Development and validation of a model for predicting the risk of brain arteriovenous malformation rupture based on three-dimensional morphological features

ObjectiveBrain arteriovenous malformation (bAVM) is an important reason for intracranial hemorrhage. This study aimed at developing and validating a model for predicting bAVMs rupture by using three-dimensional (3D) morphological features extracted from Computed Tomography (CT) angiography.Materials and methodsThe prediction model was developed in a cohort consisting of 412 patients with bAVM between January 2010 and December 2020. All cases were partitioned into training and testing sets in the ratio of 7:3. Features were extracted from the 3D model built on CT angiography. Logistic regression was used to develop the model, with features selected using L1 Regularization, presented with a nomogram, and assessed with calibration curve, receiver operating characteristic (ROC) curve and decision curve analyze (DCA).ResultsSignificant variations in associated aneurysm, deep located, number of draining veins, type of venous drainage, deep drainage, drainage vein entrance diameter (Dv), type of feeding arteries, middle cerebral artery feeding, volume, Feret diameter, surface area, roundness, elongation, mean density (HU), and median density (HU) were found by univariate analysis (p < 0.05). The prediction model consisted of associated aneurysm, deep located, number of draining veins, deep drainage, Dv, volume, Feret diameter, surface area, mean density, and median density. The model showed good discrimination, with a C-index of 0.873 (95% CI, 0.791–0.931) in the training set and 0.754 (95% CI, 0.710–0.795) in the testing set.ConclusionsThis study presented 3D morphological features could be conveniently used to predict hemorrhage from unruptured bAVMs

Association between methionine sulfoxide and risk of moyamoya disease

ObjectiveMethionine sulfoxide (MetO) has been identified as a risk factor for vascular diseases and was considered as an important indicator of oxidative stress. However, the effects of MetO and its association with moyamoya disease (MMD) remained unclear. Therefore, we performed this study to evaluate the association between serum MetO levels and the risk of MMD and its subtypes.MethodsWe eventually included consecutive 353 MMD patients and 88 healthy controls (HCs) with complete data from September 2020 to December 2021 in our analyzes. Serum levels of MetO were quantified using liquid chromatography-mass spectrometry (LC–MS) analysis. We evaluated the role of MetO in MMD using logistic regression models and confirmed by receiver-operating characteristic (ROC) curves and area under curve (AUC) values.ResultsWe found that the levels of MetO were significantly higher in MMD and its subtypes than in HCs (p < 0.001 for all). After adjusting for traditional risk factors, serum MetO levels were significantly associated with the risk of MMD and its subtypes (p < 0.001 for all). We further divided the MetO levels into low and high groups, and the high MetO level was significantly associated with the risk of MMD and its subtypes (p < 0.05 for all). When MetO levels were assessed as quartiles, we found that the third (Q3) and fourth (Q4) MetO quartiles had a significantly increased risk of MMD compared with the lowest quartile (Q3, OR: 2.323, 95%CI: 1.088–4.959, p = 0.029; Q4, OR: 5.559, 95%CI: 2.088–14.805, p = 0.001).ConclusionIn this study, we found that a high level of serum MetO was associated with an increased risk of MMD and its subtypes. Our study raised a novel perspective on the pathogenesis of MMD and suggested potential therapeutic targets

Hypo-high density lipoproteinemia is a predictor for recurrent stroke during the long-term follow-up after revascularization in adult moyamoya disease

ObjectivePrevious studies have reported that hypo-high-density lipoproteinemia (HHDL) was an independent risk factor for the cerebrovascular event. However, the risk of HHDL for stroke recurrence in moyamoya disease (MMD) during long-term follow-up after revascularization remains poorly understood. We aim to investigate the association between HHDL and stroke recurrence in adult patients with MMD.MethodsA total of 138 adult patients with MMD were prospectively recruited from 1 July to 31 December 2019. After excluding 15 patients who did not meet the inclusion criteria, all the 123 patients were enrolled. Participants were grouped according to the stroke recurrence and HHDL presentation, respectively. Clinical data and laboratory examinations were compared by the statistical analysis. The Kaplan–Meier survival analysis was conducted to compare the stroke-free survival rates between participants with HHDL and those without. Univariate and multivariate logistic regression analyses were performed to identify independent factors of the neurological status. Univariate and multivariate Cox regression analyses were conducted to identify the predictors for the recurrent stroke.ResultsParticipants with recurrent stroke group showed a lower level of high-density lipoprotein (HDL) (p = 0.030). More participants in the recurrent stroke group had HHDL (p = 0.045). What is more, there was statistical significance in the Kaplan–Meier curve of stroke incidence between the normal HDL group and the HHDL group (log-rank test, p = 0.034). Univariate logistic analysis results showed that HHDL (OR 0.916, 95% CI 0.237–3.543; p = 0.899) and HDL (OR 0.729, 95% CI 0.094–5.648; p = 0.763) were not predictive factors for the neurological status. In the multivariate Cox regression analysis, diabetes (HR 4.195, 95% CI 1.041–16.899; p = 0.044), HDL (HR 0.061, 95% CI 0.006–0.626; p = 0.019), and HHDL (HR 3.341, 95% CI 1.110–10.051; p = 0.032) were independent risk factors for the recurrent stroke.ConclusionsHypo-high-density lipoproteinemia might be a predictor or the potential therapeutic target for recurrent stroke during the long-term follow-up after revascularization in adult patients with MMD

