Visualization and identification of health space, based on personalized molecular phenotype and treatment response to relevant underlying biological processes

Abstract

BackgroundBeing able to visualize multivariate biological treatment effects can be insightful. However the axes in visualizations are often solely defined by variation and thus have no biological meaning. This makes the effects of treatment difficult to interpret.MethodsA 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.ResultsThe 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.ConclusionsThe 'health space' model allows visualization of multiple results and to interpret them. The model presents treatment group effects, subgroups and individual responses.

EURRECA—Evidence-Based Methodology for Deriving Micronutrient Recommendations

“…We built a conceptual multivariate model 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 (Bouwman et al, 2012). For instance the oxidation status can be represented by an axis which is quantified by a combination of markers (Bouwman et al, 2012;van Ommen et al, 2008).…”

“…vitamin C and E) effects on these markers (Activity 3) allows for individualised micronutrient recommendations based on the nutritional health space. Applying the health space method on data of a human intervention with an anti-inflammatory dietary mix, has shown that the model allows visualization of an individual's health status based on the assembly of omics data in biologically relevant processes multiple results and facilitates the interpretation (application of the health space is extensively described in (Bouwman et al, 2012) …”

“…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 (Bouwman et al, 2012). For instance the oxidation status can be represented by an axis which is quantified by a combination of markers (Bouwman et al, 2012;van Ommen et al, 2008). Assessment of the individual's health status by measuring these markers combined with the collated evidence for micronutrient (e.g.…”

“…----Please Insert Figure 5 Here---- (Bouwman et al, 2012;van Ommen et al, 2009). An example of a metabolomics approach to assess micronutrient related health status is described in the micronutrient specific sections on selenium (Hurst et al, 2013).…”

“…To fully appraise the impact of micronutrients on health, a more holistic view of the biological effects of multiple micronutrients is needed, including building micronutrient-centered biological networks (van Ommen et al, 2009) and developing suitable statistical methods for assessing individual micronutrient-health effects (Activity 6: 'health space' model) (Bouwman et al, 2012). As an example of this EURRECA approach, information collated from human studies that met…”