Yash Paul Sharma, Ankur Mittal, Kraticka Singhal, Naveena N, Juniali Hatwal, Akshyaya Pradhan, Ashish Singh, Bishav Mohan, Akash Batta
Multi-omics approaches may enhance precision risk stratification in ACS although prospective validation and standardization remain essential.
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.