Background
The pathogenesis of arterial and venous thrombosis is in large part interlaced. How much platelet phenotype relates to acute venous thromboembolism (VTE) independent of the underlying cardiovascular profile is presently poorly investigated.
Methods
Platelet count and mean platelet volume (MPV), platelet aggregation in whole blood and platelet rich plasma (PRP), platelet-dependent thrombin generation (TG) and platelet surface activation markers were measured under standardized conditions. Machine learning was applied to identify the most relevant characteristics associated with VTE from a large array (N = 58) of clinical and platelet-related variables. Findings: VTE cases (N = 159) presented with lower platelet count and MPV vs controls (N = 140). Whole blood aggregation showed shorter collagen/Epinephrine closure times in cases, particularly within acetylsalicylic acid (ASA) users. Within ASA users, higher PRP aggregation after adenosine diphosphate (ADP), epinephrine, collagen and arachidonic acid was observed in cases vs controls. Within non-ASA and/or subjects on anticoagulants, cases presented with lower aggregation after ADP and collagen vs controls. Lower platelet-dependent TG, higher CD63 on resting and lower PAC-1 expression after collagen/ADP in-vitro stimulated platelets further characterized VTE cases vs controls, independent of therapy. Lasso regression analysis identified 26 variables associated with VTE of which 69% were platelet-related. Interpretation: Comprehensive phenotyping of platelet function identified a large proportion of low responders to ASA in VTE cases. Lower platelet-dependent TG and lower platelet reactivity after ex-vivo stimulation characterized the "platelet exhausted syndrome" in cases. Finally, from a large array of covariates including clinical risk factors, platelet biomarkers comprised 69% of all selected variables differentiating VTE cases vs controls. Funding: German Federal Ministry of Education and Research, CTH-Mainz and Bayer AG.