Dataset Information

Genetic Risk Assessment of Elastin Gene Polymorphisms with Intracranial Aneurysm in Koreans.

ABSTRACT: Elastin encoded by elastin gene (ELN) is a crucial extracellular matrix protein responsible for arterial resilience. The objective of this study was to identify single nucleotide polymorphisms (SNPs) of ELN gene susceptible to intracranial aneurysm (IA) in Korean population. Two SNPs of ELN gene, rs2071307 (Gly422Ser) and rs2856728 (intron), were genotyped in 90 patients with IA and 90 age and frequency matched controls. Fisher's exact test was conducted to evaluate allelic association with IA. Of the two SNPs in ELN gene, T allele of rs2856728 (intron) showed statistically significant association with increased development of IA (odds ratio [OR]: 2.34, 95% confidence interval [CI]: 1.44-3.81, P = 7.6 × 10-4). However, G allele of rs2071307 (Gly422Ser) had no significant association with the development of IA (OR: 1.27, 95% CI: 1.44-3.81, P = 0.607). Interestingly, the odds of having rs2856728 variant was approximately 2-fold higher in males than that in females (OR: 3.46 vs. 1.88, P < 0.05). However, none of SNPs showed difference between single and multiple IA in this study. This preliminary study implies that the rs2856728 variant in ELN gene polymorphisms might play crucial roles in the development and pathogenesis of IA in Korean population.

SUBMITTER: Jeon JP

PROVIDER: S-EPMC5785693 | biostudies-literature | 2018 Jan

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Genetic Risk Assessment of Elastin Gene Polymorphisms with Intracranial Aneurysm in Koreans.

Jeon Jin Pyeong JP Hong Eun Pyo EP Kim Jeong Eun JE Ha Eun Jin EJ Cho Won-Sang WS Son Young-Je YJ Bang Jae Seung JS Oh Chang Wan CW

Neurologia medico-chirurgica 20171110 1

Elastin encoded by elastin gene (ELN) is a crucial extracellular matrix protein responsible for arterial resilience. The objective of this study was to identify single nucleotide polymorphisms (SNPs) of ELN gene susceptible to intracranial aneurysm (IA) in Korean population. Two SNPs of ELN gene, rs2071307 (Gly422Ser) and rs2856728 (intron), were genotyped in 90 patients with IA and 90 age and frequency matched controls. Fisher's exact test was conducted to evaluate allelic association with IA. ...[more]

PMID: 29129841

Similar Datasets

Project description:ObjectiveRebleeding is recognized as the main cause of mortality after intracranial aneurysm rupture. Though timely intervention can prevent poor prognosis, there is no agreement on the surgical priority and choosing medical treatment for a short period after rupture. The aim of this study was to investigate the risk factors related to the rebleeding after admission and establish predicting models for better clinical decision-making.MethodsThe patients with ruptured intracranial aneurysms (RIAs) between January 2018 and September 2020 were reviewed. All patients fell to the primary and the validation cohort by January 2020. The hemodynamic parameters were determined through the computational fluid dynamics simulation. Cox regression analysis was conducted to identify the risk factors of rebleeding. Based on the independent risk factors, nomogram models were built, and their predicting accuracy was assessed by using the area under the curves (AUCs).ResultA total of 577 patients with RIAs were enrolled in this present study, 86 patients of them were identified as undergoing rebleeding after admission. Thirteen parameters were identified as significantly different between stable and rebleeding aneurysms in the primary cohort. Cox regression analysis demonstrated that six parameters, including hypertension [hazard ratio (HR), 2.54; P = 0.044], bifurcation site (HR, 1.95; P = 0.013), irregular shape (HR, 4.22; P = 0.002), aspect ratio (HR, 12.91; P < 0.001), normalized wall shear stress average (HR, 0.16; P = 0.002), and oscillatory stress index (HR, 1.14; P < 0.001) were independent risk factors related to the rebleeding after admission. Two nomograms were established, the nomogram including clinical, morphological, and hemodynamic features (CMH nomogram) had the highest predicting accuracy (AUC, 0.92), followed by the nomogram including clinical and morphological features (CM nomogram; AUC, 0.83), ELAPSS score (AUC, 0.61), and PHASES score (AUC, 0.54). The calibration curve for the probability of rebleeding showed good agreement between prediction by nomograms and actual observation. In the validation cohort, the discrimination of the CMH nomogram was superior to the other models (AUC, 0.93 vs. 0.86, 0.71 and 0.48).ConclusionWe presented two nomogram models, named CMH nomogram and CM nomogram, which could assist in identifying the RIAs with high risk of rebleeding.

Project description:Background and Purpose: Precisely assessing the likelihood of an intracranial aneurysm rupturing is critical for guiding clinical decision-making. The objective of this study is to construct and validate a deep learning framework utilizing point clouds to forecast the likelihood of aneurysm rupturing. Methods: The dataset included in this study consisted of a total of 623 aneurysms, with 211 of them classified as ruptured and 412 as unruptured, which were obtained from two separate projects within the AneuX morphology database. The HUG project, which included 124 ruptured aneurysms and 340 unruptured aneurysms, was used to train and internally validate the model. For external validation, another project named @neurIST was used, which included 87 ruptured and 72 unruptured aneurysms. A standardized method was employed to isolate aneurysms and a segment of their parent vessels from the original 3D vessel models. These models were then converted into a point cloud format using open3d package to facilitate training of the deep learning network. The PointNet++ architecture was utilized to process the models and generate risk scores through a softmax layer. Finally, two models, the dome and cut1 model, were established and then subjected to a comprehensive comparison of statistical indices with the LASSO regression model built by the dataset authors. Results: The cut1 model outperformed the dome model in the 5-fold cross-validation, with the mean AUC values of 0.85 and 0.81, respectively. Furthermore, the cut1 model beat the morphology-based LASSO regression model with an AUC of 0.82. However, as the original dataset authors stated, we observed potential generalizability concerns when applying trained models to datasets with different selection biases. Nevertheless, our method outperformed the LASSO regression model in terms of generalizability, with an AUC of 0.71 versus 0.67. Conclusion: The point cloud, as a 3D visualization technique for intracranial aneurysms, can effectively capture the spatial contour and morphological aspects of aneurysms. More structural features between the aneurysm and its parent vessels can be exposed by keeping a portion of the parent vessels, enhancing the model's performance. The point cloud-based deep learning model exhibited good performance in predicting rupture risk while also facing challenges in generalizability.

Dataset Information

Genetic Risk Assessment of Elastin Gene Polymorphisms with Intracranial Aneurysm in Koreans.

Publications

Genetic Risk Assessment of Elastin Gene Polymorphisms with Intracranial Aneurysm in Koreans.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets