Post-traumatic Deep vein thrombosis (pt-DVT) is a serious health issue that often leads to considerable morbidity and mortality especially in patients with coronary heart disease (CHD). Diagnosis of DVT in a clinical setting, however, presents considerable challenges. Multiomics techniques has led to high diagnostic and prognostic accuracy for various pathological conditions. This study explored omics methods (specifically, LC-MS) for the detection of metabolites and proteins as well as improve the precision of pt-DVT diagnosis in CHD patients. A total of 502 metabolites and 524 proteins were annotated. Sixty-four differential metabolites and 121 differential proteins were identified between pt-DVT&CHD and CHD controls. Among these, six key metabolites were selected by LASSO Regression and Random Forest algorithms, and then used to develop a diagnostic model using Logistic Regression. These models demonstrated a high predictive capability for pt-DVT in CHD compared to the D-dimer alone and was validated in an independent cohort. Additionally, multiomics analysis indicated that an enhanced sphingolipid metabolism promotes thrombosis by exacerbating inflammation in endothelial cells and oxidative stress. This was underpinned by the significant upregulation of key metabolic proteins, including SMPD1, SPHK2, ENPP7 and CERK in the sphingolipid pathway, highlighting them as potential therapeutic targets. This study delineates metabolic dysregulations associated with pt-DVT and provide potential biomarkers for diagnosis of pt-DVT in CHD patients.
Aliyu et al. (Sat,) studied this question.