Evaluation of methodologies for assessing the overall diet: dietary quality scores and dietary pattern analysis

Abstract

This paper aims to describe different approaches for studying the overall diet with advantages and limitations. Studies of the overall diet have emerged because the relationship between dietary intake and health is very complex with all kinds of interactions. These cannot be captured well by studying single dietary components. Three main approaches to study the overall diet can be distinguished. The first method is researcher-defined scores or indices of diet quality. These are usually based on guidelines for a healthy diet or on diets known to be healthy. The second approach, using principal component or cluster analysis, is driven by the underlying dietary data. In principal component analysis, scales are derived based on the underlying relationships between food groups, whereas in cluster analysis, subgroups of the population are created with people that cluster together based on their dietary intake. A third approach includes methods that are driven by a combination of biological pathways and the underlying dietary data. Reduced rank regression defines linear combinations of food intakes that maximally explain nutrient intakes or intermediate markers of disease. Decision tree analysis identifies subgroups of a population whose members share dietary characteristics that influence (intermediate markers of) disease. It is concluded that all approaches have advantages and limitations and essentially answer different questions. The third approach is still more in an exploration phase, but seems to have great potential with complementary value. More insight into the utility of conducting studies on the overall diet can be gained if more attention is given to methodological issues.

Identification of data-driven Dutch dietary patterns that benefit the environment and are healthy

“…Reduced rank regression is a relatively new method to derive dietary patterns (Ocké 2013). Compared to PCA, for example, RRR derives patterns that are only slightly different but show stronger associations with diseases (DiBello et al 2008;Hoffmann et al 2005;Nettleton et al 2007).…”

“…Besides a priori dietary patterns, several statistical models can derive and identify posteriori dietary patterns actually present based on population food intake information (Ocké 2013). Examples of these models are principle component analysis (PCA) or cluster analysis (CA) (Hearty and Gibney 2009;Newby and Tucker 2004;Stricker et al 2013).…”

“…For our study, we used a priori knowledge on the healthiness of diets and environmental effects of foods to identify dietary patterns that could potentially be more sustainable and healthy. As far as we know, RRR has only been applied with biomarkers of disease or nutrient intake as response variables but not with an overall dietary index based on recommendations for a healthy diet (Ocké 2013), let alone in combination with an indicator of environmental impact.…”

Nutrition and healthy ageing: the key ingredients

“…using principal component analysis or k-means clustering) (9) . Conversely, in a priori approaches, researchers define scores or indices of the overall dietary quality on the basis of foods or diets associated with health or according to the specific dietary guidelines (9) .…”

Tracking and Changes in Dietary Patterns of Adolescents: A Longitudinal Study in Southern Brazil

“…The dietary habits of a population can be assessed through several approaches [10]. Food consumption is commonly evaluated by estimating total energy intake, micro and macronutrients of separate food-items; in addition, more comprehensive analyses may consider food groups rather than food-items separately, through eating indexes and dietary patterns (DPs) [10,11].…”