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.

酒精相关性肝病 (ALD) 是全世界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——的了解仍不完整。在这里，我们提出了一种配对的肝脏-血浆蛋白质组学方法来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 名对照者和 459 名 ALD 患者）中的诊断和预后能力，其中 360 名进行了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 个肝脏活检样本，该工作流程具有较短的梯度时间和增强的数据独立采集方案，仅用了 3 周的测量时间。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，这些面板比现有的更准确地检测到显着纤维化（接受者操作特征 - 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）临床试验（德龙试验，P  < 0.05）。发现这些生物标志物组合可准确预测未来肝脏相关事件和全因死亡率，Harrell 的C指数分别为 0.90 和 0.79。一个独立的验证队列重现了诊断模型的性能，为基于 MS 的常规肝病检测奠定了基础。