National Institute for Public Health and the Environment (RIVM), PO Box 1, 3720 BA Bilthoven, The Netherlands.
Proc Nutr Soc. 2013 May;72(2):191-9. doi: 10.1017/S0029665113000013. Epub 2013 Jan 30.
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.
本文旨在描述研究整体饮食的不同方法，包括其优缺点。研究整体饮食的出现是因为饮食摄入与健康之间的关系非常复杂，存在各种相互作用，而这些作用无法通过研究单一的饮食成分来很好地捕捉。研究整体饮食可以采用三种主要方法。第一种方法是研究人员定义的饮食质量得分或指数。这些通常基于健康饮食指南或已知健康的饮食。第二种方法是基于主成分或聚类分析，由基础饮食数据驱动。在主成分分析中，根据食物组之间的潜在关系得出尺度，而在聚类分析中，根据人群的饮食摄入情况将人群分为亚组。第三种方法包括受生物途径和基础饮食数据组合驱动的方法。降秩回归定义了最大限度地解释营养素摄入或疾病中间标志物的食物摄入的线性组合。决策树分析确定了具有共享影响（疾病中间标志物）的饮食特征的人群亚组。结论是，所有方法都有优缺点，本质上回答的是不同的问题。第三种方法仍处于探索阶段，但似乎具有很大的潜力和互补价值。如果更多地关注方法学问题，就能更深入地了解进行整体饮食研究的效用。
