Introduction: Principal component analysis is used to determine dietary behaviors of a population whereas reduced rank regression is used to construct disease-related dietary patterns. This study aimed to compare both types of DP and theirs associations with cardiovascular risk factors (CVRF). Materiel and Methods: Data were derived from the cross sectional NESCAV (Nutrition, Environment and Cardiovascular Health) study, aiming to describe the cardiovascular health of the Greater region's population (Grand duchy of Luxembourg, Wallonia (Belgium), Lorraine (France)). 2298 individuals were included for this study and dietary intake was assessed using a 134-item food frequency questionnaire. Results: We found that CVRF-related patterns also reflect eating behaviours of the population. Comparing concordant food groups between both dietary pattern methods, a diet high in fruits, oleaginous and dried fruits, vegetables, olive oil, fats rich in omega 6 and tea and low in fried foods, lean and fatty meat, processed meat, ready meal, soft drink and beer was associated with lower prevalence of CVRF. In the opposite, a pattern characterized by high intakes of fried foods, meat, offal, beer, wine and aperitifs and spirits, and low intakes of cereals, sugar and sweets and soft drinks was associated with higher prevalence of CVRF. Conclusion: In sum, we found that a "Prudent" and "Animal protein and alcohol" patterns were both associated with CVRF and behaviourally meaningful. Moreover, the relationships of those dietary patterns with lifestyle characteristics support the theory that food choices are part of a larger pattern of healthy lifestyle. Copyright © 2016 Sauvageot et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
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(2017). Correction: Correction: Association of Empirically Derived Dietary Patterns with Cardiovascular Risk Factors: A Comparison of PCA and RRR Methods. PLOS ONE, 12(1), e0171468. https://doi.org/10.1371/journal.pone.0171468