Ann Readapt Med Phys 2006 Apr 25; (Read article online)

Barat M, Dehail P, de Seze M

OBJECTIVES: To identify variables increasing fatigue following spinal cord injury (SCI) and their functional consequences. METHODS: A search of the Medline and Reedoc databases with the keywords SCI, fatigue, intrinsic muscular fatigue, chronic fatigue, aging, training, electrostimulation, quality of life and the same words in French. RESULTS: Two kinds of fatigue are identified following SCI. Intrinsic fatigue in muscles totally or partially paralysed at the level of or below the spinal cord lesion; this peripheral fatigue is due to denervation, total or partial loss of motoneurons, or histological and metabolical changes in muscle; it is well-defined by electrophysiological technology; spasticity and spasms have little influence on its development; it is reversible in part with long term electrostimulation, but at this time, electroneuroprosthetic techniques do not reduce the excessive energetic cost to stand up and walk. Chronic fatigue appears in the long term following SCI; it is linked with aging, physiological, and psychological deconditioning; some data point to chronic fatigue after SCI similar to post-polio syndrome and chronic fatigue syndrome, which may explain the central nature of the fatigue; training programs could be useful in delaying this chronic fatigue and as a consequence, increasing the latent quality of life. CONCLUSION: Muscular intrinsic fatigue after SCI is always of a peripherical nature in muscles partially or totally paralysed. Chronic fatigue during aging greatly decreases quality of life. Both intrinsic and chronic fatigue could be anticipated by electrostimulation technique on the one hand and long term training on the other.

Ann Readapt Med Phys 2006 Apr 19; (Read article online)

Maquet D, Demoulin C, Crielaard JM

OBJECTIVE: A systematic review of the literature about chronic fatigue syndrome (CFS). METHODS: A search of the Medline database (via Ovid and PubMed) with the key words chronic fatigue syndrome, diagnosis, classification, epidemiology, etiology, physiopathology, metabolism, microbiology, immunology, virology, psychology, drug therapy, rehabilitation, and therapy. The reference lists of each article were examined for additional related articles. RESULTS: CFS was defined in 1988 by the US Centes for Disease Control and Prevention. The prevalence of chronic fatigue syndrome has ranged from 0.2% to 0.7% in the general population. In 1994, the definition of CFS was revised by Fukuda et al. Despite various research in several topics (e.g. infection, immune systems, neuroendocrinology, autonomic activity, neuromuscular involvement), the pathophysiology remains unknown. CONCLUSION: CFS, with its various major clinical and functional impacts, should be associated with a "biopsychosocial model". Progressive muscular rehabilitation, combined with behavioral and cognitive treatment, is an essential part of therapy.

Philos Trans R Soc Lond B Biol Sci 2006 Apr 29; 361(1468): 681-7 (Read article online)

Peakman M, Skowera A, Hotopf M

One candidate cause of Gulf War illness is vaccination against infectious diseases including medical counter-measures against biological weapons. One influential theory has suggested that such mass-vaccination caused a shift in immune response to a Type 2 cytokine pattern (Th2), which it was suggested was accompanied by a chronic fatigue syndrome-like illness. This article critically appraises this theory. We start by examining epidemiological evidence, which indicates that single vaccines are unlikely to be a substantial cause of Gulf War illness, but that there was a modest relationship with multiple vaccines, which was strongest in those vaccinated while deployed to the Gulf. These relationships may be affected by recall bias. We conclude by examining the results of immunological studies carried out in veterans or in a relevant setting in vitro. The balance of evidence from immunological studies on veterans returning from the War, including those developing multi-symptom illness, is that the immune response has not become polarized towards Th2. In summary, the epidemiological evidence for a multiple vaccine effect on Gulf War-related illness remains a potentially important aetiological lead, but mechanistic studies available at this stage do not identify any immunological basis for it.

