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Journal of Theoretical Biology 236 (2005) 60–72 www.elsevier.com/locate/yjtbi

Metabolic setpoint control mechanisms in different physiological systems at rest and during exercise A. St Clair Gibsona,b,, J.H. Goedeckea,b, Y.X. Harleya,b, L.J. Myersa,b, M.I. Lamberta,b, T.D. Noakesa, E.V. Lamberta,b a

Brain Sciences Research Group, MRC/UCT Research Unit of Exercise Science and Sports Medicine, Sport Science Institute of South Africa, P.O. Box 115, Newlands 7725, South Africa b MRC/UCT Medical Imagining Research Unit, Department of Human Biology, University of Cape Town and Sport Science Institute of South Africa, Boundary Road, Newlands 7700, South Africa Received 23 November 2004; received in revised form 12 February 2005; accepted 17 February 2005 Available online 7 April 2005 Communicated by Athelstan Cornish-Bocoden

Abstract Using a number of different homeostatic control mechanisms in the brain and peripheral physiological systems, metabolic activity is continuously regulated at rest and during exercise to prevent catastrophic system failure. Essential for the function of these regulatory processes are baseline ‘‘setpoint’’ levels of metabolic function, which can be used to calculate the level of response required for the maintenance of system homeostasis after system perturbation, and to which the perturbed metabolic activity levels are returned to at the end of the regulatory process. How these setpoint levels of all the different metabolic variables in the different peripheral physiological systems are created and maintained, and why they are similar in different individuals, has not been well explained. In this article, putative system regulators of metabolic setpoint levels are described. These include that: (i) innate setpoint values are stored in a certain region of the central nervous system, such as the hypothalamus; (ii) setpoint values are created and maintained as a response to continuous external perturbations, such as gravity or ‘‘zeitgebers’’, (iii) setpoint values are created and maintained by complex system dynamical activity in the different peripheral systems, where setpoint levels are regulated by the ongoing feedback control activity between different peripheral variables; (iv) human anatomical and biomechanical constraints contribute to the creation and maintenance of metabolic setpoints values; or (v) a combination of all these four different mechanisms occurs. Exercise training and disease processes can affect these metabolic setpoint values, but the setpoint values are returned to pretraining or pre-disease levels if the training stimulus is removed or if the disease process is cured. Further work is required to determine what the ultimate system regulator of metabolic setpoint values is, why some setpoint values are more stringently protected by homeostatic regulatory mechanisms than others, and the role of conscious decision making processes in determining the regulation of metabolic setpoint values. r 2005 Elsevier Ltd. All rights reserved. Keywords: Complex system; Zeitgebers; Homeostasis; Hypothalamus; Metabolism

1. Introduction

Corresponding author. Brain Sciences Research Group, MRC/ UCT Research Unit of Exercise Science and Sports Medicine, Sport Science Institute of South Africa, P.O. Box 115, Newlands 7725, South Africa. Tel.: +27 21 6504577; fax: +27 21 6867530. E-mail address: [email protected] (A. St Clair Gibson).

0022-5193/$ - see front matter r 2005 Elsevier Ltd. All rights reserved. doi:10.1016/j.jtbi.2005.02.016

The control of metabolic function in different physiological systems during exercise is still not well understood. The majority of research has examined control of physiological activity at the apparent limits of performance during exercise (Bassett and Howley, 2000; Coggan and Coyle, 1987; Dempsey and Wagner, 1999;

ARTICLE IN PRESS A. St Clair Gibson et al. / Journal of Theoretical Biology 236 (2005) 60–72

Fitts, 1994; Green, 1997; Richardson et al., 1999; Sahlin et al., 1998). Because of this work, metabolic system control has been described in terms of the limiting capacity of a physiological system, where either metabolite accumulation or substrate depletion, for example, causes ‘‘catastrophic’’ failure of a particular physiological system (Edwards, 1983). Recently, however, this catastrophic model of physiological control of exercise has been suggested to be flawed, and that, in contrast, metabolic activity in different physiological systems is regulated throughout an exercise bout in order to ensure that these systems are never maximally utilized (Noakes, 1997, 2000; Noakes et al., 2004; St Clair Gibson et al., 2001a). In this model, exercise intensity is regulated continuously using feedforward commands from the brain in response to feedback from peripheral physiological activity, from knowledge derived from prior exercise bouts, and from awareness of external environmental conditions (St Clair Gibson et al., 2001b; Ulmer, 1996). Implicit in this model is the principle of homeostasis, which suggests that these regulatory processes serve the teleological protective function of preventing catastrophic system failure and the associated damage to peripheral physiological systems whose metabolic activity is increased during the exercise bout (Noakes et al., 2004, Noakes and St Clair Gibson, 2004). The ultimate goal of these regulatory processes is to return the level of metabolic activity, which is increased during exercise, to the resting ‘‘baseline’’ levels that occurred prior to the initiation of the exercise bout. These regulatory processes include the conscious desire, initiated by the increasing level of the sensation of fatigue, to reduce the intensity of the exercise bout (Noakes et al., 2004; St Clair Gibson et al., 2001a, 2003), subconscious brain processes which modulate efferent neural command from the beginning of the exercise bout (St Clair Gibson et al., 2001a, 2003), and direct metabolic complex system control mechanisms in the peripheral physiological systems themselves (Lambert et al., 2005). In this ‘‘central governor’’ model, the limits to exercise performance are created by these dynamically occurring regulatory processes, rather than by absolute limiting capacity of the different physiological systems. In order for these regulatory processes to modulate physical activity and return metabolic activity to preexercise levels, a register of the baseline ‘‘setpoint’’ of each metabolic variable that has been altered by the exercise bout is required. This setpoint register is also required as a frame of reference, in order for metabolic calculations to be performed by the regulatory processes which would maintain metabolic activity during exercise at modulated, precise levels. At present, there is little understanding of how these metabolic setpoint values are determined or maintained. The aim of this review therefore is to examine

