DIET, NEUROIMAGING BIOMARKERS AND ALZHEIMERS DISEASE: DATA FROM

DIET, NEUROIMAGING BIOMARKERS AND ALZHEIMERS DISEASE: DATA FROM

DIET, NEUROIMAGING BIOMARKERS AND ALZHEIMERS DISEASE: DATA FROM THE AUSTRALIAN IMAGING, BIOMARKERS AND Ralph N Martins, PhD LIFESTYLE STUDY OF AGEING The AIBL Study Launched in November 2006; prospective longitudinal study Aims to improve understanding of the causes and diagnosis of AD, and help develop preventative strategies Baseline Follow-up: 18 month 36 month 54 month Current Status

72 month The Cohort 40% Perth-based, 60% Melbourne-based ~1000 Participants Healthy Controls Mild Cognitive Impairment (MCI) Alzheimers Disease (AD) A Multidisciplinary Study 4 research streams: Cognitive Imaging Biomarkers Lifestyle Lifestyle - Diet

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: Healthy Controls MCI AD Total: 723 98 149 970 Food and Beverage Consumption: Classification Differences AD > HC* Sausages Ham Meat Pies Cornflakes Bran flakes

Tinned Fruit Chips Full Cream Milk Ice Cream Margarine HC > AD* Fortified Wine White Wine Red Wine Light Beer Other Spirits Vegemite Tofu Nuts Capsicum Lettuce Avocado Spinach Broccoli Yoghurt Muesli *Students unpaired t-test, p<0.05 Controlling for BMI, country of birth, gender, age and APOE allele status.

Nutrient Consumption: Classification Differences AD > HC* HC > AD* Saturated Fat Lutein Monounsaturated Fat Zeaxanthin All Fat Calcium Retinol Magnesium Sodium Vitamin C *Students unpaired t-test, p<0.05

Controlling for BMI, country of birth, gender, age and APOE allele status. The FFQ data can also be used to examine dietary patterns. Mediterranean Diet (MeDi) 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 Health Benefit s and MeDi Adherence

Higher adherence to a MeDi has been associated with lower risk of: Obesity (Bullo et al., 2011) Hypertension (Nunez-Cordoba et al., 2009) Abnormal glucose metabolism Diabetes (Salas-Salvado et al., 2011) Coronary heart disease (Gouveri et al., 2011) (Kastorini et al., 2010) Determining a MeDi Score for each A value of 0 or 1 was Participant 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 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)

Higher Adherence to MeDi in Healthy Controls compared to MCI and AD Groups *** * 5.5 5 Mean MeDi Score 4.5 4 3.5 3 2.5 2 1.5 1 0.5 0 HC MCI AD 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. Percentage of Healthy Controls with each MeDi Score Percentage of Healthy Controls 25 20 15 10 5 0 0 1 2 3

4 5 MeDi Score 6 7 8 9 Percentage of ADs with each MeDi Score 67% 25 52% % APOE 4 positive 55 % 42

Percentage of ADs % 20 66 % 50 % past smokers % 70 % 57 % 15 62 % 38 10 %

50 % 38 % 5 100 % 100 100% 100% 50 % 100% % 0 0 1 2 3

4 5 MeDi Score 6 7 8 A subset of the AIBL cohort undergoes C11 PiB-PET Imaging Healthy Control Alzheimers Disease PiB Score Residual Higher MeDi Score is associated with lower PiB Score MeDi Score Residual

Controlling for age, APOE genotype, gender and years of Are these results confounded by the Amyloid burden of the AD brain? PiB Score Residual Amongst Healthy Controls only, Higher MeDi Score is still associated with lower PiB Score MeDi Score Residual Controlling for age, APOE genotype, gender and years of 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 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. Acknowledgements and Thanks AIBL study participants, their families, and the AIBL study team 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 OHalloran 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.

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