(Hypertension. 1999;33:153-161.)
© 1999 American Heart Association, Inc.
Scientific Contributions |
Structural Autoregulation of Terminal Vascular Beds
Vascular Adaptation and Development of Hypertension
From the Deutsches Herzzentrum Berlin (A.R.P.) and the Department of Physiology, Freie Universität Berlin (P.G.), Berlin, Germany; and the Department of Physiology, University of Arizona, Tucson, Ariz (T.W.S.).
Correspondence to A.R. Pries, MD, Freie Universität Berlin, Department of Physiology, Arnimallee 22, D-14195 Berlin, Germany. E-mail pries{at}zedat.fu-berlin.de
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Abstract—It is widely accepted that the early phase of primary hypertension is characterized by elevated cardiac output, whereas in later stages the increased blood pressure is due to increased peripheral resistance. To study long-term effects of increased blood flow on peripheral resistance, structural adaptation of microvascular networks in response to changes in blood flow was simulated using a previously developed theoretical model. The diameter of each vessel segment was assumed to change in response to local levels of shear stress, transmural pressure, a metabolic stimulus dependent on blood flow rate, and a conducted stimulus. Network morphologies and topologies were derived from intravital microscopy of the rat mesentery. Adaptive responses to the 4 stimuli were quantitatively balanced to yield stable and realistic distributions of vascular diameters and blood flow rates when the total flow rate was set to observed levels. To simulate effects of increased cardiac output, network flow resistance after structural adaptation was determined for a range of flow rates. Resistance increased with increasing flow, and increases in pressure were up to 3-fold greater than proportional to the increases in flow. According to the model, flow-dependent changes of network resistance result mainly from the vascular response to transmural pressure, which also causes arteriovenous asymmetry of diameters and pressure drops. Therefore, in vascular beds that exhibit arteriovenous asymmetry, increased flow may trigger increased flow resistance by a mechanism involving the tendency of vascular segments to reduce their luminal diameters in response to increased transmural pressure.
Key Words: stress, shear • pressure • hemodynamics • modeling, mathematical
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Introduction |
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Although the pathophysiological mechanisms of primary hypertension are not fully understood, there is general agreement that in established hypertension, cardiac output is approximately normal and the increase in blood pressure is caused by a proportional increase in peripheral resistance.1 2 3 This resistance increase evidently results from structural changes of the vasculature because it is seen under conditions of resting tone and at maximal vasodilation.1 4 In contrast, an increase in cardiac output is seen only in young and borderline hypertensive patients2 and also transiently in some experimental forms of hypertension.5 6 The initial changes of cardiovascular function in primary hypertension may result from a decreased capacity of the kidneys to excrete fluid at a given arterial pressure level.7 8 Indications that the altered vascular structure typical for established hypertension represents a secondary adjustment9 support the theory of "structural resetting" of systemic resistance4 by adaptation of vascular diameters to increased levels of blood flow and driving pressure.10 11 12
This concept implicitly assumes continuous structural adaptation of vessel diameters to local hemodynamic conditions. It is known that vessels react chronically to the mechanical forces exerted by the flowing blood, ie, wall shear stress and transmural pressure,13 14 15 16 17 18 19 20 21 and continuous adaptation to local shear stress at the endothelial surface has been suggested to control vascular structure and to optimize vascular design.22 23 However, vascular growth in response to shear stress alone would result in unstable adaptation under most circumstances.24 25 If 2 parallel flow pathways experience the same driving pressure, the pathway with lower flow resistance receives a higher flow fraction and experiences a higher level of wall shear stress. If, as suggested by experimental data, an increase of shear stress triggers an increase of vessel diameter (and vice versa), the flow resistance in this pathway would decrease further while that of the high-resistance pathway would increase. This process would continue until 1 pathway is completely eliminated and lead eventually to the collapse of a vascular network to a single large arteriovenous channel. If a response to transmural pressure on vascular structure is considered in addition (pressure-shear hypothesis26 ), such unstable adaptive behavior is not eliminated. Therefore, additional local mechanisms, eg, metabolic factors,27 must be involved.