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mechanisms which may be responsible for the creation and maintenance of the setpoint values of different metabolic processes, and the substrate concentrations maintained by these different metabolic processes, in different physiological systems, and how these setpoint values may be altered by exercise training and disease processes.

2. Individual differences and similarities in homeostatic system ‘‘setpoints’’ The human body represents a truly complex system, in which there are a vast number of regulated and regulating physiological responses. These physiological processes interact with each other continuously to sustain life. Given the number of physiological process occurring at any one time in the body, one would expect large variability between different individuals for the value of any single substrate, metabolite, or regulatory factor. Furthermore, one would expect that this interindividual variability in physiological function would alter continuously with time. Indeed, Goedecke et al. (2000) showed that metabolic fuel selection at rest and during exercise differed within a group of similarly trained individuals. However, although there are differences in fuel substrate use are present in different individuals, the range of values for a particular substrate, or indeed any physiological variable, has been found to be relatively similar in different individuals who are healthy. For example, blood glucose concentrations are usually between 4 and 6 mmol/l in healthy subjects (Saunders et al., 1998). In the study of Goedecke et al. (2000), while the endogenous fuel selection of individuals differed at rest, the difference in fuel selection of any particular individual in the group was maintained during exercise as was found at rest. These findings suggest that while the use of fuel substrates may differ between individuals, the relationship between the different fuels present at rest was maintained during exercise. Therefore, a similar metabolic regulator appears to occur in all individuals, despite the individual differences which are found to occur for any variable within physiological ‘‘norms’’. The regulators of these metabolic setpoints are at present unknown. Four possible mechanisms are proposed which may individually or collectively control these physiological setpoints found in all individuals.

3. Brain regulation of homeostatic metabolic setpoint levels The first putative regulator of the metabolic setpoints is a control mechanism in the central nervous system. In

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A. St Clair Gibson et al. / Journal of Theoretical Biology 236 (2005) 60–72

this model, values for each metabolic setpoint level and knowledge of peripheral mechanical restraints and cellular architecture are present in the brain. The hypothalamus, and in particular the suprachiasmatic nucleus, has previously been suggested to be the key area of the brain where metabolic regulation occurs (Schwartz et al., 2003; Vissing et al., 1989; Weltan et al., 1998). However, other brain areas, such as the insula cortex, have also been suggested to play a role in homeostatic physiological regulation (Craig, 2002; Williamson et al., 1999). These brain regions are suggested to regulate metabolic setpoint values in the peripheral metabolic system by setting the required values for each metabolic variable using the hypothalamic–pituitary–adrenal axis and resultant hormonal activity changes, or by altering efferent neural command to the peripheral structures and systems (Uyama et al., 2004). These innate metabolic setpoint values stored in the brain would be used as a register to indicate when changes occurred in peripheral systems. When afferent input from peripheral physiological systems indicated that a change to a particular metabolic variable had occurred, such as would occur at the start of exercise, this new, altered value of the metabolic variable would be compared to the setpoint value stored in the brain register (Damasio, 2000; Parvizi and Damasio, 2001; St Clair Gibson et al., 2003). If there was a difference between the two variables, the brain would initiate efferent neural command and hormonal changes which would attempt to restore the altered variable back to the original setpoint value (Fig. 1). Recently, as part of a theory describing the origin of conscious perception, Parvizi and Damasio (2001) suggested that a ‘‘proto-self’’ exists, which is a collection of neural networks that map the physical and physiological state of the organism. In their theory, the protoself is a first-order map describing the value for every physiological variable in the body. They suggested that when a change in the internal physiological milieu or external environment occurs, these changes become a further first-order map. When the proto-self and changed map are compared, the differences between them become a second-order map. The second-order map becomes a ‘‘mental image’’ which exists at a subconscious or conscious level in the brain, and this mental image would initiate changes in function at either the psychological, physical or cellular level which would attempt to restore the altered variables to their protoself values. Parvizi and Damasio (2001) suggested that this protoself neural network, containing the setpoint values of all the metabolic variables, exists in the brainstem and spinal cord, as the brainstem is the site of arrival of afferent input from sensors in all of the different physiological systems. Furthermore, connections exist

Fig. 1. The brain register model of metabolic setpoint regulation. In this model, the setpoint values for every physiological variable are stored in a register of values located in the brain. This register is used as a comparator to initiate counter-regulatory responses when changes occur in peripheral physiological variables. These counter-regulatory responses are based on comparisons of register values with afferent information describing current metabolic activity in the periphery. Changes in activity levels are controlled using efferent commands from the brain to decrease the metabolic rate during and after exercise to the pre-exercise level set in the brain’s register (a). For example, heart rate (HR), respiratory rate (RR) and blood glucose (BG) change from the baseline setpoint values after exercise is initiated, and the values of these metabolic variables are altered continuously during exercise to ensure that no physiological system is catastrophically over-utilized, using the original brain register values as the comparator (b).

