Hansen Cecilie Bo, Møller Maria Elizabeth Engel, Pérez-Alós Laura, Israelsen Simone Bastrup, Drici Lylia, Ottenheijm Maud Eline, Nielsen Annelaura Bach, Wewer Albrechtsen Nicolai J, Benfield Thomas, Garred Peter
Laboratory of Molecular Medicine, Department of Clinical Immunology, Section 7631, Copenhagen University Hospital - Rigshospitalet, Copenhagen, Denmark.
Department of Clinical Immunology, Copenhagen University Hospital - Rigshospitalet, Copenhagen, Denmark.
iScience. 2025 Feb 17;28(3):112046. doi: 10.1016/j.isci.2025.112046. eCollection 2025 Mar 21.
Prognostic biomarkers have been widely studied in COVID-19, but their levels may be influenced by treatment strategies. This study examined plasma biomarkers and proteomic survival prediction in two unvaccinated hospitalized COVID-19 cohorts receiving different treatments. In a derivation cohort ( = 126) from early 2020, we performed plasma proteomic profiling and evaluated innate and complement system immune markers. A proteomic model based on differentially expressed proteins predicted 30-day mortality with an area under the curve (AUC) of 0.81. The model was tested in a validation cohort ( = 80) from late 2020, where patients received remdesivir and dexamethasone, and performed with an AUC of 0.75. Biomarker levels varied considerably between cohorts, sometimes in opposite directions, highlighting the impact of treatment regimens on biomarker expression. These findings underscore the need to account for treatment effects when developing prognostic models, as treatment differences may limit their generalizability across populations.
预后生物标志物在新冠病毒疾病（COVID-19）中已得到广泛研究，但其水平可能受到治疗策略的影响。本研究在两个接受不同治疗的未接种疫苗的住院COVID-19队列中检测了血浆生物标志物和蛋白质组学生存预测。在一个来自2020年初的推导队列（n = 126）中，我们进行了血浆蛋白质组学分析，并评估了先天免疫和补体系统免疫标志物。基于差异表达蛋白的蛋白质组学模型预测30天死亡率的曲线下面积（AUC）为0.81。该模型在一个来自2020年末的验证队列（n = 80）中进行了测试，该队列中的患者接受了瑞德西韦和地塞米松治疗，其AUC为0.75。各队列之间生物标志物水平差异很大，有时甚至呈相反方向，突出了治疗方案对生物标志物表达的影响。这些发现强调了在开发预后模型时考虑治疗效果的必要性，因为治疗差异可能会限制其在不同人群中的通用性。
