Effect of Empagliflozin on the plasma lipidome in patients with type 2 diabetes mellitus: results from the EmDia clinical trial

Abstract

Background: Sodium-glucose cotransporter 2 (SGLT2) inhibitors, such as Empagliflozin, are antidiabetic drugs that reduce glucose levels and have emerged as a promising therapy for patients with heart failure (HF), although the exact molecular mechanisms underlying their cardioprotective effects remain to be fully elucidated. The EmDia study, a randomized, double-blind trial conducted at the University Medical Center of Mainz, has confirmed the beneficial effects of Empagliflozin in HF patients after both one and twelve weeks of treatment. In this work, we aimed to assess whether changes in lipid profiles driven by Empagliflozin use in HF patients in the EmDia trial could assist in gaining a better understanding of its cardioprotective mechanisms.

Methods: Lipid analysis of blood plasma from 144 patients from the EmDia trial was conducted using 4D-LC-TIMS/IMS lipidomics. Lipid signatures after treatment for one and twelve weeks, respectively, were obtained with sparse group LASSO regularized regression models. Linear regression models were employed to highlight associations between significantly changed clinical traits and lipids.

Results: The lipid signatures after one week of treatment consisted of 37 lipids from the lipid groups lysophosphatidylcholine (LPC), phosphatidylcholine (PC), phosphatidylethanolamine (PE), sphingomyelin (SM), and triacylglycerol (TG). After twelve weeks, the signature comprised 24 lipids from the same five lipid groups, along with Ceramides (Cer). Three of five lipids altered at both time points showed consistent directional trends. Empagliflozin treatment led to significant alterations in the lipidome, including increases in both beneficial lipids, such as LPCs, and potentially harmful species, notably ceramides, which have been implicated in lipotoxicity and cardiovascular risk.

Conclusion: This study identified distinct lipid signatures associated with Empagliflozin treatment after both one and twelve weeks, respectively, with five lipids overlapping between signatures and three with consistent directions, revealing that some of the beneficial effects of Empagliflozin could be through lipid modulation. Notably, Empagliflozin-modulated lipids associated with changes in clinical traits and lipid-specific profiles among clinical subgroups were observed. However, challenges remain in establishing direct associations between individual lipids and clinical outcomes. Future research integrating lipidomics data with other omics datasets could provide a more comprehensive understanding of the identified lipid signatures and their potential roles in health and diseases.

Trial registration: ClinicalTrials.gov; NCT02932436. Registration date, 2016/10/13.

Keywords: Clinical trial; Empagliflozin; HFpEF; Lipidomics; SGLT2i.

Fig. 1

Sparse group LASSO regularized regression model of the lipid-signature after one week of Empagliflozin treatment. A Barplot showing the 37 individual lipids of the lipid classes LPC, PC, PE, SM and TG that were selected by sparse group LASSO regularized regression model as being significantly associated with Empagliflozin treatment after one week. B PCA plot for the selected 37 lipids by the sparse group LASSO regularized model, showing an initial separation of treatment (Empagliflozin vs placebo) clusters beginning to emerge after one week

Fig. 2

Sparse group LASSO regularized regression model of the lipid-signature after twelve weeks of Empagliflozin treatment. A Barplot showing the 24 individual lipids of the lipid classes LPC, PC, PE, SM, TG and Cer that were selected by sparse group LASSO regularized regression model as being significantly associated with Empagliflozin treatment after twelve weeks. B PCA plot for the selected 24 lipids by the sparse group LASSO regularized model, showing a distinct separation of treatment clusters (Empagliflozin vs. placebo) after twelve weeks

Fig. 3

Forest-plot of the association of individual lipids with selected subgroups after Empagliflozin treatment for twelve weeks. Using linear regression with Bonferroni-correction (threshold = 0.000183), a forest plot was generated to show the association of individual lipids with selected clinical subgroups after Empagliflozin treatment for one week (adjusted by sex, age and E/E′ at baseline)