Tag: Wichita

Gene expression profile exploration of a large dataset on chronic fatigue syndrome

Abstract:

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.

 

Source: Fang H, Xie Q, Boneva R, Fostel J, Perkins R, Tong W. Gene expression profile exploration of a large dataset on chronic fatigue syndrome. Pharmacogenomics. 2006 Apr;7(3):429-40. https://www.ncbi.nlm.nih.gov/pubmed/16610953

 

Exploration of statistical dependence between illness parameters using the entropy correlation coefficient

Abstract:

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.

 

Source: Craddock RC, Taylor R, Broderick G, Whistler T, Klimas N, Unger ER. Exploration of statistical dependence between illness parameters using the entropy correlation coefficient. Pharmacogenomics. 2006 Apr;7(3):421-8. https://www.ncbi.nlm.nih.gov/pubmed/16610952

 

An empirical delineation of the heterogeneity of chronic unexplained fatigue in women

Abstract:

OBJECTIVES: To test the hypothesis that medically unexplained chronic fatigue and chronic fatigue syndrome (CFS) are heterogeneous conditions, and to define the different conditions using both symptom and laboratory data.

METHODS: We studied 159 women from KS, USA. A total of 51 of these suffered from fatigue consistent with established criteria for CFS, 55 had chronic fatigue of insufficient symptoms/severity for a CFS diagnosis and 53 were healthy controls matched by age and body mass index (BMI) against those with CFS. We used principal components analyses to define factors that best described the variable space and to reduce the number of variables. The 38 most explanatory variables were then used in latent class analyses to define discrete subject groups.

RESULTS: Principal components analyses defined six discrete factors that explained 40% of the variance. Latent class analyses provided several interpretable solutions with four, five and six classes. The four-class solution was statistically most convincing, but the six-class solution was more interpretable. Class 1 defined 41 (26%) subjects with obesity and relative sleep hypnoea. Class 2 were 38 (24%) healthy subjects. Class 3 captured 24 (15%) obese relatively hypnoeic subjects, but with low heart rate variability and cortisol. Class 4 were 23 (14%) sleep-disturbed and myalgic subjects without obesity or significant depression. The two remaining classes with 22 (14%) and 11 (7%) subjects consisted of the most symptomatic and depressed, but without obesity or hypnoea. Class 5 had normal sleep indices. Class 6 was characterized by disturbed sleep, with low sleep heart rate variability, cortisol, and sex hormones.

CONCLUSION: Chronic medically unexplained fatigue is heterogeneous. The putative syndromes were differentiated by obesity, sleep hypnoea, depression, physiological stress response, sleep disturbance, interoception and menopausal status. If these syndromes are externally validated and replicated, they may prove useful in determining the causes, pathophysiology and treatments of CFS.

 

Source: Vollmer-Conna U, Aslakson E, White PD. An empirical delineation of the heterogeneity of chronic unexplained fatigue in women. Pharmacogenomics. 2006 Apr;7(3):355-64. https://www.ncbi.nlm.nih.gov/pubmed/16610946

 

The challenge of integrating disparate high-content data: epidemiological, clinical and laboratory data collected during an in-hospital study of chronic fatigue syndrome

Abstract:

Chronic fatigue syndrome (CFS) is a debilitating illness characterized by multiple unexplained symptoms including fatigue, cognitive impairment and pain. People with CFS have no characteristic physical signs or diagnostic laboratory abnormalities, and the etiology and pathophysiology remain unknown. CFS represents a complex illness that includes alterations in homeostatic systems, involves multiple body systems and results from the combined action of many genes, environmental factors and risk-conferring behavior. In order to achieve understanding of complex illnesses, such as CFS, studies must collect relevant epidemiological, clinical and laboratory data and then integrate, analyze and interpret the information so as to obtain meaningful clinical and biological insight. This issue of Pharmacogenomics represents such an approach to CFS.

