Integrated metagenomic and metabolomic analysis reveals distinctive stage-specific gut-microbiome-derived metabolites in intracranial aneurysms.

OBJECTIVE: Our study aimed to explore the influence of gut microbiota and their metabolites on intracranial aneurysms (IA) progression and pinpoint-related metabolic biomarkers derived from the gut microbiome. DESIGN: We recruited 358 patients with unruptured IA (UIA) and 161 with ruptured IA (RIA) from two distinct geographical regions for conducting an integrated analysis of plasma metabolomics and faecal metagenomics. Machine learning algorithms were employed to develop a classifier model, subsequently validated in an independent cohort. Mouse models of IA were established to verify the potential role of the specific metabolite identified. RESULTS: Distinct shifts in taxonomic and functional profiles of gut microbiota and their related metabolites were observed in different IA stages. Notably, tryptophan metabolites, particularly indoxyl sulfate (IS), were significantly higher in plasma of RIA. Meanwhile, upregulated tryptophanase expression and indole-producing microbiota were observed in gut microbiome of RIA. A model harnessing gut-microbiome-derived tryptophan metabolites demonstrated remarkable efficacy in distinguishing RIA from UIA patients in the validation cohort (AUC=0.97). Gut microbiota depletion by antibiotics decreased plasma IS concentration, reduced IA formation and rupture in mice, and downregulated matrix metalloproteinase-9 expression in aneurysmal walls with elastin degradation reduction. Supplement of IS reversed the effect of gut microbiota depletion. CONCLUSION: Our investigation highlights the potential of gut-microbiome-derived tryptophan metabolites as biomarkers for distinguishing RIA from UIA patients. The findings suggest a novel pathogenic role for gut-microbiome-derived IS in elastin degradation in the IA wall leading to the rupture of IA.

Transition of intracranial aneurysmal wall enhancement from high to low wall shear stress mediation with size increase: A hemodynamic study based on 7T magnetic resonance imaging.

Background: Wall shear stress (WSS) has been proved to be related to the formation, development and rupture of intracranial aneurysms. Aneurysm wall enhancement (AWE) on magnetic resonance imaging (MRI) can be caused by inflammation and have confirmed its relationship with low WSS. High WSS can also result in inflammation but the research of its correlation with AWE is lack because of the focus on large aneurysms limited by 3T MRI in most previous studies.This study aimed to assess the potential association between high or low WSS and AWE in different aneuryms. Especially the relationship between high WSS and AWE in small aneurysm. Methods: Forty-three unruptured intracranial aneurysms in 42 patients were prospectively included for analysis. 7.0 T MRI was used for imaging. Aneurysm size was measured on three-dimensional time-of-flight (TOF) images. Aneurysm-to-pituitary stalk contrast ratio (CRstalk) was calculated on post-contrast black-blood T1-weighted fast spin echo sequence images. Hemodynamics were assessed by four-dimensional flow MRI. Results: The small aneurysms group had more positive WSS-CRstalk correlation coefficient distribution (dome: 78.6 %, p = 0.009; body: 50.0 %, p = 0.025), and large group had more negative coefficient distribution (dome: 44.8 %, p = 0.001; body: 69.0 %, p = 0.002). Aneurysm size was positively correlated with the significant OSI-CRstalk correlation coefficient at the dome (p = 0.012) and body (p = 0.010) but negatively correlated with the significant WSS-CRstalk correlation coefficient at the dome (p < 0.001) and body (p = 0.017). Conclusion: AWE can be mediated by both high and low WSS, and translate from high WSS- to low WSS-mediated pathways as size increase. Additionally, AWE may serve as an indicator of the stage of aneurysm development via different correlations with hemodynamic factors.

A comprehensive analysis of patients with cerebral arteriovenous malformation with headache: assessment of risk factors and treatment effectiveness.

