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Abstract

Cardiovascular diseases continue to be the leading cause of mortality and morbidity in the world. The prevalence has been changing with time in the developing and developed world. The higher prevalence in the South Asian population compared to the rest of the world has been reviewed with multiple theories, ranging from genetic predisposition, higher prevalence of diabetes mellitus, dietary patterns, and dyslipidemia with low high-density lipoprotein. The traditional risk scores are still underestimating cardiovascular risk in the large young population of the Indian subcontinent. There is a need to risk-stratify at an early age to start preventive measures early to save lives. The development of risk scores like Framingham Risk Score, QRISK, and ASCVD has been reclassified by adding the CT coronary calcium score. Further enhancing the risk by adding polygenic risk score for coronary artery disease and cardiometabolic disorders will guide us to start preventive measures early in life.

Keywords

ASCVD risk score cardiovascular disease Framingham Risk Score polygenic risk score

Introduction

Cardiovascular diseases, such as ischemic heart disease and cerebrovascular disease, cause around 17.7 million deaths.¹ India accounts for one-fifth of the deaths accounted worldwide, especially in the younger age group. The Global Burden of Disease study shows an age-standardized cardiovascular disease (CVD) death rate of 272 per 100,000 population in India, which is much higher than the global average of 235. Cardiovascular death occurs in Indians a decade earlier than in the Western population.² This brings to the discussion for the need of early risk stratification and early preventive measures in Indians. Particular causes of concern in CVD are early age of onset, rapid progression, and high mortality rate. The advancement in genetic testing and large genome-wide association studies (GWAS) has shown many hereditary pathogenic variants predisposing to CVDs that run in families. Since the Human Genome Project facilitated population-based comprehensive genetic profiling at decreasing costs, GWAS from such endeavors continue to show multifactorial causal mechanisms for CVDs.

What is a Polygenic Risk Score?

A polygenic risk score (PRS) is a single value that estimates an individual’s genetic predisposition or risk factor for a disease based on the known presence of the causative single-nucleotide variants (SNVs). PRSs provide information that can be used to enhance or guide, but not replace, the risk prediction models. It is calculated by adding up an individual’s risk alleles and weighting them by the effect sizes of those alleles found in the large dataset of the GWAS. PRSs are also called polygenic indexes (PGIs), genome-wide scores, or genetic risk scores. PRSs have been developed to predict an individual’s genetic predisposition to many diseases like coronary artery disease (CAD), arrhythmias like atrial fibrillation (AF), sudden cardiac deaths, breast cancer, type 1 and type 2 diabetes, prostate cancer, Parkinson’s disease, Alzheimer’s disease, major depressive disorder, and schizophrenia.

The present clinical models of disease prediction, like the Framingham Risk Score³ and more, are shown in Table 1.⁴ The risk predictive scores have overestimated the clinical setting over time³; there is a gap in prediction in the expanding population. PRS in CVD is gaining importance due to the unexplained early-onset CAD in many parts of the world. Addition of genetic testing to conventional risk prediction models redefines risk scores, potentially much earlier in life, and works toward earlier risk reduction advices.⁵ Having any of the parents with a history of premature CAD is associated with higher odds of developing CVD, independent of regular clinical risk factors.⁶ In comparing monozygotic twins with dizygotic twins, it was shown that variation in the development of CAD,⁷ AF,⁸ and diabetes mellitus^{9, 10} is attributed to common genetic variations (Table 1).

Table 1.

Key Characteristics of the Most Widely Used Risk Prediction Equations.

