Combining European and US risk prediction models with polygenic risk scores to refine cardiovascular prevention: the CoLaus|PsyCoLaus Study

被引:8
|
作者
de La Harpe, Roxane [1 ,2 ]
Thorball, Christian W. [3 ,4 ]
Redin, Claire [3 ,4 ]
Fournier, Stephane [2 ,5 ]
Mueller, Olivier [2 ,5 ]
Strambo, Davide [2 ,6 ]
Michel, Patrik [2 ,6 ]
Vollenweider, Peter [1 ,2 ]
Marques-Vidal, Pedro [1 ,2 ]
Fellay, Jacques [3 ,4 ,7 ]
Vaucher, Julien [1 ,2 ]
机构
[1] Lausanne Univ Hosp, Dept Med, Div Internal Med, Rue Bugnon 46, CH-1011 Lausanne, Switzerland
[2] Univ Lausanne, Rue Bugnon 46, CH-1011 Lausanne, Switzerland
[3] Lausanne Univ Hosp, Biomed Data Sci Ctr, Precis Med Unit, Chemin Roches 1A-1B, CH-1010 Lausanne, Switzerland
[4] Univ Lausanne, Chemin Roches 1A-1B, CH-1010 Lausanne, Switzerland
[5] Lausanne Univ Hosp, Heart & Vessel Dept, Div Cardiol, Rue Bugnon 46, CH-1011 Lausanne, Switzerland
[6] Lausanne Univ Hosp, Dept Neurosci, Div Neurol, Rue Bugnon 46, CH-1011 Lausanne, Switzerland
[7] Ecole Polytech Fed Lausanne, Sch Life Sci, Stn 19, CH-1015 Lausanne, Switzerland
基金
瑞士国家科学基金会;
关键词
Adult; Primary prevention; Cardiovascular disease; Genetic predisposition to disease; Risk; Risk assessment; Sensitivity and specificity; ROC curve; Predictive value of tests; Polygenic risk score; CORONARY-ARTERY-DISEASE; VALIDATION; ACCURACY; COMMON; WOMEN;
D O I
10.1093/eurjpc/zwad012
中图分类号
R5 [内科学];
学科分类号
1002 ; 100201 ;
摘要
Lay Summary The aim of this study is to determine whether using polygenic risk scores improves the prediction of atherosclerotic cardiovascular disease risk when combined with clinical scores currently recommended by European and US guidelines on cardiovascular prevention. Aims A polygenic risk score (PRS) has the potential to improve individual atherosclerotic cardiovascular disease (ASCVD) risk assessment. To determine whether a PRS combined with two clinical risk scores, the Systematic COronary Risk Evaluation 2 (SCORE2) and the Pooled Cohort Equation (PCE) improves the prediction of ASCVD. Methods and results Using a population-based European prospective cohort, with 6733 participants at the baseline (2003-2006), the PRS presenting the best predictive accuracy was combined with SCORE2 and PCE to assess their joint performances for predicting ASCVD Discrimination, calibration, Cox proportional hazard regression, and net reclassification index were assessed. : 4218 subjects (53% women; median age, 53.4 years), with 363 prevalent and incident ASCVD, were used to compare four PRSs. The metaGRS_CAD PRS presented the best predictive capacity (AUROC = 0.77) and was used in the following analyses. 3383 subjects (median follow-up of 14.4 years), with 190 first-incident ASCVD, were employed to test ASCVD risk prediction. The changes in C statistic between SCORE2 and PCE models and those combining metaGRS_CAD with SCORE2 and PCE were 0.008 (95% CI, -0.00008-0.02, P = 0.05) and 0.007 (95% CI, 0.005-0.01, P = 0.03), respectively. Reclassification was improved for people at clinically determined intermediate-risk for both clinical scores [NRI of 9.6% (95% CI, 0.3-18.8) and 12.0% (95% CI, 1.5-22.6) for SCORE2 and PCE, respectively]. Conclusion Combining a PRS with clinical risk scores significantly improved the reclassification of risk for incident ASCVD for subjects in the clinically determined intermediate-risk category. Introducing PRSs in clinical practice may refine cardiovascular prevention for subgroups of patients in whom prevention strategies are uncertain.