In a recent theoretical analysis, a minimum set of stimuli required to produce stable microvessel networks with realistic morphology and hemodynamic properties28 was defined by comparing results of simulations with experimental data from complete microvascular networks in the rat mesentery. It was demonstrated that adaptation of vascular diameters can yield stable and realistic network structures only if it entails responses to a combination of at least 4 stimuli: shear stress at the endothelial surface; transmural pressure; local metabolic conditions, related to local red cell flow; and a conducted stimulus coupling terminal branches to their feeding or draining vessels. The responses to shear stress and transmural pressure create and maintain the arteriovenous asymmetry of microvascular networks with respect to pressure drop, wall shear stress, and vessel diameter. The metabolic stimulus prevents the collapse of vascular networks to single arteriovenous pathways, and the conducted stimulus suppresses the generation of large shunts connecting proximal arterial and venous vessel segments.
The long-term reaction of a vascular network to changes of systemic hemodynamic conditions will largely depend on these adaptive responses. For example, if blood flow rate is increased, the equilibrium state achieved by structural adaptation is perturbed and vessel diameters must change until a new equilibrium is reached. The extent of these changes, and the resulting alteration of flow resistance, depends on the relative magnitudes of the vessels' adaptive responses. In the present study, our previously developed model for local vascular adaptation28 was used to predict the effect of changes in flow rate on flow resistance, and the results are considered in the context of changes occurring in arterial hypertension.
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Methods |
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Microvessel Network Structure
The animal preparation and the setup used for the measurement of network geometry and structure by intravital microscopy have been described in detail elsewhere.26 29 Male Wistar rats (n=3; body weight, 300 to 450 g) were anesthetized (atropine 0.1 mg/kg IM, pentobarbital sodium 20 mg/kg IM followed by ketamine 100 mg/kg IM). During the experiments, the level of anesthesia and fluid balance were maintained by intravenous infusion of physiological saline (24 mL/kg per hour) containing 0.3 mg/mL pentobarbital sodium. After cannulation of the trachea, jugular vein, and carotid artery and after abdominal midline incision, the animals were transferred to the stage mounted on an intravital microscope equipped with a 25x//N.A.0.6 salt-water immersion objective (Leitz). Heart rate and arterial blood pressure (range, 105 to 140 mm Hg) were continuously monitored via the catheter in the carotid artery. The small bowel was exteriorized and superfused continuously with a temperature-controlled (36.5°C) bicarbonate-buffered saline containing papaverine (10-4 mol/L) as a precaution to prevent the development of tone and variation of vessel diameters during the experiments.
In each experiment, a vascular network in the fat-free portion of the
mesenteric membrane with an area between 25 and 80
mm2, supplied by a feeding arteriole (inner
diameter, 
30 µm) and drained by a venule (diameter,
45 µm), was selected. These networks were scanned and
recorded on videotape and on black-and-white film. A complete scan
took 30 minutes and consisted of 300 individual fields of view
(300x400 µm). In 2 networks, an additional scan was performed
using strobe-light asynchronous illumination to allow off-line
determination of flow velocity in each vessel segment with a digitized
image-analysis system.29 The photographs
exposed during the scanning procedure were used to assemble
photomontages of the complete microvascular networks, from which
network topological structure (connection matrix) and the length of all
vessel segments between branch points were determined. The diameter of
vessel segments was determined from the video recordings
obtained with the strobe-light flash illumination, where available (n=2
networks), and from the photonegatives for the remaining networks
(n=4). The numbers of vessel segments in the 3 networks
analyzed here were 383, 546, and 913.
Mathematical Simulation of Blood Flow
Details of the mathematical flow simulation and its validation
have been described previously.29 30 Volume flow
rates and hematocrit values in each segment and pressures at each
branch point within a network were calculated using an iterative
algorithm. The phase separation effect (nonproportional partition of
red cell and plasma flows at diverging bifurcations) was taken into
account, based on observations of arteriolar bifurcations of the rat
mesentery.31 The assumed dependence of effective
blood viscosity in microvessels on vessel diameter and hematocrit was
based on data obtained previously for the same
tissue.29 Boundary conditions for the
calculations include hematocrit values and volume flow rates in all
vessel segments feeding the network and volume flow rates of those
segments leaving the network, with the exception of the main venular
draining segments. In the network with 546 segments, only 1 large
draining vessel was present. This segment was assigned a pressure
of 13.8 mm Hg according to previous measurements of
micropressures in similar-sized venules in the same
tissue.29 For the 2 other networks, 2
(383-segment network) or 7 (913-segment network) additional larger
draining segments were assigned pressures slightly above (14 and
15 mm Hg) that of the main draining segment. Measured flow
velocities in the boundary segments were used to calculate overall
volume flow rates in 2 of the 3 networks. For the third network, volume
flow rates were assigned to match experimental values for corresponding
vessel diameters.
