Different disease subtypes with distinct clinical expression in XXXX : results of a cluster analysis.

@entity1 @entity625 @entity827 @entity16502 @entity2783 @entity5641 @entity177 @entity3812 @entity925 @entity13171
OBJECTIVE: The aim of this study was to evaluate whether there are clinical subgroups that may have different prognoses among @entity827 @entity1 . METHODS: The cumulative clinical features of a large group of @entity827 @entity1 [1168 @entity1 , 593 (50.8%) male, mean age 35.3 years (s.d. 12.4)] were studied. To analyse our data and identify groups of @entity827 @entity1 with similar clinical characteristics, a two-step cluster analysis using log-likelihood distance measures was performed. For clustering the @entity827 @entity1 , we evaluated the following variables: gender, current age, age at symptom onset, age at diagnosis, presence of major clinical features, variables related with therapy and family history for @entity827 , @entity177 and carriage of @entity16502 . RESULTS: Three distinct groups of @entity827 @entity1 were identified. Cluster 1 was characterized by a high prevalence of @entity625 , @entity5641 , @entity13171 -like erythema (ELE) and @entity925 . The dosage of @entity3812 and the frequency of @entity2783 were lower in cluster 1. @entity1 in cluster 2 had an earlier age of disease onset and diagnosis. @entity16502 carriage and @entity2783 prevalence were the highest in cluster 2. This group of @entity1 was using the highest dose of @entity3812 . @entity1 in cluster 3 had the lowest prevalence of @entity625 , ELE and @entity925 . The frequencies of @entity16502 carriage and @entity2783 were lower in cluster 3 than the overall @entity827 @entity1 . Non-response to @entity3812 was also slightly lower in cluster 3. CONCLUSION: @entity1 with @entity827 can be clustered into distinct patterns of clinical and genetic manifestations and these patterns may have different prognostic significance.
