Metabolic profiling–multitude of technologies with great research potential, but (when) will translation emerge?

Metabolic phenotyping, nowadays most often termed ‘metabolomics’, is becoming increasingly applied in molecular epidemiology, particularly in concert with genomics. Since Jeremy Nicholson and colleagues coined the term ‘metabonomics’ in 1999, over 15 000 publications have appeared under this conceptual and technological umbrella (a Pubmed search on 15 August 2016 at [http:// www.ncbi.nlm.nih.gov/pubmed/] with metabonomics or metabolomics or lipidomics). Most of the published works have been, and still continue to be, methodologically oriented and thereby bear little direct relevance to applied epidemiology. Particularly the spectroscopy-based chemometric approaches–typically aiming at classification of individuals with or without a particular disease–have for a long time (mis)guided metabolomics research. Many of the limitations of these types of multivariate metabolomics applications are currently well understood: overtraining of classification models with high numbers of variables (typically spectral data points), cross-sectional study settings with very small numbers of individuals and no independent replication. However, some lack of clarity still remains, partly related to some misplaced conceptions as to the scope of truly personalized medicine. Individual diagnostics of polygenic diseases, when both the disease liability and the metabolic phenotypes are continuous, fundamentally preclude diagnostic models that would provide both high sensitivity and high specificity. For example, conditions like autism, long considered rigid disease classifications, clearly involve a somewhat arbitrary division of a continuously distributed underlying liability, limiting attempts at improved binary classification. In addition, many metabolomics applications have ignored confounding in data analyses and interpretations, though it is well established in observational epidemiology that confounding–by lifestyle and socioeconomic factors, or by baseline health status, treatment and medication effects–is prone to affect many associations.