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

ABS0235 WIDE-ANGLED SERUM AUTOANTIBODY PROFILING IN SYSTEMIC LUPUS ERYTHEMATOSUS

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

Notice bibliographique

RevueAnnals of the Rheumatic Diseases · 2025
Typearticle
Langueen
DomaineMedicine
ThématiqueSystemic Lupus Erythematosus Research
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésMedicineAutoantibodyImmunologyLupus erythematosusSystemic lupusDermatologyAntibodyInternal medicineDisease

Résumé

récupéré en direct d'OpenAlex

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.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,001
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,001
Score d'incertitude au seuil0,007

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0010,001
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0010,001
Études des sciences et des technologies0,0000,000
Communication savante0,0010,000
Science ouverte0,0000,001
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0010,001

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,032
Tête enseignante GPT0,340
Écart entre enseignants0,308 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2025
Routes d'admission1
Résumé présentoui

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