<i>FCGR2A/CD32A</i>and<i>FCGR3A/CD16A</i>Variants and EULAR Response to Tumor Necrosis Factor-α Blockers in Psoriatic Arthritis: A Longitudinal Study with 6 Months of Followup
Bibliographic record
Abstract
OBJECTIVE: The efficacy of antibody-based biological therapies currently used in psoriatic arthritis (PsA) depends not only on their blocking effect on the targeted molecule but also on their binding affinity to genetically defined variants of cell-surface Fc-γ receptors. Our objective was to assess the potential influence of functionally relevant FCGR2A/CD32A (H131R) and FCGR3A/CD16A (V158F) genetic polymorphisms on the EULAR response to tumor necrosis factor-α (TNF-α) blocker therapy in PsA. METHODS: In total 103 patients with PsA starting anti-TNF-α therapy were included. The efficacy of therapy was evaluated according to EULAR response criteria at 3 and 6 months. FCGR2A-R131H and FCGR3A-F158V polymorphisms were genotyped. Potential correlations between clinical response and the FCGR2A-R131H and FCGR3A-F158V polymorphisms were evaluated. RESULTS: EULAR response (moderate plus good) was 85.4% at 3 months and 87.4% at 6 months, while good EULAR response was 61.2% and 62.1%, respectively. More patients with high-affinity FCGR2A genotypes (homozygous or heterozygous combinations) achieved a EULAR response at 6 months compared to patients with the low-affinity genotype (RR; p = 0.034, adjusted comparison error rate < 0.025). This association was due mainly to the group of patients treated with etanercept. No correlation was found for the FCGR3A polymorphism. Similarly, no effect of C-reactive protein levels was observed. CONCLUSION: Our data indicate that FCGR2A polymorphism may influence the response to TNF-α blockers (namely etanercept) in PsA in a direction opposite to that previously found in patients with rheumatoid arthritis.
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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) | 0.000 | 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) | 0.001 | 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".