E-Institute of Shanghai Municipal Education Committee, Institute of Interdisciplinary Integrative Medicine Research, Shanghai University of Traditional Chinese Medicine, Shanghai, 201203, China.
Human Metabolomics Institute, Inc., Shenzhen, 518109, Guangdong, China.
BMC Med. 2020 Jun 5;18(1):144. doi: 10.1186/s12916-020-01595-w.
Accurate and noninvasive diagnosis and staging of liver fibrosis are essential for effective clinical management of chronic liver disease (CLD). We aimed to identify serum metabolite markers that reliably predict the stage of fibrosis in CLD patients.
We quantitatively profiled serum metabolites of participants in 2 independent cohorts. Based on the metabolomics data from cohort 1 (504 HBV associated liver fibrosis patients and 502 normal controls, NC), we selected a panel of 4 predictive metabolite markers. Consequently, we constructed 3 machine learning models with the 4 metabolite markers using random forest (RF), to differentiate CLD patients from normal controls (NC), to differentiate cirrhosis patients from fibrosis patients, and to differentiate advanced fibrosis from early fibrosis, respectively.
The panel of 4 metabolite markers consisted of taurocholate, tyrosine, valine, and linoelaidic acid. The RF models of the metabolite panel demonstrated the strongest stratification ability in cohort 1 to diagnose CLD patients from NC (area under the receiver operating characteristic curve (AUROC) = 0.997 and the precision-recall curve (AUPR) = 0.994), to differentiate fibrosis from cirrhosis (0.941, 0.870), and to stage liver fibrosis (0.918, 0.892). The diagnostic accuracy of the models was further validated in an independent cohort 2 consisting of 300 CLD patients with chronic HBV infection and 90 NC. The AUCs of the models were consistently higher than APRI, FIB-4, and AST/ALT ratio, with both greater sensitivity and specificity.
Our study showed that this 4-metabolite panel has potential usefulness in clinical assessments of CLD progression in patients with chronic hepatitis B virus infection.
准确、无创的肝纤维化诊断和分期对于慢性肝病（CLD）的有效临床管理至关重要。我们旨在确定可靠预测 CLD 患者纤维化分期的血清代谢标志物。
我们对 2 个独立队列的参与者的血清代谢物进行了定量分析。基于队列 1（504 例乙型肝炎病毒相关肝纤维化患者和 502 例正常对照，NC）的代谢组学数据，我们选择了一组 4 个预测性代谢标志物。随后，我们使用随机森林（RF）构建了 3 个基于 4 个代谢标志物的机器学习模型，分别用于区分 CLD 患者与正常对照（NC）、肝硬化患者与纤维化患者、以及晚期纤维化与早期纤维化。
该标志物组合由牛磺胆酸、酪氨酸、缬氨酸和亚油酸组成。在队列 1 中，该代谢标志物组合的 RF 模型在诊断 CLD 患者与 NC 时表现出最强的分层能力（接受者操作特征曲线下面积（AUROC）= 0.997，精确-召回曲线下面积（AUPR）= 0.994）、区分纤维化与肝硬化（0.941，0.870），以及分期肝纤维化（0.918，0.892）。在由 300 例慢性乙型肝炎病毒感染的 CLD 患者和 90 例 NC 组成的独立队列 2 中，对模型的诊断准确性进行了进一步验证。模型的 AUC 始终高于 APRI、FIB-4 和 AST/ALT 比值，具有更高的敏感性和特异性。
本研究表明，该 4 代谢物标志物组合在慢性乙型肝炎病毒感染患者 CLD 进展的临床评估中具有潜在的应用价值。
