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Objective: We performed a case-control study to investigate the correlation between DNA methylation and mRNA expression of the glutathione S-transferase alpha 4 (GSTA4) gene and the risk of intracranial aneurysm (IA) in the Chinese Han population.Methods: After propensity score matching, 44 pairs of cases and controls were collected in this study. Fasting blood samples were collected for DNA and RNA extraction within 24 h of admission. Nine CpG dinucleotides were selected from the GSTA4 promoter region for DNA methylation pyrosequencing. mRNA expression of GSTA4 was measured by quantitative real-time polymerase chain reaction (RT-qPCR). In vitro cell experiments were conducted to verify the association between 5-aza-2′-deoxycytidine induced DNA hypomethylation and GSTA4 mRNA expression.Results: The mean methylation level of GSTA4 was much lower in IA patients, especially in IA patients, especially in unruptured IA (UIA), than that in controls (IA vs. Control, p < .001; ruptured IA (RIA) vs. Control, p = .005; UIA vs. Control, p < .001). With sex stratification, we further found that the association between GSTA4 methylation and IA risk presented only in women (mean methylation level: IA vs. Control, p < .001; RIA vs. Control, p = .009; UIA vs. Control, p < .001). GSTA4 mRNA expression was significantly higher in the IA group than in the control group (p < .01) and negatively correlated with DNA methylation in all individuals (r = −.746, p < .001). DNA hypomethylation can increase GSTA4 mRNA expression in human primary artery smooth muscle cells. The receiver operating characteristic (ROC) curve showed that GSTA4 mean methylation (AUC = .80, p < .001) was a reliable predictor of women intracranial aneurysm, among which CpG 1 exhibited the best predictive value (AUC = .89, p < .001). In addition, GSTA4 expression levels could also predict the risk of IA in women (AUC = .87, p = .005).Conclusion: Decreased DNA methylation and increased mRNA expression of the GSTA4 gene are associated with the risk of IA in women.

ObjectiveWe hypothesized that quantitative net water uptake (NWU), a novel neuroimaging marker of early brain edema, can predict symptomatic intracranial hemorrhage (sICH) after acute ischemic stroke (AIS).MethodsWe enrolled patients with AIS who completed admission multimodal computed tomography (CT) within 24 h after stroke onset. NWU within the ischemic core and penumbra was calculated based on admission CT, namely NWU-core and NWU-penumbra. sICH was defined as the presence of ICH in the infarct area within 7 days after stroke onset, accompanied by clinical deterioration. The predictive value of NWU-core and NWU-penumbra on sICH was evaluated by logistic regression analyses and the receiver operating characteristic (ROC) curve. A pure neuroimaging prediction model was built considering imaging markers, which has the potential to be automatically quantified with an artificial algorithm on image workstation.Results154 patients were included, of which 93 underwent mechanical thrombectomy (MT). The median time from symptom onset to admission CT was 262 min (interquartile range, 198–368). In patients with MT, NWU-penumbra (OR =1.442; 95% CI = 1.177–1.766; P < 0.001) and NWU-core (OR = 1.155; 95% CI = 1.027–1.299; P = 0.016) were independently associated with sICH with adjustments for age, sex, time from symptom onset to CT, hypertension, lesion volume, and admission National Institutes of Health Stroke Scale (NIHSS) score. ROC curve showed that NWU-penumbra had better predictive performance than NWU-core on sICH [area under the curve (AUC): 0.773 vs. 0.673]. The diagnostic efficiency of the predictive model was improved with the containing of NWU-penumbra (AUC: 0.853 vs. 0.760). A pure imaging model also presented stable predictive power (AUC = 0.812). In patients without MT, however, only admission NIHSS score (OR = 1.440; 95% CI = 1.055–1.965; P = 0.022) showed significance in predicting sICH in multivariate analyses.ConclusionsNWU-penumbra may have better predictive performance than NWU-core on sICH after MT. A pure imaging model showed potential value to automatically screen patients with sICH risk by image recognition, which may optimize treatment strategy.