Evidence mapPaperPMID 42277826Full record

ArticleBioData mining2026

Multi-omics analysis of genetic drivers linking aortic stenosis and left ventricular diastolic dysfunction in heart failure.

Zeeshan Ahmed, Prithvi Govindareddy, Jayden Mathew, Naveena Yanamala, Partho P Sengupta

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Article in BioData mining, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Zeeshan AhmedDivision of Cardiovascular Diseases and Hypertension, Department of Medicine, Robert Wood Johnson Medical School, Rutgers Health, 125 Paterson St, New Brunswick, NJ, 08901, USA. zahmed@ifh.rutgers.edu.
Prithvi GovindareddyRutgers Institute for Health, Health Care Policy and Aging Research, Rutgers University, 112 Paterson Street, New Brunswick, NJ, 08901, USA.
Jayden MathewRutgers Institute for Health, Health Care Policy and Aging Research, Rutgers University, 112 Paterson Street, New Brunswick, NJ, 08901, USA.
Naveena YanamalaDivision of Cardiovascular Diseases and Hypertension, Department of Medicine, Robert Wood Johnson Medical School, Rutgers Health, 125 Paterson St, New Brunswick, NJ, 08901, USA.
Partho P SenguptaDivision of Cardiovascular Diseases and Hypertension, Department of Medicine, Robert Wood Johnson Medical School, Rutgers Health, 125 Paterson St, New Brunswick, NJ, 08901, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundAortic stenosis (AS) and left ventricular diastolic dysfunction (LVDD) often coexist in heart failure (HF), but the mechanisms linking them remain unclear. While AS increases afterload and promotes myocardial stiffening, emerging AI-based evidence suggests LVDD can precede the development of AS or progress simultaneously, indicating shared upstream mechanobiological and inflammatory drivers. This study explores the genetic contributors connecting AS and LVDD to identify early molecular markers and convergent pathways in HF.

methodWe analyzed Whole Genome Sequence (WGS) and RNA-seq data of the HF patients, generated using their Peripheral Blood Mononuclear Cells (PBMCs) samples. Overall bioinformatics analysis was divided into two modules, 1) gene variant and annotation analysis, and 2) gene expression and enrichment analysis. We utilized our peer review published and open source WGS and RNA-seq pipelines to process Next-Generation Sequence (NGS) data. Furthermore, we performed bioinformatics and statistical analysis to identify genetic variations, expressions, regulation, enrichments, and disease annotations.

resultsWe identified genetic markers uniquely associated with AS, LVDD, and shared between them. Furthermore, we report genes with significant expression, and functional variations, and discuss their relationship with other cardiovascular diseases (e.g. Vascular and Cardiac Stiffness, Aortic Dissection, Left Atrial Enlargement, Left Ventricular Hypertrophy, Outflow Tract Obstructive Defects, Non-Compaction Coronary Artery Disease, Arrhythmia, Congestive Heart Failure, and Hypertrophic, Dilated, and Ischemic Cardiomyopathy) and non-cardiovascular diseases (non-CVDs) (e.g. Type 1 Diabetes, Diabetic Nephropathy, Skeletal Anomalies, Rheumatoid Arthritis, Atypical Femoral Fractures, Chronic Kidney Disease, Dehydrated Hereditary Stomatocytosis, Schizophrenia, Varicose Veins, High-Altitude Pulmonary Edema, Periodontitis, and Respiratory Disorder) including multiple cancer types (e.g. Breast, Lung, Colorectal, Pancreatic, Hypopharyngeal, Acute Lymphoblastic, and Oral Squamous Cell Carcinomas) and rare genetic disorders (e.g. Hypophosphatasia, Multiple Sclerosis, Campomelic Dysplasia, Lymphatic Malformation). We validated our results through state of science literature, gene-disease annotation databases, and electronic health records.

conclusionsAS and LVDD share both clinical and genomic associations, with overlapping genetic drivers that are enriched in pathways related to inflammation, extracellular matrix remodeling, and vascular stress responses. This work supports the potential of blood-based multi-omics profiling to uncover early, systemic molecular signals of cardiac dysfunction and lays the groundwork for future tissue-specific studies to guide precision diagnosis, risk stratification, and targeted therapeutics in HF.

Indexed as

Aortic stenosisDiastolic dysfunctionGenetic markersHeart failureLeft ventricular

Identifiers

PMID42277826
PMCPMC13430796

What Socratic holds

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LicenceCC BY-NC-ND
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.