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

Abstract

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.

Hypoxanthine is a pharmacodynamic marker of ischemic brain edema modified by glibenclamide

“…Final values for each metabolite were reported as a ratio to normal human pooled plasma samples. 15 , 16 , 17 We first examined the association of each metabolite with two orthogonal measures of brain edema, MLS 18 , 19 and MMP-9. 14 Regression analyses identified 18 metabolites in association with MLS at a nominal p value threshold <0.05 ( Table S1 ).…”

Rethinking the initial changes in subarachnoid haemorrhage: Focusing on real-time metabolism during early brain injury

“…Therefore, time-dependent alterations in CSF metabolites and compounds may elucidate pathophysiological processes after SAH, and machine learning-driven metabolomic evaluation of CSF might provide more in-depth information on the pathologic underpinnings of secondary brain injury after SAH. 11 …”

Systems neuroimmunology: a review of multiomics methodologies to characterize neuroimmunological interactions in spinal and cranial diseases

“…30 High-throughput methods from multiomics are enabling researchers to interrogate changes in the CSF of patients with aSAH and ICH. Here, we summarize recent work from Koch et al 7 that used metabolomics and machine learning to study biomarkers in the CSF of patients with aSAH.…”

“…In their study, Koch et al used metabolomic data derived from the CSF samples of 81 patients with aSAH to identify key metabolites in an immune-signaling pathway that were predictive of poor outcomes after aSAH. 7 CSF samples were obtained from unclamped external ventricular drains at 3 different time points (0-5 days, 6-10 days, and 11-15 days) after admission. CSF samples were additionally obtained in a control cohort of 16 patients with nonruptured cerebral aneurysms through either a lumbar drain or lumbar puncture.…”

“…In fact, the immune system has been found to have a key role in a variety of conditions treated by spine, tumor, and cerebrovascular neurosurgeons including 1) the immune-mediated progression of degenerative disc disease (DDD), 2 2) the susceptibility of brain tumors like glioblastoma (GBM) [3][4][5][6] to novel immunotherapies, and 3) the response of brain tissue to extravasated blood products during aneurysmal subarachnoid hemorrhage (aSAH). 7 Systems neuroimmunology is an emerging framework for interrogating neuroimmunological interactions at a systems level, which can be used to study the immune processes that drive neurosurgical disease. 8-10 Systems neuroimmunology leverages multiomics data from genomic, proteomic, metabolomic, and other sources to arrive at a systems-level view of which genes, proteins, and metabolites of the immune system are active during healthy versus diseased states.…”