High-throughput metabolite profiling: identification of plasma taurine as a potential biomarker of functional outcome after aneurysmal subarachnoid hemorrhage

Early Metabolic Disruption and Predictive Biomarkers of Delayed-Cerebral Ischemia in Aneurysmal Subarachnoid Hemorrhage

Delayed cerebral ischemia (DCI) following aneurysmal subarachnoid hemorrhage (aSAH) is a major cause of complications and death. Here, we set out to identify high-performance predictive biomarkers of DCI and its underlying metabolic disruptions using metabolomics and lipidomics approaches. This single-center prospective observational study enrolled 61 consecutive patients with severe aSAH; among them, 22 experienced a DCI. Nine patients without aSAH were included as validation controls. Blood and cerebrospinal fluid (CSF) were sampled within the first 24 h after admission. We identified a panel of 20 metabolites that, together, showed high predictive performance for DCI. This panel of metabolites included lactate, cotinine, salicylate, 6 phosphatidylcholines, and 4 sphingomyelins. The interplay of the metabolome and the lipidome found between CSF and plasma in our patients underscores that aSAH and its associated DCI complications can extend beyond cerebral implications, with a peripheral dimension as well. As an illustration, early biological disruptions that might explain the subsequent DCI found systemic hypoxia driven mainly by higher blood lactate, arginine, and proline metabolism likely associated with vascular NO and disrupted ceramide/sphingolipid metabolism. We conclude that targeting early peripheral hypoxia preceding DCI could provide an interesting strategy for the prevention of vascular dysfunction.

WITHDRAWN: A narrative review of techniques for surgical revascularization of the extracranial vertebral artery in vertebrobasilar insufficiency

The Publisher regrets that this article is an accidental duplication of an article that has already been published in Neurochirurgie, volume 70. https://doi.org/10.1016/j.neuchi.2023.101516. The duplicate article has therefore been withdrawn. The full Elsevier Policy on Article Withdrawal can be found at https://www.elsevier.com/about/policies/article-withdrawal.

Plasma Metabolites Link Dietary Patterns to Stroke Risk

OBJECTIVE

While dietary intake is linked to stroke risk, surrogate markers that could inform personalized dietary interventions are lacking. We identified metabolites associated with diet patterns and incident stroke in a nested cohort from the REasons for Geographic and Racial Differences in Stroke (REGARDS) study.

METHODS

Levels of 162 metabolites were measured in baseline plasma from stroke cases (n = 1,198) and random controls (n = 904). We examined associations between metabolites and a plant-based diet pattern previously linked to reduced stroke risk in REGARDS. Secondary analyses included 3 additional stroke-associated diet patterns: a Mediterranean, Dietary Approaches to Stop Hypertension (DASH), and Southern diet. Metabolites were tested using Cox proportional hazards models with incident stroke as the outcome. Replication was performed in the Jackson Heart Study (JHS). Inverse odds ratio-weighted mediation was used to determine whether metabolites mediated the association between a plant-based diet and stroke risk.

RESULTS

Metabolites associated with a plant-based diet included the gut metabolite indole-3-propionic acid (β = 0.23, 95% confidence interval [CI] [0.14, 0.33], p = 1.14 × 10-6 ), guanosine (β = -0.13, 95% CI [-0.19, -0.07], p = 6.48 × 10-5 ), gluconic acid (β = -0.11, 95% CI [-0.18, -0.04], p = 2.06 × 10-3 ), and C7 carnitine (β = -0.16, 95% CI [-0.24, -0.09], p = 4.14 × 10-5 ). All of these metabolites were associated with both additional diet patterns and altered stroke risk. Mediation analyses identified guanosine (32.6% mediation, p = 1.51 × 10-3 ), gluconic acid (35.7%, p = 2.28 × 10-3 ), and C7 carnitine (26.2%, p = 1.88 × 10-2 ) as mediators linking a plant-based diet to reduced stroke risk.

INTERPRETATION

A subset of diet-related metabolites are associated with risk of stroke. These metabolites could serve as surrogate markers that inform dietary interventions. ANN NEUROL 2023;93:500-510.