Occup Environ Med 2006 May 12; (Read article online)

Huibers MJ, Leone SS, Kant I, Knottnerus JA

Objective Many chronic fatigue syndrome (CFS) patients are unemployed or unable to work, but most prognostic studies have failed to assess occupational outcomes in CFS. We aimed to assess whether CFS-like caseness (meeting the criteria for CFS) predicts work status in the long-term. Methods We conducted a prospective study in a sample of fatigued employees absent from work. Data were collected at baseline and four years later, and included CFS-like caseness and work status (inactive work status and full work incapacity). We used multiple logistic regression models to determine the association between CFS-like caseness and work status, controlling for potential confounders. Results CFS-like cases at baseline were three times more likely to be unable to work at follow-up than fatigued employees who did not meet CFS criteria at baseline (ORs between 3 and 3.3). These associations grew even stronger when we controlled for demographic and clinical confounders (ORs between 3.4 and 4.4). Conclusion A CFS-like status (compared to non-CFS fatigue) proved to be a strong predictor of an inactive work status and full work incapacity in the long-term. Since little is known about effective interventions that prevent absenteeism and work incapacity or facilitate return to work in subjects with chronic fatigue, there is a great need for powerful early interventions that restore or preserve the ability to work, especially for workers who meet criteria for CFS.

Pharmacogenomics 2006 Apr; 7(3): 521-8 (Read article online)

Demitrack MA

Chronic fatigue syndrome (CFS) is a complex, multisymptom illness of unknown etiology. A variety of operational case definitions based on symptom report have been developed that share some common clinical features. Patients often come to clinical presentation after months or, more typically, years of symptomatic distress. Comorbid presentation with psychiatric illnesses has been noted. Due to these fundamental issues, the impact of patient selection and the specification of the methods of outcome assessment loom large in therapeutic studies of CFS. While a substantial body of research has focused on increasing our understanding of the basic pathobiology of CFS, there have been comparatively fewer studies that have addressed the problems of patient characterization and outcome assessment. The role of clinical methodology in the study of the therapeutics of CFS is not trivial, and may confound our understanding of pragmatic recommendations for treatment.

Pharmacogenomics 2006 Apr; 7(3): 475-83 (Read article online)

Goertzel BN, Pennachin C, de Souza Coelho L, Gurbaxani B, Maloney EM, Jones JF

Objective: This paper asks whether the presence of chronic fatigue syndrome (CFS) can be more accurately predicted from single nucleotide polymorphism (SNP) profiles than would occur by chance. Methods: Specifically, given SNP profiles for 43 CFS patients, together with 58 controls, we used an enumerative search to identify an ensemble of conjunctive rules that predict whether a patient has CFS. Results: The accuracy of the rules reached 76.3%, with the highest accuracy rules yielding 49 true negatives, 15 false negatives, 28 true positives and nine false positives (odds ratio [OR] 8.94, p < 0.0001). Analysis of the SNPs used most frequently in the overall ensemble of rules gave rise to a list of 'most important SNPs', which was not identical to the list of 'most differentiating SNPs' that one would calculate via studying each SNP independently. The top three genes containing the SNPs accounting for the highest accumulated importances were neuronal tryptophan hydroxylase (TPH2), catechol-O-methyltransferase (COMT) and nuclear receptor subfamily 3, group C, member 1 glucocorticoid receptor (NR3C1). Conclusion: The fact that only 28 out of several million possible SNPs predict whether a person has CFS with 76% accuracy indicates that CFS has a genetic component that may help to explain some aspects of the illness.

In Vivo 2006 Mar-Apr; 20(2): 285-91 (Read article online)

