Plasma metabolites with mechanistic and clinical links to the neurovascular disease cavernous angioma.

BACKGROUND: Cavernous angiomas (CAs) affect 0.5% of the population, predisposing to serious neurologic sequelae from brain bleeding. A leaky gut epithelium associated with a permissive gut microbiome, was identified in patients who develop CAs, favoring lipid polysaccharide producing bacterial species. Micro-ribonucleic acids along with plasma levels of proteins reflecting angiogenesis and inflammation were also previously correlated with CA and CA with symptomatic hemorrhage. METHODS: The plasma metabolome of CA patients and CA patients with symptomatic hemorrhage was assessed using liquid-chromatography mass spectrometry. Differential metabolites were identified using partial least squares-discriminant analysis (p < 0.05, FDR corrected). Interactions between these metabolites and the previously established CA transcriptome, microbiome, and differential proteins were queried for mechanistic relevance. Differential metabolites in CA patients with symptomatic hemorrhage were then validated in an independent, propensity matched cohort. A machine learning-implemented, Bayesian approach was used to integrate proteins, micro-RNAs and metabolites to develop a diagnostic model for CA patients with symptomatic hemorrhage. RESULTS: Here we identify plasma metabolites, including cholic acid and hypoxanthine distinguishing CA patients, while arachidonic and linoleic acids distinguish those with symptomatic hemorrhage. Plasma metabolites are linked to the permissive microbiome genes, and to previously implicated disease mechanisms. The metabolites distinguishing CA with symptomatic hemorrhage are validated in an independent propensity-matched cohort, and their integration, along with levels of circulating miRNAs, enhance the performance of plasma protein biomarkers (up to 85% sensitivity and 80% specificity). CONCLUSIONS: Plasma metabolites reflect CAs and their hemorrhagic activity. A model of their multiomic integration is applicable to other pathologies.

Cavernous angiomas (CAs) are clusters of abnormal blood vessels found in the brain or spinal cord. A blood test that could identify people with CAs that have recently bled would help determine who need surgery or closer medical monitoring. We looked at the blood of people with CAs to compare the levels of metabolites, a type of small molecule produced within the body, in those who had recently bled and those who had not. We found that some metabolites may contribute to CA and have an impact on CA symptoms. Monitoring the levels of these metabolites can determine whether there had been a recent bleed. In the future, drugs or other therapies could be developed that would block or change the levels of these molecules and possibly be used to treat CA disease.

Circulating Plasma miRNA Homologs in Mice and Humans Reflect Familial Cerebral Cavernous Malformation Disease.

Patients with familial cerebral cavernous malformation (CCM) inherit germline loss of function mutations and are susceptible to progressive development of brain lesions and neurological sequelae during their lifetime. To date, no homologous circulating molecules have been identified that can reflect the presence of germ line pathogenetic CCM mutations, either in animal models or patients. We hypothesize that homologous differentially expressed (DE) plasma miRNAs can reflect the CCM germline mutation in preclinical murine models and patients. Herein, homologous DE plasma miRNAs with mechanistic putative gene targets within the transcriptome of preclinical and human CCM lesions were identified. Several of these gene targets were additionally found to be associated with CCM-enriched pathways identified using the Kyoto Encyclopedia of Genes and Genomes. DE miRNAs were also identified in familial-CCM patients who developed new brain lesions within the year following blood sample collection. The miRNome results were then validated in an independent cohort of human subjects with real-time-qPCR quantification, a technique facilitating plasma assays. Finally, a Bayesian-informed machine learning approach showed that a combination of plasma levels of miRNAs and circulating proteins improves the association with familial-CCM disease in human subjects to 95% accuracy. These findings act as an important proof of concept for the future development of translatable circulating biomarkers to be tested in preclinical studies and human trials aimed at monitoring and restoring gene function in CCM and other diseases.

Perfusion and Permeability MRI Predicts Future Cavernous Angioma Hemorrhage and Growth.

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-1ß, 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.

Baseline Characteristics of Patients With Cavernous Angiomas With Symptomatic Hemorrhage in Multisite Trial Readiness Project.

