PDF(Pubmed)
Abstract：
The clinical diagnosis of biliary atresia (BA) poses challenges, particularly in distinguishing it from cholestasis (CS). Moreover, the prognosis for BA is unfavorable and there is a dearth of effective non-invasive diagnostic models for detection. Therefore, the aim of this study is to elucidate the metabolic disparities among children with BA, CS, and normal controls (NC) without any hepatic abnormalities through comprehensive metabolomics analysis. Additionally, our objective is to develop an advanced diagnostic model that enables identification of BA. The plasma samples from 90 children with BA, 48 children with CS, and 47 NC without any liver abnormalities children were subjected to metabolomics analysis, revealing significant differences in metabolite profiles among the 3 groups, particularly between BA and CS. A total of 238 differential metabolites were identified in the positive mode, while 89 differential metabolites were detected in the negative mode. Enrichment analysis revealed 10 distinct metabolic pathways that differed, such as lysine degradation, bile acid biosynthesis. A total of 18 biomarkers were identified through biomarker analysis, and in combination with the exploration of 3 additional biomarkers (LysoPC(18:2(9Z,12Z)), PC (22:5(7Z,10Z,13Z,16Z,19Z)/14:0), and Biliverdin-IX-α), a diagnostic model for BA was constructed using logistic regression analysis. The resulting ROC area under the curve was determined to be 0.968. This study presents an innovative and pioneering approach that utilizes metabolomics analysis to develop a diagnostic model for BA, thereby reducing the need for unnecessary invasive examinations and contributing to advancements in diagnosis and prognosis for patients with BA.
摘要：
胆道闭锁（BA）的临床诊断提出了挑战，特别是在区分它与胆汁淤积（CS）。此外，BA的预后不良,且缺乏有效的非侵入性诊断模型进行检测.因此,这项研究的目的是阐明BA儿童之间的代谢差异，CS,通过全面的代谢组学分析，没有任何肝脏异常的正常对照（NC）。此外,我们的目标是开发一种先进的诊断模型，能够识别BA。来自90名BA儿童的血浆样本，48名儿童CS，和47没有任何肝脏异常的NC儿童进行代谢组学分析，揭示了3组之间代谢物谱的显着差异，特别是在BA和CS之间。在阳性模式中总共鉴定出238种差异代谢物，在阴性模式下检测到89种差异代谢物。富集分析揭示了10种不同的代谢途径，如赖氨酸降解，胆汁酸生物合成。通过生物标志物分析，共鉴定出18种生物标志物，并结合3种其他生物标志物的探索(LysoPC(18：2(9Z，12Z)),PC(22:5(7Z,10Z,13Z,16Z,19Z)/14:0)，和Biliverdin-IX-α），采用logistic回归分析构建BA诊断模型.所得到的曲线下的ROC面积被确定为0.968。本研究提出了一种创新和开创性的方法，利用代谢组学分析来开发BA的诊断模型，从而减少了不必要的侵入性检查的需要，并有助于提高BA患者的诊断和预后。
