AB1110 EXPLORING LEVELS OF PROTEIN BIOMARKERS IN RESPONSE TO TREATMENT FOR PSORIASIS AND PSORIATIC ARTHRITIS
Bibliographic record
Abstract
Background Psoriatic Arthritis (PsA), an inflammatory arthritis, occurs in 30% of psoriasis patients[1]. Serum chemokine (C-X-C motif) ligand 10 (CXCL10) is elevated in psoriasis patients that will develop PsA[2]. MMP3 is associated with response to Tissue-necrosis factor inhibitor (TNFi) treatment[3]. S100A8, CCL2, and ACP5 are associated with PsA development[4,5,6]. We aimed to compare CXCL10, MMP3, S100A8, ACP5, and CCL2 levels in PsA patients before and after treatment with biologics and in cutaneous psoriasis without arthritis (PsC) patients on and off treatment with biologics. Objectives The objective of this study was to evaluate the levels of CXCL10, MMP3, S100A8, CCL2, and ACP5 in serum of psoriasis and PsA patients before and after treatment with biologic agents (TNFi and IL-17i). Methods PsA and PsC patients are followed prospectively at the Toronto Western Hospital psoriatic disease clinic. We identified 93 PsA patients on TNFi and 22 on IL-17 inhibitors (IL-17i) and retrieved serum samples before and after therapy. Samples from 30 patients with PsC treated with biologics were matched to 30 patients not treated with biologics were also retrieved from the databank. Using the Luminex Discovery assay we measured CXCL10, MMP3, S-100A8, CCL2, and ACP5 levels. Statistical analysis was performed using Wilcoxon Signed-rank test. Results CXCL10 (P=0.0007), MMP3 (P<0.0001), S100A8 (P<0.0001), ACP5 (P<0.0001), and CCL2 (P=0.01) significantly decreased after TNFi treatment in PsA patients. CXCL10 (P=0.04) and ACP5 (P=0.02) significantly increased after IL-17i treatment in PsA patients. There were no significant differences between treated and untreated PsC patients. Conclusion CXCL10, MMP3, S100A8, ACP5, and CCL2 are potential biomarkers for response to TNFi in PsA patients. CXCL10 and ACP5 are potential biomarkers for response to IL-17i treatment in PsA patients. References [1]Ritchlin, C.T., R.A. Colbert, and D.D. Gladman, Psoriatic Arthritis. N Engl J Med, 2017. 376(10): p. 957-970. [2]Abji, F., et al., Brief report: CXCL10 is a possible biomarker for the development of psoriatic arthritis among patients with psoriasis. Arthritis & Rheumatology, 2016. 68(12): p. 2911-2916. [3]Chandran, V., et al., Soluble biomarkers associated with response to treatment with tumor necrosis factor inhibitors in psoriatic arthritis. J Rheumatol, 2013. 40(6): p. 866-71 [4]Hansson, C., C. Eriksson, and G.-M. Alenius, S-Calprotectin (S100A8/S100A9): A Potential Marker of Inflammation in Patients with Psoriatic Arthritis. Journal of Immunology Research, 2014. 2014: p. 696415. [5]Lin, Y.-C., et al., Tumor necrosis factor-alpha inhibitors suppress CCL2 chemokine in monocytes via epigenetic modification. Molecular Immunology, 2017. 83: p. 82-91. [6]Ademowo, O.S., et al., Discovery and confirmation of a protein biomarker panel with potential to predict response to biological therapy in psoriatic arthritis. Ann Rheum Dis, 2016. 75(1): p. 234-41. Acknowledgements: NIL. Disclosure of Interests Rachel Offenheim: None declared, Darshini Ganatra: None declared, Dafna D Gladman Consultant of: Abbvie, Amgen, BMS, Eli Lilly, Janssen, Novartis, Pfizer and UCB, Grant/research support from: Abbvie, Amgen, BMS, Eli Lilly, Janssen, Novartis, Pfizer and UCB.
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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 | 0.001 |
| 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) | 0.004 | 0.001 |
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".