Session Type: Abstract Session
Session Time: 3:45PM-4:00PM
Background/Purpose: Prior studies of SLE clusters based on autoantibodies have utilized cross-sectional data from single centers. We applied clustering techniques to longitudinal and comprehensive autoantibody data from a large multinational, multi-ethnic inception cohort of well characterized SLE patients to identify clusters associated with disease outcomes.
Methods: We used demographic, clinical, and serological data at enrolment and follow-up visits years 3 and 5 from 805 patients who fulfilled the 1997 Updated ACR SLE Classification Criteria and were enrolled within 15 months of diagnosis. For each visit, ANA (HEp-2 indirect immunofluorescence assay), dsDNA, Sm, U1-RNP, SSA/Ro60, SSB/La, Ro52/TRIM21, histones, ribosomal P, Jo-1, centromere B, PCNA, antiphospholipid antibodies (IgG and IgM for anticardiolipin, anti–β2GP1, and aPS/PT, lupus anticoagulant (LAC), and IgG anti-β2GP1 D1), and anti-dense-fine speckled 70 were performed at a single lab (except LAC). K-means clustering algorithm on principal component analysis (10 dimensions) transformed longitudinal ANA and autoantibody profiles was used. We compared cluster demographic and clinical outcomes, including longitudinal disease activity (total and adjusted mean SLEDAI-2K [AMS]), SLICC/ACR damage index and organ-specific domains, SLE therapies and survival, using one-way ANOVA test and a Benjamini-Hochberg correction with false discovery rate alpha = 0.05. Results were visualized using t-distributed stochastic neighbor embedding (t-SNE).
Results: Four unique patient clusters were identified (Table 1, Figure 1). Cluster 1 (n=137), characterized by high frequency of anti-Sm and anti-RNP antibodies over time, was the youngest group at disease onset with a high proportion of subjects of Asian and African ancestry. At year 5, they had the highest disease activity (total SLEDAI-2K 4.3 [4.5] and AMS 4.3 [3.1]), were more likely to have active hematologic and mucocutaneous involvement, and to be on/exposed to immunosuppressants/biologics. Cluster 2 (n=377), the largest cluster, had low frequency of anti-dsDNA, were oldest at disease onset, and at year 5, had the lowest disease activity (total SLEDAI-2K 2.3 [3.3] and AMS 2.9 [2.5]), and were less likely to have nephritis and be on/exposed to immunosuppressants/biologics. Cluster 3 (n=79) had the highest frequency of antiphospholipid antibodies over time (Figure 2), were more likely to be of European ancestry, have an elevated body mass index, be former smokers, and by year 5, to have nephritis, neuropsychiatric involvement, including strokes and seizures (SLICC/ACR damage index). Cluster 4 (n=212) was characterized by anti-SSA/Ro60, SSB/La, Ro52/TRIM21, and histone antibodies, and active immunologic involvement (low complements) at year 5. Overall, survival of the 805 subjects was 94% at 5 years, and none of the clusters predicted survival.
Conclusion: Four SLE patient clusters associated with disease activity, organ involvement, and treatment were identified in this analysis of longitudinal ANA and autoantibody profiles in relation to SLE outcomes, suggesting these SLE subsets might be identifiable based on extended autoantibody profiles early in the disease and carry prognostic information.
Figure 1. Four autoantibody cluster groups identified among 805 SLE patients followed from enrolment through years 3 and 5. Latent space visualized using a t-distributed stochastic neighbor embedding (t-SNE) with colors based on cluster labels.
Figure 2. Antiphospholipid antibodies of the four cluster groups over time (E=enrolment, Y3=year 3, Y5=year 5). Group 3 had the highest frequency of antiphospholipid antibodies, including lupus anticoagulant, IgG and IgM anticardiolipin, IgG and IgM anti–β2-glycoprotein_1 (β2GP1), IgG anti-β2GP1 Domain 1 (D1), IgG and IgM antiphosphatidylserine/prothrombin (aPS/PT), over time compared to the other three clusters. Other connective tissue disease autoantibodies not shown. Line indicates the mean, shading indicates the standard deviation.
To cite this abstract in AMA style:Choi M, Chen I, Clarke A, Fritzler M, Buhler K, Urowitz M, Hanly J, Gordon C, St.Pierre Y, Bae S, Romero-Diaz J, Sanchez-Guerrero F, Bernatsky S, Wallace D, Isenberg D, Rahman A, Merrill J, Fortin P, Gladman D, Bruce I, Petri M, Ginzler E, Dooley M, Ramsey-Goldman R, Manzi S, Jnsen A, Alarcn G, van Vollenhoven R, Aranow C, Mackay M, Ruiz-Irastorza G, Lim S, Inanc M, Kalunian K, Jacobsen S, Peschken C, Kamen D, Askanase A, Sontag D, Costenbader K. Identifying Clusters of Longitudinal Autoantibody Profiles Associated with Systemic Lupus Erythematosus Disease Outcomes [abstract]. Arthritis Rheumatol. 2021; 73 (suppl 9). https://acrabstracts.org/abstract/identifying-clusters-of-longitudinal-autoantibody-profiles-associated-with-systemic-lupus-erythematosus-disease-outcomes/. Accessed November 26, 2022.
« Back to ACR Convergence 2021
ACR Meeting Abstracts - https://acrabstracts.org/abstract/identifying-clusters-of-longitudinal-autoantibody-profiles-associated-with-systemic-lupus-erythematosus-disease-outcomes/