Park, Hoyoung

BACKGROUND: Heart failure is heterogeneous syndrome with persistently high mortality. Nuclear magnetic resonance spectroscopy enables high-throughput metabolomics, suitable for precision phenotyping. We aimed to use targeted metabolomics to derive a metabolic risk score (MRS) that improved mortality risk stratification in heart failure. METHODS: Nuclear magnetic resonance was used to measure 21 metabolites (lipoprotein subspecies, branched-chain amino acids, alanine, GlycA (glycoprotein acetylation), ketone bodies, glucose, and citrate) in plasma collected from a heart failure community cohort. The MRS was derived using least absolute shrinkage and selection operator penalized Cox regression and temporal validation. The association between the MRS and mortality and whether risk stratification was improved over the Meta-Analysis Global Group in Chronic Heart Failure clinical risk score and NT-proBNP (N-terminal pro-B-type natriuretic peptide) levels were assessed. RESULTS: The study included 1382 patients (median age, 78 years, 52% men, 43% reduced ejection fraction) with a 5-year survival rate of 48% (95% CI, 46%–51%). The MRS included 9 metabolites measured. In the validation data set, a 1 standard deviation increase in the MRS was associated with a large increased rate of death (hazard ratio, 2.2 [95% CI, 1.9–2.5]) that remained after adjustment for Meta-Analysis Global Group in Chronic Heart Failure score and NT-proBNP (hazard ratio, 1.6 [95% CI, 1.3–1.9]). These associations did not differ by ejection fraction. The integrated discrimination and net reclassification indices, and Uno’s C statistic, indicated that the addition of the MRS improved discrimination over Meta-Analysis Global Group in Chronic Heart Failure and NT-proBNP. CONCLUSIONS: This MRS developed in a heart failure community cohort was associated with a large excess risk of death and improved risk stratification beyond an established risk score and clinical markers.

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.

Introduction: Heart failure (HF) is a complex syndrome with high mortality often associated with impaired kidney function. Estimated glomerular filtration rate (eGFR) is central to kidney function assessment in HF. However, novel proteins related to kidney function (lipocalin 2 [LCN2], macrophage inhibitory cytokine 1 [MIC1], T cell immunoglobulin and mucin domain 1 [TIM1]) may improve risk stratification of HF patients, beyond eGFR. Their prognostic contribution remain to be fully explored. Hypothesis: Proteomic assessment of kidney function biomarkers improves risk stratification in HF. Methods: Clinical data and biospecimens were collected from a HF community cohort in Southeastern Minnesota from 2003-2012. Serum creatinine was measured in 1,388 patients and eGFR was calculated with the 2021 Chronic Kidney Disease (CKD) Epidemiology Collaboration equation. CKD was defined as an eGFR <60 mL/min/1.73m 2 . Plasma proteins were measured using the SomaLogic aptamer-based technology SomaScan®. In HF patients with and without CKD, Cox proportional hazard models were used to estimate the association between kidney function biomarkers and mortality, after adjustment for Meta-Analysis Global Group in Chronic HF (MAGGIC) risk score. Results: At HF index, patients were on average 75±13 years of age and 48% female. Patients had a median eGFR of 57 mL/min/1.73m 2 and 64% had CKD. Adjusted geometric mean levels of LCN2, MIC1, and TIM1 levels were 20-30% higher in patients with CKD. Patients with CKD were at higher risk of dying (HR 1.6, 95% CI 1.4-1.8). Patients with above median levels of LCN2, TIM1, MIC1 were at higher risk of dying, irrespective of CKD (See Figure). Conclusions: In a HF community cohort, several novel kidney function biomarkers provided further risk stratification in patients with and without CKD.

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