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

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 72 month Current Status

The Cohort 40% Perth-based, 60% Melbourne-based ~1000 Participants Healthy Controls Alzheimer’s Disease (AD) Mild Cognitive Impairment (MCI)

A Multidisciplinary Study 4 research streams: Multi-disciplinary approach 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 723 MCI 98 AD 149 Total: 970 To understand the role of dietary factors in AD, a questionnaire was given to each participant at baseline - the CCVFFQ. Food frequency questionnaires (FFQ’s) have been developed as a means of assessing intakes of foods and nutrients in large scale epidemiological studies and record intake over the previous 12 months. Participants are asked to indicate how often they eat each of the 74 food items using 10 frequency response options ranging from ‘Never’ to ‘3 or more times per day.’ The responses are analysed for usual daily nutrient intake and grams consumed at the Cancer Council in Victoria using software based on the Nutrient Tables for use in Australia 1995. The FFQ also contains 3 photographs of scaled portions for four foods (used to calculate a portion size calibrator). Food items are grouped into four categories: 1) cereal foods, sweets and snacks 2) dairy products, meats and fish 3) fruit and 4) vegetables. A separate set of questions covers intake of alcoholic beverages. Compared intake between HC, MCI and AD’s.

*Student’s unpaired t-test, p<0.05 Food and Beverage Consumption: Classification Differences AD > HC* Sausages Ice Cream Ham Margarine Meat Pies Cornflakes Bran flakes Tinned Fruit Chips Full Cream Milk HC > AD* Fortified Wine Capsicum White Wine Lettuce Red Wine Avocado Light Beer Spinach Other Spirits Broccoli Vegemite Yoghurt Tofu Muesli Nuts *Student’s unpaired t-test, p<0.05 Controlling for BMI, country of birth, gender, age and APOE allele status.

*Student’s unpaired t-test, p<0.05 Nutrient Consumption: Classification Differences AD > HC* Saturated Fat Monounsaturated Fat All Fat Retinol Sodium HC > AD* Lutein Zeaxanthin Calcium Magnesium Vitamin C *Student’s 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. All of which are risk factors for AD.

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 This is not a specific diet but a collection of eating habits traditionally followed by people in the countries surrounding the Mediterranean Sea. It includes many of the components reported as being potentially beneficial for Alzheimer’s Disease and Cognition.

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 (Gouveri et al., 2011) Diabetes (Salas-Salvado et al., 2011) Coronary heart disease (Kastorini et al., 2010) All of which are risk factors for AD.

Determining a MeDi Score for each Participant 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 Category < Median ≥ Median Fruit 1 Vegetables Legumes Cereals Fish Meat Dairy Monounsaturated : Saturated Fats Alcohol 0 (+ zero intake)

Higher Adherence to MeDi in Healthy Controls compared to MCI and AD Groups *** * 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 Percentage of Healthy Controls with each MeDi Score MeDi Score Percentage of Healthy Controls

Percentage of ADs with each MeDi Score % past smokers % APOE ε4 positive 100% 38% 50% 57% 70% 52% 67% 42% 55% 66% 62% However, diet alone is not sufficient for prevention of AD, it is essential to use a combined approach, including other lifestyle factors. And when predicting people at higher risk of developing AD, we need to take into account their lifestyle factors and also genetic factors including the APOE genotype.

A subset of the AIBL cohort undergoes C11 PiB-PET Imaging Healthy Control Alzheimer’s Disease

Higher MeDi Score is associated with lower PiB Score MeDi Score Residual PiB Score Residual Correlation: PiB score Residual was calculated by regressing PiB score against Age, APOE, Gender and Years of Education. Similarly, Mediscore Residual was calculated by regressing Mediscore against Age, APOE, Gender and Years of Education. According to the theory of Partial Correlation, regression residual comparison of any two variables is equal to correlating the same variables controlling for covariates it was regressed against1. Following correlation graph shows correlation between PiB Score and Mediscore(conditioning Age, APOE, Gender and Years of Education). 1. Discovery of meaningful associations in genomic data using partial correlation coefficients. De la Fuente A et al. Bioinformatics. 2004 Dec 12;20(18):3565-74. Controlling for age, APOE genotype, gender and years of education.

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

Controlling for age, APOE genotype, gender and years of education. Amongst Healthy Controls only, Higher MeDi Score is still associated with lower PiB Score MeDi Score Residual PiB Score Residual Correlation: PiB score Residual was calculated by regressing PiB score against Age, APOE, Gender and Years of Education. Similarly, Mediscore Residual was calculated by regressing Mediscore against Age, APOE, Gender and Years of Education. According to the theory of Partial Correlation, regression residual comparison of any two variables is equal to correlating the same variables controlling for covariates it was regressed against1. Following correlation graph shows correlation between PiB Score and Mediscore(conditioning Age, APOE, Gender and Years of Education). 1. Discovery of meaningful associations in genomic data using partial correlation coefficients. De la Fuente A et al. Bioinformatics. 2004 Dec 12;20(18):3565-74. Controlling for age, APOE genotype, gender and years of education.

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. Re. last point: It should be noted that the vast majority of our cohort are Caucasians and inferences regarding similar effects in other populations may be limited.

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. First point could be explained by a change in dietary patterns due to the AD diagnosis. Additionally we recognise that other factors may counteract the beneficial effects of the MeDi, for example participants with a MeDi score of 8 who nevertheless develop AD, could be explained by their current smoking status, their APOE genotype or other factors. The FFQ relies on participants’ estimations of food intake over the previous 12 month period; this is a common limitation of studies of diet and disease - misclassification of food consumption due to limited accuracy. The CCVFFQ is known to under-report certain foods, including soft drinks and snack foods- although these are not used for MeDi score calculation. THAT SAID, THE CCVFFQ, AS A DIETARY DATA COLLECTION INSTRUMENT, HAS BEEN VALIDATED IN MULTIPLE EPIDEMIOLOGICAL STUDIES. The MeDi score itself has limitations; it weighs equally the underlying individual food categories, which in turn are composed of different numbers of food constituents. Frequencies of food intake are based on relatively few diet constituents, which may underestimate the overall quantity of food in each food category. Possible mechanisms through which the MeDi may be exerting its protective effect for AD could be via effects on the vascular system. There is strong evidence linking the MeDi to lower risk of vascular risk factors such as dyslipidemia , hypertension, abnormal glucose metabolism, and coronary heart disease , which are also risk factors for AD. Oxidative stress could be another biological mechanism linking the MeDi and AD. AD brains exhibit constant evidence of reactive-oxygen species (ROS) and reactive nitrogen species (RNS) – mediated injury. The protective role of the MeDi against cognitive decline may also be mediated by attenuation of the inflammation pathway.

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 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.