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. 2021 Jan 14;18(1):e1003498.
doi: 10.1371/journal.pmed.1003498. eCollection 2021 Jan.

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

Luanluan Sun  1 , Lisa Pennells  1 , Stephen Kaptoge  1 , Christopher P Nelson  2 , Scott C Ritchie  1   3 , Gad Abraham  1   3 , Matthew Arnold  1 , Steven Bell  1 , Thomas Bolton  1 , Stephen Burgess  1 , Frank Dudbridge  2   4 , Qi Guo  1 , Eleni Sofianopoulou  1 , David Stevens  1 , John R Thompson  2 , Adam S Butterworth  1 , Angela Wood  1 , John Danesh  1   5 , Nilesh J Samani  2   4 , Michael Inouye  1   3   6 , Emanuele Di Angelantonio  1
Affiliations

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

Luanluan Sun et al. PLoS Med. .

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.

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Conflict of interest statement

I have read the journal’s policy and the authors of this manuscript have the following competing interests: SK is funded by grants to institution from: British Heart Foundation, UK Medical Research Council, UK National Institute of Health Research, Cambridge Biomedical Research Centre. SB is a paid statistical reviewer for PLOS Medicine. ASB received grants outside of this work from AstraZeneca, Biogen, Bioverativ, Merck, Novartis and Sanofi, as well as personal fees from Novartis. JD serves on the International Cardiovascular and Metabolic Advisory Board for Novartis (since 2010), the Steering Committee of UK Biobank (since 2011), the MRC International Advisory Group (ING) member, London (since 2013), the MRC High Throughput Science ‘Omics Panel Member, London (since 2013), the Scientific Advisory Committee for Sanofi (since 2013), the International Cardiovascular and Metabolism Research and Development Portfolio Committee for Novartis and the Astra Zeneca Genomics Advisory Board (2018).

Figures

Fig 1
Fig 1. Study design and overview.
CHD, coronary heart disease; PRS, polygenic risk score.
Fig 2
Fig 2. Adjusted hazard ratios of conventional cardiovascular risk factors and polygenic risk scores for first-onset cardiovascular outcomes.
Hazard ratios (HRs) were estimated using Cox regression, stratified by study centre and sex, and adjusted for age at baseline, smoking status, history of diabetes, systolic blood pressure, total cholesterol, and high-density lipoprotein (HDL) cholesterol levels, where appropriate. For continuous variables, HRs are shown per SD higher of each predictor to facilitate comparison. For categorical variables, HRs are shown for men versus women, for patients with diabetes versus without, and for current smokers versus others.
Fig 3
Fig 3. Incremental predictive ability of polygenic risk scores and C-reactive protein for cardiovascular disease, above conventional risk factors.
Conventional risk factors included age at baseline, sex, smoking status, history of diabetes, systolic blood pressure, total cholesterol, and high-density lipoprotein cholesterol. C-index and related changes were estimated using Cox regression, stratified by study centre and sex, adjusted for age at baseline, smoking status, history of diabetes, systolic blood pressure, total cholesterol, and high-density lipoprotein cholesterol. 95% confidence intervals (CIs) were estimated using the efficient jackknife approach.
Fig 4
Fig 4. Incremental predictive ability of polygenic risk scores (PRSs) for cardiovascular disease (CVD) outcomes, beyond conventional risk predictors, across different baseline population characteristics.
The base model included information on the conventional risk factors, i.e., age at baseline, sex, smoking status, history of diabetes, systolic blood pressure, total cholesterol, and high-density lipoprotein (HDL) cholesterol, with stratification by study centre and sex, where appropriate. The prediction model within each subgroup was constructed using coefficients estimated among the entire population.
Fig 5
Fig 5. Estimated public health impact with targeted assessment of polygenic risk scores among 100,000 UK adults in a primary care setting.
CVD, cardiovascular disease; LDL, low-density lipoprotein; PRS, polygenic risk score.
See this image and copyright information in PMC

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