Search PubMedSearch

PubMed · 41771067

Proteomic patterns according to ejection fraction: an EMPEROR-programme analysis.

Abstract

AIMS: Left ventricular ejection fraction (LVEF) has been incorporated as an inclusion criterion in HF trials. Patient's characteristics, event risk, and treatment response vary according to LVEF. A better understanding of the biological processes across LVEF is warranted. To study proteomic biomarker expression across LVEF using data from the EMPEROR-Programme. METHODS: Two thousand two hundred and fifty-four patients who had proteomic measurements available using 1134 proteins overlapping between the Explore 1536 and 3072 Olink® platforms were included. Main analyses were performed within the EMPEROR-Preserved dataset due to differences in entry criteria between EMPEROR-Preserved and EMPEROR-Reduced with higher entry N-terminal pro B-type natriuretic peptide (NT-proBNP) levels that varied by LVEF cut-offs in the latter. Protein concentrations were compared using ordinal logistic regression across LVEF categories: 41%-49%, 50%-59%, and ≥60%. The resulting β-coefficient indicates the change in the log-odds for the outcome of being in a lower LVEF category for every NPX unit in log2 scale. Analyses were adjusted for covariates and a false-discovery-rate (FDR) correction was applied. RESULTS: A total of 297 proteins exhibited a trend of expression across LVEF categories in EMPEROR-Preserved after adjustment for potential confounders and correction for test multiplicity. Of these, the top 10 proteins were: NT-pro BNP (β = 0.18, 95% CI 0.09-0.27), Wnt inhibitory factor-1 (β = 0.40, 95% CI 0.19-0.61), sialomucin core protein 24 (β = 0.48, 95% CI 0.22-0.74), phospholipid transfer protein (β = 0.38, 95% CI 0.17-0.59), natriuretic peptides B (β = 0.13, 95% CI 0.06-0.20), intercellular adhesion molecule 5 (β = 0.31, 95% CI 0.14-0.49), neural cell adhesion molecule 2 (β = 0.45, 95% CI 0.19-0.70), neural cell adhesion molecule L1-like protein (β = 0.45, 95% CI 0.19-0.71), interactor protein for cytohesin exchange factors 1 (β = 0.12, 95% CI 0.05-0.19), and 3-ketoacyl-CoA thiolase, peroxisomal (β = 0.17, 95% CI 0.07-0.26). The correlation between these proteins and LVEF was generally weak (Rho ≤0.2). CONCLUSIONS: Within EMPEROR-Preserved, the top differentially expressed circulating proteins suggest that pathways related to natriuretic peptides, cell-adhesion, and clonal haematopoiesis are overexpressed at mildly-reduced ejection fraction, but none of the proteins passed the 5%FDR cut-off, and the correlation between circulating proteins and LVEF was weak. These findings suggest that circulating proteins may not be a good discriminant of ejection fraction.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

João Pedro Ferreira, Francesco Fioretti, Mikhail Sumin, Stefan D Anker, Gerasimos Filippatos, Manuel Monroy Kuhn, Marina Panova-Noeva, Jürgen Prochaska, Maral Saadati, Britta Stolze, Cordula Zeller, Faiez Zannad, Javed Butler. 2026-09-15. Proteomic patterns according to ejection fraction: an EMPEROR-programme analysis.. https://doi.org/10.1093/ejhf%2Fxuag013

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related citations

Global Genomic Surveillance.

Global genomic surveillance has emerged as a foundational pillar of public health in the twenty-first century, enabling real-time tracking of pathogen evolution and informing outbreak response. This chapter examines the strategic architecture of global genomic surveillance, focusing on its application to arboviruses such as chikungunya virus (CHIKV). It explores the integration of genomic data with epidemiological, clinical, and environmental information within a One Health framework, while addressing critical challenges in governance, equity, and interoperability. The discussion covers the entire genomic surveillance workflow, from sample collection and sequencing to bioinformatic analysis and phylogenetic inference, and highlights the transformative role of artificial intelligence (AI) in predictive surveillance. By analyzing global initiatives, operational barriers, and emerging technologies, this chapter underscores the necessity of sustainable, equitable, and interoperable genomic systems to proactively address current and future infectious disease threats.

Humans

Systematic Dissection of Key Driver Perturbation Signatures in Single Cells via ECCITE-seq.

CRISPR screens, such as expanded CRISPR-compatible cellular indexing of transcriptomes and epitopes by sequencing (ECCITE-seq), enable the simultaneous measurement of transcriptomes, gRNA identity, and cell-surface protein expression at single-cell resolution to systematically interrogate gene function. This platform provides a powerful and scalable experimental approach for validating disease-associated regulators identified by large-scale association studies and other computational methods, including network-based analyses of multi-omics data. Here, as an example application, we describe an ECCITE-seq framework to characterize the transcriptomic consequences of perturbing multiple neuronal key driver genes associated with Alzheimer's disease (AD) in human-induced pluripotent stem cell (hiPSC)-derived neurons. More broadly, by integrating customized pooled gRNA libraries with different CRISPR effectors across multiple cell types, this approach allows for the assessment of the regulatory impact of candidate genes implicated in development and disease processes.

Humans

Identification of Genome-Wide Chromatin Structural Aberration in Cancer by Hi-C Analysis.

Aberrant three-dimensional genome organization is a hallmark of cancer, often driving oncogene activation through mechanisms such as enhancer hijacking. High-throughput chromosome conformation capture (Hi-C) maps these interactions on a genome-wide scale. Unlike earlier dilution-based methods, in situ Hi-C performs proximity ligation within intact nuclei, minimizing random ligation noise and enabling fine-scale structure detection. This chapter describes an optimized in situ Hi-C protocol tailored for cancer cell lines using MboI digestion and biotin-mediated pull-down to generate high-complexity libraries. We further outline a computational workflow that extends beyond standard topological mapping of compartments and topologically associating domains to identify cancer-specific aberrations. Specifically, we focus on detecting chromosomal rearrangements (structural variants) and characterizing the distinct circular topology of extrachromosomal DNA. This integrated experimental and analytical framework provides the necessary tools to dissect the spatial dysregulation underlying tumor evolution.

Humans