Metabolomics as a potential tool for monitoring patients with aneurysmal subarachnoid hemorrhage

Metabolomics has evolved into a particularly useful tool to study interactions between metabolites and serves as an aid in unraveling the complexity of entire metabolomes. Nonetheless, it is increasingly viewed as a methodology with practical applications in the clinical setting, where identifying and quantifying biomarkers of interest could prove useful for diagnostics. Starting from a concise overview of the most prominent analytical techniques employed in metabolomics, herein we present a review of its application in studies of brain metabolism and cerebrovascular diseases, paying most attention to its uses in researching aneurysmal subarachnoid hemorrhage. Both animal models and human studies are considered, and metabolites identified as potential biomarkers are highlighted.

Nucleosides Associated With Incident Ischemic Stroke in the REGARDS and JHS Cohorts

BACKGROUND AND OBJECTIVES

Both genetic and environmental factors contribute to stroke risk. We sought to identify novel metabolites associated with incident stroke in the Reasons for Geographic and Racial Differences in Stroke (REGARDS) cohort and determine whether they reflected genetic or environmental variation.

METHODS

This was a stroke case-cohort observational study nested in REGARDS. Cases were defined as incident stroke and metabolomic profiles were compared to a randomly selected control cohort. In baseline plasma samples, 162 metabolites were measured using liquid chromatography-tandem mass spectrometry. Cox proportional hazards models were adjusted for age, sex, race, and age by race in the base model. Fully adjusted models included traditional stroke risk factors. Mediation analyses conducted for these stroke risk factors used the metabolite as mediator. Genome-wide associations with the leading candidate metabolites were calculated using array data. Replication analyses in the Jackson Heart Study (JHS) were conducted using random effects meta-analysis.

RESULTS

There were 2,043 participants who were followed over an average period of 7.1 years, including 1,075 stroke cases and 968 random controls. Nine metabolites were associated with stroke in the base model, 8 of which were measured and remained significant in meta-analysis with JHS. In the fully adjusted model in REGARDS, guanosine (hazard ratio [HR] 1.34, 95% CI 1.18-1.53; p = 7.26 × 10^-6) and pseudouridine (HR 1.28, 95% CI 1.13-1.45; p = 1.03 × 10^-4) were associated with incident ischemic stroke following Bonferroni adjustment. Guanosine also partially mediated the relationship between hypertension and stroke (17.6%) and pseudouridine did not mediate any risk factor. Genome-wide association analysis identified loci rs34631560 and rs34631560 associated with pseudouridine, but these did not explain the association of pseudouridine with stroke.

DISCUSSION

Guanosine and pseudouridine are nucleosides associated with incident ischemic stroke independently of other risk factors. Genetic and mediation analyses suggest that environmental exposures rather than genetic variation link nucleoside levels to stroke risk.

CLASSIFICATION OF EVIDENCE

This study provides Class II evidence that guanosine and pseudouridine are associated with incident stroke.

Multiomics Profiling Reveals Signatures of Dysmetabolism in Urban Populations in Central India

Background: Non-communicable diseases (NCDs) have become a major cause of morbidity and mortality in India. Perturbation of host–microbiome interactions may be a key mechanism by which lifestyle-related risk factors such as tobacco use, alcohol consumption, and physical inactivity may influence metabolic health. There is an urgent need to identify relevant dysmetabolic traits for predicting risk of metabolic disorders, such as diabetes, among susceptible Asian Indians where NCDs are a growing epidemic. Methods: Here, we report the first in-depth phenotypic study in which we prospectively enrolled 218 adults from urban and rural areas of Central India and used multiomic profiling to identify relationships between microbial taxa and circulating biomarkers of cardiometabolic risk. Assays included fecal microbiota analysis by 16S ribosomal RNA gene amplicon sequencing, quantification of serum short chain fatty acids by gas chromatography-mass spectrometry, and multiplex assaying of serum diabetic proteins, cytokines, chemokines, and multi-isotype antibodies. Sera was also analysed for N-glycans and immunoglobulin G Fc N-glycopeptides. Results: Multiple hallmarks of dysmetabolism were identified in urbanites and young overweight adults, the majority of whom did not have a known diagnosis of diabetes. Association analyses revealed several host–microbe and metabolic associations. Conclusions: Host–microbe and metabolic interactions are differentially shaped by body weight and geographic status in Central Indians. Further exploration of these links may help create a molecular-level map for estimating risk of developing metabolic disorders and designing early interventions.

