Polygenic Risk Scores for Cardiovascular Disease: A Scientific Statement From the American Heart Association

Abstract

Cardiovascular disease is the leading contributor to years lost due to disability or premature death among adults. Current efforts focus on risk prediction and risk factor mitigation‚ which have been recognized for the past half-century. However, despite advances, risk prediction remains imprecise with persistently high rates of incident cardiovascular disease. Genetic characterization has been proposed as an approach to enable earlier and potentially tailored prevention. Rare mendelian pathogenic variants predisposing to cardiometabolic conditions have long been known to contribute to disease risk in some families. However, twin and familial aggregation studies imply that diverse cardiovascular conditions are heritable in the general population. Significant technological and methodological advances since the Human Genome Project are facilitating population-based comprehensive genetic profiling at decreasing costs. Genome-wide association studies from such endeavors continue to elucidate causal mechanisms for cardiovascular diseases. Systematic cataloging for cardiovascular risk alleles also enabled the development of polygenic risk scores. Genetic profiling is becoming widespread in large-scale research, including in health care–associated biobanks, randomized controlled trials, and direct-to-consumer profiling in tens of millions of people. Thus, individuals and their physicians are increasingly presented with polygenic risk scores for cardiovascular conditions in clinical encounters. In this scientific statement, we review the contemporary science, clinical considerations, and future challenges for polygenic risk scores for cardiovascular diseases. We selected 5 cardiometabolic diseases (coronary artery disease, hypercholesterolemia, type 2 diabetes, atrial fibrillation, and venous thromboembolic disease) and response to drug therapy and offer provisional guidance to health care professionals, researchers, policymakers, and patients.

Reclassification of coronary artery disease risk using genetic risk score among subjects with borderline or intermediate clinical risk

“…Results from our study are generally consistent with previous studies on PRS of CAD [3] , [8] , including those utilizing the UKB data [9] , [10] , [11] , [12] . However, our study differs from published studies in the objective, design, and methodology.…”

“…While family history (FH) of premature ASCVD is a risk-enhancing factor [2] , other more objective inherited risk measures such as SNP-based polygenic risk scores (PRSs) and monogenic mutations are not included as risk-enhancing factors. This status quo is in contrast to extensive evidence supporting the association between CAD and monogenic/polygenic variants [3] . The objective of this study is to demonstrate that PRS is an informative risk-enhancing factor for subjects with borderline-/intermediate-ASCVD risk.…”

Benefits and barriers to implementing precision preventive care: results of a national physician survey

“…PRS for breast cancer and coronary artery disease are proposed as tools (or risk-enhancing factors) to identify high-risk patients eligible for screening and statin therapy, respectively, that traditional risk stratification methods miss. 15,18 Whether PRS are used in the future as protective factors to help patients safely defer preventive care will require strong evidence of benefit and effective communication to PCPs and patients that doing so is to reduce preventive care-related risks and not about rationing health care. 34,36 The barriers to clinical PRS implementation that PCPs selected are consistent with prior studies, in which common concerns about the use of PRS included lack of knowledge or confidence, [37][38][39][40] insufficient evidence or guidelines to support their use, 35,38,39,[41][42][43] and insurance discrimination and other ethical issues.…”

“…Latent class analysis (LCA) was used as the preferred analytic approach in this study due to its robust and model-based statistical properties. 30,62 Given the sample size (<500), number of initial indicators (18), and multiple response options, we employed multiple methods, including item recoding, 58 optimized variable selection, 63,64 and individual item inspection 62 to improve model identification and limit boundary solution and model convergence issues. Theoretical perspectives such as respondent subgroup plausibility and interpretation, model parsimony, and model fit statistics were also considered throughout the item selection and model building process.…”

“…Clinical trials and implementation projects are underway, [10][11][12][13][14] and several commercial laboratories already offer PRS products for clinical use. [15][16][17] Most attention has focused on the potential for PRS to identify high-risk subgroups of patients for whom targeted interventions might improve health outcomes through earlier prevention, detection, or treatment; 4,10,18 the potential for PRS to identify low-risk subgroups for whom certain preventive measures might be appropriately deferred remains more theoretical. 19,20 Whereas preventive medicine is most often the domain of primary care, the successful implementation of PRS for risk stratification and prevention will depend on the clinical utility and barriers that primary care physicians (PCPs) perceive to their use.…”