SEARCH

SEARCH BY CITATION

Keywords:

  • autonomic dysfunction;
  • chronic fatigue syndrome;
  • fatigue;
  • fibromyalgia;
  • gene expression;
  • orthostatic symptoms

Abstract

  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. Conflict of interest statement
  8. Acknowledgements
  9. References

Abstract.  Light AR, Bateman L, Jo D, Hughen RW, VanHaitsma TA, White AT, Light KC (University of Utah, Salt Lake City, UT, USA). Gene expression alterations at baseline and following moderate exercise in patients with Chronic Fatigue Syndrome and Fibromyalgia Syndrome. J Intern Med 2012; 271: 64–81.

Objectives.  To determine mRNA expression differences in genes involved in signalling and modulating sensory fatigue, and muscle pain in patients with chronic fatigue syndrome (CFS) and fibromyalgia syndrome (FM) at baseline, and following moderate exercise.

Design.  Forty-eight patients with CFS only, or CFS with comorbid FM, 18 patients with FM that did not meet criteria for CFS, and 49 healthy controls underwent moderate exercise (25 min at 70% maximum age-predicted heart rate). Visual-analogue measures of fatigue and pain were taken before, during and after exercise. Blood samples were taken before and 0.5, 8, 24 and 48 h after exercise. Leucocytes were immediately isolated from blood, number coded for blind processing and analyses and flash frozen. Using real-time, quantitative PCR, the amount of mRNA for 13 genes (relative to control genes) involved in sensory, adrenergic and immune functions was compared between groups at baseline and following exercise. Changes in amounts of mRNA were correlated with behavioural measures and functional clinical assessments.

Results.  No gene expression changes occurred following exercise in controls. In 71% of patients with CFS, moderate exercise increased most sensory and adrenergic receptor’s and one cytokine gene’s transcription for 48 h. These postexercise increases correlated with behavioural measures of fatigue and pain. In contrast, for the other 29% of patients with CFS, adrenergic α-2A receptor’s transcription was decreased at all time-points after exercise; other genes were not altered. History of orthostatic intolerance was significantly more common in the α-2A decrease subgroup. FM-only patients showed no postexercise alterations in gene expression, but their pre-exercise baseline mRNA for two sensory ion channels and one cytokine were significantly higher than controls.

Conclusions.  At least two subgroups of patients with CFS can be identified by gene expression changes following exercise. The larger subgroup showed increases in mRNA for sensory and adrenergic receptors and a cytokine. The smaller subgroup contained most of the patients with CFS with orthostatic intolerance, showed no postexercise increases in any gene and was defined by decreases in mRNA for α-2A. FM-only patients can be identified by baseline increases in three genes. Postexercise increases for four genes meet published criteria as an objective biomarker for CFS and could be useful in guiding treatment selection for different subgroups.


Introduction

  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. Conflict of interest statement
  8. Acknowledgements
  9. References

Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS), as defined by the Fukuda research criteria [1], is an otherwise unexplained cluster of symptoms lasting at least 6 months that includes profound, remitting/relapsing fatigue that impairs functioning and is not relieved by rest or recovery. Four or more of the following eight symptoms must also be present: (i) postexertional malaise (described as greatly increased fatigue and the feeling of sickness beginning 6–24 h after exercise and continuing for several days or weeks), (ii) unrefreshing sleep, (iii) muscle pain, (iv) joint pain, (v) new or a change in headaches, (vi) impairment of memory or concentration, (vii) sore throat and (viii) tender lymph nodes [1].

The Canadian Clinical Criteria for CFS require a significant degree of new onset physical and mental fatigue that substantially reduces activity levels and also requires (from above criteria) #1, #2, one of #3–#5, neurocognitive symptoms (including impairment of concentration and short-term memory, inability to focus vision, ataxia, muscle weakness, photophobia, periods of anxiety), and at least one symptom from each of the following categories: immune manifestations (tender lymph nodes, sore throat or new sensitivities to food and medications and/or chemicals), autonomic manifestations (orthostatic intolerance, POTS, extreme pallor, nausea and irritable bowel syndrome, palpitations, exertional dyspnoea) or neuroendocrine manifestations (subnormal temperature, sweating episodes, intolerance of heat and cold, weight changes, anorexia or abnormal appetite, worsening of symptoms with stress) [2].

Up to 70% of patients with ME/CFS (hereafter referred to as CFS) also meet the criteria set by the American College of Rheumatology for Fibromyalgia Syndrome (FM) as a condition of unknown aetiology that consists of widespread muscle/connective tissue pain present in four quadrants of the body (bilateral, upper and lower body) for 3 months or longer. There is also painful sensitivity to pressure at tender points (with pain reported for at least 11 of 18 tender points) [3]. Recent guidelines published by ACR experts, but not yet validated, have suggested altering this definition to permit easier application in the clinical setting [4]. In both CFS and FM, the major symptoms, and most of the ancillary symptoms, are subjectively determined.

For CFS, the major symptom, ‘fatigue’, is a clinical measure that is only loosely associated with the physiologists’ traditional definition of ‘fatigue’ which is the inability to voluntarily contract skeletal muscle [5]. The clinical ‘fatigue’ includes both the sensation of tiredness and increased effort in skeletal muscle contraction and also increased effort in mental functions. For FM, the major symptom is muscle pain, which has been poorly studied in comparison to cutaneous pain.

Recently, our laboratory was able to identify some of the molecular receptors responsible for the sensory neuron signalling of muscle fatigue and muscle pain [6]. In addition, we have discovered that adrenergic receptors can also contribute to enhanced muscle pain [7, 8]. We further discovered that these molecular receptors are expressed by human leucocytes, and that their expression is altered by exercise that worsens muscle pain and muscle fatigue [8]. In patients with CFS, we showed that mRNAs of sensory and adrenergic molecular receptors as well as two cytokines were dramatically increased 30 min to at least 48 h following moderate exercise (25 min of combined arm and leg exercise) [9]. These findings are similar to previous reports suggesting that patients with CFS have complex dysregulation of several physiological systems including the immune system (cytokines), the central nervous system, cellular energy and transport and cardiovascular system [2]. Dysregulation of these systems singly or together could contribute to some or all of the symptoms that define CFS and FM, but especially could help explain the important symptom of postexertional malaise.

Here, we report on an increased sample of patients with CFS and compare their gene regulation with that of patients with FM. With the larger sample, we confirmed our original finding of increased gene expression of a number of sensory, adrenergic and cytokine genes and discovered a major subgroup of CFS that is characterized by a large postexercise decrease in gene expression of the adrenergic alpha 2A receptor (α-2A). This subgroup was also characterized by a predominance of orthostatic intolerance when compared with other patients with CFS. We also discovered that patients with FM only (who do not meet criteria for CFS) do not show large gene expression changes following exercise, but, rather have baseline increases in gene expression for two sensory molecular receptors and the immune cytokine IL10.

