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Manuscript Submission Deadline 17 May 2023

The paradigms for precision cardiovascular medicine are undergoing continuous evolution and growth. Recent technological and computational discoveries allowed the first look into a complete, gapless genome sequence, offering new insights into the epigenetics of aging. With ongoing investigations of the role ...

The paradigms for precision cardiovascular medicine are undergoing continuous evolution and growth. Recent technological and computational discoveries allowed the first look into a complete, gapless genome sequence, offering new insights into the epigenetics of aging. With ongoing investigations of the role of genetic risk integrated into clinical assessment, current recommendations offer biomarker- and imaging-based risk-enhancing factors to individualize the approach to each patient. Most of the cardiovascular traits have polygenic underpinning which can be implicated in the heterogeneity of prognosis of those with monogenic disorders. Genome-wide association studies (GWAS) identified millions of single-nucleotide polymorphisms allowing genetic risk modeling of a given disease state or phenotype. Polygenic risk scores (PRS) can be calculated as a weighted sum of the number of trait-associated alleles for each individual. PRS aggregates the many small effects of variants across the genome to estimate an individual’s disease risk as a single quantitative risk factor. A PRS is typically normally distributed and those at the tails of the distribution may have a significantly greater or lower risk of disease than those in the middle. There is limited data on the validation and implementation of PRS in clinical practice and populations with ancestral and ethnic diversity.

Public health strategies aimed at early disease detection and prevention have the potential to advance cardiovascular health worldwide. There is a need for the development of artificial intelligence algorithms, including machine learning and natural language processing, to accommodate methods that integrate PRSs and other genetic risk information, family history, and clinical variables. Suh comprehensive risk scores with a linkage to electronic health record-based strategies are expected to increase awareness, detection, and control of various cardiovascular conditions with greater accuracy. PRSs can be leveraged to predict variation in drug response and susceptibility to adverse drug reactions. Therefore, research focusing on the use of PRSs to inform targeted therapies on the basis of cardiovascular disease pathways is needed. The Cost-effectiveness of such strategies along with the use of PRSs to enrich clinical trials with those at higher polygenic risk or variable drug efficacy is needed. There is a crucial need in developing and validating PRSs generated from various ancestry groups using novel statistical genetics approaches aimed at the reduction rather than the promotion of health disparities. Ethical, legal, and social issues (ELSI) remain relevant in the polygenic context in the practice of Cardiovascular medicine and public health.

This Research Topic aims to publish high-quality research in the Cardio-Genomics space with an emphasis on the genetic basis and architecture of cardiovascular diseases and novel applications of precision medicine. This Issue considers all types of original research articles, including studies conducted in human subjects, laboratory animals, in vitro, and in silico, review articles, methods articles, commentaries and brief communications, and other invited content related to cardiovascular health and disease. Articles focusing on clinical implementation of PRS relevant to cardiovascular phenotypes, methods papers on PRS construction and validation in underrepresented populations, point-of-care implementation of clinical decision support tools facilitating individualized care, engaging patients and families, and improving clinical outcomes are prioritized.

Keywords: polygenic architecture, Genetic Scores, cardiovascular traits, functional genomics, statistical genetics, clinical informatics, risk modeling, genetic predisposition, multi-ethnic polygenic risk scores, bioinformatics, machine learning, sequencing, genome-wide association study, phenome-wide association study, vascular aging primordial prevention, precision medicine


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