New approaches to the study of dietary patterns

The relationship between diet and health can be examined at the level of food components, foods and dietary patterns. Until recently, the study of food components, particularly nutrients, has been the dominant approach in nutritional epidemiology. This approach has clear advantages. If the development of a disease is causally related to the intake of a food component, the examination of that food component will be the approach with the greatest power to identify its effect. In addition, results for food components can be compared with associations observed in other populations, data from mechanistic studies and health effects found in intervention studies (Willett & Buzzard, 1998). Knowledge on the level of food components can then be used to produce foods with higher or lower levels of the component. For example, reduction of the amount of trans-fatty acids in margarines has probably resulted in substantial health benefits for populations (Oomen et al. 2001). However, the effect of a food component can differ depending on the food that it is derived from due to interactions between food components or physical characteristics of foods. For example, folate from beer may provide less health benefits than the same amount of folate from bread, because alcohol reduces intestinal folate uptake, interferes with folate metabolism and increases urinary loss of folate (Jiang et al. 2003). Effects of food consumption on disease risk can be different from predictions based on known effects of food components, because our knowledge on the myriad possible beneficial or detrimental aspects of foods (Jacobs & Murtaugh, 2000) is still limited. The study of foods and food groups accounts for interactions between different components of a food, and for effects of physical characteristics and unknown components. In addition to different components of a food, synergy or antagonism may also exist for components of different foods and drinks that are included in the dietary pattern of an individual. As a result, health effects of dietary patterns may be greater than for individual foods or nutrients. Results from the Dietary Approaches to Stop Hypertension (DASH) intervention study illustrate the importance of considering dietary patterns (Appel et al. 1997). The DASH diet, a diet characterized by high consumption of vegetables, fruits, low-fat dairy products and whole grains, resulted in a greater reduction in blood pressure than had been found for individual minerals in these foods. Another advantage of the study of dietary patterns in epidemiological studies is that potential dietary confounders are largely incorporated in the dietary pattern variable. Different methods have been used to study dietary patterns in epidemiological studies. Two main approaches can be distinguished. First, an exploratory approach can be used that identifies combinations of foods and drinks as they are consumed in reality in a particular population. Principal components analysis is a frequently used exploratory approach to identify dietary patterns (Hu et al. 2000). This data reduction technique constructs new variables that are linear combinations of the original variables and explain as much of the variation in the original variables as possible. Applied to dietary data, new dietary pattern variables are derived on the basis of the correlation matrix of the original food variables. The use of principal components analysis to derive dietary pattern variables requires several arbitrary choices such as the original variables to include and the number of dietary patterns to identify (Martinez et al. 1998). This underlines the importance of conducting sensitivity analyses to examine the robustness of the findings (Hu et al. 2000). Dietary patterns identified by the explanatory approach reflect dietary behaviour and are not based on known health effects of diet. As a result, the identified dietary patterns are not necessarily relevant for disease risk. However, results can increase insight into possibilities for dietary changes and can provide information for setting priorities for changing dietary patterns in a population by public health initiatives. Second, a hypothesis-oriented approach that uses predefined criteria to construct dietary pattern scores can be used. These scores reflect the degree to which a person’s diet conforms to a dietary pattern that was defined a priori based on presumed health effects. Scores based on dietary recommendations (McCullough et al. 2002) and characteristics of the traditional Mediterranean diet (Trichopoulou et al. 2003) have been used. Deciding what individual components to include, the cut-off points and the weights of different components of an a priori score still requires subjective choices. This approach does not have the advantages related to studying existing dietary behaviour or to the identification of new dietary patterns that may affect disease risk. However, the approach can capture the greater effects of the overall diet as compared with individual components, and can be used to test the validity of dietary recommendations. Hoffmann and colleagues have introduced a new method in nutritional epidemiology that combines characteristics of the explanatory and hypothesis-oriented approaches to dietary patterns (Hoffmann et al. 2004a). This reduced rank regression (RRR) method has similarities with principal components analysis, but it uses a set of response variables in addition to a set of food intake variables. The identified dietary patterns are linear combinations of the original food intake variables that maximally explain variation in the response variables (Hoffmann et al. 2004a). A priori knowledge is introduced by using a set of response variables that is known to predict the disease of interest. In a paper in this issue of BJN, Hoffmann et al. (2005) have used macronutrient intakes as response variables, resulting in dietary pattern variables that are linear combinations of the original food variables and maximally explain variation in macronutrient British Journal of Nutrition (2005), 93, 573–574 DOI: 10.1079/BJN20051453 q The Author 2005