Risk Equation	Parameters Used to Estimate Risk	Predicted Outcome
Systematic coronary risk evaluation	Age, sex, SBP, TC, and smoking status	10-year risk of cardiovascular mortality
Pooled cohort equations calculator	Age, sex, SBP, treatment for hypertension, TC, HDL-C, history of T2DM, and smoking status	10-year risk of a nonfatal MI, CHD death, and fatal or nonfatal stroke
Framingham Risk Score	Age, sex, SBP, TC, T2DM, and smoking	10-year risk of a nonfatal MI and CHD death
ASSIGN Risk Score	Age, sex, SBP, TC, T2DM, smoking, social deprivation, and family history of CVD	10-year risk of cardiovascular events
QRISK3 Score	Age, sex, SBP, TC/HDL-C ratio, T2DM, smoking status, ethnicity, social deprivation, body mass index, family history of CHD in a first-degree relative younger than 60 years, treated hypertension, rheumatoid arthritis, atrial fibrillation, stage 4 or 5 chronic kidney disease, migraine, corticosteroid use, systemic lupus erythematosus, treatment with atypical antipsychotic medications, severe mental illness, erectile dysfunction, and variability of blood pressure	10-year risk of cardiovascular events
Prospective Cardiovascular Münster Risk Score	Age, SBP, LDL-C, HDL-C, triglycerides, presence of T2DM, family history of MI, and smoking status	10-year risk of fatal or nonfatal CHD event
CUORE Risk Score	Age, sex, SBP, TC, HDL-C, presence of T2DM, treatment for hypertension, and smoking status	10-year risk of CHD and cerebrovascular events
Reynolds Risk Score	Age, sex, SBP, TC, HDL-C, HbA_1c if diabetic, smoking, hsCRP, and parental history of MI before the age of 60 years	10-year risk of cardiovascular events

Source: Sofogianni et al.⁴

Note: CHD: Coronary heart disease; CVD: Cardiovascular disease; HDL-C: High-density lipoprotein cholesterol; hsCRP: High-sensitivity C-reactive protein; LDL-C: Low-density lipoprotein cholesterol; MI: Myocardial infarction; SBP: Systolic blood pressure; T2DM: Type 2 diabetes mellitus; TC: Total cholesterol.

Early-onset myocardial infarction can be caused by some monogenic risk variants in the genes encoding the proprotein convertase subtilisin/kexin type 9 (PCSK9), apolipoprotein B (APOB), and low-density lipoprotein receptor (LDL-R), causing severe hypercholesterolemia; examples of causes of familial hyperlipidemias.^{11, 12} The examples of monogenic risk variants include variants that cause different diseases, leading to cardiovascular disorders, like glucokinase (GCK) for diabetes,¹³ KCNQ1 for AF,¹⁴ and F5 for venous thromboembolic disease.¹⁵

Building a PRS requires having large datasets, typically from GWAS. There will be many SNVs across the genome in the causative leads over time. The list of their effect size is measured by quantifying the association for the disease. Then the PRSs are prepared from the weighted sum of the risk conferred by multiple disease-associated SNVs across the genome. GWAS has shown that many SNVs scattered across the genome are associated with cardiovascular disorders. Each of these variants individually has only a small risk, but collectively, they account for substantial CVD risk with a weighted average.^{16, 17} The weighted average of risk score prediction will focus on CAD risk and offer provisional guidance to healthcare professionals and patients on the use of PRSs in CVD risk assessment and risk reduction.

Coronary Artery Disease

Hereditary genetic inheritance, a major risk factor for early-onset CAD, could not be quantified. With evolving data on GWAS, the monogenic, polygenic inheritance is better understood and quantified to refine the risk score. The proportion of phenotype explained by the additive sum of genetic factors is around 50%.¹⁸ GWAS of CAD in the general population has shown that common genetic variation also influences the risk for CAD.¹⁹ Increasingly large CAD GWAS continues to identify novel genomic loci; 167 separate genomic loci have been identified to be significantly associated with CAD to date.²⁰ Systematic pleiotropy analyses indicate that the majority of these loci do not influence CAD risk through well-recognized risk factors like diabetes mellitus, systemic hypertension, dyslipidemia, smoking, or obesity.²¹

Risk scores are predictors of subclinical coronary atherosclerosis and independently prognosticate coronary vascular events (around 1.5-fold of the CAD PRS; Figure 1).^22–24 The conventional risk scores do not detect high-risk CAD PRSs. The top fifth percentile of CAD PRS is associated with an increase of LDL-C by 8 mg/dL, which would have been classified as low risk traditionally.^{25, 26} The highest odds prediction of CAD, that is, threefold risk, is at the top 95th percentile (1 in 20) of CAD PRS, which is similar to that associated with familial hyperlipidemia without accompanying severe hypercholesterolemia.^{16, 25} Among the individuals with established atherosclerotic CAD, PRS was independently associated with higher major adverse cardiovascular events (MACE) (1.2-fold higher odds).²⁷ In the Atherosclerosis Risk in Communities (ARIC) study and Multi-Ethnic Study of Atherosclerosis (MESA) study, PRS addition has shown little improvement in the prediction of MACE.²⁸ More recent studies have shown better performance by using statistical approaches and validation in larger cohorts.²⁹ It is likely that the younger age group is going to benefit more among all the age groups. For example, the net relative index reached a peak of 15.4% (95% CI, 11.6%-19.3%) for younger subgroups (Figure 1).