Mathematical Simulation of Structural Adaptation
A model of structural adaptation was developed on the
basis of the experimentally determined network
structure.28 According to this model, each
segment in the network adapts by changing its diameter at a rate that
is proportional to a net growth stimulus for this segment,
Stot:
 | (1) |
 | (2) |
w." The logarithmic dependence ensures the
sensitivity of Stot to changes in
w over a wide range of
w values. The tendency of vessels to decrease
in diameter in response to increased transmural pressure is
represented by the term "-log
e(P)," where e(P)
increases sigmoidally with P according to experimental data relating
w and P in rat mesentery
networks26 :
 | (3) |
by the term
"+km
log[Qref/QHD+1]" where
QHD is the volume flow rate of red blood cells,
Qref is a reference flow (40 nL/min), and
km determines the sensitivity of the vessel to
blood flow. The conducted metabolic stimulus is represented by the term "+kc [Sc/(Sc+S0)]." Here, Sc is the summed conducted signal entering a given segment. It is assumed to be generated in each segment and propagated upstream in the arteriolar tree (downstream in the venular tree) and to decay exponentially with distance traveled (length constant, 1500 µm). Calculation of Sc proceeds from the most distal branch points of the arteriolar tree proximally to the main feeding vessel. At a given bifurcation, Sc is calculated as the sum of the metabolic stimuli of the downstream vessel segments and the conducted stimuli from the next downstream bifurcations. The same procedure is used in the inverse direction for the venular tree. For a given arteriolar or venular vessel, the resulting conducted stimulus is calculated from Sc using a saturable response, with a reference value of S0=20. The parameter kc represents the relative strength of the conducted stimulus.
Finally, a constant term "-ks" is included to represent the shrinking tendency, ie, the tendency of vessels to shrink in the absence of growth-stimulating stimuli. The values of the constants included in the mathematical expressions were obtained by minimizing the mean square deviation in flow velocities between experimentally measured values for the networks considered and the values predicted by the mathematical flow simulation.28 The resulting values used for the metabolic stimulus parameter km were 0.63, 0.83, and 0.97 for the 3 networks with 383, 546, and 913 segments, respectively. For the conducted signal parameter kc, the respective values were 3.19, 2.74, and 2.85, and for the shrinking tendency, ks, 1.57, 1.79, and 1.57.
Satisfactory agreement between predicted and observed network
structures and velocity distributions was possible only when all 4
stimuli were included. Each stimulus determines distinct
characteristics of the adapted network. Shear stress at the
endothelial surface (w)
controls the diameter distribution over the successive generations of
microvessels.26 Vascular adaptation in response
to pressure (P) generates the difference in average vessel diameters
between the arterial and the venous side of the vascular
bed. According to the pressure-shear
hypothesis,26 vascular reactivity to
w and P is needed to create the functionally
important arteriovenous asymmetry with respect to pressure drop, shear
rates, diameters, and intravascular volume. Stability of the adaptive
response can only be achieved if the metabolic stimulus,
reflecting the metabolic state of tissue adjacent to a
vessel segment, is included. This prevents the collapse of the network
to a single large arteriovenous pathway. In the present model, the
metabolic stimulus was assumed to depend directly on red
cell flow rate in a given vessel segment. In reality, the
metabolic stimulus experienced by a given segment must
depend not only on the flow in the segment but also on the extent and
metabolic demand of the region it serves. However,
inclusion of such effects would require further information about the
spatial distribution of vessels and of metabolic demand,
which is not available for this tissue. The conducted stimulus is
needed to maintain the diameter of vessels feeding or draining larger
numbers of terminal vessels. Without a growth signal transmitted from
dependent segments to their supply vessels, these vessels would shrink
and blood would flow mainly through short arteriovenous shunts,
bypassing the nutritive capillaries.
Mathematical Simulation of Network Reaction to Changes in Bulk
Flow Rate
For a given network flow resistance, any change in volume
flow rate will be associated with a proportional change in driving
pressure. However, vascular responses to the changed
hemodynamic conditions, elicited by an initial change
of the flow rate, may change the flow resistance, and in turn 
P,
constituting a feedback loop (Figure 1
).
The sensitivity of relative resistance changes (dR/R) to relative
changes in pressure is represented by the gain (G) of the
feedback loop:
 | (4) |
 | (5) |
P/R is the bulk flow rate through the boundary
segments feeding a network. In the case of positive feedback, when an
increase of pressure leads to an increase in flow resistance, G>0, and
A>1.
