Machine learning identifies clusters of longitudinal autoantibody profiles predictive of systemic lupus erythematosus disease outcomes

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

doi:10.1136/ard-2022-223808

Machine learning identifies clusters of longitudinal autoantibody profiles predictive of systemic lupus erythematosus disease outcomes

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

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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 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.

Originalsprog	Engelsk
Tidsskrift	Annals of the Rheumatic Diseases
Vol/bind	82
Udgave nummer	7
Sider (fra-til)	927-936
Antal sider	10
ISSN	0003-4967
DOI	https://doi.org/10.1136/ard-2022-223808
Status	Udgivet - jul. 2023

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Choi, M. Y., Chen, I., Clarke, A. E., Fritzler, M. J., Buhler, K. A., Urowitz, M., Hanly, J., St-Pierre, Y., Gordon, C., Bae, S.-C., Romero-Diaz, J., Sanchez-Guerrero, J., Bernatsky, S., Wallace, D. J., Isenberg, D. A., Rahman, A., Merrill, J. T., Fortin, P. R., Gladman, D. D., ... Costenbader, K. H. (2023). Machine learning identifies clusters of longitudinal autoantibody profiles predictive of systemic lupus erythematosus disease outcomes. Annals of the Rheumatic Diseases, 82(7), 927-936. https://doi.org/10.1136/ard-2022-223808

Choi, MY, Chen, I, Clarke, AE, Fritzler, MJ, Buhler, KA, Urowitz, M, Hanly, J, St-Pierre, Y, Gordon, C, Bae, S-C, Romero-Diaz, J, Sanchez-Guerrero, J, Bernatsky, S, Wallace, DJ, Isenberg, DA, Rahman, A, Merrill, JT, Fortin, PR, Gladman, DD, Bruce, IN, Petri, M, Ginzler, EM, Dooley, MA, Ramsey-Goldman, R, Manzi, S, Jönsen, A, Alarcón, GS, van Vollenhoven, RF, Aranow, C, Mackay, M, Ruiz-Irastorza, G, Lim, S, Inanc, M, Kalunian, K, Jacobsen, S, Peschken, C, Kamen, DL, Askanase, A, Buyon, JP, Sontag, D & Costenbader, KH 2023, 'Machine learning identifies clusters of longitudinal autoantibody profiles predictive of systemic lupus erythematosus disease outcomes', Annals of the Rheumatic Diseases, bind 82, nr. 7, s. 927-936. https://doi.org/10.1136/ard-2022-223808

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title = "Machine learning identifies clusters of longitudinal autoantibody profiles predictive of systemic lupus erythematosus disease outcomes",

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 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{\"o}gren syndrome antigen A or Ro60, anti-Sj{\"o}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.",

keywords = "Antibodies, Antinuclear, Autoantibodies, DNA, Humans, Immunosuppressive Agents, Lupus Erythematosus, Systemic, Machine Learning, systemic lupus erythematosus, autoimmunity, autoantibodies",

author = "Choi, {May Yee} and Irene Chen and Clarke, {Ann Elaine} and Fritzler, {Marvin J} and Buhler, {Katherine A} and Murray Urowitz and John Hanly and Yvan St-Pierre and Caroline Gordon and Sang-Cheol Bae and Juanita Romero-Diaz and Jorge Sanchez-Guerrero and Sasha Bernatsky and Wallace, {Daniel J} and Isenberg, {David Alan} and Anisur Rahman and Merrill, {Joan T} and Fortin, {Paul R} and Gladman, {Dafna D} and Bruce, {Ian N} and Michelle Petri and Ginzler, {Ellen M} and Dooley, {Mary Anne} and Rosalind Ramsey-Goldman and Susan Manzi and Andreas J{\"o}nsen and Alarc{\'o}n, {Graciela S} and {van Vollenhoven}, {Ronald F} and Cynthia Aranow and Meggan Mackay and Guillermo Ruiz-Irastorza and Sam Lim and Murat Inanc and Kenneth Kalunian and S{\o}ren Jacobsen and Christine Peschken and Kamen, {Diane L} and Anca Askanase and Buyon, {Jill P} and David Sontag and Costenbader, {Karen H}",

note = "{\textcopyright} Author(s) (or their employer(s)) 2023. No commercial re-use. See rights and permissions. Published by BMJ.",

year = "2023",

month = jul,

doi = "10.1136/ard-2022-223808",

language = "English",

volume = "82",

pages = "927--936",

journal = "Annals of the Rheumatic Diseases",

issn = "0003-4967",

publisher = "B M J Group",

number = "7",

}

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, Søren

AU - Peschken, Christine

AU - Kamen, Diane L

AU - Askanase, Anca

AU - Buyon, Jill P

AU - Sontag, David

AU - Costenbader, Karen H

PY - 2023/7

Y1 - 2023/7

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 - Antibodies, Antinuclear

KW - Autoantibodies

KW - DNA

KW - Humans

KW - Immunosuppressive Agents

KW - Lupus Erythematosus, Systemic

KW - Machine Learning

KW - systemic lupus erythematosus

KW - autoimmunity

KW - autoantibodies

UR - http://www.scopus.com/inward/record.url?scp=85160218881&partnerID=8YFLogxK

U2 - 10.1136/ard-2022-223808

DO - 10.1136/ard-2022-223808

M3 - Journal 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

ER -

Machine learning identifies clusters of longitudinal autoantibody profiles predictive of systemic lupus erythematosus disease outcomes

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