William Chutkow’s research while affiliated with Novartis Institutes for BioMedical Research and other places

Background Prognostic markers and biological pathways linked to detrimental clinical outcomes in heart failure with preserved ejection fraction (HFpEF) remain incompletely defined. Methods and Results We measured serum levels of 4123 unique proteins in 1117 patients with HFpEF enrolled in the PARAGON‐HF (Efficacy and Safety of LCZ696 Compared to Valsartan, on Morbidity and Mortality in Heart Failure Patients With Preserved Ejection Fraction) trial using a modified aptamer proteomic assay. Baseline circulating protein concentrations significantly associated with the primary end point and the timing and occurrence of total heart failure hospitalization and cardiovascular death were identified by recurrent events regression, accounting for multiple testing, adjusted for age, sex, treatment, and anticoagulant use, and compared with published analyses in 2515 patients with heart failure with reduced ejection fraction from the PARADIGM‐HF (Prospective Comparison of ARNI With ACEI to Determine Impact on Global Mortality and Morbidity in Heart Failure) and ATMOSPHERE (Efficacy and Safety of Aliskiren and Aliskiren/Enalapril Combination on Morbidity–Mortality in Patients With Chronic Heart Failure) clinical trials. We identified 288 proteins that were robustly associated with the risk of heart failure hospitalization and cardiovascular death in patients with HFpEF. The baseline proteins most strongly related to outcomes included B2M (β‐2 microglobulin), TIMP1 (tissue inhibitor of matrix metalloproteinase 1), SERPINA4 (serpin family A member 4), and SVEP1 (sushi, von Willebrand factor type A, EGF, and pentraxin domain containing 1). Overall, the protein–outcome associations in patients with HFpEF did not markedly differ as compared with patients with heart failure with reduced ejection fraction. A proteomic risk score derived in patients with HFpEF was not superior to a previous proteomic score derived in heart failure with reduced ejection fraction nor to clinical risk factors, NT‐proBNP (N‐terminal pro‐B‐type natriuretic peptide), or high‐sensitivity cardiac troponin. Conclusions Numerous serum proteins linked to metabolic, coagulation, and extracellular matrix regulatory pathways were associated with worse HFpEF prognosis in the PARAGON‐HF proteomic substudy. Our results demonstrate substantial similarities among serum proteomic risk markers for heart failure hospitalization and cardiovascular death when comparing clinical trial participants with heart failure across the ejection fraction spectrum. Registration URL: https://www.clinicaltrials.gov ; Unique Identifiers: NCT01920711, NCT01035255, NCT00853658.

Introduction: Prognostic markers and biological pathways linked to detrimental clinical outcomes in heart failure with preserved ejection fraction (HFpEF) remain incompletely defined. Goal: To identify serum proteins associated with risk for HF hospitalization (HFH) and cardiovascular (CV) death in patients with HFpEF, including proteins with differential associations with clinical outcomes in HFpEF compared to HFrEF. Methods: We measured serum levels of 4123 unique proteins in 1117 patients with HFpEF in the PARAGON-HF trial using a modified aptamer proteomic assay. Baseline protein concentrations significantly associated with total HFH and CV death were identified by recurrent events regression, accounting for multiple testing, adjusted for age, sex, treatment, and anticoagulant use, and compared to 2515 patients with HFrEF from the PARADIGM-HF and ATMOSPHERE trials. Results: We identified 288 proteins to be robustly associated with the risk of HFH and CV death (335 events over an average of 2.7 years of follow-up) in a HFpEF trial population. Proteins most strongly related to HFpEF outcomes included B2M, TIMP1, SERPINA4, and SVEP1 ( Figure ). Protein-outcome associations in patients with HFpEF did not markedly differ compared to HFrEF. Just 3 proteins (APOE, RTF1, and VWF) were differentially associated with outcomes between the HF subtypes (FDR <0.05). A proteomic risk score derived in HFpEF patients was not superior to a previous proteomic score derived in HFrEF nor to clinical risk factors, NT-proBNP, or high-sensitivity cardiac troponin. Conclusions: Numerous serum proteins linked to metabolic, coagulation, and extracellular matrix regulatory pathways were associated with worse HFpEF prognosis. SVEP1, a cell adhesion protein recently identified as a HFrEF prognosis biomarker, also strongly predicted risk in HFpEF. Our results demonstrate substantial similarities in serum proteomic risk markers across the EF spectrum.

