Evidence map›Paper›PMID 38848106›Full record

ArticleEuropean heart journal2024

Polygenic risk score adds to a clinical risk score in the prediction of cardiovascular disease in a clinical setting.

Nilesh J Samani, Emma Beeston, Chris Greengrass, Fernando Riveros-McKay, Radoslaw Debiec, Daniel Lawday, Qingning Wang, Charley A Budgeon, Peter S Braund, Richard Bramley and 15 more

Abstract read
In one paragraph

Article in European heart journal, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 41 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
41citing papers in PubMed, 3 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

41 citing papers in PubMed, 3 syntheses or guidelines pooled it.

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  15. Clinical utility of polygenic risk scores in cardiovascular disorders.Medizinische Genetik : Mitteilungsblatt des Berufsverbandes Medizinische Genetik e.V · 2026
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

25 authors.

Nilesh J SamaniDepartment of Cardiovascular Sciences, University of Leicester, BHF Cardiovascular Research Centre, Glenfield Hospital, Groby Road, Leicester LE3 9QP, UK.ORCID 0000-0002-3286-8133
Emma BeestonDepartment of Cardiovascular Sciences, University of Leicester, BHF Cardiovascular Research Centre, Glenfield Hospital, Groby Road, Leicester LE3 9QP, UK.
Chris GreengrassDepartment of Cardiovascular Sciences, University of Leicester, BHF Cardiovascular Research Centre, Glenfield Hospital, Groby Road, Leicester LE3 9QP, UK.
Fernando Riveros-McKayGenomics plc, King Charles House, Park End Street, Oxford OX1 1 JD, UK.
Radoslaw DebiecDepartment of Cardiovascular Sciences, University of Leicester, BHF Cardiovascular Research Centre, Glenfield Hospital, Groby Road, Leicester LE3 9QP, UK.ORCID 0000-0003-2292-467X
Daniel LawdayDepartment of Cardiovascular Sciences, University of Leicester, BHF Cardiovascular Research Centre, Glenfield Hospital, Groby Road, Leicester LE3 9QP, UK.
Qingning WangDepartment of Cardiovascular Sciences, University of Leicester, BHF Cardiovascular Research Centre, Glenfield Hospital, Groby Road, Leicester LE3 9QP, UK.
Charley A BudgeonDepartment of Cardiovascular Sciences, University of Leicester, BHF Cardiovascular Research Centre, Glenfield Hospital, Groby Road, Leicester LE3 9QP, UK.
Peter S BraundDepartment of Cardiovascular Sciences, University of Leicester, BHF Cardiovascular Research Centre, Glenfield Hospital, Groby Road, Leicester LE3 9QP, UK.
Richard BramleyDepartment of Cardiovascular Sciences, University of Leicester, BHF Cardiovascular Research Centre, Glenfield Hospital, Groby Road, Leicester LE3 9QP, UK.
Shireen KharodiaDepartment of Cardiovascular Sciences, University of Leicester, BHF Cardiovascular Research Centre, Glenfield Hospital, Groby Road, Leicester LE3 9QP, UK.
Michelle NewtonDepartment of Cardiovascular Sciences, University of Leicester, BHF Cardiovascular Research Centre, Glenfield Hospital, Groby Road, Leicester LE3 9QP, UK.
Andrea MarshallDepartment of Cardiovascular Sciences, University of Leicester, BHF Cardiovascular Research Centre, Glenfield Hospital, Groby Road, Leicester LE3 9QP, UK.
Andre KrzeminskiAlbany House Medical Centre, Wellingborough NN8 4RW, UK.
Azhar ZafarDiabetes Research Centre, University of Leicester, Leicester General Hospital, Leicester LE5 4PW, UK.
Anuj ChahalSouth Leicestershire Medical Group, Kibworth Beauchamp LE8 0LG, UK.
Amadeeep HeerLakeside Healthcare, Corby NN17 2UR, UK.
Kamlesh KhuntiNIHR Leicester Biomedical Research Centre, Glenfield Hospital, Groby Road, Leicester LE3 9QP, UK.
Nitin JoshiWillowbrook Medical Centre, Leicester LE5 2NL, UK.
Mayur LakhaniDepartment of Health Sciences, University of Leicester, Leicester LE1 7RH, UK.
Azhar FarooqiDepartment of Health Sciences, University of Leicester, Leicester LE1 7RH, UK.
Vincent PlagnolGenomics plc, King Charles House, Park End Street, Oxford OX1 1 JD, UK.
Peter DonnellyGenomics plc, King Charles House, Park End Street, Oxford OX1 1 JD, UK.
Michael E WealeGenomics plc, King Charles House, Park End Street, Oxford OX1 1 JD, UK.
Christopher P NelsonDepartment of Cardiovascular Sciences, University of Leicester, BHF Cardiovascular Research Centre, Glenfield Hospital, Groby Road, Leicester LE3 9QP, UK.

