Clinical Utility of Lipoprotein(a) and LPA Genetic Risk Score in Risk Prediction of Incident Atherosclerotic Cardiovascular Disease

Background The 10-year Atherosclerotic Cardiovascular Disease (ASCVD) risk score is the standard approach to predict risk of incident cardiovascular events and recently, addition of CAD polygenic scores (PGSCAD) have been evaluated. Although age and sex strongly predict the risk of CAD, their interaction with genetic risk prediction has not been systematically examined. Objectives This study performed an in-depth evaluation of age and sex effects in genetic CAD risk prediction. Methods The population-based Norwegian HUNT2 cohort of 51,036 individuals was used as the primary dataset. Findings were replicated in the UK Biobank (372,410 individuals). Models for 10-year CAD risk were fitted using Cox proportional hazards and Harrells concordance index, sensitivity, and specificity were compared. Results Inclusion of age and sex interactions of PGSCAD to the prediction models increased C-index and sensitivity likely countering the observed survival bias in the baseline. The sensitivity for females was lower than males in all models including genetic information. The two-step approach identified a total of 82.6% of incident CAD cases (74.1% by ASCVD risk score and an additional 8.5% by the PGSCAD interaction model). Conclusion These findings highlight the importance and complexity of genetic risk in predicting CAD. There is a need for modeling age and sex-interactions terms with polygenic scores to optimize detection of individuals at high-risk, those who warrant preventive interventions. Sex-specific studies are needed to understand and estimate CAD risk with genetic information.

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Analysis of a cardiovascular disease genetic risk score in the Diabetes Heart Study

Acta Diabetologica ◽

10.1007/s00592-015-0720-5 ◽

2015 ◽

Vol 52 (4) ◽

pp. 743-751 ◽

Cited By ~ 8

Author(s):

Laura M. Raffield ◽

Amanda J. Cox ◽

J. Jeffrey Carr ◽

Barry I. Freedman ◽

Pamela J. Hicks ◽

...

Keyword(s):

Cardiovascular Disease ◽

Risk Score ◽

Genetic Risk ◽

Genetic Risk Score ◽

Heart Study ◽

Diabetes Heart Study

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Abstract 175: Performance of a Multilocus Genetic Risk Score Derived from Top Signals in the Cardiogram+C4d Consortium in Predicting Incident Coronary Disease in the Atherosclerosis Risk in Communities Study

Arteriosclerosis Thrombosis and Vascular Biology ◽

10.1161/atvb.32.suppl_1.a175 ◽

2012 ◽

Vol 32 (suppl_1) ◽

Author(s):

Themistocles L Assimes ◽

Benjamin Goldstein ◽

Keyword(s):

Risk Prediction ◽

Risk Score ◽

Genetic Risk ◽

Association Studies ◽

Genetic Risk Score ◽

European Ancestry ◽

Genome Wide Association Studies ◽

Genome Wide ◽

Genotype Information ◽

Atherosclerosis Risk

Genome wide association studies (GWAS) to date have identified 30 CAD susceptibility loci but the ability to use this information to improve risk prediction remains limited. A meta-analysis of the GWAS and Cardio Metabochip data produced by the CARDIoGRAM+C4D consortium representing 63,253 cases and 126,820 controls has identified 1885 SNPs passing a False Discovery Rate (FDR) threshold of 0.5%. We hypothesized that an expanded multi locus genetic risk score (GRS) incorporating genotype information at all loci below an FDR of 0.5% would perform better than a GRS restricted to 42 loci reaching genome wide significance and tested this hypothesis in subjects of European ancestry participating in the Atherosclerosis Risk in the Community (ARIC) study. Models testing the GRS were either minimally (age and sex) or fully adjusted for traditional risk factors (TRFs). The Figure shows the hazard ratio (HZ) and 95% CI for incident events comparing each quintile of GRS to the middle quintile. The GRS including genotype information at all loci with an FDR of 0.5% noticeably improves risk prediction over the GRS restricted to genome wide significant loci in both the minimally and fully adjusted models based on several metrics including i) HR per GRS quintile, ii) the HR per SD of the GRS, and iii) the logistic regression pseudo R2, and iv) the c statistic. The HR per GRS quintile and per SD of GRS were all lower in the fully adjusted models compared to the respective minimally adjusted models but the reduction of the HR was more striking for the models that tested the more expansive GRS. These findings suggest that a larger proportion of novel GWAS CAD loci are mediating their effects through TRFs. While these findings demonstrate some progress in risk prediction using GWAS loci, both the limited and the expanded GRS continues to explain a relatively small proportion of the overall variance compared to TRF. Thus, the clinical utility of a CAD GRS remains to be determined.

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