Food patterns derived with multivariate statistical methods and their association with chronic disease in a multi-country setting: the European Prospective Investigation into Cancer and Nutrition

• Applicant: Brian Buijsse, Ph.D.
• Subject Area: Nutritional Sciences ; Epidemiology and Medical Biometry/Statistics
• Term: from 2014 to 2015
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 257392992

Studying overall dietary patterns is a relatively new direction in nutritional epidemiology. In addition to 'a priori' diet quality scores, virtually all studies so far have used so-called unsupervised methods, including principal component analysis. These methods extract dietary patterns without taking the disease outcome into account. Consequently, such dietary patterns may not be relevant (that is, predictive) for a disease. Thus, other multivariate statistical methods to identify dietary patterns with greater potential for disease prevention need to be tested. Supervised methods, which can make use of pre-existing knowledge on chronic disease risk, may be used for this aim. Using incident colorectal cancer as an example, this study will examine whether food patterns extracted with supervised methods are as good or better than food patterns from unsupervised methods and a previously proposed food-based 'a priori' diet quality score in terms of predicting chronic disease risk. The supervised methods to test are random survival forest analysis and support vector machine learning, and the unsupervised methods are principal component analysis and cluster analysis. The diet quality score will be constructed based on quartile rankings for food groups considered to be either healthy or unhealthy. This study will be conducted in the European Prospective Investigation into Cancer and Nutrition (EPIC), with 478,000 men and women from 10 countries without colorectal cancer at study inception (1992-2000) and in whom about 4517 incident cases of colorectal cancer occurred during a mean follow-up of 11 years. This study will clarify whether food patterns identified with supervised methods can outperform food patterns from established methods in terms of their association with chronic disease. If so, it will be tried to find out the underlying reasons.

• DFG Programme: Research Grants
• International Connection: France
• Participating Person: Nadia Slimani, Ph.D.