Data was collected during a 2-day in-hospital study of persons with CFS, other medically and psychiatrically unexplained fatiguing illnesses and nonfatigued controls identified from the general population of Wichita, KS, USA. While in the hospital, the participants’ psychiatric status, sleep characteristics and cognitive functioning was evaluated, and biological samples were collected to measure neuroendocrine status, autonomic nervous system function, systemic cytokines and peripheral blood gene expression. The data generated from these assessments was made available to a multidisciplinary group of 20 investigators from around the world who were challenged with revealing new insight and algorithms for integration of this complex, high-content data and, if possible, identifying molecular markers and elucidating pathophysiology of chronic fatigue. The group was divided into four teams with representation from the disciplines of medicine, mathematics, biology, engineering and computer science. The papers in this issue are the culmination of this 6-month challenge, and demonstrate that data integration and multidisciplinary collaboration can indeed yield novel approaches for handling large, complex datasets, and reveal new insight and relevance to a complex illness such as CFS.

Comment in: The postgenomic era and complex disease. [Pharmacogenomics. 2006]

 

Source: Vernon SD, Reeves WC. The challenge of integrating disparate high-content data: epidemiological, clinical and laboratory data collected during an in-hospital study of chronic fatigue syndrome. Pharmacogenomics. 2006 Apr;7(3):345-54. https://www.ncbi.nlm.nih.gov/pubmed/16610945

 

Psychometric properties of the CDC Symptom Inventory for assessment of chronic fatigue syndrome

Abstract:

OBJECTIVES: Validated or standardized self-report questionnaires used in research studies and clinical evaluation of chronic fatigue syndrome(CFS) generally focus on the assessment of fatigue. There are relatively few published questionnaires that evaluate case defining and other accompanying symptoms in CFS. This paper introduces the self-report CDC CFS Symptom Inventory and analyzes its psychometric properties.

METHODS: One hundred sixty-four subjects (with CFS, other fatiguing illnesses and non fatigued controls) identified from the general population of Wichita, Kansas were enrolled. Evaluation included a physical examination, a standardized psychiatric interview, three previously validated self-report questionnaires measuring fatigue and illness impact (Medical Outcomes Survey Short-Form-36 [MOS SF-36], Multidimensional Fatigue Inventory [MFI], Chalder Fatigue Scale), and the CDC CFS Symptom Inventory. Based on theoretical assumptions and statistical analyses, we developed several different Symptom Inventory scores and evaluated them on their ability to differentiate between participants with CFS and non-fatigued controls.

RESULTS: The Symptom Inventory had good internal consistency and excellent convergent validity. A Total score (all symptoms), Case Definition score (CFS case defining symptoms) and Short Form score (6 symptoms with minimal correlation) differentiated CFS cases from controls. Furthermore, both the Case Definition and Short Form scores distinguished people with CFS from fatigued subjects who did not meet criteria for CFS.

CONCLUSION: The Symptom Inventory appears to be a reliable and valid instrument to assess symptoms that accompany CFS. It is a positive addition to existing instruments measuring fatigue because it allows other dimensions of the illness to be assessed. Further research is needed to confirm and replicate the current findings in a normative population.

 

Source: Wagner D, Nisenbaum R, Heim C, Jones JF, Unger ER, Reeves WC. Psychometric properties of the CDC Symptom Inventory for assessment of chronic fatigue syndrome. Popul Health Metr. 2005 Jul 22;3:8. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1183246/ (Full article)

 

Evaluation of autoantibodies to common and neuronal cell antigens in Chronic Fatigue Syndrome

Abstract:

People with chronic fatigue syndrome (CFS) suffer from multiple symptoms including fatigue, impaired memory and concentration, unrefreshing sleep and musculoskeletal pain. The exact causes of CFS are not known, but the symptom complex resembles that of several diseases that affect the immune system and autoantibodies may provide clues to the various etiologies of CFS.

We used ELISA, immunoblot and commercially available assays to test serum from subjects enrolled in a physician-based surveillance study conducted in Atlanta, Georgia and a population-based study in Wichita, Kansas for a number of common autoantibodies and antibodies to neuron specific antigens.

