Shanghai Institute of Infectious Disease and Biosecurity, School of Public Health, Fudan University, Shanghai, China.
Department of Emergency Medicine, Zhongshan Hospital, Fudan University, Shanghai, China.
Crit Care. 2024 Jul 2;28(1):213. doi: 10.1186/s13054-024-05000-3.
The multidimensional biological mechanisms underpinning acute respiratory distress syndrome (ARDS) continue to be elucidated, and early biomarkers for predicting ARDS prognosis are yet to be identified.
We conducted a multicenter observational study, profiling the 4D-DIA proteomics and global metabolomics of serum samples collected from patients at the initial stage of ARDS, alongside samples from both disease control and healthy control groups. We identified 28-day prognosis biomarkers of ARDS in the discovery cohort using the LASSO method, fold change analysis, and the Boruta algorithm. The candidate biomarkers were validated through parallel reaction monitoring (PRM) targeted mass spectrometry in an external validation cohort. Machine learning models were applied to explore the biomarkers of ARDS prognosis.
In the discovery cohort, comprising 130 adult ARDS patients (mean age 72.5, 74.6% male), 33 disease controls, and 33 healthy controls, distinct proteomic and metabolic signatures were identified to differentiate ARDS from both control groups. Pathway analysis highlighted the upregulated sphingolipid signaling pathway as a key contributor to the pathological mechanisms underlying ARDS. MAP2K1 emerged as the hub protein, facilitating interactions with various biological functions within this pathway. Additionally, the metabolite sphingosine 1-phosphate (S1P) was closely associated with ARDS and its prognosis. Our research further highlights essential pathways contributing to the deceased ARDS, such as the downregulation of hematopoietic cell lineage and calcium signaling pathways, contrasted with the upregulation of the unfolded protein response and glycolysis. In particular, GAPDH and ENO1, critical enzymes in glycolysis, showed the highest interaction degree in the protein-protein interaction network of ARDS. In the discovery cohort, a panel of 36 proteins was identified as candidate biomarkers, with 8 proteins (VCAM1, LDHB, MSN, FLG2, TAGLN2, LMNA, MBL2, and LBP) demonstrating significant consistency in an independent validation cohort of 183 patients (mean age 72.6 years, 73.2% male), confirmed by PRM assay. The protein-based model exhibited superior predictive accuracy compared to the clinical model in both the discovery cohort (AUC: 0.893 vs. 0.784; Delong test, P < 0.001) and the validation cohort (AUC: 0.802 vs. 0.738; Delong test, P = 0.008).
Our multi-omics study demonstrated the potential biological mechanism and therapy targets in ARDS. This study unveiled several novel predictive biomarkers and established a validated prediction model for the poor prognosis of ARDS, offering valuable insights into the prognosis of individuals with ARDS.
急性呼吸窘迫综合征（ARDS）的多维生物学机制仍在不断阐明，用于预测 ARDS 预后的早期生物标志物仍有待确定。
我们进行了一项多中心观察性研究，对 ARDS 初始阶段患者的血清样本进行了 4D-DIA 蛋白质组学和整体代谢组学分析，同时还对疾病对照组和健康对照组的样本进行了分析。我们使用 LASSO 方法、倍数变化分析和 Boruta 算法，在发现队列中确定了 ARDS 28 天预后的生物标志物。通过外部验证队列中的平行反应监测（PRM）靶向质谱法对候选生物标志物进行验证。应用机器学习模型探索 ARDS 预后的生物标志物。
在发现队列中，纳入了 130 名成人 ARDS 患者（平均年龄 72.5 岁，74.6%为男性）、33 名疾病对照组和 33 名健康对照组，确定了区分 ARDS 与对照组的独特蛋白质组学和代谢特征。途径分析强调了鞘脂信号通路的上调，这是 ARDS 病理机制的关键因素。MAP2K1 作为关键蛋白出现，促进了该途径内各种生物学功能的相互作用。此外，代谢物神经鞘氨醇 1-磷酸（S1P）与 ARDS 及其预后密切相关。我们的研究还进一步强调了导致 ARDS 死亡的重要途径，如造血细胞谱系和钙信号通路的下调，而未折叠蛋白反应和糖酵解途径的上调。特别是，糖酵解中关键酶 GAPDH 和 ENO1 在 ARDS 的蛋白质-蛋白质相互作用网络中表现出最高的相互作用程度。在发现队列中，确定了一组 36 种蛋白作为候选生物标志物，其中 8 种蛋白（VCAM1、LDHB、MSN、FLG2、TAGLN2、LMNA、MBL2 和 LBP）在 183 名患者的独立验证队列中表现出显著一致性，通过 PRM 检测证实（平均年龄 72.6 岁，73.2%为男性）。在发现队列（AUC：0.893 与 0.784；Delong 检验，P < 0.001）和验证队列（AUC：0.802 与 0.738；Delong 检验，P = 0.008）中，蛋白模型的预测准确性均优于临床模型。
我们的多组学研究表明了 ARDS 中的潜在生物学机制和治疗靶点。本研究揭示了一些新的预测生物标志物，并建立了一个经过验证的 ARDS 不良预后预测模型，为 ARDS 患者的预后提供了有价值的见解。
