Polygenic Scores in Cardiovascular Disease 

For many decades, we knew that cardiovascular disease and several of its risk factors are heritable. This justifies why we often ask our patients about their family history, but is that truly the best measure for someone’s genetic liability to develop a disease?

Diseases such as myocardial infarction, type 2 diabetes, and atrial fibrillation are called complex traits. This is because their genetic liability comes from the summation of weak effects of many single nucleotide variations across the 3 billion nucleotides in our genome. This is different from monogenic diseases such as hypertrophic cardiomyopathy, which is due to a single but highly potent nucleotide variation in the cardiac sarcomere gene.

Owing to multiple large genome-wide association studies, a better understanding of the human genome and advances in statistical genomics methods, this polygenic liability of disease could now be measured in a single individual to predict where he or she falls on the genetic risk spectrum of disease. Over the past year, two high profile papers in Nature Genetics and JACC showed that polygenic scores can accurately predict coronary artery disease, as well as other diseases such as Type 2 Diabetes and Atrial Fibrillation.

A polygenic score is a number that is normally distributed in the population. Where your score is located on that bell-shaped curve determines your risk or protection from disease. While most of us will be in the middle, the unlucky person who happens to be in the upper tail of the distribution will carry several fold increased risk of CAD compared to the rest of us. This could be your 45 year-old patient, non-smoker with an LDL of 120mg/dL and no clinical risk factors who presents with a STEMI. On the other hand, I always wonder whether my 95 year-old grandpa who died of lung cancer with the cigarette in his mouth but a healthy heart actually was on the bottom tail of the CAD polygenic score distribution?

Just like the systolic blood pressure and the LDL cholesterol, polygenic scores are continuous measures of risk that require drawing cut-offs in order to practically classify patients and treat accordingly.  Defining those thresholds and determining how we act on them will be key for the successful implementation of those scores in clinical care. Anytime we draw thresholds to use in screening or treatment, there will be issues of sensitivity, specificity, outcomes, and value. After all, even cutoffs for risk factors that we’ve understood for decades, such as blood pressure and LDL cholesterol, continue to be debated from one guideline update to the other. The power of the polygenic scores is their wide availability and low cost (around $50 once for all diseases) as well as their ability to classify risk at an early stage in life (practically from birth) before clinical risk factors start appearing, which creates an opportunity to target disease early on before it develops.

The journey to clinical implementation of polygenic scores in cardiovascular disease still requires several steps. First, the current predictive ability of those scores declines in populations of non-European ancestry.  Development of scores that carry similar predictive ability across different ethnicities will be crucial to avoid widening healthcare disparities.  Second, prospective trials testing specific thresholds and interventions will be necessary to prove that implementation of those scores can lead to positive outcomes. For example, would targeting lower LDL thresholds in people with high polygenic scores reduce their risk of CAD?  Third, behavioral psychology studies could inform how young and healthy people react to information regarding their score. Ideally, you would want someone with higher score to engage in good lifestyle activities to mitigate his/her risk but also someone with a low score to not get falsely reassured and pick up poor lifestyle choices.  Fourth, clinical trials of existing and new therapies could be better informed with polygenic score stratification to pick up missed opportunities of benefit. One example would be that people at the upper tail of risk could benefit from treatment that when applied to the average population does not show a benefit.

Precision medicine in cardiovascular disease is happening and polygenic scores are one opportunity to prevent disease early on by targeting specific people at risk. Unlike the heritability informed by a positive family history which rarely changes management, the quantitative aspects of polygenic scores and our ability to validate their impact on outcomes prospectively and in different settings will change how we care for patients in the near future.


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