Mechanistic insights on age-related changes in heart-aorta-brain hemodynamic coupling using a pulse wave model of the entire circulatory system.

Age-related changes in aortic biomechanics can impact the brain by reducing blood flow and increasing pulsatile energy transmission. Clinical studies have shown that impaired cardiac function in patients with heart failure is associated with cognitive impairment. Although previous studies have attempted to elucidate the complex relationship between age-associated aortic stiffening and pulsatility transmission to the cerebral network, they have not adequately addressed the effect of interactions between aortic stiffness and left ventricle (LV) contractility (neither on energy transmission nor on brain perfusion). In this study, we use a well-established and validated one-dimensional blood flow and pulse wave computational model of the circulatory system to address how age-related changes in cardiac function and vasculature affect the underlying mechanisms involved in the LV-aorta-brain hemodynamic coupling. Our results reveal how LV contractility affects pulsatile energy transmission to the brain, even with preserved cardiac output. Our model demonstrates the existence of an optimal heart rate (near the normal human heart rate) that minimizes pulsatile energy transmission to the brain at different contractility levels. Our findings further suggest that the reduction in cerebral blood flow at low levels of LV contractility is more prominent in the setting of age-related aortic stiffening. Maintaining optimal blood flow to the brain requires either an increase in contractility or an increase in heart rate. The former consistently leads to higher pulsatile power transmission, and the latter can either increase or decrease subsequent pulsatile power transmission to the brain.NEW & NOTEWORTHY We investigated the impact of major aging mechanisms of the arterial system and cardiac function on brain hemodynamics. Our findings suggest that aging has a significant impact on heart-aorta-brain coupling through changes in both arterial stiffening and left ventricle (LV) contractility. Understanding the underlying physical mechanisms involved here can potentially be a key step for developing more effective therapeutic strategies that can mitigate the contributions of abnormal LV-arterial coupling toward neurodegenerative diseases and dementia.

Estimation of Wave Condition Number From Pressure Waveform Alone and Its Changes With Advancing Age in Healthy Women and Men.

INTRODUCTION: The wave condition number (WCN) is a non-dimensional number that determines the state of arterial wave reflections. WCN is equal to HR × L eff /PWV where HR, L eff , and PWV are the heart rate, effective length, and pulse wave velocity, respectively. It has been shown that a value of WCN = 0.1 indicates the optimum state of arterial wave reflection in which left ventricle workload is minimized. The pressure wave, flow wave, and PWV are all required to compute WCN, which may limit the potential clinical utility of WCN. The aims of this study are as follows: (1) to assess the feasibility of approximating WCN from the pressure waveform alone (WCN Pinf ), and (2) to provide the proof-of-concept that WCN Pinf can capture age related differences in arterial wave reflection among healthy women and men. METHODS: Previously published retrospective data composed of seventeen patients (age 19-54 years; 34.3 ± 9.6) were used to assess the accuracy of WCN Pinf . The exact value of WCN was computed from PWV (measured by foot-to-foot method), HR, and L eff . A quarter wavelength relationship with minimum impedance modulus were used to compute L eff . WCN Pinf was calculated using HR and the reflected wave arrival time. Previously published analyses from a healthy subset of the Anglo-Cardiff Collaborative Trial (ACCT) study population were used to investigate if non-invasive WCN Pinf captures age related differences in arterial wave reflection among healthy women and men. RESULTS: A strong correlation (r = 0.83, p-value <0.0001) between WCN Pinf and WCN was observed. The accuracy of WCN Pinf was independent from relevant physiological parameters such as PWV, pulse pressure (PP), and HR. Similar changes in WCN Pinf with advancing age were observed in both healthy men and healthy women. In young, healthy individuals (women and men) the WCN Pinf was around 0.1 (the optimum value), and reduced with aging. CONCLUSION: WCN can be approximated from a single pressure waveform and can capture age related arterial wave reflection alteration. These results are clinically significant since WCN can be extracted from a single non-invasive pressure waveform. Future studies will focus on investigating if WCN is associated with risk for onset of cardiovascular disease events.