Distinct clinical trajectories of gastrointestinal progression among patients with systemic sclerosis
Year: 2024
Type: article
Study design: Observational Study
Abstract: OBJECTIVES: Systemic sclerosis (SSc) is heterogeneous in its clinical presentation. Common manifestations cluster together, defining unique subgroups. This investigation aims to characterize gastrointestinal (GI) phenotypes and determine whether they can be distinguished by temporal progression. METHODS: We examined a well-established SSc patient cohort with a modified Medsger GI severity score measured over time to determine heterogeneity in disease progression. Growth mixture models estimated each patient's phenotype and disease severity trajectory over time. We compared the characteristics of estimated phenotypes using non-parametric statistics and linear and logistic regression to compare patient characteristics between phenotypes while adjusting for disease duration. RESULTS: We examined 2696 SSc patients with at least two Medsger GI scores, identifying four unique phenotypes. The most common phenotype ('Stable', n = 2325) had an average score of 1 that was consistent over time. Two phenotypes were progressive ('Early Progressive', n = 142, and 'Late Progressive', n = 115) with an initial average score of 1. The Early Progressive group increased initially and stabilized, and the Late Progressive group worsened slowly over time. A fourth phenotype ('Early Severe GI', n = 114) had an initial average Medsger GI score just below 3 with high mortality and improving GI severity over time. CONCLUSIONS: Clinically distinct GI phenotypes exist among patients with SSc. These phenotypes are not only distinguished by GI and extra-intestinal SSc clinical complications, but they are also temporally distinct. Distinct autoantibody profiles are associated strongly with more severe GI disease.
Source: Lara D. Veeken
Language: English
Subfield: Pathology and Forensic Medicine
Field: Medicine
Domain: Health Sciences
Open Access status: green
Awards: G1331311730, G1391463277, Optimizing the Approach to Identify Cancer-Associated Myositis, G6261990412, G6766803180, G8200804678, G9000075135, Harnessing multivariate patient- and population-level disease trajectories to predict major clinical events in scleroderma, Johns Hopkins Rheumatic Diseases Resource-based Core Center (Johns Hopkins RDRCC)
OpenAlex ID: W4402035678
