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Published on: January 3, 2025
JAK2 pathogenic variants in ischemic stroke: low prevalence and pre-screening model
Jialu Zhao1,2, Siqi Ge3, Shujun Gao4
1China National Clinical Research Center for Neurological Diseases, Beijing Tiantan Hospital, Capital Medical University, No. 119, South Fourth Ring West Road, Fengtai District, Beijing, 100070, China.
Objective:
Our study aimed to determine the prevalence and clinical phenotypes of JAK2 pathogenic mutation carriers in the CNSR-III ischemic stroke (IS) cohort, and to develop a pre-test genetic screening model for identifying high-risk individuals.
Methods:
We performed retrospective characterization of JAK2 pathogenic variants using targeted sequencing data in the CNSR-III cohort. Clinical and laboratory characteristics of JAK2 V617F mutation carriers and non-carriers were tested in a logistic regression model to identify key features. V617F screening score was developed to predict positive JAK2 V617F test results.
Results:
46 cases (0.4%, 46/10428) harbored the JAK2 V617F-exclusive mutation. Mutation carriers manifested significantly inferior functional outcomes following stroke relative to non-carriers (adjusted OR 2.74[1.07, 6.49]). Significant predictors of mutation status comprised elevated platelet count (PLT, OR 1.02[1.02, 1.03]), increased hemoglobin concentrations (HGB, OR 1.06 [1.04, 1.08]), and a reduced burden of traditional stroke risk factors, such as history of hypertension (OR 0.24[0.11, 0.52]), smoking history (OR 0.08[0.02, 0.24]), and body mass index (BMI, OR 0.8[0.75, 0.97]). We constructed the JAK2 V617F screening score, which efficiently discriminated between carriers and non-carriers (area under the ROC curve, AUC 0.98), achieving sensitivity of 85%, specificity of 94%, and accuracy of 94% for a cut-off score of 3 points. Internal validation confirmed robust performance, with a consistent AUC of 0.98.
Conclusions:
Despite low prevalence (0.4%), JAK2 V617F mutation represents a clinically actionable stroke subtype with distinct pathophysiology. The prediction model offers a precision medicine approach, potentially reducing the need for comprehensive genetic testing.
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