Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.
Department of Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Martinsried, Germany.
Nat Med. 2022 Jun;28(6):1277-1287. doi: 10.1038/s41591-022-01850-y. Epub 2022 Jun 2.
Alcohol-related liver disease (ALD) is a major cause of liver-related death worldwide, yet understanding of the three key pathological features of the disease-fibrosis, inflammation and steatosis-remains incomplete. Here, we present a paired liver-plasma proteomics approach to infer molecular pathophysiology and to explore the diagnostic and prognostic capability of plasma proteomics in 596 individuals (137 controls and 459 individuals with ALD), 360 of whom had biopsy-based histological assessment. We analyzed all plasma samples and 79 liver biopsies using a mass spectrometry (MS)-based proteomics workflow with short gradient times and an enhanced, data-independent acquisition scheme in only 3 weeks of measurement time. In plasma and liver biopsy tissues, metabolic functions were downregulated whereas fibrosis-associated signaling and immune responses were upregulated. Machine learning models identified proteomics biomarker panels that detected significant fibrosis (receiver operating characteristic-area under the curve (ROC-AUC), 0.92, accuracy, 0.82) and mild inflammation (ROC-AUC, 0.87, accuracy, 0.79) more accurately than existing clinical assays (DeLong's test, P < 0.05). These biomarker panels were found to be accurate in prediction of future liver-related events and all-cause mortality, with a Harrell's C-index of 0.90 and 0.79, respectively. An independent validation cohort reproduced the diagnostic model performance, laying the foundation for routine MS-based liver disease testing.
酒精性肝病 (ALD) 是全球范围内导致肝脏相关死亡的主要原因,但对该疾病的三个关键病理特征——纤维化、炎症和脂肪变性——的理解仍不完整。在这里,我们提出了一种配对的肝-血浆蛋白质组学方法,以推断分子病理生理学,并探索血浆蛋白质组学在 596 名个体(137 名对照和 459 名 ALD 个体)中的诊断和预后能力,其中 360 名个体具有基于活检的组织学评估。我们使用基于质谱 (MS) 的蛋白质组学工作流程分析了所有血浆样本和 79 个肝活检样本,该工作流程具有短梯度时间和增强的数据独立采集方案,仅在 3 周的测量时间内即可完成。在血浆和肝活检组织中,代谢功能下调,而纤维化相关信号和免疫反应上调。机器学习模型确定了蛋白质组学生物标志物面板,这些标志物可以更准确地检测出显著纤维化(接收者操作特征曲线下面积 (ROC-AUC),0.92,准确性,0.82)和轻度炎症(ROC-AUC,0.87,准确性,0.79),优于现有的临床检测(DeLong 检验,P<0.05)。这些生物标志物面板在预测未来的肝脏相关事件和全因死亡率方面表现出准确性,Harrell's C 指数分别为 0.90 和 0.79。一个独立的验证队列再现了诊断模型的性能,为常规基于 MS 的肝病检测奠定了基础。