Polygenic risk scores in cardiovascular risk prediction: A cohort study and modelling analyses

Abstract

Background Polygenic risk scores (PRSs) can stratify populations into cardiovascular disease (CVD) risk groups. We aimed to quantify the potential advantage of adding information on PRSs to conventional risk factors in the primary prevention of CVD. Methods and findings Using data from UK Biobank on 306,654 individuals without a history of CVD and not on lipid-lowering treatments (mean age [SD]: 56.0 [8.0] years; females: 57%; median follow-up: 8.1 years), we calculated measures of risk discrimination and reclassification upon addition of PRSs to risk factors in a conventional risk prediction model (i.e., age, sex, systolic blood pressure, smoking status, history of diabetes, and total and high-density lipoprotein cholesterol). We then modelled the implications of initiating guideline-recommended statin therapy in a primary care setting using incidence rates from 2.1 million individuals from the Clinical Practice Research Datalink. The C-index, a measure of risk discrimination, was 0.710 (95% CI 0.703–0.717) for a CVD prediction model containing conventional risk predictors alone. Addition of information on PRSs increased the C-index by 0.012 (95% CI 0.009–0.015), and resulted in continuous net reclassification improvements of about 10% and 12% in cases and non-cases, respectively. If a PRS were assessed in the entire UK primary care population aged 40–75 years, assuming that statin therapy would be initiated in accordance with the UK National Institute for Health and Care Excellence guidelines (i.e., for persons with a predicted risk of ≥10% and for those with certain other risk factors, such as diabetes, irrespective of their 10-year predicted risk), then it could help prevent 1 additional CVD event for approximately every 5,750 individuals screened. By contrast, targeted assessment only among people at intermediate (i.e., 5% to <10%) 10-year CVD risk could help prevent 1 additional CVD event for approximately every 340 individuals screened. Such a targeted strategy could help prevent 7% more CVD events than conventional risk prediction alone. Potential gains afforded by assessment of PRSs on top of conventional risk factors would be about 1.5-fold greater than those provided by assessment of C-reactive protein, a plasma biomarker included in some risk prediction guidelines. Potential limitations of this study include its restriction to European ancestry participants and a lack of health economic evaluation. Conclusions Our results suggest that addition of PRSs to conventional risk factors can modestly enhance prediction of first-onset CVD and could translate into population health benefits if used at scale.

Transferability of genetic loci and polygenic scores for cardiometabolic traits in British Pakistanis and Bangladeshis

“…Polygenic scores (PGSs) for CAD have been shown to have predictive value over risk scores based on clinical factors alone 10,11,42,[50][51][52][53][54] . To assess the transferability of PGSs for cardiometabolic traits derived from EUR populations into BPB individuals, we compared predictive performance in G&H to that in EUR individuals from eMERGE.…”

“…Previous studies in UKBB EUR samples reported similar improvement, with NRI estimates of 3.5% (95% CI: 2.4-4.5%) 10 and 3.7% (95% CI: 3.0-4.4%) 9 in two different analyses using CAD as the outcome. However, these NRI estimates are probably inflated by using UKBB samples that are healthier than the general UK population without recalibrating risk to a primary care setting 11 . In G&H, the PGS improved identification of high-risk individuals in people younger than 55 years, and correctly downclassified low-risk individuals in people older than 55 years, both of which are important in a clinical setting.…”

“…Furthermore, the potential clinical utility of a CAD PGS in a real-world healthcare system is largely unknown, since previous studies have mostly examined research cohorts composed of volunteers who are healthier and wealthier than average (e.g. UK Biobank [8][9][10][11][12] ).…”

Sex-specific survival bias and interaction modeling in coronary artery disease risk prediction

“…There is significant additive value of integrating genome-wide genetic data to enhance risk prediction using polygenic scores (PGS) (7-9(10)). Additionally, individuals with a PGS in the highest 8% of score distribution have a risk of CAD comparable to having monogenic familial hypercholesterolemia (3-fold increased risk) (10). To date, investigators have shown that adding PGS (11)(12)(13)(14)(15)(16)(17) to standard risk prediction algorithms enhances the power of the model to predict CAD, consistent with the estimated contribution of genetic factors responsible for 40-50% of CAD risk (18).…”

Body Mass Index and Birth Weight Improve Polygenic Risk Score for Type 2 Diabetes

“…Similarly, augmenting PRS with additional information such as BMI, and lab results such as HDL and LDL measures improved prediction power for T2D [ 37 ]. Similarly, augmenting PRS by traditional measures for cardiovascular disease risk modestly enhanced its prediction power [ 38 ]. In addition, a recent study added mortality risk factors to disease PRS to mark individuals with higher mortality risk [ 39 ].…”