# 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.

## Key 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.