Dietary patterns derived from principal component- and k-means cluster analysis: Long-term association with coronary heart disease and stroke

M. D. Stricker, N. C. Onland-Moret, J. M.A. Boer, Y. T. van der Schouw, W. M.M. Verschuren, A. M. May, P. H.M. Peeters, J. W.J. Beulens

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Abstract

Background and aims: Studies comparing dietary patterns derived from different a posteriori methods in view of predicting disease risk are scarce. We aimed to explore differences between dietary patterns derived from principal component- (PCA) and k-means cluster analysis (KCA) in relation to their food group composition and ability to predict CHD and stroke risk. Methods and results: The study was conducted in the EPIC-NL cohort that consists of 40,011 men and women. Baseline dietary intake was measured using a validated food-frequency questionnaire. Food items were consolidated into 31 food groups. Occurrence of CHD and stroke was assessed through linkage with registries. After 13 years of follow-up, 1,843 CHD and 588 stroke cases were documented. Both PCA and KCA extracted a prudent pattern (high intakes of fish, high-fiber products, raw vegetables, wine) and a western pattern (high consumption of French fries, fast food, low-fiber products, other alcoholic drinks, soft drinks with sugar) with small variation between components and clusters. The prudent component was associated with a reduced risk of CHD (HR for extreme quartiles: 0.87; 95%-CI: 0.75-1.00) and stroke (0.68; 0.53-0.88). The western component was not related to any outcome. The prudent cluster was related with a lower risk of CHD (0.91; 0.82-1.00) and stroke (0.79; 0.67-0.94) compared to the western cluster. Conclusion: PCA and KCA found similar underlying patterns with comparable associations with CHD and stroke risk. A prudent pattern reduced the risk of CHD and stroke.

Original languageEnglish
Pages (from-to)250-256
Number of pages7
JournalNutrition, Metabolism and Cardiovascular Diseases
Volume23
Issue number3
DOIs
Publication statusPublished - 1 Mar 2013

Keywords

  • Coronary heart disease
  • Dietary patterns
  • K-Means cluster analysis
  • Principal component analysis
  • Stroke

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