Health Commission of Henan Province Key Laboratory for Precision Diagnosis and Treatment of Pediatric Tumor, Children's Hospital Affiliated to Zhengzhou University, Zhengzhou, 450018, China.
Henan Key Laboratory of Rare Diseases, Endocrinology and Metabolism Center, The First Affiliated Hospital, and College of Clinical Medicine of Henan University of Science and Technology, Luoyang, 471003, China.
Sci Rep. 2024 Jul 9;14(1):15796. doi: 10.1038/s41598-024-66893-2.
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.
临床诊断胆道闭锁（BA）具有挑战性，尤其是在区分其与胆汁淤积（CS）方面。此外，BA 的预后不佳，并且缺乏有效的非侵入性诊断模型来进行检测。因此，本研究旨在通过全面的代谢组学分析，阐明无肝异常的 BA 患儿、CS 患儿和正常对照（NC）儿童之间的代谢差异。此外，我们的目标是开发一种先进的诊断模型，以识别 BA。对 90 名 BA 患儿、48 名 CS 患儿和 47 名无任何肝异常 NC 患儿的血浆样本进行代谢组学分析，结果显示 3 组之间的代谢物谱存在显著差异，尤其是 BA 和 CS 之间。在正模式下共鉴定出 238 个差异代谢物，而在负模式下检测到 89 个差异代谢物。富集分析显示 10 个不同的代谢途径存在差异，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 个标志物，并结合对另外 3 个生物标志物（LysoPC(18:2(9Z,12Z))、PC (22:5(7Z,10Z,13Z,16Z,19Z)/14:0) 和 Biliverdin-IX-α）的探索，构建了一个基于逻辑回归分析的 BA 诊断模型。由此产生的 ROC 曲线下面积确定为 0.968。本研究采用代谢组学分析为 BA 构建了一种诊断模型，这是一种创新且开创性的方法，可减少不必要的侵入性检查，有助于 BA 患者的诊断和预后的改善。
