Internal Medicine and Hepatology Unit, Department of Medicine, Surgery and Dentistry, "Scuola Medica Salernitana", University of Salerno, Via Salvador Allende, 84081, Baronissi, SA, Italy.
Theoreo srl, Via degli Ulivi 3, 84090, Montecorvino Pugliano, SA, Italy.
Metabolomics. 2021 Jan 16;17(2):12. doi: 10.1007/s11306-020-01756-1.
Non-Alcoholic Fatty Liver Disease encompasses a spectrum of diseases ranging from simple steatosis to steatohepatitis (or NASH), up to cirrhosis and hepatocellular carcinoma (HCC). The challenge is to recognize the more severe and/or progressive pathology. A reliable non-invasive method does not exist. Untargeted metabolomics is a novel method to discover biomarkers and give insights on diseases pathophysiology.
We applied metabolomics to understand if simple steatosis, steatohepatitis and cirrhosis in NAFLD patients have peculiar metabolites profiles that can differentiate them among each-others and from controls.
Metabolomics signatures were obtained from 307 subjects from two separated enrollments. The first collected samples from 69 controls and 144 patients (78 steatosis, 23 NASH, 15 NASH-cirrhosis, 8 HCV-cirrhosis, 20 cryptogenic cirrhosis). The second, used as validation-set, enrolled 44 controls and 50 patients (34 steatosis, 10 NASH and 6 NASH-cirrhosis).The "Partial-Least-Square Discriminant-Analysis"(PLS-DA) was used to reveal class separation in metabolomics profiles between patients and controls and among each class of patients, and to reveal the metabolites contributing to class differentiation.
Several metabolites were selected as relevant, in particular:Glycocholic acid, Taurocholic acid, Phenylalanine, branched-chain amino-acids increased at the increase of the severity of the disease from steatosis to NASH, NASH-cirrhosis, while glutathione decreased (p < 0.001 for each). Moreover, an ensemble machine learning (EML) model was built (comprehending 10 different mathematical models) to verify diagnostic performance, showing an accuracy > 80% in NAFLD clinical stages prediction.
Metabolomics profiles of NAFLD patients could be a useful tool to non-invasively diagnose NAFLD and discriminate among the various stages of the disease, giving insights into its pathophysiology.
非酒精性脂肪性肝病（NAFLD）涵盖了一系列疾病，从单纯性脂肪变性到脂肪性肝炎（NASH），直至肝硬化和肝细胞癌（HCC）。挑战在于识别更严重和/或进展性的病理。目前还没有可靠的非侵入性方法。非靶向代谢组学是一种发现生物标志物并深入了解疾病病理生理学的新方法。
我们应用代谢组学来了解 NAFLD 患者的单纯性脂肪变性、脂肪性肝炎和肝硬化是否具有独特的代谢谱，可以将它们彼此区分开来，并与对照组区分开来。
从两次独立招募中获得了 307 名受试者的代谢组学特征。第一次采集了 69 名对照者和 144 名患者（78 名单纯性脂肪变性、23 名 NASH、15 名 NASH 肝硬化、8 名 HCV 肝硬化、20 名隐匿性肝硬化）的样本。第二次作为验证集，招募了 44 名对照者和 50 名患者（34 名单纯性脂肪变性、10 名 NASH 和 6 名 NASH 肝硬化）。使用“偏最小二乘判别分析”（PLS-DA）揭示患者和对照者之间以及每个患者类别之间的代谢组学特征中的分类分离，并揭示导致分类差异的代谢物。
选择了几种相关的代谢物，特别是：甘氨胆酸、牛磺胆酸、苯丙氨酸、支链氨基酸随着疾病从单纯性脂肪变性到 NASH、NASH 肝硬化的严重程度增加而增加，而谷胱甘肽则减少（每个均<0.001）。此外，构建了一个集成机器学习（EML）模型（包含 10 种不同的数学模型）来验证诊断性能，在预测 NAFLD 临床阶段方面准确率>80%。
NAFLD 患者的代谢组学特征可能是一种有用的工具，可以非侵入性地诊断 NAFLD，并区分疾病的各个阶段，深入了解其病理生理学。
