Regulatory, Integrative and Comparative Physiology

Physiology in perspective: The Wisdom of the Body. Neural control of the kidney

Gerald F. DiBona


Cannon equated the fluid matrix of the body with Bernard’s concept of the internal environment and emphasized the importance of “the safe-guarding of an effective fluid matrix.” He further emphasized the important role of the autonomic nervous system in the establishment and maintenance of homeostasis in the internal environment. This year’s Cannon Lecture discusses the important role of the renal sympathetic nerves to regulate various aspects of overall renal function and to serve as one of the major “self-regulatory agencies which operate to preserve the constancy of the fluid matrix.”

  • renal sympathetic nerves
  • renal function
  • homeostasis

in his book, The Wisdom of the Body (2), Cannon articulated the concept of homeostasis, which he viewed as “the coordinated physiological reactions which maintain most of the steady states in the body.” He went on to further define the role of the autonomic nervous system in homeostasis as “…the sympathico-adrenal apparatus acts to keep constant the fluid matrix of the body. …”

Given that the “fluid matrix of the body” may be taken as the equivalent of total body fluid volume, it seems evident that Cannon was putting forth the concept that the autonomic nervous system was an important regulator of the organ known to have the primary responsibility for control of total body fluid volume and its composition: the kidney. However, while he acknowledged the contribution of Claude Bernard’s concept of “milieu interieur,” there is no reference to Bernard’s report that renal nerve stimulation decreased urinary flow rate while renal denervation increased it (1). Similarly, Ernest Henry Starling’s later confirmation of Bernard’s findings is not mentioned (13). This may explain why Cannon’s sketch of the autonomic nervous system not only does not show sympathetic neural innervation of the kidney, it does not even show the kidneys (2)!

The subject of neural control of renal function lay dormant for several years thereafter, largely due to two factors. One factor was the opinion of Homer Smith, the leading figure in renal physiology at the time. Smith’s view was that “denervation diuresis appears to be a release from enhanced vasoconstriction engendered by anesthesia and traumatic operative procedures (11, 12).” The other factor was the lack of unambiguous and unequivocal evidence of innervation of structures in the kidney other than the vasculature, that is, the tubules and the renin-containing juxtaglomerular granular cells.1


This section briefly reviews the development and progress in our understanding of the neural control of renal function from the time of an early detailed description of the intrarenal innervation in 1972 (8) to the time of the most recent definitive reviews of the topic in 1997 (3) and 2000 (5). Individual citations may be found in Refs. 3 (1,736 citations) and 5 (303 citations).

Intrinsic renal innervation.

The anatomic breakthrough was provided by Luciano Barajas [Muller and Barajas (8)], who found that “varicose regions of the axons, containing clusters of dense-cored (norepinephrine-containing) vesicles, are in contact with the renal tubules. At the point of contact, only the basement membrane separates the nerve endings from the tubule cells.” His conclusion was prophetic: “these studies provide an anatomical basis for a direct action of the autonomic nervous system on renal tubular function.” It is now known that norepinephrine-containing renal sympathetic nerve terminals are in direct contact with the peritubular basement membrane of all renal tubular segments, as well as the juxtaglomerular granular cells, thus making it clear that alterations in renal sympathetic nerve discharge with changes in neurotransmitter release could directly influence renal tubular transport function as well as renin secretion.

Renal nerve stimulation and renal denervation.

It was relatively straightforward to demonstrate the functional significance of this innervation. Use of low-frequency renal nerve stimulation at an intensity that did not affect glomerular filtration rate (GFR) or renal blood flow (RBF) produced a reversible decrease in urinary sodium and water excretion in the rat, rabbit, sheep, and monkey. Renal tubular micropuncture and microperfusion studies showed that this was associated with an increase in renal tubular sodium and water reabsorption throughout the nephron. In the reverse mode, renal denervation produced an increase in urinary sodium and water excretion associated with decreases in renal tubular sodium and water reabsorption throughout the nephron, again with no change in GFR or RBF (Table 1).

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Table 1.

Effect of alterations in renal sympathetic nerve activity on renal sodium handling

The renal sympathetic nerves innervate the three major neuroeffectors in the kidney (Fig. 1). Increased renal sympathetic nerve activity (RSNA) leads to increased renin secretion rate via stimulation of β1-adrenoceptors on juxtaglomerular granular cells, increased renal tubular sodium reabsorption (decreased urinary sodium excretion) via stimulation of α1B-adrenoceptors on renal tubular epithelial cells, and decreased RBF via stimulation of α1A-adrenoceptors on the renal arterial resistance vessels.

