Machine learning identifies clusters of longitudinal autoantibody profiles predictive of systemic lupus erythematosus disease outcomes
Creators: name: Sanchez-Guerrero, Jorge
name: Inanc, Murat
name: Sontag, David
name: Bernatsky, Sasha
name: Isenberg, David Alan
name: Ginzler, Ellen M
name: Peschken, Christine
name: Jacobsen, Søren
name: Askanase, Anca
name: Gordon, Caroline
name: Petri, Michelle
name: Bae, Sang-Cheol
name: Costenbader, Karen H
name: Buyon, Jill P
name: Wallace, Daniel J
name: Rahman, Anisur
name: Mackay, Meggan
name: Chen, Irene
name: Ruiz-Irastorza, Guillermo
name: Jönsen, Andreas
name: Ramsey-Goldman, Rosalind
name: Manzi, Susan
name: Fortin, Paul R
name: Aranow, Cynthia
name: Merrill, Joan T
name: Alarcón, Graciela S
name: Buhler, Katherine A
name: Lim, Sam
name: Kamen, Diane L
name: Bruce, Ian N
name: Kalunian, Kenneth
name: Gladman, Dafna D
name: Dooley, Mary Anne
name: Choi, May Yee
name: van Vollenhoven, Ronald F
name: Clarke, Ann Elaine
name: Urowitz, Murray
name: Hanly, John
name: Fritzler, Marvin J
name: St-Pierre, Yvan
name: Romero-Diaz, Juanita
Publisher: name: BMJ
Year: 2023

description: <p>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-Sj&ouml;gren syndrome antigen A or Ro60, anti-Sj&ouml;gren 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.</p>