Elevated Lipid Infiltration Is Associated With Cerebral Aneurysm Rupture

Ou, Chubin; Qian, Yi; Zhang, Xin; Liu, Jiahui; Li, Wenchao; Su, Hengxian; Zhang, Nan; Zhang, Jianbo; He, Xiaofu; Duan, Chuan Zhi

doi:10.3389/fneur.2020.00154

Cited by 18 publications

(8 citation statements)

References 33 publications

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“…These functions of HDL-C may explain why HDL-C reduces the risk of IA rupture. The pathological study indicated that an increase in lipid infiltration is associated with IA rupture [15,42], and Frösen and Ollikainen et al were the first to elucidate in depth the accumulation of LDL and HDL particles in the aneurysm wall [15,43]. Combined with our research, we speculate that this may be due to the passive repair of IA by lipids.…”

Section: F I G U R Esupporting

confidence: 78%

Effects of blood lipids and lipid‐modifying drugs on intracranial aneurysms

Zhang

Dong

Miao

et al. 2022

Euro J of Neurology

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Background and purpose:We used two-sample Mendelian randomization (MR) to examine the effects of blood lipids and lipid-modifying drugs on intracranial aneurysm (IA). Methods: Genetic variants for the effects of high-density lipoprotein cholesterol (HDL-C), apolipoprotein A1, low-density lipoprotein cholesterol (LDL-C), apolipoprotein B, and triglycerides and targets for lipid-modifying drugs were selected from the genome-wide discovery analyses of the UK Biobank. Summary-level data on IAs were obtained from the International Stroke Genetics Consortium. Univariate and multivariate MR analyses were performed. Results: Univariate MR analyses showed that the HDL-C was negatively correlated with IA (odds ratio [OR] = 0.816, 95% confidence interval [CI] = 0.715-0.932, p = 0.003) and ruptured IA (rIA; OR = 0.775, 95% CI = 0.663-0.906, p = 0.001). The multivariate MRinverse variance weighted analysis showed that the HDL-C was negatively correlated with IA (OR = 0.655, 95% CI = 0.434-0.988, p = 0.043) and rIA (OR = 0.563, 95% CI = 0.347-0.913, p = 0.02), and the LDL-C was negatively correlated with IA (OR = 0.402, 95% CI = 0.191-0.848, p = 0.017) and rIA (OR = 0.376, 95% CI = 0.160-0.883, p = 0.025).Using genetic proxies of known lipid-modifying drugs, we found that the increased HDL-C with cholesterol ester transfer protein proxies was associated with a decreased risk of rIA (OR = 0.852, 95% CI = 0.747-0.973, p = 0.018), and the decreased LDL-C with 3-hydrox y-3-methylglutaryl-coenzyme A reductase proxies was associated with increased risk of IA (OR = 1.772, 95% CI = 1.080-2.908, p = 0.024) and rIA (OR = 1.856, 95% CI = 1.022-3.371, p = 0.042).Conclusions: Genetically determined HDL-C and LDL-C reduce the risk of IA and rIA. The effects of different lipid-modifying drugs on IA need to be further investigated.

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Section: F I G U R Esupporting

confidence: 78%

Effects of blood lipids and lipid‐modifying drugs on intracranial aneurysms

Zhang

Dong

Miao

et al. 2022

Euro J of Neurology

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“…Hyperlipidemia and lipid accumulation have been suggested to be related to aneurysm rupture (18,(31)(32)(33). Triglyceride is commonly recognized as a risk factor for cardiovascular disease.…”

Section: Discussionmentioning

confidence: 99%

Rupture Risk Assessment for Cerebral Aneurysm Using Interpretable Machine Learning on Multidimensional Data

Liu

Qian

et al. 2020

Front. Neurol.

