Predicting mortality among ischemic stroke patients using pathways-derived polygenic risk scores

Jiang Li, Durgesh Chaudhary, Christoph J. Griessenauer, David J. Carey, Ramin Zand, Vida Abedi

Research output: Contribution to journalArticlepeer-review

2 Scopus citations


We aim to determine whether ischemic stroke(IS)-related PRSs are also associated with and further predict 3-year all-cause mortality. 1756 IS patients with European ancestry were randomly split into training (n = 1226) and testing (n = 530) groups with 3-year post-event observations. Univariate Cox proportional hazards regression model (CoxPH) was used for primary screening of individual prognostic PRSs. Only the significantly associated PRSs and clinical risk factors with the same direction for a causal relationship with IS were used to construct a multivariate CoxPH. Feature selection was conducted by the LASSO method. After feature selection, a prediction model with 11 disease-associated pathway-specific PRSs outperformed the base model, as demonstrated by a higher concordance index (0.751, 95%CI [0.693–0.809] versus 0.729, 95%CI [0.676–0.782]) in the testing sample. A PRS derived from endothelial cell apoptosis showed independent predictability in the multivariate CoxPH (Hazard Ratio = 1.193 [1.027–1.385], p = 0.021). These PRSs fine-tuned the model by better stratifying high, intermediate, and low-risk groups. Several pathway-specific PRSs were associated with clinical risk factors in an age-dependent manner and further confirmed some known etiologies of IS and all-cause mortality. In conclusion, Pathway-specific PRSs for IS are associated with all-cause mortality, and the integrated multivariate risk model provides prognostic value in this context.

Original languageEnglish (US)
Article number12358
JournalScientific reports
Issue number1
StatePublished - Dec 2022

All Science Journal Classification (ASJC) codes

  • General


Dive into the research topics of 'Predicting mortality among ischemic stroke patients using pathways-derived polygenic risk scores'. Together they form a unique fingerprint.

Cite this