Fig. 1.

Effects of increased renal sympathetic nerve activity (RSNA) on the 3 renal neuroeffectors: the juxtaglomerular granular cells (JGCC) with increased renin secretion rate (RSR) via stimulation of β1-adrenoceptors (AR), the renal tubular epithelial cells (T) with increased renal tubular sodium reabsorption and decreased urinary sodium excretion (UNaV) via stimulation of α1B-AR, and the renal vasculature (V) with decreased renal blood flow (RBF) via stimulation of α1A-AR.

The renal functional responses to renal sympathetic nerve stimulation are frequency dependent (Fig. 2) with increases in renin secretion rate occurring without changes in urinary sodium excretion, RBF, or GFR at low frequencies. At slightly higher frequencies, the increase in renin secretion rate is accompanied by increased renal tubular sodium reabsorption and decreased urinary sodium excretion, still without changes in RBF or GFR. At higher frequencies, there is increased renin secretion rate and decreased urinary sodium excretion, RBF, and GFR.

Fig. 2.

Relationship between the frequency of renal nerve stimulation and the magnitude of the response of renin secretion rate (increase), urinary sodium excretion (decrease), and renal blood flow (decrease).

Reflex renal sympathoexcitation and sympathoinhibition.

It is clear that low-frequency renal nerve stimulation does not mimic the naturally occurring rhythmicity of endogenous RSNA. For this reason, an extensive series of studies was performed using a variety of reflex maneuvers to both increase and decrease RSNA in a more physiological manner. Reflex-induced physiological increases in RSNA produced decreases in urinary sodium excretion and increases in renin release. Reflex-induced physiological decreases in RSNA produced increases in urinary sodium excretion and decreases in renin release. These changes occurred in the absence of changes in GFR and RBF and were abolished by renal denervation (Tables 2 and 3). Thus these studies using physiological reflexes to alter RSNA produced results similar to those with low-frequency renal nerve stimulation and renal denervation.

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Table 2.

Reflex stimuli that produce antinatriuresis and increased renin release in the absence of changes in GFR and RBF and are abolished by DNX

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Table 3.

Reflex stimuli that produce natriuresis and decreased renin release in the absence of changes in GFR and RBF and are abolished by DNX

Conscious experiments.

However, it was necessary to deal with Homer Smith’s criticism related to anesthesia and surgical stress (11, 12). This important issue was resolved by elegant studies in trained conscious dogs instrumented for the measurement of RSNA and renal function. Head-out water immersion and head-up tilt were used to produce reflex sustained decreases and increases in RSNA, respectively. Reflex renal sympathoinhibition produced natriuresis, whereas reflex renal sympathoexcitation produced antinatriuresis; these responses were not accompanied by changes in GFR or RBF and were abolished by renal denervation. (Table 4). Left atrial balloon inflation was used to produce reflex decreases in RSNA. Reflex renal sympathoinhibition produced a diuretic and natriuretic response with formation of a dilute urine. The diuretic and natriuretic responses were abolished by both cardiac and renal denervation, supporting the existence of a cardiorenal neural reflex volume control loop. These studies in conscious dogs using reflex stimuli to alter RSNA put to rest any residual concerns related to the possible confounding influence of anesthesia and surgical stress with respect to the influence of natural endogenous RSNA on renal tubular sodium and water reabsorption.

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Table 4.

Effect of reflex-induced alterations in RSNA in conscious dogs

Sodium balance.

Several studies documented an important contribution of the renal innervation to acute and chronic adjustments in sodium balance during both sodium deficit and sodium excess. In an integrated view, with acute and chronic sodium loading and depletion, the associated changes in RSNA contribute importantly to the ability of the kidney to conserve or dispose of sodium so as to maintain sodium balance (Table 5).

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Table 5.

Role of RSNA in sodium balance adjustments

This is reminiscent of Cannon’s words in describing his experiments on removing the entire sympathetic chain in cats: “…the sympathico-adrenal system, dispensable in the protected conditions of the laboratory, finds its great service at times of critical emergencies when it adjusts the internal organs of the body for use of the mechanisms responding to external emergencies (2).” In the case of the renal control of sodium balance, “the protected conditions of the laboratory” would be akin to normal sodium intake, whereas the “critical emergencies” would be similar to marked sodium restriction and sodium excess.

