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Cavernous angiomas (CA) are common vascular anomalies causing brain hemorrhage. Based on mouse studies, roles of gram-negative bacteria and altered intestinal homeostasis have been implicated in CA pathogenesis, and pilot study had suggested potential microbiome differences between non-CA and CA individuals based on 16S rRNA gene sequencing. We here assess microbiome differences in a larger cohort of human subjects with and without CA, and among subjects with different clinical features, and conduct more definitive microbial analyses using metagenomic shotgun sequencing. Relative abundance of distinct bacterial species in CA patients is shown, consistent with postulated permissive microbiome driving CA lesion genesis via lipopolysaccharide signaling, in humans as in mice. Other microbiome differences are related to CA clinical behavior. Weighted combinations of microbiome signatures and plasma inflammatory biomarkers enhance associations with disease severity and hemorrhage. This is the first demonstration of a sensitive and specific diagnostic microbiome in a human neurovascular disease.

Background: Cerebral cavernous angioma (CA) is a capillary vasculopathy affecting more than a million Americans with a small fraction of cases demonstrating lesional bleed or growth with major clinical sequelae. Perfusion and permeability are fundamental features of CA pathophysiology, but their role as prognostic biomarkers is unclear. Purpose: To investigate whether perfusion or permeability lesional descriptors derived from dynamic contrast‐enhanced quantitative perfusion (DCEQP) magnetic resonance imaging (MRI) can predict subsequent lesional bleed/growth in the year following imaging. Study Type: Single‐site case‐controlled study. Subjects: Two hundred and five consecutively enrolled patients (63.4% female). Field Strength/Sequence: Three‐Tesla/T1‐mapping with contrast‐enhanced dynamic two‐dimensional (2D) spoiled gradient recalled acquisition (SPGR) sequences. Assessment: Prognostic associations with bleed/growth (present or absent) in the following year were assessed in 745 CA lesions evaluated by DCEQP in the 205 patients in relation to lesional descriptors calculated from permeability and perfusion maps. A subgroup of 30 cases also underwent peripheral blood collection at the time of DCEQP scans and assays of plasma levels of soluble CD14, IL‐1β, VEGF, and soluble ROBO4 proteins, whose weighted combination had been previously reported in association with future CA bleeding. Statistical Tests: Mann–Whitney U‐test for univariate analyses. Logistic regression models minimizing the Bayesian information criterion (BIC), testing sensitivity and specificity (receiver operating characteristic curves) of weighted combinations of parameters. Results: The best prognostic biomarker for lesional bleed or growth included brainstem lesion location, mean lesional permeability, and low‐value perfusion cluster mean (BIC = 201.5, sensitivity = 77%, specificity = 72%, P < 0.05). Adding a previously published prognostic plasma protein biomarker improved the performance of the imaging model (sensitivity = 100%, specificity = 88%, P < 0.05). Data Conclusion: A combination of MRI‐based descriptors reflecting higher lesional permeability and lower perfusion cluster may potentially predict future bleed/growth in CAs. The sensitivity and specificity of the prognostic imaging biomarker can be enhanced when combined with brainstem lesion location and a plasma protein biomarker of CA hemorrhage. Level of Evidence: 2 Technical Efficacy: Stage 5 [ABSTRACT FROM AUTHOR]