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

 

Identifying illness parameters in fatiguing syndromes using classical projection methods

Abstract:

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.

 

Source: Broderick G, Craddock RC, Whistler T, Taylor R, Klimas N, Unger ER. Identifying illness parameters in fatiguing syndromes using classical projection methods. Pharmacogenomics. 2006 Apr;7(3):407-19. https://www.ncbi.nlm.nih.gov/pubmed/16610951

 

Gene expression correlates of unexplained fatigue

Abstract:

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.

 

Source: Whistler T, Taylor R, Craddock RC, Broderick G, Klimas N, Unger ER. Gene expression correlates of unexplained fatigue. Pharmacogenomics. 2006 Apr;7(3):395-405. https://www.ncbi.nlm.nih.gov/pubmed/16610950

 

Polymorphisms in genes regulating the HPA axis associated with empirically delineated classes of unexplained chronic fatigue

Abstract:

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.

 

Source: Smith AK, White PD, Aslakson E, Vollmer-Conna U, Rajeevan MS. Polymorphisms in genes regulating the HPA axis associated with empirically delineated classes of unexplained chronic fatigue. Pharmacogenomics. 2006 Apr;7(3):387-94. https://www.ncbi.nlm.nih.gov/pubmed/16610949

 

Gene expression profile of empirically delineated classes of unexplained chronic fatigue

Abstract:

OBJECTIVES: To identify the underlying gene expression profiles of unexplained chronic fatigue subjects classified into five or six class solutions by principal component (PCA) and latent class analyses (LCA).

METHODS: Microarray expression data were available for 15,315 genes and 111 female subjects enrolled from a population-based study on chronic fatigue syndrome. Algorithms were developed to assign gene scores and threshold values that signified the contribution of each gene to discriminate the multiclasses in each LCA solution. Unsupervised dimensionality reduction was first used to remove noise or otherwise uninformative gene combinations, followed by supervised dimensionality reduction to isolate gene combinations that best separate the classes.

RESULTS: The authors’ gene score and threshold algorithms identified 32 and 26 genes capable of discriminating the five and six multiclass solutions, respectively. Pair-wise comparisons suggested that some genes (zinc finger protein 350 [ZNF350], solute carrier family 1, member 6 [SLC1A6], F-box protein 7 [FBX07] and vacuole 14 protein homolog [VAC14]) distinguished most classes of fatigued subjects from healthy subjects, whereas others (patched homolog 2 [PTCH2] and T-cell leukemia/lymphoma [TCL1A]) differentiated specific fatigue classes.

CONCLUSION: A computational approach was developed for general use to identify discriminatory genes in any multiclass problem. Using this approach, differences in gene expression were found to discriminate some classes of unexplained chronic fatigue, particularly one termed interoception.

 

Source: Carmel L, Efroni S, White PD, Aslakson E, Vollmer-Conna U, Rajeevan MS. Gene expression profile of empirically delineated classes of unexplained chronic fatigue. Pharmacogenomics. 2006 Apr;7(3):375-86. https://www.ncbi.nlm.nih.gov/pubmed/16610948

 

The validity of an empirical delineation of heterogeneity in chronic unexplained fatigue

Abstract:

OBJECTIVES: To validate a latent class structure derived empirically from a clinical data set obtained from persons with chronic medically unexplained fatigue.

METHODS: The strategies utilized in this validation study included: recalculating latent class analysis (LCA) results varying random seeds and the number of initial random starting sets; recalculating LCA results by substituting alternate variables to demonstrate a robust solution; determining the statistical significance of between-class differences on disability, fatigue and demographic measures omitted from the data set used for LCA; cross-classifying class membership using established Centers for Disease Control and Prevention (CDC) research criteria for chronic fatigue syndrome (CFS) to compare the relative proportions of subjects designated CFS, chronic fatigue (not CFS) or healthy controls captured by the latent classes.

RESULTS: Recalculation of results and substitution of variables for low-loading variables demonstrated a robust LCA result. Highly significant between-class differences were confirmed between Class 2 (well) and those interpreted as ill/fatigued. Analysis of between-class differences for the fatigue groups revealed significant differences for all disability and fatigue variables, but with equivalent levels of reported activity and reduction in motivation. Cross-classification against established CDC criteria demonstrated that 89% of subjects constituting Class 2 (well) were indeed nonfatigued controls. A general tendency for grouping CFS cases in the multiple symptomatic classes was noted.

CONCLUSION: This study established reasonably good validity for an empirically-derived latent class solution reflecting considerable heterogeneity among subjects with medically unexplained chronic fatigue. This work strengthens the growing understanding of CFS as a heterogeneous entity comprised of several conditions with different underlying pathophysiological mechanisms.

