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

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posted on 2021-06-25, 14:24 authored by Luanluan Sun, Lisa Pennells, Stephen Kaptoge, Christopher P Nelson, Scott C Ritchie, Gad Abraham, Matthew Arnold, Steven Bell, Thomas Bolton, Stephen Burgess, Frank Dudbridge, Qi Guo, Eleni Sofianopoulou, David Stevens, John R Thompson, Adam S Butterworth, Angela Wood, John Danesh, Nilesh J Samani, Michael Inouye, Emanuele Di Angelantonio

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.

Funding

This work was supported by core funding from the UK Medical Research Council (MR/L003120/1), the British Heart Foundation (RG/13/13/30194; RG/18/13/33946), and the National Institute for Health Research (NIHR) (Cambridge Biomedical Research Centre at the Cambridge University Hospitals NHS Foundation Trust and NIHR Leicester Biomedical Research Centre). This work was supported by Health Data Research UK, which is funded by the the UK Medical Research Council, the Engineering and Physical Sciences Research Council, the Economic and Social Research Council, the Department of Health and Social Care (England), the Chief Scientist Office of the Scottish Government Health and Social Care Directorates, the Health and Social Care Research and Development Division (Welsh Government), the Public Health Agency (Northern Ireland), the British Heart Foundation, and Wellcome. Luanluan Sun, Lisa Pennells, Stephen Kaptoge, and Matthew Arnold are funded by a British Heart Foundation Programme Grant (RG/18/13/33946). Christopher P. Nelson is funded by a British Heart Foundation Grant (SP/16/4/32697). Scott Ritchie, Mike Inouye, and Stephen Burgess are funded by the National Institute for Health Research (Cambridge Biomedical Research Centre at the Cambridge University Hospitals NHS Foundation Trust). David Stevens was funded by the National Institute for Health Research (Cambridge Biomedical Research Centre at the Cambridge University Hospitals NHS Foundation Trust). Thomas Bolton is funded by the NIHR Blood and Transplant Research Unit in Donor Health and Genomics (NIHR BTRU-2014-10024). Steven Bell was funded by the NIHR Blood and Transplant Research Unit in Donor Health and Genomics (NIHR BTRU-2014-10024). Angela Wood is supported by a BHF-Turing Cardiovascular Data Science Award and by the EC-Innovative Medicines Initiative (BigData@Heart). Professor John Danesh holds a British Heart Foundation Professorship and a National Institute for Health Research Senior Investigator Award.

History

Citation

PLoS Med 18(1): e1003498

Author affiliation

Department of Cardiovascular Sciences; NIHR Leicester Biomedical Research Centre

Version

  • VoR (Version of Record)

Published in

PLoS Medicine

Volume

18

Issue

1

Publisher

Public Library of Science (PLoS)

issn

1549-1277

eissn

1549-1676

Acceptance date

2020-12-14

Copyright date

2021

Available date

2021-06-25

Spatial coverage

United States

Language

eng

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