生物活性脂质谱作为HIV/HCV合并感染患者晚期纤维化的非侵入性生物标志物

IF 3.5 3区医学 Q2 ENDOCRINOLOGY & METABOLISM

Metabolomics Pub Date : 2025-07-01 DOI:10.1007/s11306-025-02293-5

Tzu-Hao Lee, Kara Wegermann, Rachel Safeek, Meredith Mock, Sam Lusk, Lisa St John-Williams, Will J Thompson, Joseph E Lucas, M Arthur Moseley, Keyur Patel, Susanna Naggie

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引用次数: 0

摘要

对于合并丙型肝炎病毒（HCV）感染的人类免疫缺陷病毒（HIV）（PWH）患者，晚期肝纤维化的非侵入性评估的准确性有限，需要新的工具。我们的目的是发现与HIV/HCV合并感染treatment-naïve患者的晚期肝纤维化相关的氧脂质谱。对40例患有HCV的PWH患者的血清样本进行了针对性的氧化脂素分析。以AST和5(S)- hepe、8-HETE、14,15- dihetre、4-HDoHE、14- HDoHE、7-HDoHE、9,10- dihome等7种代谢物组成的模型，其受体工作特性曲线下面积为0.93，最佳灵敏度和特异性分别为86%和88%。

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Bioactive lipid profiles as non-invasive biomarkers of advanced fibrosis in people with HIV/HCV co-infection.

Non-invasive assessments for advanced liver fibrosis have limited accuracy in persons with human immunodeficiency virus (HIV) (PWH) who have hepatitis C virus (HCV) co-infection, and new tools are needed. Our aim was to discover oxylipin profiles associated with advanced liver fibrosis in treatment-naïve patients with HIV/HCV co-infection. Serum samples from 40 PWH with HCV were subjected to targeted oxylipin analysis. A model with AST and seven metabolites, including 5(S)-HEPE, 8-HETE, 14,15-DiHETrE, 4-HDoHE, 14- HDoHE, 7-HDoHE, and 9,10-DiHOME yielded an area under the receiver operating characteristic curve of 0.93, with optimal sensitivity and specificity of 86% and 88%, respectively.

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来源期刊

Metabolomics 医学-内分泌学与代谢

CiteScore

6.60

自引率

2.80%

发文量

审稿时长

2 months

期刊介绍： Metabolomics publishes current research regarding the development of technology platforms for metabolomics. This includes, but is not limited to: metabolomic applications within man, including pre-clinical and clinical pharmacometabolomics for precision medicine metabolic profiling and fingerprinting metabolite target analysis metabolomic applications within animals, plants and microbes transcriptomics and proteomics in systems biology Metabolomics is an indispensable platform for researchers using new post-genomics approaches, to discover networks and interactions between metabolites, pharmaceuticals, SNPs, proteins and more. Its articles go beyond the genome and metabolome, by including original clinical study material together with big data from new emerging technologies.