Figure 1.

Note: BMI: Body mass index.

The addition of CT coronary calcium (CAC) score into the risk assessment for CAD was associated with significant improvement in reclassification for the prediction of atherosclerotic cardiovascular events. The results of various studies support the utility of CAC scoring as an adjunct to risk-enhancing factor assessment to more accurately classify individuals with an intermediate risk of atherosclerotic cardiovascular disease (ASCVD) who might benefit from early statin therapy.³⁰ The UK Biobank study’s integrated risk tool (IRT), which combined PRS with established risk tools (American Heart Association/American College of Cardiology Pooled Cohort Equations (AHA/AACC PCE) or UK QRISK3), showed that the PRS had superior predictive power for CAD events when compared with other published PRSs. When combined with PCE in the IRT, it has superior predictive accuracy. Overall net reclassification improvement (NRI) for the IRT was 5.9% (95% CI, 4.7-7.0). When individuals were stratified into age-by-sex subgroups, the improvement was 8.3%-15.4%, and was larger for all subgroups of men in the 40-54 years age group (15.4% (95% CI, 11.6-19.3); Figure 2). Comparable results were found using a different risk tool (QRISK3 tool) with a broader definition for CVD.²⁴ Use of the IRT is estimated to help avoid up to 12,000 deaths in the USA and 50,000 deaths in India over 5 years.

Figure 2.

Cumulative Incidence of Coronary Artery Disease (CAD) in the UK Biobank Study. (A) All of the Groups. (B) The Four Subgroups According to Age (45-54 Years and 55-69 Years) and Sex. Individuals are Further Stratified by Polygenic Risk Score (PRS)—Defined Risk in the Top 5% of the PRS (Red), the Median 40%-60% Distribution of Risk (Blue), and the Bottom 5% of Risk Distribution (Green).²⁴

Observational analysis of epidemiological studies or post hoc analyses of randomized controlled trials for CAD prevention have yielded better strategies to reduce CAD risk in the setting of a high CAD PRS. Because a CAD PRS is largely additive of nongenetic factors, adherence to a healthful diet mitigates CAD risk regardless of CAD PRS; however, given the worse prognosis, the absolute risk reduction from a healthful diet among those with a high CAD PRS may be greater than for those without a high CAD PRS.^{31, 32}

A personal communication of the PRS changed the behavior of the high-risk group after 1.5 years. 20.8% of individuals in the high-risk group had seen a doctor, 12.4% reported weight loss, 14.2% of them quit smoking, and 15.4% had signed up for health coaching online. These suggest positive outcomes with early preventive lifestyle management for healthy living. A total of 42.6% of the high-risk group had made one or more health behavioral changes against 33.5% people at low/average risk. Higher baseline risk predicted a favorable change with early intervention (P = .001), with both high clinical risk (P = .001) and genomic risk (P = .003) contributing independently.³³

In a study-level meta-analysis across the WOSCOPS, ASCOT, and JUPITER primary prevention, relative risk reduction in those at high genetic risk was 46% versus 26% in all others (P = .05). Across all three studies, the absolute risk reduction with statin therapy was 3.6% (95% CI 2.0-5.1) among those in the high genetic risk group and 1.3% (95% CI 0.6-1.9) in all others. For every 1-SD increase in the PRS, there is a 1.32-fold greater likelihood of having coronary artery calcification and a 9.7% higher (95% CI 2.2-17.8) carotid plaque burden. Those at high genetic risk have a greater burden of subclinical atherosclerosis; this high-risk group gets greater relative and absolute benefit from statin therapy to prevent coronary heart disease events.^{34, 35}