引用
收藏
页码:561 / 571
页数:11
相关论文
共 50 条
  • [1] Combining European Society of Cardiology and American College of Cardiology/American Heart Association risk prediction model with polygenic risk scores to refine cardiovascular prevention
    De La Harpe, R.
    Thorball, C. W.
    Redin, C.
    Fournier, S.
    Muller, O.
    Strambo, D.
    Michel, P.
    Vollenweider, P.
    Marques-Vidal, P.
    Fellay, J.
    Vaucher, J.
    EUROPEAN HEART JOURNAL, 2022, 43 : 2270 - 2270
  • [2] Use of lipoprotein(a) for refining cardiovascular risk prediction in a low-risk population: The CoLaus/PsyCoLaus study
    Delabays, Benoit
    Marques-Vidal, Pedro
    Kronenberg, Florian
    Waeber, Gerard
    Vollenweider, Peter
    Vaucher, Julien
    EUROPEAN JOURNAL OF PREVENTIVE CARDIOLOGY, 2021, 28 (08) : E18 - E20
  • [3] Risk Prediction Using Polygenic Risk Scores for Prevention of Stroke and Other Cardiovascular Diseases
    Abraham, Gad
    Rutten-Jacobs, Loes
    Inouye, Michael
    STROKE, 2021, 52 (09) : 2983 - 2991
  • [4] Polygenic risk scores in cardiovascular risk prediction: A cohort study and modelling analyses
    Sun, Luanluan
    Pennells, Lisa
    Kaptoge, Stephen
    Nelson, Christopher P.
    Ritchie, Scott C.
    Abraham, Gad
    Arnold, Matthew
    Bell, Steven
    Bolton, Thomas
    Burgess, Stephen
    Dudbridge, Frank
    Guo, Qi
    Sofianopoulou, Eleni
    Stevens, David
    Thompson, John R.
    Butterworth, Adam S.
    Wood, Angela
    Danesh, John
    Samani, Nilesh J.
    Inouye, Michael
    Di Angelantonio, Emanuele
    PLOS MEDICINE, 2021, 18 (01)
  • [5] PREDICTION MODELS FOR CARDIOVASCULAR RISK On validation of cardiovascular risk scores
    Woodward, Mark
    BMJ-BRITISH MEDICAL JOURNAL, 2016, 354
  • [6] A Polygenic Risk Score to Refine Risk Stratification and Prediction for Severe Liver Disease by Clinical Fibrosis Scores
    De Vincentis, Antonio
    Tavaglione, Federica
    Jamialahmadi, Oveis
    Picardi, Antonio
    Incalzi, Raffaele Antonelli
    Valenti, Luca
    Romeo, Stefano
    Vespasiani-Gentilucci, Umberto
    CLINICAL GASTROENTEROLOGY AND HEPATOLOGY, 2022, 20 (03) : 658 - 673
  • [7] PREDICTION MODELS FOR CARDIOVASCULAR RISK On validation of cardiovascular risk scores Reply
    Damen, Johanna A. A. G.
    BMJ-BRITISH MEDICAL JOURNAL, 2016, 354
  • [8] Progress in cardiovascular prevention: from risk charts to polygenic scores and precision prevention
    Boccanelli, Alessandro
    Giampaoli, Simona
    Botta, Giordano
    Vanuzzo, Diego
    GIORNALE ITALIANO DI CARDIOLOGIA, 2021, 22 (08) : 599 - 605
  • [9] Cardiovascular risk prediction using metabolomic biomarkers and polygenic risk scores: a cohort study and modelling analyses
    Ritchie, S.
    Jiang, X.
    Pennells, L.
    Xu, Y.
    Coffey, C.
    Liu, Y.
    Surendran, P.
    Karthikeyan, S.
    Lambert, S.
    Danesh, J.
    Butterworth, A.
    Wood, A.
    Kaptoge, S.
    Di Angelantonio, E.
    Inouye, M.
    EUROPEAN HEART JOURNAL, 2024, 45
  • [10] Cardiovascular Disease Risk Prediction Models and Scores
    Giannakoulas, George
    Doundoulakis, Ioannis
    CURRENT PHARMACEUTICAL DESIGN, 2021, 27 (10) : 1231 - 1231