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To investigate the influence of changes of bulk flow rate on network
flow resistance, the flow rates in boundary segments were varied. The
volume flow rate under control conditions calculated from experimental
measurements (networks with 383 and 546 segments) or estimated from
typical flow velocities for mesenteric arterioles of a given diameter
(network with 913 segments) was multiplied with a factor ranging from
0.03 to 3. P was calculated as the average pressure difference
across the network ("driving pressure"), ie, the difference between
the flow-weighted means of the pressure in the feeding and draining
boundary vessels of the network. For the network with 546 segments,
additional simulations were performed in which the combination of
adaptive stimuli or the numerical values of parameters used
in the simulated vascular adaptation were altered.
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Figure 2
shows results of
simulations of network adaptation for a range of volume flow rates,
using the adaptation model as described above. For all 3 networks, P
increased with Q in a sigmoidal fashion. In an intermediate range of
flow rates, the increase of P was markedly greater than
proportional, reflected by a steep increase of flow resistance (R). For
the 3 networks, the percentage increase in flow resistance over the
entire flow rate range varied between about 440% and 660%.
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The substantial increase of flow resistance with volume flow rate in an
intermediate range implies that vascular adaptation leads to a strong
positive feedback with respect to pressure changes. Initial changes of
volume flow rate lead to greater than proportional changes of driving
pressure with an amplification (A) >1. Figure 3 shows that the amplification is maximal
in an intermediate range of driving pressures, close to the values
normally observed for microvascular networks in the same
tissue.29 In this range, initial variations of
pressure caused by flow changes are amplified because of vascular
adaptation by a factor of 2.8, corresponding to a gain of
0.64.
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In the text that follows, results of simulations are described in which
the parameters of the adaptation model were modified to
investigate the importance of different characteristics of the adaptive
response for the observed positive feedback mechanism. Figure 4 shows that changes of the sensitivity
to the metabolic stimulus result in only very minor changes
of the pressure/flow relation. There is a weak correlation between
metabolic sensitivity and maximal feedback amplification.
However, the network adaptation becomes increasingly unstable at higher
pressures or flow rates if the metabolic sensitivity is
reduced. This confirms theoretical
considerations28 showing that below a certain
level of metabolic sensitivity the stability of the
adaptive response is lost. With the form of the metabolic
stimulus used here, this instability occurs first at high flow rate
levels, because the metabolic stimulus (M) is assumed to
decline when the flow rate in a given segment approaches a preset
reference value (Qref). As shown in Figure 5, the feedback amplification is not
affected by changes of Qref, but a proportional
shift of the pressure-flow relationships to higher flow rate levels
occurs with increasing Qref.
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) and 0.8 (
) relative to those obtained
with the standard value (continuous line). Data were
obtained from the network with 546 segments.
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The responses of vascular networks to variations of bulk flow rate were
sensitive to the relationship between expected shear stress and
transmural pressure in vessel segments (the
e/P relationship). When the pressure scale of
this relationship was stretched or compressed (Figure 6), the resultant flow rate and
resistance levels for given driving pressures and the feedback
amplification were substantially altered. Even more marked effects
resulted from alterations in the amplitude of the
e/P relationship (Figure 7). A flatter
e/P relationship resulted in reductions of the
resistance increase with driving pressure and of amplification of
pressure changes. In the case of constant e,
ie, no pressure sensitivity of the vessels, flow resistance decreased
with increasing driving pressure. As a consequence, initial changes of
flow rate led to smaller changes of pressure after vascular adaptation
(A<1, corresponding to a negative gain), indicating a negative
feedback with respect to pressure.
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Vessel segments in the microcirculation can adapt continually to their local environment by structural changes of diameter. This adaptation should lead to stable network structures under a variety of hemodynamic conditions and levels of tissue demand. Vessels should not grow or shrink excessively and an adequate number of parallel flow pathways has to be maintained to adequately supply the tissue. Adaptive responses of individual vessels affect the flow resistance and thus the pressure/flow relation of vascular beds. The relation between responses of individual vessels and those of complete networks is complex. Any vessel reacting to its local environment influences the flow distribution in the whole network, including the vessel itself. Therefore, in extrapolations from the behavior of isolated vessels to that of vascular networks, interactions among vessel segments must be taken into account. Theoretical simulations can describe the overall network behavior that results from interactions of many individual vessels exhibiting local responses (as inferred from experimental studies and/or theoretical considerations). This approach is used here to study the fundamental consequences of adaptive responses of vessels for the relation between flow rate, driving pressure, and flow resistance of vascular beds.