Funding

Biomedical Research Centre BRC-1215-20010British Heart Foundation CH/03/001NIHR Applied Research Collaboration East Midlands NIHR200171UK National Institute for Health Research
6 · The paper itself

Abstract

BACKGROUND AND

aimsA cardiovascular disease polygenic risk score (CVD-PRS) can stratify individuals into different categories of cardiovascular risk, but whether the addition of a CVD-PRS to clinical risk scores improves the identification of individuals at increased risk in a real-world clinical setting is unknown.

methodsThe Genetics and the Vascular Health Check Study (GENVASC) was embedded within the UK National Health Service Health Check (NHSHC) programme which invites individuals between 40-74 years of age without known CVD to attend an assessment in a UK general practice where CVD risk factors are measured and a CVD risk score (QRISK2) is calculated. Between 2012-2020, 44,141 individuals (55.7% females, 15.8% non-white) who attended an NHSHC in 147 participating practices across two counties in England were recruited and followed. When 195 individuals (cases) had suffered a major CVD event (CVD death, myocardial infarction or acute coronary syndrome, coronary revascularisation, stroke), 396 propensity-matched controls with a similar risk profile were identified, and a nested case-control genetic study undertaken to see if the addition of a CVD-PRS to QRISK2 in the form of an integrated risk tool (IRT) combined with QRISK2 would have identified more individuals at the time of their NHSHC as at high risk (QRISK2 10-year CVD risk of ≥10%), compared with QRISK2 alone.

resultsThe distribution of the standardised CVD-PRS was significantly different in cases compared with controls (cases mean score .32; controls, -.18, P = 8.28×10-9). QRISK2 identified 61.5% (95% confidence interval [CI]: 54.3%-68.4%) of individuals who subsequently developed a major CVD event as being at high risk at their NHSHC, while the combination of QRISK2 and IRT identified 68.7% (95% CI: 61.7%-75.2%), a relative increase of 11.7% (P = 1×10-4). The odds ratio (OR) of being up-classified was 2.41 (95% CI: 1.03-5.64, P = .031) for cases compared with controls. In individuals aged 40-54 years, QRISK2 identified 26.0% (95% CI: 16.5%-37.6%) of those who developed a major CVD event, while the combination of QRISK2 and IRT identified 38.4% (95% CI: 27.2%-50.5%), indicating a stronger relative increase of 47.7% in the younger age group (P = .001). The combination of QRISK2 and IRT increased the proportion of additional cases identified similarly in women as in men, and in non-white ethnicities compared with white ethnicity. The findings were similar when the CVD-PRS was added to the atherosclerotic cardiovascular disease pooled cohort equations (ASCVD-PCE) or SCORE2 clinical scores.

conclusionsIn a clinical setting, the addition of genetic information to clinical risk assessment significantly improved the identification of individuals who went on to have a major CVD event as being at high risk, especially among younger individuals. The findings provide important real-world evidence of the potential value of implementing a CVD-PRS into health systems.

Indexed as

Cardiovascular DiseasesAdultAgedCase-Control StudiesFemaleGenetic Risk ScoreHeart Disease Risk FactorsHumansMaleMiddle AgedMultifactorial InheritanceRisk AssessmentRisk FactorsASCVD-PCECVD risk assessmentPolygenic risk scoresQRISK2SCORE2

Identifiers

PMID38848106
PMCPMC11379490

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.