Role of dietary pattern analysis in determining cognitive status in elderly Australian adults
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Ashby-Mitchell, Kimberly; Peeters, Anna; Anstey, Kaarin
Description
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...[Show more]
dc.contributor.author | Ashby-Mitchell, Kimberly | |
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dc.contributor.author | Peeters, Anna | |
dc.contributor.author | Anstey, Kaarin | |
dc.date.accessioned | 2015-05-19T02:25:01Z | |
dc.date.available | 2015-05-19T02:25:01Z | |
dc.identifier.issn | 2072-6643 | |
dc.identifier.uri | http://hdl.handle.net/1885/13527 | |
dc.description.abstract | 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. | |
dc.description.sponsorship | This research was supported by the Australian Research Council Centre of Excellence in Population Ageing Research (project number CE110001029). AP is supported by a National Health and Medical Research Council Career Development Award (1045456). KJA is funded by NHMRC Fellowship #1002560. We acknowledge support from the NHMRC Dementia Collaborative Research Centres. Also, for funding or logistical support, we are grateful to: National Health and Medical Research Council (NHMRC grants 233200 and 1007544), Australian Government Department of Health and Ageing, Abbott Australasia Pty Ltd, Alphapharm Pty Ltd, Amgen Australia, AstraZeneca, Bristol-Myers Squibb, City Health Centre-Diabetes Service-Canberra, Department of Health and Community Services —Northern Territory, Department of Health and Human Services—Tasmania, Department of Health— New South Wales, Department of Health—Western Australia, Department of Health—South Australia, Department of Human Services—Victoria, Diabetes Australia, Diabetes Australia Northern Territory, Eli Lilly Australia, Estate of the Late Edward Wilson, GlaxoSmithKline, Jack Brockhoff Foundation, Janssen-Cilag, Kidney Health Australia, Marian & FH Flack Trust, Menzies Research Institute, Merck Sharp & Dohme, Novartis Pharmaceuticals, Novo Nordisk Pharmaceuticals, Pfizer Pty Ltd, Pratt Foundation, Queensland Health, Roche Diagnostics Australia, Royal Prince Alfred Hospital, Sydney, Sanofi Aventis, Sanofi-Synthelabo, and the Victorian Government’s OIS Program. | |
dc.format | 16 pages | |
dc.publisher | MDPI AG | |
dc.rights | © 2015 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/4.0/). | |
dc.rights | http://www.sherpa.ac.uk/romeo/issn/2072-6643/ author can archive pre-print (ie pre-refereeing),author can archive post-print (ie final draft post-refereeing), author can archive publisher's version/PDF, Publisher's version/PDF may be used. Published source must be acknowledged. Creative Commons Attribution License Authors are encouraged to submit their published articles to institutional repositories. (Sherpa/Romeo as of 19/5/2015) | |
dc.source | Nutrients | |
dc.subject | dietary pattern | |
dc.subject | principal component analysis | |
dc.subject | cognitive impairment | |
dc.subject | Australia | |
dc.title | Role of dietary pattern analysis in determining cognitive status in elderly Australian adults | |
dc.type | Journal article | |
local.identifier.citationvolume | 7 | |
dcterms.dateAccepted | 2015-01-20 | |
dc.date.issued | 2015-02-04 | |
local.identifier.ariespublication | u9912193xPUB427 | |
local.publisher.url | http://www.mdpi.com/ | |
local.type.status | Published Version | |
local.contributor.affiliation | Ashby-Mitchell, Kimberly, Centre for Research on Ageing, Health and Wellbeing, CMBE Research School of Population Health, The Australian National University | |
local.contributor.affiliation | Anstey, Kaarin, J., Centre for Research on Ageing, Health and Wellbeing, CMBE Research School of Population Health, The Australian National University | |
dc.relation | http://purl.org/au-research/grants/arc/CE110001029 | |
dc.relation | http://purl.org/au-research/grants/nhmrc/1045456 | |
dc.relation | http://purl.org/au-research/grants/nhmrc/1002560 | |
dc.relation | http://purl.org/au-research/grants/nhmrc/233200 | |
dc.relation | http://purl.org/au-research/grants/nhmrc/1007544 | |
local.identifier.essn | 2072-6643 | |
local.bibliographicCitation.issue | 2 | |
local.bibliographicCitation.startpage | 1052 | |
local.bibliographicCitation.lastpage | 1067 | |
local.identifier.doi | 10.3390/nu7021052 | |
Collections | ANU Research Publications |
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