Machine learning identifies clusters of longitudinal autoantibody profiles predictive of systemic lupus erythematosus disease outcomes
- Authors
- Choi, May Yee; Chen, Irene; Clarke, Ann Elaine; Fritzler, Marvin J; Buhler, Katherine A; Urowitz, Murray; Hanly, John; St-Pierre, Yvan; Gordon, Caroline; Bae, Sang-Cheol; Romero-Diaz, Juanita; Sanchez-Guerrero, Jorge; Bernatsky, Sasha; Wallace, Daniel J; Isenberg, David Alan; Rahman, Anisur; Merrill, Joan T; Fortin, Paul R; Gladman, Dafna D; Bruce, Ian N; Petri, Michelle; Ginzler, Ellen M; Dooley, Mary Anne; Ramsey-Goldman, Rosalind; Manzi, Susan; Jönsen, Andreas; Alarcón, Graciela S; Van Vollenhoven, Ronald F; Aranow, Cynthia; MacKay, Meggan; Ruiz-Irastorza, Guillermo; Lim, Sam; Inanc, Murat; Kalunian, Kenneth; Jacobsen, Søren; Peschken, Christine; Kamen, Diane L; Askanase, Anca; Buyon, Jill P; Sontag, David; Costenbader, Karen H
- Issue Date
- Jul-2023
- Publisher
- BMJ PUBLISHING GROUP
- Keywords
- systemic lupus erythematosus; autoantibodies; autoimmunity
- Citation
- ANNALS OF THE RHEUMATIC DISEASES, v.82, no.7, pp.927 - 936
- Indexed
- SCIE
SCOPUS
- Journal Title
- ANNALS OF THE RHEUMATIC DISEASES
- Volume
- 82
- Number
- 7
- Start Page
- 927
- End Page
- 936
- URI
- https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/191972
- DOI
- 10.1136/ard-2022-223808
- ISSN
- 0003-4967
- Abstract
- Objectives: A novel longitudinal clustering technique was applied to comprehensive autoantibody data from a large, well-characterised, multinational inception systemic lupus erythematosus (SLE) cohort to determine profiles predictive of clinical outcomes.
Methods: Demographic, clinical and serological data from 805 patients with SLE obtained within 15 months of diagnosis and at 3-year and 5-year follow-up were included. For each visit, sera were assessed for 29 antinuclear antibodies (ANA) immunofluorescence patterns and 20 autoantibodies. K-means clustering on principal component analysis-transformed longitudinal autoantibody profiles identified discrete phenotypic clusters. One-way analysis of variance compared cluster enrolment demographics and clinical outcomes at 10-year follow-up. Cox proportional hazards model estimated the HR for survival adjusting for age of disease onset.
Results: Cluster 1 (n=137, high frequency of anti-Smith, anti-U1RNP, AC-5 (large nuclear speckled pattern) and high ANA titres) had the highest cumulative disease activity and immunosuppressants/biologics use at year 10. Cluster 2 (n=376, low anti-double stranded DNA (dsDNA) and ANA titres) had the lowest disease activity, frequency of lupus nephritis and immunosuppressants/biologics use. Cluster 3 (n=80, highest frequency of all five antiphospholipid antibodies) had the highest frequency of seizures and hypocomplementaemia. Cluster 4 (n=212) also had high disease activity and was characterised by multiple autoantibody reactivity including to antihistone, anti-dsDNA, antiribosomal P, anti-Sjögren syndrome antigen A or Ro60, anti-Sjögren syndrome antigen B or La, anti-Ro52/Tripartite Motif Protein 21, antiproliferating cell nuclear antigen and anticentromere B). Clusters 1 (adjusted HR 2.60 (95% CI 1.12 to 6.05), p=0.03) and 3 (adjusted HR 2.87 (95% CI 1.22 to 6.74), p=0.02) had lower survival compared with cluster 2.
Conclusion: Four discrete SLE patient longitudinal autoantibody clusters were predictive of long-term disease activity, organ involvement, treatment requirements and mortality risk.
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