Low- and high-molecular-weight urinary proteins as predictors of response to rituximab in patients with membranous nephropathy: a prospective study
Bibliographic record
Abstract
BACKGROUND: Selective urinary biomarkers have been considered superior to total proteinuria in predicting response to treatment and outcome in patients with membranous nephropathy (MN). METHODS: We prospectively tested whether urinary (U) excretion of retinol-binding protein (RBP), α1-microglobulin (α1M), albumin, immunoglobulin IgG and IgM and/or anti-phospholipase 2 receptor (PLA(2)R) levels could predict response to rituximab (RTX) therapy better than standard measures in MN. We also correlated changes in antibodies to PLA(2)R with these urinary biomarkers. RESULTS: Twenty patients with MN and proteinuria (P) >5 g/24 h received RTX (375 mg/m(2) × 4) and at 12 months, 1 patient was in complete remission (CR), 9 were in partial remission (PR), 5 had a limited response (LR) and 4 were non-responders (NR). At 24 months, CR occurred in 4, PR in 12, LR in 1, NR in 2 and 1 patient relapsed. By simple linear regression analysis, UIgG at baseline (mg/24 h) was a significant predictor of change in proteinuria at 12 months (Δ urinary protein) (P = 0.04). In addition, fractional excretion (FE) of IgG, urinary alpha 1 microglobulin (Uα1M) (mg/24 h) and URBP (μg/24 h) were also predictors of response (P = 0.05, 0.04, and 0.03, respectively). On the other hand, UIgM, FEIgM, albumin and FE albumin did not predict response (P = 0.10, 0.27, 0.22 and 0.20, respectively). However, when results were analyzed in relation to proteinuria at 24 months, none of the U markers that predicted response at 12 m could predict response at 24 m (P = 0.55, 0.42, 0.29 and 0.20). Decline in anti-PLA(2)R levels was associated with and often preceded urinary biomarker response but positivity at baseline was not a predictor of proteinuria response. CONCLUSIONS: The results suggest that in patients with MN, quantification of low-, medium- and high-molecular-weight urinary proteins may be associated with rate of response to RTX, but do not correlate with longer term outcomes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".