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Enregistrement W4411395416 · doi:10.1016/j.ard.2025.06.302

POS0947 ANTI-NOR-90 ANTIBODIES IN AN INTERNATIONAL COHORT OF 2140 SYSTEMIC SCLEROSIS SUBJECTS: CLINICAL ASSOCIATIONS

2025· article· en· W4411395416 sur OpenAlexaffabout
Hui Shen, Marie Hudson, Susanna Proudman, Jenny Walker, W. Stevens, Mandana Nikpour, Shervin Assassi, Maureen D. Mayes, M. Wang, Valérie Leclair, Yves Troyanov, Murray Baron, Maggie Larché, May Y. Choi, Mohammed Osman, Janet Pope, C. Thorne, M J Fritzler, S. Hoa

Notice bibliographique

RevueAnnals of the Rheumatic Diseases · 2025
Typearticle
Langueen
DomaineMedicine
ThématiqueSystemic Sclerosis and Related Diseases
Établissements canadiensSouthlake Regional Health CenterUniversity of AlbertaCentre Hospitalier de l’Université de MontréalMcGill UniversityUniversity of CalgaryWestern UniversityHôpital du Sacré-Cœur de MontréalMcMaster UniversityJewish General Hospital
Organismes subventionnairesnon disponible
Mots-clésMedicineCohortAntibodyMultiple sclerosisCohort studyImmunologyDermatologyInternal medicine

