The Search for Clinically Useful Biomarkers of Complex Disease: A Data Analysis Perspective

Abstract

Unmet clinical diagnostic needs exist for many complex diseases, which (it is hoped) will be solved by the discovery of metabolomics biomarkers. However, at present, no diagnostic tests based on metabolomics have yet been introduced to the clinic. This review is presented as a research perspective on how data analysis methods in metabolomics biomarker discovery may contribute to the failure of biomarker studies and suggests how such failures might be mitigated. The study design and data pretreatment steps are reviewed briefly in this context, and the actual data analysis step is examined more closely.

Keywords: biomarker discovery; complex disease; data analysis; heterogeneous disease; metabolomics; modeling; precision medicine; reproducibility.

Figure 1

Can results irreproducibility in metabolomics be explained by contextual sensitivity?

Figure 2

Simple models for biomarker discovery may be more generalisable. (PLSDA: Partial Least Squares Discriminant Analysis; MSM: Multi Scale Mathematical Models).