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Dietary patterns obtained through principal components analysis: the effect of input variable quantification.

Andrew D A C Smith1, Pauline M Emmett, P K Newby

  • 1School of Social and Community Medicine, University of Bristol, Oakfield House, Oakfield Grove, Clifton, Bristol BS8 2BN, UK.

The British Journal of Nutrition
|September 7, 2012
PubMed
Summary
This summary is machine-generated.

Principal components analysis (PCA) for dietary patterns can use different input variables. Both gram weights and binary intake data yield meaningful patterns, with distinct advantages for public health applications.

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Area of Science:

  • Nutritional Epidemiology
  • Biostatistics
  • Public Health Nutrition

Background:

  • Principal components analysis (PCA) is a widely used statistical technique for identifying dietary patterns from large datasets.
  • The selection of input variables for PCA, such as food group quantification, can influence the derived dietary patterns.
  • Understanding the impact of different quantification methods is crucial for accurate dietary pattern analysis.

Purpose of the Study:

  • To compare the effects of various input variable quantification methods on dietary patterns derived using PCA.
  • To evaluate four different methods: gram weights, energy-adjusted weights, percentage energy contribution, and binary intake.
  • To assess the influence of these methods on patterns identified in a large cohort of 10-year-old children.

Main Methods:

  • Utilized 3-day diet diary data from 7473 children in the Avon Longitudinal Study of Parents and Children.
  • Performed four separate PCA analyses, each using a different quantification of food group intake.
  • Examined gram weights, energy-adjusted weights, percentage energy contribution, and binary intake (consumed/not consumed).

Main Results:

  • Each PCA analysis yielded three or four distinct dietary patterns, including components representing 'healthy', 'less healthy', and specific food group consumption (e.g., meat, potatoes, vegetables).
  • Dietary patterns derived from percentage energy contribution and energy-adjusted weights showed no significant differences compared to those from gram weights.
  • Binary intake variables produced a component associated with reduced fat and sugar consumption, indicating general food preferences.

Conclusions:

  • Both gram weights and binary intake variables are effective for deriving meaningful dietary patterns using PCA.
  • Gram weights account for the quantity of food consumed, providing detailed intake information.
  • Binary intake variables capture general food preferences, which may be more amenable to public health interventions and easier for individuals to modify.