Pathological sodium retention.

Given the important contribution of alterations in RSNA to renal sodium handling, the role of RSNA in several clinically relevant rat models of renal sodium retention and edema formation was examined, i.e., congestive heart failure, nephrotic syndrome, and cirrhosis with ascites. In each model, we found a uniform set of characteristics incriminating increased RSNA as an important contributing factor to the pathological increase in renal sodium retention and edema formation (Table 6). In this regard, increased RSNA is part of the final common pathway leading to increased renal sodium retention in these several models. Although the alteration in sinoaortic and/or cardiopulmonary baroreflex function leading to the increase in RSNA was different in each of the models, inhibition of the central actions of ANG II decreased the basal level of RSNA and improved sinoaortic and/or cardiopulmonary baroreflex control of RSNA, both of which contributed to an improvement in the ability of the kidney to excrete both acute and chronic sodium loads.

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Table 6.

Role of RSNA in experimental models of renal sodium retention and edema formation


The coupling of the regulation of total body fluid volume to the regulation of arterial pressure depends on the kidney’s ability to excrete sodium in such a manner as to achieve external sodium balance in the face of varying sodium intake. An intrinsic or extrinsic defect in renal sodium excretory ability results in an increase in arterial pressure so as to enable the maintenance of external sodium balance via the pressure natriuresis mechanism. The cost of this adaptation is a higher arterial pressure, i.e., hypertension. With the knowledge that increased RSNA resulted in enhanced renal tubular sodium reabsorption, decreased urinary sodium excretion, and renal sodium retention, the observation that renal denervation interfered with the development of hypertension, especially in animal models of hypertension in which RSNA was known to be increased, was not unexpected. However, the finding that renal denervation prevents the development, attenuates the magnitude, or delays the onset of hypertension in a wide variety of animal models in multiple species (Table 7) suggests that increased RSNA may be a final common pathway for the defect in renal sodium excretory ability required for the development and maintenance of hypertension.

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Table 7.

Experimental animal models in which renal denervation prevents the development, attenuates the magnitude, or delays the onset of hypertension

Functionally specific renal nerves.

The scheme for the innervation and control of the three effector target structures in the kidney—the vasculature, the tubules, and the juxtaglomerular granular cells—was not known. There was anatomic evidence for two major schemes: a single fiber making contact with each of the three effectors or unique effector-specific fibers that make contact with only one effector and not any of the others. The prevailing view was that the efferent renal sympathetic nerve fibers were a uniform homogenous group. The efferent renal sympathetic nerve fibers are predominantly unmyelinated small fibers with conduction velocities <2 m/s. However, the finding that the distribution of rat renal nerve fiber diameters showed a bimodal distribution suggested a certain degree of heterogeneity. Anatomic studies of neurovascular contacts in the kidney disclosed different ultrastructural types of sympathetic axons, again supporting heterogeneity. Additional neurophysiological and renal functional studies support the existence of functionally specific renal sympathetic nerve fibers (Table 8).

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Table 8.

Functionally specific renal sympathetic nerve fibers


Vasoconstrictor vs. nonvasoconstrictor RSNA.

The repeated demonstration that low-frequency (intensity) renal sympathetic nerve stimulation increased renin release and renal tubular sodium reabsorption without affecting RBF or GFR focused attention on the difference between a nonvasoconstrictor intensity of RSNA and a vasoconstrictor intensity of RSNA. This is illustrated by the fact that low-frequency nonvasoconstrictor intensities of renal sympathetic nerve stimulation do not affect steady-state or stepwise autoregulation of RBF, whereas high-frequency vasoconstrictor intensities impair it, seen as an increase in the lowest autoregulatory pressure threshold. This would suggest that RBF autoregulation would not be affected by renal denervation that removed nonvasoconstrictor RSNA but would be improved when vasoconstrictor RSNA was removed.

This area has been further explored using frequency domain analytical techniques. The transfer function between arterial pressure (AP) and RBF describes to what extent fluctuations in AP influence fluctuations in RBF. The normalized transfer function gain is a ratio for the fractional variation in AP and RBF. A value of 1 (0 dB) indicates that the fractional variation in AP results in an identical fractional variation in RBF; a value of <1 (negative dB) indicates that the fractional variation in AP results in a smaller fractional variation in RBF (autoregulation); and a value of >1 (positive dB) indicates that the fractional variation in AP results in a larger fractional variation in RBF (no autoregulation). Two mechanisms contribute to dynamic RBF autoregulation: the faster myogenic mechanism with a time period of 5–10 s (0.1–0.2 Hz) and the slower tubuloglomerular feedback mechanism with a time period of 20–50 s (0.02–0.05 Hz).