Materials and methods

  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. Conflict of interest statement
  8. Acknowledgements
  9. References

Study population

All research reported here was approved by the University of Utah Institutional Review Board, and written informed consent was obtained from all subjects before participation. Exclusion criteria included active viral or upper respiratory infections, chronic cardiovascular or pulmonary disorders, or other chronic conditions such as anaemia, cancer or multiple sclerosis. All subjects were required to refrain from any other continuous exercise >5 min of walking, for 2 days before, and 2 days after the scheduled exercise task.

The sample for the present report included 48 patients with CFS (33 women) [19 (15 women) of these are the same patients as in our previous publication [8]], 49 Control subjects (29 women) [16 (11 women) are the same controls as in our previous report] and 18 FM-only subjects (15 women). This ratio of more women than men is typical in CFS and FM research and is also consistent with observations from large-scale incidence and prevalence studies including the Wichita sample [10]. The patients reflected the local population with 94% being Caucasian, 6% being minority; thus, our findings may not apply to minorities with CFS or FM. All patients with CFS met the CDC criteria for CFS [1], and 46 (96%) met the Canadian Criteria for ME/CFS as well [2]. Prior screening by an experienced physician (LB) ruled out all other known causes for persistent or relapsing fatigue in these patients with CFS. All patients were also screened for FM using the strict ACR research criteria, which include the presence of widespread pain for at least 6 months, and pain reported at 11 or more of 18 sites during tender point examination [3]. Thirty-three of the 48 patients with CFS (69%) also met ACR criteria for FM, similar to the high comorbidity of these disorders previously reported [11–13]. Eighteen patients met criteria for FM, but did not meet criteria for CFS (principally because of fatigue causing less compromise in normal daily activities) and were classified as FM-only patients (FM).

For primary analyses, all 48 patients with CFS were compared with the 49 controls. All later analyses were carried out after separating out the CFS subgroup in which the major identifying feature was large decreases in α-2A receptor mRNA at all times following exercise. All 18 patients with FM were also compared with this same control group and with the CFS group.

Both controls and all patient groups included individuals on prescribed antidepressants, and no subjects were withdrawn from these medications because antidepressants require a relatively long washout and patients may be at risk off medications. Also, our prior study [9] indicated that antidepressants do not substantially alter CFS patients’ expression of the genes in our profile. Thus, 11 of 49 control subjects (23%), 30 of 48 patients with CFS (63%) and nine of the patients with FM (50%) were tested while continuing their usual antidepressants. In regard to other prescribed medications, for the first 19 CFS and all 18 patients with FM, we requested that these patients be withdrawn from any prescribed pain medications and anticonvulsants for 6 days to participate, and only two of these patients with CFS failed to comply fully. For the remaining 29 patients with CFS, to increase participation by those with more severe symptoms who were unwilling to interrupt their medications, they were allowed to remain on all their usual physician-prescribed medications. This resulted in 15 (31%) patients with CFS being tested on opioid pain medications, and 11 (23%) patients with CFS being tested on anticonvulsants. This allowed secondary analyses to be performed comparing patients with CFS tested on versus not on these prescribed medications.

Disorder onset and functionality/severity ratings

For the patients with CFS, CFS symptom onset was reported as sudden by 36 patients (70% of the α-2A increase and 87% of the α-2A decrease subgroups). Thirty-five (73%) reported onset to be associated with one or several flu-like, mononucleosis or other viral illnesses or infections. Five patients (10%) associated onset with traumatic injuries and fractures. Three patients (6%) associated onset with surgery, and five patients associated onset with life stress or no memorable event. In contrast, the FM-only group included only two patients (11%) who reported sudden onset of their symptoms, linked to a virus in one and to a herniated disc in the other.

Categorical ratings of disorder severity from 1 to 4 were assigned for all patients with CFS by their physician (LB) based on observations and symptom measurements over multiple visits in the clinical setting, while blind to the gene expression and exercise results. The categories were defined by ability to function in normal daily activities, as follows: (4) Able to work full time 30–40 h but ‘nothing left over’, high symptom burden and limitations; (3) Able to do part time school/work/other activities 10–30 h per week, but easily relapses, and frequent rest needed; (2) Only able to do self-care activities of daily living (ADL), sedentary, <10 h per week of light activities, but could live alone with occasional help; (1) House- or bed-bound, with minimal activity tolerance, marked cognitive dysfunction and dependent on others for ADL. Thus, lower function scores indicated worse disorder severity.

Exercise protocol

Exercise testing was always performed at the same time of day (starting between 8 and 9:30 am). Venous blood samples were obtained immediately prior to exercise (baseline) and at 0.5, 8, 24 and 48 h postexercise. We assessed the severity of pre-existing and exercise-related fatigue and myalgia symptoms at the time of each blood draw, and at the midpoint and immediately after completing the exercise task; the subject provided numerical ratings of mental fatigue, physical fatigue and overall body pain using a 0–100 scale where 100 was defined as the greatest level of fatigue or pain the subject could ever imagine experiencing.

A combined arm-leg cycle ergometer (Schwinn Air-Dyne) was used for the 25-min, moderate exercise test. In the first 5 min of exercise, subjects were asked to increase pedalling rate until 70% age-predicted maximal heart rate was achieved. Thereafter, work rate was adjusted to maintain this target heart rate throughout the submaximal exercise protocol. Ratings of perceived exertion (RPE) were obtained on a scale of 1–10 every 5 min; heart rate was recorded every minute, and blood pressure was measured at baseline, every 10 min during exercise, and upon completion of the exercise. We elected to use a sustained moderate exercise rather than a maximal exercise test (which typically last only 5–9 min in patients with CFS) because of closer similarity to the natural exercise experiences reported to exacerbate CFS symptoms in patients’ daily lives. Our 25-min submaximal exercise task did elicit consistent worsening of fatigue and pain symptoms from 8 to 48 h postexercise (see Fig. 1). In contrast, after a briefer maximal exercise task, reports of worsening CFS symptoms were inconsistent or absent until 5 days after the challenge [14], a pattern not typically observed in real life. Maximal exercise protocols have demonstrated few differences in cardiorespiratory and perceptual responses; RPE is an exception being consistently higher in patients with CFS than controls [15]. However, it is notable that responses to submaximal exercise including VO2 do predict peak exercise performance in patients with CFS [16].

image

Figure 1. Behavioural scores for mental and physical fatigue and pain in patients versus control subjects. (a) Average visual analogue scores (maximum 100, minimum 0) for mental fatigue for chronic fatigue syndrome (CFS) subgroups, fibromyalgia syndrome (FM) and controls. Data points are at baseline (MF base), half way through the 25-min exercise period (MF mid), immediately after exercise (MF imm), 30 min after exercise (MF 30), and 8 (MF 8), 24 (MF 24) and 48 h (MF 48) after exercise. Orange line indicates averages of patients identified as showing increases in α-2A adrenergic receptor mRNA following exercise. This group consisted of 25 patients with CFS and co-morbid FM, and nine patients with CFS that did not meet criteria for FM (34 total). Purple line indicates patients with CFS that do not meet criteria for FM. This group consisted of 15 patients with CFS that did not meet criteria for FM. Green line indicates patients showing decreases in α-2A adrenergic receptor mRNA following exercise. This group consisted of eight patients that has CFS and co-morbid FM, and six patients with CFS that did not meet criteria for FM (14 total). Pink line indicates patients with FM that do not meet criteria for CFS (n = 18). Blue line indicates controls (n = 49). Asterisks indicate the only two data points for any of the patient groups that were not greater than baseline, all other data points for all groups were significantly greater than baseline. All data points for all patient groups were significantly greater than those of controls (P < 0.05). (b) Average visual analogue scores (maximum 100, minimum 0) for physical fatigue for CFS subgroups, FM and controls. # indicates the only point at which physical fatigue was significantly increased from baseline for controls (P < 0.05). All data points for all patient groups were significantly greater than controls and also significantly greater than baseline (P < 0.05). All other information as in a. (c) Average visual analogue scores (maximum 100, minimum 0) for pain for CFS subgroups, FM and controls. CFS-only patients had overall pain scores that were lower than patients with CFS and FM. All patients pain scores were higher at baseline and all time-points than controls (P < 0.05). In addition, all patients showed significant increases from baseline in pain scores during and following exercise (P < 0.05). All other markings as in a and b.