BACKGROUND: Due to the high mortality and disability rate of intracranial hemorrhage, headache is not the main focus of research on cerebral arteriovenous malformation (AVM), so research on headaches in AVM is still scarce, and the clinical understanding is shallow. This study aims to delineate the risk factors associated with headaches in AVM and to compare the effectiveness of various intervention treatments versus conservative treatment in alleviating headache symptoms. METHODS: This study conducted a retrospective analysis of AVMs who were treated in our institution from August 2011 to December 2021. Multivariable logistic regression analysis was employed to assess the risk factors for headaches in AVMs with unruptured, non-epileptic. Additionally, the effectiveness of different intervention treatments compared to conservative management in alleviating headaches was evaluated through propensity score matching (PSM). RESULTS: A total of 946 patients were included in the analysis of risk factors for headaches. Multivariate logistic regression analysis identified that female (OR 1.532, 95% CI 1.173-2.001, p = 0.002), supply artery dilatation (OR 1.423, 95% CI 1.082-1.872, p = 0.012), and occipital lobe (OR 1.785, 95% CI 1.307-2.439, p < 0.001) as independent risk factors for the occurrence of headaches. There were 443 AVMs with headache symptoms. After propensity score matching, the microsurgery group (OR 7.27, 95% CI 2.82-18.7 p < 0.001), stereotactic radiosurgery group(OR 9.46, 95% CI 2.26-39.6, p = 0.002), and multimodality treatment group (OR 8.34 95% CI 2.87-24.3, p < 0.001) demonstrate significant headache relief compared to the conservative group. However, there was no significant difference between the embolization group (OR 2.24 95% CI 0.88-5.69, p = 0.091) and the conservative group. CONCLUSIONS: This study identified potential risk factors for headaches in AVMs and found that microsurgery, stereotactic radiosurgery, and multimodal therapy had significant benefits in headache relief compared to conservative treatment. These findings provide important guidance for clinicians when developing treatment options that can help improve overall treatment outcomes and quality of life for patients.

Quantitative Analysis of Hemodynamic Changes in Branch Arteries Covered by Flow Diverters.

BACKGROUND AND OBJECTIVES: Understanding post-treatment hemodynamic alterations and their association with the patency of covered branch arteries is limited. This study aims to identify hemodynamic changes after flow diverter stenting and investigate their correlation with the patency status of covered branch arteries. METHODS: All patients treated with pipeline embolization device for anterior cerebral artery aneurysms at our center between 2016 and 2020 were screened for inclusion. Quantitative digital subtraction angiography was used to analyze changes in hemodynamic parameters pre- and post-stenting. The patency status of covered branch arteries after stenting was categorized as either patent or flow impairment (defined as artery stenosis or occlusion). RESULTS: A total of 71 patients, encompassing 89 covered branch arteries, were enrolled. Flow impairment was observed in 11.2% (10/89) of the branches. The mean transit time and full width at half maximum (FWHM) in covered branches were significantly prolonged post-stenting (P = .004 and .023, respectively). Flow-impaired branch arteries exhibited hemodynamic shifts contrary to those in patent branch arteries. Specifically, flow-impaired branches showed marked reductions in time to peak, FWHM, and mean transit time (decreases of 32.8%, 32.6%, and 29%, respectively; P = .006, .002, and .002, respectively). Further multivariate analysis revealed that reductions in FWHM in the branches (odds ratio = 0.97, 95% CI: 0.95-0.99, P = .007) and smoking (odds ratio = 14.5, 95% CI: 1.39-151.76, P = .026) were independent predictors of flow impairment of covered branches. CONCLUSION: Pipeline embolization device stenting can cause a reduction in blood flow in branch arteries. Compared with patent branches, flow-impaired branches exhibit an increase in blood flow velocity after stenting. Smoking and ΔFWHM in the covered branches indicate flow impairment.

Overloaded transnidal pressure gradient as the hemodynamic mechanism leading to arteriovenous malformation rupture: a quantitative analysis using intravascular pressure monitoring and color-coded digital subtraction angiography.

