Advancements in technology have improved biomarker discovery in the field of heart failure (HF). What was once a slow and laborious process has gained efficiency through use of high-throughput omics platforms to phenotype HF at the level of genes, transcripts, proteins, and metabolites. Furthermore, improvements in artificial intelligence (AI) have made the interpretation of large omics data sets easier and improved analysis. Use of omics and AI in biomarker discovery can aid clinicians by identifying markers of risk for developing HF, monitoring care, determining prognosis, and developing druggable targets. Combined, AI has the power to improve HF patient care.
Biomarker discovery has been enhanced by the high-throughput technologies of omics.
Artificial intelligence improves statistical analysis of the large data sets generated in omics to enhance identification of markers of potential interest.
Application of omics, especially proteomics, has aided with identification of biomarkers with clinical relevance to heart failure.
Through biomarker discovery, researchers can further study identified biomarkers to determine clinical utility, pathophysiology, and improve patient care.
Given the complex pathophysiology and etiologies of heart failure (HF), tailoring care to improve patient outcomes and quality of life involves the integration of multiple parameters.
Beyond adjusting treatments based on symptoms and physical examination findings, clinicians must rely on adjunctive testing to support their judgment; this includes imaging and measurement of circulating biomarkers. Most notably, among biomarkers measured, natriuretic peptides are considered the standard of care for treating patients with HF. , However, HF is a complicated condition, which cannot be summarized by one biomarker alone. , Although natriuretic peptides indicate cardiomyocyte stretch, they have limited ability to capture other changes associated with HF remodeling included inflammation, oxidative stress, and fibrosis. ,
For this reason, multiple markers are needed to recognize the pathophysiology of HF and tailor care to optimize patient outcomes.
The introduction of omics, including genomics, transcriptomics, proteomics, and metabolomics, has enhanced biomarker discovery.
Omics allows for analysis of biological pathways involved in aspects of the central dogma and biological modifications of products. Using high-throughput and untargeted omics technologies provides better phenotyping and unveils previously unknown HF pathophysiology. Furthermore, integration of findings of multiple omics studies gives a comprehensive understanding of pathophysiology from the level of genes to final metabolites to recognize where disease processes arise. Discovery of markers enables researchers and clinicians to recognize potential risks for developing HF, serve as prognostic indicators, or ascertain druggable targets for future therapeutic interventions.