Objective: Dietary pattern analysis is receiving increasing attention as a means of summarizing the multidimensional nature of dietary data. This research aims to compare principal component analysis (PCA) and cluster analysis using dietary data collected from young women in the UK.
Design: Diet was assessed using a 100-item interviewer-administered food frequency questionnaire. PCA and cluster analysis were used to examine dietary patterns.
Setting: Southampton, UK.
Subjects: A total of 6125 non-pregnant women aged 20-34 years.
Results: PCA identified two important patterns: a 'prudent' diet and a 'high-energy' diet. Cluster analysis defined two clusters, a 'more healthy' and a 'less healthy' cluster. There was a strong association between the prudent diet score and the two clusters, such that the mean prudent diet score in the less healthy cluster was -0.73 standard deviations and in the more healthy cluster was +0.83 standard deviations; the difference in the high-energy diet score between the two clusters was considerably smaller.
Conclusions: Both approaches revealed a similar dietary pattern. The continuous nature of the outcome of PCA was considered to be advantageous compared with the dichotomy identified using cluster analysis.
Sponsorship: The study was funded by the Dunhill Medical Trust, the University of Southampton and the Medical Research Council.