BACKGROUND: The hemodynamics of brain arteriovenous malformations (AVMs) may have implications for hemorrhage. This study aimed to explore the hemodynamics of ruptured AVMs by direct microcatheter intravascular pressure monitoring (MIPM) and indirect quantitative digital subtraction angiography (QDSA). METHODS: We recruited patients with AVMs at a tertiary neurosurgery center from October 2020 to March 2023. In terms of MIPM, we preoperatively super-selected a predominant feeding artery and main draining vein through angiography to measure intravascular pressure before embolization. In processing of QDSA, we adopted previously standardized procedure for quantitative hemodynamics analysis of pre-embolization digital subtraction angiography (DSA), encompassing main feeding artery, nidus, and the main draining vein. Subsequently, we investigated the correlation between AVM rupture and intravascular pressure from MIPM, as well as hemodynamic parameters derived from QDSA. Additionally, we explored the interrelationships between hemodynamic indicators in both dimensions. RESULTS: After strict screening of patients, our study included 10 AVMs (six ruptured and four unruptured). We found that higher transnidal pressure gradient (TPG) (53.00±6.36 vs 39.25±8.96 mmHg, p=0.042), higher feeding artery pressure (FAP) (72.83±5.46 vs 65.00±6.48 mmHg, p=0.031) and higher stasis index of nidus (3.54±0.73 vs 2.43±0.70, p=0.043) were significantly correlated with AVM rupture. In analysis of interrelationships between hemodynamic indicators in both dimensions, a strongly positive correlation (r=0.681, p=0.030) existed between TPG and stasis index of nidus. CONCLUSIONS: TPG and FAP from MIPM platform and nidus stasis index from QDSA platform were correlated with AVM rupture, and both were positively correlated, suggesting that higher pressure load within nidus may be the central mechanism leading to AVM rupture.

Quantitative Assessment of Hemodynamics Associated With Embolization Degree in Brain Arteriovenous Malformations.

BACKGROUND AND OBJECTIVES: Grading systems, including the novel brain arteriovenous malformation endovascular grading scale (NBAVMES) and arteriovenous malformation embocure score (AVMES), predict embolization outcomes based on arteriovenous malformation (AVM) morphological features. The influence of hemodynamics on embolization outcomes remains unexplored. In this study, we investigated the relationship between hemodynamics and embolization outcomes. METHODS: We conducted a retrospective study of 99 consecutive patients who underwent transarterial embolization at our institution between 2012 and 2018. Hemodynamic features of AVMs were derived from pre-embolization digital subtraction angiography sequences using quantitative digital subtraction angiography. Multivariate logistic regression analysis was performed to determine the significant factors associated with embolization outcomes. RESULTS: Complete embolization (CE) was achieved in 17 (17.2%) patients, and near-complete embolization was achieved in 18 (18.2%) patients. A slower transnidal relative velocity (TRV, odds ratio [OR] = 0.71, P = .002) was significantly associated with CE. Moreover, higher stasis index of the drainage vein (OR = 16.53, P = .023), shorter transnidal time (OR = 0.15, P = .013), and slower TRV (OR = 0.9, P = .049) were significantly associated with complete or near-complete embolization (C/nCE). The area under the receiver operating characteristic curve for predicting CE was 0.87 for TRV, 0.72 for NBAVMES scores (ρ = 0.287, P = .004), and 0.76 for AVMES scores. The area under the receiver operating characteristic curve for predicting C/nCE was 0.77 for TRV, 0.61 for NBAVMES scores, and 0.75 for AVMES scores. Significant Spearman correlation was observed between TRV and NBAVMES scores and AVMES scores (ρ = 0.512, P < .001). CONCLUSION: Preoperative hemodynamic factors have the potential to predict the outcomes of AVM embolization. A higher stasis index of the drainage vein, slower TRV, and shorter transnidal time may indicate a moderate blood flow status or favorable AVM characteristics that can potentially facilitate embolization.

Diagnosis of intracranial aneurysms by computed tomography angiography using deep learning-based detection and segmentation.

BACKGROUND: Detecting and segmenting intracranial aneurysms (IAs) from angiographic images is a laborious task. OBJECTIVE: To evaluates a novel deep-learning algorithm, named vessel attention (VA)-Unet, for the efficient detection and segmentation of IAs. METHODS: This retrospective study was conducted using head CT angiography (CTA) examinations depicting IAs from two hospitals in China between 2010 and 2021. Training included cases with subarachnoid hemorrhage (SAH) and arterial stenosis, common accompanying vascular abnormalities. Testing was performed in cohorts with reference-standard digital subtraction angiography (cohort 1), with SAH (cohort 2), acquired outside the time interval of training data (cohort 3), and an external dataset (cohort 4). The algorithm's performance was evaluated using sensitivity, recall, false positives per case (FPs/case), and Dice coefficient, with manual segmentation as the reference standard. RESULTS: The study included 3190 CTA scans with 4124 IAs. Sensitivity, recall, and FPs/case for detection of IAs were, respectively, 98.58%, 96.17%, and 2.08 in cohort 1; 95.00%, 88.8%, and 3.62 in cohort 2; 96.00%, 93.77%, and 2.60 in cohort 3; and, 96.17%, 94.05%, and 3.60 in external cohort 4. The segmentation accuracy, as measured by the Dice coefficient, was 0.78, 0.71, 0.71, and 0.66 for cohorts 1-4, respectively. VA-Unet detection recall and FPs/case and segmentation accuracy were affected by several clinical factors, including aneurysm size, bifurcation aneurysms, and the presence of arterial stenosis and SAH. CONCLUSIONS: VA-Unet accurately detected and segmented IAs in head CTA comparably to expert interpretation. The proposed algorithm has significant potential to assist radiologists in efficiently detecting and segmenting IAs from CTA images.