Download figure to PowerPoint

mRNA extraction and analysis

All blood processing and analyses were performed by personnel blinded to the subject’s group. At each of the five blood sampling times, blood was collected in EDTA tubes. Seven minutes after blood collection, the blood was centrifuged at 3200 rpm (1315 g- Clay Adams Compact II Centrifuge) for 12 min, plasma removed, and the white layer carefully collected in RLT + β-ME (Qiagen, Valencia, CA, USA) then quickly frozen using a methanol-dry ice slurry and stored at −80 º. RNA was extracted using RNeasy kits (Qiagen), according to manufacture’s directions, and treated with RNase-free DNase-I (Qiagen). Immediately following extraction, RNA was converted to a cDNA library using the ABI High Capacity cDNA Archive kit (Applied Biosystems, Inc., Foster City, CA, USA). The cDNA samples were stored at −2 °C until analysis.

RNA integrity was assessed with a Bioanalyzer and consistently found to have values greater than 9. The cycle counts for the control gene (TF2B) averaged 21.78 ± 1.67 (SD) for control subjects and 22.32 ± 2.09 (SD) for patients. These values and variations indicate consistent, high-quality integrity of the RNA, and sufficient amounts of RNA for accurate analysis of the amount of mRNA in these experiments.

The cDNA libraries were analysed using the ABI quantitative, real-time PCR system on the ABI Prism 7900 Sequence Detection System (Applied Biosystems, Inc.), using ABI TaqMan Master Mix (Applied Biosystems, Inc.). Master mix/primer probe solutions and template solutions were separately loaded onto 96-well preplates, with robot loading mixing these solutions when placed in the 384-well plates. Plates were centrifuged to remove any air bubbles in the wells. Each sample was run in duplicate with standards being run in quadruplicate. No-template control samples were also run. Each 384-well plate contained samples from two subjects/patients, and all genes were analysed on the same plate. Primer probes (all from TaqMan Gene Expression Assays; Applied Biosystems, Inc.) were as follows:

ASIC3 – Hs00245097_m1; P2X4 – Hs00175706_m1; P2X5 – Hs00175712_m1; TRPV1 – Hs00218912_m1; Adrenergic A2A (α-2A) – Hs00265081_s1; Adrenergic B-1 – Hs02330048_s1; Adrenergic B-2 – Hs00240532_s1; COMT – Hs00241349_m1; IL6 – Hs00174131_m1; IL10 – Hs00174086_m1; TNFβ (Alpha Lymphotoxin or ‘α-lym’) – Hs00236874_m1; TLR4 – Hs00152937_m1; CD14 – Hs00169122_g1. Control primer probes included TF2B – Hs00155321_m1; β-Actin – Hs99999903_m1; and PSMB6 – Hs00382586_m1. In later experiments, only TF2B was used as the reference gene. All primer probes, except for the adrenergic receptors and CD14 (these genes do not have introns), recognize sequences that cross splice sites and therefore make detection of genomic DNA unlikely. In all cases, we quenched the genomic DNA and ran no-template control wells to ensure that genomic DNA did not contaminate the final results. All of these primer probes were designed and tested to be used together and have similar efficiencies to help eliminate inaccuracy. For the genes that have rarely been described in leucocytes (Adrenergic α-2A, Adrenergic B-1, P2X5, TRPV1, ASIC3), we designed primers that contained 360–600 base-pairs, which included the regions ABI indicated the Primer probes listed above spanned. PCR product was generated from our leucocyte samples and sequenced. All of these sequences were 99–100% identical to predicted sequences of these genes. Evaluation of controls in this and previous experiments indicated that TF2B had less intrinsic variation than other candidates such as β-actin, had a count range that was similar to the genes of interest and did not increase or decrease because of the exercise protocol. Real-time PCR results were analysed with SDS 2.1 (Applied Biosystems, Inc.) and inspected to determine artefacts (loading errors, robot errors, thresholding errors, etc.). Count numbers were exported to an Excel spreadsheet and analysed according to the ddCT method described in ABI User Bulletin #2 (Applied Biosystems, Inc.). Baseline levels for each gene were computed relative to TF2B, and these baselines were used as the comparator for all measures taken after the exercise period (see statistical methods below for further analysis details).

Statistical analysis

Because the ddCT method used for mRNA analysis necessarily creates a non-normal, rightward skewed distribution, data were log transformed to yield distributions that could be appropriately analysed with parametric statistics.

Postexercise values for each gene expression measure were normalized relative to the same subject’s baseline levels (1.00 = baseline). As detailed in our previous report [8], to reduce false-positive findings associated with multiple comparisons, we did not examine each sampling time individually. Instead, the relative mRNA values from the four postexercise time-points (0.5, 8, 24 and 48 h) were summed into a single measure labelled area under the curve (AUC) and then log transformed. Initial manovas were performed for the metabolite detecting, adrenergic and immune markers that differed in our prior study [8] yielded significant group effects. These were followed by individual Anovas, and when significant, by comparisons of baseline and postexercise AUC of controls versus CFS and patients with FM determined with independent two-tailed t-tests. Two-tailed rather than 1-tailed tested were used despite the predictions of increases as an additional protection against false-positive results. This approach to multiple gene comparisons is similar (but on a much smaller scale) to pathway analysis as used to explore the many thousands of gene examined in microarray studies. Secondarily, we compared two CFS subgroups (CFS with increased postexercise α-2A and CFS with decreased postexercise α-2A) to controls using one-way Anovas. One-way Anovas were used for between group comparisons of cardiovascular, work rate and RPE measures obtained during the exercise task.

To examine whether group differences were related to differences in age, gender or body mass index (BMI), we performed one-way Anovas with each of these included in the model as covariates. In no instance did age, gender or BMI impact the significance of group differences.

Group by time, group and time differences in ratings of physical fatigue, mental fatigue and pain were analysed using 3 × 7 repeated measures (RM) Anovas. When significant group differences were present, within-group time effects were examined with RM Anovas with simple contrasts to determine which time-points differed significantly from baseline.

In addition to group comparisons, Pearson r correlations were used to examine relationships between exercise variables, pain and fatigue ratings, and gene expression measures. All data are presented as means and standard errors, with significance set at < 0.05.

