People eat mixes of foods, not isolated nutrients. Within individuals there are strong correlations between the consumption of individual nutrients so that it is often very difficult to separate the contribution to health or disease of one nutrient from a group of nutrients. Therefore, interest has grown in the identification of patterns of food consumption and the extent to which particular patterns can be linked to health outcomes (dietary pattern analysis). This interest represents an understandable expansion of awareness of the importance of particular food combinations and the role of nutrient-nutrient interactions (Hu et al., 1999; Millen et al., 2004). In large part, the interest is based on ecological comparisons between communities who differ in risk of specific diseases that can be plausibly linked to dietary habits. Where the aim is to understand an overall picture of the relationships between diet and disease, dietary patterns may be more informative than study of single nutrients in isolation (Balder et al., 2003).
The main techniques used to determine eating patterns are (a) principal components factor analysis, (b) cluster analysis, and (c) dietary indices (Newby et al., 2004a, b). The first two methods are useful in the reduction or grouping of data, whereas the third is valuable in the prospective study of dietary contributions to disease prevention. In factor analysis, dietary data are obtained by recall methods, and foods are sorted into major nutrient groups. These nutrient groups are then factor-analyzed using principal component methods (with or without rotation). The major factors produced are inspected for content validity and named accordingly, such that a factor that comprises high energy intake, saturated fats, and alcohol might be labeled high risk, where another factor comprising low energy, fruit and vegetables, unprocessed oily fish, and so on might be labeled prudent (Montonen et al., 2005). These factors are consistently derived in samples from diverse Western populations and have proven to be useful in understanding the contributions of diet rather than specific nutrients to risks of disease.
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