There was greater benefit from PCSK9 monoclonal antibodies among those with high PRS risk score versus low CAD PRS (37% vs. 13%; P = .04) in the ODYSSEY OUTCOMES trial in patients with established ASCVD on statins.²⁷ The preventive benefits of healthy lifestyle habits have been shown to reduce disease risk across all groups of PRS risk, although the greatest reductions are seen with the highest PRS risk.³⁶ Asymptomatic middle-aged adults at borderline/intermediate risk by conventional clinical risk factors, who were reclassified to high risk with a high CAD PRS, have been shown to benefit with statin therapy.²⁶ Among asymptomatic younger adults, a high CAD PRS may prompt more intensive earlier efforts for preventive lifestyle modification and potentially earlier lipid-lowering therapies initiation to reduce the high lifetime risk for CAD.³⁷

The integration of PRS for CAD into clinical practice is likely to improve current cardiovascular risk prediction tools. Currently, the AHA, ACC, and other international organizations recommend 10-year cardiovascular risk determination for all adult patients aged 40-75 years with the AHA/ACC ASCVD risk calculator.³⁸ The addition of PRS to the calculator significantly improves prediction for cardiovascular events (Figure 3).^{17, 23, 24, 26, 31} This enhanced prediction is independent of the conventional clinical risk factors (Figure 1). Risk is detectable before the emergence of clinical risk factors, and is appreciable across a spectrum of ages, sexes, and, increasingly, ancestries and ethnicities.^{22, 39–41} As the conventional risk factor tools are missing participants at the high PRS risk, it is cost-effective to include PRS in the AHA/ACC ASCVD risk calculator, which significantly improves prediction, to suggest early targeted measures.⁴²

Figure 3.

Predictive Accuracy of Polygenic Risk Score (PRS) Combined With Other Clinical Risk Tools and Compared to Traditional Risk Factors. (A) Net Reclassification Index (NRI). Comparison of Clinical Risk Tools With and Without the Integration of PRS.^{17, 24} The Clinical Risk Tool Used for Atrial Fibrillation was CHARGE-AF (Cohorts for Heart and Aging Research in Genomic Epidemiology Model for Atrial Fibrillation) with a Risk Threshold >5% Over 5 Years. Variables Included in CHARGE-AF were Age, Height, Weight, Systolic Blood Pressure, Diastolic Blood Pressure, Smoking Status, Blood Pressure-lowering Medication, Diabetes, Heart Failure, and History of Myocardial Infarction. The Clinical Tool for Coronary Artery Disease was the American Heart Association/American College of Cardiology Pooled Cohort Equation with a 7.5% Risk Threshold Over 10 Years and Included the Following Variables: Age, Diabetes, Sex, Race, Smoking, Total Cholesterol, High-density Lipoprotein (HDL), Systolic Blood Pressure, and Treatment for Hypertension. The Clinical Tool for Type 2 Diabetes was the American Diabetes Association Risk Score, Which Had a 33% Risk Threshold Over 10 Years and Included the Following Variables: Age, Sex, Body Mass Index, History of Stroke or Coronary Heart Disease, Parental History of Diabetes, Systolic Blood Pressure, Diastolic Blood Pressure, HDL, and Triglycerides. All Differences are Statistically Significant. (B). Comparison of C Statistics Between Clinical Risk Scores and a Risk Tool With a PRS Integrated into the Clinical Risk Tool. All Differences are Statistically Significant.^{17, 24}

A new GPS_CAD reference distribution was built using whole-genome sequencing from 1,522 Indian individuals. The GPS_CAD, containing 6,630,150 common DNA variants, had an odds ratio (OR) per standard deviation of 1.58 in South Asian UK Biobank participants and 1.60 in the Bangladeshi study (P < .001 for each). Compared to the middle quartile, risk for CAD was more pronounced for those in the top 5th percentile of the GPS_CAD distribution—ORs of 4.16, 2.46, and 3.22 in the South Asian UK Biobank, Bangladeshi, and Indian studies, respectively (P < .05 for each).⁴³

