ReviewARYA atherosclerosis2026
Unravelling the multi-omics interplay in atherosclerosis development: Their integration for diagnostic and therapeutic strategies of the disease.
Review in ARYA atherosclerosis, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
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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Registered trials
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 OpenQuestion graph.