between the brainstem nuclei and hypothalamus, and through these connections there may be integration of information such that awareness of changes in metabolic activity could occur. The accuracy of this theory has not been fully tested at present, although recently Paterson and Marino (2004) showed that athletes paced themselves consistently during an exercise bout based on what their pacing strategy, and associated metabolic activity, had been in a previous exercise bout. Based on their findings, Paterson and Marino (2004) suggested that an ‘‘exercise template’’ is present in the brain, which is updated by previous exercise bouts, and regulates the intensity of exercise in future exercise bouts. This work supports Parvizi and Damasio’s (2001) suggestion. In summary, it is possible that metabolic setpoint values are determined as part of neural networks in specific brain regions or may be associated with brain activity in a number of different regions simultaneously. These setpoint values are compared against afferent information indicating changes in metabolic activity away from the setpoint values, and changes are initiated by the brain to bring the metabolic activity back to the setpoint values. These changes include psychophysiological changes such as moving to a different environment, changing pace during physical activity or ingesting fuel or fluid as required, physiological changes such as altering efferent neural command, cardiac output or the hormonal milieu, and cellular changes such as increasing


4. External metabolic setpoint controllers If the metabolic setpoint registers were found in the same brain region in each individual, it is surprising that these values are similar in healthy individuals, because a similar degree of variation in metabolic activity as the variation in anatomical differences between individuals would be expected if these values were ‘‘hardwired’’ in the brain. This would be caused by normal genetic variation which occurs with evolution and the transfer of inherited genetic traits over a time period, creating an increasingly diverse array of anatomical brain features and an associated diverse array of setpoint values with time (Darwin, 1859; Dawkins, 1996). Therefore, an external agent or energy force, rather than an internal brain factor, may be responsible for establishing metabolic setpoints, either directly or by maintaining similar function in the brains of different individuals by preventing changes which would be produced by evolutionary pressure. Each individual would need to respond to the external agent or energy force in the same way, and this similar response would set a similar internal physiological milieu in all individuals. Therefore, the external agent or energy force would be responsible for similar homeostatic setpoints in different individuals. For this type of regulation to occur, the external energy or force would need to be consistently present to allow the physiological response to occur continuously in all individuals. A putative external energy force which would fulfill these criteria described above is the force of gravity. Gravity occurs over the entire surface of the earth, and energy is continuously required by all humans to counteract the effect of gravity on body structures (Courtine et al., 2002; Kawano et al., 2002; Mittelstaedt, 1996; Plaut et al., 2003). For example, merely standing upright requires constant force output and hence muscle activity, which would require a certain level of metabolic activity (Robinson and Fuller, 2000). Experiments performed in zero gravity environments show that physiological activity levels are profoundly altered by lack of gravitational force (Courtine et al., 2002; Katkovsky and Pomytov, 1976; Kawano et al., 2002). Therefore, there is a strong possibility that gravity, or other electromagnetic fields around the earth such as the coriolus force, are, at least partly, responsible for maintaining the similar homeostatic ‘‘setpoints’’ found in all healthy individuals. Another possible external homeostatic controller may be ‘‘zeitgebers’’, which are controlling factors that create and entrain the body’s cyclical rhythms (Hofman, 2004;

Lewy et al., 2003; Rensing and Ruoff, 2002). A potent cyclical zeitgeber is the day/night light cycle which sets the 24-h circadian rhythm of physiological activity (Brandstatter, 2003) (Fig. 2). The alternating presence or absence of light which is part of the day/night light cycle appears to initiate responses via receptors in the eye which are transmitted from the outside world to the suprachiasmatic nucleus of the hypothalamus via a direct retinohypothalamic tract (Hofman, 2004; Reppert and Weaver, 2002). The suprachiasmatic nucleus subsequently entrains the function of different peripheral organs such as the liver, heart and kidneys using efferent neural and hormonal changes (Reppert and Weaver, 2002; Scheer et al., 2001), and these peripheral organs alter peripheral metabolic function in an oscillatory manner (Stokkan et al., 2001). Therefore, it is logical to suggest that these circadian ‘‘clocks’’ may set and sustain low-frequency oscillatory activity of metabolic systems, and that these circadian clocks are therefore the dynamic metabolic system ‘‘setpoint’’ controller. There are a number of other zeitgebers which have been suggested to be responsible for entraining the physiological rhythms (Bartell et al., 2004; Danilenko et al., 2003; Davidson et al., 2003; Rensing and Ruoff, 2002). Changes in body temperature also occur in a circadian manner, probably as a result of external environmental temperature changes associated with the day/night cycle (Rensing and Ruoff, 2002; Robinson et al., 1993; Robinson and Fuller, 1999), or as a result of increased activity of individuals during the day and 200 heart rate (beats/min)

oxidative fuel utilization or altering fuel utilization composition, so as to maintain physiological function within homeostatic limits.