Résumé

récupéré en direct d'OpenAlex

Background: Antibodies directed against nucleolar organizing region (NOR)-90 have been associated with systemic sclerosis (SSc) [1] and malignancies [2]. However, studies have been limited by its rare prevalence in SSc. Objectives: The aim of this study was to identify the demographic, clinical and serological characteristics of SSc subjects with anti-NOR-90 antibodies in a large international, multicentered cohort. Methods: An international (Canada, Australia, USA, Mexico) cohort of 2140 SSc subjects was assembled. Demographic and clinical variables were harmonized, and sera were tested using a widely used line immunoassay (Euroimmun, Luebeck, Germany). Associations between anti-NOR-90 antibodies and outcomes of interest, including malignancy, were investigated. To optimize specificity, antibodies were reported as positive if present in moderate and high titers. Univariable and multivariable logistic regressions (adjusted for presence of overlapping anti-centromere, anti-topoisomerase-I and anti-RNA-polymerase-III) were done to compare anti-NOR-90-positive and -negative subgroups. Results: Forty-one (41; 1.9%) subjects had antibodies against NOR-90 (Table 1). Anti-NOR-90 antibodies were mostly found in overlap with other SSc antibodies (36/41, 88%), namely anti-RNA-polymerase-III (32%), anti-centromere (32%) and anti-topoisomerase-I (20%). Only 5 (12%) patients had single-specificity anti-NOR-90 antibodies. Compared to anti-NOR90-negative patients, those with anti-NOR90-positive antibodies were more likely to have overlapping anti-RNA-polymerase-III (32% vs 14%, OR 2.9, 95% CI 1.5-5.6, p=0.002). In univariable analyses, subjects with anti-NOR-90 and overlapping autoantibodies had higher frequencies of digital ulcers (36% vs. 14%; OR 3.5, 95% CI 1.7-7.1, p=0.0007), calcinosis (42% vs 24%, OR 2.2, 95% CI 1.1-4.3, p=0.02) and inflammatory arthritis (44% vs 28%, OR 2.0, 95% CI 1.0-3.9, p=0.049). On multivariable analyses, only digital ulcers remained significantly associated with anti-NOR-90 antibodies independent of overlapping SSc-specific antibodies (OR 2.6, 95% CI 1.2, 5.2, p=0.01). Extent of skin fibrosis was not different between groups. Interstitial lung disease was numerically but not significantly more frequently in anti-NOR-90-positive patients (46% vs 34%). No association with malignancy was found. Conclusion: This is one of the largest cohorts focusing on disease associations with anti-NOR-90 antibodies in SSc. Anti-NOR-90 antibodies are rare in SSc and mostly found in overlap with other SSc autoantibodies. Although clinical associations may vary across cohorts, in our large, international multi-centered cohort adjusting for overlapping antibodies, anti-NOR-90 positivity was associated with digital ulcers [3, 4]. REFERENCES: [1] Fritzler MJ, von Muhlen CA, Toffoli SM, Staub HL, Laxer RM. Autoantibodies to the nucleolar organizer antigen NOR-90 in children with systemic rheumatic diseases. J Rheumatol 1995;22:521-4. [2] Imai H, Ochs RL, Kiyosawa K, Furuta S, Nakamura RM, Tan EM. Nucleolar antigens and autoantibodies in hepatocellular carcinoma and other malignancies. Am J Pathol 1992;140:859-70. [3] Biglia A, Dourado E, Palterer B, et al. POS0863 Anti-nor90 antibodies in the setting of connective tissue disease: clinical significance and comparison with a cohort of patients with systemic sclerosis. Annals of the Rheumatic Diseases 2022;81:725-6. [4] Dima A, Vonk MC, Garaiman A, et al. Clinical significance of the anti-Nucleolar Organizer Region 90 antibodies (NOR90) in systemic sclerosis: Analysis of the European Scleroderma Trials and Research (EUSTAR) cohort and a systematic literature review. Eur J Intern Med 2024;125:104-10. Table 1Baseline characteristics of the 2140 SSc patients according to anti-NOR-90 antibody status.Total(N=2140)Anti-NOR-90 + (n=41)Single specificity a anti-NOR-90 + (n=5)Overlapping b anti-NOR-90 + (n=36)Anti-NOR-90 - (n=2099)Female, n (%)1845 (86%)35 (85%)5 (100%)30 (83%)1810 (86%)White, n (%)1653 (81%)33 (83%)4 (80%)29 (83%)1620 (81%)Age, yrs, mean ±SD55.1±12.652.7±1456.9±13.152.1±14.255.1±12.6Disease duration, yrs, mean ±SD9.7±9.410.1±9.38.3±9.810.4±9.49.7±9.4mRSS (0-51), mean ±SD10.7±10.011.3±9.46.8±7.411.9±9.610.7±10.0Limited cutaneous disease, n (%)1343 (63%)24 (59%)3 (60%)21 (58%)1319 (63%)Digital ulcers, n (%)266 (14%)12 (33%)0 (0%)12 (36%)254 (14%)Inflammatory