Interpretation of Polygenic Risk Score

Genetic research over the past decade has realized that our risk for many common conditions, such as heart disease and diabetes, is not influenced by just one gene but by many. Multiple genes interplay with the proteins secreted by them in tandem to influence the risk for developing CAD. Many small-effect genetic variations contribute to a person’s susceptibility to CAD. PRS prediction quantifies the contributing effects into a score and estimates whether the tested individual is at a high or moderate risk of developing atherosclerotic disease. It is a screening test that helps estimate the genetic predisposition of the individual. The risk is calculated from more than 6 million genetic markers²⁸ implicated in the disease and is given as a validated PRS. The usual turnaround time is 2 weeks for these results. The score indicates if the individual is at a high or moderate genetic risk. It is reported as AVERAGE RISK—You are at a not high genetic risk toward coronary events, MODERATE RISK—You are at a moderate genetic risk (1.5 times) toward CAD than an average-risk counterpart, HIGH RISK PRS—You are at a higher genetic risk (2 times) of getting CAD than an average-risk counterpart (Figure 4).²⁶

Figure 4.

Source: Aragam et al.²³

The PRS is also developed for the risk of type 2 diabetes mellitus, AF, venous thromboembolism, hypercholesterolemia, and pharmacogenomics.

Take Home Message

What is PRS? PRS is a single score reflecting the cumulative weighted risk of individual genetic variations for a set of traits that are prone to develop atherosclerotic disease. These individual genetic variants confer an incrementally small disease risk, but summated, they are predictive of many CVDs.

Polygenic versus monogenic risk variants: Monogenic risk variants are typically single, protein-truncating variants conferring a relatively large risk of disease. Examples of monogenic risk variants for CVD include LDL-R for familial hypercholesterolemia (FH), GCK for diabetes,¹³ KCNQ1 for AF,¹⁴ and F5 for venous thromboembolic disease.¹⁵

Coronary artery disease: Among middle-aged adults, a CAD PRS adds more value to conventional risk factors and provides additional prognostic information for CAD. The clinical significance of this improvement is contentious.^{17, 23, 24, 31} Observational evidence suggests that CAD PRS will have utility in guiding early interventions like pharmacological management (particularly for low-density lipoprotein cholesterol (LDL-C) lowering), attributable to increased estimated disease risk for better, healthier living.²⁷

There is evidence of predictive models for type II diabetes, AF, hypercholesterolemia, venous thromboembolism, and pharmacogenomics to guide gene-based therapies in the future.

Criteria for implementing PRS in cardiovascular clinical practice: Estimates of incremental efficacy and harm and logistical challenges are key aspects for healthcare systems to consider when evaluating PRS. There needs to be a better understanding by the clinicians and public awareness of the new technology, with its limitations.

PRS risk score to be an integral part of the investigation to risk-stratify and predict sudden cardiac deaths in the middle-aged population, which is of concern post-COVID pandemic. It can be ordered from genetic labs doing genome sequencing, order for PRS looking for specific nucleotide of significance as established by validated data.

Challenges and future directions: The addition of PRS to clinical risk tools consistently enhances the predictive ability. The lack of diversity of participant inclusion in biobanks, GWAS, and consequently PRSs is a major current limitation. The financial implication and insurance coverage for testing are going to decide the quantum of data to be produced in this big population.

Challenges and Future Directions

The new technology has limitations with the inclusion of only a fewer variants in the current state-of-the-art polygenic risk models.¹¹ The advantages of including rare variants in PRS models have yet to be investigated. The number of discoveries of this type of variant is limited by a lack of power for CVDs. There may be new variants discovered with low-frequency variants that confer a high risk of disease in the future with large datasets. Future studies should prioritize data from whole-genome sequencing to facilitate PRS models incorporating common and rare variants, such as a full allelic spectrum of PRS. In the absence of whole-genome data on patients for whom a PRS is to be calculated, genotyping array data can be imputed to whole-genome sequencing data. There is a difference of variants in different population groups. There is insufficient data generation from the Indian population which is unique for early-onset CAD. There is a need for Biobank-based data of CAD patients like the UK BIOBANK. There is a need for randomized trials with earlier identification of high-risk individuals and their follow-up with early intervention outcomes. The application of PRS in pediatric group is not validated yet and needs more studies. However, the clinical efficacy and cost-effectiveness of PRS in the Indian scenario are yet to be fully explored.

Footnotes

Acknowledgment

The author gratefully acknowledges the inspiration of his teachers and staff for their support in writing the review.

Declaration of Conflicting Interests

The author declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Ethical Approval and Informed Consent

As the article is a review of published data only, consent from patients and ethics committee is not required.