Subsets of those with CFS had higher rates of antibodies to microtubule-associated protein 2 (MAP2) (p = 0.03) and ssDNA (p = 0.04). There was no evidence of higher rates for several common nuclear and cellular antigens in people with CFS. Autoantibodies to specific host cell antigens may be a useful approach for identifying subsets of people with CFS, identify biomarkers, and provide clues to CFS etiologies.

 

Source: Vernon SD, Reeves WC.  Evaluation of autoantibodies to common and neuronal cell antigens in Chronic Fatigue Syndrome. J Autoimmune Dis. 2005 May 25;2:5. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1177983/ (Full article)

 

Factors influencing the diagnosis of chronic fatigue syndrome

Abstract:

BACKGROUND: Most of what is believed about chronic fatigue syndrome (CFS) is based on clinic-based studies. These studies may not reflect CFS cases in the population.

METHODS: We used data from a population-based study of CFS to identify factors associated with receiving a CFS diagnosis. Wichita, Kan, residents were screened by random-digit dialing. Eligible individuals completed a telephone interview. Respondents meeting CFS criteria were invited for a clinical evaluation to confirm CFS. We analyzed all persons with confirmed CFS. The main outcomes of this study, prevalence and incidence of CFS, are published elsewhere. Herein, we present an exploratory analysis with previous CFS diagnosis as the outcome, predicted by demographic and symptom characteristics.

RESULTS: We confirmed CFS in 90 subjects; 14 (16%) had been previously diagnosed as having CFS. Persons in the middle- vs the higher-income group were more likely to have been diagnosed as having CFS (9 [29%] of 31 subjects vs 3 [8%] of 39 subjects; P = .03), as were those with sudden vs gradual fatigue onset (7 [41%] of 17 subjects vs 4 [6%] of 64 subjects; P < .01), those reporting tender lymph nodes (7 [33%] of 21 subjects vs 7 [10%] of 69 subjects; P = .02), and those reporting a sore throat (6 [35%] of 17 subjects vs 8 [11%] of 73 subjects; P = .02). Only 17 (21%) of 81 subjects had sudden fatigue onset, and tender lymph nodes (reported in 21 [23%] of 90 subjects) and a sore throat (reported in 17 [19%] of 90 subjects) were the least common symptoms.

CONCLUSION: Most cases of CFS in the population are unrecognized by the medical community; persons diagnosed as having CFS may be different from persons with CFS in the general population.

 

Source: Solomon L, Reeves WC. Factors influencing the diagnosis of chronic fatigue syndrome. Arch Intern Med. 2004 Nov 8;164(20):2241-5. http://www.ncbi.nlm.nih.gov/pubmed/15534161

 

Chronic fatigue syndrome and other fatiguing illnesses in adolescents: a population-based study

Abstract:

PURPOSE: To estimate the prevalence of chronic fatigue syndrome (CFS) and describe characteristics of other fatiguing illnesses in adolescents (aged 12 through 17 years).

METHODS: We conducted a random digit dialing survey of the residents of Wichita, Kansas. Adults identified fatigued adolescents in the household and answered questions relating to the child’s health. Selected adolescents were invited to attend a clinic with a parent/guardian. After clinical evaluation they were classified as CFS or another fatigue state as defined in the 1994 CFS definition. Annual telephone interviews and clinical evaluations monitored subjects’ fatigue status. Data were analyzed using the Kruskal-Wallis test, the Mantel-Haenszel test, and the exact McNemar test.

RESULTS: The survey contacted 34,018 households with 90,316 residents. Of 8586 adolescents, 138 had fatigue for > or =1 month and most (107 or 78%) had chronic fatigue (> or =6 months) at some point during the 3-year follow-up. Twenty-eight had exclusionary diagnoses. Thirty-one were considered to have a CFS-like illness and were invited for clinical evaluation. Eleven agreed to participate and none met the CFS case definition. The baseline weighted prevalence of CFS-like illness was 338 per 100,000. Significant differences existed between parental and adolescents’ descriptions of illness.