Oxidative stress is associated with cardiovascular and neurodegenerative diseases. Here we report studies of neurovascular oxidative stress in chemogenetic transgenic mouse lines expressing yeast D-amino acid oxidase (DAAO) in neurons and vascular endothelium. When these transgenic mice are fed D-amino acids, DAAO generates hydrogen peroxide in target tissues. DAAO-TG Cdh5 transgenic mice express DAAO under control of the putatively endothelial-specific Cdh5 promoter. When we provide these mice with D-alanine, they rapidly develop sensory ataxia caused by oxidative stress and mitochondrial dysfunction in neurons within dorsal root ganglia and nodose ganglia innervating the heart. DAAO-TG Cdh5 mice also develop cardiac hypertrophy after chronic chemogenetic oxidative stress. This combination of ataxia, mitochondrial dysfunction, and cardiac hypertrophy is similar to findings in patients with Friedreich’s ataxia. Our observations indicate that neurovascular oxidative stress is sufficient to cause sensory ataxia and cardiac hypertrophy. Studies of DAAO-TG Cdh5 mice could provide mechanistic insights into Friedreich’s ataxia.

Aims: To evaluate the prevalence of pathogenic variants in genes associated with dilated cardiomyopathy (DCM) in a clinical trial population with heart failure and reduced ejection fraction (HFrEF) and describe the baseline characteristics by variant carrier status. Methods and results: This was a post hoc analysis of the Phase 3 PARADIGM-HF trial. Forty-four genes, divided into three tiers, based on definitive, moderate or limited evidence of association with DCM, were assessed for rare predicted loss-of-function (pLoF) variants, which were prioritised using ClinVar annotations, measures of gene transcriptional output and evolutionary constraint, and pLoF confidence predictions. Prevalence was reported for pLoF variant carriers based on DCM-associated gene tiers. Clinical features were compared between carriers and non-carriers. Of the 1412 HFrEF participants with whole-exome sequence data, 68 (4.8%) had at least one pLoF variant in the 8 tier-1 genes (definitive/strong association with DCM), with Titin being most commonly affected. The prevalence increased to 7.5% when considering all 44 genes. Among patients with idiopathic aetiology, 10.0% (23/229) had tier-1 variants only and 12.6% (29/229) had tier-1, -2 or -3 variants. Compared to non-carriers, tier-1 carriers were younger (4 years; adjusted p-value [padj ]=4×10-3 ), leaner (27.8 kg/m2 vs. 29.4 kg/m2 ; padj =3.2x10-3 ), had lower EF (27.3% vs. 29.8%; padj =5.8x10-3 ), and less likely to have ischaemic aetiology (37.3% vs 67.4%; padj =4×10-4 ). Conclusion: Deleterious pLoF variants in genes with definitive/strong association to DCM were identified in ~5% of HFrEF patients from a PARADIGM-HF trial subset, who were younger, had lower EF and were less likely to have had an ischaemic aetiology. This article is protected by copyright. All rights reserved.

