Urine Biomarkers Predict Treatment Response in the MENTOR Study
Bibliographic record
Abstract
Background: Membranous nephropathy (MN) is a common cause of nephrotic syndrome in adults. The outcome of patients with MN is highly variable and clinical parameters do not reliably identify which patients will respond to immunosuppressive therapy (IS). In the MENTOR trial >40% of subjects did not achieve complete or partial remission (CR/PR) of proteinuria by 12 months despite IS with rituximab or cyclosporine, exposing them to unnecessary IS and portending potentially poor prognosis. We evaluated whether a panel of urinary molecular markers of kidney inflammation and fibrosis improves the ability to identify treatment responders in the MENTOR trial beyond clinical data alone. Methods: We measured the abundance of 55 urinary cytokines, metalloproteases and their inhibitors at the time of randomization in 104 subjects using a Luminex-based multiplex assay. The primary outcome of interest was achievement of CR/PR at 12 months. Results: Patients achieving CR/PR had significantly higher CrCl (94.25 ±31.42 vs 75.17 ± 28.52 mL/min/1.73m2, p=0.002) and lower anti-PLA2R titre (168.5 IQR 20.5,341 vs 549 IQR 115.5,1345 U/mL, p= 0.0002) at baseline. Stepwise selection identified 3 clinical variables (CrCl, PLA2R, treatment) and 8 urinary proteins (IL9, IL10, GM-CSF, VEGF-A, TGFα, MMP2, MMP3, MMP10) associated with CR/PR. A model including the clinical and molecular variables improved discrimination of patients who are predicted to achieve CR/PR compared to a model containing clinical variables alone (ANOVA test p-value = 1.30x10-5, AUC 0.81 ± 0.096 vs. 0.70 ± 0.109). Conclusions: In summary, measurement of a panel urinary molecular markers improves the ability to predict remission at 12 months in patients with MN. Improved prediction of patients resistant to standard therapy using non-invasive markers has potential to offer more individualized treatment, to spare unnecessary treatment toxicity and to identify patients who may benefit from trials of novel therapeutic agents.Figure:: Receiver operating characteristic (ROC) curves for the prediction models selected by stepwise regression.
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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 | 0.002 | 0.004 |
| 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) | 0.003 | 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".