Will Genetic Data Significantly Change Cardiovascular Risk Prediction in Daily Practice?

William Young1, Julia Ramírez2, Stefan van Duijvenboden3, Andrew Tinker1, Pier Lambiase4, Patricia Munroe1, Michele Orini5
1Queen Mary University of London, 2Queen Mary University London, 3UCL institute of cardiovascular science, 4University College London, 5University College London, Department of Mechanical Engineering


Despite significant advances in cardiovascular (CV) medicine over the last three decades, risk prediction remains a challenge. Precision medicine has been heralded as an opportunity to overcome this problem, driven significantly by increasing availability of genetic data. Genetic testing for rare mutations linked with some mendelian monogenic syndromes is available in specialised clinics for risk stratification and family screening. Common/low frequency variants for the risk assessment of complex disease traits require aggregation into a “genetic risk score” (GRS), due to their small individual effect sizes. GRSs for coronary artery disease (CAD), hypertension and atrial fibrillation have had some modest successes at a population level. Testing in specific patient sub-groups has also shown potential; such as the use of a CAD GRS for CAD risk assessment in Familial Hypercholesterolaemia and predicting response to PCSK9 blockade in patients with acute coronary syndrome. A CAD GRS may also identify those at highest risk of sudden cardiac death (SCD) due to CAD. Additionally, common genomic variation may alter risk of arrhythmia in monogenic conditions such as Long QT syndrome, potentially explaining the variation seen in disease severity amongst families. Scepticism remains however, whether the genetic effects in CV traits are large enough to have meaningful clinical impact as demonstrated by the limited improvement in discrimination above commonly used clinical risk scores. The link between the genome and clinical outcomes is complex and yet to be clearly understood. Continued advancements in medical technologies may better improve the detail of patient phenotyping through non-invasive imaging and home-based wearable monitoring, to track trajectories relevant to the evolution of key cardiovascular diseases, refining predictive accuracy and risk stratification. Whether genetic tools can contribute to this, is yet to be consistently determined. This paper reviews recent GRSs for CV disease with examples of their use in CAD and SCD.