Funding

The author received no financial support for the research, authorship, and/or publication of this article.

ORCID iD

Karthik Tummala

References

World Health Organization. Global Health Estimates 2015: deaths by cause, age, sex, by country and by region, 2000-2015. Geneva: World Health Organization; 2016.

Prabhakaran

, Jeemon

, Roy

Cardiovascular diseases in India: current epidemiology and future directions. Circulation . 2016;133(16):1605–1620.

Brindle

, Emberson

, Lampe

, . Predictive accuracy of the Framingham coronary risk score in British men: prospective cohort study. BMJ 2003;327(7426):1267. https://doi.org/10.1136/bmj.327.7426.1267

Sofogianni

, Stalikas

, Antza

, Tziomalos

Cardiovascular risk prediction models and scores in the era of personalized medicine. J Pers Med . 2022;12(7):1180. https://doi.org/10.3390/jpm12071180

Ashley

, Hershberger

, Caleshu

, . on behalf of the American Heart Association Advocacy Coordinating Committee. Genetics and cardiovascular disease: a policy statement from the American Heart Association. Circulation . 2012;126(1):142–157. https://doi.org/10.1161/CIR.0b013e31825b07f8

Lloyd-Jones

, Nam

, D’Agostino

, . Parental cardiovascular disease as a risk factor for cardiovascular disease in middle-aged adults: a prospective study of parents and offspring. JAMA . 2004;291(18):2204–2211. https://doi.org/10.1001/jama.291.18.2204

Zdravkovic

, Wienke

, Pedersen

, Marenberg

, Yashin

, De Faire

Heritability of death from coronary heart disease: a 36-year follow-up of 20 966 Swedish twins. J Intern Med . 2002;252(3):247–254. https://doi.org/10.1046/j.1365-2796.2002.01029.x

Christophersen

, Ravn

, Budtz-Joergensen

, . Familial aggregation of atrial fibrillation: a study in Danish twins. Circ Arrhythm Electrophysiol . 2009;2(4):378–383. https://doi.org/10.1161/CIRCEP.108.786665

Poulsen

, Kyvik

, Vaag

, Beck-Nielsen

Heritability of type II (non-insulin-dependent) diabetes mellitus and abnormal glucose tolerance: a population-based twin study. Diabetologia . 1999;42(2):139–145. https://doi.org/10.1007/s001250051131

10.

Newman

, Selby

, King

, Slemenda

, Fabsitz

, Friedman

GD.

Concordance for type 2 (non-insulin-dependent) diabetes mellitus in male twins. Diabetologia . 1987;30(10):763–768. https://doi.org/10.1007/BF00275741

11.

Khera

, Won

, Peloso

, . Diagnostic yield and clinical utility of sequencing familial hypercholesterolemia genes in patients with severe hypercholesterolemia. J Am Coll Cardiol . 2016;67(22):2578–2589. https://doi.org/10.1016/j.jacc.2016.03.520

12.

Gidding

, Champagne

, de Ferranti

, . on behalf of the American Heart Association Atherosclerosis, Hypertension, and Obesity in Young Committee of Council on Cardiovascular Disease in Young, Council on Cardiovascular and Stroke Nursing, Council on Functional Genomics and Translational Biology, and Council on Lifestyle and Cardiometabolic Health. The agenda for familial hypercholesterolemia: a scientific statement from the American Heart Association [published correction appears in Circulation. 2015;132:e397]. Circulation . 2015;132(22):2167–2192. https://doi.org/10.1161/CIR.0000000000000297

13.

Anik

, Çatli

, Abaci

, Böber

Maturity-onset diabetes of the young (MODY): an update. J Pediatr Endocrinol Metab . 2015;28(3-4):251–263. https://doi.org/10.1515/jpem-2014-0384

14.

Mahida

, Lubitz

, Rienstra

, Milan

, Ellinor

PT.

Monogenic atrial fibrillation as pathophysiological paradigms. Cardiovasc Res . 2011;89(4):692–700. https://doi.org/10.1093/cvr/cvq381

15.

De Stefano

, Leone

Resistance to activated protein C due to mutated factor V as a novel cause of inherited thrombophilia. Haematologica . 1995;80(4):344–356.

16.

