Rituximab-Induced Depletion of Anti-PLA2R Autoantibodies Predicts Response in Membranous Nephropathy
Bibliographic record
Abstract
Autoantibodies to the M-type phospholipase A(2) receptor (PLA(2)R) are sensitive and specific for idiopathic membranous nephropathy. The anti-B cell agent rituximab is a promising therapy for this disease, but biomarkers of early response to treatment currently do not exist. Here, we investigated whether levels of anti-PLA(2)R correlate with the immunological activity of membranous nephropathy, potentially exhibiting a more rapid response to treatment than clinical parameters such as proteinuria. We measured the amount of anti-PLA(2)R using Western blot immunoassay in serial serum samples from a total of 35 patients treated with rituximab for membranous nephropathy in two distinct cohorts. Pretreatment samples from 25 of 35 (71%) patients contained anti-PLA(2)R, and these autoantibodies declined or disappeared in 17 (68%) of these patients within 12 months after rituximab. Those who demonstrated this immunologic response fared better clinically: 59% and 88% attained complete or partial remission by 12 and 24 months, respectively, compared with 0% and 33% among those with persistent anti-PLA(2)R levels. Changes in antibody levels preceded changes in proteinuria. One subject who relapsed during follow-up had a concomitant return of anti-PLA(2)R. In summary, measuring anti-PLA(2)R levels by immunoassay may be a method to follow and predict response to treatment with rituximab in membranous nephropathy.
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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 | 0.003 |
| 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 | 0.001 | 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".