Can polygenic risk scores improve CAD risk prediction?

Recent advances in polygenic risk scores (PRSs) have sparked a great interest in enhancing disease risk prediction by using the information on millions of variants across the genome [ 11, 12, 13, 14 ]. However, population health utility of PRSs in CAD risk prediction is controversial.

Can a risk prediction model improve reclassification performance?

Several studies have shown that PRSs can improve risk prediction accuracy for incident and prevalent CAD cases compared with individual conventional risk factors [ 15, 16] and combining risk prediction models (like PCE) with PRS improves the performance in terms of net reclassification improvement [ 17 ].

Can socio-demographic factors improve the clinical utility of risk prediction models?

However, incorporating socio-demographic, family history, lifestyle, and other environmental variables may further improve the performance of the risk prediction model. Future research that incorporates these factors may further improve the clinical utility of risk models.

Does CAD PRS improve risk reclassification?

The addition of the integrated CAD PRS to the PCE resulted in a statistically significant improvement in predictive accuracy for incident CAD, especially in individuals under the age of 55 years old in the White British population. It was also associated with moderate improvement in risk reclassification across all subgroups.