Concepts of scientific integrative medicine applied to the physiology and pathophysiology of catecholamine systems

Abstract

This review presents concepts of scientific integrative medicine and relates them to the physiology of catecholamine systems and to the pathophysiology of catecholamine-related disorders. The applications to catecholamine systems exemplify how scientific integrative medicine links systems biology with integrative physiology. Concepts of scientific integrative medicine include (i) negative feedback regulation, maintaining stability of the body's monitored variables; (ii) homeostats, which compare information about monitored variables with algorithms for responding; (iii) multiple effectors, enabling compensatory activation of alternative effectors and primitive specificity of stress response patterns; (iv) effector sharing, accounting for interactions among homeostats and phenomena such as hyperglycemia attending gastrointestinal bleeding and hyponatremia attending congestive heart failure; (v) stress, applying a definition as a state rather than as an environmental stimulus or stereotyped response; (vi) distress, using a noncircular definition that does not presume pathology; (vii) allostasis, corresponding to adaptive plasticity of feedback-regulated systems; and (viii) allostatic load, explaining chronic degenerative diseases in terms of effects of cumulative wear and tear. From computer models one can predict mathematically the effects of stress and allostatic load on the transition from wellness to symptomatic disease. The review describes acute and chronic clinical disorders involving catecholamine systems-especially Parkinson disease-and how these concepts relate to pathophysiology, early detection, and treatment and prevention strategies in the post-genome era.

Figure 1

A homeostatic system. The monitored variable is regulated by negative feedback. Afferent information about the monitored variable reaches a comparator homeostat, which drives an effector that influences the monitored variable. (+) sign indicates a positive relationship and (−) a negative relationship.

Figure 2

Monitored variable level in the absence of feedback regulation. In the computer model, the initial level of the “stock,” the monitored variable, is 100 units. The loss rate, indicated by the “pipe and valve,” depends on a rate constant, kLoss (in this case 1 per min), and on the level of the monitored variable (arrows). The level declines as a first order process, meaning the level falls exponentially.

Figure 3

Negative feedback, with proportionate control. The difference between the level of the monitored variable and the homeostat setting, the error signal, determines the rate of increase (Gain Rate) of the monitored variable. Note that with negative feedback, the level of the monitored variable reaches a steady state. As the value for kGain increases, the plateau level of the monitored variable increases; however, with proportionate control the plateau level is below the homeostat setting.

Figure 4

Effect of head-up tilting on beat-to-beat blood pressure in a healthy person. The blood pressure falls transiently but then returns to about the baseline level.

Figure 5

Computer model of a negative feedback loop with both proportionate and integrated control. The rate of increase in the monitored variable (Gain rate) is determined both by the error signal and the integrated error signal.

Figure 6

Predicted values for levels of the monitored variable as a function of time, with negative feedback by both proportionate and integrated control. The level of the monitored variable returns to the baseline level. The rate of attainment of the baseline level depends on the rate constant for the effector (kEffector). The predicted curve fits well the blood pressure response to tilting in Figure 4.

Figure 7

Homeostatic definition of stress. Stress is defined as a condition or state in which there is a sensed discrepancy between afferent information and the homeostatic setting. The sensed discrepancy corresponds to the “error signal” in the computer model of a negative feedback loop.

Figure 8

Introduction of a stressor into the computer model. The stressor augments the loss rate. The computer model predicts return to the set level of the monitored variable, with the time to return depending on the severity of the stressor. The integrated error signal is a measure of the accumulated stress.

Figure 9

Fever as an allostatic state. Changing the set-point of the homeostat (in this case at 12 h) increases the steady-state value for the monitored variable, the core temperature.

Figure 10

Stress, allostasis, and allostatic load in the computer model of negative feedback regulation of temperature by a thermostat. Allostasis refers to regulation of the level of the monitored variable at different steady-state values by adjusting the thermostat setting. Allostatic load refers to accumulated wear and tear on the furnace.

Figure 11

Inherited and acquired determinants of allostatic load. These determinants include genes and gene expression, environmental influences, resilience, and time. Note that decreased effector efficiency from allostatic load can induce a positive feedback loop, with all the relationships within the loop having a “+” sign.

Figure 12

Predicted effects of allostatic load on wellness. Because of wear and tear on the effector, the effector becomes less efficient, and because it is less efficient it has to be “on” more in order to maintain the level of the monitored variable; however, the more it is “on,” the more wear and tear (allostatic load). This positive feedback loop results in accelerated decline in wellness, early onset of symptomatic system failure (arbitrarily placed at 40% of ideal), and premature death.

