Allostatic overload in myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS)

Abstract:

Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) is a debilitating condition characterised by diverse symptoms such as fatigue, pain, sleep disturbance and autonomic dysfunction. There remains to be a singular biomarker identified for this illness, hence numerous theories about its development and perpetuation have been posited in the literature.

This brief report presents the model of ‘allostasis’ as a framework for understanding ME/CFS, specifically the notion that the physiological mechanisms employed in the body to deal with stress termed here as ‘allostatic states’ (e.g. elevation of inflammatory cytokines), may in and of themselves contribute to the perpetuation of the disorder. This theoretical assertion has important consequences for the understanding of ME/CFS and treatment; rather than searching for a singular pathogen responsible for this condition, ME/CFS can be conceptualised as a maladaptive stress disorder and interventions aimed at addressing the allostatic states may be incorporated into current symptom management programmes.

Copyright © 2013 Elsevier Ltd. All rights reserved.

 

Source: Arroll MA. Allostatic overload in myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS). Med Hypotheses. 2013 Sep;81(3):506-8. doi: 10.1016/j.mehy.2013.06.023. Epub 2013 Jul 11. https://www.ncbi.nlm.nih.gov/pubmed/23850395

 

Chronic fatigue syndrome reflects loss of adaptability

In this issue, Van Oosterwijck et al. [1] report that physical exercise lowered pain thresholds and was associated with exacerbation of symptoms in patients with myalgic encephalomyelitis (ME)/chronic fatigue syndrome (CFS) whereas, on the other hand, postexercise activity levels did not significantly decrease. Based on these and similar findings in patients with CFS, we present a conceptual framework that might provide a better understanding of the key features and pathophysiological mechanisms of CFS, and thus improve its diagnosis and treatment.

CFS as a failure of allostasis?

Van Oosterwijck et al. [1] correctly note the frequent cooccurrence of a chronic ‘fatigue–pain’ symptom cluster, usually diagnosed as CFS and/or fibromyalgia. Recently, it has been proposed that this cluster should be classified under the unifying label of ‘central sensitivity syndromes’– a broad range of functional somatic disorders mainly characterized by common sensory abnormalities (i.e. widespread pain, hyperalgesia, allodynia and hypersensitivity to noise, bright light and certain chemical substances) [2].

However, ‘stress intolerance and pain hypersensitivity syndromes’ may be a more appropriate umbrella term for these syndromes because it reflects these patients’ inability to adequately adapt to all kinds of physical and mental stressors, including pathological pain processing [3]. Within the innovative neurobiological stress paradigm of ‘allostasis’– the need for stability through continuous change [4] – this general loss of adaptability may be understood as a failure of allostasis.

Although the mechanisms underlying this failure are still unclear, they may include complex and interrelated disturbances of different components of the stress system, (i.e. the hypothalamic–pituitary–adrenal (HPA) axis), the sympathetic nervous system and various neurotransmitters that modulate perceptual–cognitive and affective brain circuits, all of which operate in intimate connection with the immune system and central pain mechanisms [5].

We and others have hypothesized that the pathophysiology of CFS might include a ‘switch’ from HPA axis hyperfunction to hypofunction following a period of chronic physical and/or psychosocial stress in vulnerable persons resulting in inadequate cortisol reactivity which may in turn, via low glucocorticoid signalling, increase inflammatory activity [5]. This assumption is consistent with the relatively low basal cortisol levels and blunted diurnal cortisol rhythm frequently observed in CFS patients [5], but recent data suggest that a decrease in glucocorticoid receptor sensitivity might play a role as well [6].

Abnormal activation of innate immunity involves the release of pro-inflammatory cytokines that influence the brain and give rise to ‘sickness behaviour’. This evolutionary, physiological and behavioural reaction normally occurs during infection or severe injury and its purpose is to optimally fight bodily threats by reorganizing priorities, saving energy and promoting healing and recovery. Characteristic symptoms are profound lethargy, feelings of malaise, concentration difficulties, headache, mild fever, sensory hypersensitivity and generalized pain. In CFS patients, however, this ‘flu-like’ symptom complex may be typically provoked by any kind of stressor (e.g. physical effort, mental pressure, strong emotions) and lead to a motivational shift by urging the patient to withdraw from activities [7].

Yet, the situation may be more complex. Not only is there evidence for basal hyperfunction of the sympathetic nervous system in CFS [8] and fibromyalgia [9], but dysfunctional descending pain-inhibiting pathways [10] and various psychological mechanisms may also contribute to abnormal pain perception [11].

