Predictive Performance of a Polygenic Risk Score for Incident Ischemic Stroke in a Healthy Older Population

Abstract

Background and Purpose: Polygenic risk scores (PRSs) can be used to predict ischemic stroke (IS). However, further validation of PRS performance is required in independent populations, particularly older adults in whom the majority of strokes occur. Methods: We predicted risk of incident IS events in a population of 12 792 healthy older individuals enrolled in the ASPREE trial (Aspirin in Reducing Events in the Elderly). The PRS was calculated using 3.6 million genetic variants. Participants had no previous history of cardiovascular events, dementia, or persistent physical disability at enrollment. The primary outcome was IS over 5 years, with stroke subtypes as secondary outcomes. A multivariable model including conventional risk factors was applied and reevaluated after adding PRS. Area under the curve and net reclassification were evaluated. Results: At baseline, mean population age was 75 years. In total, 173 incident IS events occurred over a median follow-up of 4.7 years. When PRS was added to the multivariable model as a continuous variable, it was independently associated with IS (hazard ratio, 1.41 [95% CI, 1.20-1.65] per SD of the PRS; P<0.001). The PRS alone was a better discriminator for IS events than most conventional risk factors. PRS as a categorical variable was a significant predictor in the highest tertile (hazard ratio, 1.74; P=0.004) compared with the lowest. The area under the curve of the conventional model was 66.6% (95% CI, 62.2-71.1) and after inclusion of the PRS, improved to 68.5 ([95% CI, 64.0-73.0] P=0.095). In subgroup analysis, the continuous PRS remained an independent predictor for large vessel and cardioembolic stroke subtypes but not for small vessel stroke. Reclassification was improved, as the continuous net reclassification index after adding PRS to the conventional model was 0.25 (95% CI, 0.17-0.43). Conclusions: PRS predicts incident IS in a healthy older population but only moderately improves prediction over conventional risk factors. Registration: URL: https://www.clinicaltrials.gov; Unique identifier: NCT01038583.