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

• Published in: Proceedings of the Nutrition Society 2013-05, Vol.72 (2), p.191-199
• Main Author: OCKE, Marga C
• Format: Article
• Language: English
• Subjects: Biological and medical sciences; Biomarkers; Cluster Analysis; Conference on ‘Translating nutrition: integrating research, practice and policy’; Decision Trees; Diet; Diet - standards; Diet Surveys; Disease; Feeding Behavior; Feeding. Feeding behavior; Food; Food groups; Fundamental and applied biological sciences. Psychology; Health care; Humans; Nutrition Assessment; Nutrition research; Population; Principal Component Analysis; Principal components analysis; R&D; Regression Analysis; Research & development; Statistical methods; Studies; Symposium 1: Innovation in diet and lifestyle assessment; Vertebrates: anatomy and physiology, studies on body, several organs or systems
• ISSN: 0029-6651; 1475-2719
• DOI: 10.1017/S0029665113000013

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.