Identifying illness parameters in fatiguing syndrome using classical projection methods

University of Alberta, Institute for Biomolecular Design, Edmonton, Alberta, T6G 2H7, Canada.
Pharmacogenomics (Impact Factor: 3.22). 05/2006; 7(3):407-19. DOI: 10.2217/14622416.7.3.407
Source: PubMed


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.
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.
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.
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.

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    • "Immunological aberrations (inflammation, immune activation, immunosuppression and immune dysfunction); Klimas et al., 1990; Fletcher et al., 2009; Lorusso et al., 2009; Meeus et al., 2009; Brenu et al., 2011; Maes et al., 2012b consistent with processes observed during (latent) infection; Lloyd et al., 1993; Kerr et al., 2008a; Broderick et al., 2010 Intestinal dysbiosis, inflammation and hyperpermeability, Maes et al., 2007a; Sheedy et al., 2009; Lakhan and Kirchgessner, 2010; De Meirleir et al., 2013; Frémont et al., 2013 associated with systemic immune system abnormalities; Maes et al., 2012c; Groeger et al., 2013 (reactivating and/or persistent) infections; Hilgers and Frank, 1996; Chia and Chia, 2003; Nicolson et al., 2003; Chia et al., 2010; Chapenko et al., 2012 Elevated oxidative and nitrosative stress; Zhang et al., 1995; Kennedy et al., 2010; Maes and Twisk, 2010; Tomic et al., 2012 Mitochondrial dysfunction and damage to mitochondria; Behan et al., 1991; Pietrangelo et al., 2009; Booth et al., 2012; Meeus et al., 2013 Hypovolemia, diminished cardiac output and Streeten and Bell, 1998; Hurwitz et al., 2009; Miwa and Fujita, 2009; Hollingsworth et al., 2012 blood and oxygen supply deficits to muscles and brain, McCully and Natelson, 1999; Biswal et al., 2011; Ocon, 2013 especially in an upright position and during exercise; LaManca et al., 1999; Peckerman et al., 2003; Wyller et al., 2007; Patrick Neary et al., 2008 Reduced (maximum) oxygen uptake; Farquhar et al., 2002; Weinstein et al., 2009; Vermeulen et al., 2010; Jones et al., 2012 Neurological abnormalities; Lange et al., 2005; Chen et al., 2008; Puri et al., 2012; Natelson, 2013 Hypocortisolism/blunted hypothalamic-pituitary-adrenal (HPA) axis response; Demitrack et al., 1991; Lorusso et al., 2009; Papadopoulos and Cleare, 2011; Tak et al., 2011 Ion channel dysfunction (channelopathy); Watson et al., 1997; Whistler et al., 2005; Broderick et al., 2006; Cameron et al., 2007 A deviant physiological responses to exertion Thambirajah et al., 2008; Jones et al., 2012; Light et al., 2012; Smylie et al., 2013; Snell et al., 2013 (Kindlon, 2012), e.g., oxygen uptake at the anaerobic threshold and maximum oxygen uptake (VO2max), and biomarkers, e.g., (exercise-induced) cytokine levels. "
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    ABSTRACT: Myalgic Encephalomyelitis (ME) was identified as a new clinical entity in 1959 and has been acknowledged as a disease of the central nervous system/neurological disease by the World Health Organisation since 1969. Cognitive impairment, (muscle) weakness, circulatory disturbances, marked variability of symptoms, and, above all, post-exertional malaise: a long-lasting increase of symptoms after minor exertion, are distinctive symptoms of ME.Chronic Fatigue Syndrome (CFS) was introduced in 1988 and was redefined into clinically evaluated, unexplained (persistent or relapsing) chronic fatigue, accompanied by at least four out of a list of eight symptoms, e.g. headaches and unrefreshing sleep, in 1994.Although the labels are used interchangeably, ME and CFS define distinct diagnostic entities. Post-exertional malaise and cognitive deficits e.g. are not mandatory for the diagnosis CFS, while obligatory for the diagnosis ME. “Fatigue” is not obligatory for the diagnosis ME.Since fatigue and other symptoms are subjective and ambiguous, research has been hampered. Despite this and other methodological issues, research has observed specific abnormalities in ME/CFS repetitively, e.g. immunological abnormalities, oxidative and nitrosative
    Frontiers in Physiology 03/2014; 5:109. DOI:10.3389/fphys.2014.00109 · 3.53 Impact Factor
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    • "Criterial symptoms are supported by a study of more than 2500 patients that determined which symptoms had the greatest efficacy to identify patients with ME [22]. Investigations into gene expression [23–27] and structure further support the criteria at a molecular level including anomalies of increased oxidative stress [4, 28], altered immune and adrenergic signalling [29, 30] and altered oestrogen receptor expression [31]. In addition, evidence supporting a genetic predisposition to ME points to modifications in serotonin transporter genes [32, 33], the glucocorticoid receptor gene [34], as well as HLA class II involvement [35]. "
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    ABSTRACT: Carruthers BM, van de Sande MI, De Meirleir KL, Klimas NG, Broderick G, Mitchell T, Staines D, Powles ACP, Speight N, Vallings R, Bateman L, Baumgarten-Austrheim B, Bell DS, Carlo-Stella N, Chia J, Darragh A, Jo D, Lewis D, Light AR, Marshall-Gradisbik S, Mena I, Mikovits JA, Murovska M, Pall ML, Stevens S (Independent, Vancouver, BC, Canada; Independent, Calgary, AB, Canada; Department of Physiology and Medicine, Vrije University of Brussels, Himmunitas Foundation, Brussels, Belgium; Department of Medicine,University of Miami Miller School of Medicine and Miami Veterans Affairs Medical Center, Miami, FL, USA; Department of Medicine, University of Alberta, Edmonton, AB, Canada; Honorary Consultant for NHS at Peterborough/Cambridge, Lowestoft, Suffolk, UK; Gold Coast Public Health Unit, Southport, Queensland; Health Sciences and Medicine, Bond University, Robina, Queensland, Australia; Faculty of Health Sciences, McMaster University and St Joseph’s Healthcare Hamilton, Hamilton, ON, Canada; Independent, Durham, UK; Howick Health