CONCLUSIONS: The prevalence of CFS among adolescents was considerably lower than among adults. Evaluation of CFS in adolescents must consider both parent and patient perception of fatigue and other illnesses that might explain the symptom complex.

 

Source: Jones JF, Nisenbaum R, Solomon L, Reyes M, Reeves WC. Chronic fatigue syndrome and other fatiguing illnesses in adolescents: a population-based study. J Adolesc Health. 2004 Jul;35(1):34-40. http://www.ncbi.nlm.nih.gov/pubmed/15193572

 

Factor analysis of symptoms among subjects with unexplained chronic fatigue: what can we learn about chronic fatigue syndrome?

Abstract:

OBJECTIVE: Chronic fatigue syndrome (CFS) case definitions agree that fatigue must be unexplained, debilitating and present for at least 6 months, but they differ over accompanying symptoms. Our objective was to compare the 1994 CFS case-defining symptoms with those identified by factor analysis.

METHODS: We surveyed the Wichita population and measured the occurrence of 21 symptoms in 1391 chronically fatigued subjects who did not report fatigue-associated medical or psychiatric conditions. We used factor analyses to identify symptom dimensions of fatigue and cluster analysis to assign subjects to subgroups.

RESULTS: Forty-three subjects had CFS. We confirmed three factors: musculoskeletal, infection and cognition-mood-sleep, essentially defined by CFS symptoms. Although factor scores were higher among CFS subjects, CFS and non-CFS distributions overlapped substantially. Three clusters also showed overlap between CFS and non-CFS subjects.

CONCLUSION: CFS symptomatology is a multidimensional phenomenon overlapping with other unexplained fatiguing syndromes and this must be considered in CFS research.

 

Source: Nisenbaum R, Reyes M, Unger ER, Reeves WC. Factor analysis of symptoms among subjects with unexplained chronic fatigue: what can we learn about chronic fatigue syndrome? J Psychosom Res. 2004 Feb;56(2):171-8. http://www.ncbi.nlm.nih.gov/pubmed/15016574

 

Medication use by persons with chronic fatigue syndrome: results of a randomized telephone survey in Wichita, Kansas

Abstract:

BACKGROUND: Chronic fatigue syndrome (CFS) is characterized by profound fatigue, which substantially interferes with daily activities, and a characteristic symptom complex. Patients use a variety of prescribed and self-administered medications, vitamins, and supplements for relief of their symptoms. The objective of this study was to describe utilization of medications and supplements by persons with CFS and non-fatigued individuals representative of the general population of Wichita, Kansas.

METHODS: We used a random-digit dialing telephone survey to identify persons with CFS in the general population of Wichita, Kansas. Subjects who on the basis of telephone interview met the CFS case definition, and randomly selected non-fatigued controls, were invited for a clinic evaluation that included self-reported use of medications and supplements. Sex-adjusted odds ratios and 95% confidence interval were estimated to measure the association between CFS and use of various drug categories.

RESULTS: We clinically evaluated and classified 90 subjects as CFS during the study and also collected clinical data on 63 who never described fatigue. Subjects with CFS reported using 316 different drugs compared to 157 reported by non-fatigued controls. CFS subjects were more likely to use any drug category than controls (p = 0.0009). Pain relievers and vitamins/supplements were the two most common agents listed by both groups. In addition CFS persons were more likely to use pain relievers, hormones, antidepressants, benzodiazepines, gastro-intestinal, and central nervous system medications (Sex-adjusted odds ratios range = 2.97 – 12.78).

CONCLUSION: Although the reasons for increased use of these agents were not elucidated, the data indicated the CFS patients’ need for symptom relief.

 

Source: Jones JF, Nisenbaum R, Reeves WC. Medication use by persons with chronic fatigue syndrome: results of a randomized telephone survey in Wichita, Kansas. Health Qual Life Outcomes. 2003 Dec 2;1:74. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC293479/ (Full article)