Multiomics of maladaptive cardiac energy metabolism in the progression of multimorbid cardiovascular disease

Abstract Background Multimorbid cardiovascular disease (CVD), refers to the co-morbid state where cardiometabolic disorders example (coronary artery disease (CAD), hypertension, diabetes, obesity, dyslipidaemia, and heart failure (HF)) co-exist in one person. It is the most common cause of disability and mortality worldwide. 50 % of CAD patients had at least one other morbidity which progresses to HF and ~13% of CVD deaths are associated with diabetes, hypertension, and dyslipidaemia (1). CVD complications and progression are linked to maladaptive cardiac energy metabolism, which triggers increased cellular stresses e.g., oxidative stress, mitochondrial dysfunction, misfolded proteins and activation of proinflammatory pathways, ultimately resulting in impaired cardiomyocyte function (2). Purpose Evidence suggests that optimising cardiac energy metabolism could provide huge therapeutic benefits and potentially reverse underlying metabolic derangements. Thus, the interplay between fuel substrates and cellular energy metabolism in multimorbid CVD progression may explain variations in disease risk and is a key target for novel therapeutic interventions (3). Methods Discovery proteomics was used to investigate plasma samples (n=138) from a well-established HF cohort (DIAMOND HFpEF, NCT03050593), with matched healthy samples as control to identify protein-based biomarkers of maladaptive energy metabolism remodelling in heart failure (Figure 1). Plasma samples were pretreated by reduction, alkylation and digested followed by LC-IMS-ToF-MS/MS analysis on the Waters Synapt G2-S mass spectrometer equipped with a nano Acquity UPLC system. Proteins were identified using Progenesis QI for proteomics with an in-house reference database. Bioinformatics workflow involved preprocessing, filtering, denoising, differential expression analysis and pathway analysis. Results Following statistical and pathway analysis 40 differentially expressed blood proteins (DEP) linked with key metabolic pathways were selected for verification. Examples of enriched terms from DEPs for HF in comparison with normal controls include: glycolysis, pyruvate metabolism, TCA cycle and ATP synthesis. An optimised MRM assay was developed for two peptides per protein using Masslynx Skyline Interface (MSI). Conclusion Omics technologies are advanced for detailed description of cardiovascular disease phenotypes. Using targeted proteomics, the DEPs will be used to develop a multimarker assay for validation and comprehensive profiling of the 40 signatures of maladaptive cardiac energy metabolism in well characterised Pre-HF and Developed HF cohorts to understand disease progression, pathophysiological mechanisms and target key regulatory enzymes for risk stratification and novel therapeutic interventions.Figure 1For image description, please refer to the figure legend and surrounding text.