ArticleNeurosurgery2021
Machine Learning-Driven Metabolomic Evaluation of Cerebrospinal Fluid: Insights Into Poor Outcomes After Aneurysmal Subarachnoid Hemorrhage.
Article in Neurosurgery, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed, 36 citations in OpenAlex.
- Metabolome Wide Mendelian Randomization Assessing the Causal Relationship Between Serum Metabolites, Cerebrospinal Metabolites, and Aneurysmal Subarachnoid Hemorrhage.Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology · 2026Article
- Phenotype-Aligned Metabolomics Identifies Plasma Arginine as a Candidate Predictor of Delayed Cerebral Ischemia after Aneurysmal Subarachnoid Hemorrhage.Translational stroke research · 2026Article
- A machine learning framework using urinary biomarkers for pancreatic ductal adenocarcinoma prediction with post hoc validation via single-cell transcriptomics.Briefings in bioinformatics · 2025Article
- Predicting Mortality in Subarachnoid Hemorrhage Patients Using Big Data and Machine Learning: A Nationwide Study in Türkiye.Journal of clinical medicine · 2025Article
- Artificial Intelligence for Patient Safety and Surgical Education in Neurosurgery.JMA journal · 2025Review
- Metabolomic and lipidomic pathways in aneurysmal subarachnoid hemorrhage.Neurotherapeutics : the journal of the American Society for Experimental NeuroTherapeutics · 2025Review
- SDMA as a marker and mediator in cerebrovascular disease.Clinical science (London, England : 1979) · 2024Review
- Iron Accumulation and Metabolic Alterations in Aneurysmal Subarachnoid Hemorrhage.Stroke (Hoboken, N.J.) · 2024Article
- Study the local metabolic changes of aneurysms through microcatheter sampling.Scientific reports · 2024Article
- CSF metabolomics alterations after aneurysmal subarachnoid hemorrhage: what do we know?Acta neurologica Belgica · 2023Review
- Predicting the Outcome of Patients with Aneurysmal Subarachnoid Hemorrhage: A Machine-Learning-Guided Scorecard.Journal of clinical medicine · 2023Article
- Artificial Intelligence in Neurosurgery: A State-of-the-Art Review from Past to Future.Diagnostics (Basel, Switzerland) · 2023Review
- Metabolomic Analysis in Neurocritical Care Patients.Metabolites · 2023Review
- Article
- Plasma levels of CD36 and glutathione as biomarkers for ruptured intracranial aneurysm.Open life sciences · 2023Article
- Additive prognostic impact of the cerebrospinal fluid arginine/ornithine ratio to established clinical scores in aneurysmal subarachnoid hemorrhage.Frontiers in neurology · 2023Article
- Long-Term Effects of Repeated Blast Exposure in United States Special Operations Forces Personnel: A Pilot Study Protocol.Journal of neurotrauma · 2022Article
- The role of L-arginine metabolism in neurocritical care patients.Neural regeneration research · 2022Review
- Hypoxanthine is a pharmacodynamic marker of ischemic brain edema modified by glibenclamide.Cell reports. Medicine · 2022Article
- Systems neuroimmunology: a review of multiomics methodologies to characterize neuroimmunological interactions in spinal and cranial diseases.Neurosurgical focus · 2022Review
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Authors and funding
8 authors at 3 institutions in 2 countries.
Funding
Abstract
backgroundAneurysmal 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.
objectiveTo ascertain potential insights into pathological mechanisms of injury after aSAH using an approach of metabolomics coupled with machine learning methods.
methodsUsing 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.
resultsEN 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).
conclusionSDMA, 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.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.