# Metabolic Profiling of the EmDia Cohort by LC‐MS Reveals Empagliflozin‐Intake Associated Regulation of 1,5‐anhydroglucitol and Urate

## TL;DR

This study uses LC-MS metabolomics to analyze how empagliflozin affects plasma metabolites in type 2 diabetes patients, identifying specific biomarkers like 1,5-anhydroglucitol.

## Contribution

A robust LC-MS workflow with pentafluorophenyl chromatography is introduced, enabling comprehensive plasma metabolite profiling and identifying drug-specific metabolic changes.

## Key findings

- Empagliflozin intake is associated with reduced plasma levels of deoxyhexoses like 1,5-anhydroglucitol.
- The LC-MS method achieves near-perfect prediction of fasting blood glucose (R2 = 0.97).
- The workflow provides comprehensive plasma metabolite coverage and scalable high-confidence metabolite annotation.

## Abstract

The EmDia trial, designed to study the effects of the sodium glucose cotransporter‐2 (SGLT2) inhibitor empagliflozin on cardiovascular comorbidities in type 2 diabetes mellitus (T2DM) patients, has been investigated for short‐term metabolic alterations by a limited set of clinical assays. To expand on this data, we report on the development of a liquid chromatography‐mass spectrometry (LC‐MS)‐based metabolomics approach employing an optimized metabolite separation by pentafluorophenyl chromatography. High‐confidence metabolite annotation based on reference standards allows for fast and robust metabolic characterization of large plasma cohorts due to scalability. Applied to EmDia, we show the high predictive power of our methodology for several clinical parameters, including a near‐perfect prediction of fasting blood glucose (R2
 = 0.97), and demonstrate how empagliflozin leads to reduced plasma levels of deoxyhexoses, such as 1,5‐anhydroglucitol, a short‐term biomarker for glycemic control.

Clinical metabolomics studies continue to gain interest due to their comprehensive metabolite coverage, offering insights into metabolic alterations in health and disease.In this study, we present a robust data‐independent acquisition liquid chromatography‐mass spectrometry‐based metabolomics workflow employing an optimized metabolite separation by pentafluorophenyl chromatography that showcases a comprehensive coverage of plasma metabolites.Applied to characterize plasma metabolite profiles in samples of EmDia, a placebo controlled study investigating the effect of the SGLT2 inhibitor empagliflozin, we assess the predictive power of metabolite signals for clinical parameters describing organ physiologies and pathophysiologies.Descriptive statistics are applied to the metabolite profiles to identify empagliflozin intake‐associated metabolite markers.

Clinical metabolomics studies continue to gain interest due to their comprehensive metabolite coverage, offering insights into metabolic alterations in health and disease.

In this study, we present a robust data‐independent acquisition liquid chromatography‐mass spectrometry‐based metabolomics workflow employing an optimized metabolite separation by pentafluorophenyl chromatography that showcases a comprehensive coverage of plasma metabolites.

Applied to characterize plasma metabolite profiles in samples of EmDia, a placebo controlled study investigating the effect of the SGLT2 inhibitor empagliflozin, we assess the predictive power of metabolite signals for clinical parameters describing organ physiologies and pathophysiologies.

Descriptive statistics are applied to the metabolite profiles to identify empagliflozin intake‐associated metabolite markers.