Guselkumab induces robust reduction in acute phase proteins and type 17 effector cytokines in active psoriatic arthritis: results from phase 3 trials
Bibliographic record
Abstract
OBJECTIVE: To investigate serum protein expression in participants with psoriatic arthritis (PsA) and changes after guselkumab treatment. METHODS: Participants with PsA were treated with guselkumab or placebo in the DISCOVER-1 and DISCOVER-2 studies. Serum levels of acute phase reactants C reactive protein (CRP) and serum amyloid A (SAA) and inflammatory cytokines/chemokines were measured at weeks 0, 4 and 24 in 300 study participants and 34 healthy controls (HCs). The PSUMMIT studies measured serum interleukin (IL)-17A, IL-17F and CRP after ustekinumab treatment and levels with ustekinumab versus guselkumab treatment were compared. RESULTS: Baseline serum levels of CRP, SAA, IL-6, IL-17A and IL-17F were elevated in participants with active PsA vs HCs (p<0.05, geometric mean (GM) ≥40% higher). Baseline T-helper cell 17 (Th17) effector cytokines were significantly associated with baseline psoriasis but not joint disease activity. Compared with placebo, guselkumab treatment resulted in decreases in serum CRP, SAA, IL-6, IL-17A, IL-17F and IL-22 as early as week 4 and continued to decrease through week 24 (p<0.05, GM decrease from baseline ≥33%). At week 24, IL-17A and IL-17F levels were not significantly different from HCs, suggesting normalisation of peripheral IL-23/Th17 axis effector cytokines postguselkumab treatment. Reductions in IL-17A/IL-17F levels were greater in guselkumab-treated versus ustekinumab-treated participants, whereas effects on CRP levels were similar. CONCLUSION: Guselkumab treatment reduced serum protein levels of acute phase and Th17 effector cytokines and achieved comparable levels to those in HCs. In participants with PsA, reductions of IL-17A and IL-17F were of greater magnitude after treatment with guselkumab than with ustekinumab.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".