Existing tests for detecting liver fibrosis, inflammation and steatosis, three stages of liver disease that are still reversible are severely hampered by limited accuracy or invasive nature. Here, we present a paired liver-plasma proteomics approach to infer molecular pathophysiology and to identify biomarkers in a cross-sectional alcohol-related liver disease cohort of nearly 600 individuals. Metabolic functions were downregulated whereas fibrosis-associated signaling and novel immune responses were upregulated, but only half of tissue proteome changes were transmitted to the circulation. Machine learning models based on our biomarker panels outperformed existing tests, laying the foundation for a generic proteomic liver health assessment.

中文翻译：

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配对的肝活检和血浆蛋白质组学研究揭示了与酒精相关的肝病的循环生物标志物

现有的用于检测肝纤维化，炎症和脂肪变性，仍可逆的三个阶段的肝病的检测方法由于准确性或侵袭性的限制而受到严重阻碍。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，并在近600名与酒精相关的横断面肝病队列中识别生物标志物。代谢功能被下调，而纤维化相关信号和新的免疫反应被上调，但是只有一半的组织蛋白质组变化被传递到循环系统。基于我们的生物标志物面板的机器学习模型优于现有测试，为通用的蛋白质组学肝脏健康评估奠定了基础。