Jitender Kumar, Corey D. Broeckling, Fredrik Wiklund, Erik Ingelsson and Jessica E. Prenni Pages 220 - 226 ( 7 )
Non-targeted metabolite profiling using ultra performance liquid chromatography-mass spectrometry (UPLCMS) was performed as part of a large-scale epidemiological study involving biobanked serum samples. The influence of both biological (age and body mass index) and technical (season of sample collection, fasting time, handling time, and storage time) covariates on the analysis was assessed. Statistical models including different sets of these covariates were compared and the results illustrate that variation in which covariates were included did not have an appreciable effect on the number or composition of biologically significant metabolite features associated with body mass index or age. Furthermore, when all covariates were included in the model, there was little overlap of metabolite features significantly associated with the different covariates. Thus, the results of this study illustrate that while some of the observed quantitative variance of metabolite features can be explained by biological and technical covariates, the use of non-targeted metabolite profiling of serum by UPLC-MS is valid for studies of biological outcomes in biobanked clinical samples from large-scale studies.
Biobanked clinical samples, biomarker discovery, covariates, Non-targeted metabolomics, statistical models, ultra performance liquid chromatography-mass spectrometry.
Proteomics and Metabolomics Facility, Colorado State University, 2021 Campus Delivery, Fort Collins, CO, 80523, USA.