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Fig. 2. Variation in daily heart rate reflects a day/night cycle of activity, illustrating the external controller model of metabolic setpoint regulation. In this model, an external agent or energy force such as gravity or a zeitgeber controls metabolic setpoint values by entraining and maintaining metabolic activity as part of the continual energy requirement of responding to the external agents. For example heart rate varies throughout the day, being higher during the day and in particular during exercise bouts, and lower during the night. This ongoing daily day/night cycle entrains metabolic activity in a continuously oscillatory manner, and because of the similarity of the daily oscillations associated with the day/night circadian rhythm, this zeitgeber is a possible setpoint controller (Data from M. Lambert et al., unpublished data).

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reduced activity of individuals during the night. Changes in physiological function occur as a response to these external or activity-related temperature changes and therefore these temperature changes can be described as a zeitgeber. Social activity, such as feeding cycles have also been proposed to be an important external zeitgeber (Davidson et al., 2003; Stokkan et al., 2001). Daily scheduled feeding, such as eating three regular meals at the same time point during the day, induces cyclical changes in the activity of physiological systems which are required to absorb and utilize or store the ingested fuel. Scheduled feeding also induces anticipatory social habituated changes to allow these feeding bouts to occur, and these social changes which allow an individual to feed at similar time points during a day become potent physiological zeitgebers themselves (Davidson and Menaker, 2003; Mistlberger and Skene, 2004). The zeitgebers described above may not operate as separate entities, but rather may have linked function. For example, the temperature and day/night cycles are likely to be linked, as are the day/night and feeding cycles (Rensing and Ruoff, 2002; Robinson and Fuller, 2000). Furthermore, it has been postulated that the temperature related circadian cycles and gravity are linked (Robinson and Fuller, 2000). There have also been shown to be internal circadian ‘‘clocks’’ in the liver, pancreas and kidney that may function independently in the absence of suprachiasmatic nucleus function (Davidson et al., 2003; Muhlbauer et al., 2004; Reppert and Weaver, 2002). However, in a healthy individual, these internal circadian clocks have been shown to be entrained by the suprachiasmatic nucleus and therefore by the day/night cycle, and are also likely to be influenced by the feeding zeitgeber (Hofman, 2004; Muhlbauer et al., 2004). Therefore, a number of different zeitgebers may function together as linked external influences that are responsible for setting the homeostatic setpoints in different physiological systems.

5. Complex system dynamical control of homeostatic setpoints Both the brain and external zeitgeber theories of homeostatic setpoint regulation propose that control of peripheral physiological systems occurs by factors ‘‘upstream’’ or external to the system itself. Another possible control mechanism is the complex dynamic system model (Gleick, 1987; Sardar and Abrams, 1998; Series, 1994). In this model, there is no single external controlling structure, but rather interacting variables in peripheral physiological systems create their own selfsustaining control mechanism (Lambert et al., 2005; St Clair Gibson and Noakes, 2004; Saunders et al., 2000).

An example of this is the relationship between blood glucose and insulin kinematics (Lambert et al., 2005; Gomis et al., 1996; Saunders et al., 1998). If blood glucose concentration falls, as a result of a metabolic challenge such as performing exercise, this induces a corresponding fall in insulin secretion which results in a reduction in blood insulin concentration (Fig. 3a). This reduction in insulin concentration results in the reduction of the activity of enzymes that convert glucose to less active forms of stored energy, and the concentration of blood glucose concentration subsequently rises (Hollingdal et al., 2000). As the blood glucose concentration increases due to these changes, insulin concentration subsequently also increases, resulting in increased anabolic conversion of glucose to other substrates, such as glycogen, therefore allowing less blood glucose to become available (Saunders et al., 1998). Blood glucose concentrations therefore become oscillatory with time, and these oscillations would occur around a blood glucose concentration which was midway between the highest peak and lowest trough levels of the amplitude of the oscillation in blood glucose

Time

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Fig. 3. The complex system dynamic model of metabolic setpoint regulation. In this model, there are no external controlling structures, but rather interacting variables in peripheral physiological systems create their own self-sustaining control mechanism. For example, blood glucose (BG) and blood insulin concentrations each change in response to alteration in the others concentrations, creating a selfsustaining control mechanism (a). Furthermore, all physiological variables affect each other continuously, and the final muscle power output and pace changes during exercise or activities of daily living are the result of oscillatory changes in all these physiological variables, including efferent neural command. The metabolic setpoint values would be the midpoint of the oscillatory amplitude of each different variable (b).


