Understanding the association of fatigue with other symptoms of fibromyalgia: Development of a cluster model

Objective. To develop a symptoms cluster model that can describe factors of fibromyalgia syndrome (FMS) associated with fatigue severity as reported by the sample and to explore FMS clinical symptom subclusters based on varying symptom intensities. Methods. FMS individuals (n = 120, 82% ages 31-60 years, 90% women, 59% white) diagnosed with the 1990 or 2010 American College of Rheumatology diagnostic criteria were enrolled. Participants completed multiple validated self-report questionnaires to measure fatigue, pain, depression, anxiety, pain catastrophizing, daytime sleepiness, cognitive function, and FMS-related polysymptomatic distress. Cluster analysis using SPSS 19.0 and structural equation modeling using AMOS 17.0 were used. Results. Final structural equation modeling the symptoms cluster model showed good fit and revealed that FMS fatigue was associated with widespread pain, symptoms severity, pain intensity, pain interference, cognitive dysfunction, catastrophizing, anxiety, and depression (x(2) = 121.72 (98df), P > 0.05, x(2)/df = 1.242, comparative fit index = 0.982, root mean square error of approximation = 0.045). Two distinct clinical symptom subclusters emerged: subcluster 1 (78% of total subjects), defined by widespread pain, unrefreshed waking, and somatic symptoms, and subcluster 2 (22% of total subjects), defined by fatigue and cognitive dysfunction with pain being a less severe and less widespread occurrence. Conclusion. Overall, subcluster 1 had more intense symptoms than subcluster 2. FMS symptoms may be categorized into 2 clinical subclusters. These findings have implications for an illness whose diagnosis and management are symptom dependent. A longitudinal study capturing the variability in the symptom experience of FMS subjects is warranted.