Technical and clinical aspects of cortisol as a biochemical marker of chronic stress

Abstract

Stress is now recognized as a universal premorbid factor associated with many risk factors of various chronic diseases. Acute stress may induce an individual's adaptive response to environmental demands. However, chronic, excessive stress causes cumulative negative impacts on health outcomes through "allostatic load". Thus, monitoring the quantified levels of long-term stress mediators would provide a timely opportunity for prevention or earlier intervention of stress-related chronic illnesses. Although either acute or chronic stress could be quantified through measurement of changes in physiological parameters such as heart rate, blood pressure, and levels of various metabolic hormones, it is still elusive to interpret whether the changes in circulating levels of stress mediators such as cortisol can reflect the acute, chronic, or diurnal variations. Both serum and salivary cortisol levels reveal acute changes at a single point in time, but the overall long-term systemic cortisol exposure is difficult to evaluate due to circadian variations and its protein-binding capacity. Scalp hair has a fairy predictable growth rate of approximately 1 cm/month, and the most 1 cm segment approximates the last month's cortisol production as the mean value. The analysis of cortisol in hair is a highly promising technique for the retrospective assessment of chronic stress.

Fig. 1.. The differential stress hormones secreted by adrenal cortex and medullar. Catecholamines cause general physiological changes that prepare the body for physical activity (fight-or-flight response) in the shortterm response. Some typical effects include increases in heart rate, blood pressure, and blood glucose levels, and other general reactions of the sympathetic nervous system. Corticoids are involved in a wide range of physiological processes including chronic stress response, immune response, and regulations of inflammation, carbohydrate metabolism, protein catabolism, blood electrolyte levels, and behavior. Data are taken from “https://www.studyblue.com/notes/note/n/07-adrenalglands/deck/1109539”.

Fig. 2.. Stress, allostasis, and allostatic load. Stress is any stimulus inducing either adaptive or maladaptive allostasis (changes in homeostasis) of stress mediators, which constitute the autonomic nervous system (blood pressure, catecholamines), metabolic hormones (cortisol, insulin), and pro- & anti-inflammatory cytokines. If stress stimuli are excessive and repetitive, recovery to the original homeostatic levels may be incomplete (indicated by the second blue arrow). As a result, chronic stress can make a body system to anticipate, as if such a new (stressful) environment would persist, demanding a newly defined set point for future adaptation. Thus, the difference between the new and old set points can be understood as a ‘cumulative burden of adaptation to stress’- i.e., allostatic load. Examples of allostatic load may be found in the primary mediators (hypercortisolemia, increased inflammatory cytokines), secondary outcomes (elevated blood pressure, overweight, insulin resistance), or tertiary outcomes (hypertension, diabetes, obesity, coronary heart disease, neurodegenerative disorders). For more details, see reference # 6.

Fig. 3.. A proposed mechanism of cortisol incorporation into hair and retrospective reflection of its chronic secretion. Cortisol may be incorporated into hair via passive diffusion from (A) blood capillary, (B) sweat, and (C) sebum, as well as from (D) external sources. Data are taken from reference #12.

Fig. 4.. Reconstructed metabolic network for systematic screening of therapeutic target point. The re-modeled metabolic structure is composed of metabolites, which consists of node (e.g. metabolite) and edge (e.g. correlation, structural similarity). The model may be extended to protein information via reaction pair that has already been built in the assembly, which could unravel “hidden” or veiled metabolic modulation, particularly in chronic disease. In this example, node color and size indicate significant difference and fold change compared to control (e.g. disease vs control), while edge presents two layers of information on structural similarity and reaction likelihood, which leads to automatic rearrangement as seen in the figure indicating proximity of biochemical module.