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Integration of a polygenic score into guideline-recommended prediction of cardiovascular disease
Ling Li1,2,3, Shichao Pang1, Fabian Starnecker1,2
1Department of Cardiology, Deutsches Herzzentrum München, Technische Universität München, Lazarettstr. 36, Munich 80636, Germany.
Insights
Combining polygenic risk scores (PRS) with clinical tools like SCORE2 improves cardiovascular disease (CVD) risk prediction. This multiplicative model enhances precision by integrating genetic and clinical risk factors for better outcomes.
Area of Science:
- Cardiovascular Medicine
- Genetics
- Epidemiology
Background:
- Guideline-recommended cardiovascular disease (CVD) risk prediction tools like SCORE2 have limitations.
- The optimal integration of polygenic risk scores (PRS) with existing clinical risk assessment tools remains unclear.
Purpose of the Study:
- To evaluate the best method for combining a polygenic risk score (PRS) with guideline-recommended cardiovascular disease (CVD) risk prediction tools.
- To assess the clinical utility of a multiplicative model integrating SCORE2 and PRS for CVD risk stratification.
Main Methods:
- Calculated a coronary artery disease (CAD) PRS in UK Biobank participants (n=432,981) and validated in Framingham/ARIC (n=10,757).
- Assessed the relationship between PRS and CVD odds ratios (ORs) across clinical risk strata.
- Tested a multiplicative model (SCORE2 × PRS-factor) for risk reclassification and predictive improvement.
Main Results:
- Polygenic risk scores (PRS) and SCORE2 demonstrated independent contributions to cardiovascular disease (CVD) risk.
- A multiplicative model (SCORE2 × PRS-factor) significantly reclassified intermediate-risk individuals to high-risk categories.
- Reclassification by PRS-factor identified individuals with a two-fold higher incidence of CVD compared to those remaining at intermediate risk.
Conclusions:
- Absolute CVD risk (clinical score) and relative genetic risk (PRS) provide complementary information.
- A simple multiplicative model combining clinical risk scores and PRS can enhance the precision of CVD risk prediction tools.
- This integrated approach offers improved identification of individuals at high risk for incident CVD.
Background And Aims:
It is not clear how a polygenic risk score (PRS) can be best combined with guideline-recommended tools for cardiovascular disease (CVD) risk prediction, e.g. SCORE2.
Methods:
A PRS for coronary artery disease (CAD) was calculated in participants of UK Biobank (n = 432 981). Within each tenth of the PRS distribution, the odds ratios (ORs)-referred to as PRS-factor-for CVD (i.e. CAD or stroke) were compared between the entire population and subgroups representing the spectrum of clinical risk. Replication was performed in the combined Framingham/Atherosclerosis Risk in Communities (ARIC) populations (n = 10 757). The clinical suitability of a multiplicative model 'SCORE2 × PRS-factor' was tested by risk reclassification.
Results:
In subgroups with highly different clinical risks, CVD ORs were stable within each PRS tenth. SCORE2 and PRS showed no significant interactive effects on CVD risk, which qualified them as multiplicative factors: SCORE2 × PRS-factor = total risk. In UK Biobank, the multiplicative model moved 9.55% of the intermediate (n = 145 337) to high-risk group increasing the individuals in this category by 56.6%. Incident CVD occurred in 8.08% of individuals reclassified by the PRS-factor from intermediate to high risk, which was about two-fold of those remained at intermediate risk (4.08%). Likewise, the PRS-factor shifted 8.29% of individuals from moderate to high risk in Framingham/ARIC.
Conclusions:
This study demonstrates that absolute CVD risk, determined by a clinical risk score, and relative genetic risk, determined by a PRS, provide independent information. The two components may form a simple multiplicative model improving precision of guideline-recommended tools in predicting incident CVD.
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