concentration. Blood insulin concentrations would be similarly oscillatory. Therefore, in an isolated system, the simple interaction between these two metabolic variables would create a metabolic homeostatic ‘‘setpoint’’ for each variable (Saunders et al., 2000; Sturis et al., 1991). The changes in these variables are deterministic to each other, and the determinism in this model would be independent of external control mechanisms such as commands from the brain or zeitgebers. However, what would be required in such a control system is a continuous system perturbation, and the passing of time itself, which would induce ongoing changes in blood glucose which would initiate the ongoing counter-regulatory response from insulin. In this complex dynamic system model, physiological variables interact with each other continuously, in a manner similar to the blood glucose/insulin example described above (Feneberg et al., 1999; Glass, 2001; Gleick, 1987; Goldberger et al., 2002; Goldbeter, 2002; Lambert et al., 2005; St Clair Gibson and Noakes, 2004). Therefore, the homeostatic setpoints of all metabolic variables in all physiological systems in the body would be the midpoint of the oscillatory amplitudes of each different variable, which in turn would be set by the interaction of every other variable that interacts with that particular metabolic variable (Fig. 3b). The initial and ongoing responses that would ‘‘maintain’’ the oscillatory system required to establish the homeostatic setpoints would depend on the continuous responses of the individual to the continuously changing external environment or continuing social interactions with other individuals. The social and functional changes induced by these physiological responses would in turn alter the external environment and behavior of the other individuals that initiated the changes in the physiological function of the particular individual. Therefore, the behavior of all individuals, and the state of the external environment around an individual, are dependent on the interaction between the different systems and response to changes in any component in the entire system (Gleick, 1987; Sardar and Abrams, 1998). Therefore, in this complex system model, the original and continuous system perturbation from prior activity, which resulted in changes to all the different system variables becomes a self-sustaining controller of the entire system. This external perturbation, in the context of a particular individual, would be alteration of the activity of that individual, which itself would be a response to ongoing environmental or social changes such as moving to avoid a car, moving to avoid a place which is too hot, or running a race to achieve material and social reward (Palmer et al., 1994). In this model therefore, there is determinism in every activity, and each action would be a reaction to a prior action (St Clair Gibson and Noakes, 2004). The system becomes

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self-sustaining and overall control of the metabolic setpoints is created by the counter-regulatory responses themselves. In this model the concept of ‘‘external’’ and ‘‘internal’’ perturbations also become artificial, as the individual is also part of a larger complex, inter-related system consisting of a number of social and environmental interactions, and there is no boundary between ‘‘external’’ and ‘‘internal’’ physiological activity (Brown et al., 2002; Gleick, 1987; Schneider et al., 2002). Therefore, in this model, changes in brain efferent commands to the periphery are also responses to prior perturbations in either external or internal environments, rather than being an absolute system controller (Marder, 2001; Getty et al., 2000; St Clair Gibson and Noakes, 2004). Furthermore, in this model, both brain and external environment are part of the complex dynamical activity that controls metabolic activity and maintains the setpoint level of each metabolic variable in different physiological systems.

6. Mechanical and anatomical regulators of homeostatic setpoint activity While homeostatic setpoints may be regulated by brain structures, by external forces or energy systems, or by complex dynamical system interaction, the responses these controlling factors exert is dependent in part on anatomical structures and mechanical structural constraints (Guyton, 1987; Weibel, 2002). Therefore, maintenance of specific levels of blood glucose in peripheral skeletal muscles is partly dependent in this example on the anatomical structure and connections of blood vessels and capillaries, the rate of transfer of blood glucose across the cell membrane, and it’s rate of utilization in the cell (Alpert et al., 2002; Gudbjornsdottir et al., 2003; Howlett et al., 2003; Kuikka, 2002; Vincent et al., 2003; Williams et al., 2003; Zhang et al., 2004). Similarly, the concentrations of organelles responsible for the utilization of glucose, or knowledge of these concentrations by the brain, such as the concentration of mitochondria responsible for the oxidation of glucose-derived pyruvate, and the concentration of enzymes and enzymatic cofactors present in the mitochondria and cytosol may also be a factor in regulating glucose concentrations in the peripheral skeletal muscle, and therefore may also be a factor in maintaining homeostatic setpoint values (Bruce et al., 2003; Fisher et al., 2002; Jones et al., 2003; Kawanaka et al., 2000). Therefore, the mechanical barriers such as the walls of blood vessels, the muscle sarcolemma, and mitochondrial cell membrane, which are similar in design in all humans, and the concentration of different organelles and enzymes in the different peripheral physiological systems may be a factor in controlling metabolic

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setpoint values. However, although these mechanical factors may be a factor in regulating metabolic setpoint values, they are unlikely to be the ultimate controller of the system, as they are mechanical conduits processing the physiological response to command processes which are generated from either a high level of control, such as the brain or from external sources of control, or through complex system interaction of the different physiological systems in the body.

7. The effect of long-term exercise and disease on homeostatic setpoints In the previous sections, different putative mechanisms for maintaining resting metabolic setpoint values have been described. Performing long-term exercise training can alter metabolic setpoint values in different physiological systems. For example, resting heart rate is reduced, blood lipid and cholesterol profiles are reduced, and muscle enzymatic and mitochondrial function are altered by continuous, long term athletic training (Adhihetty et al., 2003; Izquierdo et al., 2003; Noakes, 2003; Puente-Maestu et al., 2003; Sugawara et al., 2001). These alterations are described as being related to the individual being ‘‘fitter’’ after training than prior to training. The changes in different metabolic setpoint values are probably due to adaptations in protein regulatory function at the genetic and molecular level, which alters the physical and neural structures associated with physiological activity by changing their size, number and efficiency (Puente-Maestu et al., 2003). These physical alterations result in a system which interacts differently compared to the non-trained state and therefore alters the metabolic setpoint values of all the different metabolic variables in the different physiological systems. However, these alterations in metabolic setpoint values that occur with long-term training are maintained only as long as the training bouts continue. Once the training stimulus is removed, the metabolic setpoint values in different physiological systems return to their original values associated with the ‘‘detrained state’’ (Mujika and Padilla, 2001a, b; Snow et al., 2001). Therefore, physical training does not induce permanent alterations in metabolic setpoints. Interestingly, this ‘‘detraining’’ effect occurs at a faster rate than the ‘‘training’’ effect, indicating that it is easier to return metabolic setpoint values to their untrained values, than it is to alter the setpoint values away from the untrained values (Sugawara et al., 2001). The reason for this difference in rate of change of metabolic setpoint values associated with training and detraining is currently unknown, and further work is required to clarify the reasons for this difference.

