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dc.contributor.authorSun, Luanluan
dc.contributor.authorPennells, Lisa
dc.contributor.authorKaptoge, Stephen
dc.contributor.authorNelson, Christopher P.
dc.contributor.authorRitchie, Scott C.
dc.contributor.authorAbraham, Gad
dc.contributor.authorArnold, Matthew
dc.contributor.authorBell, Steven
dc.contributor.authorBolton, Thomas
dc.contributor.authorBurgess, Stephen
dc.contributor.authorDudbridge, Frank
dc.contributor.authorGuo, Qi
dc.contributor.authorSofianopoulou, Eleni
dc.contributor.authorStevens, David
dc.contributor.authorThompson, John R.
dc.contributor.authorButterworth, Adam S.
dc.contributor.authorWood, Angela
dc.contributor.authorDanesh, John
dc.contributor.authorSamani, Nilesh J.
dc.contributor.authorInouye, Michael
dc.contributor.authorDi Angelantonio, Emanuele
dc.date.accessioned2021-01-15T04:15:16Z
dc.date.available2021-01-15T04:15:16Z
dc.date.issued2021-01-14
dc.date.submitted2020-01-31
dc.identifier.issn1549-1277
dc.identifier.otherpmedicine-d-20-00284
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/316211
dc.description.abstractBackground: 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.
dc.languageen
dc.publisherPublic Library of Science
dc.rightsAttribution 4.0 International (CC BY 4.0)en
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en
dc.subjectResearch Article
dc.subjectMedicine and health sciences
dc.subjectBiology and life sciences
dc.titlePolygenic risk scores in cardiovascular risk prediction: A cohort study and modelling analyses
dc.typeArticle
dc.date.updated2021-01-15T04:15:15Z
prism.issueIdentifier1
prism.publicationNamePLOS Medicine
prism.volume18
dc.identifier.doi10.17863/CAM.63319
dcterms.dateAccepted2020-12-14
rioxxterms.versionofrecord10.1371/journal.pmed.1003498
rioxxterms.versionVoR
rioxxterms.licenseref.urihttp://creativecommons.org/licenses/by/4.0/
datacite.contributor.supervisoreditor: Hindy, George
dc.contributor.orcidPennells, Lisa [0000-0002-8594-3061]
dc.contributor.orcidNelson, Christopher P. [0000-0001-8025-2897]
dc.contributor.orcidRitchie, Scott C. [0000-0002-8454-9548]
dc.contributor.orcidArnold, Matthew [0000-0001-6339-1115]
dc.contributor.orcidBell, Steven [0000-0001-6774-3149]
dc.contributor.orcidBurgess, Stephen [0000-0001-5365-8760]
dc.contributor.orcidDudbridge, Frank [0000-0002-8817-8908]
dc.contributor.orcidStevens, David [0000-0001-8874-7122]
dc.contributor.orcidThompson, John R. [0000-0003-4819-1611]
dc.contributor.orcidWood, Angela [0000-0002-7937-304X]
dc.contributor.orcidSamani, Nilesh J. [0000-0002-3286-8133]
dc.contributor.orcidInouye, Michael [0000-0001-9413-6520]
dc.contributor.orcidDi Angelantonio, Emanuele [0000-0001-8776-6719]
dc.identifier.eissn1549-1676


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Attribution 4.0 International (CC BY 4.0)
Except where otherwise noted, this item's licence is described as Attribution 4.0 International (CC BY 4.0)