Data-driven pathway level analysis of metabolomics data in the Estonian Biobank associates carbon metabolism with incident hypertension

Purpose: The purpose of this study was to find metabolic changes associated with incident hypertension in the volunteer-based Estonian Biobank. Methods: We used a subcohort of the Estonian Biobank where metabolite levels had been measured by mass-spectrometry (LC-MS, Metabolon platform). We divided annotated metabolites of 989 individuals into KEGG pathways, followed by principal component analysis of metabolites in each pathway, resulting in a dataset of 91 pathway components. Next, we defined incident hypertension cases and controls based on electronic health records, resulting in a dataset of 101 incident hypertension cases and 450 controls. We used Cox proportional hazards models and replicated the results in a separate cohort of the Estonian Biobank, assayed with LC-MS dataset of the Broad platform and including 582 individuals. Results: Our results show that body mass index and a component of the carbon metabolism KEGG pathway are associated with incident hypertension in both discovery and replication cohorts. Conclusion: We demonstrate that a high-dimensional dataset can be meaningfully reduced into informative pathway components that can subsequently be analysed in an interpretable way, and replicated in a metabolomics dataset from a different platform.