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.

The authors have declared no competing interest.

Aaron Gusdon received support from the National Institute of Neurological Disorders and Stroke under award (K23NS121628). Huimahn Alex Choi received support from the National Institute of Neurological Disorders and Stroke under award (1R61NS119640-01A1 and R01NS131469). Xuefang Ren received support from the National Institutes of Health (1R01NS117606-01A1 and 1-R01-AG-073659-01A1) and new faculty start-up funds from the University of Texas Health Science Center at Houston. Neeraj Badjatia received support from the Neurocritical care Society Investing in CLINical Neurocticial CarE Research (INCLINE) Grant. Alice Ryan received support from a Senior Research Career Scientist Award (IK6 RX003977) from the U.S. Department of Veterans Affairs Rehabilitation R&D Service and VA Baltimore GRECC, National Institute on Aging Claude D. Pepper Older Americans Independence Center (P30-AG028747). Folfefac Atem received support from the National Institutes of Health (R01AR074989) and the Patient-Centered Outcomes Research Institute (PCS-1605-35413).

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. Institutional review boards (IRB) at both institutions reviewed and approved this study. IRB approvals were as follows: UTHSC-MHH (HSC-MS-12-0637) and UMSOM (HP-00074174). All methods were performed in accordance with the Declaration of Helsinki. Prior to enrollment, informed consent was obtained from all patients or their legal guardians.

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