BACKGROUND: Acute coronary syndrome (ACS) remains a leading cause of cardiovascular morbidity and mortality worldwide. High-throughput omics technologies offer opportunities for more comprehensive understanding of ACS pathophysiology. METHODS: A systematic review was conducted in accordance with PRISMA 2020 guidelines. Searches of PubMed/MEDLINE, Scopus, Web of Science and Embase (January 2015-April 2026) identified 8,731 records. Study quality and risk of bias were assessed for studies selected for detailed evidence synthesis using the Newcastle-Ottawa Scale, QUADAS-2 and AMSTAR-2. RESULTS: Systematic searching identified 866 primary studies and 47 systematic reviews. Detailed synthesis focused on the most robust and replicated biomarker candidates where ceramide risk scores, GDF-15, MPO and miR-208a emerged as particularly promising while multi-omics machine-learning models improved ACS classification. CONCLUSIONS: Multi-omics approaches may enhance precision risk stratification in ACS although prospective validation and standardization remain essential.
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Sharma et al. (2026) studied this question.
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