Figure 13

Relationship between extent of adrenaline and ACTH responses across multiple stressors, from a meta-analysis of literature (138).

Figure 14

Labile blood pressure in patients with baroreflex failure as a late sequela of irradiation of the neck. Blood pressure lability in this setting exemplifies loss of control of the level of the monitored variable, by disruption of the barostatic negative feedback loop.

Figure 15

Some effectors regulating levels of monitored variables. The effectors are grouped arbitrarily into those of the autonomic nervous system (ANS), pituitary/endocrine (Pitu./Endo.) systems, and others. ANS effectors include the sympathetic noradrenergic system (SNS), sympathetic cholinergic system (SCS), sympathetic adrenergic system (SAS), parasympathetic nervous system (PNS), the DOPA-dopamine system (DDA), and the enteric nervous system (ENS). Pitu./Endo. systems include the hypothalamic-pituitary-adrenocortical (HPA) axis, renin-angiotensin-aldosterone system (RAS), thyroid hormone (THY), growth hormone (GH), gonadotrophic hormones (GON), prolactin/oxytocin (PRO), arginine vasopressin (AVP), insulin (INS), and glucagon (GLU). Other effectors include cytokines (CYT), endogenous opiate species (EOS), atrial natriuretic peptide (ANP), bradykinins (BRK), and nitric oxide (NO).

Figure 16

Compensatory activation. When a homeostatic system contains more than one effector, disabling of an effector leads to compensatory activation of the other effectors. Compensatory activation is one advantage of having multiple effectors.

Figure 17

Computer model of multiple effectors.

Figure 18

Computer-generated curves predicting effects of disabling one effector on activity of an alternative effector. As the rate constant for Effector 1 declines (green to red to black curves), the extent of activation of Effector 2 increases (compensatory activation).

Figure 19

Effector sharing. Two homeostatic systems involving negative feedback loops share the same effector.

Figure 20

Computer model of effector sharing. In this model, two homeostats determine the state of activity of the same effector, which in turn affects levels of two monitored variables. Via effector sharing, a stressor affecting levels of one monitored variable results in altered levels of a different monitored variable.

Figure 21

Predicted effects of effector sharing on levels of monitored variables. As the magnitude of stress increases in one homeostatic system (green to red to black curves), the level of the monitored variable for that homeostatic system returns to the baseline value, while the level of the monitored variable for the second homeostatic system reaches a different steady-state value. Increasing stress therefore results in maintenance of the first monitored variable at the set value, while levels of the second monitored variable increase to a new steady state. The extent of increase in the level of the second monitored variable depends on the extent of activation of the shared effector.

Figure 22

Complex involvement of multiple effectors and homeostats in the integrated response to orthostasis.

Figure 23

Complex involvement of multiple effectors and homeostats in the integrated response to exercise.

Figure 24

Minimum scientific integrative medicine model. The minimum model incorporates at least one monitored variable that is regulated by multiple effectors and at least one effector that is shared by multiple homeostats.

Figure 25

Compensatory activation of alternative effectors upon disabling of the SNS effector.

Figure 26

Catecholaminergic effectors associated with different homeostats. The different effector patterns result in “primitive specificity” of responses to different stressors. Effectors involving the catecholamines norepinephrine (sympathetic nervous system, SNS), epinephrine (sympathetic adrenergic system, SAS), or dopamine (DOPA-dopamine system, DDS) are in color. Other effectors depicted are the renin-angiotensin-aldosterone system (RAS), arginine vasopressin system (AVP), insulin (INS), glucagon (GLU), the parasympathetic nervous system (PNS), the hypothalamic-pituitary-thyroid system (HPT), and the sympathetic cholinergic system (SCS).

Figure 27

Primitive specificity in different domains. For each stressor there is a particular pattern of autonomic, somatic changes, and experiential changes.

Figure 28

Cannon’s experiment in which he exposed an instrumented cat to a barking dog. Blood taken from the vena cava of the stressed cat relaxed a rhythmically contracting intestinal strip in a bioassay preparation (40). “Excited” blood was added at (b) and (f), and “quiet” blood from the same animal was added at (d).

Figure 29

Illustration of Cannon’s use of the heart rate of a denervated heart as a measure of adrenal EPI secretion (35).

