title: Time-varying properties of renal autoregulatory mechanisms.
abstract: In order to assess the possible time-varying properties of renal autoregulation, time-frequency and time-scaling methods were applied to renal blood flow under broad-band forced arterial blood pressure fluctuations and single-nephron renal blood flow with spontaneous oscillations obtained from normotensive (Sprague-Dawley, Wistar, and Long-Evans) rats, and spontaneously hypertensive rats. Time-frequency analyses of normotensive and hypertensive blood flow data obtained from either the whole kidney or the single-nephron show that indeed both the myogenic and tubuloglomerular feedback (TGF) mechanisms have time-varying characteristics. Furthermore, we utilized the Renyi entropy to measure the complexity of blood-flow dynamics in the time-frequency plane in an effort to discern differences between normotensive and hypertensive recordings. We found a clear difference in Renyi entropy between normotensive and hypertensive blood flow recordings at the whole kidney level for both forced (p < 0.037) and spontaneous arterial pressure fluctuations (p < 0.033), and at the single-nephron level (p < 0.008). Especially at the single-nephron level, the mean Renyi entropy is significantly larger for hypertensive than normotensive rats, suggesting more complex dynamics in the hypertensive condition. To further evaluate whether or not the separation of dynamics between normotensive and hypertensive rats is found in the prescribed frequency ranges of the myogenic and TGF mechanisms, we employed multiresolution wavelet transform. Our analysis revealed that exclusively over scale ranges corresponding to the frequency intervals of the myogenic and TGF mechanisms, the widths of the blood flow wavelet coefficients fall into disjoint sets for normotensive and hypertensive rats. The separation of the scales at the myogenic and TGF frequency ranges is distinct and obtained with 100% accuracy. However, this observation remains valid only for the whole kidney blood pressure/flow data. The results suggest that understanding of the time-varying properties of the two mechanisms is required for a complete description of renal autoregulation.
authors.affiliation: Department of Biomedical Engineering, State University of New York at Stony Brook, 11794, USA.
authors.forename: Rui
authors.initials: R
authors.lastname: Zou
authors.forename: Will A
authors.initials: WA
authors.lastname: Cupples
authors.forename: K P
authors.initials: KP
authors.lastname: Yip
authors.forename: Niels H
authors.initials: NH
authors.lastname: Holstein-Rathlou
authors.forename: Ki H
authors.initials: KH
authors.lastname: Chon
mesh_terms: D000465:Algorithms; D000818:Animals; D001794:Blood Pressure; D002339:Carotid Arteries / Q000503:physiopathology; D005246:Feedback; D005583:Fourier Analysis; D006706:Homeostasis; D006977:Hypertension, Renal / Q000503:physiopathology*; D008954:Models, Biological; D009399:Nephrons / Q000098:blood supply / Q000503:physiopathology*; D051381:Rats; D020318:Rats, Long-Evans; D017208:Rats, Wistar; D012079:Renal Circulation; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted; D013269:Stochastic Processes; D013997:Time Factors