The data presented by Van Oosterwijck et al. [1] fit within the stress adaptability hypothesis, which includes immune-related central pain sensitization, and thus make a strong case for refining current diagnostic criteria of CFS [12] to incorporate – as a mandatory criterion –patients’ maladaptive postexertional response. Novel clinical diagnostic criteria have meanwhile been developed [13] but it remains to be seen whether these criteria will empirically prove to be appropriate in identifying the key features of the illness.

You can read the rest of this comment here: http://onlinelibrary.wiley.com/doi/10.1111/j.1365-2796.2010.02240.x/full

Comment on: Pain inhibition and postexertional malaise in myalgic encephalomyelitis/chronic fatigue syndrome: an experimental study. [J Intern Med. 2010]

 

Source: Van Houdenhove B, Luyten P. Chronic fatigue syndrome reflects loss of adaptability. J Intern Med. 2010 Sep;268(3):249-51. doi: 10.1111/j.1365-2796.2010.02240.x. http://onlinelibrary.wiley.com/doi/10.1111/j.1365-2796.2010.02240.x/full (Full article)

 

Chronic fatigue syndrome and high allostatic load: results from a population-based case-control study in Georgia

Abstract:

OBJECTIVE: To confirm the association of chronic fatigue syndrome (CFS) with high allostatic load (AL) level, examine the association of subsyndromal CFS with AL level, and investigate the effect of depression on these relationships and the association of AL with functional impairment, fatigue, symptom severity, fatigue duration, and type of CFS onset. AL represents the cumulative physiologic effect of demands to adapt to stress.

METHODS: Population-based case-control study of 83 persons with CFS, 202 persons with insufficient symptoms or fatigue for CFS (ISF), and 109 well controls living in Georgia. Unconditional logistic regression was used to generate odds ratios (ORs) as measures of the association of AL with CFS.

RESULTS: Relative to well controls, each 1-point increase in allostatic load index (ALI) was associated with a 26% increase in likelihood of having CFS (OR(adjusted) = 1.26, 95% Confidence Interval (CI) = 1.00, 1.59). This association remained in the presence and absence of depression (OR(adjusted) = 1.35, CI = 1.07, 1.72; OR(adjusted) = 1.35, CI = 1.10, 1.65). Compared with the ISF group, each 1-point increase in ALI was associated with a 10% increase in likelihood of having CFS (OR(adjusted) = 1.10, CI = 0.93, 1.31). Among persons with CFS, the duration of fatigue was inversely correlated with ALI (r = -.26, p = .047).

CONCLUSIONS: Compared with well controls, persons with CFS were significantly more likely to have a high AL. AL increased in a gradient across well, ISF, and CFS groups.

 

Source: Maloney EM, Boneva R, Nater UM, Reeves WC. Chronic fatigue syndrome and high allostatic load: results from a population-based case-control study in Georgia. Psychosom Med. 2009 Jun;71(5):549-56. doi: 10.1097/PSY.0b013e3181a4fea8. Epub 2009 May 4. https://www.ncbi.nlm.nih.gov/pubmed/19414615 (Full article)

 

Allostatic load is associated with symptoms in chronic fatigue syndrome patients

Abstract:

OBJECTIVES: To further explore the relationship between chronic fatigue syndrome (CFS) and allostatic load (AL), we conducted a computational analysis involving 43 patients with CFS and 60 nonfatigued, healthy controls (NF) enrolled in a population-based case-control study in Wichita (KS, USA). We used traditional biostatistical methods to measure the association of high AL to standardized measures of physical and mental functioning, disability, fatigue and general symptom severity. We also used nonlinear regression technology embedded in machine learning algorithms to learn equations predicting various CFS symptoms based on the individual components of the allostatic load index (ALI).

METHODS: An ALI was computed for all study participants using available laboratory and clinical data on metabolic, cardiovascular and hypothalamic-pituitary-adrenal (HPA) axis factors. Physical and mental functioning/impairment was measured using the Medical Outcomes Study 36-item Short Form Health Survey (SF-36); current fatigue was measured using the 20-item multidimensional fatigue inventory (MFI); frequency and intensity of symptoms was measured using the 19-item symptom inventory (SI). Genetic programming, a nonlinear regression technique, was used to learn an ensemble of different predictive equations rather just than a single one. Statistical analysis was based on the calculation of the percentage of equations in the ensemble that utilized each input variable, producing a measure of the ‘utility’ of the variable for the predictive problem at hand. Traditional biostatistics methods include the median and Wilcoxon tests for comparing the median levels of subscale scores obtained on the SF-36, the MFI and the SI summary score.

