%0 Journal Article
%T Visualization and identification of health space, based on personalized molecular phenotype and treatment response to relevant underlying biological processes
%A Jildau Bouwman
%A Jack TWE Vogels
%A Suzan Wopereis
%A Carina M Rubingh
%A Sabina Bijlsma
%A Ben van Ommen
%J BMC Medical Genomics
%D 2012
%I BioMed Central
%R 10.1186/1755-8794-5-1
%X A statistical visualization method is presented, which analyses and visualizes the effects of treatment in individual subjects. The visualization is based on predefined biological processes as determined by systems-biological datasets (metabolomics proteomics and transcriptomics). This allows one to evaluate biological effects depending on shifts of either groups or subjects in the space predefined by the axes, which illustrate specific biological processes. We built validated multivariate models for each axis to represent several biological processes. In this space each subject has his or her own score on each axis/process, indicating to which extent the treatment affects the related process.The health space model was applied to visualize the effects of a nutritional intervention, with the goal of applying diet to improve health. The model was therefore named the 'health space' model. The 36 study subjects received a 5-week dietary intervention containing several anti-inflammatory ingredients. Plasma concentrations of 79 proteins and 145 metabolites were quantified prior to and after treatment. The principal processes modulated by the intervention were oxidative stress, inflammation, and metabolism. These processes formed the axes of the 'health space'. The approach distinguished the treated and untreated groups, as well as two different response subgroups. One subgroup reacted mainly by modulating its metabolic stress profile, while a second subgroup showed a specific inflammatory and oxidative response to treatment.The 'health space' model allows visualization of multiple results and to interpret them. The model presents treatment group effects, subgroups and individual responses.Emerging technologies in biological research can provide an enormous wealth of data. At the same time, researchers are challenged to interpret this data in an integrated and meaningful manner. Multiple parameters describe processes and interactions between processes. Multivariate statist
%U http://www.biomedcentral.com/1755-8794/5/1