POS0702 SEX DIFFERENCES IN SERUM PROTEIN PROFILES OF MALES AND FEMALES WITH PSORIATIC ARTHRITIS
Bibliographic record
Abstract
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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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.003 |
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".