and Medical Centre, Howick, New Zealand; Fatigue Consultation Clinic, Salt Lake Regional Medical Center; Internal Medicine, Family Practice, University of Utah, Salt Lake City, UT, USA; ME/CFS Center, Oslo University Hospital HF, Norway; Department of Paediatrics, State University of New York, Buffalo, NY; Independent, Pavia, Italy; Harbor-UCLA Medical Center, University of California, Los Angeles, CA; EV Med Research, Lomita, CA, USA; University of Limerick, Limerick, Ireland; Pain Clinic, Konyang University Hospital, Daejeon, Korea; Donvale Specialist Medical Centre, Donvale, Victoria, Australia; Departments or Anesthesiology, Neurobiology and Anatomy, University of Utah, Salt Lake City, Utah, USA; Health Sciences and Medicine, Bond University, Robina, Queensland, Australia; Department of Medicina Nuclear, Clinica Las Condes, Santiago, Chile; Whittemore Peterson Institute, University of Nevada, Reno, NV, USA; Miwa Naika Clinic, Toyama, Japan; A. Kirchenstein Institute of Microbiology and Virology, Riga Stradins University, Riga, Latvia; Department of Biochemistry & Basic Medical Sciences, Washington State University, Portland, OR; Department of Sports Sciences, University of the Pacific, Stockton, CA USA). Myalgic encephalomyelitis: International Consensus Criteria (Review). J Intern Med 2011; 270: 327–338. The label ‘chronic fatigue syndrome’ (CFS) has persisted for many years because of the lack of knowledge of the aetiological agents and the disease process. In view of more recent research and clinical experience that strongly point to widespread inflammation and multisystemic neuropathology, it is more appropriate and correct to use the term ‘myalgic encephalomyelitis’ (ME) because it indicates an underlying pathophysiology. It is also consistent with the neurological classification of ME in the World Health Organization’s International Classification of Diseases (ICD G93.3). Consequently, an International Consensus Panel consisting of clinicians, researchers, teaching faculty and an independent patient advocate was formed with the purpose of developing criteria based on current knowledge. Thirteen countries and a wide range of specialties were represented. Collectively, members have approximately 400 years of both clinical and teaching experience, authored hundreds of peer-reviewed publications, diagnosed or treated approximately 50 000 patients with ME, and several members coauthored previous criteria. The expertise and experience of the panel members as well as PubMed and other medical sources were utilized in a progression of suggestions/drafts/reviews/revisions. The authors, free of any sponsoring organization, achieved 100% consensus through a Delphi-type process. The scope of this paper is limited to criteria of ME and their application. Accordingly, the criteria reflect the complex symptomatology. Operational notes enhance clarity and specificity by providing guidance in the expression and interpretation of symptoms. Clinical and research application guidelines promote optimal recognition of ME by primary physicians and other healthcare providers, improve the consistency of diagnoses in adult and paediatric patients internationally and facilitate clearer identification of patients for research studies.
    Journal of Internal Medicine 07/2011; 270(4):327-38. DOI:10.1111/j.1365-2796.2011.02428.x · 6.06 Impact Factor
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    • "The strength of multivariate methods is the possibility to define combinations of proteins that maximizes the model predictive ability. The use of multivariate methods such as Partial Least Squares (PLS) [11,12], where the expression of several genes or proteins are studied simultaneously, is increasing and has earlier shown to be powerful in tumour classification and biomarker discovery [13-17]. In this study, PLS was utilized to select the most important proteins for distinguishing between normal and tumour samples. "
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    ABSTRACT: There is a vast need to find clinically applicable protein biomarkers as support in cancer diagnosis and tumour classification. In proteomics research, a number of methods can be used to obtain systemic information on protein and pathway level on cells and tissues. One fundamental tool in analysing protein expression has been two-dimensional gel electrophoresis (2DE). Several cancer 2DE studies have reported partially redundant lists of differently expressed proteins. To be able to further extract valuable information from existing 2DE data, the power of a multivariate meta-analysis will be evaluated in this work. We here demonstrate a multivariate meta-analysis of 2DE proteomics data from human prostate and colon tumours. We developed a bioinformatic workflow for identifying common patterns over two tumour types. This included dealing with pre-processing of data and handling of missing values followed by the development of a multivariate Partial Least Squares (PLS) model for prediction and variable selection. The variable selection was based on the variables performance in the PLS model in combination with stability in the validation. The PLS model development and variable selection was rigorously evaluated using a double cross-validation scheme. The most stable variables from a bootstrap validation gave a mean prediction success of 93% when predicting left out test sets on models discriminating between normal and tumour tissue, common for the two tumour types. The analysis conducted in this study identified 14 proteins with a common trend between the tumour types prostate and colon, i.e. the same expression profile between normal and tumour samples. The workflow for meta-analysis developed in this study enabled the finding of a common protein profile for two malign tumour types, which was not possible to identify when analysing the data sets separately.
    BMC Bioinformatics 09/2010; 11(1):468. DOI:10.1186/1471-2105-11-468 · 2.58 Impact Factor
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