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To investigate in type-1 diabetes mellitus (DM1) patients the role of hypertension and of DM1 itself on aortic stiffness by using magnetic resonance imaging (MRI). Consecutive patients from the diabetes and hypertension outpatient clinic and healthy volunteers were included in our study. Subjects were divided into four groups: 32 healthy volunteers (mean age: 54.5 ± 6.8 years), 20 DM1 patients (mean age: 48.3 ± 5.9 years), 31 hypertensive patients (mean age: 59.9 ± 7.2 years) and 28 patients with both DM1 and hypertension (mean age: 50.1 ± 6.2 years). Aortic stiffness was measured by means of pulse wave velocity (PWV) using velocity-encoded MRI. Analysis of variance (ANOVA), uni- and multivariable regression models and the Bonferroni-test for multiple testing, were used for statistical analyses. Mean aortic PWV was 5.7 ± 1.2 m/s in healthy volunteers, 5.9 ± 1.2 m/s in DM1 patients without hypertension, 7.3 ± 1.2 m/s in hypertensive patients and 7.3 ± 1.3 m/s in patients with both DM1 and hypertension. Compared to healthy control subjects, aortic PWV was significantly higher in patients with hypertension (P < 0.001) and in patients with both DM1 and hypertension (P < 0.001), whereas aortic PWV was not increased in patients having DM1 alone. Furthermore, aortic PWV was significantly higher in DM1 patients with hypertension than in patients with DM1 alone (P = 0.002). These findings remained after adjustment for confounding factors. Hypertension has a predominant contributive effect on aortic stiffness in DM1 patients whereas the direct diabetic effect on aortic stiffness is small.

Magnetic resonance imaging (MRI) can be used in vivo in combination with computational fluid dynamics (CFD) to derive velocity profiles in space and time and accordingly, pressure drop and wall shear stress distribution in natural or artificial vessel segments. These hemodynamic data are difficult or impossible to acquire directly in vivo. Therefore, research has been performed combining MRI and CFD for flow simulations in flow phantoms, such as bends or anastomoses, and even in human vessels such as the aorta, the carotid, and the abdominal bifurcation. There is, however, no unanimity concerning the use of MRI velocity measurements as input for the inflow boundary condition of a CFD simulation. In this study, different input possibilities for the inflow boundary conditions are compared. MRI measurements of steady and pulsatile flow were performed on a U bend phantom, representing the aorta geometry. PAMFLOW (ESI Software, Krimpen aan den Ussel, The Netherlands), an industrial CFD software package, was used to solve the Navier-Stokes equations for incompressible flow. Three main parameters were found to influence the choice of an inflow boundary condition type. First, the flow rate through a vessel should be exact, since it proves to be a determining factor for the accuracy of the velocity profile. The other decisive parameters are the physiology of the flow profile and the required computer processing unit time. Our comparative study indicates that the best way to handle an inflow boundary condition is to use the velocities measured by MRI at the inflow plane as being fixed velocities. However, before using these MRI velocity data, they first should be corrected for the partial volume effect by filtering and second scaled in order to obtain the correct flow rate. This implies that a reliable flow rate measurement absolutely is needed for CFD calculations based on MRI velocity measurements.