Machine learning identifies clusters of longitudinal autoantibody profiles predictive of systemic lupus erythematosus disease outcomes
(2023) In Annals of the Rheumatic Diseases 82(7). p.927-936- 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... (More)
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.
(Less)
- author
- organization
- publishing date
- 2023-07-01
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- autoantibodies, autoimmunity, systemic lupus erythematosus
- in
- Annals of the Rheumatic Diseases
- volume
- 82
- issue
- 7
- pages
- 10 pages
- publisher
- BMJ Publishing Group
- external identifiers
-
- scopus:85160218881
- pmid:37085289
- ISSN
- 0003-4967
- DOI
- 10.1136/ard-2022-223808
- language
- English
- LU publication?
- yes
- id
- 54171058-1762-4ca6-a39e-031934bebb81
- date added to LUP
- 2023-09-18 11:46:28
- date last changed
- 2023-11-21 22:31:48
@article{54171058-1762-4ca6-a39e-031934bebb81,
abstract = {{<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-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.</p>}},
author = {{Choi, May Yee and Chen, Irene and Clarke, Ann Elaine and Fritzler, Marvin J. and Buhler, Katherine A. and Urowitz, Murray and Hanly, John and St-Pierre, Yvan and Gordon, Caroline and Bae, Sang Cheol and Romero-Diaz, Juanita and Sanchez-Guerrero, Jorge and Bernatsky, Sasha and Wallace, Daniel J. and Isenberg, David Alan and Rahman, Anisur and Merrill, Joan T. and Fortin, Paul R. and Gladman, Dafna D. and Bruce, Ian N. and Petri, Michelle and Ginzler, Ellen M. and Dooley, Mary Anne and Ramsey-Goldman, Rosalind and Manzi, Susan and Jönsen, Andreas and Alarcón, Graciela S. and Van Vollenhoven, Ronald F. and Aranow, Cynthia and Mackay, Meggan and Ruiz-Irastorza, Guillermo and Lim, Sam and Inanc, Murat and Kalunian, Kenneth and Jacobsen, Søren and Peschken, Christine and Kamen, Diane L. and Askanase, Anca and Buyon, Jill P. and Sontag, David and Costenbader, Karen H.}},
issn = {{0003-4967}},
keywords = {{autoantibodies; autoimmunity; systemic lupus erythematosus}},
language = {{eng}},
month = {{07}},
number = {{7}},
pages = {{927--936}},
publisher = {{BMJ Publishing Group}},
series = {{Annals of the Rheumatic Diseases}},
title = {{Machine learning identifies clusters of longitudinal autoantibody profiles predictive of systemic lupus erythematosus disease outcomes}},
url = {{http://dx.doi.org/10.1136/ard-2022-223808}},
doi = {{10.1136/ard-2022-223808}},
volume = {{82}},
year = {{2023}},
}