Researchers at the University of Utah revealed new data on the gene expression pattern associated with conditions like fibromyalgia, chronic fatigue syndrome (CFS) and depression. The study was published in the journal Arthritis Care and Research and is entitled “Gene expression factor analysis to differentiate pathways linked to fibromyalgia, chronic fatigue syndrome, and depression in a diverse patient sample”.
Fibromyalgia is a medical disorder characterized by a set of symptoms that includes widespread chronic musculoskeletal pain, incapacitating fatigue, stiffness and numbness in certain parts of the body, painful response to pressure, headaches, unrefreshing sleep (poor sleep quality), anxiety or depression and mood alterations.
Fibromyalgia shares features with another medical condition called CFS, a complex disorder characterized by extreme, remitting/relapsing fatigue that interferes with a person’s well-being and is not relieved by rest or recovery. Other symptoms include post-exertional malaise, muscle and/or joint pain, headaches, loss of memory or concentration, sore throat, enlarged lymph nodes and unrefreshing sleep. Both disorders often co-occur and can affect people’s ability to conduct simple daily tasks, compromising their quality of life. Women are usually more affected than men.
There is a close association between fibromyalgia, CFS and depression, with approximately 50% of the patients suffering from depression. Depression has been reported to be linked to worsening of pain, poor sleep, functional impairment and poor health outcomes.
Previous studies have shown that the expression of certain genes is altered in fibromyalgia and CFS patients, and that some of these genes were also altered in depressed individuals.
The goal of the study was to analyze the gene expression pattern associated with fibromyalgia, CFS and depression in order to determine the pathways shared by these medical conditions. Researchers used an Exploratory Factor Analysis (EFA) and assessed whether independent candidate genes could be grouped into relevant biological factors and whether these factors could be linked to a fibromyalgia and/or CFS diagnosis.
The team analyzed leukocyte gene expression from blood samples of 61 healthy controls, 15 fibromyalgia patients, 33 CFS patients, 79 patients with both fibromyalgia and CFS, 42 patients who are resistant to depression medication and 31 who are medication-responsive. In total, 34 candidate genes were assessed through EFA.
Researchers found that the 34 genes could be clustered into four independent groups of biological factors that were categorized by function into: 1) purinergic and cellular modulators, 2) neuronal growth and immune function, 3) nociception and stress mediators, 4) energy and mitochondrial function. Factors 1 and 3 were found to be associated with CFS but not with fibromyalgia, and with a lower depression severity.
The team concluded that gene expression relevant to fibromyalgia, CFS and depression can be grouped into meaningful biological clusters, with CFS and depression being associated with the same two clusters although CFS is linked to increased expression while depression is linked to a decreased expression of these particular genes. The team proposes that future studies should look into multi-modal treatments and perceive these medical conditions as a combination of diseases and symptoms.