Liu Shiyang, Xu Wen, Tu Bo, Xiao Zhiqing, Li Xue, Huang Lei, Yuan Xin, Zhou Juanjuan, Yang Xinxin, Yang Junlian, Chang De, Chen Weiwei, Wang Fu-Sheng
Medical School of Chinese PLA, Beijing 100853, China.
Senior Department of Infectious Diseases, The Fifth Medical Center of Chinese PLA General Hospital, National Clinical Research Center for Infectious Diseases, Beijing 100073, China.
Biomedicines. 2025 May 10;13(5):1162. doi: 10.3390/biomedicines13051162.
Elderly patients infected with SARS-CoV-2 are at higher risk of developing cytokine storm and severe outcomes; however, specific immunological and proteomic biomarkers for early prediction remain unclear in this vulnerable group. We enrolled 182 elderly COVID-19 patients from the Chinese PLA General Hospital between November 2022 and April 2023, categorizing them based on progression to respiratory failure requiring mechanical ventilation (defined as severe progression). Olink proteomic analysis was performed on admission serum from 40 propensity score-matched samples, with differentially expressed proteins (DEPs) validated by cytometric bead array (CBA) in 178 patients. To predict severe progression, a model was developed using a 70% training set and validated on a 30% validation set. LASSO regression screened features followed by logistic regression and receiver operating characteristic (ROC) analysis to optimize the model by incrementally incorporating features ranked by random forest importance. Elderly patients progressing to severe COVID-19 exhibited early immune dysregulation, including neutrophilia, lymphopenia, monocytopenia, elevated procalcitonin (PCT), C-reactive protein (CRP), interleukin-6 (IL-6), neutrophil-to-lymphocyte ratio (NLR), and systemic immune-inflammation index (SII), as well as coagulation dysfunction and multi-organ injury. Proteomics identified a set of biomarkers, including tumor necrosis factor-related apoptosis-inducing ligand (TRAIL), and revealed disruptions in signaling pathways, including the mTOR and VEGF signaling pathways. The optimal predictive model, which incorporated PCT, IL-6, monocyte percentage, lymphocyte count, and TRAIL, achieved an area under curve (AUC) of 0.870 (0.729-1.000) during validation. TRAIL levels negatively correlated with fibrinogen ( < 0.05). Elderly COVID-19 patients with severe progression demonstrate early immune dysregulation, hyperinflammation, coagulation dysfunction, and multi-organ injury. The model we proposed effectively predicts disease progression in elderly COVID-19 patients, providing potential biomarkers for early clinical risk stratification in this vulnerable population.
感染新型冠状病毒2（SARS-CoV-2）的老年患者发生细胞因子风暴和出现严重后果的风险更高；然而，在这一脆弱群体中，用于早期预测的特定免疫和蛋白质组学生物标志物仍不明确。我们于2022年11月至2023年4月在中国人民解放军总医院招募了182例老年新型冠状病毒肺炎（COVID-19）患者，并根据是否进展为需要机械通气的呼吸衰竭（定义为严重进展）对他们进行分类。对40个倾向评分匹配样本的入院血清进行了Olink蛋白质组学分析，并在178例患者中通过细胞计数珠阵列（CBA）对差异表达蛋白（DEP）进行了验证。为了预测严重进展情况，使用70%的训练集建立模型，并在30%的验证集上进行验证。套索回归筛选特征，随后进行逻辑回归和受试者工作特征（ROC）分析，通过逐步纳入按随机森林重要性排序的特征来优化模型。进展为重症COVID-19的老年患者表现出早期免疫失调，包括中性粒细胞增多、淋巴细胞减少、单核细胞减少、降钙素原（PCT）、C反应蛋白（CRP）、白细胞介素-6（IL-6）、中性粒细胞与淋巴细胞比值（NLR）和全身免疫炎症指数（SII）升高，以及凝血功能障碍和多器官损伤。蛋白质组学鉴定出一组生物标志物，包括肿瘤坏死因子相关凋亡诱导配体（TRAIL），并揭示了信号通路的破坏，包括mTOR和VEGF信号通路。纳入PCT、IL-6、单核细胞百分比、淋巴细胞计数和TRAIL的最佳预测模型在验证期间的曲线下面积（AUC）为0.870（0.729 - 1.000）。TRAIL水平与纤维蛋白原呈负相关（<0.05）。进展为重症的老年COVID-19患者表现出早期免疫失调、炎症反应过度、凝血功能障碍和多器官损伤。我们提出的模型有效地预测了老年COVID-19患者的疾病进展，为这一脆弱人群的早期临床风险分层提供了潜在的生物标志物。