Renal denervation in the Wistar-Kyoto (WKY) rat does not affect RBF, indicating that the prevailing level of RSNA in WKY is of nonvasoconstrictor intensity (4). In addition, there was no effect on dynamic RBF autoregulation, analyzed as the transfer function relationship between spontaneous fluctuations in AP and RBF (Fig. 3, left). Conversely, renal denervation in spontaneously hypertensive rats (SHR), in which basal RSNA is known to be elevated, significantly increased RBF, indicating that the level of RSNA in SHR is of vasoconstrictor intensity. Before renal denervation, dynamic RBF autoregulation was absent in SHR (Fig. 3, right) with positive values for gain over the entire frequency range and a peak in gain at the signature frequency (0.02–0.05 Hz) of the tubuloglomerular feedback (TGF) component of RBF autoregulation. After renal denervation in SHR, dynamic autoregulation was substantially improved with negative values for gain at frequencies <0.1 Hz and reappearance of a nadir in gain at the TGF frequency.

Fig. 3.

Effect of renal denervation (DNX) on the transfer function between arterial pressure (AP) and RBF for Wistar-Kyoto rats (WKY; left) and spontaneously hypertensive rats (SHR; right). Renal DNX did not affect total RBF in WKY (nonvasoconstrictor intensity of RSNA), whereas it significantly increased total RBF in SHR (vasoconstrictor intensity of RSNA). Because there was no significant effect in WKY (n = 9; left), only mean data are shown. For SHR (n = 10; right), the bracketing lines represent 1 SE. For comparison between rats, gain has been normalized to the value at DC (0 frequency).

In companion studies, we used peripheral thermal receptor stimulation (insertion of the rat’s tail in hot water) to reflexively increase RSNA. After the initial peak RSNA response of 46%, there was a steady-state 16% increase in RSNA that was sustained for an average of 14 min and led to a 15% decrease in RBF, i.e., a vasoconstrictor intensity of RSNA. Dynamic RBF autoregulation was substantially affected. The transfer function gain between AP and RBF during hot tail (Fig. 4, top) showed a shift to positive gain values over the entire frequency range, an absence of the TGF signature, and an obscuring of the plateau normally observed in the frequency range of the myogenic component of RBF autoregulation (0.1–0.2 Hz). On further analysis, it was found that the transfer function gain between AP and RSNA (i.e., arterial baroreflex control of RSNA; Fig. 4, middle) was not affected. Thus the overall effect was localized to the transfer function gain between RSNA and RBF (Fig. 4, bottom), where, at every frequency, there was less attenuation of the RSNA signal during hot tail than during control.

Fig. 4.

Effect of peripheral thermal receptor stimulation (hot tail) on normalized transfer function gain between AP and RBF (top), AP and RSNA (middle), and RSNA and RBF (bottom) in normal rats (n = 6). Hot tail significantly decreased total RBF. Bracketing lines at top and bottom represent 1 SE.

Although the analyses were made during the steady state, the nonstationary nature of most physiological signals is an important consideration. The transfer function spectra shown in Fig. 4 assess the data set over the entire time interval assuming stationarity of the respective signals; this is referred to as a time-invariant transfer function. To account for possible nonstationarity, an algorithm for determining the time-variant transfer function was used (14); this permits examination of the transfer function serially over the time interval of data collection. As shown in Fig. 5, A and B, the transfer function exhibited reasonable stability over a 500-s time interval during both the control and hot tail period.

Fig. 5.

Time-variant transfer function (TVTF) between AP and RBF during control (A) and peripheral thermal receptor stimulation (hot tail; B) in a single anesthetized normal rat. These contour plots display frequency (Hz) on the vertical y-axis, time (s) on the horizontal x-axis, and gain (dB) on the z-axis perpendicular to the x-y plane. Stationarity of the transfer function is shown as an unchanging color (i.e., gain) at a given frequency over the entire time course, e.g., during both control (A) and hot tail (B), the color is constant over the entire time course at a frequency of 0.05 Hz, the approximate frequency of the tubuloglomerular feedback (TGF) mechanism. Analysis was performed with the kind assistance of Ki H. Chon and the time-variant transfer function algorithm (14).