Khera

, Chaffin

, Aragam

, . Genome-wide polygenic scores for common diseases identify individuals with risk equivalent to monogenic mutations. Nat Genet . 2018;50(9):1219–1224. https://doi.org/10.1038/s41588-018-0183-z

17.

Mars

, Koskela

, Ripatti

, . Polygenic and clinical risk scores and their impact on age at onset and prediction of cardiometabolic diseases and common cancers. Nat Med . 2020;26(4):549–557. https://doi.org/10.1038/s41591-020-0800-0

18.

Marenberg

, Risch

, Berkman

, Floderus

, de Faire

Genetic susceptibility to death from coronary heart disease in a study of twins. N Engl J Med . 1994;330(15):1041–1046. https://doi.org/10.1056/NEJM199404143301503

19.

Klarin

, Zhu

, Emdin

, . CARDIoGRAMplusC4D Consortium. Genetic analysis in UK Biobank links insulin resistance and transendothelial migration pathways to coronary artery disease. Nat Genet . 2017;49(9):1392–1397. https://doi.org/10.1038/ng.3914

20.

Erdmann

, Kessler

, Munoz Venegas

, Schunkert

A decade of genome-wide association studies for coronary artery disease: the challenges ahead. Cardiovasc Res . 2018;114(9):1241–1257. https://doi.org/10.1093/cvr/cvy084

21.

Webb

, Erdmann

, Stirrups

, . Wellcome Trust Case Control Consortium; MORGAM Investigators; Myocardial Infarction Genetics and CARDIoGRAM Exome Consortia Investigators. Systematic evaluation of pleiotropy identifies 6 further loci associated with coronary artery disease. J Am Coll Cardiol . 2017;69(7):823–836. https://doi.org/10.1016/j.jacc.2016.11.056

22.

Weale

, Riveros-Mckay

, Selzam

, . Validation of an integrated risk tool, including polygenic risk score, for atherosclerotic cardiovascular disease in multiple ethnicities and ancestries. Am J Cardiol . 2021;148:157–164. https://doi.org/10.1016/j.amjcard.2021.02.032

23.

Inouye

, Abraham

, Nelson

, . UK Biobank CardioMetabolic Consortium CHD Working Group. Genomic risk prediction of coronary artery disease in 480,000 adults: implications for primary prevention. J Am Coll Cardiol . 2018;72(16):1883–1893. https://doi.org/10.1016/j.jacc.2018.07.079

24.

Riveros-Mckay

, Weale

, Moore

, . An integrated polygenic tool substantially enhances coronary artery disease prediction. Circ Genom Precis Med . 2021;14(2):e003304. https://doi.org/10.1161/CIRCGEN.120.003304

25.

Khera

, Chaffin

, Zekavat

, . Whole-genome sequencing to characterize monogenic and polygenic contributions in patients hospitalized with early-onset myocardial infarction. Circulation . 2019;139(13):1593–1602. https://doi.org/10.1161/CIRCULATIONAHA.118.035658

26.

Aragam

, Dobbyn

, Judy

, . Limitations of contemporary guidelines for managing patients at high genetic risk of coronary artery disease. J Am Coll Cardiol . 2020;75(22):2769–2780. https://doi.org/10.1016/j.jacc.2020.04.027

27.

Damask

, Steg

, Schwartz

, . Regeneron Genetics Center and the ODYSSEY OUTCOMES Investigators. Patients with high genome-wide polygenic risk scores for coronary artery disease may receive greater clinical benefit from alirocumab treatment in the ODYSSEY OUTCOMES trial. Circulation . 2020;141(8):624–636. https://doi.org/10.1161/CIRCULATIONAHA.119.044434

28.

Mosley

, Gupta

, Tan

, . Predictive accuracy of a polygenic risk score compared with a clinical risk score for incident coronary heart disease. JAMA . 2020;323(7):627–635. https://doi.org/10.1001/jama.2019.21782

29.

Elliott

, Bodinier

, Bond

, . Predictive accuracy of a polygenic risk score-enhanced prediction model vs a clinical risk score for coronary artery disease. JAMA . 2020;323(7):636–645. https://doi.org/10.1001/jama.2019.22241

30.

Patel

, Pallazola

, Dudum

, . Assessment of coronary artery calcium scoring to guide statin therapy allocation according to risk-enhancing factors: the multi-ethnic study of atherosclerosis. JAMA Cardiol . 2021;6(10):1161–1170. https://doi.org/10.1001/jamacardio.2021.2321

31.

