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Published on: November 19, 2020
Systemic Metabolic Alterations after Aneurysmal Subarachnoid Hemorrhage: A Plasma Metabolomics Approach
Bosco Seong Kyu Yang1, Jude P J Savarraj1, Hua Chen1
1Division of Neurocritical Care, Department of Neurosurgery, McGovern School of Medicine, University of Texas Health Science Center, Houston, TX, USA.
Background:
Aneurysmal subarachnoid hemorrhage (aSAH) causes systemic changes that contribute to delayed cerebral ischemia (DCI) and morbidity. Circulating metabolites reflecting underlying pathophysiological mechanisms warrant investigation as biomarker candidates.
Methods:
Blood samples, prospectively collected within 24 hours (T1) of admission and 7-days (T2) post ictus, from patients with acute aSAH from two tertiary care centers were retrospectively analyzed. Samples from healthy subjects and patients with non-neurologic critical illness served as controls. A validated external analysis platform was used to perform untargeted metabolomics. Bioinformatics analyses were conducted to identify metabolomic profiles defining each group and delineate metabolic pathways altered in each group. Machine learning (ML) models were developed incorporating key metabolites to improve DCI prediction.
Results:
Among 70 aSAH, 30 healthy control, and 17 sick control subjects, a total of 1,117 metabolites were detected. Groups were matched among key clinical variables. DCI occurred in 36% of aSAH subjects, and poor functional outcome was observed in 70% at discharge. Metabolomic profiles readily discriminated the groups. aSAH subjects demonstrated a robust mobilization of lipid metabolites, with increased levels of free fatty acids (FFAs), mono- and diacylglycerols (MAG, DAG) compared with both control groups. aSAH subjects also had decreased circulating amino acid derived metabolites, consistent with increased catabolism. DCI was associated with increased sphingolipids (sphingosine and sphinganine) and decreased acylcarnitines and S-adenosylhomocysteine at T1. Decreased lysophospholipids and acylcarnitines were associated with poor outcomes. Incorporating metabolites into ML models improved prediction of DCI compared with clinical variables alone.
Conclusions:
Profound metabolic shifts occur after aSAH with characteristic increases in lipid and decreases in amino acid metabolites. Key lipid metabolites associated with outcomes (sphingolipids, lysophospholipids, and acylcarnitines) provide insight into systemic changes driving secondary complications. These metabolites may also prove to be useful biomarkers to improve prognostication and personalize aSAH care.
