Discovery and characterisation of dietary patterns in two Nordic countries

Using non-supervised and supervised multivariate statistical techniques to analyse dietary survey data

image of Discovery and characterisation of dietary patterns in two Nordic countries

This Nordic study encompasses multivariate data analysis (MDA) of preschool Danish as well as pre- and elementary school Swedish consumers. Contrary to other counterparts the study incorporates two separate MDA varieties - Pattern discovery (PD) and predictive modelling (PM). PD, i.e. hierarchical cluster analysis (HCA) and factor analysis (using PCA), helped identifying distinct consumer aggregations and relationships across food groups, respectively, whereas PM enabled the disclosure of deeply entrenched associations. 17 clusters - here defined as dietary prototypes - were identified by means of HCA in the entire bi-national data set. These prototypes underwent further processing, which disclosed several intriguing consumption data relationships: Striking disparity between consumption patterns of Danish and Swedish preschool children was unveiled and further dissected by PM. Two prudent and mutually similar dietary prototypes appeared among each of two Swedish elementary school children data subsets. Dietary prototypes rich in sweetened soft beverages appeared among Danish and Swedish children alike. The results suggest prototype-specific risk assessment and study design.




Multivariate data analysis (MDA) has an established and acknowledged position in diverse fundamental and applied sciences, especially those of engineering but likewise various biology and biomedical disciplines. Since decades back MDA is an integral part of nutritional epidemiology and has, more recently, even found inroads to dietary surveys. Nonetheless, the latter field has hitherto witnessed but scattered MDA application and very little, if any, of advanced and exploratory nature. This situation contrasts sharply to the quite extensive accumulated resources allocated to such surveys globally, including those accomplished in the Nordic countries. Actually, dietary surveys – commonly conducted periodically within the respective national Nordic food regulatory agencies – are typically designed to capture eating habits at comparatively high levels of differentiation, thereby comprising rich data sets. Without taking advantage of appropriate computational technology, however, much information embedded in these compilations will remain curtailed. Consequently, a more regular implementation of relevant MDA within the dietary survey area will undoubtedly render intricate eating patterns, within and across countries, accessible to inspection and construal.


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