The step response (10) is often used to characterize the response of a system. In this case, the response of a model system to a unit input is often characterized by an initial overshoot, followed by oscillatory behavior that slowly diminishes to a steady-state plateau value. As shown in Fig. 6, the steady-state plateau value was significantly greater in hot tail than in control, reflecting a lesser ability to stabilize output in response to the unit step input. This is a measure of the overall impairment in dynamic autoregulation of RBF seen after hot tail (Fig. 4, top). The oscillatory behavior may be further defined by the natural frequency (that frequency at which the system would oscillate if there were no damping), the damping ratio (the ratio of actual damping to the critical damping where there is no oscillation), and the damped natural frequency (a measure of how the damping ratio affects the natural frequency). The values for the damping ratio (0.51 ± 0.05 vs. 0.47 ± 0.04), the natural frequency (1.41 ± 0.08 vs. 1.45 ± 0.08 Hz), and damped natural frequency (1.21 ± 0.05 vs. 1.25 ± 0.05 Hz), representing the dynamics of the process compared with the absolute steady-state magnitude of the overall response, were not significantly different between control and hot tail.

Fig. 6.

Step response for control and peripheral thermal receptor stimulation (hot tail) in 6 normal rats. At time 0, a unit step was applied. The magnitude of the overshoot, the oscillations, and the steady-state plateau were higher during hot tail than during control, consistent with impaired dynamic RBF autoregulation during hot tail.


It is possible to consider RBF as the single output of a system with two inputs, AP and RSNA. This variant of a multiple input, single output (MISO) is capable of being modeled using linear difference equations such as autoregressive moving average with exogenous input (ARMAX) (6). With the use of least-squares methods, the overall goal is to minimize the difference between the measured RBF and the simulated RBF (i.e., the residual) by using either single input alone or both inputs combined. Figure 7 shows the observed RBF (processed and scaled to unit variance) together with the modeled RBF with AP and RSNA as single inputs and APRSNA as a combined input in an anesthetized normal rat. The AP and APRSNA model RBF are closer to the observed RBF than the RSNA model RBF. The efficacy of the simulation is quantitatively assessed by measuring the standard deviation (SD) of the residuals (Fig. 8). Under normal conditions, as well as conditions of reflex increases in RSNA (hot tail), AP as a single input was more efficacious than RSNA as a single input with the combined input of APRSNA being equal to AP alone. These results suggest that the variations in AP are more dominant than the variations in RSNA in explaining the variations in RBF during both control and hot tail. During hot tail, the overall values for SD residuals were lower compared with those during control. This may relate to a lower level of absolute RBF, as well as a decrease in RBF variability, both related to the increase in renal vasoconstrictor tone during hot tail.

Fig. 7.

Comparison of the observed RBF and the model RBF using either AP or RSNA as a single input or using both as a combined input (APRSNA). Data are from a 2-min segment from a single anesthetized normal rat and have been processed and scaled to unit variance. The approximation of the observed RBF is best with the AP and APRSNA model and less with the RSNA model.

Fig. 8.

Comparison of the SD residuals for the different models (n = 6). The SD residual for the AP and APRSNA models are similar and significantly lower than that for the RSNA model during both control and peripheral thermal receptor stimulation (hot tail). The overall values are significantly lower during hot tail compared with control, possibly reflecting lesser RBF variability during hot tail-induced renal vasoconstriction.

A recent model of TGF and the myogenic mechanism in afferent arterioles incorporates a tubular, glomerular, and an arteriolar model (7). The tubular model permits one to increase or decrease proximal tubular fluid reabsorption, producing an inverse change in fluid delivery to the macula densa, an important signal for TGF. The model was used to examine the effects of changes in proximal tubular fluid reabsorption that would be expected from alterations in RSNA over the low-frequency range, that range known not to affect whole kidney GFR or RBF. As shown in Fig. 9, going leftward along the horizontal axis, decreases in RSNA cause decreases in proximal tubular reabsorption of up to 40%. This produces an increase in macula densa delivery, activating TGF, and results in a decrease in both single nephron glomerular filtration rate (SNGFR) and single nephron glomerular plasma flow (SNGPF). The decrease in SNGFR, 25% at maximum, is greater than the decrease in SNGPF of 10%. This can be viewed as a protective response against possible excessive renal loss of sodium and water. Going rightward along the horizontal axis, over the range of increases in RSNA that do not affect whole kidney GFR and RBF, there is an increase in proximal tubular reabsorption of up to 30%. The resultant decrease in macula densa delivery inhibits TGF, resulting in quantitatively smaller increases in SNGFR and SNGPF. This can be viewed as a protective response against possible excessive renal retention of sodium and water.