Kodama M, Kodama T

Since 1996 in our clinic, the regular practice of megadose vitamin C infusion with dehydroepiandrosterone-cortisol annex and the continuous intake of erythromycin and chloramphenicol have been found useful for the clinical control of the autoimmune disease interstitial pneumonia, also known as chronic fatigue syndrome. The long-term use of these two systems for the treatment of the autoimmune disease has led to the emergence of four problems of theoretical or practical importance, as described below: i) Should maintenance of the above core treatments be continued for prophylactic purposes in the absence of acute signs of pneumonia? Evidence indicated that their use was essential to arrest the dynamic activity of an intrapulmonary bacterial colony in the immunodeficient host, and that the 5-year survival rate of interstitial pneumonia patients would have been worse without the prophylactic practice of the 2 treatments. ii) Evidence was presented to suggest that the activity of the intrapulmonary bacterial colony was becoming less responsive because of the emergence of a drug-resistent mutant bacterium. The introduction of new antibiotics (kanamycin) was found to improve the acute signs of pneumonia. iii) The bone marrow function of one male patient with interstitial pneumonia was found to decline during the observation period of 9 years. It was speculated that his bone marrow, like his lungs, was in the course of fibrosis. iv) One female patient was diagnosed with breast cancer in the course of interstitial pneumonia treatment--an example indicating that the persistence of an autoimmune disease in an elderly subject might be associated with the emergence of malignancy. Dehydroepiandrosterone was shown to promote the recovery of hepatic function in the course of cancer chemotherapy with cyclophosphamide. The beneficial effect of the adrenal androgen was dose-dependent. The significance of this finding is discussed in the light of the steroid carcinogenesis concept. The reasoning behind the view that interstitial pneumonia and chronic fatigue syndrome are one disease is also discussed.

Pharmacogenomics 2006 Apr; 7(3): 441-454 (Read article online)

Fostel J, Boneva R, Lloyd A

Objective: To identify biomarkers of chronic fatigue syndrome (CFS) and related disorders through analysis of microarray data, pathology test results and self-report symptom profiles. Method: To empirically derive the symptom domains of the illnesses, factor analysis was performed on responses to self-report questionnaires (multidimensional fatigue inventory, Centers for Disease Control and Prevention (CDC) symptom inventory and Zung depression scale) before validation with independent datasets. Gene expression patterns that distinguished subjects across each factor dimension were then sought. Results: A four-factor solution was favored, featuring 'fatigue' and 'mood disturbance' factors. Scores on these factors correlated with measures of disability on the Short Form (SF)-36. A total of 57 genes that distinguished subjects along each factor dimension were identified, although the separation was significant only for subjects beyond the extreme (15(th) and 85(th)) percentiles of severity. Clustering of laboratory parameters with expression of these genes revealed associations with serum measurements of pH, electrolytes, glucose, urea, creatinine, and liver enzymes (aspartate amino transferase [AST] and alanine amino transferase [AST]); as well as hematocrit and white cell count. Conclusion: CFS is a complex syndrome that cannot simply be associated with changes in individual laboratory tests or expression levels of individual genes. No clear association with gene expression and individual symptom domains was found. However, analysis of such multifacetted datasets is likely to be an important means to elucidate the pathogenesis of CFS.

Pharmacogenomics 2006 Apr; 7(3): 467-73 (Read article online)

Maloney EM, Gurbaxani BM, Jones JF, de Souza Coelho L, Pennachin C, Goertzel BN

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.

Pharmacogenomics 2006 Apr; 7(3): 407-419 (Read article online)

Broderick G, Craddock RC, Whistler T, Taylor R, Klimas N, Unger ER

Objectives: To examine the potential of multivariate projection methods in identifying common patterns of change in clinical and gene expression data that capture the illness state of subjects with unexplained fatigue and nonfatigued control participants. Methods: Data for 111 female subjects was examined. A total of 59 indicators, including multidimensional fatigue inventory (MFI), medical outcome Short Form 36 (SF-36), Centers for Disease Control and Prevention (CDC) symptom inventory and cognitive response described illness. Partial least squares (PLS) was used to construct two feature spaces: one describing the symptom space from gene expression in peripheral blood mononuclear cells (PBMC) and one based on 117 clinical variables. Multiplicative scatter correction followed by quantile normalization was applied for trend removal and range adjustment of microarray data. Microarray quality was assessed using mean Pearson correlation between samples. Benjamini-Hochberg multiple testing criteria served to identify significantly expressed probes. Results: A single common trend in 59 symptom constructs isolates of nonfatigued subjects from the overall group. This segregation is supported by two co-regulation patterns representing 10% of the overall microarray variation. Of the 39 principal contributors, the 17 probes annotated related to basic cellular processes involved in cell signaling, ion transport and immune system function. The single most influential gene was sestrin 1 (SESN1), supporting recent evidence of oxidative stress involvement in chronic fatigue syndrome (CFS). Dominant variables in the clinical feature space described heart rate variability (HRV) during sleep. Potassium and free thyroxine (T4) also figure prominently. Conclusion: Combining multiple symptom, gene or clinical variables into composite features provides better discrimination of the illness state than even the most influential variable used alone. Although the exact mechanism is unclear, results suggest a common link between oxidative stress, immune system dysfunction and potassium imbalance in CFS patients leading to impaired sympatho-vagal balance strongly reflected in abnormal HRV.

