Neurocritical care · 2025
Systemic Metabolic Alterations After Aneurysmal Subarachnoid Hemorrhage: A Plasma Metabolomics Approach.
СтатьяПоддержка NIH: внешняя программаНазвание и аннотация: Перевод пока не подготовлен
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Yang Bosco Seong Kyu, Savarraj Jude P J, Chen Hua, Hinds Sarah N, Torres Glenda L, Ryan Alice S, Atem Folefac D, Lorenzi Philip L, Ren Xuefang S, McCullough Louise D, Badjatia Neeraj, Choi Huimahn A, Gusdon Aaron M
Аннотация
BACKGROUND: Aneurysmal subarachnoid hemorrhage (aSAH) causes systemic changes that contribute to delayed cerebral ischemia (DCI). Systemic metabolomic profiles following aSAH were characterized and examined for their ability to predict outcomes.
METHODS: Blood samples prospectively collected within 24 h (T1) of admission and 7 days (T2) post ictus from patients with aSAH at two tertiary care centers were retrospectively analyzed. Samples from healthy individuals and patients with non-neurologic critical illness served as controls. A validated external analysis platform was used to perform untargeted metabolomics. Clinical data were prospectively collected and adjudicated. Bioinformatics analyses were conducted to identify metabolomic profiles defining each group and delineating relevant metabolic pathways. Machine learning (ML) models for outcome prediction were developed, incorporating key metabolites.
RESULTS: A total of 70 subjects with aSAH, 30 healthy controls, and 17 critically ill (sick) controls were included. Groups were matched among key clinical variables. DCI occurred in 36% of subjects with aSAH, and poor functional outcome occurred in 70% at discharge. Metabolomic profiles readily discriminated the groups. aSAH subjects demonstrated mobilization of lipid metabolites, with increased levels of free fatty acids, monoacylglycerols, and diacylglycerols compared with control groups (average 1.8-fold increase; q < 0.05). Circulating amino acid-derived metabolites were significantly decreased, showing an average 30% reduction (q < 0.05), consistent with increased catabolism. DCI was associated with increased sphingolipids (2.1-fold) and decreased acylcarnitines (1.9-fold) and S-adenosylhomocysteine (1.2-fold) at T1 (p < 0.05). Decreased lysophospholipids (1.4-fold) and acylcarnitines (1.5-fold) were associated with poor outcomes (p < 0.05). Incorporating metabolites into ML models improved prediction of DCI compared with clinical variables alone (elastic net linear regression p < 0.01, extreme gradient boosting p = 0.016).
CONCLUSIONS: Profound metabolic shifts occur after aSAH, with characteristic increases in lipid and decreases in amino acid metabolite levels. Key lipid metabolites (sphingolipids, lysophospholipids, and acylcarnitines) associated with outcomes provide insight into the pathophysiological changes driving secondary complications after aSAH. These metabolites may be useful biomarkers to improve prognostication and personalize aSAH care.
METHODS: Blood samples prospectively collected within 24 h (T1) of admission and 7 days (T2) post ictus from patients with aSAH at two tertiary care centers were retrospectively analyzed. Samples from healthy individuals and patients with non-neurologic critical illness served as controls. A validated external analysis platform was used to perform untargeted metabolomics. Clinical data were prospectively collected and adjudicated. Bioinformatics analyses were conducted to identify metabolomic profiles defining each group and delineating relevant metabolic pathways. Machine learning (ML) models for outcome prediction were developed, incorporating key metabolites.
RESULTS: A total of 70 subjects with aSAH, 30 healthy controls, and 17 critically ill (sick) controls were included. Groups were matched among key clinical variables. DCI occurred in 36% of subjects with aSAH, and poor functional outcome occurred in 70% at discharge. Metabolomic profiles readily discriminated the groups. aSAH subjects demonstrated mobilization of lipid metabolites, with increased levels of free fatty acids, monoacylglycerols, and diacylglycerols compared with control groups (average 1.8-fold increase; q < 0.05). Circulating amino acid-derived metabolites were significantly decreased, showing an average 30% reduction (q < 0.05), consistent with increased catabolism. DCI was associated with increased sphingolipids (2.1-fold) and decreased acylcarnitines (1.9-fold) and S-adenosylhomocysteine (1.2-fold) at T1 (p < 0.05). Decreased lysophospholipids (1.4-fold) and acylcarnitines (1.5-fold) were associated with poor outcomes (p < 0.05). Incorporating metabolites into ML models improved prediction of DCI compared with clinical variables alone (elastic net linear regression p < 0.01, extreme gradient boosting p = 0.016).
CONCLUSIONS: Profound metabolic shifts occur after aSAH, with characteristic increases in lipid and decreases in amino acid metabolite levels. Key lipid metabolites (sphingolipids, lysophospholipids, and acylcarnitines) associated with outcomes provide insight into the pathophysiological changes driving secondary complications after aSAH. These metabolites may be useful biomarkers to improve prognostication and personalize aSAH care.