Pewowaruk et al.’s results show that the arterial stiffness of the carotid artery and the regional cfPWV is, as expected, higher in hypertensive individuals than in controls, but this was not observed in the brachial artery [4]. Stewart et al. [8] also obtained similar results for the carotid artery of middle-aged individuals. Furthermore, the observed response to NTG-mediated vasodilation of the different arterial beds analyzed in the study of Pewowaruk et al. is noteworthy; while the stiffness of the elastic carotid artery, as well as cfPWV, increased, the stiffness of the muscular brachial artery did not change significantly. These findings were independent of hypertensive status. In contrast, Stewart et al. [8] observed a decrease in stiffness in response to NTG in control participants but no change in hypertensive individuals.

Further model analyses by Pewowaruk et al. revealed that in the carotid artery, the active VSMC stiffness index parameter was lower than the passive ECM stiffness index, whereas these two indices were almost equal in the brachial artery. This leads to the hypothesis that the different stiffness responses to vasodilation are a result of the ratio of active to passive stiffness contributions, which is in line with previous research these researchers performed with their model using data available from other clinical studies [7]. When this difference is positive (active stiffness > passive stiffness), decreasing arterial tone will decrease the overall wall stiffness as the active contribution decreases. In contrast, when this difference is negative, vasodilation will lead to an increase in stiffness. This could potentially explain the discrepancy between the present results and those of Stewart et al. [8]; in the older age group in the study by Pewowaruk et al., vascular aging is likely to be more pronounced than in a middle-aged group, which causes more stiffening of the (passive) ECM [1, 2]. This could reverse the ratio between the active and passive stiffness components and therefore cause the observed stiffening (rather than destiffening) in response to vasodilation.

Since only NTG is used as a vasodilatory agent, the magnitude of the vasodilation-induced change in stiffness may not be directly comparable to that induced by other vasodilatory drugs. Nevertheless, sublingual NTG administration is easily applicable in research because of the rapid onset of vasodilation and its short half-life and action time [5]. As an organic nitrate, NTG generates the vasodilatory compound NO [5]. At therapeutic doses, NO-mediated vasodilation is most prominent in the veins [1, 5] but also affects arteries and even other types of smooth muscle cells [5]. Other antihypertensive drugs may also induce vasodilation via different pathways, including the inhibition of the renin-angiotensin-aldosterone system or blocking calcium influx [5]. Although the amount of vasodilation and stiffness modulation induced by different compounds may differ, Pewowaruk et al.’s results highlight a potential adverse effect of antihypertensive drugs on vascular stiffness.

By developing a computational model, Pewowaruk et al. [4] were able to extrapolate the measured datapoints to the overall mechanical behavior of the vascular wall and eventually investigate stiffness independently of the confounding influence of blood pressure. The nature of the model also gives insight into the separate mechanical contributions of the passive and active components of the arterial wall. Other researchers also investigated the role of VSMC contraction on the arterial wall [9,10,11], resulting in models of varying degrees of complexity. The strength of Pewowaruk et al.’s model [4] is its applicability to situations with limited clinical data due to the low number of fitting parameters. However, the inherent assumptions and constraints therein also result in limitations in the use of the model. In particular, in the carotid artery data of three participants, their model was not able to capture the mechanical behavior without residual errors (Fig. 1B), despite having enough degrees of freedom [4]. In two of these participants, the active contribution to pressure decreased from diastole to systole. This could not be described by the model, given the assumption of an exponential VSMC pressure contribution with positive parameter constraints. Physiologically, VSMC contraction is generated by actin-myosin interactions, leading to length-dependent force generation, showing a maximum at optimal filament overlap while decreasing above/below this optimum [9,10,11]. We, therefore, hypothesized that in these two participants, VSMCs operated beyond their optimal length.

We adjusted the original model to include the length dependency of active VSMC tension as a Gaussian-shaped function [9] to capture the decrease in active pressure contribution from diastole to systole in two participants:

$$T\left( D \right) = \fracP_}}}}}}}}D_}}}}}}}}\left[ }}}}}}}}\left( }}}}}}}}}} - 1} \right)} + \frac}}}}}}}}}}e^}}}}}}}}}} - C_}}}} \right)}}}}}}}}}}}}}}} \right],$$

(1)

where Pref and Dref are the reference pressure (fixed at 80 mmHg) and diameter [7], respectively; β0,pas is a passive stiffness index parameter [7]; k and kref govern the contractile state (kref = 0.1) [7]; cact is a normalized optimal working length [9]; and ωact governs the width of the Gaussian function [9]. This implementation showed that the VSMC contribution to tension was nearly diameter independent in Participant 1, indicating VSMCs were near their maximum contraction length (Fig. 1C, D). In Participant 2, the VSMC contribution showed a strong negative relationship with diameter (Fig. 1E, F), indicating that VSMCs were beyond their maximum contraction length. This approach also has its own limitations, as it requires three parameters to describe the width, mean, and maximum of the Gaussian curve, compared to only two parameters for the exponential active curve in the original model. Given that only four datapoints are available, this is not enough to fit all five fitting parameters of the new model. Therefore, ωact is set constant to 0.374, as suggested by Carlson and Secomb [9]. Moreover, since the active and passive contributions are no longer represented by a similar exponential function, the passive and active stiffness terms cannot be directly compared anymore through comparison of two β0 parameters, as was possible in the original model [4]. However, a comparison in terms of passive vs. active pressure load bearing remains possible. Furthermore, as previously indicated by Giudici and Spronck [12], Pewowaruk’s model may not be able to capture passive behavior at small diameters, since the elastin contribution is not described well by the exponential function. This could also (partially) explain the fitting problem observed for these two participants. However, given the old age of the participants, the impact of this limitation is expected to be limited [12].

In summary, the research from Pewowaruk et al. [4] showed 1) how the degree of vasoconstriction could alter vascular stiffness in both the muscular brachial artery and elastic carotid artery and 2) how clinical research benefits from innovative modeling approaches. Their study highlights the importance of investigating whether vasodilatory drugs, used as antihypertensive medication, have an adverse effect on large arteries by increasing their stiffness, which has inherent potential cardiovascular risks.