Causal relationships between human blood metabolites and intracranial aneurysm and aneurysmal subarachnoid hemorrhage: a Mendelian randomization study.

Objective: The aim of this study was to assess the causal relationships between blood metabolites and intracranial aneurysm, aneurysmal subarachnoid hemorrhage, and unruptured intracranial aneurysm. Methods: Our exposure sample consisted of 7,824 individuals from a genome-wide association study of human blood metabolites. Our outcome sample consisted of 79,429 individuals (7,495 cases and 71,934 controls) from the International Stroke Genetics Consortium, which conducted a genome-wide association study of intracranial aneurysm, aneurysmal subarachnoid hemorrhage, and unruptured intracranial aneurysm. We identified blood metabolites with a potential causal effect on intracranial aneurysms and conducted sensitivity analyses to validate our findings. Results: After rigorous screening and Mendelian randomization tests, we found four, two, and three serum metabolites causally associated with intracranial aneurysm, aneurysmal subarachnoid hemorrhage, and unruptured intracranial aneurysm, respectively (all P < 0.05). Sensitivity analyses confirmed the robustness of these associations. Conclusions: Our Mendelian randomization analysis demonstrated causal relationships between human blood metabolites and intracranial aneurysm, aneurysmal subarachnoid hemorrhage, and unruptured intracranial aneurysm. Further research is required to explore the potential of targeting these metabolites in the management of intracranial aneurysm.

Automated anatomical labeling of the intracranial arteries via deep learning in computed tomography angiography.

Background and purpose: Anatomical labeling of the cerebral vasculature is a crucial topic in determining the morphological nature and characterizing the vital variations of vessels, yet precise labeling of the intracranial arteries is time-consuming and challenging, given anatomical structural variability and surging imaging data. We present a U-Net-based deep learning (DL) model to automatically label detailed anatomical segments in computed tomography angiography (CTA) for the first time. The trained DL algorithm was further tested on a clinically relevant set for the localization of intracranial aneurysms (IAs). Methods: 457 examinations with varying degrees of arterial stenosis were used to train, validate, and test the model, aiming to automatically label 42 segments of the intracranial arteries [e.g., 7 segments of the internal carotid artery (ICA)]. Evaluation metrics included Dice similarity coefficient (DSC), mean surface distance (MSD), and Hausdorff distance (HD). Additionally, 96 examinations containing at least one IA were enrolled to assess the model's potential in enhancing clinicians' precision in IA localization. A total of 5 clinicians with different experience levels participated as readers in the clinical experiment and identified the precise location of IA without and with algorithm assistance, where there was a washout period of 14 days between two interpretations. The diagnostic accuracy, time, and mean interrater agreement (Fleiss' Kappa) were calculated to assess the differences in clinical performance of clinicians. Results: The proposed model exhibited notable labeling performance on 42 segments that included 7 anatomical segments of ICA, with the mean DSC of 0.88, MSD of 0.82 mm and HD of 6.59 mm. Furthermore, the model demonstrated superior labeling performance in healthy subjects compared to patients with stenosis (DSC: 0.91 vs. 0.89, p < 0.05; HD: 4.75 vs. 6.19, p < 0.05). Concurrently, clinicians with model predictions achieved significant improvements when interpreting the precise location of IA. The clinicians' mean accuracy increased by 0.04 (p = 0.003), mean time to diagnosis reduced by 9.76 s (p < 0.001), and mean interrater agreement (Fleiss' Kappa) increased by 0.07 (p = 0.029). Conclusion: Our model stands proficient for labeling intracranial arteries using the largest CTA dataset. Crucially, it demonstrates clinical utility, helping prioritize the patients with high risks and ease clinical workload.