Integrated multi-omics predictive analysis of atherosclerosis: a sub-study from the Mineralocorticoid Receptor Antagonism in Diabetic Atherosclerosis (MAGMA) trial

Abstract Background/Introduction Mineralocorticoid receptor (MR) antagonists (MRA) are beneficial in cardiorenal outcomes in randomized controlled trials but the mechanisms are unclear. MAGMA was an NHLBI sponsored randomized, double-blind, placebo controlled, 12-month trial comparing Spironolactone (n = 37) vs. placebo (n = 42) in Type 2 diabetics with CKD stages 3-4 on maximal renin-angiotensin system (RAS) blockade and a prior atherosclerotic event and/or left ventricular (LV) hypertrophy. The primary outcome of percent (%) change in total aortic wall volume (TWV) at 12 months, measured by magnetic resonance imaging (MRI), was significantly reduced by Spironolactone. Purpose/Originality The purpose of this analysis was to understand mechanistic pathways of MRAs using a multi-omics approach that could help tease out molecular mechanisms of benefit. Revealing for the first time which of these integrated pathways are predictive of the primary outcome will help design treatments for targeted interventions. Methods Plasma and peripheral blood mononuclear cells from patients randomized to Spironolactone or placebo at baseline and 3-months were measured for aptamer-based proteomic biomarkers (7,596 proteins) and 10-X platform-based single-cell RNA-sequencing (scRNAseq), respectively. Pre-selected candidate predictors were used as inputs of a predictive model of changes of TWV. We fit a Mixed-Multivariate Random Forest (RF) model, a variation of the RF supervised tree-based machine learning method to take the experimental design into account in the regression formulation. The two sources of "omics predictors" were integrated into a supervised multi-omics model to jointly explain the outcome using an extension of Sparse Generalized Canonical Correlation Analysis (SGCCA). Integrated multiome functional analyses with graphical visualizations were carried out by statistical enrichment analysis using Over Representation Analysis (ORA) and Gene Set Enrichment Analysis (GSEA) against databases of gene ontologies, biological pathways, putative regulatory motifs, proteins, or disease annotations. Results The plasma proteome in response to Spironolactone revealed downregulation of MR targets including fibrosis, immune activation/inflammation, leukocyte activation, proliferation and pathways involved in cytokine stimulation. scRNAseq pathways revealed negative regulation of cytokines production such as IL-2 and redistribution of multiple cell types. Predictors of plaque progression involved cytokine-receptor, complement-coagulation, cell adhesion and axonal guidance targets. Multiome integrated functional analyses results of predictive pathways will be presented. Conclusions The changes in plasma proteomic profile with Spironolactone were consistent with the phenotype of reduced atherosclerosis and downregulation of multiple inflammatory, immune response and profibrotic pathways.