Khera

, Emdin

, Drake

, . Genetic risk, adherence to a healthy lifestyle, and coronary disease. N Engl J Med . 2016;375(24):2349–2358. https://doi.org/10.1056/NEJMoa1605086

32.

Said

, Verweij

, van der Harst

Associations of combined genetic and lifestyle risks with incident cardiovascular disease and diabetes in the UK Biobank Study. JAMA Cardiol . 2018;3(8):693–702. https://doi.org/10.1001/jamacardio.2018.1717

33.

Widen

, Junna

, Ruotsalainen

, . How communicating polygenic and clinical risk for atherosclerotic cardiovascular disease impacts health behavior: an observational follow-up study. Circ Genom Precis Med . 2022;15(2):e003459.

34.

Natarajan

, Young

, Stitziel

, . Polygenic risk score identifies subgroup with higher burden of atherosclerosis and greater relative benefit from statin therapy in the primary prevention setting. Circulation . 2017;135(22):2091–2101. https://doi.org/10.1161/CIRCULATIONAHA.116.024436

35.

Mega

, Stitziel

, Smith

, . Genetic risk, coronary heart disease events, and the clinical benefit of statin therapy: an analysis of primary and secondary prevention trials. Lancet . 2015;385(9984):2264–2271. https://doi.org/10.1016/S0140-6736(14)61730-X

36.

, Chen

, Han

, Jiang

, Natarajan

, Zhao

Interactions between enhanced polygenic risk scores and lifestyle for cardiovascular disease, diabetes, and lipid levels. Circ Genom Precis Med . 2021;14(1):e003128. https://doi.org/10.1161/CIRCGEN.120.003128

37.

Natarajan

Polygenic risk scoring for coronary heart disease: the first risk factor. J Am Coll Cardiol . 2018;72(16):1894–1897. https://doi.org/10.1016/j.jacc.2018.08.1041

38.

Arnett

, Blumenthal

, Albert

, . 2019 ACC/AHA guideline on the primary prevention of cardiovascular disease: a report of the American College of Cardiology/American Heart Association Task Force on Clinical Practice Guidelines [published corrections appear in Circulation. 2019;140:e649–e650, Circulation . 2020;141:e60, and Circulation . 2020;141:e774]. Circulation . 2019;140(11):e596–e646. https://doi.org/10.1161/CIR.0000000000000678

39.

Koyama

, Ito

, Terao

, . Population-specific and trans-ancestry genome-wide analyses identify distinct and shared genetic risk loci for coronary artery disease. Nat Genet . 2020;52(11):1169–1177. https://doi.org/10.1038/s41588-020-0705-3

40.

Huang

, Sallah

, Dunca

, . Transferability of genetic loci and polygenic scores for cardiometabolic traits in British Pakistanis and Bangladeshis. medRxiv . Preprint posted online June 24, 2021. https://doi.org/10.1101/2021.06.22.21259323

41.

, Liu

, Cui

, . A polygenic risk score improves risk stratification of coronary artery disease: a large-scale prospective Chinese cohort study. Eur Heart J . 2022;43(18):1702–1711. https://doi.org/10.1093/eurheartj/ehac093

42.

Sun

, Pennells

, Kaptoge

, . Polygenic risk scores in cardiovascular risk prediction: a cohort study and modelling analyses. PLoS Med . 2021;18(1):e1003498. https://doi.org/10.1371/journal.pmed.1003498

43.

Wang

, Menon

, Mishra

, . Validation of a genome-wide polygenic score for coronary artery disease in South Asians. J Am Coll Cardiol . 2020;76(6):703–714. https://doi.org/10.1016/j.jacc.2020.06.024

Polygenic Risk Score in Clinical Practice: A Review

Abstract

Keywords

Introduction

What is a Polygenic Risk Score?

Key Characteristics of the Most Widely Used Risk Prediction Equations.

Coronary Artery Disease

Interpretation of Polygenic Risk Score

Take Home Message

Challenges and Future Directions

Footnotes

Acknowledgment

Declaration of Conflicting Interests

Ethical Approval and Informed Consent

Funding

ORCID iD

References