POS0947 ANTI-NOR-90 ANTIBODIES IN AN INTERNATIONAL COHORT OF 2140 SYSTEMIC SCLEROSIS SUBJECTS: CLINICAL ASSOCIATIONS
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".