Background Though current heart failure (HF) biomarkers are highly prognostic, systematically characterizing associations between circulating proteins and risk of subsequent events may improve clinical risk prediction and illuminate new biological pathways. Large-scale assays measuring thousands of proteins now enable unbiased proteomic investigation in clinical trials. Purpose To identify and replicate serum proteins associated with HF events in patients with chronic HF with reduced ejection fraction (HFrEF), and to develop and validate a proteomic risk score. Methods Serum levels of 4076 proteins were measured at baseline in the ATMOSPHERE (n=1261, 487 events over 6 years) and PARADIGM-HF (n=1257, 287 events over 4 years) trials of chronic HFrEF using a modified aptamer-based proteomics assay. Proteins associated with the primary endpoint, HF hospitalization or cardiovascular death, were identified in the ATMOSPHERE discovery cohort (false discovery rate<0.05) by Cox regression adjusted for age, sex, treatment arm, and anticoagulant use, and replicated in PARADIGM-HF (Bonferroni-corrected p<0.05). A proteomic risk score was derived in ATMOSPHERE using Cox LASSO penalized regression and evaluated in PARADIGM-HF compared to the MAGGIC clinical risk score and N-terminal pro-B-type natriuretic peptide (NT-proBNP). For proteins associated with the primary endpoint, pathway analysis was conducted using Ingenuity Pathway analysis and an exploratory two-sample Mendelian randomization was performed using genetic and outcome data from both trials and protein quantitative trait loci from deCODE to infer which identified proteins contribute to HF prognosis. Results We identified 377 serum proteins associated with the primary endpoint in ATMOSPHERE and replicated 167 in PARADIGM-HF. Prognostic proteins included known HF biomarkers Growth Differentiation Factor 15, NT-proBNP, and Angiopoietin-2, and also a previously unrecognized HF biomarker: Sushi, Von Willebrand Factor Type A, EGF And Pentraxin Domain Containing 1 (SVEP1) (HR 1.60 [95% CI 1.44–1.79] per standard deviation [SD], p=2x10–17) (Table 1). Proteins related to hepatic fibrosis, granulocyte adhesion, and inhibition of matrix metalloproteinases were over-represented. A 64-protein risk score derived in ATMOSPHERE predicted clinical events in PARADIGM-HF with greater discrimination (c-statistic 0.70) than the MAGGIC clinical score (c-statistic 0.61), NT-proBNP (c-statistic 0.65), or both (c-statistic 0.66) (Figure 1). Genetically predicted levels of NT-proBNP, WISP2, FSTL1, and CTSS were associated with the primary endpoint by Mendelian randomization. Conclusions We identify SVEP1, an extracellular matrix protein known to cause inflammation in vascular smooth muscle cells, as a previously unrecognized HF biomarker. A 64-protein score improved risk discrimination compared with NT-proBNP and may assist in identifying high-risk patients for clinical trials or disease management programs. Funding Acknowledgement Type of funding sources: Private company. Main funding source(s): The ATMOSPHERE and PARADIGM-HF trials were sponsored by Novartis

