Combining Clinical and Polygenic Risk Improves Stroke Prediction Among Individuals With Atrial Fibrillation

Area of Science:

• Cardiology
• Genetics
• Stroke Medicine

Background:

• Atrial fibrillation (AF) significantly increases ischemic stroke risk, with a heritable component.
• Current risk tools like CHA₂DS₂-VASc do not incorporate genetic or family history data.
• There is a need for improved risk stratification in AF patients to prevent stroke.

Purpose of the Study:

• To develop and validate a polygenic risk score (PRS) for predicting ischemic stroke in patients with AF.
• To assess the added value of PRS, alone and integrated with clinical factors, for stroke risk prediction.
• To compare the predictive performance of an integrated PRS and clinical risk tool against existing methods.

Main Methods:

• Constructed a PRS using over half a million genetic variants from a large genome-wide association study in Europeans.
• Externally validated the PRS in the UK Biobank dataset, assessing its performance independently and integrated with clinical risk factors.
• Compared the integrated tool's predictive ability against the CHA₂DS₂-VASc score using Net Reclassification Index and model fit.

Main Results:

• The integrated PRS and clinical risk factors tool demonstrated the greatest predictive ability for ischemic stroke in AF patients.
• The integrated tool significantly improved risk classification compared to CHA₂DS₂-VASc (Net Reclassification Index 2.3%, P=0.002).
• Independently, PRS was a significant predictor of stroke, and it was uncorrelated with clinical risk factors.

Conclusions:

• Polygenic risk scores are significantly associated with ischemic stroke risk in patients with AF.
• Integrating PRS with clinical factors offers superior predictive ability for stroke risk in AF.
• Despite improvements, predicting stroke in AF remains a complex clinical challenge.