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Machine learning identifies clusters of longitudinal autoantibody profiles predictive of systemic lupus erythematosus disease outcomes
- 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
Published Web Location
https://doi.org/10.1136/ard-2022-223808Abstract
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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