Results

  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. Conflict of interest statement
  8. Acknowledgements
  9. References

Data were obtained from 48 patients with CFS, 18 patients with FM and 49 control subjects. Table 1 summarizes the characteristics of these groups, with the patients with CFS divided into the two major subgroups suggested by the data in this study. Age and gender were well matched between controls and the CFS patient groups, but less well matched for the FM group; however, neither gender nor age were significant covariates in the patient with FM analyses.

Table 1. Subject characteristics for controls, chronic fatigue syndrome (CFS) patients, and fibromyalgia syndrome (FM) patients
CharacteristicControls (n = 49; 29F)All CFS Patients (n = 48; 33F)α-2A increase CFS Patients (n = 34; 25F)α-2A decrease CFS Patients (n = 14; 8F)FM patients (n = 18, 15F)All CFS P*α-2A+ CFS Patients P*α-2A− CFS Patients P*FM P*
  1. Values are mean ± SE.

  2. BMI, body mass index; SBP, systolic blood pressure; DBP, diastolic blood pressure; %PMHR, percent of age-predicted maximal heart rate; WR, work rate; RPE, rating of perceived exertion.

  3. Bold indicates P < 0.01.

  4. Bold (in italic) indicates 0.01 < P < 0.05.

Age (years)42.0 ± 1.941.8 ± 1.941.4 ± 2.242.8 ± 3.550.4 ± 2.60.9980.9790.9710.037
Median4546464651
BMI (kg m−2)23.9 ± 0.726.2 ± 0.826.5 ± 0.925.6 ± 1.326.5 ± 1.40.0550.0500.4480.135
Resting SBP (mmHg)126.4 ± 1.9123.0 ± 2.6120.9 ± 3.0128.3 ± 5.0126.1 ± 3.00.5500.2160.9020.994
Resting DBP (mmHg)80.5 ± 1.680.6 ± 1.580.2 ± 1.881.7 ± 2.987.1 ± 2.20.9960.9900.9090.048
Exercise SBP (mmHg)157.0 ± 3.2148.7 ± 3.7147.8 ± 4.6150.9 ± 6.4161.1 ± 5.90.1990.1950.6600.766
Exercise DBP (mmHg)90.8 ± 1.888.6 ± 1.688.2 ± 2.189.6 ± 2.695.7 ± 3.10.6000.5450.9310.253
Resting HR (baseline)76.7 ± 1.882.4 ± 2.483.4 ± 2.780.1 ± 5.080.2 ± 3.80.1440.0870.6870.683
Exercise HR (BPM)126.8 ± 1.4119.8 ± 2.2121.6 ± 2.5115.6 ± 4.4122.8 ± 3.20.0170.1720.0670.446
Exercise HR (%PMHR)71.3 ± 0.567.3 ± 1.168.1 ± 1.165.4 ± 2.561.7 ± 1.90.0040.0310.081<0.001
Exercise WR (Kcal kg−1 per min)7.6 ± 0.74.3 ± 0.24.1 ± 0.24.7 ± 0.45.1 ± 0.4<0.001<0.0010.0020.023
Exercise RPE3.1 ± 0.15.0 ± 0.25.0 ± 0.35.1 ± 0.54.5 ± 0.4<0.001<0.0010.0040.012
No. of Ortho intoleranceNA18 (38%)8 (24%)10 (71%)0 0.00030.008 
Viral onsetNA29 (60%)19 (56%)10 (71%)1 (5%)    
Tested on pain med115 (31%)11 (32%)4 (29%)3 (16%)    
On anti-convulsants011 (23%)6 (18%)5 (36%)5 (28%)    
On anti-depressant12 (24%)35 (73%)24 (71%)11 (79%)10 (56%)    

We attempted to adjust for fitness-level mismatches by exercising all patients and controls at the same relative exercise intensity, to 70% of age-predicted, maximal heart rate. The majority of patients with CFS and FM were able to attain the 70% level, but a few could not, leading to small, but significantly lower percentages of maximal heart rate in all patient groups. In spite of lower relative exercise intensities and lower work rates, all patient groups reported higher perceived exertion (RPE) during the exercise task than controls (see Table 1).

Figure 1 depicts mean ratings of mental fatigue, physical fatigue and pain on our 0–100 scale in the various groups before, during and after the exercise task. In control subjects, exercise did not increase ratings of mental fatigue or pain at any time-point; and physical fatigue was increased only at mid-exercise and not at any postexercise time. In sharp contrast, exercise caused significant increases in all fatigue and pain measures at all time-points during and after exercise in CFS only and in CFS + FM patients. Patients with FM reported increases in pain and physical fatigue at all time-points and increases in mental fatigue at all time-points except during and immediately after exercise. As can be seen in Fig. 1, the two CFS subgroups distinguished by postexercise adrenergic α-2A increases versus decreases (described in detail below) did not differ from each other in ratings of pain or fatigue. Not surprisingly (because these patients were defined by having less pain than FM or CFS + FM patients), patients with CFS only had lower pain scores during and immediately after exercise than the CFS + FM or FM groups. Thus, this very moderate level of exercise for 25 min caused postexertional malaise lasting 48 h in all CFS and FM patient groups, but not controls.

Gene expression results

Patients with CFS compared with patients with CFS who also have comorbid FM.  Initially, we divided the patients with CFS into those that had CFS, but did not meet criteria for FM, and those patients with CFS who also met criteria for FM, i.e., they had both CFS and FM (CFS + FM). None of the descriptive variables in Table 1 were significantly different between these groups. Likewise, comparison of these two groups showed very similar gene expression both before and after exercise. Only postexercise ASIC3 AUC was greater in the CFS + FM versus the CFS-only patients (P < 0.046). For this reason, in all of the following analyses, CFS-only and CFS + FM patients are grouped together.

Patients with CFS showed no baseline changes in gene expression from controls; patients with FM showed baseline increases in three genes. Table 2 contains the average baseline values and significance values for postexercise AUC (0.5, 8, 24 and 48 h after exercise) for mRNA increases from baseline levels. None of the CFS subgroups differed from controls in expression of any gene at baseline, either as separate subgroups or combined into a single CFS group (all P values >0.145). In contrast, the FM group showed several differences from controls at baseline before exercise. They had significantly higher baseline quantities of mRNA for sensory receptors P2X4 (P < 0.001) and TRPV1 (P < 0.005), and for the cytokine IL10 (P < 0.031). Figure 2 graphs these differences. These differences were unaltered after covarying for age and gender differences.

Table 2. manova results, baseline means + SEs and anova results for all mRNAs relative to TF2B, and anova results for postexercise Area Under Curve (AUC) mRNA differences from controls in Patients with chronic fatigue syndrome (CFS) and patients with fibromyalgia syndrome (FM)
 Controls Baseline N = 49All Patients with CFS Baseline N = 48CFS α-2A Increase Baseline N = 34CFS α-2A Decrease Baseline N = 14FM Baseline N = 18All Patients with CFS Baseline PFM Baseline PAll CFS Patients AUC P N = 48CFS α-2A Increase AUC P N = 34CFS α-2A Decrease AUC P N = 14FM AUC P N = 18
  1. Bold = P < 0.01, Bold (italics) 0.01 > P > 0.05 as compared with controls (all two tailed comparisons).

