Aims The canonical Wnt signaling pathway plays an essential role in blood‐brain barrier integrity and intracerebral hemorrhage in preclinical stroke models. Here, we sought to explore the association between canonical Wnt signaling and hemorrhagic transformation (HT) following intravenous thrombolysis (IVT) in acute ischemic stroke (AIS) patients as well as to determine the underlying cellular mechanisms. Methods 355 consecutive AIS patients receiving IVT were included. Blood samples were collected on admission, and HT was detected at 24 hours after IVT. 117 single‐nucleotide polymorphisms (SNPs) of 28 Wnt signaling genes and exon sequences of 4 core cerebrovascular Wnt signaling components (GPR124, RECK, FZD4, and CTNNB1) were determined using a customized sequencing chip. The impact of identified genetic variants was further studied in HEK 293T cells using cellular and biochemical assays. Results During the study period, 80 patients experienced HT with 27 parenchymal hematoma (PH). Compared to the non‐PH patients, WNT7A SNPs (rs2163910, P = .001, OR 2.727; rs1124480, P = .002, OR 2.404) and GPR124 SNPs (rs61738775, P = .012, OR 4.883; rs146016051, P < .001, OR 7.607; rs75336000, P = .044, OR 2.503) were selectively enriched in the PH patients. Interestingly, a missense variant of GPR124 (rs75336000, c.3587G>A) identified in the PH patients resulted in a single amino acid alteration (p.Cys1196Tyr) in the intracellular domain of GPR124. This variant substantially reduced the activity of WNT7B‐induced canonical Wnt signaling by decreasing the ability of GPR124 to recruit cytoplasmic DVL1 to the cellular membrane. Conclusion Variants of WNT7A and GPR124 are associated with increased risk of PH in patients with AIS after intravenous thrombolysis, likely through regulating the activity of canonical Wnt signaling.

Background: Cardiometabolic index (CMI) is associated with several risk factors for stroke; however, few studies have assessed the role of CMI in stroke risk. Objective: This study aimed to assess the association between CMI and stroke in a population-based cross-sectional study. Methods: This study included 4445 general residents aged ≥40 years selected by multistage stratified random cluster sampling. CMI was calculated as the product of the ratio of waist circumference to height (WHtR) and the ratio of triglyceride levels to high-density lipoprotein cholesterol levels (TG/HDL-C). Participants were categorized according to CMI quartiles: quartile 1 (Q1), quartile 2 (Q2), quartile 3 (Q3), and quartile 4 (Q4). Multivariate logistic regression analysis and receiver operating characteristic (ROC) curves were used to assess the association between CMI and stroke. Results: A total of 4052 participants were included in the study, with an overall stroke prevalence of 7.2%. The prevalence of stroke increased with CMI quartiles, ranging from 4.4% to 9.2% (p for trend <0.001). Compared with Q1, stroke risk for Q2, Q3, and Q4 were 1.550-, 1.693-, and 1.704- fold, respectively. The area under the ROC curve (AUC) [95% CI] was (0.574 [0.558−0.589]) for CMI, 0.627 [0.612−0.642]) (p=0.0024) for WHtR, 0.556 [0.540−0.571]) (p<0.0001) for TG/HDL-C. CMI was inferior to WHtR, but CMI had marginal advantage over TG/HDL-C in terms of its stroke discrimination ability. Conclusion: Although there was a strong and independent association between CMI and stroke in the general population, CMI had limited discriminating ability for stroke. Thus, new parameters should be developed.