Cluster analysis identifies three clinical patterns of patients with systemic autoimmune diseases and anti-Ku antibodies

Abstract

Objective: To determine distinct patterns of patients with autoimmune diseases harbouring anti-Ku antibodies and their respective prognosis.

Methods: Anti-Ku-positive patients were retrieved through four immunology departments. Clusters were derived from unsupervised multiple correspondence analysis, not including the disease's diagnosis, followed by hierarchical clustering. Baseline characteristics and risk of disease progression, defined as a composite of new organ involvement or the need for new immunosuppressants, were compared across the retrieved clusters.

Results: Among 154 anti-Ku-positive patients, three clusters were identified. At disease's onset, all patients included in cluster 1 (n=42/154, 27%) had muscle involvement, 34% displayed cardiac manifestations. Inflammatory myopathies (n=35/42, 83%) and/or systemic sclerosis (n=17/42, 40%) were the most frequent diagnoses. Cluster 2 (n=69/154, 45%) included the lowest proportion of women (68% vs 83% and 84% in clusters 1 and 3), 54% of patients had lung involvement, and 25% fulfilled Sjögren's disease criteria. Cluster 3 (n=43/154, 28%) included younger patients (median age 25 years), with 79% of them fulfilling systemic lupus erythematosus criteria. These three clusters have distinct outcomes (p=0.001): cluster 1 developed lung involvement and displayed the higher risk of disease progression, cluster 2 was prone to myositis development and cluster 3 developed various clinical manifestations. The proportion of patients with heart involvement doubled over time in all clusters, with a majority of myocarditis in cluster 1, pulmonary hypertension in cluster 2 and pericarditis in cluster 3.

Conclusion: Three distinct groups of anti-Ku-positive patients were identified; cardiac involvement should be carefully tracked throughout the follow-up in all of them.

Keywords: Autoantibodies; Autoimmune Diseases; Sjogren's Syndrome; Systemic Lupus Erythematosus; Systemic Sclerosis.

Figure 1. Identification of clusters. Factor map (A) showing the raw individual data used to generate the dendrogram (B). Horizontal branches represent the combination of two clusters, and vertical branches the degree of dissimilarity between combined clusters; the grey zone represents the level of truncation, resulting in three groups. The colours indicate individuals according to the cluster to which they belong.

Figure 2. Kaplan-Meier plot estimating the risk of disease evolution in each cluster. Kaplan-Meier curves estimating (A) the risk of new organ involvement and/or new immunosuppressant prescription according to the clusters, (B) the risk of new organ involvement, (C) the risk of immunosuppressive treatment (N=154). P value was calculated by log-rank test. Shading represents the 95% CIs.

Figure 3. Spider plots illustrating the development of organ involvement over time in each cluster. Shading represents organ involvement at baseline. Dotted line shows the development of new organ involvement during follow-up.