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Presentation
Identifying illness parameters in fatiguing syndromes using classical projection methods
Pharmacogenomics
  • Gordon Broderick, Nova Southeastern University
  • R. C. Craddock
  • T. Whistler
  • R. Taylor
  • Nancy G. Klimas, Nova Southeastern University
ISBN or ISSN
1470-269X
Publication Date / Copyright Date
4-1-2006
Peer Reviewed
1
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.
Citation Information
Gordon Broderick, R. C. Craddock, T. Whistler, R. Taylor, et al.. "Identifying illness parameters in fatiguing syndromes using classical projection methods" Pharmacogenomics Vol. 7 Iss. 3 (2006) p. 407 - 419
Available at: http://works.bepress.com/gordon-broderick/75/