Dulek, Brittany

Introduction: Coronary disease is the most common cause of heart failure (HF). HF is classified as ischemic (IHF) or non-ischemic (NIHF) based on this etiology. IHF and NIHF have distinct mechanisms not accounted for by ejection fraction (EF) and an improved understanding of the differences could drive the discovery of novel therapeutic targets. We therefore sought to identify proteomic signatures between IHF and NIHF. Methods: Proteomic profiling using the SomaScan®1.7K was performed using plasma samples from HF patients in a community cohort (Rochester Epidemiology Project, 2003-2012). IHF was defined by prior myocardial infarction, percutaneous coronary intervention or coronary artery bypass graft, or obstructive disease in at least 1 vessel on coronary angiography. We compared the protein expression levels between IHF and NIHF using t-test and applied the Benjamini-Hochberg false-discovery rate (FDR) procedure for multiple comparisons. We explored pathway analysis for functional annotations using the biological process component of the Gene Ontology Resource (Panther 17v). Results: Among 1,388 patients, there were 518 IHF patients (age: 75±12years, 39.4% female, NYHA Class III/IV: 72.5%, EF:45±15%), and 870 NIHF patients (age: 75±14years, 53.6% female, NYHA Class III/IV: 66.9%, EF:49±17%). Among 7335 plasma proteins quantified, 63 unique proteins (FDR < 0.05) were differentially expressed between IHF and NIHF. Thirty-seven proteins including Troponin-I and T, and Myoglobin showed increased expression in IHF compared to NIHF and 26 including IGFBP-3 showed decreased expression. Pathophysiologic pathways including angiogenesis, coagulation, and lipid metabolism were represented in IHF, while inflammation and remodeling signaling pathways were dominant in relation to NIHF. Conclusions: High-throughput proteomic techniques are able to delineate IHF versus NIHF patients based on distinct biomarkers and pathophysiologic processes.

Abstract Background Heart failure (HF) is a complex clinical syndrome that affects over 60 million persons worldwide.1 The 5-year survival rate of HF is less than 50% irrespective of ejection fraction (EF) and the limitations of EF in sufficiently phenotyping HF are recognized.2,3 High throughput proteomics assays are an attractive option for precision phenotyping.4 Purpose To explore the proteomic profiles and biomarker pathways associated with death in an HF community cohort. Methods We measured proteomics in plasma specimens collected from an HF cohort (2003–2012) of patients predominantly of European Ancestry (92%). The relative concentrations of circulating plasma proteins were measured using an aptamer-based assay containing 7335 human protein targets. Proteins significantly associated with mortality were selected while adjusting for age, sex, and eGFR. These proteins were employed in clustering analysis and subjected to 10-fold cross-validation. We quantified and corrected the clustering bias via bootstrapping and subsequently validated the results via semi-supervised clustering. Multivariable Cox regression was implemented for the cluster variable while adjusting for the Meta-analysis Global Group in Chronic Heart Failure (MAGGIC) risk score. Additionally, we performed enrichment and pathway analysis of the outcome-associated proteins considering significance thresholds of −log10(P-value) &gt;2, FDR corrected P&lt;0.05 (regulator analysis) and Z-scores of &gt;2 and &lt;−2 as thresholds for activation and inhibition of proteins/pathways. Results From a total of 1,388 patients (mean age 75±13; 52% male), 2 distinct clusters, (HR: 2.04; 95% CI [1.78–2.34]; p-value: &lt;0.0001 independent of MAGGIC) were identified, cluster 1; n=722, mean age 72±14; NYHA class III/IV: 41%/26%; EF: 47%±17 and cluster 2; n=666, mean age 79±11; NYHA class III/IV: 41%/31%, EF: 48%±16 based on the 447 proteins [354 positively and 93 negatively] significantly associated with mortality (1,158 events). Gene-set enrichment analysis of all 447 proteins revealed cell adhesion, extracellular matrix organization, enzyme-linked protein/tyrosine kinase signaling pathways, humoral immune response, tissue development, growth factor signaling, post-translational protein modification, and platelet degranulation as the dominant biological themes. Finally, we found 18 upstream regulators predicted to be driving the expressions of the proteins in the dataset including a predicted outcome-associated protein-Transforming Growth Factor Beta 1 – TGFB1 (p=7.46E-09), Interleukin 1B, IL1B (p=9.40E-08), and oncostatin M, OSM (p=6.61E-05) as the top 3 regulators (predicted activation) based on FDR-corrected statistical significance of P&lt;0.05 (Figure 1) Conclusion We identified distinct clusters based on proteomic profiles associated with HF mortality independently of clinical prognostic makers. Further, we delineated protein regulators driving the major pathways associated with HF survival. Funding Acknowledgement Type of funding sources: Public Institution(s). Main funding source(s): Department of Intramural Research, NIH/NHLBI