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Published on: September 20, 2024
Multiomics approaches to cardiovascular disease: technological innovations and clinical translation
Binte Zehra1, Nidhina Vinod1, Shuhd BinEshaq1
1College of Medicine, Mohammed Bin Rashid University of Medicine and Health Sciences, Dubai Health, Dubai, United Arab Emirates.
Insights
Emerging multi-omics technologies are revolutionizing cardiovascular disease (CVD) research by revealing complex molecular mechanisms. These advanced tools enable a deeper understanding of CVD, paving the way for precision cardiovascular medicine.
Area of Science:
- Cardiovascular Biology
- Genomics
- Proteomics
- Metabolomics
Background:
- Cardiovascular diseases (CVDs) are a leading cause of death globally.
- A gap exists between clinical understanding and the molecular basis of CVD.
- Existing research often overlooks the complex molecular drivers of CVD.
Purpose of the Study:
- To review how emerging multi-omic and functional genomics technologies are redefining cardiovascular disease research.
- To highlight innovations in single-cell, spatial, and long-read sequencing, proteomics, metabolomics, and integrative data modeling.
- To frame omics-enabled strategies for translating molecular insights into clinical applications for precision cardiovascular medicine.
Main Methods:
- Utilizing high-resolution, cross-layer profiling (genomic, epigenomic, transcriptomic, proteomic, metabolomic, lipidomic, glycomic, fluxomic) at single-cell and spatial resolutions.
- Applying computational and functional genomics, including genome-scale perturbation screens and single-cell perturbation frameworks.
- Synthesizing data from emerging technologies to dissect regulatory circuits and identify disease drivers.
Main Results:
- Multi-omic approaches reveal CVD as a multi-layered process involving dynamic interactions among cell types, regulatory programs, and metabolic states.
- Innovations enable mechanistic dissection of regulatory circuits, distinguishing primary disease drivers from secondary adaptations.
- These platforms advance the field from associative biomarker discovery to mechanism-guided target prioritization.
Conclusions:
- Emerging omics technologies provide a unified, physiologically grounded framework for understanding CVD.
- Integrating technological innovation with computational rigor and functional validation is key.
- These strategies are crucial for translating molecular insights into clinically meaningful cardiovascular phenotypes and advancing precision cardiovascular medicine.
Abstract:
Cardiovascular diseases (CVDs) remain the leading cause of global morbidity and mortality, reflecting a persistent gap between clinical phenotyping and the molecular mechanisms that govern disease initiation, progression, and interindividual variability. Recent advances in emerging technologies have fundamentally reshaped cardiovascular physiology by enabling high-resolution, cross-layer profiling of the heart and vasculature across genomic, epigenomic, transcriptomic, proteomic, metabolomic, lipidomic, glycomic, and fluxomic layers, increasingly at single-cell and spatial resolution. These approaches reveal CVD as a coordinated, multilayered process driven by dynamic interactions among cell types, regulatory programs, and metabolic states, rather than isolated gene-level defects. In this review, we synthesize how emerging multiomic, computational, and functional genomic technologies are redefining the study of cardiovascular disease across molecular, cellular, and tissue levels. We highlight recent innovations in single-cell and spatial atlases, long-read sequencing, proteomics and metabolomics, integrative data modeling, and functional omics approaches, including genome-scale perturbation screens and single-cell perturbation frameworks. These platforms enable mechanistic dissection of regulatory circuits, distinguish primary disease drivers from secondary adaptations, and directly assess therapeutic reversibility, advancing the field beyond associative biomarker discovery toward mechanism-guided target prioritization. We further discuss key methodological and translational challenges accompanying high-dimensional cardiovascular data, including preanalytical variability, control selection, temporal misalignment across molecular layers, population diversity, and reference bias. By integrating technological innovation with computational rigor and functional validation, this review frames emerging omics-enabled strategies as a unified, physiologically grounded framework for translating molecular insight into clinically meaningful cardiovascular phenotypes and advancing precision cardiovascular medicine.
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