OBJECTIVES: Assessing risk of cardiovascular disease is central to early detection, prevention, and clinical decision-making. Until now, clinical risk prediction relies on demographic characteristics, lifestyle, health parameters and family history. Routine genetic testing however is absent from this list. Yet, genetics are the earliest measurable contributor to common adult-onset disease risk. Novel genetic profiling methods have been developed to estimate the probabilistic susceptibility of an individual to disease based on their Polygenic Risk Score (PRS). That is a weighted sum of the number of risk alleles carried by an individual, where the risk alleles and their weights are defined by their measured effects as detected by Genome Wide Association Studies (GWAS). The aim of this study was to develop an adjusted PRS that estimates the combined risk from both genetic and phenotypic parameters, for use in medical practice.

METHODS: We developed a novel PRS to estimate comprehensive risk for six common cardiovascular conditions, comprising coronary artery disease, dilated cardiomyopathy, hypertrophic cardiomyopathy, heart failure, atrial fibrillation, and ischemic stroke. Specifically, we designed three unique algorithms to i) search for Single Nucleotide Polymorphisms (SNPs) associated with disease predisposition in major databases with published GWAS, ii) detect the appropriate SNPs by assessing p-value, beta coefficient, odds ratio, and linkage disequilibrium metrics, and iii) calculate PRS for each cardiovascular condition under investigation. Finally, we employed the American Heart Association Life’s Simple 7 (LS7) lifestyle and phenotypic characteristics scoring system to assess individual’s cardiovascular health status.

RESULTS: Using LS7 categorization, we were capable to generate an adjusted PRS that can dynamically fine-tune risk prediction based on current health status and age.

CONCLUSIONS: Our novel methodology, that at its core encompasses genetic risk, is then combined with traditional clinical risk prediction metrics to revolutionize cardiovascular disease detection, prevention, and monitoring, thus enabling implementation of personalized medicine at its best.