Medical Physiology, 3rd Edition

Interaction among the Different Cardiovascular Control Systems

The control of the cardiovascular system involves “linear,” “branched,” and “connected” interactions

In the previous chapters, we often presented physiological responses in a linear sequence or on a linear chart. For instance, we might represent the carotid-baroreceptor feedback loop (see p. 534) as a linear sequence of events (Fig. 25-1A). However, cardiovascular parameters and associated physiological responses are often related by multiple factors, requiring a more complex diagram called a branching tree (or an algorithmic tree). For example, in our discussion of the control of cardiac output (see pp. 545–548), we started with the knowledge that cardiac output depends on two parameters—stroke volume and heart rate. Therefore, with our very first step, we come to a fork in the road—an example of a branching tree (see Fig. 25-1B). At the next level, we encounter a pair of forks because stroke volume and heart rate both depend on two parameters. Finally, at the third level, we see that each of the determinants of stroke volume and heart rate depends on multiple factors (i.e., multiple forks).

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FIGURE 25-1 Patterns of cardiovascular control. A, The baroreceptor reflex is depicted as if increased blood pressure affected a single stretch receptor, which ultimately would influence a single effector (i.e., vascular smooth muscle). B, Cardiac output depends on multiple parameters, which in turn depend on multiple parameters, and so on. However, we ignore potential interactions among parameters. C, A branching tree represents the control of mean arterial pressure. The left limb repeats B. Superimposed on this simple tree are three more complex interactions: (1) feedback loops (red arrows), (2) two occurrences of the same parameter (connected by the red dashed line), and (3) examples of parameters exerting effects on two different branches of the tree (brown arrows). CN, cranial nerve.

The control of some cardiovascular parameters is even more complex, requiring that we graft branches from smaller trees. For example, we know from Ohm's law that mean arterial pressure depends on both cardiac output (and all the elements in its branching tree in Fig. 25-1B) and total peripheral resistance, which requires a branching tree of its own (see Fig. 25-1C). Moreover, sometimes an element in one part of the “forest” interacts with another element that is far away. A physiological system with such complex interactions is best represented by a connected diagram, which may include feedback loops (see Fig. 25-1C, red arrows), parameters that appear more than once in the tree (connected by a red dashed line), or factors that modulate parameters in two different branches of the tree (connected by brown arrows). Although not shown in Figure 25-1C, several feedback loops may impinge on a single element, and some loops are more dominant than others. The complex interactions among parameters make it difficult to distinguish factors of overriding importance from those of lesser weight. Moreover, when one disturbs a single parameter in a complex physiological system, the initial state of other parameters determines the end state of the system. In previous chapters, we have chosen situations that artificially isolate one portion of the cardiovascular system (e.g., heart, microcirculation) to explain in a simple way the homeostatic control mechanisms that govern that subsystem. However, conditions isolating subsystems rarely apply to a real person.

How can we evaluate which parameters are crucial? As an example, consider one subsystem, the heart. Let us assume that we can rigorously analyze all determinants of cardiac function—such as Starling's law, force-velocity relationships, effect of heart rate on contractility, and so forth. These analyses take the form of mathematical expressions, which we can combine by systems analysis to create a model that describes the behavior of the entire heart—at least theoretically. How can we test whether our model is reasonable? We can compare the physiological response of the heart in vivo with the response predicted by a computer simulation of the model. Using this approach, we may be able to establish whether we have used the correct feedback loops, whether we have assigned proper values to various elements, and whether we have assigned the proper weight to each interaction. In this way, we can use any agreement between the experimental data and the performance of the model as evidence—but not proof—that the concepts contained in the model are reasonable.

Regulation of the entire cardiovascular system depends on the integrated action of multiple subsystem controls as well as noncardiovascular controls

In performing a systems analysis of the entire cardiovascular system, we must consider the interrelationships among the various subsystems summarized in the central yellow block of Figure 25-2. Not surprisingly, we cannot fully understand how a particular disturbance affects the overall circulation unless we consider all subsystems in an integrated fashion. For instance, consider the effects of administering norepinephrine, which has a high affinity for α1 adrenoceptors, less for β1 adrenoceptors, and far less for β2 adrenoceptors. These receptors are present, in varying degrees, in both the blood vessels and the heart. A branching tree would predict the following. Because α1 adrenoceptors (high affinity) are present in most vascular beds, we expect widespread vasoconstriction. Because β2 adrenoceptors (low affinity) are present in only a few vascular beds, we predict little vasodilation. Because β1 adrenoceptors (intermediate affinity) are present in pacemaker and myocardial cells of the heart, we would anticipate an increase in both heart rate and contractility and therefore an increase in cardiac output.

