Pathway level metabolomics analysis identifies carbon metabolism as a key factor of incident hypertension in the Estonian Biobank

TL;DR

This study finds that changes in carbon metabolism are linked to the development of hypertension using data from the Estonian Biobank.

Contribution

The novel approach of reducing high-dimensional metabolomics data into interpretable pathway components is successfully applied and replicated.

Findings

Carbon metabolism pathway components are associated with incident hypertension in both discovery and replication cohorts.

Body mass index is also found to be associated with incident hypertension.

High-dimensional metabolomics data can be effectively reduced into meaningful pathway components for analysis.

Abstract

The purpose of this study was to find metabolic changes associated with incident hypertension in the volunteer-based Estonian Biobank. 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. 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. 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.

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