Endothelial Activation in Deep Vein Thrombosis and Chronic Venous Insufficiency: An Exploratory Pilot Study

Background: Deep vein thrombosis (DVT), chronic venous insufficiency (CVI), and atherosclerosis have traditionally been regarded as distinct conditions, although they may share mechanisms involving inflammation and endothelial dysfunction. Objective: To explore the relationship between DVT, CVI, inflammatory biomarkers, and carotid plaque burden using machine learning. Methods: We included 10 patients with DVT (>1 year after diagnosis), 10 with CVI, and 10 age-, sex-, and cardiovascular risk factor- frequency-matched controls. Circulating endothelial cells (CEC), ICAM-1, CXCL7, and high-sensitivity C-reactive protein (hs-CRP) were measured. Participants underwent carotid magnetic resonance imaging, with plaques classified according to the American Heart Association (AHA) score. Kernel principal component analysis (kPCA) was followed by k-nearest neighbors (kNN) (k = 3). Results: Only ICAM-1 differed significantly between clinical groups (median [IQR]: controls, 0.41 [0.37–0.43]; DVT, 0.70 [0.61–0.79]; CVI, 0.64 [0.61–0.79] pg/mL; p < 0.001). kPCA followed by kNN identified three classes: Class 1 included 9 controls; Class 2 included 8 CVI participants and 1 control; and Class 3 included 10 DVT and 2 CVI. The prevalence of AHA plaque scores ≥3 was descriptively higher in Classes 2 and 3 than in Class 1 (77.8%, 83.3%, and 33.3%, respectively), although the overall difference did not reach statistical significance (p = 0.071). Conclusions: ICAM-1 was the only biomarker differing significantly between clinical groups. Exploratory kPCA and kNN analyses identified multidimensional patterns associated with DVT and CVI. Differences in the prevalence of AHA plaque scores ≥3 across kNN classes did not reach statistical significance. These findings are hypothesis-generating and require validation in larger cohorts.