Yukiko Fukuda, M.D., is a Research Scientist in the MEI Lab. Her research focuses on the development of non-invasive sensors for heart and vascular monitoring and understanding cardiovascular physiology’s contribution to patient health. She is interested in clinical pathophysiology. Prior to joining the MEI Lab, she completed a residency in Internal Medicine at Takasaki General Hospital in Japan. Dr. Fukuda received her M.D. from Gunma University, and she has business experience as a systems engineer at IBM Japan.
To build out the cardiovascular biodigital twin (CVBioDT) into a more realistic and human model, the MEI Lab has been working to incorporate physiological functions and data regarding the effects of drugs. In an in-vivo study using laboratory rats, Dr. Fukuda and colleagues from the MEI Lab, the National Cerebral and Cardiovascular Center (NCVC), and the Circulatory System Research Foundation focused on blood flow in the kidney system (renal hemodynamics), the hormone angiotensin (ANG)II, which regulates blood pressure and fluid balance, and telmisartan (TELM), an ANGII receptor blocker used to treat hypertension. To study the relationships between the renal system, the hormone and the drug, the team used a technique that measures in fine detail the resistance of blood flow within the renal arteries. Their work, “Influence of angiotensin II and telmisartan on in vivo high-resolution renal arterial impedance in rats,” was recently published in the American Journal of Physiology. To learn more about this research, we conducted the following Q&A with Dr. Fukuda:
How did you conceive of this experiment? Were there gaps in the medical research that you were trying to fill? Was random ventricular pacing, which delivers a somewhat irregular pattern of electrical impulses to the heart, one of your novel contributions, and part of your attempt to generate a wider span of data?
The renal system, primarily consisting of the kidneys, is essential for filtering blood, removing waste, and maintaining fluid and electrolyte balance. It especially plays a vital role in blood pressure regulation through the renin-angiotensin-aldosterone system (RAS). Thus, we position the renal model as one of the enhanced models in our CVBioDT. Our CvBioDT consists of an electrical analogue model, and we needed a lumped-parameter model of the kidney system that was validated by experimental data.
The kidneys have an autoregulation function that helps maintain a stable blood flow and glomerular filtration rate (GFR) despite fluctuations in systemic blood pressure. This function protects the delicate structures of kidneys and preserves overall kidney function. Furthermore, RAS is another key regulator of renal hemodynamics. While these renal physiological mechanisms have been widely investigated through in-vitro experiments, few papers have explored their dynamic characteristics in terms of renal hemodynamics.
The research groups by Dr. Sunagawa and Dr. Joe Alexander had developed a method to obtain high-resolution arterial impedance in in-vivo using random pacing to the left ventricular to elucidate the characteristics of systemic arterial vasculature (Alexander, AmJ P, 1989). While their method was used to investigate not only systemic arterial impedance, but also applied to elucidate pulmonary arterial impedances (Nishikawa, Life Sci, 2018), there were no studies that applied the method to obtain renal arterial impedance. We tried to apply their established techniques to elucidate the dynamic properties of renal hemodynamics.
Some of your results are displayed in a table showing the output of multiple linear regressions of the relationships between mean renal arterial pressure and a range of parameters. At the end of the paper, you conclude 1) that ANGII may increase the estimated downstream pressure via a vasoconstrictive effect on the resistance of efferent arterioles, possibly to maintain urinary flow; and 2) that TELM may enhance pressure diuresis by increasing renal blood flow without affecting estimated downstream pressure when renal pressure is maintained. In what ways do those results advance the field? Were they at all surprising or were you looking primarily to quantify these relationships in more precise terms? Is there another way to summarize these results, and the somewhat contrary impact of ANGII and TELM on renal hemodynamics?
Unlike other organs, the kidney has a unique structure with two capillaries, the glomerulus and the tubule. The anterior and posterior arterioles of glomeruli are named as the afferent and efferent arterioles. It is an established physiological mechanism that ANGII and its blocker (TELM, etc.) mainly act on both arterioles, especially the efferent arterioles. In our study, the renal arterial impedance was quantified using a three-element Windkessel model, and the findings indicated that ANGII and TELM affect mainly the distal resistances. Our observations support the previously established results. In other words, it indicates that ANGII and its blocker have little effect on proximal side arterial resistance. This is consistent with the fact that the proximal resistance in the three-element Windkessel model represents the properties of resistance of elastic arteries, and our results confirmed that ANGII mainly affects the resistance vessels, but not elastic arteries in kidney. I think this is the first step in demonstrating how dynamic parameters of renal hemodynamics can be quantified by the present protocol.
How would you incorporate these kinds of results into the CVBioDT model? Would you be waiting for more definitive data – perhaps clinical trials – before doing so? (Are there limits on how complex the CVBioDT model should become? Could it contain too many parameters?)
As mentioned earlier, the renal model is considered to be one of our CVBioDT’s enhanced models. Even though the present findings were obtained from experiments using rats, incorporating these findings into the renal model in the CVBioDT would enable us to simulate basic renal hemodynamics: autoregulation, pressure diuresis, and RAS. Parameter estimation of the CVBioDT model is currently being studied by other MEI colleagues, starting with the basic model. We believe that in the future more detailed parameter estimation will be possible using clinical data regarding renal hemodynamics, such as renal arterial flow from echocardiograms.
In terms of the future, the paper mentions possible in vitro experimentation. What would you be looking for in that kind of testing scenario? There’s also the suggestion that you look at spontaneously hypertensive rats. Would that again be part of the effort to gain a wider spectrum of data?
Our next goal is to reproduce the findings from this study in silico (i.e., in the CVBioDT) and verify them with in-vivo experiments.
Currently, one limitation of this study was that we administered exogenous ANGII to normotensive rats to mimic the condition of increased ANGII ( = hyperactivity states of RAS). In physiological conditions, hyperactivation of RAS, which is induced by prolonged renal blood flow reduction, induces several cardiovascular diseases, such as hypertension and chronic kidney disease. This underlying pathophysiological condition is far different from our artificial experiment settings that used exogenous ANGII on normotensive rats. In order to expand the scope of CVBioDT to the comorbidities of cardiovascular diseases, such as essential hypertension and chronic kidney disease, it would be necessary to obtain further data from animal models that can reproduce such pathophysiological conditions.
This experiment seems to have called for a combination of surgical skills, data analytics and expertise in cardiovascular and renal systems. How did you and your co-authors cover all this territory? You are an M.D who previously worked in systems engineering, correct? How did your background and interests lead you to this kind of research?
With my background as an M.D., I believe my understanding of how various organ systems interact and influence each other (physiology), and what pathological conditions can arise when their homeostasis is disrupted (pathophysiology) enabled me to make this research possible.
This achievement would not have been possible without the support of the NCVC, especially Dr. Kawada. This study required highly specialized experimental techniques to obtain reproducible data from rats. It would have been unattainable without his expertise, knowledge and surgical skills. Moreover, Dr. Sunagawa provided invaluable advice on the experimental protocols, analysis and data interpretation. If I were not a part of the NTT Research MEI Lab, I would not have had the opportunity to work with such distinguished doctors and conduct the research. I am truly grateful to be able to perform my research in such an auspicious environment.
Learn more about Yukiko Fukuda in her profile video here: – NTT Research (ntt-research.com)