Exploration of the gene expression correlates of chronic unexplained fatigue using factor analysis

Abstract

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 (15th and 85th) 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.