PB1011 - Research on the novel biomarkers of extracellular vesicles in the diagnosis of post-traumatic deep vein thrombosis

Background: Deep vein thrombosis (DVT) is a common complication after trauma and mostly without specific symptoms. Timely diagnosis and early appropriate treatment measures can prevent further development of thrombosis for patients with traumatic lower extremity fractures. Although extracellular vesicles (EVs) are confirmed as promising disease biomarkers, little is known about the level and composition of their subgroups and their role in the diagnosis of post-traumatic DVT.

Aims: This study aimed to identify novel biomarkers for diagnosing DVT in trauma patients.

Methods: The levels of circulating large EVs (IEVs 0.1-1μm) subgroups were measured using flow cytometry. Isolated EVs were characterized and subjected to proteomics analysis to screen for differential expressed proteins (DEPs) between DVT and non-DVT patients. Regularized logistic regression analysis based on L2 penalty terms using R’s caret package (version 6.0-93) was applied to build a model for DVT diagnosis.

Results: Compared to patients without DVT, DVT patients had higher circulating IEVs subgroups. As shown in table.1, phosphatidylserine-positive hepatocyte-derived IEVs (hIEVs) had the best predictive value for post-traumatic DVT diagnosis with the area under the curve (AUC 0.894). The results of the proteomic analysis showed that differentially expressed proteins (DEPs) of circulating EVs between the DVT group and non-DVT group were enriched in the complement and coagulation cascade. Finally, we developed a model of nine biomarkers including F2, F9, SERPINC1, SERPING1, C8G, CFH, C7, CFP, and hIEVs level, for post-traumatic DVT diagnosis. This model was able to identify the traumatic patients with DVT and without DVT with AUC of 0.990 (figure.1).

Conclusion(s): This is the first study on circulating EVs levels and protein expression profiling analysis in post-traumatic DVT patients. We identified DEPs and established a novel model of nine biomarkers for diagnosing post-traumatic DVT.