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. 2012 Oct;26(7):1047-56.
doi: 10.1016/j.bbi.2012.06.002. Epub 2012 Jun 9.

Linking Disease Symptoms and Subtypes With Personalized Systems-Based Phenotypes: A Proof of Concept Study

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Linking Disease Symptoms and Subtypes With Personalized Systems-Based Phenotypes: A Proof of Concept Study

Kirstin Aschbacher et al. Brain Behav Immun. .
Free PMC article

Abstract

A dynamic systems model was used to generate parameters describing a phenotype of Hypothalamic-Pituitary-Adrenal (HPA) behavior in a sample of 36 patients with chronic fatigue syndrome (CFS) and/or fibromyalgia (FM) and 36 case-matched healthy controls. Altered neuroendocrine function, particularly in relation to somatic symptoms and poor sleep quality, may contribute to the pathophysiology of these disorders. Blood plasma was assayed for cortisol and ACTH every 10 min for 24h. The dynamic model was specified with an ordinary differential equation using three parameters: (1) ACTH-adrenal signaling, (2) inhibitory feedback, and (3) non-ACTH influences. The model was "personalized" by estimating an individualized set of parameters from each participant's data. Day and nighttime parameters were assessed separately. Two nocturnal parameters (ACTH-adrenal signaling and inhibitory feedback) significantly differentiated the two patient subgroups ("fatigue-predominant" patients with CFS only versus "pain-predominant" patients with FM and comorbid chronic fatigue) from controls (all p's<.05), whereas daytime parameters and diurnal/nocturnal slopes did not. The same nocturnal parameters were significantly associated with somatic symptoms among patients (p's<.05). There was a significantly different pattern of association between nocturnal non-ACTH influences and sleep quality among patients versus controls (p<.05). Although speculative, the finding that patient somatic symptoms decreased when more cortisol was produced per unit ACTH, is consistent with cortisol's anti-inflammatory and sleep-modulatory effects. Patients' HPA systems may compensate by promoting more rapid or sustained cortisol production. Mapping "behavioral phenotypes" of stress-arousal systems onto symptom clusters may help disentangle the pathophysiology of complex disorders with frequent comorbidity.

Figures

Fig. 1
Fig. 1
Fit of the dynamic and slope models compared to measured cortisol. Note: this graph illustrates model fit for nocturnal circadian cortisol secretion in one representative healthy participant. As expected, the evening period is quiescent, while the dynamics become increasingly more agitated in the early morning period.
Fig. 2
Fig. 2
HPA system parameter differences in patient subgroups versus matched healthy controls. **p≤ .01, *p≤ .05, p≤ .10. Paired t-test comparisons of patients and individually-matched healthy controls on systems-derived neuroendocrine outcomes. Fatigue-predominant patients (n = 14) met criteria for CFS and not FM, whereas pain-predominant patients (n = 22) met criteria for FM with or without chronic fatigue. These subtypes are associated with significant differences in neuroendocrine system activity. The y-axis represents the mean differences (from case-control paired t-tests) of two system parameters (ACTH-adrenal signaling and inhibitory cortisol feedback signaling). Positive values indicate that patients exhibited amplified signaling relative to controls, whereas negative values indicate decreased signaling of patients relative to controls.
Fig. 3
Fig. 3
Loss of regulatory association between sleep quality and nocturnal cortisol secretion among patients with chronic fatigue syndrome and/or fibromyalgia compared to healthy controls. PSQI, Pittsburgh Sleep Quality Index global score, in which higher scores = worse sleep quality over the previous month. Among healthy controls, worse sleep quality is associated with significantly lower levels of the parameter reflecting the intercept in the dynamic model. This parameter reflects non-ACTH influences on cortisol and is primarily correlated with the level of cortisol during the circadian nadir in the early evening. See Tables 3 and 4.

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