Use of Cluster Analysis to Delineate Symptom Profiles in an Ehlers-Danlos Syndrome Patient Population

Abstract

ContextThe Ehlers-Danlos Syndromes (EDSs) are a set of rare heritable disorders of connective tissue, characterized by defects in the structure and synthesis of extracellular matrix elements that lead to a myriad of problems including joint hypermobility and skin abnormalities. Because EDS affects multiple organ systems, defining clear boundaries and recognizing overlapping clinical features shared by disease phenotypes is challenging. ObjectivesThe objective of this study was to seek evidence of phenotypic subgroups of patients with distinctive symptom profiles and describe these resulting subgroups. MethodsData were extracted from a repository assembled 2001–2013 by the National Institute on Aging Intramural Research Program. Agglomerative hierarchical clustering was used to form distinct subgroups of patients with respect to the domains of pain, physical and mental fatigue, daytime sleepiness, and nighttime sleep. Domains were selected based on literature review, clinician expertise, and guidance from patient advisors. ResultsOne hundred seventy-five patients met all inclusion criteria. Three subgroups were identified. The Pain Dominant subgroup (39 patients) had the highest mean pain values, but lowest mean values of other symptoms. The High Symptom Burden subgroup (71 patients) had high mean values in all domains. The Mental Fatigue subgroup (65 patients) had a high mean value for mental fatigue and daytime sleepiness, but a lower mean value for pain. ConclusionThe subgroups aligned with clinical observation of the heterogeneous nature of EDS, with overlapping symptoms between subtypes and a wide divergence in degree of symptoms within subtypes. This exploratory study helps characterize the various phenotypes and comorbidities of patients with EDS.