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Am J Clin Nutr. 2012 Nov;96(5):1079-92. doi: 10.3945/ajcn.112.038109. Epub 2012 Oct 3.

Assessment of dietary patterns in nutritional epidemiology: principal component analysis compared with confirmatory factor analysis.

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INSERM U1018/CESP, Respiratory and Environmental Epidemiology team (team 5), 16 avenue Paul Vaillant Couturier, 94807 Villejuif Cedex, France.



In the field of nutritional epidemiology, principal component analysis (PCA) has been used to derive patterns, but the robustness of interpretation might be an issue when the sample size is small. The authors proposed the alternative use of confirmatory factor analysis (CFA) to define such patterns.


The aim was to compare dietary patterns derived through PCA and CFA used as equivalent approaches in terms of stability and relevance.


PCA and CFA were performed in 2 different studies: the Epidemiological Study on the Genetics and Environment of Asthma 2-France (EGEA2-France; n = 1236) and the Phenotype and Course of Chronic Obstructive Pulmonary Disease study-Spain (n = 274). To check for stability, PCA and CFA were also performed in 2 subsamples from the EGEA2 study (n = 618 and 309). Statistical proprieties were evaluated by 1000 bootstrapped random sets of observations for each of the 4 subsamples. For each random set of observations, the distribution of the factor loading for each pattern was obtained and represented by using box-plots. To check for relevance, partial correlations between different nutrients and the different patterns derived by either PCA or CFA were calculated.


With the use of CFA, 2 consistent dietary patterns were derived in each subsample (the Prudent and the Western patterns), whereas dietary factors were less interpretable with the use of PCA (smaller median of factor loadings and higher dispersion), especially for the smallest subsample. Higher correlations were reported among total fiber, vitamins, minerals, and total lipids with patterns derived by using CFA than with patterns derived by using PCA.


The current study shows that CFA may be a useful alternative to PCA in epidemiologic studies, especially when the sample size is small.

[Indexed for MEDLINE]

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