4.7 Article

Role of Dietary Pattern Analysis in Determining Cognitive Status in Elderly Australian Adults

Journal

NUTRIENTS
Volume 7, Issue 2, Pages 1052-1067

Publisher

MDPI
DOI: 10.3390/nu7021052

Keywords

-

Funding

  1. Australian Research Council Centre of Excellence in Population Ageing Research [CE110001029]
  2. National Health and Medical Research Council Career Development Award [1045456]
  3. NHMRC [1002560]
  4. NHMRC Dementia Collaborative Research Centres
  5. National Health and Medical Research Council (NHMRC) [233200, 1007544]
  6. Australian Government Department of Health and Ageing
  7. Abbott Australasia Pty Ltd
  8. Alphapharm Pty Ltd
  9. Amgen Australia
  10. AstraZeneca
  11. Bristol-Myers Squibb
  12. City Health Centre-Diabetes Service-Canberra
  13. Department of Health and Community Services -Northern Territory
  14. Department of Health and Human Services-Tasmania
  15. Department of Health-New South Wales
  16. Department of Health-Western Australia
  17. Department of Health-South Australia
  18. Department of Human Services-Victoria
  19. Diabetes Australia
  20. Diabetes Australia Northern Territory
  21. Eli Lilly Australia
  22. Estate of the Late Edward Wilson
  23. GlaxoSmithKline
  24. Jack Brockhoff Foundation
  25. Janssen-Cilag
  26. Kidney Health Australia
  27. Marian FH Flack Trust
  28. Menzies Research Institute
  29. Merck Sharp Dohme
  30. Novartis Pharmaceuticals
  31. Novo Nordisk Pharmaceuticals
  32. Pfizer Pty Ltd
  33. Pratt Foundation
  34. Queensland Health
  35. Roche Diagnostics Australia
  36. Royal Prince Alfred Hospital, Sydney
  37. Sanofi Aventis
  38. Sanofi-Synthelabo
  39. Victorian Government's OIS Program

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Principal Component Analysis (PCA) was used to determine the association between dietary patterns and cognitive function and to examine how classification systems based on food groups and food items affect levels of association between diet and cognitive function. The present study focuses on the older segment of the Australian Diabetes, Obesity and Lifestyle Study (AusDiab) sample (age 60+) that completed the food frequency questionnaire at Wave 1 (1999/2000) and the mini-mental state examination and tests of memory, verbal ability and processing speed at Wave 3 (2012). Three methods were used in order to classify these foods before applying PCA. In the first instance, the 101 individual food items asked about in the questionnaire were used (no categorisation). In the second and third instances, foods were combined and reduced to 32 and 20 food groups, respectively, based on nutrient content and culinary usage-a method employed in several other published studies for PCA. Logistic regression analysis and generalized linear modelling was used to analyse the relationship between PCA-derived dietary patterns and cognitive outcome. Broader food group classifications resulted in a greater proportion of food use variance in the sample being explained (use of 101 individual foods explained 23.22% of total food use, while use of 32 and 20 food groups explained 29.74% and 30.74% of total variance in food use in the sample, respectively). Three dietary patterns were found to be associated with decreased odds of cognitive impairment (CI). Dietary patterns derived from 101 individual food items showed that for every one unit increase in ((Fruit and Vegetable Pattern: p = 0.030, OR 1.061, confidence interval: 1.006-1.118); (Fish, Legumes and Vegetable Pattern: p = 0.040, OR 1.032, confidence interval: 1.001-1.064); (Dairy, Cereal and Eggs Pattern: p = 0.003, OR 1.020, confidence interval: 1.007-1.033)), the odds of cognitive impairment decreased. Different results were observed when the effect of dietary patterns on memory, processing speed and vocabulary were examined. Complex patterns of associations between dietary factors and cognition were evident, with the most consistent finding being the protective effects of high vegetable and plant-based food item consumption and negative effects of 'Western' patterns on cognition. Further long-term studies and investigation of the best methods for dietary measurement are needed to better understand diet-disease relationships in this age group.

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