POS0702 SEX DIFFERENCES IN SERUM PROTEIN PROFILES OF MALES AND FEMALES WITH PSORIATIC ARTHRITIS
Notice bibliographique
Résumé
Background: Psoriatic arthritis (PsA) is an immune-mediated disease with equal prevalence among males and females. However, sex-related differences have been reported in the clinical presentation and treatment response outcomes. The biological mechanisms driving these differences remain unknown. Objectives: The overall objective of the study is to understand how sex, as a biological variable, influences PsA. Specifically, our first aim was to identify sex-specific differences in serum proteins and biological pathways in males and females with PsA. Our second aim was to create classification models to distinguish disease from controls in males and females. Methods: This cross-sectional study included patients with active PsA from the University of Toronto Psoriatic Arthritis cohort. Patients were included if they met the following criteria: 1) diagnosis of PsA and meeting the classification of psoriatic arthritis criteria (CASPAR); 2) about to start systemic therapy for active musculoskeletal manifestations of PsA; 3) serum samples stored in the biobank. Patients were excluded if they had active cancer, end-stage major organ disease, or were currently on systemic corticosteroids. Serum proteins were analyzed using an aptamer-based assay. The differential expression analysis of the proteins between PsA males vs. PsA females and PsA vs. Controls (overall and by sex) was performed using the limma package in R. Differentially expressed proteins (DEPs) were defined as false-discovery rate p < 0.05 and fold change > 1.2. PathDIP version 5 was used for pathway enrichment analysis on DEPs from PsA males vs. females. The protein-pathway relationship in PsA males vs. females was created using NAViGaTOR version 3. Multi-protein classification models were created to distinguish PsA from controls in males and females using logistic regression with elastic net, random forest, support vector machine, and linear discriminant analysis. From random forest, we performed variable importance analysis to identify sex-specific proteins significantly contributing to the model's predictive accuracy. Results: We measured 6402 serum proteins using an aptamer-based assay in 100 active PsA patients (50 males, 50 females) and 50 age- and sex-matched healthy controls (25 males, 25 females). The mean age of PsA males was 49.5 years (± 12.40), and PsA females was 49.4 years (± 14.75). Overall, 71% of patients were naïve to biologic therapies. Disease activity measures, including swollen and tender joint counts and psoriasis severity, were not statistically different between the sexes. Protein expression levels did not differ significantly between pre- and post-menopausal females or between biologic-exposed and biologic-naïve groups. Differential expression revealed more than a 20-fold increase in deregulated proteins among PsA males vs. controls (741) compared to PsA females vs. controls (31), and 200 that were shared (Figure 1A-D). Several sex-specific pathways from DEPs of PsA males vs. females were identified through pathway analysis, including Rho GTPase, Kit receptor, focal adhesion, phosphatidylinositol signaling, Fc gamma R-mediated phagocytosis, neutrophil extracellular trap formation, epithelial mesenchymal transition regulators, insulin signaling, necroptosis, and IL-18 signaling (Figure 1E). There were more male-differential proteins associated with the sex-specific pathways (11 proteins, e.g., SRC, LYN, SPHK1) than in females (1 protein, PPIF). The classification models performed well to distinguish disease from controls by sex, with the area under the curve scores between 0.8-0.99 (Figure 2A-B). Variable importance analysis identified mutual proteins between males and females with PsA (e.g., macrophage migration inhibitory factor, C3b) and others that were female-specific (leukotriene A4-hydrolase) and male-specific (e.g., IL-36A, NEK7, PIK3CA/PIK3R1). Conclusion: In this untargeted proteomic study, we provide evidence of sex-related differences in serum proteins and biological pathways between male and female patients with PsA. More unique serum proteins and biological pathways were deregulated in male PsA patients than in females. These sex-specific pathways are related to immune cell function (phagocytosis, neutrophil trap formation), cytokine signaling (IL-18), vascular function (angiogenesis, platelet function), and intracellular signaling (Rho GTPase). These proteins and pathways offer potential new targets for future sex-based research in PsA. REFERENCES: NIL . Acknowledgements: NIL . Disclosure of Interests: Steven Dang: None declared, Xianwei Li: None declared, Liqun Diao: None declared, Vincent Piguet Sanofi, LEO Pharma, Novartis, Sanofi, Union Therapeutics, Abbvie and UCB, AbbVie, Bausch Health, Boehringer Ingelheim, Bristol Myers Squibb, Celgene, Eli Lilly, Incyte, Janssen, LEO Pharma, L'Oréal, Novartis, Organon, Pfizer, Sandoz and Sanofi, David Croitoru: None declared, Joan Wither AstraZeneca, Pfizer, Igor Jurisica: None declared, Vinod Chandran Bristol-Myers Squibb, Eli Lilly, Janssen, Novartis, UCB, AbbVie/Abbott, Lihi Eder Abbvie, UCB, Pfizer, Janssen, Novartis, Eli Lilly, Sandoz, Fresenius Kabi. © 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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi 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.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,018 | 0,003 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».