For a given vascular bed, changes of systemic arterial pressure, caused for example by modifications of cardiac function or flow resistance in other vascular beds, will initially lead to parallel, proportional changes of flow and pressure: The fundamental vascular reactions to hemodynamic stimuli, which have been described in many studies, predict opposite vascular reactions to these shear stress (or flow) and pressure signals. It is generally accepted that an increase in shear stress leads to an increase in diameter of the vessel lumen, acutely by reduction of vessel tone and chronically by vessel growth.12 13 32 33 Conversely, increased pressure results in a diameter decrease, acutely by an increase of myogenic tone34 35 36 and chronically by remodeling or concentric growth.10 11 37 A number of studies have investigated the relation between these reactions under defined experimental conditions.38 39 40 41 42 However, the complex nonlinear interactions in complete vascular networks discussed above render the interpretation of such measurements difficult and prevent their generalization to complete vascular beds.
Moreover, vascular response to shear stress alone cannot generate
stable and realistic network structures.28
Stability can be achieved by balancing the shear stress signals
(w) with a metabolic signal (M)
that decreases with increasing blood flow in a given vessel. These 2
stimuli represent a minimal set of signals for stable local
adaptation. A conducted signal (C) prevents the generation of short,
large-diameter arteriovenous shunts, which would otherwise carry a
large fraction of the total flow. The inclusion of a pressure
sensitivity (P) of the vascular response establishes the arteriovenous
asymmetry of vascular networks.26 Shear stress,
pressure drop, and flow velocity are greater on the
arterial side of the systemic circulation, whereas vessel
diameters and intravascular volume are greater on the venous side. This
asymmetry is functionally important because it determines
physiological characteristics, including the low
average capillary pressure level.
According to the model, an increase in arteriolar pressure causes an
adaptive reduction in vessel luminal diameters until a new equilibrium
state is reached. If pressure is held constant, the resulting increase
in flow resistance attenuates the change in flow rate. This long-term
behavior can be characterized according to
Folkow4 43 as "structural autoregulation." It
resembles acute functional autoregulation in that flow variations are
damped, and the capillary bed is protected from large pressure changes
(Figure 8). For a reduction (increase) of
the bulk flow rate by 20%, arteriolar driving pressure decreases
(increases) from 88 mm Hg to 49 (137) mm Hg, whereas mean
capillary pressure, which is 24 mm Hg under control conditions,
is decreased (increased) to only 20.8 (31.8) mm Hg. It is of note
that the value for the gain of "structural autoregulation"
determined in the present study (0.64) is close to the
experimental values reported for the gain of acute "whole body
autoregulation,"7 which ranges from 0.4 to 0.6.
Because of structural autoregulation, the long-term level of volume
flow rate into a vascular bed is primarily under local control and can
be modified by changes of the local metabolic demand. In
the present model such modifications are represented by
changes of the reference flow value (Figure 5).
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Local control of flow rate and conserved capillary pressure levels
resulting from structural autoregulation are potentially beneficial for
the function of the tissue involved. However, as changes of flow rate
are attenuated, changes of driving pressure are amplified (Figure 3).
Structural adaptation of individual vessels in response to local
stimuli results in a positive feedback of vascular beds with respect to
pressure (Figure 1). Thus, an initial small and functional pressure
increase elicited, for example, by an increase of cardiac output can
lead to a larger structural increase in pressure and flow resistance.
In the development of hypertension, the initial cause may thus be
elusive and lead to only small rises of pressure, whereas the
progression of the disease with strong, sustained pressure escalation
is related to adaptive responses according to the positive feedback
mechanism and vascular
hypertrophy.44
Folkow's theory of structural autoregulation4 43
is based on the assumption that vessels adapt to keep average
circumferential stress (wall tension per unit of wall thickness) to a
constant level throughout vascular networks and for different systemic
hemodynamic conditions. According to Laplace's law,
=P · r/w, where is average circumferential stress, P is
transmural pressure, r is vessel radius, and w is wall thickness, this
implies that an increase of pressure elicits a proportional increase of
normalized wall thickness, w/r. If this change of wall structure
involves a decrease of the luminal diameter of the vessel, vascular
flow resistance will be increased. Folkow also hypothesized that the
thickening of the vessel wall would amplify the
hemodynamic effect of vascular smooth muscle tone, as
for a given shortening of the smooth muscle layer the degree of luminal
narrowing would increase with increasing w/r.