Chronic disease processes also affect the metabolic setpoint values. For example, after an individual suffers a myocardial infarct, where there is permanent loss of cardiac muscle tissue in the area of the infarct, the hearts contractile rhythm and function will change (Weidemann et al., 2003). These changes in cardiac function may lead to changes in other physiological systems due to the complex interactions between the different systems described previously, and may also lead to multiple organ failure, where eventually all systems catastrophically fail (Haywood et al., 1995; Lindholm et al., 2003). Because the tissue involved in the myocardial infarct is dead, and is replaced by fibrotic tissue, which cannot regenerate, the change in cardiac function is permanent (Weidemann et al., 2003). Therefore, the changes induced in the metabolic setpoint values of all the other physiological systems also become permanent, until death occurs (Lindholm et al., 2003). In complex system theory terminology, this would be known as a bifurcation (Glass, 2001). Bifurcations are changes in the oscillatory patterns of complex systems, which remain either after the initial stimulus is removed, or when the stimulus continues indefinitely (Glass, 2001). In the example of cardiac failure, the bifurcation caused by cardiac insufficiency, when it leads to multiple organ failure, would be an alteration of metabolic setpoint values and physiological function which does not allow the maintenance of physiological function compatible with life. The bifurcation could therefore not be classified as a permanent change in metabolic setpoint levels which could maintain continuing physiological function. However, if cardiac function is improved by giving a cardiac inotropic agent as a medical intervention, the other physiological systems, such as the kidneys, liver and lungs, which were in the process of failing, improve in function (Bristow et al., 2001). A state of relative ‘‘wellness’’ can be maintained with permanent use of cardiac inotropic agents, although different homeostatic setpoints for the different physiological variables will be maintained (Buchman, 2002). If one attempts to return these altered setpoint values in these disease states to those found in healthy individuals, it may be harmful rather than beneficial, and result paradoxically in the development of multiple organ failure (Buchman, 2002). Therefore, in this example, a stable, beneficial alteration of metabolic homeostatic variables has occurred, as long as the inotropic agents are prescribed continuously. As with fitness training however, when these inotropic agents are removed, the altered state is not sustainable, and the system attempts to return to the metabolic setpoint values which were induced by the myocardial infarct, and multiple organ failure occurs. This multiple organ failure is probably due to a cascade of events which induce changes in metabolic function that fall beyond the ‘‘high’’ and ‘‘low’’ ends of the cellular


metabolic thresholds. As control processes are not initiated in the absence of inotropic agents to prevent this multiple organ failure, this example may indicate that the metabolic setpoint values are not set by brain control processes, and are rather set by external environmental or complex system dynamical factors, as there is no attenuation of the changes in the different physiological systems in the absence of inotropic cardiac support, when the baseline metabolic setpoints are not optimal to an individual with cardiac dysfunction. In diseases such as diabetes mellitus, there appears to be marked changes in the concentrations of blood glucose, with levels measured at different times of the day either being higher or lower than the concentrations present in healthy individuals (Hollingdal et al., 2000). However, these changes appear to be caused by the increased variation in blood glucose concentration associated with changes in the gain, and time constant of the gain, of the blood glucose control system in individuals with diabetes mellitus, rather than by changes in the metabolic setpoint values (Hollingdal et al., 2000; Lin et al., 2002).

8. The affect of acute exercise training on homeostatic metabolic setpoints and the ‘‘setpoint controversy’’ In this article, we have suggested that metabolic setpoint values are controlled by structures or activity in the brain, by the external environment, by complex system interaction, or by a combination of all these factors. We have further suggested that any change of a variable away from these metabolic setpoint values initiates a chain of events which returns the variable to these setpoint levels. In this model, the setpoint register may philosophically have a teleological purpose, which is to defend the different physiological systems from catastrophic failure by maintaining the physiological variables with these systems within ‘‘safe’’ functional limits (Buchman, 2002; Noakes and St Clair Gibson, 2004; Lambert et al., 2005). From this perspective, any alteration to a peripheral variable away from the homeostatic ‘‘setpoint’’ values would be a threat to the system’s integrity. While it is scientifically difficult to test a teleological argument, this perspective does intuitively appear reasonable. However, this is a somewhat simplistic interpretation of this homeostatic system control, as changes in peripheral variables away from a baseline level may be beneficial, as in the case of multiple system setpoint changes which occur in individuals suffering from cardiac failure. A further example of the beneficial value of a movement of a variable away from baseline levels is evident in temperature regulation (Briese, 1998). The usual response of an animal or individual to an increased core temperature (hyperthermia) is to seek out

