Zhao Zihe, Chen Taicai, Liu Qingyuan, Hu Jianhang, Ling Tong, Tong Yuanhao, Han Yuexue, Zhu Zhengyang, Duan Jianfeng, Jin Yi, Fu Dongsheng, Wang Yuzhu, Pan Chaohui, Keyoumu Reyaguli, Sun Lili, Li Wendong, Gao Xia, Shi Yinghuan, Dou Huan, Liu Zhao
Department of Vascular Surgery, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, People's Republic of China.
The State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, People's Republic of China.
J Inflamm Res. 2025 Jan 10;18:533-547. doi: 10.2147/JIR.S494191. eCollection 2025.
Stanford Type B Aortic Dissection (TBAD), a critical aortic disease, has exhibited stable mortality rates over the past decade. However, diagnostic approaches for TBAD during routine health check-ups are currently lacking. This study focused on developing a model to improve the diagnosis in a population.
Serum biomarkers were investigated in 88 participants using proteomic profiling combined with machine learning. The findings were validated using ELISA in other 80 participants. Subsequently, a diagnostic model for TBAD integrating biomarkers with clinical indicators was developed and assessed using machine learning.
Six differentially expressed proteins (DEPs) were identified through proteomic profiling and machine learning in discovery and derivation cohorts. Five of these (GDF-15, IL6, CD58, LY9, and Siglec-7) were further verified through ELISA validation within the validation cohort. In addition, ten blood-related indicators were selected as clinical indicators. Combining biomarkers and clinical indicators, the machine learning-based models performed well (AUC of the biomarker model = 0.865, AUC of the clinical model = 0.904, and AUC of the combined model = 0.909) using relative quantitation. The performance of the three models was verified (AUC of biomarker model = 0.866, AUC of clinical model = 0.868, and AUC of combined model = 0.886) using absolute quantitation. Crucially, the combined models outperformed individual biomarkers and clinical models, demonstrating superior efficacy.
Using proteomic profiling, we identified serum IL-6, GDF-15, CD58, LY9, and Siglec-7 as TBAD biomarkers. The machine-learning-based diagnostic model exhibited significant potential for TBAD diagnosis using only blood samples within the population.
B型主动脉夹层（TBAD）是一种严重的主动脉疾病，在过去十年中死亡率一直保持稳定。然而，目前在常规健康检查中缺乏针对TBAD的诊断方法。本研究旨在开发一种模型，以改善对人群的诊断。
对88名参与者进行血清生物标志物研究，采用蛋白质组分析结合机器学习方法。研究结果在另外80名参与者中通过酶联免疫吸附测定（ELISA）进行验证。随后，开发了一种将生物标志物与临床指标相结合的TBAD诊断模型，并使用机器学习进行评估。
通过蛋白质组分析和机器学习在发现队列和推导队列中鉴定出六种差异表达蛋白（DEP）。其中五种（生长分化因子15、白细胞介素6、CD58、LY9和唾液酸结合免疫球蛋白样凝集素7）在验证队列中通过ELISA验证得到进一步证实。此外，选择了十个血液相关指标作为临床指标。结合生物标志物和临床指标，基于机器学习的模型使用相对定量表现良好（生物标志物模型的曲线下面积[AUC]=0.865，临床模型的AUC=0.904，组合模型的AUC=0.909）。使用绝对定量验证了三种模型的性能（生物标志物模型的AUC=0.866，临床模型的AUC=0.868，组合模型的AUC=0.886）。至关重要的是，组合模型优于单个生物标志物和临床模型，显示出卓越的效能。
通过蛋白质组分析，我们鉴定出血清白细胞介素6、生长分化因子15、CD58、LY9和唾液酸结合免疫球蛋白样凝集素7为TBAD生物标志物。基于机器学习的诊断模型在人群中仅使用血液样本进行TBAD诊断具有显著潜力。
