TY - JOUR
T1 - Machine learning identifies clusters of longitudinal autoantibody profiles predictive of systemic lupus erythematosus disease outcomes
AU - Choi, May Yee
AU - Chen, Irene
AU - Clarke, Ann Elaine
AU - Fritzler, Marvin J.
AU - Buhler, Katherine A.
AU - Urowitz, Murray
AU - Hanly, John
AU - St-Pierre, Yvan
AU - Gordon, Caroline
AU - Bae, Sang-Cheol
AU - Romero-Diaz, Juanita
AU - Sanchez-Guerrero, Jorge
AU - Bernatsky, Sasha
AU - Wallace, Daniel J.
AU - Isenberg, David Alan
AU - Rahman, Anisur
AU - Merrill, Joan T.
AU - Fortin, Paul R.
AU - Gladman, Dafna D.
AU - Bruce, Ian N.
AU - Petri, Michelle
AU - Ginzler, Ellen M.
AU - Dooley, Mary Anne
AU - Ramsey-Goldman, Rosalind
AU - Manzi, Susan
AU - Jönsen, Andreas
AU - Alarcón, Graciela S.
AU - van Vollenhoven, Ronald F.
AU - Aranow, Cynthia
AU - MacKay, Meggan
AU - Ruiz-Irastorza, Guillermo
AU - Lim, Sam
AU - Inanc, Murat
AU - Kalunian, Kenneth
AU - Jacobsen, S. ren
AU - Peschken, Christine
AU - Kamen, Diane L.
AU - Askanase, Anca
AU - Buyon, Jill P.
AU - Sontag, David
AU - Costenbader, Karen H.
N1 - Funding Information: MYC is supported by the Lupus Foundation of America Gary S. Gilkeson Career Development Award and research gifts in kind from MitogenDx (Calgary, Canada). AEC holds The Arthritis Society Research Chair in Rheumatic Diseases at the University of Calgary. JH’s work was supported by the Canadian Institutes of Health Research (research grant MOP-88526). CG’s work was supported by Lupus UK, Sandwell and West Birmingham Hospitals NHS Trust and the NIHR/Wellcome Trust Clinical Research Facility in Birmingham. S-CB’s work was supported by Basic Science Research Programme through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2021R1A6A1A03038899). The Montreal General Hospital Lupus Clinic is partially supported by the Singer Family Fund for Lupus Research. AR and DAI are supported by the National Institute for Health Research University College London Hospitals Biomedical Research Centre. The Hopkins Lupus Cohort is supported by NIH Grants AR043727 and AR069572. PRF presently holds a tier 1 Canada Research Chair on Systemic Autoimmune Rheumatic Diseases at Université Laval, and part of this work was done while he was still holding a Distinguished Senior Investigator of The Arthritis Society. INB is an NIHR Senior Investigator and is funded by Arthritis Research UK, the National Institute for Health Research Manchester Biomedical Research Centre and the NIHR/Wellcome Trust Manchester Clinical Research Facility. MAD’s work was supported by the NIH grant RR00046. RR-G’s work was supported by the NIH (grants 1U54TR001353 formerly 8UL1TR000150 and UL-1RR-025741, K24-AR-02318 and P60AR064464 formerly P60-AR-48098). SM is supported by grants R01 AR046588 and K24 AR002213. GR-I is supported by the Department of Education, Universities and Research of the Basque Government. SJ is supported by the Danish Rheumatism Association (A1028) and the Novo Nordisk Foundation (A05990). KHC is supported by NIH K24 AR066109. Publisher Copyright: © 2023 BMJ Publishing Group. All rights reserved.
PY - 2023/7/1
Y1 - 2023/7/1
N2 - 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.
AB - 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.
KW - autoantibodies
KW - autoimmunity
KW - systemic lupus erythematosus
UR - http://www.scopus.com/inward/record.url?scp=85160218881&partnerID=8YFLogxK
U2 - https://doi.org/10.1136/ard-2022-223808
DO - https://doi.org/10.1136/ard-2022-223808
M3 - Article
C2 - 37085289
SN - 0003-4967
VL - 82
SP - 927
EP - 936
JO - Annals of the rheumatic diseases
JF - Annals of the rheumatic diseases
IS - 7
M1 - 223808
ER -