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Record W4411400277 · doi:10.1016/j.ard.2025.06.1825

ABS0235 WIDE-ANGLED SERUM AUTOANTIBODY PROFILING IN SYSTEMIC LUPUS ERYTHEMATOSUS

2025· article· en· W4411400277 on OpenAlexaboutno aff
Laura Eades, Artemis Papadaki, Katie Lennard, Alberta Hoi, Rangi Kandane‐Rathnayake, Eric F. Morand, William A. Figgett, Fabien B. Vincent

Bibliographic record

VenueAnnals of the Rheumatic Diseases · 2025
Typearticle
Languageen
FieldMedicine
TopicSystemic Lupus Erythematosus Research
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineAutoantibodyImmunologyLupus erythematosusSystemic lupusDermatologyAntibodyInternal medicineDisease

Abstract

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Background: Autoantibody specificities routinely measured in the assessment of patients with systemic lupus erythematosus (SLE) are limited to finite panels testing only a few autoantibodies. Few studies have examined autoantibody profiles and clinical associations in SLE using unbiased wide-angled approaches profiling hundreds of autoantibody specificities. Objectives: We aimed to determine serum autoantibody profiles in patients with SLE and examine associations with socio-demographics, SLE phenotype and outcomes using unbiased analysis of a wide-angled antibody panel. Methods: This single-centre cohort study included patients with SLE enrolled in the Australian Lupus Registry and Biobank at Monash Lupus Clinic (Melbourne, Australia). The participant cohort was balanced in terms of self-reported ethnicity (European or Asian) and disease activity (SLE Disease Activity Index-2000 (SLEDAI-2K)). Organ damage was assessed using the Systemic Lupus International Collaborating Clinics SLE Damage Index (SDI). Low disease activity was defined as achieving Lupus Low Disease Activity State (LLDAS). Remission was defined according to the Definition of Remission in SLE (DORIS). Wide-angled serum autoantibody profiling was performed using an i-Ome® protein array (Sengenics), customised to include three interferon targets (IFNα, IFNγ and IFNω). This array allows relative quantification of 1,613 circulating IgG and IgA autoantibodies. Unsupervised partitioning around medoids clustering was performed, using maximum silhouette distance to estimate the optimal number of clusters. Associations between clusters and clinical features were assessed using multivariable multinomial regression. Differential abundance analysis was carried out using multivariable linear regression modelling, adjusted for multiple testing. Supervised learning was performed using the least absolute shrinkage and selection operator (LASSO) and cross-validation. Covariates adjusted for in regression models included age, sex and ethnicity. Results: 60 patients were studied, with a median (interquartile range) age of 42 (34 - 52) years; 90% were female, 50% were of Asian ethnicity and 50% of European ethnicity. After applying a filter that removes autoantibodies closely correlating with negative control spot data, 533 IgG and 534 IgA autoantibodies were analysed. Four and five patient clusters were identified based on IgG and IgA autoantibody profiling, respectively. Patients in IgG cluster four had an autoantibody profile characterised by anti-SSB and anti-Ro60, were significantly more likely to be of Asian ethnicity (adjusted OR 14.6, 95% CI 1.51 - 140, p =0.02) and had higher ESR levels (adjusted β-coef 1.13, 95% CI 1.03 - 1.24, p =0.01) than those in cluster one. Patients in IgA cluster four had an autoantibody profile characterised by anti-PRKAR1 and anti-TACC1, and were more likely to have biopsy-proven lupus nephritis than those in cluster one (adjusted OR 7.83, 95% CI 1.01 - 61.0, p =0.049) or cluster three (adjusted OR 12.9, 95% CI 1.58 - 106, p =0.02). In addition, patients in IgA cluster four were more likely to use immunosuppression (adjusted OR 18.2, 95% CI 1.52 - 216, p =0.02) than those in cluster two; and were more likely to be of Asian ethnicity than those in cluster one on univariable regression