Sex differences in serum proteomic profiles in psoriatic arthritis
Bibliographic record
Abstract
OBJECTIVES: Sex-related differences exist in the clinical presentation and treatment outcomes of patients with PsA. The biological pathways driving these differences remain unknown. We conducted an untargeted proteomic study to identify sex-specific serum proteins and biological pathways in males and females with PsA. METHODS: We used an aptamer-based panel to measure 6402 serum proteins in 50 male and 50 female patients with active PsA and 50 age- and sex-matched non-psoriatic controls. Differential expression and pathway enrichment analysis identified differentially expressed proteins (DEPs) and enriched pathways between male and female PsA patients. Machine learning classifiers were used to develop sex-specific multi-biomarker models to distinguish PsA patients from controls. Proteins with the highest predictive performances were highlighted from random forest models. RESULTS: The differential analysis revealed over 20 times more sex-specific DEPs in PsA males vs controls (n = 741) than in PsA females vs controls (n = 31). The enriched pathways among DEPs in PsA males vs PsA females were related to intracellular signalling, vascular function, cytokine signalling and immune cell functions. All models discriminated PsA from controls for both sexes with an area under the curve of 0.85-0.99. Variable importance analysis identified leukotriene A-4 hydrolase as a significant predictor in PsA females vs controls, whereas IL-36 alpha, NEK7 and PIK3CA/PIK3R1 were significant in PsA males vs controls. CONCLUSION: Significantly more dysregulated proteins and biological pathways were found in males than in females with PsA. The identified proteins and pathways offer potential new targets for sex-based research in PsA.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".