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

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 ol...

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Autores principales: Ashby-Mitchell, Kimberly, Peeters, Anna, Anstey, Kaarin J.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4344574/
https://www.ncbi.nlm.nih.gov/pubmed/25658241
http://dx.doi.org/10.3390/nu7021052
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author Ashby-Mitchell, Kimberly
Peeters, Anna
Anstey, Kaarin J.
author_facet Ashby-Mitchell, Kimberly
Peeters, Anna
Anstey, Kaarin J.
author_sort Ashby-Mitchell, Kimberly
collection PubMed
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 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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spelling pubmed-43445742015-03-18 Role of Dietary Pattern Analysis in Determining Cognitive Status in Elderly Australian Adults Ashby-Mitchell, Kimberly Peeters, Anna Anstey, Kaarin J. Nutrients Article 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. MDPI 2015-02-04 /pmc/articles/PMC4344574/ /pubmed/25658241 http://dx.doi.org/10.3390/nu7021052 Text en © 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/).
spellingShingle Article
Ashby-Mitchell, Kimberly
Peeters, Anna
Anstey, Kaarin J.
Role of Dietary Pattern Analysis in Determining Cognitive Status in Elderly Australian Adults
title Role of Dietary Pattern Analysis in Determining Cognitive Status in Elderly Australian Adults
title_full Role of Dietary Pattern Analysis in Determining Cognitive Status in Elderly Australian Adults
title_fullStr Role of Dietary Pattern Analysis in Determining Cognitive Status in Elderly Australian Adults
title_full_unstemmed Role of Dietary Pattern Analysis in Determining Cognitive Status in Elderly Australian Adults
title_short Role of Dietary Pattern Analysis in Determining Cognitive Status in Elderly Australian Adults
title_sort role of dietary pattern analysis in determining cognitive status in elderly australian adults
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4344574/
https://www.ncbi.nlm.nih.gov/pubmed/25658241
http://dx.doi.org/10.3390/nu7021052
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