A workflow for UPLC-MS non-targeted metabolomic profiling in large human population-based studies

CC-BY-ND 4.0 International license peer-reviewed) is the author/funder. It is made available under a The copyright holder for this preprint (which was not. ABSTRACT Metabolomic profiling is an emerging technique in life sciences. Human studies using these techniques have been performed in a small number of individuals or have been targeted at a restricted number of metabolites. In this article, we propose a data analysis workflow to perform non-targeted metabolomic profiling in large human population-based studies using ultra performance liquid chromatography-mass spectrometry (UPLC-MS). We describe challenges and propose solutions for quality control, statistical analysis and annotation of metabolic features. Using the data analysis workflow, we detected more than 8,000 metabolic features in serum samples from 2,489 fasting individuals. As an illustrative example, we performed a non-targeted metabolome-wide association analysis of high-sensitive C-reactive protein (hsCRP) and detected 407 metabolic features corresponding to 90 unique metabolites that could be replicated in an external population. Our results reveal unexpected biological associations, such as metabolites identified as monoacylphosphorylcholines (LysoPC) being negatively associated with hsCRP. R code and fragmentation spectra for all metabolites are made publically available. In conclusion, the results presented here illustrate the viability and potential of non-targeted metabolomic profiling in large population-based studies.. CC-BY-ND 4.0 International license peer-reviewed) is the author/funder. It is made available under a The copyright holder for this preprint (which was not .