In Europe, 15 million people suffer from heart failure syndrome (HF). It is the most common cause of hospitalization in people over the age of 65 and is associated with poor long-term survival. Thus, it places a significant burden on the health care system. Currently, three clinical subtypes of HF have been characterized and treatment options are limited. The underlying molecular mechanisms leading to HF are still largely unknown. The DIASyM research core employs a comprehensive systems medicine approach that will include proteomic, metabolomic, lipidomic and genetic phenotyping. Multi-OMICs data will be analysed by knowledge mining, agnostic and supervised learning approaches to identify novel molecular, pathophysiological subphenotypes leading to HF.

The MyoVasc and Gutenberg Health Study (GHS) are large prospective cohorts of patients with heart failure and population-representative individuals, situated in Germany. Both studies provide sequential clinical characterization of individuals by deep phenotyping and comprehensive biobanking. Building on this unique resource, the DIASyM research core will develop, optimize, and standardize mass spectrometric workflows in order to achieve the best possible results. Here, predominantly data independent acquisition" (DIA) mass spectrometric measurement methods will be employed to provide detailed analyses of the patients on the proteome, lipidome and metabolome level.

Within the DIASyM research core, we will develop and optimize standardized processes for reproducible sample processing, subsequent mass spectrometric data acquisition and multidimensional data analyses. The integration of three bioinformatics research groups enables the development of dedicated software and analysis workflows for data processing, interpretation and high-dimensional data integration in close collaboration with the mass spectrometry groups. This close and interdisciplinary integration in turn allows a coordinated translation of the MS-based OMICs data into clinical applications.