Hypofibrinolysis in type 2 diabetes: the role of the inflammatory pathway and complement C3

AIMS/HYPOTHESIS Plasminogen activator inhibitor-1 (PAI-1) has been regarded as the main antifibrinolytic protein in diabetes, but recent work indicates that complement C3 (C3), an inflammatory protein, directly compromises fibrinolysis in type 1 diabetes. The aim of the current project was to investigate associations between C3 and fibrinolysis in a large cohort of individuals with type 2 diabetes. METHODS Plasma levels of C3, C-reactive protein (CRP), PAI-1 and fibrinogen were analysed by ELISA in 837 patients enrolled in the Edinburgh Type 2 Diabetes Study. Fibrin clot lysis was analysed using a validated turbidimetric assay. RESULTS Clot lysis time correlated with C3 and PAI-1 plasma levels (r = 0.24, p < 0.001 and r = 0.22, p < 0.001, respectively). In a multivariable regression model involving age, sex, BMI, C3, PAI-1, CRP and fibrinogen, and using log-transformed data as appropriate, C3 was associated with clot lysis time (regression coefficient 0.227 [95% CI 0.161, 0.292], p < 0.001), as was PAI-1 (regression coefficient 0.033 [95% CI 0.020, 0.064], p < 0.05) but not fibrinogen (regression coefficient 0.003 [95% CI -0.046, 0.051], p = 0.92) or CRP (regression coefficient 0.024 [95% CI -0.008, 0.056], p = 0.14). No correlation was demonstrated between plasma levels of C3 and PAI-1 (r = -0.03, p = 0.44), consistent with previous observations that the two proteins affect different pathways in the fibrinolytic system. CONCLUSIONS/INTERPRETATION Similarly to PAI-1, C3 plasma levels are independently associated with fibrin clot lysis in individuals with type 2 diabetes. Therefore, future studies should analyse C3 plasma levels as a surrogate marker of fibrinolysis potential in this population.

Computational fluid dynamics modelling in cardiovascular medicine.

This paper reviews the methods, benefits and challenges associated with the adoption and translation of computational fluid dynamics (CFD) modelling within cardiovascular medicine. CFD, a specialist area of mathematics and a branch of fluid mechanics, is used routinely in a diverse range of safety-critical engineering systems, which increasingly is being applied to the cardiovascular system. By facilitating rapid, economical, low-risk prototyping, CFD modelling has already revolutionised research and development of devices such as stents, valve prostheses, and ventricular assist devices. Combined with cardiovascular imaging, CFD simulation enables detailed characterisation of complex physiological pressure and flow fields and the computation of metrics which cannot be directly measured, for example, wall shear stress. CFD models are now being translated into clinical tools for physicians to use across the spectrum of coronary, valvular, congenital, myocardial and peripheral vascular diseases. CFD modelling is apposite for minimally-invasive patient assessment. Patient-specific (incorporating data unique to the individual) and multi-scale (combining models of different length- and time-scales) modelling enables individualised risk prediction and virtual treatment planning. This represents a significant departure from traditional dependence upon registry-based, population-averaged data. Model integration is progressively moving towards 'digital patient' or 'virtual physiological human' representations. When combined with population-scale numerical models, these models have the potential to reduce the cost, time and risk associated with clinical trials. The adoption of CFD modelling signals a new era in cardiovascular medicine. While potentially highly beneficial, a number of academic and commercial groups are addressing the associated methodological, regulatory, education- and service-related challenges.