Clustering and interpretation on real nutritional data

Abstract

Nutritional Genomics studies diet-gene-disease interactions and aims to promote health and disease prevention. It is based on the idea that everything ingested into a person’s body affects the genome of the individual and, therefore, both genes and nutrients modify the same metabolic processes. This paper presents an application of clustering and interpretation over real heterogeneous data coming from a nutritional study. The individuals are clustered by their diet and physical activity habits and the resulting clustering is interpreted. This work is part of a methodology to deal with data from dietary intervention studies.