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Background: Assessment of cerebral aneurysm rupture risk is an important task, but it remains challenging. Recent works applying machine learning to rupture risk evaluation presented positive results. Yet they were based on limited aspects of data, and lack of interpretability may limit their use in clinical setting. We aimed to develop interpretable machine learning models on multidimensional data for aneurysm rupture risk assessment.Methods: Three hundred seventy-four aneurysms were included in the study. Demographic, medical history, lifestyle behaviors, lipid profile, and morphologies were collected for each patient. Prediction models were derived using machine learning methods (support vector machine, artificial neural network, and XGBoost) and conventional logistic regression. The derived models were compared with the PHASES score method. The Shapley Additive Explanations (SHAP) analysis was applied to improve the interpretability of the best machine learning model and reveal the reasoning behind the predictions made by the model.Results: The best machine learning model (XGBoost) achieved an area under the receiver operating characteristic curve of 0.882 [95% confidence interval (CI) = 0.838–0.927], significantly better than the logistic regression model (0.779; 95% CI = 0.729–0.829; P = 0.002) and the PHASES score method (0.758; 95% CI = 0.713–0.800; P = 0.001). Location, size ratio, and triglyceride level were the three most important features in predicting rupture. Two typical cases were analyzed to demonstrate the interpretability of the model.Conclusions: This study demonstrated the potential of using machine learning for aneurysm rupture risk assessment. Machine learning models performed better than conventional statistical model and the PHASES score method. The SHAP analysis can improve the interpretability of machine learning models and facilitate their use in a clinical setting.

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“…The repeated microbleeds from these new fragile vasa vasorum further promote inflammatory cell accumulation ( 28 ). Acute and chronic inflammatory cell accumulations accelerate aneurysm enlargement and induce aneurysm rupture ( 29 ).…”

Section: Discussionmentioning

confidence: 99%

Hemodynamic Characteristic Analysis of Aneurysm Wall Enhancement in Unruptured Middle Cerebral Artery Aneurysm

Zhong

Kuang

et al. 2022

Front. Neurol.

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Background and PurposeAneurysm wall enhancement (AWE) on vessel wall magnetic resonance imaging has been suggested as a marker of the unstable status of intracranial aneurysm (IA) and may predict IA rupture risk. However, the role of abnormal hemodynamics in unruptured IAs with AWE remains poorly understood. This study aimed to determine the association between abnormal hemodynamics and AWE in unruptured middle cerebral artery (MCA) aneurysms.MethodsA total of 28 patients with 32 bifurcation aneurysms of the middle cerebral artery>3mm in size were retrospectively selected for this study. Vessel wall magnetic resonance images were reviewed, and the AWE pattern of each aneurysm was classified as no AWE, partial AWE, and circumferential AWE. Computational fluid dynamics were used to calculate the hemodynamic variables of each aneurysm. Univariate and multivariate analyses investigated the association between AWE and hemodynamic variables.ResultsAWE was present in 13 aneurysms (40.6%), with 7 (21.9%) showing partial AWE and 6 (18.7%) showing circumferential AWE. Kruskal–Wallis H analysis revealed that hemodynamic variables including wall shear stress (WSS), oscillatory shear index, aneurysm pressure (AP), relative residence time, and low shear area (LSA) were significantly associated with AWE (p < 0.05). Further ordinal logistic regression analysis found that WSS was the only factor with a significant association with AWE (p = 0.048); similar trends were identified for LSA (p = 0.055) and AP (p = 0.058). Spearman's correlation analysis showed that AWE was negatively correlated with WSS (rs = −0.622, p < 0.001) and AP (rs = −0.535, p = 0.002) but positively correlated with LSA (rs = 0.774, p < 0.001).ConclusionLow wall shear stress, low aneurysm pressure, and increased low shear area were associated with aneurysm wall enhancement on vessel wall magnetic resonance imaging in unruptured cerebral aneurysms. These abnormal hemodynamic parameters may induce inflammation and cause aneurysm wall enhancement. However, the association between these parameters and their underlying pathological mechanisms requires further investigation.

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Elevated Lipid Infiltration Is Associated With Cerebral Aneurysm Rupture

Cited by 18 publications

References 33 publications

Effects of blood lipids and lipid‐modifying drugs on intracranial aneurysms

Effects of blood lipids and lipid‐modifying drugs on intracranial aneurysms

Rupture Risk Assessment for Cerebral Aneurysm Using Interpretable Machine Learning on Multidimensional Data

Hemodynamic Characteristic Analysis of Aneurysm Wall Enhancement in Unruptured Middle Cerebral Artery Aneurysm

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