broccoli

Vegemite

Yoghurt

Tofu

Muesli

Nuts

AD > HC*

Sausages

Ice

Cream

Ham

Margarine

Meat Pies

Cornflakes

Bran flakes

Tinned Fruit

Chips

Full Cream Milk

*Student’s unpaired t-test, p<0.05

Controlling for BMI, country of birth, gender, age and APOE allele status.

Food and Beverage Consumption: Classification Differences

*Student’s unpaired t-test, p<0.05Controlling for BMI, country of birth, gender, age and APOE allele status.

HC > AD*

Lutein

Zeaxanthin

Calcium

Magnesium

Vitamin C

AD > HC*

Saturated Fat

Monounsaturated Fat

All Fat

Retinol

Sodium

Nutrient Consumption: Classification Differences

The FFQ data can also be used to examine dietary patterns.

High intake of fruit and vegetables

Moderate to high fish intakeModerate to high cereal intake

High unsaturated fatty acidsLow saturated fatty acids

Low to moderate dairy product intake

Low meat and poultry intakeRegular but moderate alcohol intake

Mediterranean Diet (MeDi)

Higher adherence to a MeDi has been associated with lower risk of:

Obesity

(Bullo et al., 2011)H

ypertension (Nunez-Cordoba et al., 2009)

Abnormal glucose metabolism

(Gouveri et al., 2011)

D

iabetes

(Salas-

Salvado et al., 2011)C

oronary heart disease (Kastorini

et al., 2010)Health Benefit s and MeDi Adherence

A value of 0 or 1 was assigned to each of the following categories using sex specific medians as cut-offs

MeDi score generated for each participant (0-9 point scale): higher score indicates higher adherence

Determining a MeDi Score for each Participant

Category

< Median

≥ Median

Fruit

0

1

Vegetables

0

1

Legumes

0

1

Cereals

0

1

Fish

0

1

Meat

1

0

Dairy

1

0

Monounsaturated

: Saturated Fats

0

1

Alcohol

1

0

(+ zero intake)

***

*

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.

Higher Adherence to MeDi in Healthy Controls compared to MCI and AD Groups

MeDi Score

Percentage

of

Healthy Controls

Percentage of Healthy Controls with each MeDi Score

100%

100%

38%

50%

57%

70%

52

%

67

%

42%

55%

50%

66%

38%

62%

100%

50%

100%

100%

MeDi Score

Percentage

of

ADs

% past

smokers

% APOE

ε

4 positive

Percentage of ADs with each MeDi Score

Healthy

Control

Alzheimer’s Disease

A subset of the AIBL cohort undergoes C

11

PiB-PET Imaging

Higher MeDi Score is associated with lower PiB Score

Controlling for age, APOE genotype, gender and years of education.

MeDi Score Residual

PiB Score Residual

Are these results confounded by the Amyloid burden of the AD brain?

Amongst Healthy Controls

only, Higher MeDi Score is

still associated with lower PiB Score

Controlling for age, APOE genotype, gender and years of education.

MeDi Score Residual

PiB Score Residual

Summary - 1

In this Australian cohort, both MCI and AD participants have a lower adherence to the MeDi compared to Healthy Controls at baseline.

This is the first study of its kind to use an elderly Australian cohort.

Our analysis suggests that higher MeDi adherence appears to reduce the risk of AD - agrees with previous reports on US and French populations

(Scarmeas et al., 2006; 2009; Feart et al., 2009).

The association between MeDi and AD remained unchanged when data was adjusted for potential confounders; age, sex, education, APOE genotype, country of birth, caloric intake, BMI, smoking status, history of hypertension, angina, stroke, diabetes and heart attack.

Our Australian cohort is unlikely to adhere strictly to a diet typical of Mediterranean countries; ‘true MeDi’ adherence in our population may be lower than Mediterranean populations. However, our results support the notion that the beneficial effects of the MeDi are transferable to different populations.

Summary - 2

This is a cross-sectional report, so we cannot

assume that our results show decreased MeDi adherence is a risk factor for AD.

However, our finding that higher MeDi Score is associated with lower PiB Score adds weight to our argument.

The hypothesis gains momentum given that we find higher MeDi Score is

still associated with lower PiB Score when MCI and AD groups are excluded from the analysis.

To our knowledge, this represents the first study to assess the relationship between PiB-PET-determined amyloid burden and diet.

The longitudinal nature of the AIBL study will enable further investigation of the relationship between diet and AD risk.

Acknowledgements - Authors

Samantha Gardener, Stephanie R. Rainey-Smith, Yian

Gu, Alinda Mondal, Kevin Taddei, Simon Laws, Veer Gupta, David Ames, Kathryn A. Ellis, Richard Head, S. Lance Macaulay, Colin Masters, Christopher Rowe, Cassandra Szoeke, Peter Clifton, Jennifer Keogh, Nikos Scarmeas,

Ralph N. Martins, and the AIBL Research Group.

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

FauxJonathan FosterJurgen FrippChristopher FowlerSamantha GardenerVeer Gupta Gareth JonesAdrian KamerJane Khoo Asawari KilledarNeil KilleenTae Wan KimAdam KowalczykEleftheria KotsopoulosGobhathai KunarakRebecca LachovitskiSimon LawsNat LenzoQiao-Xin Li Xiao Liang Kathleen LucasJames LuiGeorgia MartinsRalph Martins Paul MaruffColin MastersYumiko MatsumotoSabine MatthaesSimon McBrideAndrew MilnerClaire MontagueLynette MooreAudrey MuirChristopher O’HalloranGraeme O'KeefeAnita PanayiotouAthena PatonJacqui PatonJeremiah PeifferSvetlana PejoskaKelly PertileKerryn Pike Lorien PorterRoger PriceParnesh RanigaAlan RembachCarolina RestrepoMiroslava RimajovaJo RobertsonElizabeth RonsisvalleRebecca RumbleMark RodriguesChristopher RoweStephanie Rainey-SmithOlivier SalvadoJack SachGreg SavageCassandra SzoekeKevin TaddeiTania TaddeiBrett TrounsonMarinos Tsikkos Victor Villemagne Stacey Walker Vanessa WardMichael WeinbornAndrea WilsonBill WilsonMichael WoodwardOlga YastrubetskayaPing ZhangAIBL 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.