Journal
SMALL METHODS
Volume 7, Issue 3, Pages -Publisher
WILEY-V C H VERLAG GMBH
DOI: 10.1002/smtd.202201486
Keywords
biomarkers; cerebrovascular disease; mass spectrometry; metabolic fingerprints; nanoparticles
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Unruptured intracranial aneurysm (UIA) is a high-risk cerebrovascular saccular dilatation. Serum metabolic fingerprints are a promising alternative for early diagnosis. This study applied nanoparticle enhanced laser desorption/ionization mass spectrometry to obtain high-performance UIA-specific serum metabolic fingerprints. The constructed machine learning model achieved a diagnostic performance with an AUC of 0.842 and identified lactate, glutamine, homoarginine, and 3-methylglutaconic acid as the metabolic biomarker panel.
Unruptured intracranial aneurysm (UIA) is a high-risk cerebrovascular saccular dilatation, the effective medical management of which depends on high-performance diagnosis. However, most UIAs are diagnosed incidentally during neurovascular imaging modalities, which are time-consuming and harmful (e.g., radiation). Serum metabolic fingerprints is a promising alternative for early diagnosis of UIA. Here, nanoparticle enhanced laser desorption/ionization mass spectrometry is applied to obtain high-performance UIA-specific serum metabolic fingerprints. Diagnostic performance with an area-under-the-curve (AUC) of 0.842 (95% confidence interval (CI): 0.783-0.891) is achieved by the constructed machine learning (ML) model, including ML algorithm selection and feature selection. Lactate, glutamine, homoarginine, and 3-methylglutaconic acid are identified as the metabolic biomarker panel, which showed satisfactory diagnosis (AUC of 0.812, 95% CI: 0.727-0.897) and effective growth risk assessment (p<0.05, two-tailed t-test) of UIAs. This work aims to promote the diagnostics of UIAs and metabolic biomarker screening for medical management.
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