Genetic risk score for intracranial aneurysms to predict aneurysmal subarachnoid hemorrhage and identify associations with patient characteristics

Abstract

BackgroundRupture of an intracranial aneurysm (IA) causes aneurysmal subarachnoid hemorrhage (ASAH). There is no accurate prediction model for IA or ASAH in the general population. Recent discoveries in genetic risk for IA may allow improved risk prediction.MethodsWe constructed a genetic risk score including genetic association data for IA and 17 traits related to IA (a metaGRS) to predict ASAH incidence and IA presence. The metaGRS was trained in 1,161 IA cases and 407,392 controls in the UK Biobank and validated in combination with risk factors blood pressure, sex, and smoking in 828 IA cases and 68,568 controls from the Nordic HUNT study. We further assessed association between genetic risk load and patient characteristics in a cohort of 5,560 IA patients.ResultsThe hazard ratio for ASAH incidence was 1.34 (95% confidence interval = 1.20-1.51) per SD increase of metaGRS. Concordance index increased from 0.63 [0.59-0.67] to 0.65 [0.62-0.69] upon including the metaGRS on top of clinical risk factors. The odds ratio for prediction of IA presence was 1.09 [95% confidence interval: 1.01-1.18], but did not improve area under the curve. The metaGRS was statistically significantly associated with age at ASAH (β=-4.82×10−3 per year [-6.49×10−3 to -3.14×10−3], P=1.82×10−8), and location at the internal carotid artery (OR=0.92 [0.86 to 0.98], P=0.0041).ConclusionsThe metaGRS was predictive of ASAH incidence with modest added value over clinical risk factors. Genetic risk plays a role in clinical heterogeneity of IA. Additional studies are needed to identify the biological mechanisms underlying this heterogeneity.KEY MESSAGESWhat is already known on this topicRecent advanced in the understanding of genetic risk for IA opened and opportunity for risk prediction by combining genetic and conventional risk factors.What this study addsHere, we developed a genetic risk score based on genetic association information for IA and 17 related traits. This risk score improved prediction compared to a model including only conventional risk factors. Further, genetic risk was associated with age at ASAH and IA location.How this study might affect research, practice, or policyThis study emphasizes the importance of combining conventional and genetic risk factors in prediction of IA. It provides a metric to develop an accurate risk assessment method including conventional and genetic risk factors.