Metabolite detectingmanova0.0300.0090.0220.0060.6340.576
 ASIC39.02E-03 ± 5.17E-047.79E-03 ± 7.03E-048.02E-03 ± 8.56E-047.26E-03 ± 8.32E-049.81E-03 ± 1.09E-030.2980.4620.2000.540.4430.925
 P2X42.00E-01 ± 1.05E-021.86E-01 ± 1.38E-021.97E-01 ± 1.80E-021.62E-01 ± 1.50E-022.74E-01 ± 2.36E-020.6930.0010.0020.0020.5470.570
 P2X52.33E-01 ± 2.00E-022.43E-01 ± 2.47E-021.97E-01 ± 1.80E-023.23E-01 ± 4.23E-022.75E-01 ± 3.35E-020.3950.2820.0430.0220.8880.629
 TRPV11.23E-02 ± 7.94E-041.41E-02 ± 1.00E-031.35E-02 ± 1.12E-031.51E-02 ± 1.64E-031.66E-02 ± 1.09E-030.2720.0050.0270.0200.6450.939
Adrenergicmanova0.4720.3040.007<0.0005<0.00050.112
 α-2A5.42E-03 ± 6.87E-047.04E-03 ± 1.59E-037.68E-03 ± 2.10E-035.75E-03 ± 1.40E-033.61E-03 ± 6.32E-040.6190.1280.0230.00050.0190.297
 β-13.25E-02 ± 1.03E-027.27E-02 ± 5.30E-029.15E-02 ± 7.43E-023.13E-02 ± 1.80E-025.92E-02 ± 3.68E-020.6480.3440.0560.0120.9940.999
 β-21.13E+00 ± 8.85E-029.98E-01 ± 7.17E-021.97E+00 ± 5.87E-019.09E-01 ± 6.96E-021.05E+00 ± 2.02E-010.4810.6730.0020.0010.9980.594
 COMT2.33E-01 ± 1.88E-021.89E-01 ± 1.75E-021.90E-01 ± 1.91E-021.89E-01 ± 3.30E-022.29E-01 ± 1.82E-020.1450.9060.0090.0320.1020.845
Cytokinemanova0.5060.0180.1510.0930.7650.498
 IL63.69E-03 ± 1.19E-033.98E-03 ± 4.86E-044.17E-03 ± 6.07E-043.55E-03 ± 7.14E-044.64E-03 ± 1.17E-030.9660.6510.2530.1350.9980.701
 IL104.61E-03 ± 3.81E-042.09E-02 ± 1.45E-022.85E-02 ± 2.02E-025.04E-03 ± 7.51E-046.24E-03 ± 6.47E-040.5090.0310.0030.0020.1530.799
 αLT7.95E-02 ± 1.41E-028.21E-02 ± 1.29E-028.38E-02 ± 1.64E-027.84E-02 ± 1.75E-026.55E-02 ± 7.54E-030.9850.5500.3070.1400.9740.980
 TLR44.54E-01 ± 6.13E-024.34E-01 ± 3.95E-024.80E-01 ± 4.88E-023.31E-01 ± 5.10E-023.64E-01 ± 2.09E-020.9390.3760.1960.1100.9950.973
 CD142.14E+00 ± 1.34E-012.11E+00 ± 1.80E-012.30E+00 ± 2.27E-011.73E+00 ± 2.01E-012.37E+00 ± 2.21E-010.9900.3740.6580.7240.8770.840
image

Figure 2. Comparison of baseline gene expression in patients with fibromyalgia syndrome (FM) to that of age- and gender-matched controls. Because of the large differences in the amount of mRNA for different genes, the average baseline value of each of the significantly increased genes for the patients with FM was matched in magnitude for this graph. The scale for each of the genes (in amount relative to the control gene, TF2B) is indicated to the left of each bar. Con = Control subjects. Controls also marked with cross hatching. Colours are the same as for genes in Fig. 3.

Download figure to PowerPoint

Postexercise gene expression changes in patient subgroups versus controls.  Although their gene expression did not differ from controls prior to exercise, after exercise patients with CFS showed greater increases in mRNA than controls for seven of the genes under study. With all CFS subgroups combined, patients with CFS showed greater postexercise AUC increases than controls for P2X4, P2X5, TRPV1, α-2A, β-2, COMT and IL10 (P < 0.05 to P < 0.001;see Table 2).

In our previous study, unusual gene expression patterns in some patients with CFS suggested a possible subgroup, which was confirmed clinically by Dr Bateman. With the additional data from the present study, on the basis of changes in gene expression levels following exercise, we could define two major subgroups of patients with CFS. These subgroups were defined by postexercise increases versus decreases in mRNA of the adrenergic α-2A receptor. The larger CFS subgroup (71% of all patients with CFS) had increases in the α-2A receptor mRNA at one or more time-points following exercise (α-2A increase patients with CFS). This group showed large increases in all seven genes listed above as well as increases in β-1 AUC (See Table 2). As can be seen in Fig. 3 (compare A with B), increases in expression of the genes listed above compared to controls were observed at 30 min following the exercise period and lasted for the duration of the study, 48 h after the exercise period. This was confirmed by repeated measures analyses showing significant time effects after exercise indicating increases above baseline that were sustained throughout the postexercise period.

image

Figure 3. Graphs comparing gene expression increases following moderate exercise in patients with chronic fatigue syndrome (CFS) and fibromyalgia syndrome (FM). All graphs plotted in log10 scale. For all graphs, baselines for all genes were normalized to 1. Scale on graphs is fold changes from baseline in mRNA quantity. Significant differences in the sum of all time-points for each gene (area under the curve) are indicated by * in the legend boxes to the right of each graph. Metabolite-detecting receptors are coloured in blues, adrenergic receptors and COMT are coloured in reds, and cytokine-related genes are coloured in greens. (a) Averages of mRNA fold changes after exercise for 49 Control subjects. No significant differences from baselines were noted. (b) Averages of mRNA fold changes after exercise in 34 patients with CFS and patients with CFS that also made criteria for FM that did not show decreases in α-2A adrenergic receptor mRNA following exercise. Significant differences are noted by asterisks in the legend box at right. See key under this graph for P value of asterisks. (c) Averages of mRNA fold changes after exercise for 14 patients with CFS and patients with CFS that also made criteria for FM that showed decreases in α-2A adrenergic receptor mRNA at all times following exercise. Only α-2A adrenergic receptor mRNA showed significant changes. For this graph, the data for ASIC3 from one extreme outlier were dropped. (d) Averages of mRNA fold changes after exercise for 18 patients with FM only. No differences from controls were seen at any time after exercise. This contrasts with baseline changes seen in Fig. 2.