Pharmacogenomics 2006 Apr; 7(3): 455-65 (Read article online)

Gurbaxani BM, Jones JF, Goertzel BN, Maloney EM

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.

Harefuah 2006 Apr; 145(4): 276-80, 318 (Read article online)

Greenberg S, Frid M

One of the major symptoms of chronic fatigue syndrome (CFS) is reduced exercise and functional capacity and increased fatigue symptoms following physical effort. A review of the literature indicates that patients that suffer from CFS are characterized by: low aerobic capacity, higher heart rate during sub-maximal exercise, higher subjective effort prescription, reduced muscle strength, and prolonged recovery period. Although several symptoms are a result of lack of physical activity, several mechanisms were suggested to explain those symptoms: pathological heart rate control, reduced aerobic metabolic capacity, reduced blood supply to the working muscles and nerve system dysfunction. Participating in guided exercise programs was found to be the most effective treatment in improving exercise and functional capacity, reducing fatigue syndromes and improving patients' daily function.

Pharmacogenomics 2006 Apr; 7(3): 485-94 (Read article online)

Goertzel BN, Pennachin C, de Souza Coelho L, Maloney EM, Jones JF, Gurbaxani B

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.

Pharmacogenomics 2006 Apr; 7(3): 503-9 (Read article online)

Waltman P, Pearlman A, Mishra B

Comprehensive, systematic and integrated data-centric statistical approaches to disease modeling can provide powerful frameworks for understanding disease etiology. Here, one such computational framework based on redescription mining in both its incarnations, static and dynamic, is discussed. The static framework provides bioinformatic tools applicable to multifaceted datasets, containing genetic, transcriptomic, proteomic, and clinical data for diseased patients and normal subjects. The dynamic redescription framework provides systems biology tools to model complex sets of regulatory, metabolic and signaling pathways in the initiation and progression of a disease. As an example, the case of chronic fatigue syndrome (CFS) is considered, which has so far remained intractable and unpredictable in its etiology and nosology. The redescription mining approaches can be applied to the Centers for Disease Control and Prevention's Wichita (KS, USA) dataset, integrating transcriptomic, epidemiological and clinical data, and can also be used to study how pathways in the hypothalamic-pituitary-adrenal axis affect CFS patients.

J Am Dent Assoc 2006 Apr; 137(4): 488-93 (Read article online)

Brooks JK, Francis LA

BACKGROUND: Postural orthostatic tachycardia syndrome (POTS) is a chronic, relatively common autonomic disorder typically affecting younger females. It is distinguished by a dramatic increase in heart rate on the assumption of an upright posture from the supine position. METHODS: The authors provide an overview of the demographics, clinical assessment, diagnostic features, differential diagnoses, pathogeneses and medical treatment of patients with POTS, with an emphasis on the clinical treatment of the dental patient affected by the syndrome. CONCLUSION: Patients frequently exhibit symptoms of lightheadedness, fatigue, palpitations and syncope. Patients with POTS may have Ehlers-Danlos syndrome, mitral valve prolapse, chronic fatigue syndrome or, rarely, the Brugada syndrome. Despite widespread dissemination of information regarding POTS in the medical literature, scant information on it has appeared in dental publications. PRACTICE IMPLICATIONS: Dentists need to be familiar with the clinical features of POTS and be prepared to treat patients at risk of developing syncope.

