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A comparison of the dietary patterns derived by principal component analysis and cluster analysis in older Australians

BACKGROUND: Despite increased use of dietary pattern methods in nutritional epidemiology, there have been few direct comparisons of methods. Older adults are a particularly understudied population in the dietary pattern literature. This study aimed to compare dietary patterns derived by principal co...

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Autores principales: Thorpe, Maree G., Milte, Catherine M., Crawford, David, McNaughton, Sarah A.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4772350/
https://www.ncbi.nlm.nih.gov/pubmed/26928406
http://dx.doi.org/10.1186/s12966-016-0353-2
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author Thorpe, Maree G.
Milte, Catherine M.
Crawford, David
McNaughton, Sarah A.
author_facet Thorpe, Maree G.
Milte, Catherine M.
Crawford, David
McNaughton, Sarah A.
author_sort Thorpe, Maree G.
collection PubMed
description BACKGROUND: Despite increased use of dietary pattern methods in nutritional epidemiology, there have been few direct comparisons of methods. Older adults are a particularly understudied population in the dietary pattern literature. This study aimed to compare dietary patterns derived by principal component analysis (PCA) and cluster analysis (CA) in older adults and to examine their associations with socio-demographic and health behaviours. METHODS: Men (n = 1888) and women (n = 2071) aged 55–65 years completed a 111-item food frequency questionnaire in 2010. Food items were collapsed into 52 food groups and dietary patterns were determined by PCA and CA. Associations between dietary patterns and participant characteristics were examined using Chi-square analysis. The standardised PCA-derived dietary patterns were compared across the clusters using one-way ANOVA. RESULTS: PCA identified four dietary patterns in men and two dietary patterns in women. CA identified three dietary patterns in both men and women. Men in cluster 1 (fruit, vegetables, wholegrains, fish and poultry) scored higher on PCA factor 1 (vegetable dishes, fruit, fish and poultry) and factor 4 (vegetables) compared to factor 2 (spreads, biscuits, cakes and confectionery) and factor 3 (red meat, processed meat, white-bread and hot chips) (mean, 95 % CI; 0.92, 0.82–1.02 vs. 0.74, 0.63–0.84 vs. −0.43, −0.50– −0.35 vs. 0.60 0.46–0.74, respectively). Women in cluster 1 (fruit, vegetables and fish) scored highest on PCA factor 1 (fruit, vegetables and fish) compared to factor 2 (processed meat, hot chips cakes and confectionery) (1.05, 0.97–1.14 vs. −0.14, −0.21– −0.07, respectively). Cluster 3 (small eaters) in both men and women had negative factor scores for all the identified PCA dietary patterns. Those with dietary patterns characterised by higher consumption of red and processed meat and refined grains were more likely to be Australian-born, have a lower level of education, a higher BMI, smoke and did not meet physical activity recommendations (all P < 0.05). CONCLUSIONS: PCA and CA identified comparable dietary patterns within older Australians. However, PCA may provide some advantages compared to CA with respect to interpretability of the resulting dietary patterns. Older adults with poor dietary patterns also displayed other negative lifestyle behaviours. Food-based dietary pattern methods may inform dietary advice that is understood by the community. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12966-016-0353-2) contains supplementary material, which is available to authorized users.
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spelling pubmed-47723502016-03-02 A comparison of the dietary patterns derived by principal component analysis and cluster analysis in older Australians Thorpe, Maree G. Milte, Catherine M. Crawford, David McNaughton, Sarah A. Int J Behav Nutr Phys Act Research BACKGROUND: Despite increased use of dietary pattern methods in nutritional epidemiology, there have been few direct comparisons of methods. Older adults are a particularly understudied population in the dietary pattern literature. This study aimed to compare dietary patterns derived by principal component analysis (PCA) and cluster analysis (CA) in older adults and to examine their associations with socio-demographic and health behaviours. METHODS: Men (n = 1888) and women (n = 2071) aged 55–65 years completed a 111-item food frequency questionnaire in 2010. Food items were collapsed into 52 food groups and dietary patterns were determined by PCA and CA. Associations between dietary patterns and participant characteristics were examined using Chi-square analysis. The standardised PCA-derived dietary patterns were compared across the clusters using one-way ANOVA. RESULTS: PCA identified four dietary patterns in men and two dietary patterns in women. CA identified three dietary patterns in both men and women. Men in cluster 1 (fruit, vegetables, wholegrains, fish and poultry) scored higher on PCA factor 1 (vegetable dishes, fruit, fish and poultry) and factor 4 (vegetables) compared to factor 2 (spreads, biscuits, cakes and confectionery) and factor 3 (red meat, processed meat, white-bread and hot chips) (mean, 95 % CI; 0.92, 0.82–1.02 vs. 0.74, 0.63–0.84 vs. −0.43, −0.50– −0.35 vs. 0.60 0.46–0.74, respectively). Women in cluster 1 (fruit, vegetables and fish) scored highest on PCA factor 1 (fruit, vegetables and fish) compared to factor 2 (processed meat, hot chips cakes and confectionery) (1.05, 0.97–1.14 vs. −0.14, −0.21– −0.07, respectively). Cluster 3 (small eaters) in both men and women had negative factor scores for all the identified PCA dietary patterns. Those with dietary patterns characterised by higher consumption of red and processed meat and refined grains were more likely to be Australian-born, have a lower level of education, a higher BMI, smoke and did not meet physical activity recommendations (all P < 0.05). CONCLUSIONS: PCA and CA identified comparable dietary patterns within older Australians. However, PCA may provide some advantages compared to CA with respect to interpretability of the resulting dietary patterns. Older adults with poor dietary patterns also displayed other negative lifestyle behaviours. Food-based dietary pattern methods may inform dietary advice that is understood by the community. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12966-016-0353-2) contains supplementary material, which is available to authorized users. BioMed Central 2016-02-29 /pmc/articles/PMC4772350/ /pubmed/26928406 http://dx.doi.org/10.1186/s12966-016-0353-2 Text en © Thorpe et al. 2016 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research
Thorpe, Maree G.
Milte, Catherine M.
Crawford, David
McNaughton, Sarah A.
A comparison of the dietary patterns derived by principal component analysis and cluster analysis in older Australians
title A comparison of the dietary patterns derived by principal component analysis and cluster analysis in older Australians
title_full A comparison of the dietary patterns derived by principal component analysis and cluster analysis in older Australians
title_fullStr A comparison of the dietary patterns derived by principal component analysis and cluster analysis in older Australians
title_full_unstemmed A comparison of the dietary patterns derived by principal component analysis and cluster analysis in older Australians
title_short A comparison of the dietary patterns derived by principal component analysis and cluster analysis in older Australians
title_sort comparison of the dietary patterns derived by principal component analysis and cluster analysis in older australians
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4772350/
https://www.ncbi.nlm.nih.gov/pubmed/26928406
http://dx.doi.org/10.1186/s12966-016-0353-2
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