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FIGURE 25-2 Interaction among cardiovascular subsystems and noncirculatory systems. GI, gastrointestinal.

Although our analysis predicts that the heart rate should increase, in most cases the dominant effect of intravenous norepinephrine injection is to slow down the heart. The explanation, which relies on a connected diagram, is that increased peripheral resistance (caused by stimulation of α1 receptors) and increased cardiac output (caused by stimulation of β1 receptors) combine to cause a substantial rise in mean arterial pressure. The baroreceptor reflex (see red arrow on right in Fig. 25-1C) then intervenes to instruct the heart to slow down (see p. 534). However, bradycardia may not occur if many vascular beds were dilated before the administration of norepinephrine; in this case, the rise in blood pressure would be modest. Bradycardia might also not occur if the baroreceptor reflex were less sensitive, as would be the case in a chronically hypertensive patient (see Box 23-1). Thus, the effect of intravenous administration of norepinephrine on heart rate depends on the pre-existing state of various subsystems.

In trying to understand the integrated response of the cardiovascular system to an insult, we must include in our analysis not only all the subsystems of the cardiovascular system but also the pertinent control systems outside the circulation (see blue boxes in Fig. 25-2):

1. Autonomic nervous system (ANS). Part of the ANS is intimately involved in cardiovascular control (e.g., high-pressure baroreceptor response). On the other hand, a generalized activation of the ANS, such as occurs with the fight-or-flight response (see p. 347) also affects the circulation.

2. Respiratory system. We have already seen that ventilatory activity converts the intrinsic bradycardia response to tachycardia during stimulation of the peripheral chemoreceptors (see p. 545). In addition, the action of the respiratory muscles during inspiration causes intrathoracic pressure to become more negative (see p. 607), thereby increasing venous return. A third example is that the evaporative loss of water during breathing reduces total body water and, ultimately, blood volume.

3. Hematopoietic organs and liver. These systems control blood composition in terms of cell constituents and plasma proteins. The hematocrit and large proteins (e.g., fibrinogen) are major determinants of blood viscosity (see pp. 437–439) and therefore of blood flow. Because the plasma proteins also determine colloid osmotic pressure (see p. 470), they are a major component of the Starling forces (see pp. 467–468), which determine the distribution of extracellular fluid (ECF) between the interstitium and the blood plasma.

4. Gastrointestinal and urinary systems. Because the gastrointestinal tract and kidneys are the principal organs determining input and output of electrolytes and water, they are mainly responsible for controlling the volume and electrolyte composition of ECF. ECF volume plays a central role in the long-term control of blood pressure (see p. 838).

5. Endocrine system. Part of the endocrine system is intimately involved in cardiovascular control (e.g., epinephrine release by the adrenal medulla). Other hormones influence the cardiovascular system either because they are vasoactive agents (see pp. 551–554) or because they regulate fluid volume and electrolyte composition by acting on the kidney and gastrointestinal system.

6. Temperature control system. The cardiovascular system is a major effector organ for thermoregulation, carrying blood from the body core to the skin, where heat loss then occurs (see pp. 1200–1201). In part, this heat loss occurs as sweat glands secrete fluid that then evaporates. However, the loss of ECF volume reduces the effective circulating volume (see pp. 554–555).

Inclusion of control elements outside the circulation (see Fig. 25-2) in our connected diagram of the cardiovascular system (see Fig. 25-1C) would expand the computer model to include hundreds of independent and dependent variables. Rather than trying to grasp such an exhaustive model, we work here through the integrated cardiovascular responses to four important circulatory “stresses”: (1) orthostasis (i.e., standing up), (2) emotional stress, (3) exercise, and (4) hemorrhage.