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Accepted (AM)Open Access (License Unspecified), Open
Journal article
Machine learning identifies clusters of longitudinal autoantibody profiles predictive of systemic lupus erythematosus disease outcomes
Annals of the rheumatic diseases, v 82(7), pp 927-936
01 Jul 2023
PMID: 37085289
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
ObjectivesA 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.MethodsDemographic, 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.ResultsCluster 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.ConclusionFour discrete SLE patient longitudinal autoantibody clusters were predictive of long-term disease activity, organ involvement, treatment requirements and mortality risk.
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Details
- Title
- Machine learning identifies clusters of longitudinal autoantibody profiles predictive of systemic lupus erythematosus disease outcomes
- Creators
- May Yee Choi - University of CalgaryIrene Chen - Massachusetts Institute of TechnologyAnn Elaine Clarke - University of CalgaryMarvin J Fritzler - University of CalgaryKatherine A Buhler - University of CalgaryMurray Urowitz - Toronto Western HospitalJohn Hanly - Queen Elizabeth II Health Sciences CentreYvan St-Pierre - McGill University Health CentreCaroline Gordon - University of BirminghamSang-Cheol Bae - Hanyang University Seoul HospitalJuanita Romero-Diaz - Instituto Nacional de Ciencias Médicas y Nutrición Salvador ZubiránJorge Sanchez-Guerrero - Mount Sinai HospitalSasha Bernatsky - McGill University Health CentreDaniel J Wallace - Cedars-Sinai Medical CenterDavid Alan Isenberg - University College LondonAnisur Rahman - University College LondonJoan T Merrill - Oklahoma Medical Research FoundationPaul R Fortin - Université LavalDafna D Gladman - University of TorontoIan N Bruce - University of ManchesterMichelle Petri - Johns Hopkins MedicineEllen M Ginzler - SUNY Downstate Health Sciences UniversityMary Anne Dooley - University of North Carolina at Chapel HillRosalind Ramsey-Goldman - Northwestern UniversitySusan Manzi - Allegheny Health NetworkAndreas Jönsen - Lund UniversityGraciela S Alarcón - University of Alabama at BirminghamRonald F van Vollenhoven - University of AmsterdamCynthia Aranow - Feinstein Institute for Medical ResearchMeggan Mackay - Feinstein Institute for Medical ResearchGuillermo Ruiz-Irastorza - BioCruces Health research InstituteSam Lim - Emory UniversityMurat Inanc - Istanbul UniversityKenneth Kalunian - University of California, San DiegoSøren Jacobsen - Copenhagen University HospitalChristine Peschken - University of ManitobaDiane L Kamen - Medical University of South CarolinaAnca Askanase - New York University Langone Orthopedic HospitalJill P Buyon - New York UniversityDavid Sontag - Massachusetts Institute of TechnologyKaren H Costenbader - Brigham and Women's Hospital
- Publication Details
- Annals of the rheumatic diseases, v 82(7), pp 927-936
- Publisher
- BMJ Publishing Group Ltd and European League Against Rheumatism
- Number of pages
- 10
- Grant note
- London Hospitals Biomedical Research Centre Gary S. Gilkeson Career Development Award / Lupus Foundation of America (http://dx.doi.org/10.13039/100002193) LUPUS UK (http://dx.doi.org/10.13039/501100018817) 1U54TR001353; AR043727; AR046588; AR069572; K24 AR002213; K24 AR066109; K24-AR-02318; P60AR064464; RR00046; UL-1RR-025741 / National Institute for Health Research (http://dx.doi.org/10.13039/501100000272) NRF-2021R1A6A1A03038899 / National Research Foundation of Korea (http://dx.doi.org/10.13039/501100003725) Distinguished Senior Investigator of The Arthritis / Arthritis Society (http://dx.doi.org/10.13039/501100000142) A05990 / National Institute for Health Research Manchester Biomedical Research Centre Novo Nordisk Foundation Clinical Research Facility in Birmingham Mancheste / Wellcome Trust (http://dx.doi.org/10.13039/100010269) A1028 / Department of Education, Universities and Research of the Basque Government Gigtforeningen Singer Family Fund for Lupus Research
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- General Internal Medicine
- Web of Science ID
- WOS:000991880300001
- Scopus ID
- 2-s2.0-85160218881
- Other Identifier
- 991021934013404721
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- Collaboration types
- Domestic collaboration
- International collaboration
- Web of Science research areas
- Rheumatology