Abstract

Objective: This study aimed to develop a novel genetic tool, the Polygenic Risk Score (PRS), to non-invasively and routinely estimate predisposition to cardiovascular diseases (CVDs), comprising coronary artery disease, dilated cardiomyopathy, hypertrophic cardiomyopathy, atrial fibrillation, ischemic stroke, and heart failure. The study also aimed to improve risk estimation by incorporating chronological age, physiological characteristics, and behavioral parameters to calculate an adjusted PRS. Design and method: Three algorithms were designed to i) search literature databases for GWAS on CVD predisposition, ii) derive Single Nucleotide Polymorphisms (SNPs) by assessing p-values, beta coefficients, odds ratios, and linkage disequilibrium metrics, and then iii) calculate the PRS for each CVD. Manual scientific evaluation was also employed. Thus, a novel genetic panel (termed iDNA Cardio Health) was developed to assess polygenic risk for the six above mentioned CVDs, following buccal swab sampling. Finally, we employed the American Heart Association's (AHA) Life's Simple 7 (LS7) lifestyle and phenotypic characteristics scoring system to assess current cardiovascular health status and then use this information, as well as chronological age, to dynamically calculate an adjusted PRS. Results: After testing the iDNA Cardio Health on a Greek population sample, we were capable to examine CVD risk stratification. Additionally, we could identify individuals with high polygenic risk, who could significantly benefit from lifestyle and/or clinical modifications. Furthermore, employing the LS7 categorization, we were capable to fine-tune risk prediction based on the adjusted PRS. Conclusions: It is evident that current clinical tools for CVD risk estimation may underestimate the risk, while the PRS can be employed to optimally reclassify individuals with marginal intermediate risk to a high-risk category. Our novel methodology, which at its core encompasses polygenic risk, can be combined with LS7 traditional clinical risk prediction metrics and chronological age to revolutionize CVD prevention though screening, monitoring, and clinical management, thus enabling the implementation of PRS and adjusted PRS in clinical care to empower personalized medicine.

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