Pharmacogenomics 2006 Apr; 7(3): 395-405 (Read article online)

Whistler T, Taylor R, Craddock RC, Broderick G, Klimas N, Unger ER

Quantitative trait analysis (QTA) can be used to test whether the expression of a particular gene significantly correlates with some ordinal variable. To limit the number of false discoveries in the gene list, a multivariate permutation test can also be performed. The purpose of this study is to identify peripheral blood gene expression correlates of fatigue using quantitative trait analysis on gene expression data from 20,000 genes and fatigue traits measured using the multidimensional fatigue inventory (MFI). A total of 839 genes were statistically associated with fatigue measures. These mapped to biological pathways such as oxidative phosphorylation, gluconeogenesis, lipid metabolism, and several signal transduction pathways. However, more than 50% are not functionally annotated or associated with identified pathways. There is some overlap with genes implicated in other studies using differential gene expression. However, QTA allows detection of alterations that may not reach statistical significance in class comparison analyses, but which could contribute to disease pathophysiology. This study supports the use of phenotypic measures of chronic fatigue syndrome (CFS) and QTA as important for additional studies of this complex illness. Gene expression correlates of other phenotypic measures in the CFS Computational Challenge (C3) data set could be useful. Future studies of CFS should include as many precise measures of disease phenotype as is practical.

Curr Opin Psychiatry 2006 Jan; 19(1): 67-73 (Read article online)

Cho HJ, Skowera A, Cleare A, Wessely S

PURPOSE OF REVIEW: Chronic fatigue syndrome is a controversial condition especially concerning its clinical definition and aetiopathogenesis. Most recent research progress has been made in phenomenology and pathophysiology and we focused our review on these two areas. RECENT FINDINGS: The phenomenology research supports the notion of a discrete fatigue syndrome which can be distinguished from depression and anxiety. The current case definition, however, may need an improvement based on empirical data. Recent advances in understanding the pathophysiology of chronic fatigue syndrome continue to demonstrate the involvement of the central nervous system. Hyperserotonergic state and hypoactivity of the hypothalamic-pituitary-adrenal axis constitute other findings, but the question of whether these alterations are a cause or consequence of chronic fatigue syndrome still remains unanswered. Immune system involvement in the pathogenesis seems certain but the findings on the specific mechanisms are still inconsistent. Genetic studies provide some evidence of the syndrome being a partly genetic condition, but environmental effects seem to be still predominant and identification of specific genes is still at a very early stage. SUMMARY: The recent findings suggest that further research is needed in improving the current case definition; investigating overlaps and boundaries among various functional somatic syndromes; answering the question of whether the pathophysiologic findings are a cause or consequence; and elucidating the involvement of the central nervous system, immune system and genetic factors.

Pharmacogenomics 2006 Apr; 7(3): 421-428 (Read article online)

Craddock RC, Taylor R, Broderick G, Whistler T, Klimas N, Unger ER

The entropy correlation coefficient (ECC) is a useful tool for measuring statistical dependence between variables. We employed this tool to search for pairs of variables that correlated in the chronic fatigue syndrome (CFS) Computational Challenge dataset. Highly related variables are candidates for data reduction, and novel relationships could lead to hypotheses regarding the pathogenesis of CFS. Methods: Data for 130 female participants in the Wichita (KS, USA) clinical study [1] was coded into numerical values. Metric data was grouped using Gaussian mixture models; the number of groups was chosen using Bayesian information content. The pair-wise correlation between all variables was computed using the ECC. Significance was estimated from 1000 iterations of a permutation test and a threshold of 0.01 was used to identify significantly correlated variables. Results: The five dimensions of multidimensional fatigue inventory (MFI) were all highly correlated with each other. Seven Short Form (SF)-36 measures, four CFS case-defining symptoms and the Zung self-rating depression scale all correlated with all MFI dimensions. No physiological variables correlate with more than one MFI dimension. MFI, SF-36, CDC symptom inventory, the Zung self-rating depression scale and three Cambridge Neuropsychological Test Automated Battery (CANTAB) measures are highly correlated with CFS disease status. Discussion: Correlations between the five dimensions of MFI are expected since they are measured from the same instrument. The relationship between MFI and Zung depression index has been previously reported. MFI, SF-36, and Centers for Disease Control and Prevention (CDC) symptom inventory are used to classify CFS; it is not surprising that they are correlated with disease status. Only one of the three CANTAB measures that correlate with disease status has been previously found, indicating the ECC identifies relationships not found with other statistical tools. Conclusion: The ECC is a useful tool for measuring statistical dependence between variables in clinical and laboratory datasets. The ECC needs to be further studied to gain a better understanding of its meaning for clinical data.