Download figure to PowerPoint

As shown inFig. 3candTable 2, the smaller subgroup (29% of all patients with CFS) demonstrated large decreases in α-2A mRNA at all time-points following exercise (α-2A decrease group). This group was also distinguished by a clinical history of orthostatic intolerance in most of the patients (see Table 1, No. of Ortho intolerance). Ten of the 14 α-2A decrease patients had clinical orthostatic intolerance compared with only 8 of 34 α-2A increase patients with CFS (71% vs. 18%, Χ2P < 0.008). In addition to this high rate of orthostatic intolerance, the α-2A decrease patients demonstrated no significant increases in expression of any of the other genes measured in this study compared to the Control group (see Fig. 3c and Table 2). Chronic fatigue syndrome-only and CFS + FM patients were similarly represented in both subgroups; the α-2A decrease group had six CFS-only patients and eight CFS + FM patients, while the α-2A increase group had nine CFS-only patients and 25 CFS + FM patients.

To ensure that these findings were not solely because of differences in exertion, we performed a secondary analysis after reducing our sample to the CFS and Control subjects who were matched on RPE. This analysis confirmed our central findings. This comparison examined 15 controls with the highest RPE (mean 3.89) matched with 27 patients with overlapping RPEs (mean 3.87). Even though this reduced our sample size and statistical power substantially, 6 of the 7 genes’ AUCs were still significantly greater in patients with CFS than controls [P2X4 (P < 0.05), TRPV1 (P < 0.02), α-2A (P < 0.03), β-2 (P < 0.03), COMT (P < 0.04), and IL10 (P < 0.01)]. The only gene not reliably different was P2X5 (P = 0.12).

Gene expression varied with the clinical severity of CFS. Figure 4 graphs the four severity groups for the α-2A increase patients with CFS (n = 34). In this subgroup, there were three patients with severity 1 (9%), 11 with severity 2 (32%), 14 with severity 3 (41%) and four with severity 4 (12%). Two patients were not included for this analysis because they were less severe than 4. This graph indicates that, relative to controls, gene expression was increased most in patients with the highest severity and least increased in patients with the lowest severity. When the average of all gene AUC was compared, groups 1 and 2 were significantly greater that that of groups 3 and 4 (P < 0.011).

image

Figure 4. Graph of severity groups’ gene expression. For this graph, gene expression increases for all four time periods of exercise were summed and a single value shown. Severity scale was: (1) House- or bed-bound, with minimal activity tolerance, marked cognitive dysfunction, and dependent on others for activities of daily living. (2) Only able to do self-care activities of daily living, sedentary, <10 h per week of light activities, but could live alone with occasional help. (3) Able to do part-time school/work/other activities 10–30 h per week, but easily relapses, and frequent rest needed 4. Able to work full time 30–40 h but ‘nothing left over’, high symptom burden and limitations. Controls are graphed on top of each of the severity groups in grey cross hatching so that patients can be easily compared with control gene expression levels.

Download figure to PowerPoint

The minor subgroup, the α-2A decrease group (n = 14), had one patient with severity 1 (7%), four patients with severity 2 (29%), six patients with severity 3 (43%) and two patients with severity 4 (14%). One patient had a severity score less severe than 4. This distribution was very similar to that of the major subgroup (Χ2P > 0.85).

Effects of medications

As described previously, 11 patients with CFS were on anticonvulsants, and 15 were on opioids during testing while the other patients with CFS either had not used them or were withdrawn from them prior to testing. To determine whether these medications might have affected the gene expression, we compared the total postexercise AUC gene expression of patients on these medications with the AUC of those not on the medications. There was a trend in the direction of lesser postexercise increases in our selected genes among patients with CFS on anticonvulsants versus CFS on no medications (P = 0.055), but even this medicated CFS group still differed from controls (P < 0.05). Patients with CFS on opioid pain medications alone did not differ from other patients with CFS (P > 0.245). These findings suggest that anticonvulsant drugs may reduce the postexercise gene expression increases in some patients with CFS, but they also indicate that withdrawal from these medications prior to testing is not critical when attempting to differentiate their responses from those of healthy individuals.

When the 30 patients with CFS tested on versus the 18 not on antidepressants were compared, no statistical difference in expression of mRNA was observed (P > 0.614). Likewise, when the 11 control subjects also were on antidepressants were compared with the 38 other controls, there were no differences in gene expression.

Correlations

Table 3 shows the correlations between the behavioural scores for fatigue and pain and AUC gene expression measures, as well as inter-correlations between the various genes. These indicate strong positive relationships between postexercise pain and fatigue and increases in P2X4, TRPV1, α-2A, β-2 and IL10. Relationships between the behavioural measures were weaker for the ASIC3, P2X5 and the other cytokine genes measured.

Table 3. Correlations of post-exercise area under the curve (AUC) gene expression measures with post-exercise AUC fatigue and pain and with each other (All α-2A increase CFS patients and controls included in this analysis)
 ASIC3P2X4P2X5TRPV1α-2Aβ-1β-2COMTIL6IL10TLR4
  1. For all listed correlations < 0.05. For those correlations > +0.30, < 0.01. NS = nonsignificant, P > 0.05.

Mental fatigueNS+0.51+0.28+0.34+0.60+0.32+0.45+0.39+0.25+0.42+0.27
Physical fatigueNS+0.51+0.26+0.34+0.60+0.32+0.47+0.36NS+0.41+0.25
PainNS+0.43NS+0.32+0.48+0.30+0.44+0.27+0.25+0.38NS
ASIC3NS+0.42+0.35NS-0.38NS+0.40NSNSNS
P2X4 +0.36+0.41+0.57+0.38+0.72+0.59+0.37+0.57+0.36
P2X5  +0.76+0.27NS+0.30+0.61+0.33+0.24NS
TRPV1   +0.30NS+0.31+0.63+0.55+0.55+0.46
α-2A    +0.59+0.76+0.29+0.34+0.49+0.34
β-1     +0.62NS+0.30+0.45NS
β-2      +0.37+0.45+0.51+0.36
COMT       +0.33+0.44+0.29
IL6        +0.66+0.34
IL10         +0.50

Discussion

  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. Conflict of interest statement
  8. Acknowledgements
  9. References

Confirmation that gene expression changes occur in CFS

The present study confirms our prior findings [9] that moderate exercise in patients with CFS leads to increased expression of certain sensory ion channel, adrenergic and immune genes (P2X4, P2X5, TRPV1, α-2A, β-2, COMT and IL10) that do not occur in healthy individuals. The functions of these genes and how they contribute to the symptoms of CFS and FM were discussed in our previous report [9]. These mRNA increases in CFS are evident within 30 min and are maintained for at least 48 h. This includes one ion channel receptor, the heat-sensitive capsaicin receptor TRPV1, which was not significantly different between CFS and controls in our prior study with smaller group sizes. However, we did not replicate our prior study in all respects. First, in the present study, postexercise increases in mRNA for ASIC3 or adrenergic β-1 receptors or in the immune marker TLR4 were not statistically greater in all patients with CFS combined versus controls. Second, we previously reported that baseline mRNA for β-2 and α-2A adrenergic receptors tended to be lower in patients with CFS; the present study, however, indicated no differences prior to exercise in expression of any gene in patients with CFS compared with controls. These differences are most likely due to the increased sample and a broader range of ages, disease severity, and possibly partly due to the allowed use of pain medications and anticonvulsant medications in the present study. Our present findings indicate that although both milder disorder severity and current use of anticonvulsant medications may be associated with lesser postexercise increases in expression of these sensory and adrenergic receptors, the patients with CFS who are more functional (and all the less functional patients as well) or are tested on medications still differ from healthy controls.