The effects of pressure on wall structure and the effects of wall
structure on flow resistance and sensitivity to smooth muscle tone
described above are generally accepted. However, the question of what
physical quantity represents the main signal for the vascular
responses is unresolved. Experimental data from morphometric
studies37 45 46 47 48 49 50 51 52 show a strong increase of w/r
with decreasing vessel diameter for arterioles and venules (Figure 9). The relatively thick walls of small
microvessels may serve structural and functional needs other than the
hemodynamic control of a vascular bed. As a
result, average circumferential stress in the smallest microvessels is
only about 1/10 of that in the larger feeding vessels, making it an
unlikely candidate to be the main signal for the vascular adaptation
that leads to increased flow resistance in hypertension. In contrast,
vascular reactivity to changes of pressure renders the adaptation to
hemodynamic conditions independent of specific
differences in wall thickness. Furthermore, pressure is the only
hemodynamic parameter that exhibits a
predictable monotonic decline along arteriovenous pathways and can thus
serve to control systematic structural differences between
arterial and venous parts of the circulation. It is not
known what sensing mechanisms could generate the proposed vascular
reactions to pressure. However, such mechanisms are physically
conceivable. If, for example, the circumferential tension is primarily
carried by a specific circumferential structure in the vessel wall,
whose thickness is proportional to vessel diameter, the circumferential
stress in this structure could serve as a signal proportional to
pressure.
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The response of a network to a change in pressure or flow depends on
the response characteristics of the vessels, which in this discussion
are assumed to be identical for all vessels. Simulations with modified
vascular response characteristics show that the presence and extent of
structural autoregulation depends on the pressure sensitivity, defined
in the model by the relationship between expected wall shear stress,
e, and pressure, P. In contrast, changes in
other parameters of the response do not lead to major
changes in the reactive pattern, as long as pressure stability is
maintained (see Figure 4). The initial increase of driving pressure
used to test the adaptive characteristics of vascular beds leads to a
proportional increase of flow and wall shear stress
(w). The separate vascular responses to
increases of pressure (diameter reduction) and wall shear stress
(diameter increase) result in opposite changes of flow resistance.
Therefore, when both parameters are increased, the final
change of flow resistance is difficult to predict. In the standard
model considered, a e/P relationship based on
experimental findings is used, which predicts a strong sigmoidal
increase of e with changes in P. Here, the
increase of e dictated by the initial increase
of pressure is greater than the parallel increase of
w for most vessels. The resultant vascular
reaction is thus dominated by the effect of increased pressure and
vessel diameter decreases. This in turn leads to an increase of flow
resistance and hence driving pressure (Figure 6). If in contrast the
pressure sensitivity is abolished (e
constant), the increase of w determines
vascular responses to increased driving pressure, and a decrease of
flow resistance results (Figure 7).
Current knowledge concerning structural responses of vessels during adaptation is far from complete. Experiments at the scale of individual cells and vascular segments have not yet provided sufficient data on the quantitative relationships between hemodynamic conditions, tissue metabolic state, and structural vascular reactions to allow us to predict the behavior of terminal vascular beds in a "bottom up" inductive approach. Therefore, the present model was based on a "top down" deductive approach, in which observed properties of microvascular networks were used to deduce a minimal set of stimuli and their quantitative effects.28 Available experimental data on the underlying mechanisms were used to guide the process. This approach has led to new hypotheses and predictions on the interaction of adaptive vascular reactions and on the effects of changing hemodynamic conditions on network adaptation. However, further experimental work is required to test the model and its predictions. The approach used here may help to stimulate the necessary experiments and can provide a framework for interpreting their results.
Vascular beds of the systemic circulation are characterized by hemodynamic arteriovenous asymmetry, as evidenced by capillary pressure levels that are much lower than the arithmetic mean of arterial feeding and venous draining pressures. The pressure sensitivity of vascular adaptation is largely responsible for generating this asymmetry.26 28 The present study shows that such pressure sensitivity can also lead to increased flow resistance when driving pressure is increased, ie, to structural autoregulation. This phenomenon may have an important role in amplifying and stabilizing the sustained increase of arterial blood pressure seen in essential hypertension.
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Acknowledgments |
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This study was supported by the Deutsche Forschungsgemeinschaft (Pr 271/5-1, 5-2, and 5-3) and by National Institutes of Health grant HL34555. The expert technical help of Bettina Giesicke as well as the assistance of Anja Scheuermann in preparing the manuscript are gratefully acknowledged.
Received July 7, 1998; first decision July 23, 1998; accepted August 7, 1998.
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