Pulsatile Flow

Cardiovascular diseases (CVD) account annually for almost one third of all deaths worldwide. Among the CVD, systemic arterial hypertension (SAH) is related to more than half of those outcomes. Type 2 diabetes mellitus is an independent risk factor for SAH because it causes functional and structural damage to the arterial wall, leading to stiffness. Several studies have related oxidative stress, production of free radicals, and neuroendocrine and genetic changes to the physiopathogenesis of vascular aging. Indirect ways to analyze that aging process have been widely studied, pulse wave velocity (PWV) being considered gold standard to assess arterial stiffness, because there is large epidemiological evidence of its predictive value for cardiovascular events, and it requires little technical knowledge to be performed. A pulse wave is generated during each cardiac contraction and travels along the arterial bed until finding peripheral resistance or any bifurcation point, determining the appearance of a reflected wave. In young individuals, arteries tend to be more elastic, therefore, the reflected wave occurs later in the cardiac cycle, reaching the heart during diastole. In older individuals, however, the reflected wave occurs earlier, reaching the heart during systole. Because PWV is an important biomarker of vascular damage, highly valuable in determining the patient's global cardiovascular risk, we chose to review the articles on vascular aging in the context of cardiovascular risk factors and the tools available to the early identification of that damage.

Aims and backgroundParameters of arterial stiffness such as pulse wave velocity (PWV) were recently proposed as independent risk factors of cardiovascular events. We analyse three PWV parameters in the large population-based study LIFE-Adult to identify risk factors, normal and reference values.Methods and resultsBrachial-ankle (ba), brachial-femoral (bf) and carotid-femoral (cf) PWV assessment was performed using Vicorder device. 8509 participants aged 19-80 were analysed. PWV parameters were moderately correlated (r(ba/bf) = 0.6, r(ba/cf) = 0.46, r(bf/cf) = 0.59). Age and blood pressure are the dominant determinants of PWV parameters explaining > 18% of variability. Sex was only relevant for bfPWV and cfPWV. All further analysed cardiovascular and other risk factors are of minor importance. We provide age-dependent percentiles for the population (reference values) and for the subgroup of normotonic individuals. All percentiles show a strong increase with age. The difference between normotonic and all individuals is small for younger age groups but increases up to 1 m/s for elderly subjects.ConclusionOur study confirms and further underpins the strong impact of age and blood pressure on arterial stiffness and the relatively weak contribution of other factors, supporting an independent role of arterial stiffness in cardiovascular disease development. Age-dependent reference and normal values were provided on the basis of the so far largest study sample facilitating the implementation of PWV assessment in clinical practice. Due to better compliance, handling and stronger association with age and blood pressure, baPWV could serve as an alternative to cfPWV. Follow-up data are required to estimate the clinical significance of specified PWV cut-offs.

Cerebral pulsatility reflects a balance between the transmission and damping of pulsatility in the cerebrovasculature. Females experience greater cerebral pulsatility with aging, which may have implications for sex differences in stroke risk and cognitive decline. This study sought to explore vascular contributors to cerebral pulsatility and pulsatile damping in men and women. Adults (n = 282, 53% female) underwent measurements of cerebral (middle cerebral artery) pulsatility, pulsatile damping (ratio of cerebral to carotid pulsatility), large artery stiffening (ratio of aortic to carotid pulse wave velocity), and carotid wave transmission/reflection dynamics using wave intensity analysis. Multiple regression revealed that older age, female sex, greater large artery stiffening, higher carotid pulse pressure, and greater forward wave energy was associated with increased cerebral pulsatility (adjusted R2 = 0.44, P 2 = 0.51, P NEW & NOTEWORTHY Cerebral pulsatility contributes to brain health and depends on a balance between transmission and damping of pulsatile hemodynamics into the cerebrovasculature. Our data indicate that cerebral pulsatility increases with age, female sex, extracranial artery stiffening, forward wave energy, and pulse pressure, whereas pulsatile damping decreases with age and female sex and increases with greater carotid wave reflections. These novel data identify pulsatile damping as a potential contributor to sex differences in cerebral pulsatile burden.

IntroductionThe clearance of brain metabolites increases during sleep, in association with increased spectral power of the three main cerebrospinal fluid (CSF) flow drivers: cardiovascular, respiratory, and vasomotor brain pulsations. However, little is known about how the increased power of these pulsations affects the velocity and direction of fluid flow in the sleeping brain.ObjectivesTo address this knowledge gap, we mapped the CSF oscillatory flow velocity in relation to the changing physiological pulsations in the brains of 22 healthy volunteers during sleep and waking.MethodsWe used the ultrafast magnetic resonance imaging sequence known as magnetic resonance encephalography (MREG) for tracing the pulsatile movement of water molecules inside the cranium. First, we conducted a phantom validation study with optical flow analysis to confirm that MREG accurately tracks pulsatile water molecule flow in a porous tissue medium. Next, we obtained MREG recordings for mapping the three physiological pulsations without aliasing in the human brain across the awake and sleep states; we thereby quantified the brain-wide 3D velocity →V$\mathop \to \limits_{\mathrm{V}} $ vectors (i.e., the velocity vs and 3D direction v̂${\mathrm{\hat{v}}}$ ) of each pulsation band, using comprehensive dense optical flow analysis during EEG-verified sleep in comparison to the awake state. Finally, we assessed relationships among the spectral power of the physiological pulsations, their 3D velocity →V$\mathop \to \limits_{\mathrm{V}} $ , and slow-delta EEG power, which is known to depict the increased interstitial volume during sleep.ResultsIn our phantom study, dense optical flow analysis reliably detected water flow in tissue driven by external pulsations. In healthy volunteers, sleep increased flow velocities ( →V$\mathop \to \limits_{\mathrm{V}} $ ) of the pulsations by more than 20% in concert with elevations in respiratory pulsations and vasomotor waves, while the velocity of cardiovascular pulsations (vs) declined by the same percentage. There was a significant anticorrelation between cardiac mean spectral power and slow delta EEG mean power, and a significant correlation between vasomotor mean spectral power and slow delta EEG mean power over the whole brain.ConclusionsPhantom studies validated the optic flow analysis of fast MREG recordings. Sleep altered the 3D velocity dynamics of all neurofluidic brain pulsations in a manner consistent with increased interstitial space and greater fluid exchange, thus supporting the glymphatic model wherein physiological pulsations drive bulk flow during sleep.