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a cold environment, reduce locomotion, increase vasodilatation and reduce metabolic rate. However, during a bout of fever, changes occur which are not ‘‘corrective’’ and which return the core temperature to previous homeostatic setpoint values, but rather create conditions which would lead to further increases in temperature, or maintenance of the raised temperature, away from the homeostatic setpoints (Briese, 1998). These include seeking a warm environment, increased vasonconstriction and shivering, all which increase metabolic rate and increase generation of heat, despite an individual already having an increased core temperature (Briese, 1998). Therefore, the responses to hyperthermia and fever, which similarly cause core temperature to increase above homeostatic setpoint levels, induce directly opposite effects, which in the case of hyperthermia, lead to a reduction in core temperature, and in fever, maintains the increase in core temperature (Fig. 4). These differences have been described as the temperature ‘‘setpoint controversy’’ (Briese, 1998). The teleological value of the responses to hyperthermia would be to prevent catastrophic overheating of physiological systems. The teleological value of the responses to fever would be to allow optimal function of the inflammatory and immune response to remove the threat posed by the organism or process which induced the fever. How the decision to initiate either of these different strategies in response to an increase in core temperature is reached is not apparent. A bout of physical activity could also perhaps be thought of as another type of a ‘‘setpoint controversy’’. The exercise bout would result in changes in physiological systems, with many metabolic variables increasing or decreasing from their normal homeostatic setpoints. These changes would induce corrective behavior through one of the mechanisms described previously, in order to return the perturbed systems to baseline setpoint levels, and to avoid catastrophic failure of any physiological system that may be caused by excessive levels of exercise intensity (Noakes et al., 2004). These Hyperthemia 39°C Exercise

39°C Fever

Fever

37°C Baseline Temperature

37°C Exercise

Fig. 4. The ‘‘setpoint controversy’’ in temperature regulation. Both exercise and fever induce a hyperthermic response. The counterregulatory responses to the hyperthermia associated with exercise attempts to attenuate the hyperthermic condition. The counterregulatory response to the hyperthermia associated with fever attempt to maintain the hyperthermic condition.

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changes would include conscious symptoms of fatigue which would reduce the desire of the individual to continue exercise (St Clair Gibson et al., 2001a; Kayser, 2003; St Clair Gibson and Noakes, 2004), reductions in efferent neural command to peripheral skeletal muscle to reduce the exercise intensity and thus the metabolic rate (Gandevia, 2001; Ikai and Steinhaus, 1961), and changes in peripheral physiological systems which would attenuate the capacity of the peripheral systems to continue working at a rate which would induce a metabolic catastrophe (Lambert et al., 2005). A problem with this model of control of metabolic setpoints during an exercise bout is that the desire to initiate exercise itself then becomes an anti-homeostatic impulse. If the homeostatic activity during exercise is designed to return the altered metabolic values to resting baseline setpoint levels, the reason for initiating the exercise bout appears counter-intuitive. The large majority of exercise science studies of the last 50 years have indicated that exercise is beneficial, reducing the morbidity of both acute and chronic diseases, particularly lifestyle-associated diseases such as obesity, high blood pressure and diabetes (Noakes, 2003; Paffenbarger et al., 1986). However recently, several studies have shown that high volumes or high-intensity exercise may be harmful to some individuals, with studies showing increased incidence of permanent muscle damage (Collins et al., 2003; Grobler et al., 2004), and evidence of acquired training intolerance (St Clair Gibson, 2002) and chronic fatigue syndrome (Rowbottom et al., 1998) associated with high-intensity and high mileage athletic activity. Furthermore, as many as 70% of runners at some point develop musculoskeletal injuries (van Mechelen, 1992, 1995), which is clearly not beneficial. Therefore, it is not clearly why individuals would initiate exercise activity, from the perspective that the increase in metabolic rate associated with exercise is anti-homeostatic, and leads to the initiation of a number of processes designed to reduce the exercise intensity or halt the exercise bout to restore metabolic function to pre-exercise setpoint levels. A possible reason may therefore be that exercise may feel good, and there is a degree of addiction or motivation to exercise (not associated with the athletic addiction itself), such as material or social reward, which induces athletes to initiate exercise bouts. Therefore, there may be a ‘‘cognitive dissonance’’ between the subconscious homeostatic control processes and conscious desires or motivation of the individual, and the conscious desires appear to be anti-homeostatic. However, as conscious knowledge of desires and motivation have been suggested to originate in subconscious psychological processes (St Clair Gibson et al., 2001b, 2003), it may be that these apparently anti-homeostatic goals are part of more complex, higher order social ‘‘homeostatic’’ setpoints which are beneficial to the long-term future of

the individual, such as enhanced social and financial status, and the capacity of these factors to enhance the reproductive capacity and longevity of the individual deciding to initiate the athletic activity. Rowland (1998) has suggested that there is a biological controller in the central nervous system, which he described as an ‘‘activity-stat’’, which regulates energy intake and output. He suggested that exercise is a strategy used by the ‘‘activity-stat’’ to regulate energy expenditure, and suggested that exercise by an adult is similar to children playing, which is also possibly a form of energy regulation (Rowland, 1998). Rowland (1998) also suggested that this ‘‘activity-stat’’ could be overridden by extrinsic influences such as personal desire, peer influences, and environmental conditions, which may be a reason why many adults reduce activity and become obese. In support of Rowland’s (1998) theory is the concept that there must be a degree of forward planning of exercise bouts (St Clair Gibson and Noakes, 2004), as temperature has been show to increase prior to initiation of an exercise bout (Briese, 1998), indicating a degree of subconscious feedforward metabolic future ‘‘planning’’ is occurring, and this increased temperature in anticipation of exercise would also be an anti-homeostatic phenomenon. Whatever the reason for this pre-exercise increase in temperature, it does indicate a degree of interplay between physiological and psychological factors in the control of metabolic setpoint values.

