Diagnosis of Unruptured Intracranial Aneurysm by High‐Performance Serum Metabolic Fingerprints. Issue 3 (12th January 2023)

• Record Type: Journal Article
• Authors: Su, Jiabin; Cao, Jing; Yang, Heng; Xu, Wei; Liu, Wanshan; Wang, Ruimin; Huang, Yida; Wu, Jiao; Gao, Xinjie; Weng, Ruiyuan; Pu, Jun; Liu, Ning; Gu, Yuxiang; Qian, Kun; Ni, Wei
• Is Part Of: Small methods. Volume 7:Issue 3(2023)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2023-01-12
• Subjects: biomarkers -- cerebrovascular disease -- mass spectrometry -- metabolic fingerprints -- nanoparticles; Nanotechnology -- Methodology -- Periodicals; Nanotechnology -- Periodicals; Periodicals; 620.5028
• DOI: 10.1002/smtd.202201486
• Languages: English
• ISSNs: 2366-9608

Abstract

Abstract: 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. Abstract : Nanoparticle enhanced laser desorption/ionization mass spectrometry is applied to obtain high‐performance unruptured intracranial aneurysm (UIA)‐specific serum metabolic fingerprints. Diagnostic performance with an area‐under‐the‐curve (AUC) of 0.842 is achieved by machine learning. The screened metabolic‐biomarker‐panel showed satisfactory diagnosis (AUC of 0.812) and effective growth risk assessment ( p <0.05) of UIAs. This work will contribute to diagnostics and biomarker screening of UIAs.