Cross-platform analysis of longitudinal data in metabolomics

Abstract

Metabolic profiling is considered to be a very promising tool for diagnostic purposes, for assessing nutritional status and response to drugs. However, it is also evident that human metabolic profiles have a complex nature, influenced by many external factors. This, together with the understanding of the difficulty to assign people to distinct groups and a general move in clinical science towards personalized medicine, raises the interest to explore individual and variable metabolic features for each individual separately in longitudinal study design. In the current paper we have analyzed a set of metabolic profiles of a selection of six urine samples per person from a set of healthy individuals by ¹H NMR and reversed-phase UPLC-MS. We have demonstrated that the method for recovery of individual metabolic phenotypes can give complementary information to another established method for analysis of longitudinal data—multilevel component analysis. We also show that individual metabolic signatures can be found not only in ¹H NMR data, as has been demonstrated before, but also even more strongly in LC-MS data.