Serum Kynurenic Acid and Kynurenine Are Negatively Associated with the Risk of Adult Moyamoya Disease

Background and aim. Kynurenine (KYN) and kynurenic acid (KYNA) are key intermediate metabolites associated with inflammation and immune responses in the kynurenine pathway. It remains unknown whether KYN or KYNA is associated with the risk of adult moyamoya disease (MMD). The aim of this study was to prospectively investigate the association between serum KYN or KYNA and the risk of adult MMD. Methods. The study was conducted from July 2020 to December 2021. We measured serum KYN and KYNA levels for 360 adult MMD patients (259 cases of ischemic MMD, 101 cases of hemorrhagic MMD) and 89 age-sex-matched healthy controls. Clinical and laboratory characteristics were collected from the medical record. Results. After multivariate adjustment, decreased serum KYNA (OR, 0.085; 95% CI, 0.035–0.206; p = 0.000) or KYN (OR, 0.430; 95% CI, 0.225–0.820; p = 0.010) levels were associated with increased risk of MMD when upper and lower tertiles were compared. In addition, a higher trend of hemorrhagic MMD was found in MMD patients in KYNA tertile 1 compared with those in tertile 2 to 3 (OR, 0.584; 95% CI, 0.345–0.987; p = 0.044). Addition of serum KYNA (net reclassification improvement: 73.24%, p = 0.000; integrated discrimination improvement: 9.60%, p = 0.000) or KYN (integrated discrimination improvement: 1.70%, p = 0.037) to conventional risk factors significantly improved the risk prediction of MMD. In the exploratory analysis, we observed an interaction between KYN and age (≥40 versus <40 years) or homocysteine levels (≥13.0 versus <13.0 μmol/L) on the risk of MMD. Conclusions. Decreased serum KYNA or KYN levels were associated with an increased risk of adult MMD, suggesting that serum KYNA or KYN may be a valuable predictive biomarker for adult MMD

Multiomics and blood-based biomarkers of moyamoya disease: protocol of Moyamoya Omics Atlas (MOYAOMICS)

Abstract Background Moyamoya disease (MMD) is a rare and complex cerebrovascular disorder characterized by the progressive narrowing of the internal carotid arteries and the formation of compensatory collateral vessels. The etiology of MMD remains enigmatic, making diagnosis and management challenging. The MOYAOMICS project was initiated to investigate the molecular underpinnings of MMD and explore potential diagnostic and therapeutic strategies. Methods The MOYAOMICS project employs a multidisciplinary approach, integrating various omics technologies, including genomics, transcriptomics, proteomics, and metabolomics, to comprehensively examine the molecular signatures associated with MMD pathogenesis. Additionally, we will investigate the potential influence of gut microbiota and brain-gut peptides on MMD development, assessing their suitability as targets for therapeutic strategies and dietary interventions. Radiomics, a specialized field in medical imaging, is utilized to analyze neuroimaging data for early detection and characterization of MMD-related brain changes. Deep learning algorithms are employed to differentiate MMD from other conditions, automating the diagnostic process. We also employ single-cellomics and mass cytometry to precisely study cellular heterogeneity in peripheral blood samples from MMD patients. Conclusions The MOYAOMICS project represents a significant step toward comprehending MMD’s molecular underpinnings. This multidisciplinary approach has the potential to revolutionize early diagnosis, patient stratification, and the development of targeted therapies for MMD. The identification of blood-based biomarkers and the integration of multiple omics data are critical for improving the clinical management of MMD and enhancing patient outcomes for this complex disease