Plasma metabolic signatures for intracranial aneurysm and its rupture identified by pseudotargeted metabolomics.
Plasma metabolic signatures for intracranial aneurysm and its rupture identified by pseudotargeted metabolomics.
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DOI：
10.1016/j.cca.2022.11.002
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发表时间：
2022-11
期刊：
影响因子：
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通讯作者：
Kaijian Sun;Xin Zhang;X. Li;Xi-feng Li;Shi-xing Su;Yuan Luo;Hao Tian;M. Zeng;Cheng Wang
中科院分区：
文献类型：
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作者：
Kaijian Sun;Xin Zhang;X. Li;Xi-feng Li;Shi-xing Su;Yuan Luo;Hao Tian;M. Zeng;Cheng Wang
背景和目的IA危险分层的重要代谢特征及其潜在的生物学基础仍然难以捉摸。我们的研究旨在通过分析IA患者的血浆代谢谱来开发早期诊断模型和破裂分类模型。材料和方法来自105名参与者的队列的血浆样本，包括75名未破裂和破裂状态的IA患者（国际律师联合会，RIA）和30名对照参与者被收集用于使用超高效液相色谱-质谱法进行综合代谢评估。的假靶向代谢组学方法。此外，一个集成的机器学习策略的基础上LASSO，随机森林和逻辑回归用于特征选择和modelconstruction.ResultsThe代谢谱干扰显着UIA和RIA患者。值得注意的是，腺苷含量显着下调UIA，和各种甘氨酸结合的二级胆汁酸减少RIA患者。富集的KEGG途径包括谷胱甘肽代谢和胆汁酸代谢。两套生物标志物面板被定义为区分IA和其破裂的受试者工作特征曲线下的面积分别为0.843和0.929的validationsets.ConclusionsThe本研究可以有助于更好地了解IA的发病机制，并促进新的治疗靶点的发现。代谢物组可作为IA的潜在非侵入性诊断和风险分层工具。
Background and aimsThe vital metabolic signatures for IA risk stratification and its potential biological underpinnings remain elusive. Our study aimed to develop an early diagnosis model and rupture classification model by analyzing plasma metabolic profiles of IA patients.Materials and methodsPlasma samples from a cohort of 105 participants, including 75 IA patients in unruptured and ruptured status (UIA, RIA) and 30 control participants were collected for comprehensive metabolic evaluation using ultra-high-performance liquid chromatography–mass spectrometry-based pseudotargeted metabolomics method. Furthermore, an integrated machine learning strategy based on LASSO, random forest and logistic regression were used for feature selection and model construction.ResultsThe metabolic profiling disturbed significantly in UIA and RIA patients. Notably, adenosine content was significantly downregulated in UIA, and various glycine-conjugated secondary bile acids were decreased in RIA patients. Enriched KEGG pathways included glutathione metabolism and bile acid metabolism. Two sets of biomarker panels were defined to discriminate IA and its rupture with the area under receiver operating characteristic curve of 0.843 and 0.929 on the validation sets, respectively.ConclusionsThe present study could contribute to a better understanding of IA etiopathogenesis and facilitate discovery of new therapeutic targets. The metabolite panels may serve as potential non-invasive diagnostic and risk stratification tool for IA.