Systemic Metabolic Alterations After Aneurysmal Subarachnoid Hemorrhage: A Plasma Metabolomics Approach

Area of Science:

• Neuroscience
• Metabolomics
• Biomarker Discovery

Background:

• Aneurysmal subarachnoid hemorrhage (aSAH) triggers systemic changes contributing to delayed cerebral ischemia (DCI).
• Understanding these systemic metabolic shifts is crucial for predicting patient outcomes.
• Metabolomic profiling offers a novel approach to characterize these changes.

Purpose of the Study:

• To characterize systemic metabolomic profiles in patients following aSAH.
• To identify specific metabolites associated with DCI and poor functional outcomes.
• To evaluate the utility of metabolomic data in machine learning models for outcome prediction.

Main Methods:

• Untargeted metabolomic analysis of blood samples from aSAH patients, healthy controls, and critically ill controls.
• Prospective collection of clinical data and retrospective analysis of metabolomic data.
• Development of machine learning models incorporating key metabolites for DCI prediction.

Main Results:

• aSAH patients exhibited increased lipid metabolites (free fatty acids, monoacylglycerols, diacylglycerols) and decreased amino acid-derived metabolites.
• Specific lipid metabolites, including sphingolipids and acylcarnitines, were associated with DCI and poor outcomes.
• Machine learning models incorporating metabolites improved DCI prediction compared to clinical variables alone.

Conclusions:

• Significant metabolic alterations, particularly in lipid and amino acid metabolism, characterize aSAH.
• Key lipid metabolites serve as potential biomarkers for predicting DCI and functional outcomes.
• Metabolomic data can enhance prognostication and personalize treatment strategies for aSAH patients.