only (OR 7.20, 95% CI 1.08 - 48.0, p =0.04). There were no significant associations between clusters and other clinical variables of interest, including disease activity, LLDAS, remission and organ damage. With regard to individual autoantibodies, none were associated with ethnicity on either differential abundance analysis or LASSO. Anti-intracellular PTK7 IgG autoantibody abundance was positively associated with ESR (adjusted β-coef 0.05, 95% CI 0.03 - 0.07, p =0.004), and anti-SSB IgG autoantibody abundance was positively associated with ESR on univariable regression only (β-coef 0.04, 95% CI 0.02-0.06, p =0.01). However, these autoantibodies were not retained in a LASSO model predicting ESR. DORIS remission was associated with higher levels of IgG autoantibodies to PSIP1 on both differential abundance analysis (adjusted β-coef 0.94, 95% CI 0.66 - 1.69, p =0.01) and using LASSO, as well as higher levels of LRR binding FLII interacting protein 2 (LRRFIP2), a MyD88 interacting protein, on both differential abundance analysis (adjusted β-coef 1.18, 95% CI 0.66 - 1.69, p <0.001) and using LASSO. Conclusion: Autoantibody specificity profiles are highly heterogeneous in SLE; however, cluster analysis revealed a subgroup of patients with anti-SSB and anti-Ro60 IgG autoantibody profiles, which was associated with Asian ethnicity and higher ESR, in keeping with known clinical associations between SLE patients of Asian ethnicity and anti-SSB and anti-Ro60. There were no associations between individual autoantibodies and ethnicity or most clinical outcomes. However, anti-PSIP1 IgG and anti-LRRFIP2 IgG were associated with DORIS remission, highlighting the potential for comprehensive autoantibody profiling to contribute clinically informative biomarkers. The role of LRRFIP2 in regulating MyD88-driven inflammation in SLE should be examined in future studies. REFERENCES: NIL . Acknowledgements: Laura Eades and Artemis Papadaki contributed equally to this work and are co-first authors (Laura Eades is presenting author). William Figgett and Fabien Vincent contributed equally to this work and are co-senior authors. Disclosure of Interests: Laura Eades: None declared. Artemis Papadaki: None declared. Katie Lennard: None declared. Alberta Hoi AH is on advisory boards for AstraZeneca, GSK, and Janssen. AH has received sponsorship for the Australian Lupus Registry & Biobank and Asia Pacific Lupus Collaboration from AstraZeneca, BMS, Eli Lilly, and UCB and contract research from AstraZeneca and Merck Serono. Rangi Kandane-Rathnayake RKR has received research grants from BMS, GSK, and Novartis. Eric Morand EM has received consulting fees and/or speaker honoraria from AstraZeneca, Biogen, Bristol Myers Squibb, DragonFly, Eli Lilly, EMD Serono, GlaxoSmithKline, Novartis, Remegen, Quell, UCB, and Zenas, EM has received consulting fees and/or speaker honoraria from AstraZeneca, Biogen, Bristol Myers Squibb, DragonFly, Eli Lilly, EMD Serono, GlaxoSmithKline, Novartis, Remegen, Quell, UCB, and Zenas. EM has received research funding from AbbVie, Amgen, AstraZeneca, Biogen, Bristol Myers Squibb, Eli Lilly, EMD Serono, Genentech-Hoffman, La Roche, GSK, Janssen, Novartis, Takeda, and Union Chimique Belge. William A Figgett: None declared. Fabien Vincent FV has received research grants from Janssen-Cilag, CSL Limited, and AstraZeneca. © The Authors 2025. This abstract is an open access article published in Annals of Rheumatic Diseases under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Neither EULAR nor the publisher make any representation as to the accuracy of the content. The authors are solely responsible for the content in their abstract including accuracy of the facts, statements, results, conclusion, citing resources etc.

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How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.032
GPT teacher head0.340
Teacher spread0.308 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2025
Admission routes1
Has abstractyes

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