In our previous report [9], we suggested that patients with CFS that did not meet criteria for FM were a defined subgroup separate from patients with CFS that had comorbid FM. With the additional sample size in the present study, we found that these two groups had similar gene expression profiles before and after exercise except for ASIC3, which was higher after exercise in the CFS + FM patients than in the CFS-only patients (These two groups also both divided into the α-2A subgroups described below, with similar proportions in each group). Pre- and postexercise ratings of mental and physical fatigue severity were likewise similar in these two patient groups, although the CFS-only group had lower pain scores at nearly all times before, during and after the exercise task. Whether CFS only versus CFS + FM belong to one or two different CFS populations cannot be determined from these selected outcome measures. Based on the specific genes in our profile, however, it was clear that these CFS + FM patients were much more similar to CFS alone patients than they were to FM alone patients, who showed greater mRNA for three genes at baseline compared with controls, but did not show greater increases in any of these genes after exercise.

Discovery of a significant subgroup defined by gene expression

A subgroup (29% of all patients with CFS tested here) was defined by consistent decreases in α-2A following exercise. This subgroup (α-2A decrease patients with CFS) did not show increases or decreases in other genes relative to controls. The majority (71%) of this subgroup also had clinical orthostatic intolerance, as opposed to only 24% of the major subgroup (those that showed increases in α-2A following exercise ‘α-2A increase patients with CFS). The distribution of clinical severity was not different between these subgroups, indicating that both groups had similarly debilitating fatigue. Both groups also demonstrated very similar fatigue and pain responses to exercise (see Fig. 1). In the α-2A decrease patients with CFS, the very large and consistent decreases in α-2A expression following exercise combined with orthostatic intolerance suggest that different mechanisms cause the debilitating fatigue in this subgroup. The large decreases in α-2A mRNA may reflect a particular type of dysregulation of the sympathetic nervous system. For example, activation of α-2A normally causes a decrease in sympathetic outflow (decreased release of norepinephrine). It is possible that the decrease in transcription of α-2A observed is a response to abnormally low levels of sympathetic outflow (or low levels of vascular and cardiac responses to norepinephrine), and resulting decreased cardiac output and possibly global vasodilatation. These effects would cause inadequate blood flow to working muscles and the brain. Thus, this decrease in α-2A transcription may be an attempt to compensate for a dysfunctional sympathetic response (by increasing norepinephrine release) that does not adequately increase blood flow to working muscles and the brain during and following exercise. This interpretation is supported by previous reports, which found a significant subgroup of patients with CFS that were defined by more severe orthostatic intolerance and possibly by cardiac bioenergetic abnormalities that would lead to inadequate increases in blood flow during exercise [17–20]. These investigators also demonstrated that at least some patients with CFS showed improper efflux of metabolites from the muscles of patients with CFS that also could be a result of abnormally low levels of sympathetic outflow, or reduced vascular and cardiac responses to norepinephrine. One report suggested that postural orthostatic tachycardia syndrome patients (POTS) comprise about 30% of patients with CFS [20]. Another group suggested that while some patients with CFS did conform to the POTS diagnosis, other patients with CFS without POTS also had autonomic disturbances as well [21]. The relationship between the α-2A decrease patients with CFS and POTS and the degree and type of autonomic disturbances needs further study. Given that the α-2A increase patients with CFS also have dysregulation of α-2A as well as other adrenergic receptors and COMT, it is likely that dividing the patients by the gene expression measures will lead to better understanding of the specific problems in patients with CFS and lead to better treatment options.

Possible transcription factors involved in gene expression increases in the larger subgroup

The larger subgroup of patients (α-2A increase patients with CFS) demonstrated large gene expression increases following exercise in many different genes. These include P2X4, P2X5, TRPV1, α-2A, β-1, β-2, COMT and IL10. The increase of so many genes might indicate that upstream transcription factors common to all of these genes are dysregulated and control these downstream genes in a pathological fashion in CFS. Interestingly, all of the genes measured here are interconnected by the transcription factors CREB, GR-alpha (part of NR3C1, the glucocorticoid receptor) and NF-Kappa B1. Previously, NR3C1 polymorphisms were found to be associated with CFS [22–26]. In addition, Maes et al. [27] have shown increased NF-Kappa B production in leucocytes of patients with CFS, and Kim et al. [28] have shown increased DNA binding of NF-Kappa B in direct proportion to increases in exercise intensity. Whether these associations predict, or cause future CFS, should be investigated. Interestingly, the genes found to be dysregulated in the present study represent most of the pathways hypothesized by others to be altered in CFS. These include the immune system (IL10 and leucocytes in general) [29–31], cellular energy (P2X4 and 5 that encode ATP levels, and TRPV1 encoding temperature) [32] and the cardiovascular system (adrenergic receptors α-2A, β-1, β-2, and the catecholamine processing gene COMT) [33, 34].

Previously, most groups have used microarrays using RNA collected at a single time-point, from relatively few subjects to examine thousands of genes to attempt to determine genes whose expression differed in patients with CFS from controls or other disease groups e.g. [22, 35–44]. As opposed to these findings of differences, a recent microarray study using identical twins discordant for chronic fatigue found no gene expression differences in CFS [45]. None of the genes that we assayed in the present study were found in these previous studies to be increased in patients with CFS. This is not surprising because the gene expression differences reported here were not observed at baseline, but required moderate exercise to expose them. Additionally, previous exercise studies using a single postexercise time-point would also likely have missed the gene increases demonstrated here. This is because the presence of the α-2A decrease group greatly increases the variance of the overall CFS population. With the small sample sizes used for microarray, it is unlikely that the gene expression increases observed in the α-2A increase group could have been discerned, and even more unlikely that the gene expression decreases in the α-2A decrease group would have been found. Dividing these subgroups greatly decreased the variance in gene expression measures in both groups.

FM-only patients are defined by increases in gene expression at baseline

Contrary to expectations, patients with FM who did not meet criteria for CFS, that is, without chronically diminished function specifically linked to fatigue, did show evidence of postexertional malaise reflected as increases in self-reported fatigue and pain measures for 48 h after moderate exercise. However, the patients with FM did not show reliable postexercise increases in mRNA for any gene under study. Although their average work rate was higher than the patients with CFS, patients with FM had lower average increase in heart rate than patients with CFS. This differential in work rate and heart rate increase may be due to less de-conditioning on average in the FM group versus the CFS group. However, it is unlikely that this explains the postexercise gene expression differences between the CFS and FM-only patients because a post hoc analysis using only those patients with CFS whose work rates were matched to those of the patients with FM indicated that these patients with CFS still showed greater increases in mRNA than controls (P < 0.05), unlike the lack of increases observed in patients with FM.

