Emerging biomarkers derived from multiomics technologies and artificial intelligence may enhance cardiovascular disease risk prediction and therapy monitoring compared to conventional approaches.
Abstract The current research examines the creation of biomarkers for measuring cardiovascular disease (CVD) risk and tracking therapy effectiveness. While current biomarkers like cholesterol levels and troponins are useful, developing and innovating new biomarkers gives fresh insights into CVD etiology and drug response. The limits of known biomarkers are discussed, leading to the quest for new signs that might enhance risk categorization and therapy monitoring. Advancements in multiomics technologies, such as genomics, proteomics, metabolomics, and transcriptomics, have been utilized to uncover possible biomarkers, offering a complete perspective of molecular pathways implicated in CVD. Artificial intelligence and machine learning play a vital role in biomarker development and validation, allowing the investigation of massive omics datasets and detecting patterns and links that may not be obvious using conventional approaches. The clinical translation of new biomarkers needs comprehensive validation and evaluation of their efficacy in improving patient outcomes. Incorporating these indicators into clinical practice might boost risk prediction, modify treatment regimens, and improve overall CVD care.
Omar Elsaka (Mon,) conducted a review in Cardiovascular disease. Emerging and innovative biomarkers was evaluated. Emerging biomarkers derived from multiomics technologies and artificial intelligence may enhance cardiovascular disease risk prediction and therapy monitoring compared to conventional approaches.
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