BACKGROUND: Recently, common genetic risk factors for intracranial aneurysm (IA) and aneurysmal subarachnoid hemorrhage (ASAH) were found to explain a large amount of disease heritability and therefore have potential to be used for genetic risk prediction. We constructed a genetic risk score to (1) predict ASAH incidence and IA presence (combined set of unruptured IA and ASAH) and (2) assess its association with patient characteristics. METHODS: A genetic risk score incorporating genetic association data for IA and 17 traits related to IA (so-called metaGRS) was created using 1161 IA cases and 407 392 controls from the UK Biobank population study. The metaGRS was validated in combination with risk factors blood pressure, sex, and smoking in 828 IA cases and 68 568 controls from the Nordic HUNT population study. Furthermore, we assessed association between the metaGRS and patient characteristics in a cohort of 5560 IA patients. RESULTS: Per SD increase of metaGRS, the hazard ratio for ASAH incidence was 1.34 (95% CI, 1.20–1.51) and the odds ratio for IA presence 1.09 (95% CI, 1.01–1.18). Upon including the metaGRS on top of clinical risk factors, the concordance index to predict ASAH hazard increased from 0.63 (95% CI, 0.59–0.67) to 0.65 (95% CI, 0.62–0.69), while prediction of IA presence did not improve. The metaGRS was statistically significantly associated with age at ASAH (β=−4.82×10 −3 per year [95% CI, −6.49×10 −3 to −3.14×10 −3 ]; P =1.82×10 −8 ), and location of IA at the internal carotid artery (odds ratio=0.92 [95% CI, 0.86–0.98]; P =0.0041). CONCLUSIONS: The metaGRS was predictive of ASAH incidence, although with limited added value over clinical risk factors. The metaGRS was not predictive of IA presence. Therefore, we do not recommend using this metaGRS in daily clinical care. Genetic risk does partly explain the clinical heterogeneity of IA warranting prioritization of clinical heterogeneity in future genetic prediction studies of IA and ASAH.

Background Prediction models for risk of cardiovascular events generally do not include young adults, and cardiovascular risk factors differ between women and men. Therefore, this study aimed to develop prediction models for first‐ever cardiovascular event risk in men and women aged 30 to 49 years. Methods and Results We included patients aged 30 to 49 years without cardiovascular disease from a Dutch routine care database. Outcome was defined as first‐ever cardiovascular event. Our reference models were sex‐specific Cox proportional hazards models based on traditional cardiovascular predictors, which we compared with models using 2 predictor subsets with the 20 or 50 most important predictors based on the Cox elastic net model regularization coefficients. We assessed the C‐index and calibration curve slopes at 10 years of follow‐up. We stratified our analyses based on 30‐ to 39‐year and 40‐ to 49‐year age groups at baseline. We included 542 141 patients (mean age 39.7, 51% women). During follow‐up, 10 767 cardiovascular events occurred. Discrimination of reference models including traditional cardiovascular predictors was moderate (women: C‐index, 0.648 [95% CI, 0.645–0.652]; men: C‐index, 0.661 [95%CI, 0.658–0.664]). In women and men, the Cox proportional hazard models including 50 most important predictors resulted in an increase in C‐index (0.030 and 0.012, respectively), and a net correct reclassification of 3.7% of the events in women and 1.2% in men compared with the reference model. Conclusions Sex‐specific electronic health record‐derived prediction models for first‐ever cardiovascular events in the general population aged <50 years have moderate discriminatory performance. Data‐driven predictor selection leads to identification of nontraditional cardiovascular predictors, which modestly increase performance of models.

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.