Even though they did not show exercise-induced increases in gene expression, patients with FM were clearly different from healthy controls and from the patients with CFS at baseline. Unlike CFS only or CFS + FM, they showed higher baseline mRNA than controls for two sensory ion channel genes, P2X4 and TRPV1, and one cytokine, IL10. Given the results for the CFS + FM patients and the similarity of their postexertional symptoms, this finding was surprising.

What is not surprising is that the affected genes were P2X4, TRPV1 and IL10. In our mouse experiments, genes that seemed most likely to encode noxious levels of muscle-produced metabolites were a combination of ASIC3 (and/or ASIC1), P2X4 and TRPV1 [6]. The increases in these receptors on leucocytes could make them more sensitive to muscle-produced metabolites, and more likely to produce sensitizing cytokines when muscles are activated (see a recent review of how P2X4 on leucocytes could be involved in inflammatory pain [46]). This could lead to more muscle pain at all times. It is also possible that numbers of these same molecular receptors (P2X4, and TRPV1) are increased in sensory neurons in as well as in leucocytes. If increased in sensory neurons, an increased signal for muscle metabolites would occur at all levels of exercise, leading to widespread increases in muscle pain. As Sluka et al. [47] have shown, long-term muscle pain can lead to secondary hyperalgesia in skin throughout the body. This secondary hyperalgesia seems to be mediated by ASIC3 [48]. It is important to note that the higher mRNA for P2X4, TRPV1 and IL10 at baseline in FM-only patients did not diminish after exercise – these increases were simply maintained from the pre- to postexercise time-points.

The increased gene expression in patients with FM at baseline that is maintained but not increased after moderate exercise is a novel finding that has not been reported previously. One possible explanation for the increased gene expression at baseline in patients with FM is that, although all subjects refrained from formal exercise, their general activity level over 24–48 h prior to their baseline blood draw was high enough to cause the mRNA increases. It is also possible that the CFS + FM patients had lower general activity levels prior to testing, which may explain why they do not show baseline increases. Although this possibility needs to be examined more formally in future studies, comments volunteered by many of the CFS + FM patients are consistent with this possibility, reporting many hours spent totally inactive either seated or in bed each week.

Among the array of immune markers that we used for this gene expression profile, only the anti-inflammatory cytokine IL10 differed between patients and controls. It is very intriguing that IL10 was elevated at baseline in the FM-only group and was more increased throughout the postexercise period in patients with CFS. The findings of enhanced postexercise anti-inflammatory cytokines response in at least a subset of patients with CFS have been reported previously using serum levels, which are more definitive measures [49, 50]. Immune activation may also play an important role in postexertional worsening of fatigue and pain.

Gene expression as biomarkers for CFS and FM

Previously, we suggested that mRNA expression of some of the genes measured here might be useful as objective, blood-based biomarkers for CFS [9]. For the major subgroup reported here (the α-2A increase patients), a combination of PX4, α-2A, β-2 and IL10 at all time-points after moderate exercise, sensitivity can be as great as 0.93 with a specificity of 0.77, or specificity can be as great as 0.91 with a sensitivity of 0.77. The AUC of the receiver operating curve was 0.91 with a 95% confidence interval of 0.83–0.98. In either case, accuracy was 0.80. This would be considered a Very Good to Excellent diagnostic tool [51]. Reliable diagnostic values for the α-2A decreasing patients could not be accurately computed because of small sample size (n = 14).

We have shown in a recently submitted manuscript that these gene markers are not similarly increased in patients with MS who exhibit unexplained increases in fatigue (White, A. T., Light A. R., Hughen R. W., VanHaitsma T. A., and Light K. C. Differences in metabolite-detecting, adrenergic, and immune gene expression following moderate exercise in multiple sclerosis, CFS, and healthy controls, accepted in Psychosomatic Medicine). We also have preliminary data indicating that these genes are not increased before or after exercise in patients with unexplained fatigue who have advanced prostate cancer. Furthermore, the observations from this study on the subset of controls tested while on antidepressants prescribed for mild clinical depression suggest that these genes are not increased in medication-responsive depression; we are currently examining patients with moderate to severe medication-refractory depression to reinforce this tentative finding.

For patients with FM, using the three genes that were significantly different at baseline weighted to compensate for their quantity (P2X4 + 15xTRPV1 + 40xIL10), sensitivity could be as great as 0.89 with a specificity of 0.6, or specificity could be as great as 0.89 with a sensitivity of 0.56. The AUC of the ROC was 0.78, with a 95% confidence interval of 0.662–0.894, and the accuracy was 0.72. This would be less than ideal as a biomarker for diagnostic purposes, but could still identify the majority of patients with FM. This test would have the advantage that a single blood draw could be used, and no exercise test would be required.

It is possible that these gene expression tests could be used in combination with behavioural measures before, during and after moderate exercise to more accurately determine the presence of CFS or FM in patients. Other promising candidate biomarkers have been supported by recent studies including other gene expression measures [43, 52], noradrenergic markers like Neuropeptide Y [53], hypothalamic pituitary–adrenal markers including NR3C1 gene expression and polymorphisms [23–25], immune markers including natural killer cell activity and cytokines [31, 54] mitochondrial dysfunction markers [32] and proteomic markers in cerebrospinal fluid [55]. It is very plausible that some of these other biomarkers could potentially be combined with the gene expression measures reported here to produce a very accurate diagnostic test and clearly demark different subgroups within the ME/CFS umbrella.

Causes of CFS and FM

Genes for this study were selected because they were directly involved in signalling of fatigue by skeletal muscle. Therefore, they were not selected to determine the primary cause of CFS or FM in the subjects tested. Gene expression changes in the patients tested here (both increases and decreases) could be caused by viral infections. Most previous investigations of humans during viral infections have indicated strong Th1 immune responses involving increases in pro-inflammatory cytokines [56], which we did not observe in most of our patients with CFS or FM either at baseline or after exercise. On the contrary, the increase in IL10 mRNA we observed at baseline in patients with FM and in patients with CFS following exercise is more similar to an enhanced Th2 response, which could potentially lead to greater susceptibility to viral infections and tumours [57]. A very recent study of identical twins discordant for CFS found no unique viruses consistently associated with CFS [58], but did find that 9% of twins with CFS had undiagnosed GB-type C virus infections while none of the identical twins without CFS had such infections. Until causal factors are determined, studies like this one are helpful in providing objective biological markers indicating neural and immune pathways that become dysregulated in CFS and FM and that are directly associated with severity of symptoms. These pathways also provide new information that may be used to develop and assess treatments in both disorders.

Conflict of interest statement

  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. Conflict of interest statement
  8. Acknowledgements
  9. References

Dr Bateman has received income from Eli Lilly, Forest, Jazz, Pfizer and Hemispherx Biopharma as a principal investigator for phase III trials of investigational drugs for FM and CFS. She is also a paid speaker and consultant for Eli Lilly, Forest and Pfizer regarding FDA approved drugs for FM. The other authors have no conflicts of interest.

Acknowledgements

  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. Conflict of interest statement
  8. Acknowledgements
  9. References

Supported by extramural grants from CFIDS, AFSA and NIH R21 NS057821 from NINDS and NIAMS, and by intramural grants from the University of Utah.

References

  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. Conflict of interest statement
  8. Acknowledgements
  9. References