Predictive modeling in neurocritical care using causal artificial intelligence

Artificial intelligence (AI) and digital twin models of various systems have long been used in industry to test products quickly and efficiently. Use of digital twins in clinical medicine caught attention with the development of Archimedes, an AI model of diabetes, in 2003. More recently, AI models have been applied to the fields of cardiology, endocrinology, and undergraduate medical education. The use of digital twins and AI thus far has focused mainly on chronic disease management, their application in the field of critical care medicine remains much less explored. In neurocritical care, current AI technology focuses on interpreting electroencephalography, monitoring intracranial pressure, and prognosticating outcomes. AI models have been developed to interpret electroencephalograms by helping to annotate the tracings, detecting seizures, and identifying brain activation in unresponsive patients. In this mini-review we describe the challenges and opportunities in building an actionable AI model pertinent to neurocritical care that can be used to educate the newer generation of clinicians and augment clinical decision making.

Machine Learning-Driven Metabolomic Evaluation of Cerebrospinal Fluid: Insights Into Poor Outcomes After Aneurysmal Subarachnoid Hemorrhage

BACKGROUND

Aneurysmal subarachnoid hemorrhage (aSAH) is associated with a high mortality and poor neurologic outcomes. The biologic underpinnings of the morbidity and mortality associated with aSAH remain poorly understood.

OBJECTIVE

To ascertain potential insights into pathological mechanisms of injury after aSAH using an approach of metabolomics coupled with machine learning methods.

METHODS

Using cerebrospinal fluid (CSF) samples from 81 aSAH enrolled in a retrospective cohort biorepository, samples collected during the peak of delayed cerebral ischemia were analyzed using liquid chromatography-tandem mass spectrometry. A total of 138 metabolites were measured and quantified in each sample. Data were analyzed using elastic net (EN) machine learning and orthogonal partial least squares-discriminant analysis (OPLS-DA) to identify the leading CSF metabolites associated with poor outcome, as determined by the modified Rankin Scale (mRS) at discharge and at 90 d. Repeated measures analysis determined the effect size for each metabolite on poor outcome.

RESULTS

EN machine learning and OPLS-DA analysis identified 8 and 10 metabolites, respectively, that predicted poor mRS (mRS 3-6) at discharge and at 90 d. Of these candidates, symmetric dimethylarginine (SDMA), dimethylguanidine valeric acid (DMGV), and ornithine were consistent markers, with an association with poor mRS at discharge (P = .0005, .002, and .0001, respectively) and at 90 d (P = .0036, .0001, and .004, respectively). SDMA also demonstrated a significantly elevated CSF concentration compared with nonaneurysmal subarachnoid hemorrhage controls (P = .0087).

CONCLUSION

SDMA, DMGV, and ornithine are vasoactive molecules linked to the nitric oxide pathway that predicts poor outcome after severe aSAH. Further study of dimethylarginine metabolites in brain injury after aSAH is warranted.

Machine Learning Applications in the Neuro ICU: A Solution to Big Data Mayhem?

The neurological ICU (neuro ICU) often suffers from significant limitations due to scarce resource availability for their neurocritical care patients. Neuro ICU patients require frequent neurological evaluations, continuous monitoring of various physiological parameters, frequent imaging, and routine lab testing. This amasses large amounts of data specific to each patient. Neuro ICU teams are often overburdened by the resulting complexity of data for each patient. Machine Learning algorithms (ML), are uniquely capable of interpreting high-dimensional datasets that are too difficult for humans to comprehend. Therefore, the application of ML in the neuro ICU could alleviate the burden of analyzing big datasets for each patient. This review serves to (1) briefly summarize ML and compare the different types of MLs, (2) review recent ML applications to improve neuro ICU management and (3) describe the future implications of ML to neuro ICU management.