9. Hierarchal and redundant regulation of metabolic setpoint function As described previously in this review, the homeostatic setpoint register of all metabolic variables in all different physiological systems in the body may be maintained or created by control processes in the brain, or as responses to external conditions, or as a result of complex system interaction of all the different physiological variables. The ‘‘setpoint controversy’’ described above, suggests that the setpoint register may be shifted because of a ‘‘higher’’ order of homeostatic control function. A question raised by this setpoint controversy, is whether the homeostatic setpoint values of certain metabolic variables are more tightly controlled than others, and whether there would be differences in the amplitude of deviation allowed from the baseline levels for different variables in the context of allowing this to occur for the benefit of a ‘‘higher’’ order of physiological system control. Previous research has shown that certain metabolic variables are more tightly controlled than other variables. For example, in an isolated compartment experiment, examining the interaction between blood glucose, insulin and glucagons concentrations, blood glucose concentrations were shown to oscillate with significantly


lower amplitudes away from its baseline levels compared to the amplitude of oscillations of blood insulin and glucagon concentrations (Goodner et al., 1977). This would indicate that blood glucose concentration is more tightly ‘‘defended’’ compared to the concentration of blood insulin or glucagon concentrations. Or, interpreted another way, the increased amplitude of the oscillations of blood insulin or glucagon are acceptable as they are necessary to maintain blood glucose concentrations within more narrow setpoint limits. It is not clear what feature of the system control mechanisms described previously allows for the creation of hierarchal regulation of the homeostatic setpoint values of different variables. From a teleological perspective, it could be argued that glucose is a fuel essential for creating ATP, which is the basic energetic requirement of all metabolic functions. The function of insulin and glucagon are to maintain the level of glucose concentrations necessary for survival. As both insulin and glucagon can both regulate blood glucose concentration, there is a degree of redundancy to their function, and blood glucose can still be maintained, albeit with reduced efficiency, if the supply of either of these hormones was compromised. Therefore, perhaps the homeostatic setpoint values of metabolic variables, which are to a degree redundant, are not defended as stringently as those whose function is essential and without which, death would occur. If this hypothesis is correct, the setpoint concentrations of essential metabolic fuels such as oxygen and glucose would be most stringently defended, followed by those with the lowest level of system redundancy, and those variables which are most redundant would be least protected. If this is indeed the case, as it appears to be from the example described above, a high degree of knowledge of the hierarchy of importance of the different metabolic variables, and their level of redundancy, is required by the system controllers. Further work is required to assess the veracity of this hypothesis. Examining redundancy in control mechanisms may provide insight into which system controllers described previously in this review are most important in setting the homeostatic setpoint values of the different physiological systems.

10. Summary Previous work has shown that physiological systems are precisely controlled at rest and during exercise to ensure that they operate at submaximal levels of activity, in order to prevent catastrophic failure of any single peripheral metabolic system. This control system could not counteract a system perturbation, if there was no baseline or setpoint register that would be used to calculate the level of response which would be required

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to return the perturbed systems to values which occurred prior to the onset of the perturbation. Therefore, the setpoint values of different metabolic variables are a fundamental factor in the homeostatic regulation of physiological systems. This article has examined different mechanisms of how metabolic setpoint values are maintained and regulated. The values for these different metabolic variables may be stored in the central nervous system, in a region such as the hypothalamus, and efferent neural commands originating from the central nervous system may continuously regulate the setpoint values of different metabolic variables in different physiological systems. The metabolic setpoint values may be created as a continuous response to external system perturbations such as gravity, or zeitgebers such as the day/night light cycle by the different physiological systems. They may also be created as a response to system perturbations caused by prior activity in the different physiological systems, which result in ongoing and continuous responses that become a sustained self-regulating controller of metabolic setpoint levels, as part of complex system control dynamics. The regulation of metabolic setpoint values may also be the result of a combination of all these different possible control systems. Disease processes and exercise training both appear to affect the setpoint level of metabolic variables. But, unless the disease processes or exercise induces a permanent bifurcation, such as occurs with permanent loss of cardiac tissue after a myocardial infarction, these changes do not appear to be permanent, and the value of different metabolic variables return to the original setpoint levels once the disease process or training stimulus is removed. The temperature setpoint controversy, which describes that temperature may be increased or decreased as part of different homeostatic control purposes, indicates that complex and hierarchal factors may be involved in setpoint control. It also appears that certain setpoint variables are more tightly maintained than others, and this may be due to the level of redundancy which occurs for each variable, and the necessity of that variable for the acute survival of the individual. In conclusion, metabolic setpoint regulation is crucial for initiating homeostatic regulation of physiological systems at rest and during exercise. Further research is required to determine what are the principal controllers of these metabolic setpoints, and how ‘‘knowledge’’ of these setpoint registers is attained and utilized by the various homeostatic processes that operate to maintain system integrity at rest and during exercise. Further research is also required to understand why regulation of metabolic setpoint values is altered in obese individuals and individuals suffering from chronic diseases such as hypertension and myocardial ischaemia. An understanding of factors involved in metabolic

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setpoint regulation may lead to more successful treatment of these chronic diseases.

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