arthritis, n (%)595 (29%)16 (41%)1 (20%)15 (44%)579 (28%)Calcinosis, n (%)523 (25%)15 (37%)0 (0%)15 (42%)508 (24%)Myositis, n (%)180 (9%)5 (13%)1 (20%)4 (12%)175 (9%)PH, n (%)250 (14%)5 (14%)1 (33%)4 (13.5%)245 (14%)ILD, n (%)717 (34%)19 (46%)2 (40%)17 (47%)698 (34%)GERD, n (%)1741 (82%)30 (73%)2 (40%)28 (78%)1711 (82%)Dysphagia, n (%)1124 (53%)19 (48%)4 (80%)15 (43%)1105 (53%)Antibiotics for bacterial overgrowth, n (%)123 (6%)0 (0%)0 (0%)0 (0%)123 (6%)Pseudo-obstruction, n (%)63 (3%)0 (0%)0 (0%)0 (0%)63 (3%)Scleroderma renal crisis, n (%)76 (4%)3 (7%)0 (0%)3 (8%)73 (4%)Malignancy, n (%)163 (8%)4 (10%)0 (0%)4 (11%)159 (8%)Statistically significant results are highlighted in bold (p≤0.05)SSc: systemic sclerosis, mRSS: modified Rodnan skin score, PH: pulmonary hypertension, ILD: interstitial lung disease,GERD: gastroesophageal reflux diseasea Single specificity anti-NOR-90+ group was exclusive of anti-CENP, -topoisomerase I, -RNA polymerase III, -fibrillarin, -Ku, -Th/To, -Ro52, -PDGFR, and -PmScl75/100 antibodiesb Overlapping anti-NOR-90+ group had positive NOR-90 antibodies with at least one other of anti-CENP, -topoisomerase I, -RNA polymerase III, -fibrillarin, -Ku, -Th/To, -Ro52, -PDGFR, or -PmScl75/100 antibodies. Acknowledgements: Investigators of the Canadian Scleroderma Research Group: M. Baron, Montreal, Quebec; M. Hudson, Montreal, Quebec; G. Gyger, Montreal, Quebec; S. Hoa, Montreal, Quebec; J. Pope, London, Ontario; M. Larché, Hamilton, Ontario; N. Khalidi, Hamilton, Ontario; A. Masetto, Sherbrooke, Quebec; E. Sutton, Halifax, Nova Scotia; T.S. Rodriguez-Reyna, Mexico City, Mexico; N. Maltez, Ottawa, Ontario; C. Thorne, Newmarket, Ontario; P.R. Fortin, Quebec, Quebec; A. Ikic, Quebec, Quebec; D. Robinson, Winnipeg, Manitoba; N. Jones, Edmonton, Alberta; S. LeClercq, Calgary, Alberta; E. Kaminska, Calgary, Alberta; J-P Mathieu, Montreal, Quebec; P. Docherty, Moncton, New Brunswick; D. Smith, Ottawa, Ontario; M. Osman, Edmonton, Alberta; M. Choy, Calgary, Alberta; D. Smith, Ottawa, Ontario; M. J. Fritzler, Calgary, Alberta; Investigators of the Australian Scleroderma Interest Group: C. Hill, Adelaide, South Australia; S. Lester, Adelaide, South Australia; P. Nash, Sunshine Coast, Queensland; M. Nikpour, Melbourne, Victoria; J. Roddy, Perth, Western Australia; K. Patterson, Adelaide, South Australia; S. Proudman, Adelaide, South Australia; M. Rischmueller, Adelaide, South Australia; J. Sahhar, Melbourne, Victoria; W. Stevens, Melbourne, Victoria; J. Walker, Adelaide, South Australia; J. Zochling, Hobart, Tasmania. Investigators of GENISOS: Shervin Assassi, Houston, Texas; Maureen D. Mayes, Houston, Texas; Terry A. McNearney, Galveston, Texas; Gloria Salazar, Houston, Texas. Disclosure of Interests: Hao Cheng Shen: None declared, Marie Hudson Astra-Zeneca, Boehringer Ingelheim, Merck, Merck, Boehringer Ingelheim, Pfizer, Susanna M. Proudman Janssen, Boehringer Ingelheim, Janssen, Boehringer Ingelheim, MSD, Janssen, Boehringer Ingelheim, Jennifer G. Walker Boehringer Ingelheim, Wendy Stevens: None declared, Mandana Nikpour AstraZeneca, Boehringer Ingelheim, GSK, Janssen, AstraZeneca, Boehringer Ingelheim, GSK, Janssen, Syntara, Boehringer Ingelheim, Janssen, Shervin Assassi Abbvie, AstraZeneca, aTyr, Boehringer Ingelheim, CSL Behring, Merck, Mitsubishi Tanabe, Takeda, TeneoFour, Janssen, Boehringer Ingelheim, aTyr, Maureen D. Mayes: None declared, Mianbo Wang: None declared, Valérie Leclair: None declared, yves troyanov AstraZeneca, Kezar Life Science, Eli Lilly, UCB, Murray Baron: None declared, Maggie Larché Boehringer Ingelheim, AstraZeneca, BMS, May Y. Choi MitogenDx, Werfen, Astra Zeneca, GSK, Celltrion, Organon, Mallinkrodt Pharmaceuticals, Astra Zeneca, Mohammed Osman: None declared, Janet Pope: None declared, Carter Thorne: None declared, Marvin Fritzler Werfen, Werfen, Sabrina Hoa: None declared. © 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 enseignants0,002
Score d'incertitude au seuil0,006

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 technologies0,0010,000
Communication savante0,0010,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0020,000

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 Opus0,080
Tête enseignante GPT0,385
Écart entre enseignants0,305 · 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'admission2
Résumé présentoui

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