BACKGROUND AND PURPOSE: Brain cavernous angiomas with symptomatic hemorrhage (CASH) have a high risk of neurological disability from recurrent bleeding. Systematic assessment of baseline features and multisite validation of novel magnetic resonance imaging biomarkers are needed to optimize clinical trial design aimed at novel pharmacotherapies in CASH. METHODS: This prospective, multicenter, observational cohort study included adults with unresected, adjudicated brain CASH within the prior year. Six US sites screened and enrolled patients starting August 2018. Baseline demographics, clinical and imaging features, functional status (modified Rankin Scale and National Institutes of Health Stroke Scale), and patient quality of life outcomes (Patient-Reported Outcomes Measurement Information System-29 and EuroQol-5D) were summarized using descriptive statistics. Patient-Reported Outcomes Measurement Information System-29 scores were standardized against a reference population (mean 50, SD 10), and one-sample t test was performed for each domain. A subgroup underwent harmonized magnetic resonance imaging assessment of lesional iron content with quantitative susceptibility mapping and vascular permeability with dynamic contrast-enhanced quantitative perfusion. RESULTS: As of May 2020, 849 patients were screened and 110 CASH cases enrolled (13% prevalence of trial eligible cases). The average age at consent was 46±16 years, 53% were female, 41% were familial, and 43% were brainstem lesions. At enrollment, ≥90% of the cohort had independent functional outcome (modified Rankin Scale score ≤2 and National Institutes of Health Stroke Scale score <5). However, perceived health problems affecting quality of life were reported in >30% of patients (EuroQol-5D). Patients had significantly worse Patient-Reported Outcomes Measurement Information System-29 scores for anxiety (P=0.007), but better depression (P=0.002) and social satisfaction scores (P=0.012) compared with the general reference population. Mean baseline quantitative susceptibility mapping and permeability of CASH lesion were 0.45±0.17 ppm and 0.39±0.31 mL/100 g per minute, respectively, which were similar to historical CASH cases and consistent across sites. CONCLUSIONS: These baseline features will aid investigators in patient stratification and determining the most appropriate outcome measures for clinical trials of emerging pharmacotherapies in CASH.

Perfusion and permeability as diagnostic biomarkers of cavernous angioma with symptomatic hemorrhage.

Cavernous angiomas with symptomatic hemorrhage (CASH) have a high risk of rebleeding, and hence an accurate diagnosis is needed. With blood flow and vascular leak as established mechanisms, we analyzed perfusion and permeability derivations of dynamic contrast-enhanced quantitative perfusion (DCEQP) MRI in 745 lesions of 205 consecutive patients. Thirteen respective derivations of lesional perfusion and permeability were compared between lesions that bled within a year prior to imaging (N = 86), versus non-CASH (N = 659) using machine learning and univariate analyses. Based on logistic regression and minimizing the Bayesian information criterion (BIC), the best diagnostic biomarker of CASH within the prior year included brainstem lesion location, sporadic genotype, perfusion skewness, and high-perfusion cluster area (BIC = 414.9, sensitivity = 74%, specificity = 87%). Adding a diagnostic plasma protein biomarker enhanced sensitivity to 100% and specificity to 85%. A slightly modified derivation achieved similar accuracy (BIC = 321.6, sensitivity = 80%, specificity = 82%) in the cohort where CASH occurred 3-12 months prior to imaging after signs of hemorrhage would have disappeared on conventional MRI sequences. Adding the same plasma biomarker enhanced sensitivity to 100% and specificity to 87%. Lesional blood flow on DCEQP may distinguish CASH after hemorrhagic signs on conventional MRI have disappeared and are enhanced in combination with a plasma biomarker.

A Roadmap for Developing Plasma Diagnostic and Prognostic Biomarkers of Cerebral Cavernous Angioma With Symptomatic Hemorrhage (CASH).

BACKGROUND: Cerebral cavernous angioma (CA) is a capillary microangiopathy predisposing more than a million Americans to premature risk of brain hemorrhage. CA with recent symptomatic hemorrhage (SH), most likely to re-bleed with serious clinical sequelae, is the primary focus of therapeutic development. Signaling aberrations in CA include proliferative dysangiogenesis, blood-brain barrier hyperpermeability, inflammatory/immune processes, and anticoagulant vascular domain. Plasma levels of molecules reflecting these mechanisms and measures of vascular permeability and iron deposition on magnetic resonance imaging are biomarkers that have been correlated with CA hemorrhage. OBJECTIVE: To optimize these biomarkers to accurately diagnose cavernous angioma with symptomatic hemorrhage (CASH), prognosticate the risk of future SH, and monitor cases after a bleed and in response to therapy. METHODS: Additional candidate biomarkers, emerging from ongoing mechanistic and differential transcriptome studies, would further enhance the sensitivity and specificity of diagnosis and prediction of CASH. Integrative combinations of levels of plasma proteins and characteristic micro-ribonucleic acids may further strengthen biomarker associations. We will deploy advanced statistical and machine learning approaches for the integration of novel candidate biomarkers, rejecting noncorrelated candidates, and determining the best clustering and weighing of combined biomarker contributions. EXPECTED OUTCOMES: With the expertise of leading CA researchers, this project anticipates the development of future blood tests for the diagnosis and prediction of CASH to clinically advance towards precision medicine. DISCUSSION: The project tests a novel integrational approach of biomarker development in a mechanistically defined cerebrovascular disease with a relevant context of use, with an approach applicable to other neurological diseases with similar pathobiologic features.