Use of Urinary Proteins as Predictors of Response to Immunosuppressive Treatment in Membranous Nephropathy
Bibliographic record
Abstract
Background: Membranous nephropathy (MN) is the most common cause of nephrotic syndrome in adults. Prognosis is defined by remission in proteinuria. Response to immunosuppression agents such as calcineurin inhibitors and rituximab vary. While the presence of anti-PLA2R antibodies may help to guide prognosis and treatment strategies, further identifying biomarkers that could refine treatment decision would be useful. Using data from the MENTOR trial (NEJM 2019), we evaluated whether 24-hour total urinary protein, urinary albumin, immunoglobulin M (uIgM), immunoglobulin G (uIgG), and urinary alpha 1 microglobulin (uα1m) at baseline could be used to predict response to immunosuppressive therapy at 12 months in patients with MN. Methods: Logistic regression models were used to study the relationship between baseline urinary proteins with patients' treatment outcomes by treatment drug (rituximab or cyclosporine). The treatment outcome was defined as patients achieving either complete (CR; <0.3g/24 hours) or partial remission (PR; >0.3-<3.5g/24 hours) of proteinuria at 12 months. Results: In both cyclosporine and rituximab arm, all urinary proteins exhibited a decline from baseline to 12 months post treatment. However, none of the baseline urinary proteins were found to be significantly associated with treatment response at 12 months (p>0.05 for all). Results were similar when restricted to patients with positive anti-PLA2R at baseline. Conclusions: Baseline measures of the urinary albumin, uIgM, uIgG, and uα1m are not predictors of patients going into CR or PR at 12 months after treatment with rituximab or cyclosporine.Table 1:: Odds ratio of urinary protein predicting CR or PR at 12 months in patients treated with Rituximab or Cyclosporine (Fully Adjusted model)*Adjusted for adjust for age, sex, eGFR, and creatinine clearance
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".