#2561 URINE PROTEOMICS FOR PREDICTION OF DISEASE PROGRESSION IN PATIENTS WITH IGA NEPHROPATHY
Bibliographic record
Abstract
Abstract Background and Aims IgA nephropathy (IgAN) may lead to kidney failure. The urinary proteomics biomarker-based classifier (IgAN237) may predict progression at the time of kidney biopsy [1]. We now studied whether IgAN237 predicts disease progression not only at the time of biopsy but also later in the course of IgAN. Method Urine samples from 103 patients with biopsy-proven IgAN were analyzed using capillary electrophoresis-mass spectrometry at baseline (IgAN237 score-1) and 89 also at follow-up (IgAN237 score-2). Patients were grouped into ‘non-progressors’ (IgAN237 score ≤0.38 Units) and ‘progressors’ (IgAN237 score >0.38 Units). Historical and follow-up data included, e.g., estimated glomerular filtration rate (eGFR)-slopes, urinary albumin/creatinine ratio (UACR)-slopes and medication. Multiple logistic regression analysis, using a stepwise model, was done with progressors and non-progressors as dependent factors including explanatory variables age, sex, eGFR- and UACR-slopes. Results Median age at biopsy was 44 years (range 11-92); 63% were male. Median interval between biopsy and IgAN237 score-1 was 65 months (0-606), and between IgAN237 score-1 and score-2 was 258 days (71-531). IgAN237 score-1 and score-2 values did not differ significantly and were correlated (rho = 0.44, p<0.001). Twenty-eight and 26% of patients were classified as progressors based on IgAN237 score-1 and score-2, respectively. The IgAN237 score inversely correlated with the chronic eGFR slopes (rho = -0.278, p = 0.02 for score-1; rho = -0.409, p = 0.002 for score-2) and with ±180days eGFR slopes (rho = -0.31, p = 0.009 and rho = -0.439, p = 0.001, respectively) (Figure 1). The ±180days eGFR-slopes were more reduced for progressors than non-progressors (median -5.98 versus -1.22 mL/min/1.73m2 per year for score-1, p<0.001; -3.02 vs 1.08 mL/min/1.73m2 per year for score-2, p = 0.047) (Figure 2). The only significant variable maintained in a multiple logistic regression analysis for IgAN237 score-1 (progressor vs non-progressor) was ±180days eGFR slope (p<0.001) and for IgAN237 score-2 the UACR (p = 0.002) and age at baseline (p = 0.016). Conclusion The urinary IgAN237 classifier represents a risk stratification tool in IgAN not only at the time of biopsy but also later in the course of the disease. It may guide physicians in management and follow-up strategies in an individualized manner.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".