Polygenic risk scores in cardiovascular risk prediction: A cohort study and modelling analyses.
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Sun, L., Pennells, L., Kaptoge, S., Nelson, C. P., Ritchie, S., Abraham, G., Arnold, M., et al. (2021). Polygenic risk scores in cardiovascular risk prediction: A cohort study and modelling analyses.. PLoS medicine, 18 (1), e1003498. https://doi.org/10.1371/journal.pmed.1003498
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 (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, total- and HDL-cholesterol). We then modelled 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 by about 10% and 12% in cases and controls, respectively. If PRS was to be assessed in the entire UK primary care population aged 40-75, 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 one additional CVD event for every approximately 5750 individuals screened. By contrast, targeted assessment only among people at intermediate (i.e., 5-10%) 10-year CVD risk could help prevent one additional CVD event for 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.
Humans, Cardiovascular Diseases, Incidence, Risk Assessment, Cohort Studies, Adult, Aged, Middle Aged, Female, Male, Biomarkers, United Kingdom, Heart Disease Risk Factors
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 [Cambridge Biomedical Research Centre at the Cambridge University Hospitals NHS Foundation Trust] [*]. This work was supported by Health Data Research UK, which is funded by the UK Medical Research Council, Engineering and Physical Sciences Research Council, Economic and Social Research Council, Department of Health and Social Care (England), Chief Scientist Office of the Scottish Government Health and Social Care Directorates, Health and Social Care Research and Development Division (Welsh Government), Public Health Agency (Northern Ireland), British Heart Foundation and Wellcome. LuanLuan Sun, Lisa Pennells, Stephen Kaptoge and Matt Arnold are funded by a British Heart Foundation Programme Grant (RG/18/13/33946). 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
British Heart Foundation (RG/13/13/30194)
British Heart Foundation (RG/18/13/33946)
Cambridge University Hospitals NHS Foundation Trust (CUH) (unknown)
British Heart Foundation (CH/12/2/29428)
Medical Research Council (MC_UU_00002/7)
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External DOI: https://doi.org/10.1371/journal.pmed.1003498
This record's URL: https://www.repository.cam.ac.uk/handle/1810/315315
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