RESULTS: Among CFS patients, but not controls, a high level of AL was significantly associated with lower median values (indicating worse health) of bodily pain, physical functioning and general symptom frequency/intensity. Using genetic programming, the ALI was determined to be a better predictor of these three health measures than any subcombination of ALI components among cases, but not controls.

 

Source: Goertzel BN, Pennachin C, de Souza Coelho L, Maloney EM, Jones JF, Gurbaxani B. Allostatic load is associated with symptoms in chronic fatigue syndrome patients. Pharmacogenomics. 2006 Apr;7(3):485-94. https://www.ncbi.nlm.nih.gov/pubmed/16610958

 

Chronic fatigue syndrome and high allostatic load

Abstract:

STUDY POPULATION: We examined the relationship between chronic fatigue syndrome (CFS) and allostatic load in a population-based, case-control study of 43 CFS patients and 60 nonfatigued, healthy controls from Wichita, KS, USA.

METHODS: An allostatic load index was computed for all study participants using available laboratory and clinical data, according to a standard algorithm for allostatic load. Logistic regression analysis was used to compute odds ratios (ORs) as estimates of relative risk in models that included adjustment for matching factors and education; 95% confidence intervals (CIs) were computed to estimate the precision of the ORs.

RESULTS: CFS patients were 1.9-times more likely to have a high allostatic load index than controls (95% CI = 0.75, 4.75) after adjusting for education level, in addition to matching factors. The strength of this association increased in a linear trend across categories of low, medium and high levels of allostatic load (p = 0.06).

CONCLUSION: CFS was associated with a high level of allostatic load. The three allostatic load components that best discriminated cases from controls were waist:hip ratio, aldosterone and urinary cortisol.

 

Source: Maloney EM, Gurbaxani BM, Jones JF, de Souza Coelho L, Pennachin C, Goertzel BN. Chronic fatigue syndrome and high allostatic load. Pharmacogenomics. 2006 Apr;7(3):467-73. https://www.ncbi.nlm.nih.gov/pubmed/16610956

 

Linear data mining the Wichita clinical matrix suggests sleep and allostatic load involvement in chronic fatigue syndrome

Abstract:

OBJECTIVES: To provide a mathematical introduction to the Wichita (KS, USA) clinical dataset, which is all of the nongenetic data (no microarray or single nucleotide polymorphism data) from the 2-day clinical evaluation, and show the preliminary findings and limitations, of popular, matrix algebra-based data mining techniques.

METHODS: An initial matrix of 440 variables by 227 human subjects was reduced to 183 variables by 164 subjects. Variables were excluded that strongly correlated with chronic fatigue syndrome (CFS) case classification by design (for example, the multidimensional fatigue inventory [MFI] data), that were otherwise self reporting in nature and also tended to correlate strongly with CFS classification, or were sparse or nonvarying between case and control. Subjects were excluded if they did not clearly fall into well-defined CFS classifications, had comorbid depression with melancholic features, or other medical or psychiatric exclusions. The popular data mining techniques, principle components analysis (PCA) and linear discriminant analysis (LDA), were used to determine how well the data separated into groups. Two different feature selection methods helped identify the most discriminating parameters.

RESULTS: Although purely biological features (variables) were found to separate CFS cases from controls, including many allostatic load and sleep-related variables, most parameters were not statistically significant individually. However, biological correlates of CFS, such as heart rate and heart rate variability, require further investigation.

CONCLUSIONS: Feature selection of a limited number of variables from the purely biological dataset produced better separation between groups than a PCA of the entire dataset. Feature selection highlighted the importance of many of the allostatic load variables studied in more detail by Maloney and colleagues in this issue [1] , as well as some sleep-related variables. Nonetheless, matrix linear algebra-based data mining approaches appeared to be of limited utility when compared with more sophisticated nonlinear analyses on richer data types, such as those found in Maloney and colleagues [1] and Goertzel and colleagues [2] in this issue.

 

Source: Gurbaxani BM, Jones JF, Goertzel BN, Maloney EM. Linear data mining the Wichita clinical matrix suggests sleep and allostatic load involvement in chronic fatigue syndrome. Pharmacogenomics. 2006 Apr;7(3):455-65. https://www.ncbi.nlm.nih.gov/pubmed/16610955