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1704 Identifying clusters of longitudinal autoantibody profiles associated with systemic lupus erythematosus disease outcomes
  1. May Y Choi1,
  2. Irene Chen2,
  3. Ann Clarke1,
  4. Marvin J Fritzler1,
  5. Katherine A Buhler1,
  6. Murray Urowitz3,
  7. John G Hanly4,
  8. Caroline Gordon5,
  9. Yvan St Pierre6,
  10. Sang-Cheol Bae7,
  11. Juanita Romero Diaz8,
  12. Jorge Sanchez-Guerrero9,
  13. Sasha Bernatsky10,
  14. Daniel Wallace11,
  15. David Isenberg12,
  16. Anisur Rahman12,
  17. Joan T Merrill13,
  18. Paul R Fortin14,
  19. Dafna D Gladman3,
  20. Ian Bruce15,
  21. Michelle A Petri16,
  22. Ellen Ginzler17,
  23. Mary Anne Dooley18,
  24. Rosalind Ramsey-Goldman19,
  25. Susan Manzi20,
  26. Andreas Jonsen21,
  27. Graciela S Alarcon22,
  28. Ronald FVan Vollenhoven23,
  29. Cynthia Aranow24,
  30. Meggan Mackay24,
  31. Guillermo Ruiz-Irastorza25,
  32. Sam Lim26,
  33. Murat Inanc27,
  34. Kenneth C Kalunian28,
  35. Soren Jacobsen29,
  36. Christine Peschken30,
  37. Diane Kamen31,
  38. Anca Askanase32,
  39. David Sontag2,
  40. Jill Buyon33 and
  41. Karen H Costenbader34
  1. 1University of Calgary, Cumming School of Medicine, Calgary, Alberta, Canada
  2. 2Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, Cambridge, MA, USA
  3. 3Lupus Program, Centre for Prognosis Studies in The Rheumatic Disease and Krembil Research Institute, Toronto Western Hospital, University of Toronto, Toronto, Ontario, Canada
  4. 4Division of Rheumatology, Department of Medicine and Department of Pathology, Queen Elizabeth II Health Sciences Centre and Dalhousie University, Halifax, Nova Scotia, Canada
  5. 5Rheumatology Research Group, Institute of Inflammation and Ageing, College of Medical and Dental Sciences, University of Birmingham, Birmingham, UK
  6. 6Research Institute of the McGill University Health Centre; Montreal, Quebec, Canada
  7. 7Department of Rheumatology, Hanyang University Hospital for Rheumatic Diseases, Seoul, Korea
  8. 8Instituto Nacional de Ciencias Médicas y Nutrición, Mexico City, Mexico
  9. 9Mount Sinai Hospital and University Health Network, University of Toronto, Canada
  10. 10Divisions of Rheumatology and Clinical Epidemiology, McGill University Health Centre
  11. 11Cedars-Sinai/David Geffen School of Medicine at UCLA, Los Angeles, California, USA
  12. 12Centre for Rheumatology, Department of Medicine, University College London, UK
  13. 13Department of Clinical Pharmacology, Oklahoma Medical Research Foundation, Oklahoma City, Oklahoma, USA
  14. 14Division of Rheumatology, CHU de Québec – Université Laval, Québec City, Canada
  15. 15University of Manchester, Manchester, UK
  16. 16Division of Rheumatology, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
  17. 17Department of Medicine, SUNY Downstate Medical Center, Brooklyn, New York, USA
  18. 18Thurston Arthritis Research Center, University of North Carolina, Chapel Hill, North Carolina, USA
  19. 19Northwestern University and Feinberg School of Medicine, Chicago, Illinois, USA
  20. 20Allegheny Health Network, Pittsburgh, Pennsylvania, USA
  21. 21Lund University, Lund, Sweden
  22. 22Department of Medicine, University of Alabama at Birmingham, Birmingham, Alabama, USA
  23. 23University of Amsterdam, Rheumatology and Immunology Center, Amsterdam, Noord-Holland, The Netherlands
  24. 24Feinstein Institute for Medical Research, Manhasset, New York, USA
  25. 25Autoimmune Diseases Research Unit, Department of Internal Medicine, BioCruces Health Research Institute, Hospital Universitario Cruces, University of the Basque Country, Barakaldo, Spain
  26. 26Emory University School of Medicine, Division of Rheumatology, Atlanta, Georgia, USA
  27. 27Division of Rheumatology, Department of Internal Medicine, Istanbul Medical Faculty, Istanbul University, Istanbul, Turkey
  28. 28University of California San Diego School of Medicine, La Jolla, California, USA
  29. 29Department of Rheumatology, Rigshospitalet, Copenhagen University Hospital, , Copenhagen, Denmark
  30. 30University of Manitoba, Winnipeg, Manitoba, Canada
  31. 31Medical University of South Carolina, Charleston, South Carolina, USA
  32. 32Hospital for Joint Diseases, New York University, Seligman Centre for Advanced Therapeutics, New York New York, USA
  33. 33New York University School of Medicine, New York, NY, USA
  34. 34Division of Rheumatology, Inflammation and Immunity, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, USA


Background 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 criteria and were enrolled within 15 months of diagnosis. For each visit, ANA, dsDNA, Sm, U1-RNP, SSA/Ro60, SSB/La, Ro52/TRIM21, histones, ribosomal P, Jo-1, centromere B, PCNA, anti-DFS70, lupus anticoagulant (LAC), IgG and IgM for anticardiolipin, anti–β2GP1, and aPS/PT, and IgG anti-β2GP1 D1 were performed at a single lab (except LAC). K-means clustering algorithm on principal component analysis (10 dimensions) transformed longitudinal ANA/autoantibody profiles was used. We compared cluster demographic/clinical outcomes, including longitudinal disease activity (total and adjusted mean SLEDAI-2K), 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.

Results Four unique patient clusters were identified (table 1). Cluster 1, characterized by high frequency of anti-Sm and anti-RNP 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, were more likely to have active hematologic and mucocutaneous involvement, and to be on/exposed to immunosuppressants/biologics. Cluster 2, the largest cluster, had low frequency of anti-dsDNA, were oldest at disease onset, and at year 5, had the lowest disease activity, and were least likely to have nephritis and be on/exposed to immunosuppressants/biologics. Cluster 3 had the highest frequency of antiphospholipid antibodies over time, were more likely to be of European ancestry, have an elevated BMI, be former smokers, and by year 5, to have nephritis, neuropsychiatric involvement, including strokes and seizures (SLICC/ACR damage index). Cluster 4 was characterized by anti-SSA/Ro60, SSB/La, Ro52/TRIM21, histone antibodies, and low complements at year 5. Overall, survival of the 805 subjects was 94% at 5 years, and none of the clusters predicted survival.

Abstract 1704 Table 1

Demographic and clinical characteristics that were statistically significant1 at baseline and five-year follow-up between the four SLE longitudinal autoantibody clusters

Conclusions Four SLE patient clusters associated with disease activity, organ involvement, and treatment were identified in this analysis of longitudinal ANA/autoantibody profiles in relation to SLE outcomes, suggesting these subsets might be identifiable based on extended autoantibody profiles early in disease and carry prognostic information.

Acknowledgments This study is presented on behalf of SLICC. We would also like to acknowledge the technical assistance of Ms. Haiyan Hou (MitogenDx Laboratory).

This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See:

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