Background Systematically characterizing associations between circulating proteins and risk for subsequent clinical events may improve clinical risk prediction and shed light on unrecognized biological pathways in heart failure (HF). Large-scale assays measuring thousands of proteins now enable broad proteomic investigation in clinical trials. Methods Serum levels of 4076 proteins were measured at baseline in the ATMOSPHERE (n=1258, 487 events over 6 years) and PARADIGM-HF (n=1257, 287 events over 4 years) trials of chronic HF with reduced ejection fraction using a modified aptamer-based proteomics assay. Proteins associated with the primary endpoint of HF hospitalization or cardiovascular death were identified in the ATMOSPHERE discovery cohort by Cox regression adjusted for age, sex, treatment arm, and anticoagulant use (false discovery rate<0.05), and were replicated in PARADIGM-HF (Bonferroni-corrected p<0.05). A proteomic risk score was derived in ATMOSPHERE using Cox LASSO penalized regression and evaluated in PARADIGM-HF compared to the MAGGIC clinical risk score and N-terminal pro-B-type natriuretic peptide (NT-proBNP) immunoassay. For proteins that were associated with the primary endpoint, two-sample Mendelian randomization was performed using genetic and outcome data from both trials and protein quantitative trait loci from deCODE to infer causal associations. Results We identified 377 serum proteins that were associated with the primary endpoint in ATMOSPHERE and replicated 167 in PARADIGM-HF. Prognostic proteins included known HF biomarkers such as Growth Differentiation Factor 15, NT-BNP, and Angiopoietin-2, and also a previously unrecognized HF biomarker: Sushi, Von Willebrand Factor Type A, EGF and Pentraxin Domain Containing 1 (SVEP1, HR 1.60 [95% CI 1.44-1.79] per standard deviation [SD], p=2×10 ⁻¹⁷ ). A 64-protein risk score derived in ATMOSPHERE predicted the primary endpoint in PARADIGM-HF with greater discrimination (C-statistic 0.70) than the MAGGIC clinical score (C-statistic 0.61), NT-proBNP (C-statistic 0.65), or both (C-statistic 0.66). Genetically controlled levels of BNP, WISP2, FSTL1, and CTSS were associated with the primary endpoint by Mendelian randomization. Conclusions We identified SVEP1, an extracellular matrix protein known to cause inflammation in vascular smooth muscle cells, as a new HF biomarker associated with risk of hospitalization or death. A 64-protein score improved risk discrimination compared with NT-proBNP and may assist in identifying high-risk patients.

Aims The HERMES (HEart failure Molecular Epidemiology for Therapeutic targetS) consortium aims to identify the genomic and molecular basis of heart failure. Methods and results The consortium currently includes 51 studies from 11 countries, including 68 157 heart failure cases and 949 888 controls, with data on heart failure events and prognosis. All studies collected biological samples and performed genome-wide genotyping of common genetic variants. The enrolment of subjects into participating studies ranged from 1948 to the present day, and the median follow-up following heart failure diagnosis ranged from 2 to 116 months. Forty-nine of 51 individual studies enrolled participants of both sexes; in these studies, participants with heart failure were predominantly male (34–90%). The mean age at diagnosis or ascertainment across all studies ranged from 54 to 84 years. Based on the aggregate sample, we estimated 80% power to genetic variant associations with risk of heart failure with an odds ratio of ≥1.10 for common variants (allele frequency ≥ 0.05) and ≥1.20 for low-frequency variants (allele frequency 0.01–0.05) at P < 5 × 10⁻⁸ under an additive genetic model. Conclusions HERMES is a global collaboration aiming to (i) identify the genetic determinants of heart failure; (ii) generate insights into the causal pathways leading to heart failure and enable genetic approaches to target prioritization; and (iii) develop genomic tools for disease stratification and risk prediction.

AIMS: The HERMES (HEart failure Molecular Epidemiology for Therapeutic targetS) consortium aims to identify the genomic and molecular basis of heart failure. METHODS AND RESULTS: The consortium currently includes 51 studies from 11 countries, including 68 157 heart failure cases and 949 888 controls, with data on heart failure events and prognosis. All studies collected biological samples and performed genome-wide genotyping of common genetic variants. The enrolment of subjects into participating studies ranged from 1948 to the present day, and the median follow-up following heart failure diagnosis ranged from 2 to 116 months. Forty-nine of 51 individual studies enrolled participants of both sexes; in these studies, participants with heart failure were predominantly male (34–90%). The mean age at diagnosis or ascertainment across all studies ranged from 54 to 84 years. Based on the aggregate sample, we estimated 80% power to genetic variant associations with risk of heart failure with an odds ratio of ≥1.10 for common variants (allele frequency ≥ 0.05) and ≥1.20 for low-frequency variants (allele frequency 0.01–0.05) at P < 5 × 10^{-8} under an additive genetic model. CONCLUSIONS: HERMES is a global collaboration aiming to (i) identify the genetic determinants of heart failure; (ii) generate insights into the causal pathways leading to heart failure and enable genetic approaches to target prioritization; and (iii) develop genomic tools for disease stratification and risk prediction.