Pharmacogenomics 2006 Apr; 7(3): 429-440 (Read article online)

Fang H, Xie Q, Boneva R, Fostel J, Perkins R, Tong W

Objective: To gain understanding of the molecular basis of chronic fatigue syndrome (CFS) through gene expression analysis using a large microarray data set in conjunction with clinically administrated questionnaires. Method: Data from the Wichita (KS, USA) CFS Surveillance Study was used, comprising 167 participants with two self-report questionnaires (multidimensional fatigue inventory [MFI] and Zung depression scale [Zung]), microarray data, empiric classification, and others. Microarray data was analyzed using bioinformatics tools from ArrayTrack. Results: Correspondence analysis was applied to the MFI questionnaire to select the 23 samples having either the most or the least fatigue, and to the Zung questionnaire to select the 26 samples having either the most or least depression; ten samples were common, resulting in a total of 39 samples. The MFI and Zung-based CFS/non-CFS (NF) classifications on the 39 samples were consistent with the empiric classification. Two differentially-expressed gene lists were determined, 188 fatigue-related genes and 164 depression-related genes, which shared 24 common genes and involved 11 common pathways. Principal component analysis based on 24 genes clearly separates 39 samples with respect to their likelihood to be CFS. Most of the 24 genes are not previously reported for CFS, yet their functions are consistent with the prevailing model of CFS, such as immune response, apoptosis, ion channel activity, signal transduction, cell-cell signaling, regulation of cell growth and neuronal activity. Hierarchical cluster analysis was performed based on 24 genes to classify 128 (=167-39) unassigned samples. Several of the 11 identified common pathways are supported by earlier findings for CFS, such as cytokine-cytokine receptor interaction and neuroactive ligand-receptor interaction. Importantly, most of the 11 common pathways are interrelated, suggesting complex biological mechanisms associated with CFS. Conclusion: Bioinformatics is critical in this study to select definitive sample groups, analyze gene expression data and gain insight into biological mechanisms. The 24 identified common genes and 11 common pathways could be important in future studies of CFS at the molecular level.

Pharmacogenomics 2006 Apr; 7(3): 387-394 (Read article online)

Smith AK, White PD, Aslakson E, Vollmer-Conna U, Rajeevan MS

Chronic fatigue syndrome (CFS) is characterized by persistent or relapsing fatigue that is not alleviated by rest, causes substantial reduction in activities and is accompanied by a variety of symptoms. Its unknown etiology may reflect that CFS is heterogeneous. Latent class analyses of symptoms and physiological systems were used to delineate subgroups within a population-based sample of fatigued and nonfatigued subjects [1] . This study examined whether genetic differences underlie the individual subgroups of the latent class solution. Polymorphisms in 11 candidate genes related to both hypothalamic-pituitary-adrenal (HPA) axis function and mood-related neurotransmitter systems were evaluated by comparing each of the five ill classes (Class 1, n = 33; Class 3, n = 22; Class 4, n = 22; Class 5, n = 17; Class 6, n = 11) of fatigued subjects with subjects defined as well (Class 2, n = 35). Of the five classes of subjects with unexplained fatigue, three classes were distinguished by gene polymorphsims involved in either HPA axis function or neurotransmitter systems, including proopiomelanocortin (POMC), nuclear receptor subfamily 3, group C, member 1 (NR3C1), monoamine oxidase A (MAOA), monoamine oxidase B (MAOB), and tryptophan hydroxylase 2 (TPH2). These data support the hypothesis that medically unexplained chronic fatigue is heterogeneous and presents preliminary evidence of the genetic mechanisms underlying some of the putative conditions.