Unravelling the multi-omics interplay in atherosclerosis development: Their integration for diagnostic and therapeutic strategies of the disease
- 1Razi Herbal Medicines Research Center, School of Medicine, Lorestan University of Medical Sciences, Khorramabad, Iran
- 2Department of Medical Biotechnology, School of Paramedical, Gerash University of Medical Sciences, Gerash, Iran
- 3Hepatitis Research Center, Department of Medical Biotechnology, School of Medicine, Lorestan University of Medical Sciences, Khorramabad, Iran
Abstract
Atherosclerosis (AS) is a leading cause of coronary artery disease (CAD), characterized by the gradual accumulation of plaques within arterial walls, resulting in severe health complications such as myocardial infarctions and strokes. Recent advancements in multi-omics technologies including genomics, transcriptomics, proteomics, and metabolomics have significantly enhanced our understanding of the complex molecular mechanisms underlying atherosclerosis. These technologies provide a comprehensive view of the disease by integrating diverse biological data that extend beyond traditional risk factors. The integration of network medicine with multi-omics approaches further enhances our ability to elucidate the intricate interactions among biological components in both health and disease states related to atherosclerosis. This interdisciplinary framework not only facilitates the identification of novel diagnostic biomarkers and therapeutic targets but also improves patient stratification for personalized interventions. By employing machine learning (ML) techniques, we can combine multiple omics and clinical datasets into innovative tools that support the development of tailored diagnostic and therapeutic strategies. However, challenges such as data quality, integration, and privacy remain significant barriers to progress. This review offers a comprehensive overview of various multi-omics technologies and their contributions to precision medicine in managing atherosclerotic cardiovascular disease (ASCVD). Additionally, we address current challenges and limitations while outlining future research directions aimed at improving patient outcomes through innovative methodologies. The convergence of multi-omics data with network medicine signifies a critical shift toward more effective and individualized treatment paradigms in combating ASCVD, presenting substantial opportunities for advancements in clinical practice and patient management.
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BibTeX
@article{barfi2026unravelling,
title = {{Unravelling the multi-omics interplay in atherosclerosis development: Their integration for diagnostic and therapeutic strategies of the disease}},
author = {Elahe Barfi and Mahdi Barazesh and Shiva Mohammadi and Leila Abkhooie},
journal = {ARYA Atherosclerosis},
year = {2026},
month = oct,
doi = {10.48305/arya.2026.45754.3113},
url = {https://doi.org/10.48305/arya.2026.45754.3113},
note = {CC BY-NC 4.0},
}
RIS
TY - JOUR
TI - Unravelling the multi-omics interplay in atherosclerosis development: Their integration for diagnostic and therapeutic strategies of the disease
AU - Elahe Barfi
AU - Mahdi Barazesh
AU - Shiva Mohammadi
AU - Leila Abkhooie
PY - 2026
DA - 2026/10/30
JO - ARYA Atherosclerosis
DO - 10.48305/arya.2026.45754.3113
UR - https://doi.org/10.48305/arya.2026.45754.3113
AB - Atherosclerosis (AS) is a leading cause of coronary artery disease (CAD), characterized by the gradual accumulation of plaques within arterial walls, resulting in severe health complications such as myocardial infarctions and strokes. Recent advancements in multi-omics technologies including genomics, transcriptomics, proteomics, and metabolomics have significantly enhanced our understanding of the complex molecular mechanisms underlying atherosclerosis. These technologies provide a comprehensive view of the disease by integrating diverse biological data that extend beyond traditional risk factors. The integration of network medicine with multi-omics approaches further enhances our ability to elucidate the intricate interactions among biological components in both health and disease states related to atherosclerosis. This interdisciplinary framework not only facilitates the identification of novel diagnostic biomarkers and therapeutic targets but also improves patient stratification for personalized interventions. By employing machine learning (ML) techniques, we can combine multiple omics and clinical datasets into innovative tools that support the development of tailored diagnostic and therapeutic strategies. However, challenges such as data quality, integration, and privacy remain significant barriers to progress. This review offers a comprehensive overview of various multi-omics technologies and their contributions to precision medicine in managing atherosclerotic cardiovascular disease (ASCVD). Additionally, we address current challenges and limitations while outlining future research directions aimed at improving patient outcomes through innovative methodologies. The convergence of multi-omics data with network medicine signifies a critical shift toward more effective and individualized treatment paradigms in combating ASCVD, presenting substantial opportunities for advancements in clinical practice and patient management.
SN - 1735-3955
SN - 2251-6638
ER -