Fig. 9.

Effect of alterations in RSNA that affect proximal tubular reabsorption but not whole kidney glomerular filtration rate (GFR) or RBF on a single nephron TGF model. With decreases in RSNA, proximal tubular reabsorption decreases, macula densa delivery increases, and TGF is activated with relatively greater decreases in single nephron glomerular filtration rate (SNGFR) than in single nephron glomerular plasma flow (SNGPF). With increases in RSNA, proximal tubular reabsorption increases, macula densa delivery decreases, and TGF is inhibited with small increases in both SNGFR and SNGPF. Analysis (Figs. 9 and 10) was performed using the TGF model with the kind assistance of Donald J. Marsh (7).

The strength of the TGF mechanism can be represented as the maximum power seen in the power spectrum of SNGPF (Fig. 10); this maximum power always occurred in the frequency range characteristic of TGF in the rat and the dog, 0.023 Hz. With decreased RSNA leading to subsequent activation of TGF, maximum SNGPF power is greater than with increased RSNA leading to inhibition of TGF. As shown in Fig. 10, inset, decreased RSNA and TGF activation increases maximum TGF power more than twofold from baseline. With increased RSNA and inhibition of TGF, maximum power decreases to less than one-half of baseline.

Fig. 10.

Strength of TGF represented as the SNGPF power spectrum. For each of the steps, the peak power occurred at the same frequency, 0.023 Hz. Inset: with decreases in RSNA and TGF activation, peak SNGPF power was increased more than 2-fold over baseline. With increases in RSNA and TGF inhibition, peak SNGPF power was decreased to less than one-half of baseline.

The model suggests that changes in the level of RSNA which do not affect whole kidney GFR or RBF produce alterations in proximal tubular fluid reabsorption and macula densa delivery sufficient to influence the TGF mechanism, resulting in compensatory regulatory changes in SNGFR and SNGPF.

In summary, this lecture has emphasized the important role of the renal sympathetic nerves in the regulation of renal function in both physiological and pathophysiological conditions. Despite the earlier work of both Claude Bernard and Ernest Henry Starling, this field is relatively new in terms of growth and development. Over the past 40 years, the subject of renal nerves has emerged from obscurity with fewer than 10 publications per year in 1965 and risen to a level of reasonable interest and respect with some 80–90 publications annually since 1985. In response to the question posed by an editorialist, “Renal nerves: are they essential?” (9), we are now able to respond with a strong affirmative.


Work in this laboratory was supported by National Institute of Diabetes and Digestive and Kidney Diseases Grant DK-15843 and a Department of Veterans Affairs Merit Review Award. Additional support was received from the Wennergren Foundations and the Karolinska Institute Research Foundation, Stockholm, Sweden, and the Adlerbertska Forskningsstiftelsen, The Royal Society of Arts and Sciences, Göteborg, Sweden.


This lecture was delivered at the Annual Meeting of the American Physiological Society at the combined meeting of Experimental Biology and 35th International Union of Physiological Sciences, April 2, 2005, San Diego, CA.

I am grateful to Dr. Ki H. Chon (Department of Biomedical Engineering, State University of New York at Stony Brook, Stony Brook, NY) for a copy of the algorithm for time-variant transfer function (14).

I am grateful to Dr. Donald J. Marsh (Dept. of Molecular Pharmacology, Physiology, and Biotechnology, Brown University, Providence, RI) for running the tubular model of TGF at varying levels of proximal tubular reabsorption (7).


  • 1 The Walter B. Cannon Memorial Award Lecture is sponsored by The Grass Foundation. The connections between Albert and Ellen Grass and Walter Cannon originated during the time that Albert was working as a part-time research instrument engineer in the Department of Physiology at Harvard Medical School, then chaired by Cannon. Ellen was a PhD candidate studying auditory action potentials. The Grass Instrument Company originated in Quincy, MA. Like Albert Grass, the author was also born and raised in Quincy, MA, less than a mile from the original headquarters of the Grass Instrument Company. The author’s father, also a physician, had the privilege of being involved in the health care of the Grass family.


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