Section of Computational and Systems Medicine, Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, London, UK.
National Institute for Health Research Biomedical Research Unit (Gastrointestinal and Liver), Nottingham University Hospitals NHS Trust and University of Nottingham, Nottingham, UK.
Am J Gastroenterol. 2015 Jan;110(1):159-69. doi: 10.1038/ajg.2014.370. Epub 2014 Dec 23.
The invasive nature of biopsy alongside issues with categorical staging and sampling error has driven research into noninvasive biomarkers for the assessment of liver fibrosis in order to stratify and personalize treatment of patients with liver disease. Here, we sought to determine whether a metabonomic approach could be used to identify signatures reflective of the dynamic, pathological metabolic perturbations associated with fibrosis in chronic hepatitis C (CHC) patients.
Plasma nuclear magnetic resonance (NMR) spectral profiles were generated for two independent cohorts of CHC patients and healthy controls (n=50 original and n=63 validation). Spectral data were analyzed and significant discriminant biomarkers associated with fibrosis (as graded by enhanced liver fibrosis (ELF) and METAVIR scores) identified using orthogonal projection to latent structures (O-PLS).
Increased severity of fibrosis was associated with higher tyrosine, phenylalanine, methionine, citrate and, very-low-density lipoprotein (vLDL) and lower creatine, low-density lipoprotein (LDL), phosphatidylcholine, and N-Acetyl-α1-acid-glycoprotein. Although area under the receiver operator characteristic curve analysis revealed a high predictive performance for classification based on METAVIR-derived models, <40% of identified biomarkers were validated in the second cohort. In the ELF-derived models, however, over 80% of the biomarkers were validated.
Our findings suggest that modeling against a continuous ELF-derived score of fibrosis provides a more robust assessment of the metabolic changes associated with fibrosis than modeling against the categorical METAVIR score. Plasma metabolic phenotypes reflective of CHC-induced fibrosis primarily define alterations in amino-acid and lipid metabolism, and hence identify mechanistically relevant pathways for further investigation as therapeutic targets.
活检的侵袭性，以及分类分期和采样误差的问题，促使人们研究非侵入性生物标志物，以评估肝纤维化，从而对肝病患者进行分层和个体化治疗。在这里，我们试图确定代谢组学方法是否可以用于识别与慢性丙型肝炎（CHC）患者纤维化相关的动态病理代谢扰动的特征。
为两个独立的 CHC 患者和健康对照组（n=50 个原始组和 n=63 个验证组）生成了血浆核磁共振（NMR）光谱图谱。使用正交投影到潜在结构（O-PLS）分析光谱数据，并确定与纤维化相关的显著判别生物标志物（如增强肝纤维化（ELF）和 METAVIR 评分分级）。
纤维化的严重程度增加与酪氨酸、苯丙氨酸、蛋氨酸、柠檬酸和极低密度脂蛋白（vLDL）升高以及肌酸、低密度脂蛋白（LDL）、磷脂酰胆碱和 N-乙酰-α1-酸性糖蛋白降低有关。虽然基于 METAVIR 衍生模型的分类的接收器操作特性曲线分析显示出较高的预测性能，但在第二个队列中仅验证了 <40%的鉴定生物标志物。然而，在 ELF 衍生模型中，超过 80%的生物标志物得到了验证。
我们的研究结果表明，针对纤维化的连续 ELF 衍生评分进行建模比针对分类 METAVIR 评分进行建模提供了对纤维化相关代谢变化的更稳健评估。反映 CHC 诱导的纤维化的血浆代谢表型主要定义了氨基酸和脂质代谢的改变，从而确定了作为治疗靶点的进一步研究的机制相关途径。