Figure 30

Articles culled from PubMed using the search term, “allostatic load,” as a function of 2-year periods since 1996. The number of articles on allostatic load increased exponentially.

Figure 31

Diagrams of feedback loops that may be involved in fainting reactions (neurocardiogenic syncope, reflex syncope, vaso-vagal syncope). According to the collapse firing hypothesis, syncope results from a combination of SNS activation and decreased cardiac filling (such as from orthostasis or acute hemorrhage), which evokes a pattern of SNS withdrawal and PNS stimulation. According to a schema derived from concepts of scientific integrative medicine, syncope results from positive feedback loops and interference with negative feedback loops, at least partly due to sharing of the SNS and SAS effectors. The result is a specific neuroendocrine pattern that includes PNS activation and sympathoadrenal imbalance.

Figure 32

Mean arterial pressure (MAF), forearm vascular resistance (FVR), and arterial plasma levels of catecholamines in a patient with tilt-induced hypotension and syncope. The arrows emphasize the mirrored trends in FVR and plasma EPI. EPI becomes dissociated from NE (sympathoadrenal imbalance) and FVR falls below baseline several minutes before hypotension and syncope.

Figure 33

Multiple sites of interference with baroreflex regulation in Parkinson disease (PD) with orthostatic hypotension. Carotid wall thickening interferes with transduction of blood pressure information into baroreceptor afferent traffic. Alpha-synucleinopathy or neuronal loss in brainstem nuclei interferes with central barostatic function. Neuroimaging and neurochemical evidence indicates substantial noradrenergic denervation or dysfunction in the left ventricular myocardium, renal cortex, and other extra-cranial sites.

Figure 34

The getaway car analogy. A car’s engine uses energy for locomotion. The bank robber’s getaway car is kept idling, so that the driver can rapidly shift from “park” to “drive.” As the engine idles, toxic combustion products are produced, which are detoxified by a catalytic converter. The oil lubricates the pistons. Eventually, the engine fails, and deposits are found in the engine and oil.

Figure 35

Catecholamine neurons are like the idling getaway car engine. Catecholamines such as dopamine leak from storage vesicles into the cytoplasm, where they undergo enzymatic oxidative deamination catalyzed by MAO-A to form toxic catecholaldehydes such as DOPAL. DOPAL is detoxified by aldehyde dehydrogenase (ALDH). Eventually the catecholaminergic neurons die, and deposits of alpha-synuclein are found in Lewy bodies.

Figure 36

The “catecholaldehyde hypothesis.” According to this hypothesis, decreased vesicular sequestration of cytosolic catecholamines and impaired catecholaldehyde detoxification cause the death of catecholamine neurons that characterizes Parkinson disease. Under resting conditions, most of the irreversible loss of dopamine (DA) from the neurons is due to passive leakage from vesicles (DAv) into the cytosol (DAc) and efficient but imperfect vesicular uptake mediated by the type 2 vesicular monoamine transporter (VMAT2). This loss is balanced by ongoing catecholamine biosynthesis from the action of L-aromatic-amino-acid decarboxylase (LAAAD) on 3,4-dihydroxyphenylalanine (DOPA) produced from tyrosine (TYR) by tyrosine hydroxylase (TH). Release by exocytosis is followed by reuptake mediated by the cell membrane DA transporter (DAT). Intra-neuronal metabolism of DA is channeled through enzymatic deamination catalyzed by monoamine oxidase (MAO), producing the catecholaldehyde 3,4-dihydroxyphenylacetaldehyde (DOPAL). DOPAL is detoxified mainly by aldehyde dehydrogenase (ALDH), to form the acid, 3,4-dihydroxyphenylacetic acid (DOPAC), with 3,4-dihydroxyphenylethanol (DOPET) a minor metabolite formed via aldose/aldehyde reductase (AR). Both DAc and DOPAL spontaneously auto-oxidize to quinones, which augment generation of reactive oxygen species (ROS), resulting in lipid peroxidation. 4-Hydroxynonenal (4HNE), a major lipid peroxidation product, inhibits ALDH. DOPAL cross-links with proteins, augmenting oligomerization of alpha-synuclein and inhibiting TH.

Figure 37

Pathogenetic mechanisms resulting in loss of catecholaminergic neurons may reflect induction of a variety of positive feedback loops.

Figure 38

Computer model-generated curves illustrating that compensatory activation prolongs the time before a disease process manifests clinically. Tracking the rate of compensatory activation may inform decision-making about appropriate timing for initiation of neuroprotective treatment.