 

Source: Aslakson E, Vollmer-Conna U, White PD. The validity of an empirical delineation of heterogeneity in chronic unexplained fatigue. Pharmacogenomics. 2006 Apr;7(3):365-73. https://www.ncbi.nlm.nih.gov/pubmed/16610947

 

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

 

Postulated vasoactive neuropeptide autoimmunity in fatigue-related conditions: a brief review and hypothesis

Abstract:

Disorders such as chronic fatigue syndrome (CFS) and gulf war syndrome (GWS) are characterised by prolonged fatigue and a range of debilitating symptoms of pain, intellectual and emotional impairment, chemical sensitivities and immunological dysfunction. Sudden infant death syndrome (SIDS) surprisingly may have certain features in common with these conditions. Post-infection sequelae may be possible contributing factors although ongoing infection is unproven. Immunological aberration may prove to be associated with certain vasoactive neuropeptides (VN) in the context of molecular mimicry, inappropriate immunological memory and autoimmunity.

Adenylate cyclase-activating VNs including pituitary adenylate cyclase-activating polypeptide (PACAP), vasoactive intestinal peptide (VIP) and calcitonin gene-related peptide (CGRP) act as hormones, neurotransmitters, neuroregulators, immune modulators and neurotrophic substances. They and their receptors are potentially immunogenic.

VNs are widely distributed in the body particularly in the central and peripheral nervous systems and have been identified in the gut, adrenal gland, blood cells, reproductive system, lung, heart and other tissues. They have a vital role in maintaining cardio-respiratory function, thermoregulation, memory, concentration and executive functions such as emotional responses including social cues and appropriate behaviour. They are co-transmitters for a number of neurotransmitters including acetylcholine and gaseous transmitters, are potent immune regulators with primarily anti-inflammatory activity, and have a significant role in protection of the nervous system against toxic assault as well as being important in the maintenance of homeostasis.

This paper describes a biologically plausible mechanism for the development of certain fatigue-related syndromes based on loss of immunological tolerance to these VNs or their receptors following infection, other events or de novo resulting in significant pathophysiology possibly mediated via CpG fragments and heat shock (stress) proteins. These conditions extend the public health context of autoimmunity and VN dysregulation and have implications for military medicine where radiological, biological and chemical agents may have a role in pathogenesis. Possible treatment and prevention options are considered.

 

Source: Staines DR. Postulated vasoactive neuropeptide autoimmunity in fatigue-related conditions: a brief review and hypothesis. Clin Dev Immunol. 2006 Mar;13(1):25-39. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2270748/ (Full article)

 

Fatigue Intervention by Nurses Evaluation–the FINE Trial. A randomised controlled trial of nurse led self-help treatment for patients in primary care with chronic fatigue syndrome: study protocol.[ISRCTN74156610]

Abstract:

BACKGROUND: Chronic fatigue syndrome, also known as ME (CFS/ME), is a condition characterised primarily by severe, disabling fatigue, of unknown origin, which has a poor prognosis and serious personal and economic consequences. Evidence for the effectiveness of any treatment for CFS/ME in primary care, where most patients are seen, is sparse. Recently, a brief, pragmatic treatment for CFS/ME, based on a physiological dysregulation model of the condition, was shown to be successful in improving fatigue and physical functioning in patients in secondary care. The treatment involves providing patients with a readily understandable explanation of their symptoms, from which flows the rationale for a graded rehabilitative plan, developed collaboratively with the therapist. The present trial will test the effectiveness and cost-effectiveness of pragmatic rehabilitation when delivered by specially trained general nurses in primary care. We selected a client-centred counselling intervention, called supportive listening, as a comparison treatment. Counselling has been shown to be as effective as cognitive behaviour therapy for treating fatigue in primary care, is more readily available, and controls for supportive therapist contact time. Our control condition is treatment as usual by the general practitioner (GP).

METHODS AND DESIGN: This study protocol describes the design of an ongoing, single-blind, pragmatic randomized controlled trial of a brief (18 week) self-help treatment, pragmatic rehabilitation, delivered by specially trained nurse-therapists in patients’ homes, compared with nurse-therapist delivered supportive listening and treatment as usual by the GP. An economic evaluation, taking a societal viewpoint, is being carried out alongside the clinical trial. Three adult general nurses were trained over a six month period to deliver the two interventions. Patients aged over 18 and fulfilling the Oxford criteria for CFS are assessed at baseline, after the intervention, and again one year later. Primary outcomes are self-reported physical functioning and fatigue at one year, and will be analysed on an intention-to-treat basis. A qualitative study will examine the interventions’ mechanisms of change, and also GPs’ drivers and barriers towards referral.

 

Source: Wearden AJ, Riste L, Dowrick C, Chew-Graham C, Bentall RP, Morriss RK, Peters S, Dunn G, Richardson G, Lovell K, Powell P. Fatigue Intervention by Nurses Evaluation–the FINE Trial. A randomised controlled trial of nurse led self-help treatment for patients in primary care with chronic fatigue syndrome: study protocol. [ISRCTN74156610]. BMC Med. 2006 Apr 7;4:9. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1456982/ (Full article)